Heritable variation in plant phenotypes, and thus potential for evolutionary change, can in principle not only be caused by variation in DNA sequence, but also by underlying epigenetic variation. ...However, the potential scope of such phenotypic effects and their evolutionary significance are largely unexplored.
Here, we conducted a glasshouse experiment in which we tested the response of a large number of epigenetic recombinant inbred lines (epiRILs) of Arabidopsis thaliana – lines that are nearly isogenic but highly variable at the level of DNA methylation – to drought and increased nutrient conditions.
We found significant heritable variation among epiRILs both in the means of several ecologically important plant traits and in their plasticities to drought and nutrients. Significant selection gradients, that is, fitness correlations, of several mean traits and plasticities suggest that selection could act on this epigenetically based phenotypic variation.
Our study provides evidence that variation in DNA methylation can cause substantial heritable variation of ecologically important plant traits, including root allocation, drought tolerance and nutrient plasticity, and that rapid evolution based on epigenetic variation alone should thus be possible.
Several methods were developed to mine gene–gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected ...graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC’s encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.
The adaptive value of transgenerational effects (the ancestor environmental effects on offspring) in changing environments has received much attention in recent years, but the related empirical ...evidence remains equivocal. Here, we conducted a meta‐analysis summarising 139 experimental studies in plants and animals with 1170 effect sizes to investigate the generality of transgenerational effects across taxa, traits, and environmental contexts. It was found that transgenerational effects generally enhanced offspring performance in response to both stressful and benign conditions. The strongest effects are in annual plants and invertebrates, whereas vertebrates appear to benefit mostly under benign conditions, and perennial plants show hardly any transgenerational responses at all. These differences among taxonomic/life‐history groups possibly reflect that vertebrates can avoid stressful conditions through their mobility, and longer‐lived plants have alternative strategies. In addition to environmental contexts and taxonomic/life‐history groups, transgenerational effects also varied among traits and developmental stages of ancestors and offspring, but the effects were similarly strong across three generations of offspring. By way of a more comprehensive data set and a different effect size, our results differ from those of a recent meta‐analysis, suggesting that transgenerational effects are widespread, strong and persistent and can substantially impact the responses of plants and animals to changing environments.
Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that ...can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG). GCNG encodes the spatial information as a graph and combines it with expression data using supervised training. GCNG improves upon prior methods used to analyze spatial transcriptomics data and can propose novel pairs of extracellular interacting genes. The output of GCNG can also be used for downstream analysis including functional gene assignment.Supporting website with software and data: https://github.com/xiaoyeye/GCNG .
As the internet has become popularized in recent years, cognitive behavioral therapy for insomnia (CBT-i) has shifted from a face-to-face approach to delivery via the internet (internet-based CBT-i, ...ICBT-i). Several studies have investigated the effects of ICBT-i on comorbid anxiety and depression; however, the results remain inconclusive. Thus, a meta-analysis was conducted to determine the effects of ICBT-i on anxiety and depression. Electronic databases, including PubMed, EMBASE, PsycINFO and the Cochrane Library (throughout May 28, 2015), were systematically searched for randomized controlled trials (RCTs) of ICBT-i. Data were extracted from the qualified studies and pooled together. The standardized mean difference (SMD) and 95% confidence interval (95% CI) were calculated to assess the effects of ICBT-i on comorbid anxiety and depression. Nine records that included ten studies were ultimately qualified. The effect sizes (ESs) were -0.35 -0.46, -0.25 for anxiety and -0.36 -0.47, -0.26 for depression, which were stable using a between-group or within-group comparison and suggest positive effects of ICBT-i on both comorbid disorders. Although positive results were identified in this meta-analysis, additional high-quality studies with larger sample sizes are needed in the future.
Selective extraction of uranium from water has attracted worldwide attention because the largest source of uranium is seawater with various interference ions (Na+, K+, Mg2+, Ca2+, etc.). However, ...traditional adsorbents encapsulate most of their functional sites in their dense structure, leading to problems with low selectivity and adsorption capacities. In this work, the tailor‐made binding sites are first decorated into porous skeletons, and a series of molecularly imprinted porous aromatic frameworks are prepared for uranium extraction. Because the porous architecture provides numerous accessible sites, the resultant material has a fourfold increased ion capacity compared with traditional molecularly imprinted polymers and presents the highest selectivity among all reported uranium adsorbents. Moreover, the porous framework can be dispersed into commercial polymers to form composite components for the practical extraction of uranium ions from simulated seawater.
