Abstract: Abstract In small hibernators, global downregulation of the endocannabinoid system (ECS), which is involved in modulating neuronal signaling, feeding behavior, energy metabolism, and circannual rhythms, has been reported to possibly drive physiological adaptation to the hibernating state. In hibernating brown bears (Ursus arctos), we hypothesized that beyond an overall suppression of the ECS, seasonal shift in endocannabinoids compounds could be linked to bear’s peculiar features that include hibernation without arousal episodes and capacity to react to external disturbance. We explored circulating lipids in serum and the ECS in plasma and metabolically active tissues in free-ranging subadult Scandinavian brown bears when both active and hibernating. In winter bear serum, in addition to a 2-fold increase in total fatty acid concentration, we found significant changes in relative proportions of circulating fatty acids, such as a 2-fold increase in docosahexaenoic acid C22:6 n-3 and a decrease in arachidonic acid C20:4 n-6. In adipose and muscle tissues of hibernating bears, we found significant lower concentrations of 2-arachidonoylglycerol (2-AG), a major ligand of cannabinoid receptors 1 (CB1) and 2 (CB2). Lower mRNA level for genes encoding CB1 and CB2 were also found in winter muscle and adipose tissue, respectively. The observed reduction in ECS tone may promote fatty acid mobilization from body fat stores, and favor carbohydrate metabolism in skeletal muscle of hibernating bears. Additionally, high circulating level of the endocannabinoid-like compound N-oleoylethanolamide (OEA) in winter could favor lipolysis and fatty acid oxidation in peripheral tissues. We also speculated on a role of OEA in the conservation of an anorexigenic signal and in the maintenance of torpor during hibernation, while sustaining the capacity of bears to sense stimuli from the environment.

Specific shifts in the endocannabinoid system in hibernating brown bears;Christian Boyer;2020;figshare


CCDC 2016285: Experimental Crystal Structure Determination;Kniazeva, Mariia V.;2020;Cambridge Crystallographic Data Centre

Related Article: Mariia V. Kniazeva, Alexander S. Ovsyannikov, Daut R. Islamov, Aida I. Samigullina, Aidar T. Gubaidullin, Pavel V. Dorovatovskii, Svetlana E. Solovieva, Igor S. Antipin, Sylvie Ferlay|2020|CrystEngComm|22|7693|doi:10.1039/D0CE01232G


CCDC 2016286: Experimental Crystal Structure Determination;Kniazeva, Mariia V.;2020;Cambridge Crystallographic Data Centre


CCDC 2016287: Experimental Crystal Structure Determination;Kniazeva, Mariia V.;2020;Cambridge Crystallographic Data Centre


25,27-dihydroxy-26,28-bis((4-carboxyphenyl)methoxy)calix(4)arene dimethyl sulfoxide solvate

CCDC 2016288: Experimental Crystal Structure Determination;Kniazeva, Mariia V.;2020;Cambridge Crystallographic Data Centre


Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines;Clotilde Théry;2018;Universität des Saarlandes


CCDC 2014872: Experimental Crystal Structure Determination;Chevalier, Aline;2020;Cambridge Crystallographic Data Centre

Related Article: Aline Chevalier, Artem Osypenko, Jean-Marie Lehn, Daniel Meyer|2020|Chemical Science|11|11468|doi:10.1039/D0SC04098C


CCDC 2013385: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre

Related Article: Pierre Thuéry, Youssef Atoini, Jack Harrowfield|2020|Cryst.Growth Des.|20|7368|doi:10.1021/acs.cgd.0c01050


CCDC 2013386: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013387: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013388: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013389: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013390: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013391: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013392: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013393: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013394: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


CCDC 2013395: Experimental Crystal Structure Determination;Thuéry, Pierre;2020;Cambridge Crystallographic Data Centre


Abstract: Abstract Background Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon–intron structures. Even the best eukaryotic gene prediction algorithms can make serious errors that will significantly affect subsequent analyses. Results We first investigated the prevalence of gene prediction errors in a large set of 176,478 proteins from ten primate proteomes available in public databases. Using the well-studied human proteins as a reference, a total of 82,305 potential errors were detected, including 44,001 deletions, 27,289 insertions and 11,015 mismatched segments where part of the correct protein sequence is replaced with an alternative erroneous sequence. We then focused on the mismatched sequence errors that cause particular problems for downstream applications. A detailed characterization allowed us to identify the potential causes for the gene misprediction in approximately half (5446) of these cases. As a proof-of-concept, we also developed a simple method which allowed us to propose improved sequences for 603 primate proteins. Conclusions Gene prediction errors in primate proteomes affect up to 50% of the sequences. Major causes of errors include undetermined genome regions, genome sequencing or assembly issues, and limitations in the models used to represent gene exon–intron structures. Nevertheless, existing genome sequences can still be exploited to improve protein sequence quality. Perspectives of the work include the characterization of other types of gene prediction errors, as well as the development of a more comprehensive algorithm for protein sequence error correction.

Understanding the causes of errors in eukaryotic protein-coding gene prediction: a case study of primate proteomes;Corentin Meyer;2020;figshare


Abstract: Additional file 1: Characterization of the 5446 mismatch errors associated with gene mispredictions.

Additional file 1 of Understanding the causes of errors in eukaryotic protein-coding gene prediction: a case study of primate proteomes;Corentin Meyer;2020;figshare


CCDC 1903453: Experimental Crystal Structure Determination;Le Fouler, Vincent;2019;Cambridge Crystallographic Data Centre

Related Article: Vincent Le Fouler, Yu Chen, Vincent Gandon, Vincent Bizet, Christophe Salomé, Thomas Fessard, Fang Liu, K. N. Houk, Nicolas Blanchard|2019|J.Am.Chem.Soc.|141|15901|doi:10.1021/jacs.9b07037


CCDC 1903455: Experimental Crystal Structure Determination;Le Fouler, Vincent;2019;Cambridge Crystallographic Data Centre


Abstract: Abstract Mutations in the RYR1 gene, encoding the skeletal muscle calcium channel RyR1, lead to congenital myopathies, through expression of a channel with abnormal permeability and/or in reduced amount, but the direct functional whole organism consequences of exclusive reduction in RyR1 amount have never been studied. We have developed and characterized a mouse model with inducible muscle specific RYR1 deletion. Tamoxifen-induced recombination in the RYR1 gene at adult age resulted in a progressive reduction in the protein amount reaching a stable level of 50% of the initial amount, and was associated with a progressive muscle weakness and atrophy. Measurement of calcium fluxes in isolated muscle fibers demonstrated a reduction in the amplitude of RyR1-related calcium release mirroring the reduction in the protein amount. Alterations in the muscle structure were observed, with fibers atrophy, abnormal mitochondria distribution and membrane remodeling. An increase in the expression level of many proteins was observed, as well as an inhibition of the autophagy process. This model demonstrates that RyR1 reduction is sufficient to recapitulate most features of Central Core Disease, and accordingly similar alterations were observed in muscle biopsies from Dusty Core Disease patients (a subtype of Central Core Disease), pointing to common pathophysiological mechanisms related to RyR1 reduction.

In vivo RyR1 reduction in muscle triggers a core-like myopathy;Laurent Pelletier;2020;figshare


CCDC 1995952: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre

Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633


CCDC 1995953: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre


CCDC 1995955: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre


CCDC 1995956: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre


CCDC 1995957: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre


CCDC 1995958: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre


CCDC 1995960: Experimental Crystal Structure Determination;Mamane, Victor;2020;Cambridge Crystallographic Data Centre


bis[2,6-bis(1H-pyrazol-1-yl)pyridine-4-carbonitrile]-iron(ii) bis(tetrafluoroborate) acetonitrile solvate

CCDC 1961768: Experimental Crystal Structure Determination;Kumar, Kuppusamy Senthil;2020;Cambridge Crystallographic Data Centre

Related Article: Kuppusamy Senthil Kumar, Nicolas Del Giudice, Benoît Heinrich, Laurent Douce, Mario Ruben|2020|Dalton Trans.|49|14258|doi:10.1039/D0DT02214D


CCDC 2010806: Experimental Crystal Structure Determination;Kumar, Kuppusamy Senthil;2020;Cambridge Crystallographic Data Centre


CCDC 2021378: Experimental Crystal Structure Determination;del Pozo, Juan;2020;Cambridge Crystallographic Data Centre

Related Article: Juan del Pozo, Shaochen Zhang, Filippo Romiti, Shibo Xu, Ryan P. Conger, Amir H. Hoveyda|2020|J.Am.Chem.Soc.|142|18200|doi:10.1021/jacs.0c08732


CCDC 2021379: Experimental Crystal Structure Determination;del Pozo, Juan;2020;Cambridge Crystallographic Data Centre


CCDC 2021381: Experimental Crystal Structure Determination;del Pozo, Juan;2020;Cambridge Crystallographic Data Centre


CCDC 2021382: Experimental Crystal Structure Determination;del Pozo, Juan;2020;Cambridge Crystallographic Data Centre


Abstract: Abstract Background Infectious and tropical diseases (ID) physicians are needed now more than ever to tackle existing and emerging global threats. However, in many countries, ID is not recognized as a qualifying specialty. The creation of ID residency in 2017 in France offers the opportunity to know how and why the specialty is chosen by medical students. Methods We first analyzed the choice of specialty of all French medical students in 2017 and 2018 according to their rank at the national exam that ends medical studies. A web questionnaire was then sent in January 2019 to all ID residents in France (n = 100) to assess the factors influencing their choice of specialty and their career plan. Results We analyzed the choice of 17,087 medical students. ID was the first-chosen specialty with a median national rank of 526/8539, followed by plastic surgery and ophthalmology. The questionnaire was completed by 90% of the French ID residents (n = 100). The most encouraging factors to choose ID were the multi-system approach of the specialty, the importance of diagnostic medicine and having done an internship in ID during medical school. The potential deterrents were the work-life balance, the workload and the salary. Conclusions The recent recognition of ID as a qualifying specialty in France can be considered a success insofar as the specialty is the most popular among all medical and surgical specialties. Individuals who choose ID are attracted by the intellectual stimulation of the specialty but express concerns about the working conditions and salaries.

