Potential role of Bcl2 in lipid metabolism and synaptic dysfunction of age-related hearing loss.

Age-related hearing loss (ARHL) is a prevalent condition affecting millions of individuals globally. This study investigated the role of the cell survival regulator Bcl2 in ARHL through in vitro and in vivo experiments and metabolomics analysis. The results showed that the lack of Bcl2 in the auditory cortex affects lipid metabolism, resulting in reduced synaptic function and neurodegeneration. Immunohistochemical analysis demonstrated enrichment of Bcl2 in specific areas of the auditory cortex, including the secondary auditory cortex, dorsal and ventral areas, and primary somatosensory cortex. In ARHL rats, a significant decrease in Bcl2 expression was observed in these areas. RNAseq analysis showed that the downregulation of Bcl2 altered lipid metabolism pathways within the auditory pathway, which was further confirmed by metabolomics analysis. These results suggest that Bcl2 plays a crucial role in regulating lipid metabolism, synaptic function, and neurodegeneration in ARHL; thereby, it could be a potential therapeutic target. We also revealed that Bcl2 probably has a close connection with lipid peroxidation and reactive oxygen species (ROS) production occurring in cochlear hair cells and cortical neurons in ARHL. The study also identified changes in hair cells, spiral ganglion cells, and nerve fiber density as consequences of Bcl2 deficiency, which could potentially contribute to the inner ear nerve blockage and subsequent hearing loss. Therefore, targeting Bcl2 may be a promising potential therapeutic intervention for ARHL. These findings provide valuable insights into the molecular mechanisms underlying ARHL and may pave the way for novel treatment approaches for this prevalent age-related disorder.

Comparative Genomics Revealing the Genomic Characteristics of Klebsiella variicola Clinical Isolates in China.

Klebsiella variicola is an opportunistic pathogen often misidentified as Klebsiella pneumoniae, leading to misdiagnoses and inappropriate treatment in clinical settings. The genetic and molecular characteristics of clinically isolated K. variicola remain largely unexplored. We aim to fill this knowledge gap by examining the genomic properties of and evolutionary relationships between clinical isolates of K. variicola. The genomic data of 70 K. variicola strains were analyzed using whole-genome sequencing. A phylogenetic tree was generated based on the gene sequences from these K. variicola strains and public databases. Among the K. variicola strains, the drug resistance genes with the highest carrying rates were beta-lactamase and aminoglycoside. Locally isolated strains had a higher detection rate for virulence genes than those in public databases, with yersiniabactin genes being the most prevalent. The K locus types and MLST subtypes of the strains exhibited a dispersed distribution, with O3/O3a being the predominant subtype within the O category. In total, 28 isolates carried both IncFIB(K)_Kpn3 and IncFII_pKP91 replicons. This study underscores the importance of developing more effective diagnostic tools and therapeutic strategies for K. variicola infections. The continued surveillance and monitoring of K. variicola strains is essential for understanding the epidemiology of infections and informing public health strategies.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework.

BACKGROUND: Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. METHODS: Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. FINDINGS: The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. INTERPRETATION: iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. FUNDING: The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

Estimation of drinking water volume of laboratory animals based on image processing.

This paper describes an image processing-based technique used to measure the volume of residual water in the drinking water bottle for the laboratory mouse. This technique uses a camera to capture the bottle's image and then processes the image to calculate the volume of water in the bottle. Firstly, the Grabcut method separates the foreground and background to avoid the influence of background on image feature extraction. Then Canny operator was used to detect the edge of the water bottle and the edge of the liquid surface. The cumulative probability Hough detection identified the water bottle edge line segment and the liquid surface line segment from the edge image. Finally, the spatial coordinate system is constructed, and the length of each line segment on the water bottle is calculated by using plane analytical geometry. Then the volume of water is calculated. By comparing image processing time, the pixel number of liquid level, and other indexes, the optimal illuminance and water bottle color were obtained. The experimental results show that the average deviation rate of this method is less than 5%, which significantly improves the accuracy and efficiency of measurement compared with traditional manual measurement.

