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Rift Valley Fever (RVF) is a zoonosis transmitted by Aedes and Culex mosquitoes, and is considered a priority pathogen by the WHO. RVF epidemics mostly occur in Africa and can decimate livestock herds, causing significant economic losses and posing health risks for humans. RVF transmission is associated with the occurrence of El Niño events that cause floods in eastern Africa and favour the emergence of mosquitoes in wetlands. Different risk models have been developed to forecast RVF transmission risk but very few studies have validated models at pan-African scale. This study aims to validate the skill of the Liverpool Rift Valley Fever model (LRVF) in reproducing RVF epidemics over Africa and to explore the relationship between simulated climatic suitability for RVF transmission and large-scale climate modes of variability such as the El Niño Southern Oscillation (ENSO) and the Dipole Mode Index (DMI). Our results show that the LRVF model correctly simulates RVF transmission hotspots and reproduces large epidemics that affected African countries. LRVF was able to correctly reproduce major RVF epidemics in Somalia, Kenya, Zambia and to a lesser extent for Mauritania and Senegal. The positive phases of ENSO and DMI are associated with an increased risk of RVF over the Horn of Africa, with important time lags. Following research activities should focus on the development of predictive modelling systems at different time scales., (© 2024. The Author(s).)

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Introduction: Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression., Methods: To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava., Results: Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction., Conclusion: We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Long, Romay, Ramstein, Buckler and Robbins.)

Between the Last Glacial Maximum and the mid-Holocene, the Mediterranean Sea experienced major hydrological changes. The deposition of the last sapropel, S1, during the Early Holocene is a consequence of these changes. In order to cause anoxia in the Eastern Mediterranean Sea (EMS) bottom water, a long preconditioning period of a few thousand years would need to occur throughout the deglaciation prior to S1. It is generally believed that this freshwater was of North Atlantic origin, later supplemented by the African Humid period (AHP). Here, we investigate another potentially important source of freshwater to the EMS: the Fennoscandian ice sheet (FIS) meltwater, running into the Caspian and Black Seas. A few scenarios of continental hydrologic perturbation have been developed to drive a high-resolution Mediterranean Sea general circulation model. We demonstrate that, during the last deglaciation, FIS meltwater flowing into the Black Sea reduced surface salinity and ventilation over the main convection areas in the EMS. By including continental hydrological changes, a more consistent framework is produced to characterize the hydrology of the Mediterranean Sea during the last deglaciation and the Early Holocene., (© 2022. The Author(s).)

Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada., Competing Interests: The authors have declared that no competing interests exist.

Key Message: Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Studies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments.

Warm periods in Earth's history offer opportunities to understand the dynamics of the Earth system under conditions that are similar to those expected in the near future. The Middle Pliocene warm period (MPWP), from 3.3 to 3.0 My B.P, is the most recent time when atmospheric CO 2 levels were as high as today. However, climate model simulations of the Pliocene underestimate high-latitude warming that has been reconstructed from fossil pollen samples and other geological archives. One possible reason for this is that enhanced non-CO 2 trace gas radiative forcing during the Pliocene, including from methane (CH 4 ), has not been included in modeling. We use a suite of terrestrial biogeochemistry models forced with MPWP climate model simulations from four different climate models to produce a comprehensive reconstruction of the MPWP CH 4 cycle, including uncertainty. We simulate an atmospheric CH 4 mixing ratio of 1,000 to 1,200 ppbv, which in combination with estimates of radiative forcing from N 2 O and O 3 , contributes a non-CO 2 radiative forcing of 0.9 [Formula: see text] (range 0.6 to 1.1), which is 43% (range 36 to 56%) of the CO 2 radiative forcing used in MPWP climate simulations. This additional forcing would cause a global surface temperature increase of 0.6 to 1.0 °C, with amplified changes at high latitudes, improving agreement with geological evidence of Middle Pliocene climate. We conclude that natural trace gas feedbacks are critical for interpreting climate warmth during the Pliocene and potentially many other warm phases of the Cenezoic. These results also imply that using Pliocene CO 2 and temperature reconstructions alone may lead to overestimates of the fast or Charney climate sensitivity., Competing Interests: The authors declare no competing interest.

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: ( i ) gene-family-guided splitting and ( ii ) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3' UTR is more important for fine-tuning mRNA abundance levels while the 5' UTR is more important for large-scale changes., Competing Interests: The authors declare no conflict of interest.