How and why do French medical students choose the specialty of infectious and tropical diseases? A national cross-sectional study;Nathan Peiffer-Smadja;2020;figshare


Abstract: Abstract Background CREB-dependent transcription necessary for long-term memory is driven by interactions with CREB-binding protein (CBP), a multi-domain protein that binds numerous transcription factors potentially affecting expression of thousands of genes. Identifying specific domain functions for multi-domain proteins is essential to understand processes such as cognitive function and circadian clocks. We investigated the function of the CBP KIX domain in hippocampal memory and gene expression using CBPKIX/KIX mice with mutations that prevent phospho-CREB (Ser133) binding. Results We found that CBPKIX/KIX mice were impaired in long-term memory, but not learning acquisition or short-term memory for the Morris water maze. Using an unbiased analysis of gene expression in the dorsal hippocampus after training in the Morris water maze or contextual fear conditioning, we discovered dysregulation of CREB, CLOCK, and BMAL1 target genes and downregulation of circadian genes in CBPKIX/KIX mice. Given our finding that the CBP KIX domain was important for transcription of circadian genes, we profiled circadian activity and phase resetting in CBPKIX/KIX mice. CBPKIX/KIX mice exhibited delayed activity peaks after light offset and longer free-running periods in constant dark. Interestingly, CBPKIX/KIX mice displayed phase delays and advances in response to photic stimulation comparable to wildtype littermates. Thus, this work delineates site-specific regulation of the circadian clock by a multi-domain protein. Conclusions These studies provide insight into the significance of the CBP KIX domain by defining targets of CBP transcriptional co-activation in memory and the role of the CBP KIX domain in vivo on circadian rhythms. Graphical abstract

The CBP KIX domain regulates long-term memory and circadian activity;Snehajyoti Chatterjee;2020;figshare


Abstract: Additional file 2 : Table S1. List of Genes Differentially Expressed in CBPKIX/KIX mutants compared to wild-type littermates after spatial learning. Table S2. List of the upstream regulators of the DEGs in CBPKIX/KIX mutants. Table S3. List of Genes Differentially Expressed in wildtype mice after spatial learning compared to homecage. Table S4. Positive controls for validation of differential gene expression analysis. Table S5. List of the upstream regulators of the DEGs in wild-type mice following learning. Table S6. High confidence (80%) changes in homecage gene expression in CBPKIX/KIX mice. Table S7. List of qPCR primers used to study gene expression.

Additional file 2 of The CBP KIX domain regulates long-term memory and circadian activity;Snehajyoti Chatterjee;2020;figshare


CCDC 2027729: Experimental Crystal Structure Determination;Ruiz, Angélina Torres;2020;Cambridge Crystallographic Data Centre

Related Article: Angélina Torres Ruiz, Manon H. E. Bousquet, Simon Pascal, Gabriel Canard, Valérie Mazan, Mourad Elhabiri, Denis Jacquemin, Olivier Siri|2020|Org.Lett.|22|7997|doi:10.1021/acs.orglett.0c02926


CCDC 1946672: Experimental Crystal Structure Determination;Jouaiti, Abdelaziz;2020;Cambridge Crystallographic Data Centre

Related Article: Abdelaziz Jouaiti, Philippe Grosshans, Nathalie Kyritsakas, Sylvie Ferlay, Marc Henry, Mir Wais Hosseini|2020|CrystEngComm|22|1746|doi:10.1039/C9CE01781J


CCDC 1946673: Experimental Crystal Structure Determination;Jouaiti, Abdelaziz;2020;Cambridge Crystallographic Data Centre


CCDC 1540885: Experimental Crystal Structure Determination;Bauder, Claude;2018;Cambridge Crystallographic Data Centre

Related Article: Claude Bauder|2018|Eur.J.Org.Chem.|2018|4874|doi:10.1002/ejoc.201800875


CCDC 1986254: Experimental Crystal Structure Determination;Mathivathanan, Logesh;2020;Cambridge Crystallographic Data Centre

Related Article: Logesh Mathivathanan, Guillaume Rogez, Nadia Ben Amor, Vincent Robert, Raphael G. Raptis, Athanassios K. Boudalis|2020|Chem.-Eur.J.|26|12769|doi:10.1002/chem.202001028


CCDC 1995095: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre

Related Article: Joanna Hammoud, Fatima Abou-Khalil, Thierry Roisnel, Vincent Dorcet, Christophe Bour, Vincent Gandon, David Lebœuf, Jean-François Carpentier, Yann Sarazin|2020|Dalton Trans.|49|13017|doi:10.1039/D0DT02573A


CCDC 1995096: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 1995097: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 1995098: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 1995099: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 1995100: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 1995101: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 1995102: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


Abstract: Additional file 1: Table S1. Frequencies of somatotype components in Cameroonians and Senegalese. This table presents the somatotype profiles in the sample used for the development of the BOSHAS.

Additional file 1 of Development and validation of the body shape scale (BOSHAS) for assessing body shape perception in African populations;Emmanuel Cohen;2020;figshare

Earth and related environmental sciences


Abstract: Additional file 2: Table S2. Original culturally relevant questionnaire for the convergent validity of the BOSHAS.

Additional file 2 of Development and validation of the body shape scale (BOSHAS) for assessing body shape perception in African populations;Emmanuel Cohen;2020;figshare


Abstract: Abstract Background As a consequence of ‘Western’ acculturation, eating disorders and body image disturbances, such as fatness phobia and body dysmorphic disorders towards musculature and body shape, are emerging in Africa, with young people the most affected. It is therefore important to accurately assess perceptions of body shape. However, the existing body image assessment scales lack sufficient accuracy and validity testing to compare body shape perception across different African populations. The purpose of this study was to develop and validate the Body Shape Scale (BOSHAS) to evaluate body shape perceptions related body image disorders in African populations. Methods To develop the BOSHAS, anthropometric measures of 80 Cameroonians and 81 Senegalese (both sexes included; 40.1% females overall) were taken for three body shape criteria: somatotype components, body mass index (BMI) and waist-to-hip ratio. Subjects were selected to cover a wide variability in body shape and were photographed in full face and profile positions. To validate the BOSHAS, the scale was administered twice (2 weeks apart) to 106 participants (aged 31.2 ± 12.6 years) to assess its reliability. In addition, a questionnaire measuring different aspects of body shape (e.g. musculature) was also administered (n = 597; aged 36.7 ± 15.6 years) to assess its convergent validity. Results The BOSHAS includes two sex-specific subscales of 10 photographs each. Most participants were able to repeat their BOSHAS preference order. Test-retest reliability was also consistent in estimating Current Body Shape (CBS), Desired Body Shape and Ideal Body Shape for participants and their partners. CBS was correlated with BMI, and different BOSHAS indices were consistent with declarations obtained by questionnaire. Conclusions The BOSHAS is the first sex-specific scale of real African models photographed in face and profile, including large body shape variability. The validation protocol showed good validity and reliability for evaluating body shape perceptions and dissatisfaction of Africans.

Development and validation of the body shape scale (BOSHAS) for assessing body shape perception in African populations;Emmanuel Cohen;2020;figshare


CCDC 2009320: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 2009385: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 2009421: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


CCDC 2009437: Experimental Crystal Structure Determination;Hammoud, Joanna;2020;Cambridge Crystallographic Data Centre


Abstract: The video shows how it worked the Test Phase in the outdoor area: the experimenter followed the subject (here is Walt, a Tonkean macaque) without giving him any cue about the direction he had to take. When the subject touched a (baited) box, the experimenter unlocked it with the remote control.

Video 2, the Test Phase (link in the manuscript) from Where and what? Frugivory is associated with more efficient foraging in three semi-free ranging primate species.;Cinzia Trapanese;2019;The Royal Society


Abstract: The video shows the moment in which the experimenter presents the personal starting cue to the subject (Yannick, a Tonkean macaque) at the starting point.

Video 1, the personal starting cue (link in the manuscript) from Where and what? Frugivory is associated with more efficient foraging in three semi-free ranging primate species.;Cinzia Trapanese;2019;The Royal Society


CCDC 1977549: Experimental Crystal Structure Determination;Tian, Jiaxin;2020;Cambridge Crystallographic Data Centre

Related Article: Jiaxin Tian, Yan Chen, Marie Vayer, Alexandre Djurovic, Régis Guillot, Refka Guermazi, Samuel Dagorne, Chritophe Bour, Vincent Gandon|2020|Chem.-Eur.J.|26|12831|doi:10.1002/chem.202001376


CCDC 1977550: Experimental Crystal Structure Determination;Tian, Jiaxin;2020;Cambridge Crystallographic Data Centre


Abstract: Dataset as associated material

Dataset from Negative phenotypic and genetic correlation between natal dispersal propensity and nest-defence behaviour in a wild bird.;Pierre Bize;2017;The Royal Society


Abstract: Excel spreadsheet containing, in separate sheets, data for statistical analysis and visualization of post-illumination changes in pupil diameter and response speed, data underlying the pupil diameter trace during the red and blue light exposure paradigm, and sex and age characteristics of the participants in Experiment 1.

S1 Data from Sustained effects of prior red light on pupil diameter and vigilance during subsequent darkness.;Wisse P. Van Der Meijden;2018;The Royal Society


Abstract: Excel spreadsheet containing, in separate sheets, data for statistical analysis and visualization of post-illumination changes in sleep propensity, and sex and age characteristics of the participants in Experiment 3.

S3 Data from Sustained effects of prior red light on pupil diameter and vigilance during subsequent darkness.;Wisse P. Van Der Meijden;2018;The Royal Society


Abstract: Excel spreadsheet containing, in separate sheets, data for statistical analysis and visualization of post-illumination changes in pupil diameter, response speed, R-R interval, ln SDNN, ln RMSSD, and ln SDNN/RMSSD, and sex and age characteristics of the participants in Experiment 2.

S2 Data from Sustained effects of prior red light on pupil diameter and vigilance during subsequent darkness.;Wisse P. Van Der Meijden;2018;The Royal Society


Abstract: Abstract Background Hyperoxemia has been associated with increased mortality in critically ill patients, but little is known about its effect in trauma patients. The objective of this study was to assess the association between early hyperoxemia and in-hospital mortality after severe trauma. We hypothesized that a PaO2 ≥ 150 mmHg on admission was associated with increased in-hospital mortality. Methods Using data issued from a multicenter prospective trauma registry in France, we included trauma patients managed by the emergency medical services between May 2016 and March 2019 and admitted to a level I trauma center. Early hyperoxemia was defined as an arterial oxygen tension (PaO2) above 150 mmHg measured on hospital admission. In-hospital mortality was compared between normoxemic (150 > PaO2 ≥ 60 mmHg) and hyperoxemic patients using a propensity-score model with predetermined variables (gender, age, prehospital heart rate and systolic blood pressure, temperature, hemoglobin and arterial lactate, use of mechanical ventilation, presence of traumatic brain injury (TBI), initial Glasgow Coma Scale score, Injury Severity Score (ISS), American Society of Anesthesiologists physical health class > I, and presence of hemorrhagic shock). Results A total of 5912 patients were analyzed. The median age was 39 [26–55] years and 78% were male. More than half (53%) of the patients had an ISS above 15, and 32% had traumatic brain injury. On univariate analysis, the in-hospital mortality was higher in hyperoxemic patients compared to normoxemic patients (12% versus 9%, p

Early hyperoxemia is associated with lower adjusted mortality after severe trauma: results from a French registry;Josefine S. Baekgaard;2020;figshare


Abstract: This zip file contains all the matlab scripts and an example

3D cylindric matlab scripts and example from Three-dimensional microscopy and image analysis methodology for mapping and quantification of nuclear positions in tissues with approximate cylindrical geometry.;Pedro Campinho;2018;The Royal Society


Abstract: From around 2750 to 2500 BC, Bell Beaker pottery became widespread across western and central Europe, before it disappeared between 2200 and 1800 BC. The forces that propelled its expansion are a matter of long-standing debate, and there is support for both cultural diffusion and migration having a role in this process. Here we present genome-wide data from 400 Neolithic, Copper Age and Bronze Age Europeans, including 226 individuals associated with Beaker-complex artefacts. We detected limited genetic affinity between Beaker-complex-associated individuals from Iberia and central Europe, and thus exclude migration as an important mechanism of spread between these two regions. However, migration had a key role in the further dissemination of the Beaker complex. We document this phenomenon most clearly in Britain, where the spread of the Beaker complex introduced high levels of steppe-related ancestry and was associated with the replacement of approximately 90% of Britain’s gene pool within a few hundred years, continuing the east-to-west expansion that had brought steppe-related ancestry into central and northern Europe over the previous centuries.

