Genomic analyses provide insights into peach local adaptation and responses to climate change

The environment has constantly shaped plant genomes, but the genetic bases underlying how plants adapt to environmental influences remain largely unknown. We constructed a high-density genomic variation map of 263 geographically representative peach landraces and wild relatives. A combination of whole-genome selection scans and genome-wide environmental association studies (GWEAS) was performed to reveal the genomic bases of peach adaptation to diverse climates. A total of 2092 selective sweeps that underlie local adaptation to both mild and extreme climates were identified, including 339 sweeps conferring genomic pattern of adaptation to high altitudes. Using genome-wide environmental association studies (GWEAS), a total of 2755 genomic loci strongly associated with 51 specific environmental variables were detected. The molecular mechanism underlying adaptive evolution of high drought, strong UVB, cold hardiness, sugar content, flesh color, and bloom date were revealed. Finally, based on 30 yr of observation, a candidate gene associated with bloom date advance, representing peach responses to global warming, was identified. Collectively, our study provides insights into molecular bases of how environments have shaped peach genomes by natural selection and adds candidate genes for future studies on evolutionary genetics, adaptation to climate changes, and breeding.info:eu-repo/semantics/publishedVersio

Impeded Nedd4-1-Mediated Ras Degradation Underlies Ras-Driven Tumorigenesis

RAS genes are among the most frequently mutated proto-oncogenes in cancer. However, how Ras stability is regulated remains largely unknown. Here, we report a regulatory loop involving the E3 ligase Nedd4-1, Ras, and PTEN. We found that Ras signaling stimulates the expression of Nedd4-1, which in turn acts as an E3 ubiquitin ligase that regulates Ras levels. Importantly, Ras activation, either by oncogenic mutations or by epidermal growth factor (EGF) signaling, prevents Nedd4-1-mediated Ras ubiquitination. This leads to Ras-induced Nedd4-1 overexpression, and subsequent degradation of the tumor suppressor PTEN in both human cancer samples and cancer cells. Our study thus unravels the molecular mechanisms underlying the interplay of Ras, Nedd4-1, and PTEN and suggests a basis for the high prevalence of Ras-activating mutations and EGF hypersignaling in cancer. © 2014 The Authors

Real-Time Solution of Unsteady Inverse Heat Conduction Problem Based on Parameter-Adaptive PID with Improved Whale Optimization Algorithm

To solve the problem of the common unsteady inverse heat conduction problem in the industrial field, a real-time solution method of improving the whale optimization algorithm (IWOA) and parameter-adaptive proportional-integral-differential (PID) is proposed in the paper. A feedback control system with IWOA-PID, which can inversely solve the boundary heat flux, is established. The deviation between the calculated temperature and the measured temperature of the measured point obtained by solving the direct heat conduction problem (DHCP) is used as the system input. The heat flux which is iteration-solved by IWOA-PID is used as system output. The method improves the initial solution distribution, global search capability and population diversity generalization of the traditional whale optimization algorithm (WOA), which effectively improves the parameter-adaptive capability of PID. The experimental results show that the solution method of inverse heat transfer proposed in the paper can accurately retrieve the variation of the boundary heat flux in real time and has good resistance and self-adaptability

Therapeutic Evidence of Human Mesenchymal Stem Cell Transplantation for Cerebral Palsy: A Meta-Analysis of Randomized Controlled Trials

Cerebral palsy (CP) is a kind of movement and posture disorder syndrome in early childhood. In recent years, human mesenchymal stem cell (hMSC) transplantation has become a promising therapeutic strategy for CP. However, clinical evidence is still limited and controversial about clinical efficacy of hMSC therapy for CP. Our aim is to evaluate the efficacy and safety of hMSC transplantation for children with CP using a meta-analysis of randomized controlled trials (RCTs). We conducted a systematic literature search including Embase, PubMed, ClinicalTrials.gov, Cochrane Controlled Trials Register databases, Chinese Clinical Trial Registry, and Web of Science from building database to February 2020. We used Cochrane bias risk assessment for the included studies. The result of pooled analysis showed that hMSC therapy significantly increased gross motor function measure (GMFM) scores (standardized mean difference SMD=1.10, 95%CI=0.66‐1.53, P<0.00001, high-quality evidence) and comprehensive function assessment (CFA) (SMD=1.30, 95%CI=0.71‐1.90, P<0.0001, high-quality evidence) in children with CP, compared with the control group. In the subgroup analysis, the results showed that hMSC therapy significantly increased GMFM scores of 3, 6, and 12 months and CFA of 3, 6, and 12 months. Adverse event (AE) of upper respiratory infection, diarrhea, and constipation was not statistically significant between the two groups. This meta-analysis synthesized the primary outcomes and suggested that hMSC therapy is beneficial, effective, and safe in improving GMFM scores and CFA scores in children with CP. In addition, subgroup analysis showed that hMSC therapy has a lasting positive benefit for CP in 3, 6, and 12 months

An Opposition-Based Learning CRO Algorithm for Solving the Shortest Common Supersequence Problem