Effects of plant diversity and abiotic factors on the multifunctionality of an arid desert ecosystem.

Many studies suggest that species diversity and abiotic factors promote ecosystem multifunctionality. However, whether ecosystem multifunctionality is impacted by phylogenetic diversity remains controversial. The present study tested this in an arid desert ecosystem in Ebinur Lake Basin using soil C:N ratio, soil pH, and soil salinity as abiotic factors, and species diversity and phylogenetic diversity as indicators of plant diversity. The effects of plant diversity and abiotic factors on single ecosystem functions (nutrient cycling, carbon stocks, water regulation, and wood production) and ecosystem multifunctionality were studied. We used structural equation modeling to assess the relationships among different functional groups and factors. The results showed that: (1) abiotic factors, particularly pH and C:N ratio in soil, had the strongest positive impact on multifunctionality (P < 0.001). The phylogenetic diversity and species diversity showed inconsistent changes, and their contribution to multifunctionality were not outstanding. (2) Abiotic factors were closely related to different ecosystem functions. Soil C:N had a significant positive effect on carbon stocks (P < 0.001), with an effect index of 0.89. Soil pH significantly enhanced nutrient cycling and water regulation. The role of plant diversity varied with the combination of different ecosystem functions. Phylogenetic diversity and species diversity influenced wood production, but showed opposite functions. (3) The importance of four single-ecosystem functions in an arid region was ranked as follows: carbon stocks > water regulation > nutrient cycling > wood production, emphasizing the importance of carbon elements in these ecosystems. These results improve our understanding of the drivers of multifunctionality in arid ecosystems, facilitating the elucidation of the influence of abiotic factors and phylogenetic diversity.

Machine learning of pair-contact process with diffusion.

The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified into the directed percolation (DP) universality class, the model of PCPD has been controversially discussed since its infancy. To our best knowledge, there is so far no consensus on whether the phase transition of the PCPD falls into the unknown university classes or else conveys a new kind of non-equilibrium phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original configurations of the model and provide reasonable estimates of thresholds. Therefore, no matter whether the non-equilibrium lattice model is a random process of unitary (for instance the DP) or binary (for instance the PCP), or whether it contains the diffusion motion of particles, unsupervised learning can capture the essential, hidden information. Beyond that, supervised learning is also applied to learning the PCPD at different diffusion rates. We proposed a more accurate numerical method to determine the spatial correlation exponent [Formula: see text], which, to a large degree, avoids the uncertainty of data collapses through naked eyes.

Detection of bulk feed volume based on binocular stereo vision.

The volume detection of medical mice feed is crucial to understand the food intake requirements of mice at different growth stages and to grasp their growth, development, and health status. Aiming at the problem of volume calculation in the way of feed bulk in mice, a method for detecting the bulk volume of feed in mice based on binocular stereo vision was proposed. Firstly, the three-dimensional point coordinates of the feed's surface were calculated using the binocular stereo vision three-dimensional reconstruction technology. The coordinates of these dense points formed a point cloud, and then the projection method was used to calculate the volume of the point cloud; and finally, the volume of the mice feed was obtained. We use the stereo matching data set provided by the Middlebury evaluation platform to conduct experimental verification. The results show that our method effectively improves the matching degree of stereo matching and makes the three-dimensional point coordinates of the obtained feed's surface more accurate. The point cloud is then denoised and Delaunay triangulated, and the volume of the tetrahedron obtained after the triangulation is calculated and summed to obtain the total volume. We used different sizes of wood instead of feed for multiple volume calculations, and the average error between the calculated volume and the real volume was 7.12%. The experimental results show that the volume of the remaining feed of mice can be calculated by binocular stereo vision.

Transfer learning of phase transitions in percolation and directed percolation.