Traditional adsorbents for the selective extraction of uranium have the disadvantages of low available surface areas, capacities, and selectivity. Thus, porous aromatic frameworks are decorated with tailor‐made binding sites to obtain molecularly imprinted porous aromatic frameworks, which possess excellent capacities and selectivity due to more accessible porous spaces and the hierarchical porosity.
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Diabetic nephropathy (DN) is a serious complication of diabetes mellitus, and persistent inflammation in circulatory and renal tissues is an important pathophysiological basis for DN. ...The essence of the microinflammatory state is the innate immune response, which is central to the occurrence and development of DN. Members of the inflammasome family, including both “receptors” and “regulators”, are key to the inflammatory immune response. Nucleotide binding and oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) and other inflammasome components are able to detect endogenous danger signals, resulting in activation of caspase-1 as well as interleukin (IL)-1β, IL-18 and other cytokines; these events stimulate the inflammatory cascade reaction, which is crucial for DN. Hyperglycaemia, hyperlipidaemia and hyperuricaemia can activate the NLRP3 inflammasome, which then mediates the occurrence and development of DN through the K+ channel model, the lysosomal damage model and the active oxygen cluster model. In this review, we survey the involvement of the NLRP3 inflammasome in various signalling pathways and highlight different aspects of their influence on DN. We also explore the important effects of the NLRP3 inflammasome on kidney function and structural changes that occur during DN development and progression. It is becoming more evident that NLRP3 inflammasome targeting has therapeutic potential for the treatment of DN.
A novel compact initiative braking system orienting intelligent vehicles and autonomous driving is revealed. The delicate arrangement of on-off switch valves guarantees precise hydraulic pressure ...modulation. Integrated stroke simulator provides a well-tuned pedal force feedback. The fallback level is intensively designed to be nondegraded. A hierarchical control frame with the underlying hydraulic controller is designed to govern operation procedures. The underlying hydraulic controller is set up based on adaptive gain scheduling proportion differentiation controller and logic threshold control. Hardware-in-loop tests are carried out in full perspectives. The test result of slope-sine combination tracking shows that, compared with the conventional proportion integration differentiation controller, the designed underlying controller achieves higher pressure modulation accuracy with no chattering effect. Controller robustness to accumulator pressure fluctuation is proven by the dual-cylinder tracking test. A batch of step-response tests under different accumulator pressures shows a rapid pressure building capability in emergency situations under all pressure range. The fail-safe test result indicates that the conventional hydraulic brake can be restored in 1.5 s with the operation of the driver, which significantly increases the margin of brake safety for highly autonomous vehicles. The regenerative braking test result suggests the immense potential of the developed system in an application to electrified vehicles.
Video summarization is one of the critical techniques in video retrieval, video browsing, and management. It is still a challenging research task due to user subjectivity, excessive redundant ...information, and lack of spatio-temporal dependency. In this paper, we propose an unsupervised video summarization approach via reinforcement learning with shot-level semantics. The primary idea of this unsupervised method is based on the encoder-decoder model. We use a novel field size dataset to train a convolutional neural network as an encoder to extract the convolutional feature matrix from the video. Then, a bidirectional LSTM is utilized as a decoder to obtain probability weights for selecting keyframes, which preserves the spatio-temporal dependence of video summarization. Specifically, to reduce the influence of user subjectivity, we design a shot-level semantic reward function to generate more representative summarization results. The shot-level semantics are the rules followed by the video shooting process without being changed by the preferences of different viewers. Finally, we evaluate our approach on four classical datasets, SumMe, TVSum, CoSum, and VTW. The results suggest that our algorithm outperforms others and achieves satisfactory results.
Investigators in China report the results of an open-label, randomized clinical trial of lopinavir–ritonavir for the treatment of Covid-19 in 199 infected adult patients. The primary end point was ...the time to clinical improvement.
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