As a non-deterministic polynomial hard (NP-hard) problem, the shortest common supersequence (SCS) problem is normally solved by heuristic or metaheuristic algorithms. One type of metaheuristic algorithms that has relatively good performance for solving SCS problems is the chemical reaction optimization (CRO) algorithm. Several CRO-based proposals exist; however, they face such problems as unstable molecular population quality, uneven distribution, and local optimum (premature) solutions. To overcome these problems, we propose a new approach for the search mechanism of CRO-based algorithms. It combines the opposition-based learning (OBL) mechanism with the previously studied improved chemical reaction optimization (IMCRO) algorithm. This upgraded version is dubbed OBLIMCRO. In its initialization phase, the opposite population is constructed from a random population based on OBL; then, the initial population is generated by selecting molecules with the lowest potential energy from the random and opposite populations. In the iterative phase, reaction operators create new molecules, where the final population update is performed. Experiments show that the average running time of OBLIMCRO is more than 50% less than the average running time of CRO_SCS and its baseline algorithm, IMCRO, for the desoxyribonucleic acid (DNA) and protein datasets

A Flexible Reinforced Bin Packing Framework with Automatic Slack Selection

The slack-based algorithms are popular bin-focus heuristics for the bin packing problem (BPP). The selection of slacks in existing methods only consider predetermined policies, ignoring the dynamic exploration of the global data structure, which leads to nonfully utilization of the information in the data space. In this paper, we propose a novel slack-based flexible bin packing framework called reinforced bin packing framework (RBF) for the one-dimensional BPP. RBF considers the RL-system, the instance-eigenvalue mapping process, and the reinforced-MBS strategy simultaneously. In our work, the slack is generated with a reinforcement learning strategy, in which the performance-driven rewards are used to capture the intuition of learning the current state of the container space, the action is the choice of the packing container, and the state is the remaining capacity after packing. During the construction of the slack, an instance-eigenvalue mapping process is designed and utilized to generate the representative and classified validate set. Furthermore, the provision of the slack coefficient is integrated into MBS-based packing process. Experimental results show that, in comparison with fit algorithms, MBS and MBS’, RBF achieves state-of-the-art performance on BINDATA and SCH_WAE datasets. In particular, it outperforms its baseline MBS and MBS’, averaging the number increase of optimal solutions of 189.05% and 27.41%, respectively

A Meta Reinforcement Learning-Based Task Offloading Strategy for IoT Devices in an Edge Cloud Computing Environment

Developing an effective task offloading strategy has been a focus of research to improve the task processing speed of IoT devices in recent years. Some of the reinforcement learning-based policies can improve the dependence of heuristic algorithms on models through continuous interactive exploration of the edge environment; however, when the environment changes, such reinforcement learning algorithms cannot adapt to the environment and need to spend time on retraining. This paper proposes an adaptive task offloading strategy based on meta reinforcement learning with task latency and device energy consumption as optimization targets to overcome this challenge. An edge system model with a wireless charging module is developed to improve the ability of IoT devices to provide service constantly. A Seq2Seq-based neural network is built as a task strategy network to solve the problem of difficult network training due to different dimensions of task sequences. A first-order approximation method is proposed to accelerate the calculation of the Seq2Seq network meta-strategy training, which involves quadratic gradients. The experimental results show that, compared with existing methods, the algorithm in this paper has better performance in different tasks and network environments, can effectively reduce the task processing delay and device energy consumption, and can quickly adapt to new environments

Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers

Efficient resource management in a Cloud data center relies on minimizing energy consumption and utilizing physical resource efficiently while maintaining the service-level agreement (SLA) at its highest level. To achieve this goal, dynamically consolidating virtual machines (VMs) is considered a promising method, because it eliminates the hotspots resulting from overloaded hosts and switches the underloaded hosts to sleep mode through the live migration of VMs. However, during the consolidation, each VM migration consumes additional resource, leading to performance degradation and SLA violation. To address this issue, this study proposes a novel adaptive performance-to-power-ratio (PPR)-aware dynamic VM consolidation framework based on both the predicted resource utilization and PPR of the heterogeneous hosts to resolve the trade-off of performance and energy. The proposed framework consists of four stages: (1) host overload detection based on residual available computing capacity; (2) selection of the appropriate VMs for migration from the overloaded hosts based on minimum data transfer; (3) host underload detection based on multi-criteria Z-score approach; (4) allocating the VMs selected for migration from the overloaded and underloaded hosts based on the modified power-aware best-fit decreasing algorithm. To validate the reliability and scalability of the proposed method, we performed experimental evaluation in both real and simulated environments. The experimental results demonstrate that the proposed approach can reduce the energy consumption effectively and ensure maximal conformity to the quality of service (QoS) requirements across heterogeneous infrastructures, in comparison with the existing competitive approaches

Spatial and Temporal Variability of Polycyclic Aromatic Hydrocarbons in Sediments from Yellow River-Dominated Margin

Polycyclic aromatic hydrocarbons (PAHs) were analyzed for surface sediments and a sediment core from the Yellow River-dominated margin. The concentration of 16 USEPA priority PAHs in surface sediments ranged from 5.6 to 175.4 ng g−1 dry weight sediment (dws) with a mean of 49.1 ng g−1 dws. From 1930 to 2011, the distribution of PAHs (37.2 to 210.6 ng g−1 dws) was consistent with the socioeconomic development of China. The PAHs’ concentration peaked in 1964 and 1986, corresponding to the rapid economic growth in China (1958–1965) and the initiation of the “Reform and Open” policy in 1978, respectively. The applications of molecular diagnostic ratios and principal component analysis suggest that PAHs are predominantly produced by the coal and biomass combustion, whereas the contribution of petroleum combustions slightly increased after the 1970s, synchronous with an increasing usage of oil and gas in China