Puzzle Hi-C: An accurate scaffolding software.

High-quality, chromosome-scale genomes are essential for genomic analyses. Analyses, including 3D genomics, epigenetics, and comparative genomics rely on a high-quality genome assembly, which is often accomplished with the assistance of Hi-C data. Curation of genomes reveal that current Hi-C-assisted scaffolding algorithms either generate ordering and orientation errors or fail to assemble high-quality chromosome-level scaffolds. Here, we offer the software Puzzle Hi-C, which uses Hi-C reads to accurately assign contigs or scaffolds to chromosomes. Puzzle Hi-C uses the triangle region instead of the square region to count interactions in a Hi-C heatmap. This strategy dramatically diminishes scaffolding interference caused by long-range interactions. This software also introduces a dynamic, triangle window strategy during assembly. Initially small, the window expands with interactions to produce more effective clustering. Puzzle Hi-C outperforms available scaffolding tools.

Synergistic Effects of Rotavirus and Co-Infecting Viral Enteric Pathogens on Diarrheal Disease - Guangzhou City, Guangdong Province, China, 2019.

What is already known about this topic?: Diarrhea represents a substantial public health issue, contributing globally to a high number of pediatric medical consultations, hospital admissions, and mortality rates. What is added by this report?: An increase in diarrheal frequency serves as a critical benchmark for evaluating severity. The predominant pathogens associated with pediatric diarrhea are rotavirus and norovirus, with co-infections exerting a notable compounding effect that leads to more severe diarrhea. What are the implications for public health practice?: Implementing sensitive diagnostic techniques and comprehensive monitoring is paramount in identifying co-infections. Such strategies can provide physicians with critical insights into disease progression, thus considerably reducing the burden of diarrhea.

Assessing the association between the circulating levels of inflammatory cytokines and the risk of tuberculosis: A bidirectional two-sample mendelian randomization study.

BACKGROUND: Numerous observational studies have previously reported an association between inflammatory cytokines and tuberculosis (TB). However, the causal relationship between these factors remains unclear. Consequently, we conducted two-sample Mendelian randomization (MR) analyses to ascertain the causal link between levels of inflammatory cytokines and the risk of TB. METHODS: Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene. SNP was obtained from genome-wide association studies (GWAS) of 8293 individuals of Finnish. TB data was obtained from the UK Biobank, which included 46,293 individuals of European ancestry (comprising 2277 TB cases and 46,056 controls). Two-sample, bi-directional MR analyses using inverse-variance weighted (IVW) method as the primary analysis. Followed by comprehensive sensitivity analyses to validate the robustness of results. RESULT: The study showed that the causal relationship between circulating levels of interleukin (IL)-7 and risk of TB (odds ratio [OR] = 1.001, 95% confidence intervals [CIs]: 1.000, 1.003. p = 0.047). No causal associations were observed between other influencing factors and the occurrence of TB. Furthermore, the analysis revealed that TB infection exhibited negative causal associations with macrophage inflammatory protein 1 alpha ([MIP-1α], OR = 0.007, 95% CI: 0.000, 0.192. p = 0.004), IL-2 (OR = 0.014, 95% CI: 0.010, 0.427. p = 0.014), interleukin-2 receptor alpha chain([IL-2rα], OR = 0.019, 95% CI: 0.001, 0.525. p = 0.019) and basic fibroblast growth factor ([bFGF], OR = 0.066, 95% CI: 0.006, 0.700. p = 0.024). CONCLUSION: The study has illuminated the causal link between inflammatory cytokines and TB, thereby enhancing our comprehension of the potential mechanisms underlying TB pathogenesis. This discovery offers promising avenues for the identification of novel therapeutic targets in TB treatment. These insights may ultimately pave the way for more effective treatment approaches, thereby improving patient outcomes.

Impact factors of Blastocystis hominis infection in persons living with human immunodeficiency virus: a large-scale, multi-center observational study from China.