The Beaker phenomenon and the genomic transformation of northwest Europe;Iñigo Olalde;2018;Online Ancient Genome Repository


Abstract: Abstract Background Pre-pregnancy obesity and excessive gestational weight gain (GWG) are established risk factors for adverse pregnancy, delivery and birth outcomes. Pregnancy is an ideal moment for nutritional interventions in order to establish healthier lifestyle behaviors in women at high risk of obstetric and neonatal complications. Methods Electronic-Personalized Program for Obesity during Pregnancy to Improve Delivery (ePPOP-ID) is an open multicenter randomized controlled trial which will assess the efficacy of an e-health web-based platform offering a personalized lifestyle program to obese pregnant women in order to reduce the rate of labor procedures and delivery interventions in comparison to standard care. A total of 860 eligible pregnant women will be recruited in 18 centers in France between 12 and 22 weeks of gestation, randomized into the intervention or the control arm and followed until 10 weeks of postpartum. The intervention is based on nutrition, eating behavior, physical activity, motivation and well-being advices in which personalization is central, as well as the use of a mobile/tablet application. Inputs includes data from the medical record of participants (medical history, anthropometric data), from the web platform (questionnaires on dietary habits, eating behavior, physical activity and motivation in both groups), and adherence to the program (time of connection for the intervention group only). Data are collected at inclusion, 32 weeks, delivery and 10 weeks postpartum. As primary outcome, we will use a composite endpoint score of obstetrical interventions during labor and delivery, defined as caesarean section and instrumental delivery (forceps and vacuum extractor). Secondary outcomes will consist of data routinely collected as part of usual antenatal and perinatal care, such as GWG, hypertension, preeclampsia, as well as fetal and neonatal outcomes including premature birth, gestational age at birth, birth weight, macrosomia, Apgar score, arterial umbilical cord pH, neonatal traumatism, hyperbilirubinemia, respiratory distress syndrome, transfer in neonatal intensive care unit, and neonatal adiposity. Post-natal outcomes will be duration of breastfeeding, maternal weight retention and child weight at postnatal visit. Discussion The findings of the ePPOP-ID trial will help design e-health intervention program for obese women in pregnancy. Trial registration ClinicalTrials.gov Identifier: NCT02924636 / October 5th 2016.

Rationale and design of ePPOP-ID: a multicenter randomized controlled trial using an electronic-personalized program for obesity in pregnancy to improve delivery;Philippe Deruelle;2020;figshare


Abstract: Supplemental Figures and Tables

Supplemental Material for Gómez-Saldivar et al., 2020;Georgina Gómez-Saldivar;2020;GSA Journals


Related Article: Michał Kołodziejski, Artur R. Stefankiewicz, Jean-Marie Lehn|2019|Chemical Science|10|1836|doi:10.1039/C8SC04598D

CCDC 1853998: Experimental Crystal Structure Determination;Kołodziejski, Michał;2018;Cambridge Crystallographic Data Centre


CCDC 1853999: Experimental Crystal Structure Determination;Kołodziejski, Michał;2018;Cambridge Crystallographic Data Centre


Related Article: Patrick Larpent, Abdelaziz Jouaiti, Nathalie Kyritsakas, Mir Wais Hosseini|2019|Chem.Commun.|55|91|doi:10.1039/C8CC08877B

CCDC 1413936: Experimental Crystal Structure Determination;Larpent, Patrick;2018;Cambridge Crystallographic Data Centre


CCDC 1413937: Experimental Crystal Structure Determination;Larpent, Patrick;2018;Cambridge Crystallographic Data Centre


CCDC 1983127: Experimental Crystal Structure Determination;Bonfiglio, Anna;2020;Cambridge Crystallographic Data Centre

Related Article: Anna Bonfiglio, Lenka Pallova, Vincent César, Christophe Gourlaouen, Stéphane Bellemin-Laponnaz, Chantal Daniel, Federico Polo, Matteo Mauro|2020|Chem.-Eur.J.|26|11751|doi:10.1002/chem.202002767


CCDC 2004107: Experimental Crystal Structure Determination;Barbero, Héctor;2020;Cambridge Crystallographic Data Centre

Related Article: Héctor Barbero, Antoine Meunier, Kondalarao Kotturi, Ashton Smith, Nathalie Kyritsakas, Adam Killmeyer, Ramin Rabbani, Md Nazimuddin, Eric Masson|2020|Phys.Chem.Chem.Phys.(PCCP)|22|20602|doi:10.1039/D0CP03285A


CCDC 2004108: Experimental Crystal Structure Determination;Barbero, Héctor;2020;Cambridge Crystallographic Data Centre


Abstract: Abstract Background The mammalian Major Histocompatibility Complex (MHC) is a genetic region containing highly polymorphic genes with immunological functions. MHC class I and class II genes encode antigen-presenting molecules expressed on the cell surface. The MHC class II sub-region contains genes expressed in antigen presenting cells. The antigen binding site is encoded by the second exon of genes encoding antigen presenting molecules. The exon 2 sequences of these MHC genes have evolved under the selective pressure of pathogens. Interspecific differences can be observed in the class II sub-region. The family Equidae includes a variety of domesticated, and free-ranging species inhabiting a range of habitats exposed to different pathogens and represents a model for studying this important part of the immunogenome. While equine MHC class II DRA and DQA loci have received attention, the genetic diversity and effects of selection on DRB and DQB loci have been largely overlooked. This study aimed to provide the first in-depth analysis of the MHC class II DRB and DQB loci in the Equidae family. Results Three DRB and two DQB genes were identified in the genomes of all equids. The genes DRB2, DRB3 and DQB3 showed high sequence conservation, while polymorphisms were more frequent at DRB1 and DQB1 across all species analyzed. DQB2 was not found in the genome of the Asiatic asses Equus hemionus kulan and E. h. onager. The bioinformatic analysis of non-zero-coverage-bases of DRB and DQB genes in 14 equine individual genomes revealed differences among individual genes. Evidence for recombination was found for DRB1, DRB2, DQB1 and DQB2 genes. Trans-species allele sharing was identified in all genes except DRB1. Site-specific selection analysis predicted genes evolving under positive selection both at DRB and DQB loci. No selected amino acid sites were identified in DQB3. Conclusions The organization of the MHC class II sub-region of equids is similar across all species of the family. Genomic sequences, along with phylogenetic trees suggesting effects of selection as well as trans-species polymorphism support the contention that pathogen-driven positive selection has shaped the MHC class II DRB/DQB sub-regions in the Equidae.

Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 11. Sharing of positively selected amino acid sites (SAASs) in the DRB and DQB locus in Equids and other species.

Additional file 11 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 2. Amplification of individual DRB and DQB genes from different horse breeds.

Additional file 2 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 8. Functional effects of amino acid substitutions.

Additional file 8 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 1. BLAST pairwise sequence alignments of DQB2 and DQB-like flanking sequences.

Additional file 1 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 10. Allele sharing in individual DRB and DQB loci.

Additional file 10 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 7. Recombination identified by the Single breakpoint (SBP) method.

Additional file 7 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


Abstract: Additional file 9. Positively and negatively selected sites identified using different methods in DRB and DQB loci.

Additional file 9 of Genetic diversity, evolution and selection in the major histocompatibility complex DRB and DQB loci in the family Equidae;Marie Klumplerova;2020;figshare


CCDC 2007067: Experimental Crystal Structure Determination;Yu, Fei;2020;Cambridge Crystallographic Data Centre

Related Article: Fei Yu, Bao-Qian Ji, Marko Jagodič, Yan-Min Su, Shan-Shan Zhang, Lei Feng, Mohamedally Kurmoo, Zvonko Jagličić, Di Sun|2020|Inorg.Chem.|59|13524|doi:10.1021/acs.inorgchem.0c01915


CCDC 2007070: Experimental Crystal Structure Determination;Yu, Fei;2020;Cambridge Crystallographic Data Centre


CCDC 1869722: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre

Related Article: Pierre Thuéry, Youssef Atoini, Jack Harrowfield|2019|Inorg.Chem.|58|567|doi:10.1021/acs.inorgchem.8b02762


CCDC 1869723: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869724: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869725: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869726: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869727: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869728: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869729: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1869730: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1874637: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre

Related Article: Pierre Thuéry,  Youssef Atoini,  Jack Harrowfield|2019|Inorg.Chem.|58|870|doi:10.1021/acs.inorgchem.8b02992


CCDC 1874638: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1874639: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


tris(ammonium) tris(methyl(triphenyl)phosphonium) nonakis(mu-1,2,2-trimethylcyclopentane-1,3-dicarboxylato)-dodecaoxo-hexa-uranium unknown solvate

CCDC 1874640: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 1874636: Experimental Crystal Structure Determination;Thuéry, Pierre;2018;Cambridge Crystallographic Data Centre


CCDC 2007065: Experimental Crystal Structure Determination;Yu, Fei;2020;Cambridge Crystallographic Data Centre


CCDC 2007066: Experimental Crystal Structure Determination;Yu, Fei;2020;Cambridge Crystallographic Data Centre


CCDC 2007068: Experimental Crystal Structure Determination;Yu, Fei;2020;Cambridge Crystallographic Data Centre


CCDC 2007069: Experimental Crystal Structure Determination;Yu, Fei;2020;Cambridge Crystallographic Data Centre


CCDC 1972177: Experimental Crystal Structure Determination;Gimeno, Lea;2020;Cambridge Crystallographic Data Centre

Related Article: Lea Gimeno, Errol Blart, Jean-Noël Rebilly, Marina Coupeau, Magali Allain, Thierry Roisnel, Alexis Quarré de Verneuil, Christophe Gourlaouen, Chantal Daniel, Yann Pellegrin|2020|Chem.-Eur.J.|26|11887|doi:10.1002/chem.202001209


CCDC 2007757: Experimental Crystal Structure Determination;Egly, Julien;2020;Cambridge Crystallographic Data Centre

