scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data.

With the emergence of large amount of single-cell RNA sequencing (scRNA-seq) data, the exploration of computational methods has become critical in revealing biological mechanisms. Clustering is a representative for deciphering cellular heterogeneity embedded in scRNA-seq data. However, due to the diversity of datasets, none of the existing single-cell clustering methods shows overwhelming performance on all datasets. Weighted ensemble methods are proposed to integrate multiple results to improve heterogeneity analysis performance. These methods are usually weighted by considering the reliability of the base clustering results, ignoring the performance difference of the same base clustering on different cells. In this paper, we propose a high-order element-wise weighting strategy based self-representative ensemble learning framework: scEWE. By assigning different base clustering weights to individual cells, we construct and optimize the consensus matrix in a careful and exquisite way. In addition, we extracted the high-order information between cells, which enhanced the ability to represent the similarity relationship between cells. scEWE is experimentally shown to significantly outperform the state-of-the-art methods, which strongly demonstrates the effectiveness of the method and supports the potential applications in complex single-cell data analytical problems.

Effects of Na/K-Cl Salts on Hydrolysis of Aluminosilicate Glass Using Ab Initio Molecular Dynamics.

The structural and chemical modifications on the surface of pure and alkali-doped aluminosilicate (AS) glasses due to hydrolysis are investigated using ab initio molecular dynamics. The effects of water on the glass network are fully elucidated by analyzing the short- and intermediate-range structural orders embedded in the pair distribution function, bond length and angle distribution, coordination number, and interatomic bonding. A novel concept of total bond order is used to quantify and compare the strength of bonds in hydrated and unhydrated glasses. We show that AS glass is hydrolyzed by water diffusion near the surface and by proton (H+) transfers into the bulk, which increases with time. Hence, a dissolved glass-water interface becomes rich in Si-OH and Al-OH. The alkali ions associated with the nonbridging oxygen accelerate the hydrolysis by facilitating water and H+ diffusion. Al is more impacted by hydrolysis than Si, resulting in greater variation in the Al-O bond order than Si-O. Doping of NaCl and KCl enhances the ionization of water and the hydrolysis of ASs with increased salt concentration. The KCl doping ionizes more water molecules and causes more degradation of the glass network than NaCl. Co-doping of Na and K results in a mixed alkali effect due to complex interatomic bonding from different-sized ions. These exceptionally detailed findings in highly complex glasses with varying salt compositions provide new and unprecedented atomistic insights that can help to understand the hydrolysis and dissolution mechanisms of ASs and other silicate glasses.

AGML: Adaptive Graph-based Multi-label Learning for Prediction of RBP and AS Event Associations During EMT.

Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediating alternative splicing (AS) events during epithelial-mesenchymal transition (EMT). However, due to the substantial cost and complexity of biological experiments, how AS events are regulated and influenced remains largely unknown. Thus, it is important to construct effective models for inferring hidden RBP-AS event associations during EMT process. In this paper, a novel and efficient model was developed to identify AS event-related candidate RBPs based on Adaptive Graph-based Multi-Label learning (AGML). In particular, we propose to adaptively learn a new affinity graph to capture the intrinsic structure of data for both RBPs and AS events. Multi-view similarity matrices are employed for maintaining the intrinsic structure and guiding the adaptive graph learning. We then simultaneously update the RBP and AS event associations that are predicted from both spaces by applying multi-label learning. The experimental results have shown that our AGML achieved AUC values of 0.9521 and 0.9873 by 5-fold and leave-one-out cross-validations, respectively, indicating the superiority and effectiveness of our proposed model. Furthermore, AGML can serve as an efficient and reliable tool for uncovering novel AS events-associated RBPs and is applicable for predicting the associations between other biological entities. The source code of AGML is available at https://github.com/yushanqiu/AGML.

Exploring multiplex qPCR as a diagnostic tool for detecting microfilarial DNA in dogs infected with Dirofilaria immitis: A comparative analysis with the modified Knott's test.

Current recommendations to diagnose cardiopulmonary dirofilariosis in dogs caused by Dirofilaria immitis involves tandem antigen and circulating microfilariae tests. The modified Knott's test is an important tool in heartworm diagnosis, allowing identification of circulating microfilariae. However, the subjective nature of the modified Knott's test affects its accuracy and diagnostic laboratories usually do not provide a quantitative outcome. Quantitative enumeration of microfilariae enables clinicians to track treatment progress and acts as a proxy for detecting emerging macrocyclic lactone resistance. There is a need for better diagnostic tools suitable for routine use to efficiently and accurately quantify the presence of D. immitis microfilaremia. The aim of this study was to determine whether the quantitative modified Knott's test can be substituted by multiplex quantitative polymerase chain reaction (qPCR) targeting D. immitis and associated Wolbachia endosymbiont DNA in canine blood samples. To do this, genomic DNA samples (n = 161) from Australian dogs, collected as part of a previous 2021 study, were assessed in a TaqMan qPCR targeting DNA of D. immitis, Wolbachia sp. and Canis lupus familiaris. Of the 161 genomic DNA samples, eight were considered positive for D. immitis microfilariae. The qPCR assay demonstrated good efficiency (E = 90 to 110%, R2 > 0.94). Considering the performance and efficient use of bench time, this TaqMan qPCR assay is a suitable alternative to the modified Knott's test for quantitative enumeration of microfilariae (Cohen's kappa coefficient [κ]: κ = 1 using D. immitis qPCR marker, κ = 0.93 using Wolbachia qPCR marker). The qPCR data demonstrated a comparable result to that of the quantitative modified Knott's test in a 2022 survey of D. immitis in Australian dogs (n = 23) before and after macrocyclic lactone (ML) administration. Improving the detection and diagnosis of canine heartworm infections will assist veterinarians in better managing and controlling disease outcomes and will be valuable for tracking the spread of ML resistance in Australia.

Cytochrome oxidase I DNA barcodes of crocodilians meat selling in Hong Kong.

The crocodilians include true crocodiles, alligators, caimans, and gharial, and the trade of crocodilian products is regulated in accordance with the Convention of Wild Fauna and Flora (CITES). Hong Kong does not have her own wild crocodilians; thus, all crocodilians meat available is presumably imported with proper license. Here, we obtained a dataset of cytochrome oxidase I (COI) gene markers of 114 crocodilian meat samples (including frozen and dried crocodilian meat products) available in the contemporary market. We have also validated these barcodes in a phylogenetic approach with other data deposited on the GenBank, and detected 112 samples belonging to four crocodile species Crocodylus siamensis, C. porosus, C. niloticus and Alligator mississippiensis, and 2 samples belonging to snake Malayopython reticulatus. The dataset generated in this study will be useful for further studies including meat inspection, illegal trading, and enhancement of international and local legislations on illegal reptile importation.

Porosity modeling in a TiNbTaZrMo high-entropy alloy for biomedical applications.

High-entropy alloys (HEAs) have attracted great attention for many biomedical applications. However, the nature of interatomic interactions in this class of complex multicomponent alloys is not fully understood. We report, for the first time, the results of theoretical modeling for porosity in a large biocompatible HEA TiNbTaZrMo using an atomistic supercell of 1024 atoms that provides new insights and understanding. Our results demonstrated the deficiency of using the valence electron count, quantification of large lattice distortion, validation of mechanical properties with available experimental data to reduce Young's modulus. We utilized the novel concepts of the total bond order density (TBOD) and partial bond order density (PBOD) via ab initio quantum mechanical calculations as an effective theoretical means to chart a road map for the rational design of complex multicomponent HEAs for biomedical applications.