The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural network (DANN) based on transfer learning to studying nonequilibrium and equilibrium phase transition models, which are percolation model and directed percolation (DP) model, respectively. With the DANN, only a small fraction of input configurations (two-dimensional images) needs to be labeled, which is automatically chosen, to capture the critical point. To learn the DP model, the method is refined by an iterative procedure in determining the critical point, which is a prerequisite for the data collapse in calculating the critical exponent ν_{⊥}. We then apply the DANN to a two-dimensional site percolation with configurations filtered to include only the largest cluster which may contain the information related to the order parameter. The DANN learning of both models yields reliable results which are comparable to the ones from Monte Carlo simulations. Our study also shows that the DANN can achieve quite high accuracy at much lower cost, compared to the supervised learning.

Metabolomic characterization of congenital microtia: a possible analysis for early diagnosis.

Background: Although metabolic abnormalities have been deemed one of the essential risk factors for growth and development, the relationship between metabolic abnormalities and microtia is still unclear. In this study, we aimed to establish a cell model of microtia and the changes of serum metabolites in patients with microtia. Methods: After constructing a cell model of microtia with low expression of BMP5, we performed integrative metabolomics analysis. For the altered metabolites, the content of glycerophosphocholine (PC), triacylglycerol (TG), and choline in the serum of 28 patients (15 patients with microtia and 13 controls) with microtia was verified by enzyme-linked immunosorbent assay (ELISA). Results: Detailed metabolomic evaluation showed distinct clusters of metabolites between BMP5-low expressing cells and normal control (NC) cells. The cell model of microtia had significantly higher levels of TG, PC, glycerophosphoethanolamine (PE), sphingomyelin, sulfatide, glycerophosphoglycerol, diacylglycerol, and glycosphingolipid. The main abnormal metabolites were mainly concentrated in the glycerophospholipid metabolism pathway, and PC and choline were closely related. In the serum of patients with microtia, the contents of PC, TG, and choline were significantly increased. Conclusions: The individual serum samples confirmed the different metabolites between patients with microtia and controls. In particular, we showed that a newly developed metabolic biomarker panel has a high sensitivity and specificity for separating patients with microtia from controls.

Mitochondrial dysfunction resulting from the down-regulation of bone morphogenetic protein 5 may cause microtia.

BACKGROUND: Bone morphogenetic protein 5 (BMP5) has been identified as one of the important risk factors for microtia; however, the link between them has yet to be clarified. In this study, we aimed to demonstrate the relationship of BMP5 with mitochondrial function and investigate the specific role of mitochondria in regulating microtia development. METHODS: BMP5 expression was measured in auricular cartilage tissues from patients with and without microtia. The effects of BMP5 knockdown on cellular function and mitochondrial function were also analyzed in vitro. Changes in genome-wide expression profiles were measured in BMP5-knockdown cells. Finally, the specific impact of BMP5 down-regulation on mitochondrial fat oxidation was analyzed in vitro. RESULTS: BMP5 expression was down-regulated in the auricular cartilage tissues of microtia patients. BMP5 down-regulation inhibited various cellular functions in vitro, including cell proliferation, mobility, and cytoactivity. The functional integrity of mitochondria was also damaged, accompanied by a decrease in mitochondrial membrane potential, reactive oxygen species (ROS) neutralization, and reduced adenosine triphosphate (ATP) production. Carnitine O-palmitoyltransferase 2 and diacylglycerol acyltransferase 2, two of the key regulators of mitochondrial lipid oxidation, were also found to be decreased by BMP5 down-regulation. CONCLUSIONS: Down-regulation of BMP5 affects glycerolipid metabolism and fatty acid degradation, leading to mitochondrial dysfunction, reduced ATP production, and changes in cell function, and ultimately resulting in microtia. This research provides supporting evidence for an important role of BMP5 down-regulation in affecting mitochondrial metabolism in cells, and sheds new light on the mechanisms underlying the pathogenesis of microtia.