BACKGROUND: Blastocystis hominis (Bh) is zoonotic parasitic pathogen with a high prevalent globally, causing opportunistic infections and diarrhea disease. Human immunodeficiency virus (HIV) infection disrupts the immune system by depleting CD4+ T lymphocyte (CD4+ T) cell counts, thereby increasing Bh infection risk among persons living with HIV (PLWH). However, the precise association between Bh infection risk and HIV-related biological markers and treatment processes remains poorly understood. Hence, the purpose of the study was to explore the association between Bh infection risk and CD4+ T cell counts, HIV viral load (VL), and duration of interruption in antiviral therapy among PLWH. METHODS: A large-scale multi-center cross-sectional study was conducted in China from June 2020 to December 2022. The genetic presence of Bh in fecal samples was detected by real-time fluorescence quantitative polymerase chain reaction, the CD4+ T cell counts in venous blood was measured using flowcytometry, and the HIV VL in serum was quantified using fluorescence-based instruments. Restricted cubic spline (RCS) was applied to assess the non-linear association between Bh infection risk and CD4+ T cell counts, HIV VL, and duration of interruption in highly active antiretroviral therapy (HARRT). RESULTS: A total of 1245 PLWH were enrolled in the study, the average age of PLWH was 43 years [interquartile range (IQR): 33, 52], with 452 (36.3%) being female, 50.4% (n = 628) had no immunosuppression (CD4+ T cell counts > 500 cells/µl), and 78.1% (n = 972) achieved full virological suppression (HIV VL < 50 copies/ml). Approximately 10.5% (n = 131) of PLWH had interruption. The prevalence of Bh was found to be 4.9% [95% confidence interval (CI): 3.8-6.4%] among PLWH. Significant nonlinear associations were observed between the Bh infection risk and CD4+ T cell counts (Pfor nonlinearity < 0.001, L-shaped), HIV VL (Pfor nonlinearity < 0.001, inverted U-shaped), and duration of interruption in HARRT (Pfor nonlinearity < 0.001, inverted U-shaped). CONCLUSIONS: The study revealed that VL was a better predictor of Bh infection than CD4+ T cell counts. It is crucial to consider the simultaneous surveillance of HIV VL and CD4+ T cell counts in PLWH in the regions with high level of socioeconomic development. The integrated approach can offer more comprehensive and accurate understanding in the aspects of Bh infection and other opportunistic infections, the efficacy of therapeutic drugs, and the assessment of preventive and control strategies.

Prediction of HLA-DRB1*0401 binding peptides using support vector machine.

In recent years, many machine learning methods have been developed to predict HLA binding peptides. However, because only limited types of descriptors characterising the protein features are included in these approaches, these methods have poor prediction accuracy. In this study, we applied support vector machine methods to predict the peptides that bind to the major histocompatibility complexes Class II molecule HLA-DRBl*0401 using six sets of molecular descriptors characterising the primary structures of the peptides. We found that some feature groups provided good prediction accuracies and the overall accuracies were greater than 95% and some feature groups had poor accuracies of only 50%. The performance was improved significantly by additional feature selection and the overall accuracies from each group or combination of descriptors were greater than 90%. Of note, the inclusion of necessary informative and discriminative descriptors improved the prediction accuracies.

Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method.

OBJECTIVE: Accurate prediction of major histocompatibility complex (MHC) class II binding peptides helps reducing the experimental cost for identifying helper T cell epitopes, which has been a challenging problem partly because of the variable length of the binding peptides. This work is to develop an accurate model for predicting MHC-binding peptides using machine learning methods. METHODS: In this work, a machine learning method, continuous kernel discrimination (CKD), was used for predicting MHC class II binders of variable lengths. The composition transition and distribution features were used for encoding peptide sequence and the Metropolis Monte Carlo simulated annealing approach was used for feature selection. RESULTS: Feature selection was found to significantly improve the performance of the model. For benchmark dataset Dataset-1, the number of features is reduced from 147 to 24 and the area under the receiver operating characteristic curve (AUC) is improved from 0.8088 to 0.9034, while for benchmark dataset Dataset-2, the number of features is reduced from 147 to 44 and the AUC is improved from 0.7349 to 0.8499. An optimal CKD model was derived from the feature selection and bandwidth optimization using 10-fold cross-validation. Its AUC values are between 0.831 and 0.980 evaluated on benchmark datasets BM-Set1 and are between 0.806 and 0.949 on benchmark datasets BM-Set2 for MHC class II alleles. These results indicate a significantly better performance for our CKD model over other earlier models based on the training and testing of the same datasets. CONCLUSIONS: Our study suggested that the CKD method outperforms other machine learning methods proposed earlier in the prediction of MHC class II biding peptides. Moreover, the choice of the cut-off for CKD classifier is crucial for its performance.