Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Codon Bias of the DDR1 Gene and Transcription Factor EHF in Multiple Species

Milk production is an essential economic trait in cattle, and understanding the genetic regulation of this trait can enhance breeding strategies. The discoidin domain receptor 1 (DDR1) gene has been identified as a key candidate gene that influences milk production, and ETS homologous factor (EHF) is recognized as a critical transcription factor that regulates DDR1 expression. Codon usage bias, which affects gene expression and protein function, has not been fully explored in cattle. This study aims to examine the codon usage bias of DDR1 and EHF transcription factors to understand their roles in dairy production traits. Data from 24 species revealed that both DDR1 and EHF predominantly used G/C-ending codons, with the GC3 content averaging 75.49% for DDR1 and 61.72% for EHF. Synonymous codon usage analysis identified high-frequency codons for both DDR1 and EHF, with 17 codons common to both genes. Correlation analysis indicated a negative relationship between the effective number of codons and codon adaptation index for both DDR1 and EHF. Phylogenetic and clustering analyses revealed similar codon usage patterns among closely related species. These findings suggest that EHF plays a crucial role in regulating DDR1 expression, offering new insights into genetically regulating milk production in cattle.

Association between estimated glucose disposal rate control level and stroke incidence in middle-aged and elderly adults

BACKGROUND

To estimate glucose disposal rate (eGDR) as a newly validated surrogate marker of insulin resistance. Few studies have explored the association between changes in eGDR levels and stroke incidence. This study aims to explore the effect of the level of eGDR control on stroke and events.

METHODS

Data were obtained from the China Longitudinal Study on Health and Retirement (CHARLS). The eGDR control level was classified using K-means cluster analysis. Logistic regression analysis was used to explore the association between different eGDR control levels and incident stroke. Restrictive cubic spline regression was used to test the potential nonlinear association between cumulative eGDR and stroke incidence.

RESULTS

Of the 4790 participants, 304 (6.3%) had a stroke within 3 years. The odds ratio (OR) was 2.34 (95% confidence interval [CI], 1.42-3.86) for the poorly controlled class 4 and 2.56 (95% CI, 1.53-4.30) for the worst controlled class 5 compared with class 1 with the best controlled eGDR. The OR for well-controlled class 2 was 1.28 (95% CI, 0.79-2.05), and the OR for moderately controlled class 3 was 1.95 (95% CI, 1.14-3.32). In restrictive cubic spline regression analysis, eGDR changes are linearly correlated with stroke occurrence. Weighted quartile and regression analysis identified waist circumference and hypertension as key variables of eGDR for predicting incident stroke.

CONCLUSIONS

Poorly controlled eGDR level is associated with an increased risk of stroke in middle-aged and elderly people. Monitoring changes in eGDR may help identify individuals at high risk of stroke early.

Unveiling Allosteric Regulation and Binding Mechanism of BRD9 through Molecular Dynamics Simulations and Markov Modeling

Bromodomain-containing protein 9 (BRD9) is a key player in chromatin remodeling and gene expression regulation, and it is closely associated with the development of various diseases, including cancers. Recent studies have indicated that inhibition of BRD9 may have potential value in the treatment of certain cancers. Molecular dynamics (MD) simulations, Markov modeling and principal component analysis were performed to investigate the binding mechanisms of allosteric inhibitor POJ and orthosteric inhibitor 82I to BRD9 and its allosteric regulation. Our results indicate that binding of these two types of inhibitors induces significant structural changes in the protein, particularly in the formation and dissolution of α-helical regions. Markov flux analysis reveals notable changes occurring in the α-helicity near the ZA loop during the inhibitor binding process. Calculations of binding free energies reveal that the cooperation of orthosteric and allosteric inhibitors affects binding ability of inhibitors to BRD9 and modifies the active sites of orthosteric and allosteric positions. This research is expected to provide new insights into the inhibitory mechanism of 82I and POJ on BRD9 and offers a theoretical foundation for development of cancer treatment strategies targeting BRD9.

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research.