It is generally considered that the perennial glaciation of Greenland lasting several orbital cycles began around 2.7 Ma along with the intensification of Northern Hemisphere glaciation (NHG). Both data and model studies have demonstrated that a decline in atmospheric pCO 2 was instrumental in establishing a perennial Greenland ice sheet (GrIS), yet models have generally used simplistic pCO 2 constraints rather than data-inferred pCO 2 evolution. Here, using a method designed for the long-term coupling of climate and cryosphere models and pCO 2 scenarios from different studies, we highlight the pivotal role of pCO 2 on the GrIS expansion across the Plio-Pleistocene Transition (PPT, 3.0-2.5 Ma), in particular in the range between 280 and 320 ppm. Good qualitative agreement is obtained between various IRD reconstructions and some of the possible evolutions of the GrIS simulated by our model. Our results underline the dynamism of the GrIS waxing and waning under pCO 2 levels similar to or lower than today, which supports recent evidence of a dynamic GrIS during the Plio-Pleistocene.

The nearly cosmopolitan tribe Desmodieae (Fabaceae) includes many important genera for medicine and forage. However, the phylogenetic relationships among the infratribal groups circumscribed using morphological traits are still poorly known. In this study, we used chloroplast (rbcL, psbA-trnH) and nuclear (ITS-1) DNA sequences to investigate the molecular phylogeny and historical biogeography of Desmodieae, and infer ancestral states for several vegetative and reproductive traits. Three groups, corresponding to the Desmodium, Lespedeza, and Phyllodium groups sensu Ohashi were retrieved in the phylogenetic analyses. Conflicts in the topologies inferred from the chloroplast and nuclear datasets were detected. For instance, the Lespedeza clade was sister to the groups Phyllodium+Desmodium based on chloroplast DNA, but nested within the Desmodium group based on ITS-1. Moreover, the New Caledonian endemic genera Arthroclianthus and Nephrodesmus were not monophyletic but together formed a clade, which also included Hanslia and Ohwia based on chloroplast DNA. The hypothetical common ancestor of Desmodieae was dated to the Middle Oligocene (ca. 28.3Ma) and was likely an Asian shrub or tree producing indehiscent loments. Several colonization events towards Oceania, America, and Africa occurred (all less than ca. 17.5Ma), most probably through long distance dispersal. The fruits of Desmodieae repeatedly evolved from indehiscence to dehiscence. We also showed that indehiscent loments allow for more variability in the number of seeds per fruit than indehiscent legumes. Modularity seems here to allow variability in the number of ovules produced in a single ovary., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Geological evidence indicates that grounded ice sheets reached sea level at all latitudes during two long-lived Cryogenian (58 and ≥5 My) glaciations. Combined uranium-lead and rhenium-osmium dating suggests that the older (Sturtian) glacial onset and both terminations were globally synchronous. Geochemical data imply that CO 2 was 10 2 PAL (present atmospheric level) at the younger termination, consistent with a global ice cover. Sturtian glaciation followed breakup of a tropical supercontinent, and its onset coincided with the equatorial emplacement of a large igneous province. Modeling shows that the small thermal inertia of a globally frozen surface reverses the annual mean tropical atmospheric circulation, producing an equatorial desert and net snow and frost accumulation elsewhere. Oceanic ice thickens, forming a sea glacier that flows gravitationally toward the equator, sustained by the hydrologic cycle and by basal freezing and melting. Tropical ice sheets flow faster as CO 2 rises but lose mass and become sensitive to orbital changes. Equatorial dust accumulation engenders supraglacial oligotrophic meltwater ecosystems, favorable for cyanobacteria and certain eukaryotes. Meltwater flushing through cracks enables organic burial and submarine deposition of airborne volcanic ash. The subglacial ocean is turbulent and well mixed, in response to geothermal heating and heat loss through the ice cover, increasing with latitude. Terminal carbonate deposits, unique to Cryogenian glaciations, are products of intense weathering and ocean stratification. Whole-ocean warming and collapsing peripheral bulges allow marine coastal flooding to continue long after ice-sheet disappearance. The evolutionary legacy of Snowball Earth is perceptible in fossils and living organisms.