Related Article: Julien Egly, Damien Bissessar, Thierry Achard, Benoît Heinrich, Pascal Steffanut, Matteo Mauro, Stéphane Bellemin-Laponnaz|2021|Inorg.Chim.Acta|514|119971|doi:10.1016/j.ica.2020.119971


CCDC 2007758: Experimental Crystal Structure Determination;Egly, Julien;2020;Cambridge Crystallographic Data Centre


CCDC 2007759: Experimental Crystal Structure Determination;Egly, Julien;2020;Cambridge Crystallographic Data Centre


CCDC 2007760: Experimental Crystal Structure Determination;Egly, Julien;2020;Cambridge Crystallographic Data Centre


CCDC 2007761: Experimental Crystal Structure Determination;Egly, Julien;2020;Cambridge Crystallographic Data Centre


CCDC 1982032: Experimental Crystal Structure Determination;Saranya, Sundar;2020;Cambridge Crystallographic Data Centre

Related Article: Sundar Saranya, Rengan Ramesh, David Sémeril|2020|Organometallics|39|3194|doi:10.1021/acs.organomet.0c00367


CCDC 1982033: Experimental Crystal Structure Determination;Saranya, Sundar;2020;Cambridge Crystallographic Data Centre


Abstract: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

CCDC 1846774: Experimental Crystal Structure Determination;Mathilde Berville;2018;Cambridge Crystallographic Data Centre

Related Article: Mathilde Berville,  Jimmy Richard,  Monika Stolar,  Sylvie Choua,  Nolwenn Le Breton,  Christophe Gourlaouen,  Corinne Boudon,  Laurent Ruhlmann,  Thomas Baumgartner,  Jennifer A. Wytko,  Jean Weiss|2018|Org.Lett.|20|8004|doi:10.1021/acs.orglett.8b03579


CCDC 1849378: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre

Related Article: Fei Yu, Jia-Qian Li, Mohamedally Kurmoo, Jing-Lin Zuo|2018|Inorg.Chem.|57|15040|doi:10.1021/acs.inorgchem.8b02878


CCDC 1849379: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre


CCDC 1849380: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre


CCDC 1849381: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre


CCDC 1862104: Experimental Crystal Structure Determination;Wenting Cai;2018;Cambridge Crystallographic Data Centre

(2,3,7,8,12,13,17,18-octaethylporphyrinato)-nickel(ii) C80fulleride thorium benzene solvate

Related Article: Wenting Cai, Laura Abella, Jiaxin Zhuang, Xingxing Zhang, Lai Feng, Yaofeng Wang, Roser Morales-Martínez, Ronda Esper, Mauro Boero, Alejandro Metta-Magaña, Antonio Rodríguez-Fortea, Josep M. Poblet, Luis Echegoyen, Ning Chen|2018|J.Am.Chem.Soc.|140|18039|doi:10.1021/jacs.8b10435


CCDC 1835947: Experimental Crystal Structure Determination;Wenting Cai;2018;Cambridge Crystallographic Data Centre

(2,3,7,8,12,13,17,18-octaethylporphyrinato)-nickel(ii) C76fulleride uranium chloroform solvate


CCDC 1862103: Experimental Crystal Structure Determination;Wenting Cai;2018;Cambridge Crystallographic Data Centre

(2,3,7,8,12,13,17,18-octaethylporphyrinato)-nickel(ii) C80fulleride uranium chloroform solvate


Abstract: Ageing is characterized by the impairment of the acute innate immune response and the upregulation of low-grade inflammation, i.e. inflammaging. At the cellular level, telomeres are considered as a marker of biological ageing as their length is progressively eroded in the absence of repair mechanisms. However, the link between telomeres and inflammaging remains underexplored. We aimed to identify proteins that are differentially expressed between age classes in response to an acute inflammatory challenge. We challenged young (two months) and old (12 months) C57BL/6 mice using bacterial lipopolysaccharide (LPS) and measured telomere length and proteomic profiles in splenocytes. In total, 233 out of the 1966 proteins we quantified differed among experimental groups. A hierarchical clustering analysis revealed that nine of those 233 proteins were differently expressed among the experimental groups. Young mice responded to LPS by increasing the expression of proteins involved in the innate immune response, and interestingly, in telomere length maintenance. However, this regulation was impaired at older ages. These results are in agreement with the assumption that the strength of selection declines with age, potentially explaining the maintenance of costly, dysregulated, immune responses at old age. We suggest that the immune response is competing with the telomere maintenance process, highlighting how telomeres reflect the ageing trade-off even in a species where telomere length is not related to lifespan.

Supplementary material from "Age-related response to an acute innate immune challenge in mice: proteomics reveals a telomere maintenance-related cost";François Criscuolo;2018;Figshare


CCDC 1537426: Experimental Crystal Structure Determination;Gang Liu;2018;Cambridge Crystallographic Data Centre

Related Article: Gang Liu, Xing Yang, Antoine Bonnefont,Yaokang Lv, Jun Chen, Wenyan Dan, Zuofeng Chen,  Laurent Ruhlmann, Dominic S. Wright, Cheng Zhang|2018|Chem.Commun.|54|14132|doi:10.1039/C8CC07527A


CCDC 1864945: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre

Related Article: Nelson Y. S. Lam, Dianna Truong, Hilke Burmeister, Maria V. Babak, Hannah U. Holtkamp, Sanam Movassaghi, Daniel Moscoh Ayine-Tora, Ayesha Zafar, Mario Kubanik, Luciano Oehninger, Tilo Söhnel, Jóhannes Reynisson, Stephen M. F. Jamieson, Christian Gaiddon, Ingo Ott, Christian G. Hartinger|2018|Inorg.Chem.|57|14427|doi:10.1021/acs.inorgchem.8b02634


CCDC 1864946: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864947: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864948: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864949: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864950: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864951: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864952: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


CCDC 1864953: Experimental Crystal Structure Determination;Nelson Y. S. Lam;2018;Cambridge Crystallographic Data Centre


Related Article: Gérald Chaplais, Guillaume Fraux, Jean-Louis Paillaud, Claire Marichal, Alain H.  Fuchs, François-Xavier Coudert and Joël Patarin|2018|J.Phys.Chem.C|122|26945|doi:10.1021/acs.jpcc.8b08706

CCDC 1860456: Experimental Crystal Structure Determination;Gérald Chaplais;2018;Cambridge Crystallographic Data Centre


CCDC 1860457: Experimental Crystal Structure Determination;Gérald Chaplais;2018;Cambridge Crystallographic Data Centre


Related Article: Soumyaditya Mula, Nicolas Leclerc, Patrick Lévêque, Pascal Retailleau, and Gilles Ulrich|2018|J.Org.Chem.|83|14406|doi:10.1021/acs.joc.8b01948

CCDC 1576619: Experimental Crystal Structure Determination;Soumyaditya Mula;2018;Cambridge Crystallographic Data Centre


CCDC 1848046: Experimental Crystal Structure Determination;Jean-David Peltier;2018;Cambridge Crystallographic Data Centre

Related Article: Jean-David Peltier, Benoît Heinrich, Bertrand Donnio, olivier Jeannin, Joelle Rault - Berthelot, Emmanuel Jacques, Cyril Poriel|2018|J.Mater.Chem.C|6|13197|doi:10.1039/C8TC04313B


CCDC 1848047: Experimental Crystal Structure Determination;Jean-David Peltier;2018;Cambridge Crystallographic Data Centre


CCDC 1855503: Experimental Crystal Structure Determination;Nicolas Lentz;2018;Cambridge Crystallographic Data Centre

Related Article: Nicolas Lentz, Sonia Mallet-Ladeira, Antoine Baceiredo, Tsuyoshi Kato, David Madec|2018|Dalton Trans.|47|15751|doi:10.1039/C8DT03669A


CCDC 1855504: Experimental Crystal Structure Determination;Nicolas Lentz;2018;Cambridge Crystallographic Data Centre


CCDC 1855505: Experimental Crystal Structure Determination;Nicolas Lentz;2018;Cambridge Crystallographic Data Centre


CCDC 1874215: Experimental Crystal Structure Determination;B. Sarr;2018;Cambridge Crystallographic Data Centre

Related Article: B. Sarr, A. Mbaye, C.A.K. Diop, F. Melin, P. Hellwig, M. Sidibé, Y. Rousselin|2018|Acta Crystallogr.,Sect.E:Cryst.Commun.|74|1682|doi:10.1107/S2056989018014755


Abstract: Near Eastern Neolithic farmers introduced several species of domestic plants and animals as they dispersed into Europe. Dogs were the only domestic species present in both Europe and the Near East prior to the Neolithic. Here, we assessed whether early Near Eastern dogs possessed a unique mitochondrial lineage that differentiated them from Mesolithic European populations. We then analysed mitochondrial DNA sequences from 99 ancient European and Near Eastern dogs spanning the Upper Palaeolithic to the Bronze Age to assess if incoming farmers brought Near Eastern dogs with them, or instead primarily adopted indigenous European dogs after they arrived. Our results show that European pre-Neolithic dogs all possessed the mitochondrial haplogroup C, and that the Neolithic and Post-Neolithic dogs associated with farmers from Southeastern Europe mainly possessed haplogroup D. Thus, the appearance of haplogroup D most probably resulted from the dissemination of dogs from the Near East into Europe. In Western and Northern Europe, the turnover is incomplete and C haplogroup persists well into the Chalcolithic at least. These results suggest that dogs were an integral component of the Neolithic farming package and a mitochondrial lineage associated with the Near East was introduced into Europe alongside pigs, cows, sheep and goats. It got diluted into the native dog population when reaching the Western and Northern margins of Europe.