Genome sequencing of drake semen micobiome with correlation with their compositions, sources and potential mechanisms affecting semen quality

Artificial insemination (AI) technology has greatly promoted the development of the chicken industry. Recently, AI technology has also begun to be used in the duck industry, but there are some problems. Numerous researchers have shown that microbes colonizing in semen can degrade semen quality, and AI can increase the harmful microbial load in hen's reproductive tract. Different from the degraded external genitalia of roosters, drakes have well-developed external genitalia, which may cause drake semen to be more susceptible to microbial contamination. However, information on the compositions, sources, and effects of semen microbes on semen quality remains unknown in drakes. In the current study, high-throughput sequencing technology was used to detect microbial communities in drake semen, environmental swabs, cloacal swabs, and the spermaduct after quantifying the semen quality of drakes to investigate the effects of microbes in the environment, cloaca, and spermaduct on semen microbiota and the relationships between semen microbes and semen quality. Taxonomic analysis showed that the microbes in the semen, environment, cloaca, and spermaduct samples were all classified into 4 phyla and 25 genera. Firmicutes and Proteobacteria were the dominant phyla. Phyllobacterium only existed in the environment, while Marinococcus did not exist in the cloaca. Of the 24 genera present in semen: Brachybacterium, Brochothrix, Chryseobacterium, Kocuria, Marinococcus, Micrococcus, Rothia, Salinicoccus, and Staphylococcus originated from the environment; Achromobacter, Aerococcus, Corynebacterium, Desemzia, Enterococcus, Jeotgalicoccus, Pseudomonas, Psychrobacter, and Turicibacter originated from the cloaca; and Agrobacterium, Carnobacterium, Chelativorans, Devosia, Halomonas, and Oceanicaulis originated from the spermaduct. In addition, K-means clustering analysis showed that semen samples could be divided into 2 clusters based on microbial compositions, and compared with cluster 1, the counts of Chelativorans (P < 0.05), Devosia (P < 0.01), Halomonas (P < 0.05), and Oceanicaulis (P < 0.05) were higher in cluster 2, while the sperm viability (P < 0.05), total sperm number (P < 0.01), and semen quality factor (SQF) (P < 0.01) were lower in cluster 2. Furthermore, functional prediction analysis of microbes showed that the activities of starch and sucrose metabolism, phosphotransferase system, ABC transporters, microbial metabolism in diverse environments, and quorum sensing pathways between cluster 1 and cluster 2 were significantly different (P < 0.05). Overall, environmental/cloacal microbes resulted in semen contamination, and microbes from the Chelativorans, Devosia, Halomonas, and Oceanicaulis genera may have negative effects on semen quality in drakes by affecting the activities of starch and sucrose metabolism, phosphotransferase system, ABC transporters, and quorum sensing pathways that are associated with carbohydrate metabolism. These data will provide a basis for developing strategies to prevent microbial contamination of drake semen.

Molecular Dynamics Simulations Combined with Markov Model to Explore the Effect of Allosteric Inhibitor Binding on Bromodomain-Containing Protein 4

Bromodomain-Containing Protein 4 (BRD4) can play an important role in gene transcriptional regulation of tumor development and survival by participating in histone modification epigenetic mechanism. Although it has been reported that novel allosteric inhibitors such as ZL0590 have a high affinity with target protein BRD4 and good efficacy, their inhibitory mechanism has not been studied further. The aim of this study was to reveal the inhibition mechanism of allosteric inhibitor ZL0590 on Free-BRD4 and BRD4 binding MS436 (orthosteric inhibitor) by molecular dynamics simulation combined with a Markov model. Our results showed that BRD4-ZL0590 led to α-helices formation of 100-105 compared with Free-BRD4; the combination of MS436 caused residues 30-40 and 95-105 to form α-helices, while the combination of allosteric inhibitors untangled the α-helices formed by the MS436. The results of Markov flux analysis showed that the binding process of inhibitors mainly involved changes in the degree of α-helices at ZA loop. The binding of ZL0590 reduced the distance between ZA loop and BC loop, blocked the conformation at the active site, and inhibited the binding of MS436. After the allosteric inhibitor binding, the MS436 that could normally penetrate into the interior of the pocket was floating on the edge of the active pocket and did not continue to penetrate into the active pocket as expected. In summary, we provide a theoretical basis for the inhibition mechanism of ZL0590 against BRD4, which can be used as a reference for improving the development of drug targets for cancer therapy.