Transferability,development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials.  The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.     

Evolutionary history of almond tree domestication in the Mediterranean basin

Genetic diversity of contemporary domesticated species is shaped by both natural and human‐driven processes. However, until now, little is known about how domestication has imprinted the variation of fruit tree species. In this study, we reconstruct the recent evolutionary history of the domesticated almond tree, Prunus dulcis, around the Mediterranean basin, using a combination of nuclear and chloroplast microsatellites [i.e. simple sequence repeat (SSRs)] to investigate patterns of genetic diversity. Whereas conservative chloroplast SSRs show a widespread haplotype and rare locally distributed variants, nuclear SSRs show a pattern of isolation by distance with clines of diversity from the East to the West of the Mediterranean basin, while Bayesian genetic clustering reveals a substantial longitudinal genetic structure. Both kinds of markers thus support a single domestication event, in the eastern side of the Mediterranean basin. In addition, model‐based estimation of the timing of genetic divergence among those clusters is estimated sometime during the Holocene, a result that is compatible with human‐mediated dispersal of almond tree out of its centre of origin. Still, the detection of region‐specific alleles suggests that gene flow from relictual wild preglacial populations (in North Africa) or from wild counterparts (in the Near East) could account for a fraction of the diversity observed.     

Disturbances, elevation, topography and spatial proximity drive vegetation patterns along an altitudinal gradient of a top biodiversity hotspot

The correlation between vegetation patterns (species distribution and richness) and altitudinal variation has been widely reported for tropical forests, thereby providing theoretical basis for biodiversity conservation. However, this relationship may have been oversimplified, as many other factors may influence vegetation patterns, such as disturbances, topography and geographic distance. Considering these other factors, our primary question was: is there a vegetation pattern associated with substantial altitudinal variation (10–1,093 m a.s.l.) in the Atlantic Rainforest—a top hotspot for biodiversity conservation—and, if so, what are the main factors driving this pattern? We addressed this question by sampling 11 1-ha plots, applying multivariate methods, correlations and variance partitioning. The Restinga (forest on sandbanks along the coastal plains of Brazil) and a lowland area that was selectively logged 40 years ago were floristically isolated from the other plots. The maximum species richness (>200 spp. per hectare) occurred at approximately 350 m a.s.l. (submontane forest). Gaps, multiple stemmed trees, average elevation and the standard deviation of the slope significantly affected the vegetation pattern. Spatial proximity also influenced the vegetation pattern as a structuring environmental variable or via dispersal constraints. Our results clarify, for the first time, the key variables that drive species distribution and richness across a large altitudinal range within the Atlantic Rainforest.     

Screening for Unruptured Intracranial Aneurysms in Autosomal Dominant Polycystic Kidney Disease: A Survey of 420 Nephrologists

BackgroundDespite a high prevalence of intracranial aneurysm (ICA) in autosomal dominant polycystic kidney disease (ADPKD), rupture events are rare. The current recommendations for ICA screening are based on expert opinions and studies with low levels of evidence.ObjectivesThe aim of our study was to describe the attitudes of practicing nephrologists in Europe towards screening for ICA using magnetic resonance angiography (MRA).MethodsWe conducted a web-based survey among 1315 European French-speaking nephrologists and nephrology residents. An anonymous, electronic questionnaire including 24 independent questions related to ICA screening modalities, indications and participant profiles was sent by email between September and December 2014. Four hundred and twenty nephrologists (mostly from France) participated, including 31 nephrology residents; the response rate was 32%.ResultsSystematic screening for ICA was advocated by 28% of the nephrologists. A family history of ICA rupture, sudden death, stroke and migraine were consensual indications for screening (> 90% of the panel). In other clinical situations largely not covered by the recommendations (pregnancy, nephrectomy, kidney transplantation, cardiac or hepatic surgery, uncontrolled hypertension, lack of familial ADPKD history, at-risk activity, tobacco use), the attitudes towards screening were highly divergent. ICA screening was influenced by nephrologists experience with ADPKD and by their practice setting. The majority of participants (57%) would not repeat a normal ICA screening. Only a few participants (22%) knew that non-contrast MRA was the reference diagnostic tool for ICA screening, whereas most participants thought that contrast enhancement was necessary to screen for ICA. The results from the nephrology residents were analyzed separately and yielded similar results.ConclusionThis practice survey revealed that most nephrologists follow the current recommendations for the initial screening of ICAs. However, more than a quarter of the panel was in favor of systematic ICA screening, most nephrologists did not know that contrast medium was not necessary to screen for ICA using MRA, and many areas of uncertainty remain.