Supplementary material from "Dogs accompanied humans during the Neolithic expansion into Europe";Morgane Ollivier;2018;Figshare


CCDC 1829948: Experimental Crystal Structure Determination;Frederik Rainer Ehrmann;2018;Cambridge Crystallographic Data Centre

Related Article: Frederik Rainer Ehrmann,  Jorna Kalim,  Toni Pfaffeneder,  Bruno Bernet,  Christoph Hohn,  Elisabeth Schäfer,  Thomas Botzanowski,  Sarah Cianférani,  Andreas Heine,  Klaus Reuter,  François Diederich,  Gerhard Klebe|2018|Angew.Chem.,Int.Ed.|57|10085|doi:10.1002/anie.201804627


CCDC 1829949: Experimental Crystal Structure Determination;Frederik Rainer Ehrmann;2018;Cambridge Crystallographic Data Centre


9,9'-[pyridine-2,6-diylbis(3,1-phenylene)]bis(10-methylacridin-10-ium) bis(hexafluorophosphate) acetonitrile solvate

CCDC 1856039: Experimental Crystal Structure Determination;Henri-Pierre Jacquot De Rouville;2018;Cambridge Crystallographic Data Centre

Related Article: Henri-Pierre Jacquot de Rouville, Nathalie Zorn, Emmanuelle Leize-Wagner, Valérie Heitz|2018|Chem.Commun.|54|10966|doi:10.1039/C8CC05958F


CCDC 1851284: Experimental Crystal Structure Determination;Yasmine K. Redjel;2018;Cambridge Crystallographic Data Centre

Related Article: Yasmine K. Redjel, Lucas Thevenin, Jean-Claude Daran, Meriem Benslimane, Rinaldo Poli, Christophe Fliedel|2018|Polyhedron|158|83|doi:10.1016/j.poly.2018.09.021


CCDC 1851285: Experimental Crystal Structure Determination;Yasmine K. Redjel;2018;Cambridge Crystallographic Data Centre


CCDC 1851286: Experimental Crystal Structure Determination;Yasmine K. Redjel;2018;Cambridge Crystallographic Data Centre


CCDC 1851287: Experimental Crystal Structure Determination;Yasmine K. Redjel;2018;Cambridge Crystallographic Data Centre


CCDC 1851288: Experimental Crystal Structure Determination;Yasmine K. Redjel;2018;Cambridge Crystallographic Data Centre


CCDC 1847599: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre

Related Article: Meng He, Xiao Chen, Tilmann Bodenstein, Andreas Nyvang, Sebastian F. M. Schmidt, Yan Peng, Eufemio Moreno-Pineda, Mario Ruben, Karin Fink, Michael T. Gamer, Annie K. Powell, Peter W. Roesky|2018|Organometallics|37|3708|doi:10.1021/acs.organomet.8b00412


CCDC 1847600: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847601: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847602: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847603: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847604: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847605: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847606: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


CCDC 1847607: Experimental Crystal Structure Determination;Meng He;2018;Cambridge Crystallographic Data Centre


Abstract: Abstract Background Criteria for the Sepsis-3 definition of septic shock include vasopressor treatment to maintain a mean arterial pressure > 65 mmHg and a lactate concentration > 2 mmol/L. The impact of hyperoxia in patients with septic shock using these criteria is unknown. Methods A post hoc analysis was performed of the HYPER2S trial assessing hyperoxia versus normoxia in septic patients requiring vasopressor therapy, in whom a plasma lactate value was available at study inclusion. Mortality was compared between patients fulfilling the Sepsis-3 septic shock criteria and patients requiring vasopressors for hypotension only (i.e., with lactate ≤ 2 mmol/L). Results Of the 434 patients enrolled, 397 had available data for lactate at inclusion. 230 had lactate > 2 mmol/L and 167 ≤ 2 mmol/L. Among patients with lactate > 2 mmol/L, 108 and 122 were “hyperoxia”- and “normoxia”-treated, respectively. Patients with lactate > 2 mmol/L had significantly less COPD more cirrhosis and required surgery more frequently. They also had higher illness severity (SOFA 10.6 ± 2.8 vs. 9.5 ± 2.5, p = 0.0001), required more renal replacement therapy (RRT), and received vasopressor and mechanical ventilation for longer time. Mortality rate at day 28 was higher in the “hyperoxia”-treated patients with lactate > 2 mmol/L as compared to “normoxia”-treated patients (57.4% vs. 44.3%, p = 0.054), despite similar RRT requirements as well as vasopressor and mechanical ventilation-free days. A multivariate analysis showed an independent association between hyperoxia and mortality at day 28 and 90. In patients with lactate ≤ 2 mmol/L, hyperoxia had no effect on mortality nor on other outcomes. Conclusions Our results suggest that hyperoxia may be associated with a higher mortality rate in patients with septic shock using the Sepsis-3 criteria, but not in patients with hypotension alone.

Hyperoxia toxicity in septic shock patients according to the Sepsis-3 criteria: a post hoc analysis of the HYPER2S trial;Julien Demiselle;2018;Figshare


Abstract: RNA uridylation consists of the untemplated addition of uridines at the 3′ extremity of an RNA molecule. RNA uridylation is catalysed by terminal uridylyltransferases (TUTases), which form a subgroup of the terminal nucleotidyltransferase family, to which poly(A) polymerases also belong. The key role of RNA uridylation is to regulate RNA degradation in a variety of eukaryotes, including fission yeast, plants and animals. In plants, RNA uridylation has been mostly studied in two model species, the green algae Chlamydomonas reinhardtii and the flowering plant Arabidopsis thaliana. Plant TUTases target a variety of RNA substrates, differing in size and function. These RNA substrates include microRNAs (miRNAs), small interfering silencing RNAs (siRNAs), ribosomal RNAs (rRNAs), messenger RNAs (mRNAs) and mRNA fragments generated during post-transcriptional gene silencing. Viral RNAs can also get uridylated during plant infection. We describe here the evolutionary history of plant TUTases and we summarize the diverse molecular functions of uridylation during RNA degradation processes in plants. We also outline key points of future research.This article is part of the theme issue ‘5′ and 3′ modifications controlling RNA degradation’.

Supplementary material from "RNA uridylation and decay in plants";Caroline De Almeida;2018;Figshare


CCDC 1824219: Experimental Crystal Structure Determination;Quentin Huaulmé;2018;Cambridge Crystallographic Data Centre

Related Article: Quentin Huaulmé, Alexandra Sutter, Sadiara Fall, Denis Jacquemin, Patrick Lévêque, Pascal Retailleau, Gilles Ulrich, Nicolas Leclerc|2018|J.Mater.Chem.C|6|9925|doi:10.1039/C8TC03138J


CCDC 1824220: Experimental Crystal Structure Determination;Quentin Huaulmé;2018;Cambridge Crystallographic Data Centre


CCDC 1841726: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre

Related Article: Fei-Long Hu, Yan Mi, Chen Zhu, Brendan F. Abrahams, Pierre Braunstein, and Jian-Ping Lang|2018|Angew.Chem.,Int.Ed.|57|12696|doi:10.1002/anie.201806076


CCDC 1825517: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825518: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825519: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825520: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825521: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825522: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1827503: Experimental Crystal Structure Determination;Pengfei Ai;2018;Cambridge Crystallographic Data Centre

Related Article: Pengfei Ai,  Kirill Yu. Monakhov,  Jan van Leusen,  Paul Kögerler,  Christophe Gourlaouen,  Moniek Tromp,  Richard Welter,  Andreas A. Danopoulos,  Pierre Braunstein|2018|Chem.-Eur.J.|24|8787|doi:10.1002/chem.201801170


CCDC 1827504: Experimental Crystal Structure Determination;Pengfei Ai;2018;Cambridge Crystallographic Data Centre


CCDC 1827505: Experimental Crystal Structure Determination;Pengfei Ai;2018;Cambridge Crystallographic Data Centre


CCDC 1827506: Experimental Crystal Structure Determination;Pengfei Ai;2018;Cambridge Crystallographic Data Centre


CCDC 1841714: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1019498: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841716: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841717: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841718: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841719: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841720: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841721: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841722: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825515: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841723: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1825516: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841724: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


CCDC 1841725: Experimental Crystal Structure Determination;Fei-Long Hu;2018;Cambridge Crystallographic Data Centre


Abstract: Abstract Background Oleaginous yeast Yarrowia lipolytica is an organism of choice for the development of biofuel and oleochemicals. It has become a chassis for metabolic engineering in order to produce targeted lipids. Understanding the function of key-enzymes involved in lipid metabolism is essential to design better routes for enhanced lipid production and for strains producing lipids of interest. Because medium chain fatty acids (MCFA) are valuable compounds for biokerosene production, we previously generated strains capable of producing MCFA up to 12% of total lipid content (Rigouin et al. in ACS Synth Biol 6:1870â  1879, 2017). In order to improve accumulation and content of C14 fatty acid (FA), the elongation, degradation and accumulation of these MCFA in Yarrowia lipolytica were studied. Results We brought evidence of the role of YALI0F0654 (YlELO1) protein in the elongation of exogenous or de novo synthesized C14 FA into C16 FA and C18 FA. YlELO1 deletion into a ÎąFAS_I1220W expressing strain leads to the sole production of C14 FA. However, because this strain does not provide the FA essential for its growth, it requires being cultivated with essential fatty acids and C14 FA yield is limited. To promote MCFA accumulation in Y. lipolytica without compromising the growth, we overexpressed a plant diglyceride acyltransferase specific for MCFA and reached an accumulation of MCFA up to 45% of total lipid content. Conclusion We characterized the role of YlELO1 in Y. lipolytica by proving its involvement in Medium chain fatty acids elongation. We showed that MCFA content can be increased in Yarrowia lipolytica by promoting their accumulation into a stable storage form (triacylglycerides) to limit their elongation and their degradation.

Increasing medium chain fatty acids production in Yarrowia lipolytica by metabolic engineering;Coraline Rigouin;2018;Figshare


CCDC 1833993: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre

Related Article: Carlos Bartual-Murgui, Rosa Diego, Sergi Vela, Simon J. Teat, Olivier Roubeau, Guillem Aromí|2018|Inorg.Chem.|57|11019|doi:10.1021/acs.inorgchem.8b01625


CCDC 1833994: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1833995: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1833996: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1833997: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1833998: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1833999: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834000: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834001: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834002: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834003: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834004: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834005: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834006: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834007: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834008: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834009: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1834010: Experimental Crystal Structure Determination;Carlos Bartual-Murgui;2018;Cambridge Crystallographic Data Centre


CCDC 1540075: Experimental Crystal Structure Determination;Amandine Conte‐Daban;2018;Cambridge Crystallographic Data Centre

Related Article: Amandine Conte‐Daban,  Maryline Beyler,  Raphaël Tripier,  Christelle Hureau|2018|Chem.-Eur.J.|24|8447|doi:10.1002/chem.201801520


CCDC 1851716: Experimental Crystal Structure Determination;Olga V. Kazarina;2018;Cambridge Crystallographic Data Centre

Related Article: Olga V. Kazarina, Christophe Gourlaouen, Lydia Karmazin, Alexander G. Morozov, Igor L. Fedushkin, Samuel Dagorne|2018|Dalton Trans.|47|13800|doi:10.1039/C8DT02614A


CCDC 1851718: Experimental Crystal Structure Determination;Olga V. Kazarina;2018;Cambridge Crystallographic Data Centre


CCDC 1811738: Experimental Crystal Structure Determination;Julien Bortoluzzi;2018;Cambridge Crystallographic Data Centre

Related Article: Julien Bortoluzzi,  Vishwajeet Jha,  Guillaume Levitre,  Mickaël J. Fer,  Jordan Berreur,  Géraldine Masson,  Armen Panossian,  and Frédéric R. Leroux|2018|J.Org.Chem.|83|7751|doi:10.1021/acs.joc.8b00648


CCDC 1811739: Experimental Crystal Structure Determination;Julien Bortoluzzi;2018;Cambridge Crystallographic Data Centre


CCDC 1811740: Experimental Crystal Structure Determination;Julien Bortoluzzi;2018;Cambridge Crystallographic Data Centre