Tumor microenvironment including endothelial and immune cells plays a crucial role in tumor progression and has been shown to dramatically influence cancer survival. In this study, we investigated the clinical relevance of the gene expression of key mediators of angiogenesis, VEGF isoforms 121, 165, and 189, and their receptors (VEGFR-1 and R-2) in a cohort of patients ( n = 37) with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) from the Collaborative Trial in Relapsed Aggressive Lymphoma (CORAL). In patients with ABC-like DLBCL, but not in patients with GCB-like DLBCL, low VEGF 121 expression was associated with a significantly better survival than in those with high VEGF 121 level: 4-year overall survival at 100% vs 36% ( p = .011), respectively. A specific gene signature including 57 genes was correlated to VEGF 121 expression level and was analyzed using a discovery process in 1,842 GSE datasets of public microarray studies. This gene signature was significantly expressed in other cancer datasets and was associated with immune response. In conclusion, low VEGF 121 expression level was significantly associated with a good prognosis in relapsed/refractory ABC-like DLBCL, and with a well-conserved gene-expression profiling signature related to immune response. These findings pave the way for rationalization of drugs targeting immune response in refractory/relapsed ABC-like DLBCL., Competing Interests: CONFLICTS OF INTEREST Professor C. Gisselbrecht: Research funding by Roche. The other authors declare no competing financial interests.

The acceleration of ice sheet melting has been observed over the last few decades. Recent observations and modeling studies have suggested that the ice sheet contribution to future sea level rise could have been underestimated in the latest Intergovernmental Panel on Climate Change report. The ensuing freshwater discharge coming from ice sheets could have significant impacts on global climate, and especially on the vulnerable tropical areas. During the last glacial/deglacial period, megadrought episodes were observed in the Sahel region at the time of massive iceberg surges, leading to large freshwater discharges. In the future, such episodes have the potential to induce a drastic destabilization of the Sahelian agroecosystem. Using a climate modeling approach, we investigate this issue by superimposing on the Representative Concentration Pathways 8.5 (RCP8.5) baseline experiment a Greenland flash melting scenario corresponding to an additional sea level rise ranging from 0.5 m to 3 m. Our model response to freshwater discharge coming from Greenland melting reveals a significant decrease of the West African monsoon rainfall, leading to changes in agricultural practices. Combined with a strong population increase, described by different demography projections, important human migration flows could be potentially induced. We estimate that, without any adaptation measures, tens to hundreds million people could be forced to leave the Sahel by the end of this century. On top of this quantification, the sea level rise impact over coastal areas has to be superimposed, implying that the Sahel population could be strongly at threat in case of rapid Greenland melting., Competing Interests: The authors declare no conflict of interest.

New challenges in renal transplantation include using biological information to devise a useful clinical test for discerning high- and low-risk patients for individual therapy and ascertaining the best combination and appropriate dosages of drugs. Based on a 20-gene signature from a microarray meta-analysis performed on 46 operationally tolerant patients and 266 renal transplant recipients with stable function, we applied the sparse Bolasso methodology to identify a minimal and robust combination of six genes and two demographic parameters associated with operational tolerance. This composite score of operational tolerance discriminated operationally tolerant patients with an area under the curve of 0.97 (95% confidence interval 0.94-1.00). The score was not influenced by immunosuppressive treatment, center of origin, donor type, or post-transplant lymphoproliferative disorder history of the patients. This composite score of operational tolerance was significantly associated with both de novo anti-HLA antibodies and tolerance loss. It was validated by quantitative polymerase chain reaction using independent samples and demonstrated specificity toward a model of tolerance induction. Thus, our score would allow clinicians to improve follow-up of patients, paving the way for individual therapy., (Copyright © 2017 International Society of Nephrology. All rights reserved.)

Flowering time is a major determinant of biomass yield in switchgrass (Panicum virgatum), a perennial bioenergy crop, because later flowering allows for an extended period of vegetative growth and increased biomass production. A better understanding of the genetic regulation of flowering time in switchgrass will aid the development of switchgrass varieties with increased biomass yields, particularly at northern latitudes, where late-flowering but southern-adapted varieties have high winter mortality. We use genotypes derived from recently published exome-capture sequencing, which mitigates challenges related to the large, highly repetitive and polyploid switchgrass genome, to perform genome-wide association studies (GWAS) using flowering time data from a switchgrass association panel in an effort to characterize the genetic architecture and genes underlying flowering time regulation in switchgrass. We identify associations with flowering time at multiple loci, including in a homolog of FLOWERING LOCUS T and in a locus containing TIMELESS, a homolog of a key circadian regulator in animals. Our results suggest that flowering time variation in switchgrass is due to variation at many positions across the genome. The relationship of flowering time and geographic origin indicates likely roles for genes in the photoperiod and autonomous pathways in generating switchgrass flowering time variation., (No claim to original US government works. New Phytologist © 2016 New Phytologist Trust.)

