The repetitive nature and complexity of some medically relevant genes poses a challenge for their accurate analysis in a clinical setting. The Genome in a Bottle Consortium has provided variant ...benchmark sets, but these exclude nearly 400 medically relevant genes due to their repetitiveness or polymorphic complexity. Here, we characterize 273 of these 395 challenging autosomal genes using a haplotype-resolved whole-genome assembly. This curated benchmark reports over 17,000 single-nucleotide variations, 3,600 insertions and deletions and 200 structural variations each for human genome reference GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically relevant genes, including CBS, CRYAA and KCNE1. When masking these false duplications, variant recall can improve from 8% to 100%. Forming benchmarks from a haplotype-resolved whole-genome assembly may become a prototype for future benchmarks covering the whole genome.
When executing CPU and GPU applications in CPU-GPU heterogeneous systems, a common phenomenon arises where CPU applications performance is often interfered by GPU applications. This study ...substantiates this observation through an analysis of resource contention and identifies the limitations of the Virtual Channel Partitioning (VCP) approach in the crossbar switch allocation stage. In response to the resource contention problem in crossbar switch allocation stage, we propose a Probability-Based CPU-first Arbitration Strategy that enhances the priority of CPU packets in contention through specific probabilities. Furthermore, we introduce a Dynamic Probability-Based CPU-first Arbitration Strategy (DPBC) that dynamically selects probability values based on application execution phases to strike a balance between optimal CPU and GPU performance. Moreover, we combine this dynamic strategy with VCP to further enhance CPU performance, propose the DPBC-VCP method. The DPBC-VCP, a combination of network partitioning and prioritization techniques, yields an average enhancement of 48% in CPU performance compared to the baseline, with only a marginal 2.45% reduction in GPU performance.
The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society
. However, it still has many gaps and errors, and does ...not represent a biological genome as it is a blend of multiple individuals
. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome
. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity
. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.
Transfer, particularly far transfer through analogic mapping and reasoning, is considered one of the mechanisms for creative problem solving. However, the cognitive paradigm of training for ...analogical problem solving has not met with much success. It is argued by these authors that a pedagogical model that exclusively focuses on training for analogical mapping is not effective. In this study, an alternative pedagogical approach, QEOSA, standing for Question, Explore, Optimize, Show, and Act, was tested on analogical transfer of problem solving with preschool children. This pedagogical model was informed by the creativity research with respect to creative problem solving as soft constraint satisfaction, involving both divergent and convergent processes. A randomly assigned experiment with 221 preschool children was conducted using QEOSA for 4 months (one semester). The results provide evidence of effectiveness of QEOSA in promoting far transfer. Theoretical and practical implications of the study for building an effective pedagogy of creative problem solving are discussed.
To explore the correlation between miR-34c-5p and NOTCH1 in nasopharyngeal carcinoma (NPC).
qPCR was employed to quantify miR-34c-5p and NOTCH1 mRNA in NPC, and Western blot to detect NOTCH1. ...MiR-34c-5p mimics/inhibitor and NOTCH1 siRNA were constructed to analyze the role of miR-34c-5p/NOTCH1 on the biological function of NPC cells.
NPC cells showed lower miR-34c-5p expression and higher NOTCH1 expression than normal cells, and up-regulating miR-34c-5p or inhibiting NOTCH1 could strongly suppress the epithelial-mesenchymal transition (EMT), proliferation, invasion and migration of NPC cells, and induce apoptosis in them. Up-regulating miR-34c-5p could inhibit NOTCH1, and miR-34c-5p was negatively correlated with NOTCH1. Rescue experiment results revealed that NOTCH1 up-regulation could counteract the changes of cell process induced by increased miR-34c-5p.
MiR-34c-5p inhibits the growth of NPC by down-regulating NOTCH1, so up-regulating miR-34c-5p or down-regulating NOTCH1 may become the potential direction of NPC treatment.
The problem of intelligent robust controller design for flight vehicles with asynchronous switching is investigated based on deep reinforcement learning. The switched model of flight vehicle in full ...envelope is established based on Jacobian linearization according to the nonlinear dynamic model. The asynchronous switching caused by packet loss are taken into consideration and the asynchronous dynamic model of controllers and subsystems are introduced. Then the robust controller is provided. The stability of the system is analyzed, and the sufficient conditions to ensure the stability with prescribed interference suppression index are given based on average dwell time method and multiple Lyapunov functional method. The solutions of controllers are obtained by linear matrix inequality. Moreover, the obtained controller is optimized based on deep reinforcement learning, and the dynamic response performance of the system is improved while ensuring the stability and robustness. Numerical examples in the end are gi
This paper studies the problem of weighted H∞ consensus for discrete-time multi-agent systems(MASs) with modedependent average dwell time (MDADT) method under switching topologies. The asynchronous ...switching has been taken into consideration, which means the switching of controllers have a lag to the switching of system modes. Based on the outputs of MASs, a control protocol is proposed. By conducting a linear transformation, the weighted H∞ consensus design can be reduced to a weighted H∞ control problem. Then we establish Lyapunov function to analyse the stability and weighted H∞ performance, by which the sufficient conditions are derived through a set of linear matrix inequalities (LMIs). Finally, a simulation result is given to verify the effectiveness of the proposed protocol and design method.
This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the ...time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper.
The problem of H ∞ consesus control for asynchronously switched continuous-time multi-agent systems is investigated in this paper. There always exists asynchronous switching due to time delay and ...packet losses of network transmission which means that the switching of controllers will lag behind the switching of system modes, which will lead to performance degradation. First, the model of switched multi-agent systems is established. The H ∞ consensus issue can be converted as a H ∞ control problem via system reduction. Considering asynchronous switching phenomenon, the mean-square asymptotic stability is analyzed by the aid of multiple Lyapunov functional method and average dwell time (ADT) method. The sufficient conditions to ensure the closed-loop stable with prescribed performance are given in the form of linear matrix inequalities (LMIs). In the end , an example is given to verify the feasibility of the proposed approach.
PDF