CCDC 1839874: Experimental Crystal Structure Determination;Nicolas Genevaz;2018;Cambridge Crystallographic Data Centre

Related Article: Nicolas Genevaz, Patricia Chávez, Viktoriia Untilova, Alex Boeglin, Corinne Bailly, Lydia Karmazin, Laure Biniek|2018|J.Mater.Chem.C|6|9140|doi:10.1039/C8TC02022A


CCDC 1839875: Experimental Crystal Structure Determination;Nicolas Genevaz;2018;Cambridge Crystallographic Data Centre


CCDC 1844367: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre

Related Article: Fei Yu, Mohamedally Kurmoo, Gui-Lin Zhuang, Jing-Lin Zuo|2018|Chemical Science|9|7498|doi:10.1039/C8SC02851F

hexakis(mu-5,5'-di(pyridin-2-yl)-3,3'-bipyrazolato)-tetra-copper(i)-penta-copper(ii) sulfate methanol solvate


CCDC 1844368: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre


CCDC 1567148: Experimental Crystal Structure Determination;Eufemio Moreno-Pineda;2018;Cambridge Crystallographic Data Centre

Related Article: Eufemio Moreno-Pineda, Svetlana Klyatskaya, Ping Du, Marko Damjanovic, Gheorghe Taran, Wolfgang Wernsdorfer, Mario Ruben|2018|Inorg.Chem.|57|9873|doi:10.1021/acs.inorgchem.8b00823


CCDC 1567149: Experimental Crystal Structure Determination;Eufemio Moreno-Pineda;2018;Cambridge Crystallographic Data Centre


CCDC 1845198: Experimental Crystal Structure Determination;Mathilde Bouché;2018;Cambridge Crystallographic Data Centre

Related Article: Mathilde Bouché, Antoine Bonnefont, Thierry Achard, Stéphane Bellemin-Laponnaz|2018|Dalton Trans.|47|11491|doi:10.1039/C8DT02113A


CCDC 1845199: Experimental Crystal Structure Determination;Mathilde Bouché;2018;Cambridge Crystallographic Data Centre


CCDC 1845200: Experimental Crystal Structure Determination;Mathilde Bouché;2018;Cambridge Crystallographic Data Centre


CCDC 1845770: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre

Related Article: Pierre Thuéry, Jack Harrowfield|2018|Cryst.Growth Des.|18|5512|doi:10.1021/acs.cgd.8b00834


CCDC 1845771: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1845772: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1845773: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1845774: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1833630: Experimental Crystal Structure Determination;Xin Da Haung;2018;Cambridge Crystallographic Data Centre

Related Article: Xin Da haung, Yan Xu, Kun Fan, Song Song Bao, Mohamedally Kurmoo, Li Min zheng|2018|Angew.Chem.,Int.Ed.|57|8577|doi:10.1002/anie.201804102


CCDC 1833631: Experimental Crystal Structure Determination;Xin Da Haung;2018;Cambridge Crystallographic Data Centre


CCDC 1821587: Experimental Crystal Structure Determination;Fatih Sirindil;2018;Cambridge Crystallographic Data Centre

Related Article: Fatih Sirindil, Steven P. Nolan, Samuel Dagorne, Patrick Pale,Aurélien Blanc, and Pierre de Frémont|2018|Chem.-Eur.J.|24|12630|doi:10.1002/chem.201801648


CCDC 1821588: Experimental Crystal Structure Determination;Fatih Sirindil;2018;Cambridge Crystallographic Data Centre


CCDC 1848077: Experimental Crystal Structure Determination;Declan McKearney;2018;Cambridge Crystallographic Data Centre

Related Article: Declan McKearney, Sylvie Choua, Wen Zhou, Yumeela Ganga-Sah, Romain Ruppert, Jennifer A. Wytko, Jean Weiss, Daniel B. Leznoff|2018|Inorg.Chem.|57|9644|doi:10.1021/acs.inorgchem.8b01579


CCDC 1848078: Experimental Crystal Structure Determination;Declan McKearney;2018;Cambridge Crystallographic Data Centre


CCDC 1848079: Experimental Crystal Structure Determination;Declan McKearney;2018;Cambridge Crystallographic Data Centre

(29H,31H-phthalocyaninato radical)-tetrahydrofuran-zinc tetrakis(pentafluorophenyl)borate toluene solvate


CCDC 1848080: Experimental Crystal Structure Determination;Declan McKearney;2018;Cambridge Crystallographic Data Centre


CCDC 1833632: Experimental Crystal Structure Determination;Xin Da Haung;2018;Cambridge Crystallographic Data Centre


CCDC 1833633: Experimental Crystal Structure Determination;Xin Da Haung;2018;Cambridge Crystallographic Data Centre


CCDC 1833634: Experimental Crystal Structure Determination;Xin Da Haung;2018;Cambridge Crystallographic Data Centre


CCDC 1831483: Experimental Crystal Structure Determination;Sébastien Gauthier;2018;Cambridge Crystallographic Data Centre

Related Article: Sébastien Gauthier, Ariel Porter, Sylvain Achelle, Thierry Roisnel, Vincent Dorcet, Alberto Barsella, Nicolas Le Poul, Patricia Guevara Level, Denis Jacquemin, Françoise Robin-Le Guen|2018|Organometallics|37|2232|doi:10.1021/acs.organomet.8b00223


CCDC 1831485: Experimental Crystal Structure Determination;Sébastien Gauthier;2018;Cambridge Crystallographic Data Centre


CCDC 1587472: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre

Related Article: Dawid Lichosyt, Sylwia Wasiłek, Paweł Dydio, Janusz Jurczak|2018|Chem.-Eur.J.|24|11683|doi:10.1002/chem.201801460


CCDC 1587473: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587474: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587475: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587476: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587477: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587478: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587479: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1587480: Experimental Crystal Structure Determination;Dawid Lichosyt;2018;Cambridge Crystallographic Data Centre


CCDC 1849017: Experimental Crystal Structure Determination;S. Ouis;2018;Cambridge Crystallographic Data Centre

(mu-diphenylphosphanato)-(mu-hydrido)-dibromo-bis(triphenylphosphine)-di-platinum(ii) diethyl ether solvate

Related Article: S. Ouis, D.A. Rouag, L. Bendjeddou, C. Bailly|2018|Acta Crystallogr.,Sect.E:Cryst.Commun.|74|977|doi:10.1107/S205698901800868X


CCDC 1576103: Experimental Crystal Structure Determination;Yann Trolez;2018;Cambridge Crystallographic Data Centre

Related Article: Yann Trolez, Aaron D. Finke, Fabio Silvestri, Filippo Monti, Barbara Ventura, Corinne Boudon, Jean-Paul Gisselbrecht, W. Bernd Schweizer, Jean-Pierre Sauvage, Nicola Armaroli, François Diederich|2018|Chem.-Eur.J.|24|10422|doi:10.1002/chem.201801161


CCDC 1560744: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre

Related Article: Sébastien Dhers, Abhishake Mondal, David Aguilà, Juan Ramirez, Sergi Vela, Pierre Dechambenoit, Mathieu Rouzieres, Jonathan R. Nitschke, Rodolphe Clérac, Jean-Marie Lehn|2018|J.Am.Chem.Soc.|140|8218|doi:10.1021/jacs.8b03735


CCDC 1560745: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre


CCDC 1560746: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre


CCDC 1560747: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre


CCDC 1560748: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre


CCDC 1835254: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre


CCDC 1835255: Experimental Crystal Structure Determination;Sébastien Dhers;2018;Cambridge Crystallographic Data Centre


CCDC 1510489: Experimental Crystal Structure Determination;Aline Nonat;2018;Cambridge Crystallographic Data Centre

Related Article: Aline Nonat, Tao Liu, Olivier Jeannin, Franck Camerel, Loïc J. Charbonnière|2018|Chem.-Eur.J.|24|3784|doi:10.1002/chem.201705532


CCDC 1510490: Experimental Crystal Structure Determination;Aline Nonat;2018;Cambridge Crystallographic Data Centre


CCDC 1837482: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre

Related Article: Pierre Thuéry, Youssef Atoini, Jack Harrowfield|2018|Inorg.Chem.|57|7932|doi:10.1021/acs.inorgchem.8b01047

bis(ammonium) bis(tetraphenylphosphanium) hexakis(mu-2,2'-(tricyclo[3.3.1.13,7]decane-1,3-diyl)diacetato)-octaoxo-tetra-uranium(vi) unknown solvate


CCDC 1837483: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre

bis(ammonium) bis(methyl(triphenyl)phosphanium) hexakis(mu-2,2'-(tricyclo[3.3.1.13,7]decane-1,3-diyl)diacetato)-octaoxo-tetra-uranium(vi) unknown solvate


CCDC 1837484: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1815448: Experimental Crystal Structure Determination;Hui-Li Zheng;2018;Cambridge Crystallographic Data Centre

Related Article: Hui-Li Zheng, Xue-Li Chen, Tian Li, Zheng Yin, Yuexing Zhang, Mohamedally Kurmoo, Ming-Hua Zeng|2018|Chem.-Eur.J.|24|7906|doi:10.1002/chem.201800516


CCDC 1589355: Experimental Crystal Structure Determination;Jérémy Salaam;2018;Cambridge Crystallographic Data Centre

Related Article: Jérémy Salaam, Lilia Tabti, Sylvana Bahamyirou, Alexandre Lecointre, Oscar Hernandez Alba, Olivier Jeannin, Franck Camerel, Sarah Cianférani, Embarek Bentouhami, Aline M. Nonat, Loïc J. Charbonnière|2018|Inorg.Chem.|57|6095|doi:10.1021/acs.inorgchem.8b00666


CCDC 1589357: Experimental Crystal Structure Determination;Jérémy Salaam;2018;Cambridge Crystallographic Data Centre


CCDC 1817366: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre

Related Article: Pierre Thuéry, Youssef Atoini, Jack Harrowfield|2018|Inorg.Chem.|57|6283|doi:10.1021/acs.inorgchem.8b00147


CCDC 1817367: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1817368: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre

ammonium tetraphenylphosphonium nonakis(mu-cyclohexane-1,2-dicarboxylato)-hexaaqua-hexadecaoxo-octa-uranium trihydrate


CCDC 1831206: Experimental Crystal Structure Determination;Bougar Sarr;2018;Cambridge Crystallographic Data Centre

Related Article: Bougar Sarr, Cheikh A.K. Diop, Frederic Melin, Mamadou Sidibe, Petra Hellwig, Francois Michaud, Francis Maury, Francois Senocq, Abdou Mbaye, Yoann Rousselin|2018|J.Mol.Struct.|1170|44|doi:10.1016/j.molstruc.2018.05.055


CCDC 1831207: Experimental Crystal Structure Determination;Bougar Sarr;2018;Cambridge Crystallographic Data Centre


