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Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF , chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development ( FOXA2/3 , PDX1 and MNX1 ). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine ( NR5A2 and RBPJL ), and endocrine differentiation ( NEUROD1 and NKX2-2 ). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development. An integrated genomic analysis of 456 human pancreatic ductal adenocarcinomas identifies four subtypes defined by transcriptional expression profiles and show that these are associated with distinct histopathological characteristics and differential prognosis. Pancreatic cancer genomics Sean Grimmond and colleagues report integrated genomic analysis of 456 pancreatic ductal adenocarcinomas. They identify four subtypes defined by expression profiles, characterize their transcriptional networks, and show that these are associated with distinct histopathological characteristics and differential survival.

One of the most widely conserved hallmarks of aging is a decline in functional capabilities. Mobility loss is particularly burdensome due to its association with negative health outcomes, loss of independence and disability, and the heavy impact on quality of life. Recently, a new condition, physical frailty and sarcopenia, has been proposed to define a critical stage in the disabling cascade. Physical frailty and sarcopenia are characterized by weakness, slowness, and reduced muscle mass, yet with preserved ability to move independently. One of the strategies that have shown some benefits in combatting mobility loss and its consequences for older adults is physical activity. Here, we describe the opportunities and challenges for the development of physical activity interventions in people with physical frailty and sarcopenia. The aim of this article is to review age-related physio(patho)logical changes that impact mobility in old age and to provide recommendations and procedures in accordance with the available literature.

Dryland biomes cover two-fifths of Earth’s land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than 210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high temporal resolution, which are available through the Google Earth platform. We show that in 2015, 1327 million hectares of drylands had more than 10% tree-cover, and 1079 million hectares comprised forest. Our estimate is 40 to 47% higher than previous estimates, corresponding to 467 million hectares of forest that have never been reported before. This increases current estimates of global forest cover by at least 9%.

Low maize (Zea maysL.) yields and the impacts of climate change on maize production highlight the need to improve yields in eastern and southern Africa. Climate projections suggest higher temperatures within drought‐prone areas. Research in model species suggests that tolerance to combined drought and heat stress is genetically distinct from tolerance to either stress alone, but this has not been confirmed in maize. In this study we evaluated 300 maize inbred lines testcrossed to CML539. Experiments were conducted under optimal conditions, reproductive stage drought stress, heat stress, and combined drought and heat stress. Lines with high levels of tolerance to drought and combined drought and heat stress were identified. Significant genotype × trial interaction and very large plot residuals were observed; consequently, the repeatability of individual managed stress trials was low. Tolerance to combined drought and heat stress in maize was genetically distinct from tolerance to individual stresses, and tolerance to either stress alone did not confer tolerance to combined drought and heat stress. This finding has major implications for maize drought breeding. Many current drought donors and key inbreds used in widely grown African hybrids were susceptible to drought stress at elevated temperatures. Several donors tolerant to drought and combined drought and heat stress, notably La Posta Sequia C7‐F64‐2‐6‐2‐2 and DTPYC9‐F46‐1‐2‐1‐2, need to be incorporated into maize breeding pipelines.

Chicken Parvovirus (ChPV) belongs to the genus Aveparvovirus and is implicated in enteric diseases like runting–stunting syndrome (RSS) in poultry. In RSS, chicken health is affected by diarrhea, depression, and increased mortality, causing significant economic losses in the poultry industry. This study aimed to characterize the ChPV genomes detected in chickens with RSS through a metagenomic approach and compare the molecular and evolutionary characteristics within the Aveparvovirus galliform1 species. The intestinal content of broiler flocks affected with RSS was submitted to viral metagenomics. The assembled prevalent genomes were identified as ChPV after sequence and phylogenetic analysis, which consistently clustered separately from Turkey Parvovirus (TuPV). The strain USP-574-A presented signs of genomic recombination. The selective pressure analysis indicated that most of the coding genes in A. galliform1 are evolving under diversifying (negative) selection. Protein modeling of ChPV and TuPV viral capsids identified high conservancy over the VP2 region. The prediction of epitopes identified several co-localized antigenic peptides from ChPV and TuPV, especially for T-cell epitopes, highlighting the immunological significance of these sites. However, most of these peptides presented host-specific variability, obeying an adaptive scenario. The results of this study show the evolutionary path of ChPV and TuPV, which are influenced by diversifying events such as genomic recombination and selective pressure, as well as by adaptation processes, and their subsequent immunological impact.

As the Earth warms, many species are likely to disappear, often because of changing disease dynamics. Here we show that a recent mass extinction associated with pathogen outbreaks is tied to global warming. Seventeen years ago, in the mountains of Costa Rica, the Monteverde harlequin frog ( Atelopus sp.) vanished along with the golden toad ( Bufo periglenes ). An estimated 67% of the 110 or so species of Atelopus , which are endemic to the American tropics, have met the same fate, and a pathogenic chytrid fungus ( Batrachochytrium dendrobatidis ) is implicated. Analysing the timing of losses in relation to changes in sea surface and air temperatures, we conclude with ‘very high confidence’ (> 99%, following the Intergovernmental Panel on Climate Change, IPCC) that large-scale warming is a key factor in the disappearances. We propose that temperatures at many highland localities are shifting towards the growth optimum of Batrachochytrium , thus encouraging outbreaks. With climate change promoting infectious disease and eroding biodiversity, the urgency of reducing greenhouse-gas concentrations is now undeniable. Frogs can't stand the heat The Monteverde harlequin frog became a cause célèbre in the debate on global warming and biodiversity when it, together with the golden toad, disappeared from Costa Rican forests in the 1980s. Some claimed global warming as the cause; others that deforestation had damaged the habitat. A new analysis that links the extinction of these and many other amphibians endemic to the American tropics to changes in sea surface and air temperatures may prove conclusive in this round of the debate. The probable agent of the amphibians' demise has been identified too: the warming trend has benefited a fungal pathogen called Batrachochytrium dendrobatidis , the cause of chytridiomycosis, and tipped the balance against survival of its hosts.

Background The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE. Methods Data from the ATTICA prospective study ( n  = 2020 adults), enrolled during 2001–02 and followed-up in 2011–12 were used. Three different machine-learning classifiers (k-NN, random forest, and decision tree) were trained and evaluated against 10-year CVD incidence, in comparison with the HellenicSCORE tool (a calibration of the ESC SCORE). Training datasets, consisting from 16 variables to only 5 variables, were chosen, with or without bootstrapping, in an attempt to achieve the best overall performance for the machine learning classifiers. Results Depending on the classifier and the training dataset the outcome varied in efficiency but was comparable between the two methodological approaches. In particular, the HellenicSCORE showed accuracy 85%, specificity 20%, sensitivity 97%, positive predictive value 87%, and negative predictive value 58%, whereas for the machine learning methodologies, accuracy ranged from 65 to 84%, specificity from 46 to 56%, sensitivity from 67 to 89%, positive predictive value from 89 to 91%, and negative predictive value from 24 to 45%; random forest gave the best results, while the k-NN gave the poorest results. Conclusions The alternative approach of machine learning classification produced results comparable to that of risk prediction scores and, thus, it can be used as a method of CVD prediction, taking into consideration the advantages that machine learning methodologies may offer.