Despite major advances in first-line treatment, a significant proportion of patients with diffuse large B-cell lymphoma (DLBCL) will experience treatment failure. Prognosis is particularly poor for relapses occurring less than one year after the end of first-line treatment (early relapses/ER) compared to those occurring more than one year after (late relapses/LR). To better understand genomic alterations underlying the delay of relapse, we identified copy number variations (CNVs) on 39 tumor samples from a homogeneous series of patients included in the Collaborative Trial in Relapsed Aggressive Lymphoma (CORAL) prospective study. To identify CNVs associated with ER or LR, we devised an original method based on Significance Analysis of Microarrays, a permutation-based method which allows control of false positives due to multiple testing. Deletions of CDKN2A/B (28%) and IBTK (23%) were frequent events in relapsed DLBCLs. We identified 56 protein-coding genes and 25 long non-coding RNAs with significantly differential CNVs distribution between ER and LR DLBCLs, with a false discovery rate < 0.05. In ER DLBCLs, CNVs were related to transcription regulation, cell cycle and apoptosis, with duplications of histone H1T (31%), deletions of DIABLO (26%), PTMS (21%) and CK2B (15%). In LR DLBCLs, CNVs were related to immune response, with deletions of B2M (20%) and CD58 (10%), cell proliferation regulation, with duplications of HES1 (25%) and DVL3 (20%), and transcription regulation, with MTERF4 deletions (20%). This study provides new insights into the genetic aberrations in relapsed DLBCLs and suggest pathway-targeted therapies in ER and LR DLBCLs.

Objective: In multiple sclerosis (MS), central nervous system (CNS), cerebrospinal fluid (CSF), and blood display TCR clonal expansions of CD8(+) T cells. These clones have been assumed - but never demonstrated - to be similar in the three compartments. Addressing this key question is essential to infer the implication of peripheral clonally expanded CD8(+) T cells in the disease., Methods: For the first time, TCR Vβ repertoire from paired blood (purified CD8(+) and CD4(+) T cells), CSF and CNS (22 lesions, various inflammatory and demyelination statuses) samples from three MS patients was studied using complementary determining region 3 (CDR3) spectratyping and high-throughput sequencing. In parallel, blood and CNS clonally expanded CD8(+) T cells were characterized by fluorescent staining., Results: TCR Vβ repertoire analysis revealed strong sharing of predominant T-cell clones between CNS lesions, CSF, and blood CD8(+) T cells. In parallel, we showed that blood oligoclonal CD8(+) T cells exhibit characteristics of pathogenic cells, as they displayed a bias toward a memory phenotype in MS patients, with increased expression of CCR5, CD11a and Granzyme B (GZM-B) compared to non oligoclonal counterparts. CNS-infiltrating T cells were mainly CD8 expressing CD11a and GZM-B., Interpretation: This study highlights the predominant implication of CD8(+) T cells in MS pathophysiology and demonstrates that potentially aggressive CD8(+) T cells can be easily identified and characterized from blood and CSF samples.

Patients tolerant to a kidney graft display a specific blood cell transcriptional pattern but results from five different studies were inconsistent, raising the question of relevance for future clinical application. To resolve this, we sought to identify a common gene signature, specific functional and cellular components, and discriminating biomarkers for tolerance following kidney transplantation. A meta-analysis of studies identified a robust gene signature involving proliferation of B and CD4 T cells, and inhibition of CD14 monocyte related functions among 96 tolerant samples. This signature was further supported through a cross-validation approach, yielding 92.5% accuracy independent of the study of origin. Experimental validation, performed on new tolerant samples and using a selection of the top-20 biomarkers, returned 91.7% of good classification. Beyond the confirmation of B-cell involvement, our data also indicated participation of other cell subsets in tolerance. Thus, the use of the top 20 biomarkers, mostly centered on B cells, may provide a common and standardized tool towards personalized medicine for the monitoring of tolerant or low-risk patients among kidney allotransplant recipients. These data point to a global preservation of genes favoring the maintenance of a homeostatic and 'healthy' environment in tolerant patients and may contribute to a better understanding of tolerance maintenance mechanisms.