Abstract: Videos and photos of hydrogel injection for ESD

Injectable hybrid hydrogels for tumor resection and cell-responsive degradation;Giuseppe Alonci;2018;Mendeley


Related Article: Souad Dekar, Kamel Ouari, Sabrina Bendia, Douniazed Hannachi, Jean Weiss|2018|J.Organomet.Chem.|866|165|doi:10.1016/j.jorganchem.2018.04.015

CCDC 1516191: Experimental Crystal Structure Determination;Souad Dekar;2018;Cambridge Crystallographic Data Centre


CCDC 1824390: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre

catena-[(mu-oxalato)-bis(mu-cyclohexane-1,3-dicarboxylato)-tetraoxo-di-uranium penta-aqua-dioxo-uranium 12-crown-4 solvate dihydrate]

Related Article: Pierre Thuéry, Youssef Atoini, Jack Harrowfield|2018|Cryst.Growth Des.|18|3167|doi:10.1021/acs.cgd.8b00266


CCDC 1824391: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1824393: Experimental Crystal Structure Determination;Pierre Thuéry;2018;Cambridge Crystallographic Data Centre


CCDC 1829310: Experimental Crystal Structure Determination;Georges Khalil;2018;Cambridge Crystallographic Data Centre

Related Article: Georges Khalil, Laurent Barloy, Nathalie Kyritsakas, Pierre Mobian, Marc Henry|2018|Dalton Trans.|47|11113|doi:10.1039/C8DT01343H


CCDC 1829311: Experimental Crystal Structure Determination;Georges Khalil;2018;Cambridge Crystallographic Data Centre


CCDC 1829312: Experimental Crystal Structure Determination;Georges Khalil;2018;Cambridge Crystallographic Data Centre


CCDC 1829313: Experimental Crystal Structure Determination;Georges Khalil;2018;Cambridge Crystallographic Data Centre


CCDC 1829314: Experimental Crystal Structure Determination;Georges Khalil;2018;Cambridge Crystallographic Data Centre

bis(pyridine)-bis(1,1':3',1'':3'',1'''-quaterphenyl-2',2''-diolato)-titanium(iv) diethyl ether solvate


CCDC 1579356: Experimental Crystal Structure Determination;Amandine Boulmier;2018;Cambridge Crystallographic Data Centre

Related Article: Amandine Boulmier, Antoine Vacher, Dejin Zang, Shu Yang, Ali Saad, Jérôme Marrot, Olivier Oms, Pierre Mialane, Isabelle Ledoux, Laurent Ruhlmann, Dominique Lorcy, Anne Dolbecq|2018|Inorg.Chem.|57|3742|doi:10.1021/acs.inorgchem.7b02976


CCDC 1579357: Experimental Crystal Structure Determination;Amandine Boulmier;2018;Cambridge Crystallographic Data Centre


CCDC 1565592: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre

Related Article: Fei Yu, Jing Li, Zi-Heng Cao, Mohamedally Kurmoo, Jing-Lin Zuo|2018|Inorg.Chem.|57|3443|doi:10.1021/acs.inorgchem.8b00243


CCDC 1565594: Experimental Crystal Structure Determination;Fei Yu;2018;Cambridge Crystallographic Data Centre


CCDC 1817538: Experimental Crystal Structure Determination;Magdalena O. Cichocka;2018;Cambridge Crystallographic Data Centre

Related Article: Magdalena O. Cichocka, Yannick Lorgouilloux, Stef Smeets, Jie Su, Wei Wan, Philippe Caullet, Nicolas Bats, Lynne B. McCusker, Jean-Louis Paillaud, Xiaodong Zou|2018|Cryst.Growth Des.|18|2441|doi:10.1021/acs.cgd.8b00078


CCDC 1817539: Experimental Crystal Structure Determination;Magdalena O. Cichocka;2018;Cambridge Crystallographic Data Centre


CCDC 1586275: Experimental Crystal Structure Determination;Rafael Stieler;2017;Cambridge Crystallographic Data Centre

Related Article: Rafael Stieler, Marcelo Priebe Gil, Alessandra Caovilla|2017|CSD Communication||| Related Article: Alessandra Caovilla, Juliana S. Penning, Adriana C. Pinheiro, Frédéric Hild, Rafael Stieler, Samuel Dagorne, Osvaldo L. Casagrande Junior, Marcelo P. Gil|2018|J.Organomet.Chem.|863|95|doi:10.1016/j.jorganchem.2018.03.040


Abstract: This data represent the original western blots covering the in vitro data and original ChIP assays with % to input data from the "Cockayne syndrome A and B proteins regulate the timing of transcription arrest upon genotoxic stress through a ubiquitin/proteasome degradation process" publication.

Cockayne’s syndrome A and B proteins regulate transcription arrest after genotoxic stress by promoting ATF3 degradation;Alexey Epanchintsev;2017;Mendeley


CCDC 1501082: Experimental Crystal Structure Determination;Jeremy Cheeseman;2017;Cambridge Crystallographic Data Centre

Related Article: Ernest Graf, Jack Harrowfield, Jean-Pierre Kintzinger, Jean-Marie Lehn, Jacques Le Moigne, Kari Rissanen|2018|ChemPlusChem|83|605|doi:10.1002/cplu.201700557 Related Article: Jeremy Cheeseman, Ernest Graf, Jack Harrowfield, Jean-Pierre Kintzinger, Jean-Marie Lehn, Jacques Le Moigne and Kari Rissanen|2017|CSD Communication|||


CCDC 1584575: Experimental Crystal Structure Determination;Rafael Stieler;2017;Cambridge Crystallographic Data Centre


CCDC 1569735: Experimental Crystal Structure Determination;Fethi Elaieb;2017;Cambridge Crystallographic Data Centre

Related Article: Fethi Elaieb, David Sémeril, Claude Bauder, Dominique Matt, Corinne Bailly, Lydia Karmazin|2018|Polyhedron|139|172|doi:10.1016/j.poly.2017.10.020


CCDC 1569736: Experimental Crystal Structure Determination;Fethi Elaieb;2017;Cambridge Crystallographic Data Centre


CCDC 1532448: Experimental Crystal Structure Determination;Noel Angel Espinosa-Jalapa;2017;Cambridge Crystallographic Data Centre

Related Article: Noel Angel Espinosa-Jalapa, Manuel Alejandro Roque Ramires, Rubén Alfredo Toscano, Jean-Pierre Djukic, Ronan Le Lagadec|2017|J.Organomet.Chem.|845|125|doi:10.1016/j.jorganchem.2017.04.007


CCDC 1532449: Experimental Crystal Structure Determination;Noel Angel Espinosa-Jalapa;2017;Cambridge Crystallographic Data Centre


CCDC 1532450: Experimental Crystal Structure Determination;Noel Angel Espinosa-Jalapa;2017;Cambridge Crystallographic Data Centre


CCDC 1532451: Experimental Crystal Structure Determination;Noel Angel Espinosa-Jalapa;2017;Cambridge Crystallographic Data Centre


CCDC 1529806: Experimental Crystal Structure Determination;M.H. Noamane;2017;Cambridge Crystallographic Data Centre

Related Article: M.H. Noamane, S. Ferlay, R. Abidi, N. Kyritsakas, M.W. Hosseini|2017|Inorg.Chim.Acta|468|260|doi:10.1016/j.ica.2017.04.047


CCDC 1529800: Experimental Crystal Structure Determination;M.H. Noamane;2017;Cambridge Crystallographic Data Centre


CCDC 1529803: Experimental Crystal Structure Determination;M.H. Noamane;2017;Cambridge Crystallographic Data Centre


CCDC 1529801: Experimental Crystal Structure Determination;M.H. Noamane;2017;Cambridge Crystallographic Data Centre


CCDC 1529802: Experimental Crystal Structure Determination;M.H. Noamane;2017;Cambridge Crystallographic Data Centre


Abstract: The development of real-time, wide-field and quantitative diffuse optical imaging methods to visualize functional and structural biomarkers of living tissues is a pressing need for numerous clinical applications including image-guided surgery. In this context, Spatial Frequency Domain Imaging (SFDI) is an attractive method allowing for the fast estimation of optical properties using the Single Snapshot of Optical Properties (SSOP) approach. Herein, we present a novel implementation of SSOP based on a combination of deep learning network at the filtering stage and Graphics Processing Units (GPU) capable of simultaneous high quality image reconstruction, surface profile correction and accurate optical property (OP) extraction in real-time across large fields of view. The presented methodology demonstrates megapixel profile-corrected OP imaging with results comparable to that of profile-corrected SFDI, with a processing time of 18ms and errors relative to SFDI method less than 10% in both profilometry and profile-corrected OPs. This novel processing framework lays the foundation for real-time multispectral quantitative diffuse optical imaging for surgical guidance and healthcare applications. All code and data used for this work is publicly available at www.healthphotonics.org under the resources tab.

Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning;ENAGNON AGUÉNOUNON;2020;The Optical Society

Artificial Intelligence and Image Processing


CCDC 1038552: Experimental Crystal Structure Determination;Cyril Rajnák;2016;Cambridge Crystallographic Data Centre

Related Article: Cyril Rajnák, Ján Titiš, Olaf Fuhr, Mario Ruben, Roman Boča|2017|Polyhedron|123|122|doi:10.1016/j.poly.2016.11.009


CCDC 1038553: Experimental Crystal Structure Determination;Cyril Rajnák;2016;Cambridge Crystallographic Data Centre

bis(2-(3-(1-methyl-1H-benzimidazol-2-yl)phenyl)benzimidazol-1-ido)-iron diethyl ether methanol solvate


CCDC 1038554: Experimental Crystal Structure Determination;Cyril Rajnák;2016;Cambridge Crystallographic Data Centre

2-(6-(1H-benzimidazol-2-yl)pyridin-2-yl)-1-ethyl-1H-benzimidazole dimethyl sulfoxide solvate monohydrate


CCDC 1038555: Experimental Crystal Structure Determination;Cyril Rajnák;2016;Cambridge Crystallographic Data Centre


CCDC 1038556: Experimental Crystal Structure Determination;Cyril Rajnák;2016;Cambridge Crystallographic Data Centre


CCDC 1038557: Experimental Crystal Structure Determination;Cyril Rajnák;2016;Cambridge Crystallographic Data Centre

2-(6-(1H-benzimidazol-2-yl)pyridin-2-yl)-1-(3,5-di-t-butylbenzyl)-1H-benzimidazole dimethyl sulfoxide solvate monohydrate


CCDC 1406375: Experimental Crystal Structure Determination;Denis Frath;2016;Cambridge Crystallographic Data Centre

Related Article: Denis Frath, Sébastien Azizi, Gilles Ulrich, Pascal Retailleau, Raymond Ziessel|2016|CSD Communication|||


CCDC 1406376: Experimental Crystal Structure Determination;Denis Frath;2016;Cambridge Crystallographic Data Centre


