Ischemic cardiomyopathy (ICM) caused by coronary artery disease always leads to myocardial infarction and heart failure. Identification of novel transcriptional regulators in ICM is an effective method to establish new diagnostic and therapeutic strategies. In this study, we used two RNA-seq datasets and one microarray dataset from different studies, including 25 ICM and 21 non-failing control (NF) samples of human left ventricle tissues for further analysis. In total, 208 differentially expressed genes (DEGs) were found by combining two RNA-seq datasets with batch effects removed. GO and KEGG analyses of DEGs indicated that the response to wounding, positive regulation of smooth muscle contraction, chromatin, PI3K-Akt signaling pathway, and transporters pathways are involved in ICM. Simple Enrichment Analysis found that NFIC-binding motifs are enriched in promoter regions of downregulated genes. The Gene Importance Calculator further proved that NFIC is vital. NFIC and its downstream genes were verified in the validating microarray dataset. Meanwhile, in rat cardiomyocyte cell line H9C2 cells, two genes (Tspan1 and Hopx) were confirmed, which decreased significantly along with knocking down Nfic expression. In conclusion, NFIC participates in the ICM process by regulating TSPAN1 and HOPX. NFIC and its downstream genes may be marker genes and potential diagnostic and therapeutic targets for ICM.

Androgen-inducible genes (AIGs), which can be regulated by androgen level, constitute a group of genes characterized by the presence of the AIG/FAR-17a domain in its protein sequence. Previous studies on AIGs demonstrated that one member of the gene family, AIG1, is involved in many biological processes in cancer cell lines and that ADTRP is associated with cardiovascular diseases. It has been shown that the numbers of AIG paralogs in humans, mice, and zebrafish are 2, 2, and 3, respectively, indicating possible gene duplication events during vertebrate evolution. Therefore, classifying subgroups of AIGs and identifying the homologs of each AIG member are important to characterize this novel gene family further. In this study, vertebrate AIGs were phylogenetically grouped into three major clades, ADTRP, AIG1, and AIG-L, with AIG-L also evident in an outgroup consisting of invertebrsate species. In this case, AIG-L, as the ancestral AIG, gave rise to ADTRP and AIG1 after two rounds of whole-genome duplications during vertebrate evolution. Then, the AIG family, which was exposed to purifying forces during evolution, lost or gained some of its members in some species. For example, in eutherians, Neognathae, and Percomorphaceae, AIG-L was lost; in contrast, Salmonidae and Cyprinidae acquired additional AIG copies. In conclusion, this study provides a comprehensive molecular phylogenetic analysis of vertebrate AIGs, which can be employed for future functional characterization of AIGs.

With the rapid development of information technology, big data is gradually being popularized and promoted in various industries. Smart wearable devices have attracted much attention for their objective, accurate and continuous measurement of the amount of exercise. With the continuous improvement of intelligent equipment performance, large data analysis and intelligent wearable device technology can real-time capture the movement of the students in class data: trajectory, heart rate, speed, cost, etc., through data analysis, to monitor and intervene student’s sports activities in the class at the same time, can help the teacher more scientific system of the training plan, a more comprehensive understanding of the students in the class level and performance, enhance the level of student’s physical stamina and physical health and will bring far-reaching influence to sports research.

A key challenge of big data analytics is how to collect a large volume of (labeled) data. Crowdsourcing aims to address this challenge via aggregating and estimating high-quality data (e.g., sentiment label for text) from pervasive clients/users. Existing studies on crowdsourcing focus on designing new methods to improve the aggregated data quality from unreliable/noisy clients. However, the security aspects of such crowdsourcing systems remain under-explored to date. We aim to bridge this gap in this work. Specifically, we show that crowdsourcing is vulnerable to data poisoning attacks, in which malicious clients provide carefully crafted data to corrupt the aggregated data. We formulate our proposed data poisoning attacks as an optimization problem that maximizes the error of the aggregated data. Our evaluation results on one synthetic and two real-world benchmark datasets demonstrate that the proposed attacks can substantially increase the estimation errors of the aggregated data. We also propose two defenses to reduce the impact of malicious clients. Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.

Reinforcement learning (RL) is increasingly applied to Protein Language Models (PLMs), yet its effectiveness varies across tasks, and standard metrics such as pass@k can rise even when the model's solvable problem set is shrinking. We introduce two capability-level diagnostics. The Expansion-Shrinkage Ratio (ESR) measures how RL shifts the set of problems a PLM can solve, separating genuine gain from probability redistribution. Dual-Reward ESR reports ESR under both the training reward and an orthogonal evaluator; the gap ΔESR quantifies reward hacking as a single observable number. Applied across four protein design tasks, three RL algorithms (DPO, PPO, GRPO), and two PLM architectures, the diagnostics reveal that RL on PLMs is governed by two reward properties: verifiability, whether the reward is a fixed environment or a learned surrogate vulnerable to distribution shift, and coverage, the fraction of sequence space giving an informative gradient. The two axes produce three regimes with distinct ΔESR signatures: well-covered verifiable rewards yield genuine expansion; sparse verifiable rewards induce a coverage bottleneck; predicted rewards induce reward hacking. Controlled analyses isolate these two factors as operative, letting practitioners predict an RL run's outcome before committing to costly fine-tuning.