It is widely believed that the Sahara desert is no more than ∼2-3 million years (Myr) old, with geological evidence showing a remarkable aridification of north Africa at the onset of the Quaternary ice ages. Before that time, north African aridity was mainly controlled by the African summer monsoon (ASM), which oscillated with Earth's orbital precession cycles. Afterwards, the Northern Hemisphere glaciation added an ice volume forcing on the ASM, which additionally oscillated with glacial-interglacial cycles. These findings led to the idea that the Sahara desert came into existence when the Northern Hemisphere glaciated ∼2-3 Myr ago. The later discovery, however, of aeolian dune deposits ∼7 Myr old suggested a much older age, although this interpretation is hotly challenged and there is no clear mechanism for aridification around this time. Here we use climate model simulations to identify the Tortonian stage (∼7-11 Myr ago) of the Late Miocene epoch as the pivotal period for triggering north African aridity and creating the Sahara desert. Through a set of experiments with the Norwegian Earth System Model and the Community Atmosphere Model, we demonstrate that the African summer monsoon was drastically weakened by the Tethys Sea shrinkage during the Tortonian, allowing arid, desert conditions to expand across north Africa. Not only did the Tethys shrinkage alter the mean climate of the region, it also enhanced the sensitivity of the African monsoon to orbital forcing, which subsequently became the major driver of Sahara extent fluctuations. These important climatic changes probably caused the shifts in Asian and African flora and fauna observed during the same period, with possible links to the emergence of early hominins in north Africa.

The Last Glacial Maximum (LGM) was a global climate event, which had significant repercussions for the spatial distribution and demographic history of prehistoric populations. In Eurasia, the LGM coincides with a potential bottleneck for modern humans and may mark the divergence date for Asian and European populations (Keinan et al., 2007). In this research, the impact of climate variability on human populations in the Iberian Peninsula during the Last Glacial Maximum (LGM) is examined with the aid of downscaled high-resolution (16 × 16 km) numerical climate experiments. Human sensitivity to short time-scale (inter-annual) climate variability during this key time period, which follows the initial modern human colonisation of Eurasia and the extinction of the Neanderthals, is tested using the spatial distribution of archaeological sites. Results indicate that anatomically modern human populations responded to small-scale spatial patterning in climate variability, specifically inter-annual variability in precipitation levels as measured by the standard precipitation index. Climate variability at less than millennial scale, therefore, is shown to be an important component of ecological risk, one that played a role in regulating the spatial behaviour of prehistoric human populations and consequently affected their social networks., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The mid-Piacenzian climate represents the most geologically recent interval of long-term average warmth relative to the last million years, and shares similarities with the climate projected for the end of the 21(st) century. As such, it represents a natural experiment from which we can gain insight into potential climate change impacts, enabling more informed policy decisions for mitigation and adaptation. Here, we present the first systematic comparison of Pliocene sea surface temperature (SST) between an ensemble of eight climate model simulations produced as part of PlioMIP (Pliocene Model Intercomparison Project) with the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project mean annual SST field. Our results highlight key regional and dynamic situations where there is discord between the palaeoenvironmental reconstruction and the climate model simulations. These differences have led to improved strategies for both experimental design and temporal refinement of the palaeoenvironmental reconstruction.

Unlabelled: MADGene is a software environment comprising a web-based database and a java application. This platform aims at unifying gene identifiers (ids) and performing gene set analysis. MADGene allows the user to perform inter-conversion of clone and gene ids over a large range of nomenclatures relative to 17 species. We propose a set of 23 functions to facilitate the analysis of gene sets and we give two microarray applications to show how MADGene can be used to conduct meta-analyses., Availability: The MADGene resources are freely available online from http://www.madtools.org, a website dedicated to the analysis and annotation of DNA microarray data.

Background: DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc., Description: We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies., Conclusions: Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.

Duchenne Muscular Dystrophy (DMD) is a complex process involving multiple pathways downstream of the primary genetic insult leading to fatal muscle degeneration. Aging muscle is a multifactorial neuromuscular process characterized by impaired muscle regeneration leading to progressive atrophy. We hypothesized that these chronic atrophying situations may share specific myogenic adaptative responses at transcriptional level according to tissue remodeling. Muscle biopsies from four young DMD and four AGED subjects were referred to a group of seven muscle biopsies from young subjects without any neuromuscular disorder and explored through a dedicated expression microarray. We identified 528 differentially expressed genes (out of 2,745 analyzed), of which 328 could be validated by an exhaustive meta-analysis of public microarray datasets referring to DMD and Aging in skeletal muscle. Among the 328 validated co-expressed genes, 50% had the same expression profile in both groups and corresponded to immune/fibrosis responses and mitochondrial metabolism. Generalizing these observed meta-signatures with large compendia of public datasets reinforced our results as they could be also identified in other pathological processes and in diverse physiological conditions. Focusing on the common gene signatures in these two atrophying conditions, we observed enrichment in motifs for candidate transcription factors that may coordinate either the immune/fibrosis responses (ETS1, IRF1, NF1) or the mitochondrial metabolism (ESRRA). Deregulation in their expression could be responsible, at least in part, for the same transcriptome changes initiating the chronic muscle atrophy. This study suggests that distinct pathophysiological processes may share common gene responses and pathways related to specific transcription factors.

Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in-house microarray containing 4217 muscular organ-relevant genes. Based on their clinical status, patients were classified into three HF-severity groups: deteriorating (n= 12), intermediate (n= 19) and stable (n= 13). Two-class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170-gene and a 129-gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.

The history of Eastern African hominids has been linked to a progressive increase of open grassland during the past 8 million years. This trend was explained by global climatic processes, which do not account for the massive uplift of eastern African topography that occurred during this period. Atmosphere and biosphere simulations quantify the role played by these tectonic events. The reduced topographic barrier before 8 million years ago permitted a zonal circulation with associated moisture transport and strong precipitation. Our results suggest that the uplift itself led to a drastic reorganization of atmospheric circulation, engendering the strong aridification and paleoenvironmental changes suggested by the data.

We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene spots as well as from replicate microarrays for each biological situation under study. A carefully constructed experimental design thus allows to detect outlying expression values and to identify statistically significant expression values, together with a list of quality controls with proposed threshold values. The integrated processing procedure described here, based on multiple measurements per gene, is decisive for reliably monitoring subtle gene expression changes typical for most biological events.

Geological and palaeomagnetic studies indicate that ice sheets may have reached the Equator at the end of the Proterozoic eon, 800 to 550 million years ago, leading to the suggestion of a fully ice-covered 'snowball Earth'. Climate model simulations indicate that such a snowball state for the Earth depends on anomalously low atmospheric carbon dioxide concentrations, in addition to the Sun being 6 per cent fainter than it is today. However, the mechanisms producing such low carbon dioxide concentrations remain controversial. Here we assess the effect of the palaeogeographic changes preceding the Sturtian glacial period, 750 million years ago, on the long-term evolution of atmospheric carbon dioxide levels using the coupled climate-geochemical model GEOCLIM. In our simulation, the continental break-up of Rodinia leads to an increase in runoff and hence consumption of carbon dioxide through continental weathering that decreases atmospheric carbon dioxide concentrations by 1,320 p.p.m. This indicates that tectonic changes could have triggered a progressive transition from a 'greenhouse' to an 'icehouse' climate during the Neoproterozoic era. When we combine these results with the concomitant weathering effect of the voluminous basaltic traps erupted throughout the break-up of Rodinia, our simulation results in a snowball glaciation.

According to Milankovitch theory, the lower summer insolation at high latitudes about 115,000 years ago allowed winter snow to persist throughout summer, leading to ice-sheet build-up and glaciation. But attempts to simulate the last glaciation using global atmospheric models have failed to produce this outcome when forced by insolation changes only. These results point towards the importance of feedback effects-for example, through changes in vegetation or the ocean circulation-for the amplification of solar forcing. Here we present a fully coupled ocean-atmosphere model of the last glaciation that produces a build-up of perennial snow cover at known locations of ice sheets during this period. We show that ocean feedbacks lead to a cooling of the high northern latitudes, along with an increase in atmospheric moisture transport from the Equator to the poles. These changes agree with available geological data and, together, they lead to an increased delivery of snow to high northern latitudes. The mechanism we present explains the onset of glaciation-which would be amplified by changes in vegetation-in response to weak orbital forcing.

Chromosome, a knowledge-based analysis system has been designed for the classification of human chromosomes. Its aim is to perform an optimal classification by driving a tool box containing the procedures of image processing, pattern recognition and classification. This paper presents the general architecture of Chromosome, based on a multiagent system generator. The image processing tool box is described from the met aphasic enhancement to the fine classification. Emphasis is then put on the knowledge base intended for the chromosome recognition. The global classification process is also presented, showing how Chromosome proceeds to classify a given chromosome. Finally, we discuss further extensions of the system for the karyotype building.