CCDC 1504773: Experimental Crystal Structure Determination;Paolino, Marco;2020;Cambridge Crystallographic Data Centre

Related Article: Marco Paolino, Annalisa Reale, Giulia Magrini, Vincenzo Razzano, Germano Giuliani, Alessandro Donati, Gianluca Giorgi, Filippo Samperi, Maurizio Canetti, Matteo Mauro, Francesca Villafiorita-Monteleone, Ettore Fois, Chiara Botta, Andrea Cappelli|2020|Eur.Polym.J.|137|109923|doi:10.1016/j.eurpolymj.2020.109923


CCDC 1841381: Experimental Crystal Structure Determination;Paolino, Marco;2020;Cambridge Crystallographic Data Centre


Abstract: Abstract Objectives To assess long-term efficacy of tocilizumab in treatment-naive patients with Takayasu arteritis (TAK). Methods Prospective open-labeled trial in naïve patients with TAK who received steroids at the dose of 0.7 mg/kg/day and 7 infusions of 8 mg/kg/month of tocilizumab. The primary endpoint was the number of patients who discontinued steroids after 7 infusions of tocilizumab. Secondary endpoints included disease activity and the number of relapses during 18-month follow-up. Results Thirteen patients with TAK were included, with a median age of 32 years [19–45] and 12 (92%) females. Six (54%) patients met the primary end-point. A significant decrease of disease activity was observed after 6 months of tocilizumab therapy: decrease of median NIH scale (3 [3, 4] at baseline, versus 1 [0–2] after 6 months; p

Tocilizumab in treatment-naïve patients with Takayasu arteritis: TOCITAKA French prospective multicenter open-labeled trial;Arsene Mekinian;2020;figshare


Abstract: Abstract Background Mandibulofacial dysostosis with microcephaly (MFDM) is a rare autosomal dominant genetic disease characterized by intellectual and growth retardations, as well as major microcephaly, induced by missense and splice site variants or microdeletions in the EFTUD2 gene. Case presentation Here, we investigate the case of a young girl with symptoms of MFDM and a normal karyotype. Whole-exome sequencing of the family was performed to identify genetic alterations responsible for this phenotype. We identified a de novo synonymous variant in the EFTUD2 gene. We demonstrated that this synonymous variant disrupts the donor splice-site in intron 9 resulting in the skipping of exon 9 and a frameshift that leads to a premature stop codon. Conclusions We present the first case of MFDM caused by a synonymous variant disrupting the donor splice site, leading to exon skipping.

A de novo synonymous variant in EFTUD2 disrupts normal splicing and causes mandibulofacial dysostosis with microcephaly: case report;Arthur Jacob;2020;figshare


CCDC 2017558: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre

Related Article: Damien Bechu, Alexandru Mihai Petre, Mir Wais Hosseini, Stéphane A. Baudron|2020|CrystEngComm|22|5760|doi:10.1039/D0CE01090A


CCDC 2017559: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre


CCDC 2017560: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre


CCDC 2017561: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre


CCDC 2017562: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre


CCDC 2017563: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre


CCDC 2017564: Experimental Crystal Structure Determination;Bechu, Damien;2020;Cambridge Crystallographic Data Centre


CCDC 1991678: Experimental Crystal Structure Determination;Scarpi-Luttenauer, Matthieu;2020;Cambridge Crystallographic Data Centre

Related Article: Matthieu Scarpi-Luttenauer, Loris Geminiani, Pauline Lebrun, Nathalie Kyritsakas, Alain Chaumont, Marc Henry, Pierre Mobian|2020|Inorg.Chem.|59|12005|doi:10.1021/acs.inorgchem.0c00915


CCDC 1991679: Experimental Crystal Structure Determination;Scarpi-Luttenauer, Matthieu;2020;Cambridge Crystallographic Data Centre


CCDC 1991711: Experimental Crystal Structure Determination;Scarpi-Luttenauer, Matthieu;2020;Cambridge Crystallographic Data Centre


CCDC 1991710: Experimental Crystal Structure Determination;Scarpi-Luttenauer, Matthieu;2020;Cambridge Crystallographic Data Centre


CCDC 1991709: Experimental Crystal Structure Determination;Scarpi-Luttenauer, Matthieu;2020;Cambridge Crystallographic Data Centre


CCDC 1989153: Experimental Crystal Structure Determination;Scarpi-Luttenauer, Matthieu;2020;Cambridge Crystallographic Data Centre


CCDC 2013353: Experimental Crystal Structure Determination;Moutier, F.;2020;Cambridge Crystallographic Data Centre

Related Article: F. Moutier, A. M. Khalil, S. A. Baudron, C. Lescop|2020|Chem.Commun.|56|10501|doi:10.1039/D0CC04862C


CCDC 2013354: Experimental Crystal Structure Determination;Moutier, F.;2020;Cambridge Crystallographic Data Centre


CCDC 2013464: Experimental Crystal Structure Determination;Moutier, F.;2020;Cambridge Crystallographic Data Centre


CCDC 2013465: Experimental Crystal Structure Determination;Moutier, F.;2020;Cambridge Crystallographic Data Centre


Abstract: Abstract Background A key step in domestication of the grapevine was the transition from separate sexes (dioecy) in wild Vitis vinifera ssp. sylvestris (V. sylvestris) to hermaphroditism in cultivated Vitis vinifera ssp. sativa (V. vinifera). It is known that V. sylvestris has an XY system and V. vinifera a modified Y haplotype (Yh) and that the sex locus is small, but it has not previously been precisely characterized. Results We generate a high-quality de novo reference genome for V. sylvestris, onto which we map whole-genome re-sequencing data of a cross to locate the sex locus. Assembly of the full X, Y, and Yh haplotypes of V. sylvestris and V. vinifera sex locus and examining their gene content and expression profiles during flower development in wild and cultivated accessions show that truncation and deletion of tapetum and pollen development genes on the X haplotype likely causes male sterility, while the upregulation of a Y allele of a cytokinin regulator (APRT3) may cause female sterility. The downregulation of this cytokinin regulator in the Yh haplotype may be sufficient to trigger reversal to hermaphroditism. Molecular dating of X and Y haplotypes is consistent with the sex locus being as old as the Vitis genus, but the mechanism by which recombination was suppressed remains undetermined. Conclusions We describe the genomic and evolutionary characterization of the sex locus of cultivated and wild grapevine, providing a coherent model of sex determination in the latter and for transition from dioecy to hermaphroditism during domestication.

The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication;Hélène Badouin;2020;figshare


Abstract: Additional file 2. Assembly statistics.

Additional file 2 of The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication;Hélène Badouin;2020;figshare


Abstract: Additional file 3. List of X/Y SNPs.

Additional file 3 of The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication;Hélène Badouin;2020;figshare


Abstract: Additional file 4 Transposable element positions on the X haplotype of the V. sylvestris genome.

Additional file 4 of The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication;Hélène Badouin;2020;figshare


Abstract: Additional file 5. Transposable element content of the different BAC-derived haplotypes.

Additional file 5 of The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication;Hélène Badouin;2020;figshare


Abstract: Additional file 6. Pairwise dS values of X vs Y and Y vs Yh haplotypes.

Additional file 6 of The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication;Hélène Badouin;2020;figshare


Abstract:
- Consensus SNP calls dataset from RADseq data for the Emperor penguin.
Input files for the analyses described in Cristofari et al. 2016, "Full circumpolar migration ensures evolutionary unity in the Emperor penguin":
- Extended Bayesian Skyline Plot input files,- fastsimcoal2 analysis input files,- ngsAdmix genotype likelihood input file,- generic Structure / fastStructure input file,- Migrate-n analysis input files,

Full circumpolar migration ensures evolutionary unity in the Emperor penguin;Robin Cristofari;2016;figshare


Abstract: In Staphylococcus aureus, peptidoglycan metabolism plays a role in the host inflammatory response and pathogenesis. Transcription of the peptidoglycan hydrolases is activated by the essential 2-component system WalKR at low cell density. During stationary growth phase, WalKR is not active and transcription of the peptidoglycan hydrolase genes is repressed. In this work, we studied regulation of expression of the glycylglycine endopeptidase LytM. We show that, in addition to the transcriptional regulation mediated by WalKR, the synthesis of LytM is negatively controlled by a unique mechanism at the stationary growth phase. We have identified 2 different mRNAs encoding lytM, which vary in the length of their 5′ untranslated (5′UTR) regions. LytM is predominantly produced from the WalKR-regulated mRNA transcript carrying a short 5′UTR. The lytM mRNA is also transcribed as part of a polycistronic operon with the upstream SA0264 gene and is constitutively expressed. Although SA0264 protein can be synthesized from the longer operon transcript, lytM cannot be translated because its ribosome-binding site is sequestered into a translationally inactive secondary structure. In addition, the effector of the agr system, RNAIII, can inhibit translation of lytM present on the operon without altering the transcript level but does not have an effect on the translation of the upstream gene. We propose that this dual regulation of lytM expression, at the transcriptional and post-transcriptional levels, contributes to prevent cell wall damage during the stationary phase of growth.

Various checkpoints prevent the synthesis of <i>Staphylococcus aureus</i> peptidoglycan hydrolase LytM in the stationary growth phase;Efthimia Lioliou;2016;Taylor & Francis


Abstract: Plutonic bodies of the Central and Southern Vosges Mts can be assigned to two major early Carboniferous magmatic events: a Visean Mg–K event (c. 345 and 340–336 Ma) and a younger S-type event (329–322 Ma). New petrological, geochemical and Sr–Nd isotopic data highlight the existence of two groups of Mg–K intrusions that might be related to the nature of their primary magma sources; that is, CHUR-like and enriched mantle, which interacted with juvenile and mature crustal material, respectively. The differences between these two groups are explained by a geodynamic scenario involving deep subduction and relamination of the Saxothuringian continental crust under the Moldanubian continent. The relaminated radiogenic Saxothuringian material is thought to have been responsible for dehydration melting of both subducted crust and underlying metasomatized mantle, thereby generating the Mg–K magma subsequently emplaced at middle crustal depth. During their ascent, the mafic magmas interacted with crustally derived felsic melts. Significantly later (c. 10–15 myr) a widespread mid-crustal anatexis occurred, generating voluminous granite intrusions from mixed crustal sources (paragneisses and/or immature felsic–intermediate metaigneous rocks mixed with Mg–K plutons). The principal heat source for such a major melting event is related to the presence of Mg–K plutons rich in heat-producing elements, which were responsible, after the time lag specified, for a temperature increase at mid-crustal levels by in situ radiogenic heat production. The current study underlines the importance of deep continental crust subduction and relamination for the magmatism and development of collisional orogens.

Earth and related environmental sciences

Chronology, petrogenesis and heat sources for successive Carboniferous magmatic events in the Southern–Central Variscan Vosges Mts (NE France);Anne-Sophie Tabaud;2016;Geological Society of London