Decoding transcriptomic signatures of cysteine string protein alpha-mediated synapse maintenance.

Synapse maintenance is essential for generating functional circuitry, and decrement in this process is a hallmark of neurodegenerative disease. Yet, little is known about synapse maintenance in vivo. Cysteine string protein α (CSPα), encoded by the Dnajc5 gene, is a synaptic vesicle chaperone that is necessary for synapse maintenance and linked to neurodegeneration. To investigate the transcriptional changes associated with synapse maintenance, we performed single-nucleus transcriptomics on the cortex of young CSPα knockout (KO) mice and littermate controls. Through differential expression and gene ontology analysis, we observed that both neurons and glial cells exhibit unique signatures in the CSPα KO brain. Significantly, all neuronal classes in CSPα KO brains show strong signatures of repression in synaptic pathways, while up-regulating autophagy-related genes. Through visualization of synapses and autophagosomes by electron microscopy, we confirmed these alterations especially in inhibitory synapses. Glial responses varied by cell type, with microglia exhibiting activation. By imputing cell-cell interactions, we found that neuron-glia interactions were specifically increased in CSPα KO mice. This was mediated by synaptogenic adhesion molecules, with the classical Neurexin1-Neuroligin 1 pair being the most prominent, suggesting that communication of glial cells with neurons is strengthened in CSPα KO mice to preserve synapse maintenance. Together, this study provides a rich dataset of transcriptional changes in the CSPα KO cortex and reveals insights into synapse maintenance and neurodegeneration.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Glis2 is an early effector of polycystin signaling and a target for therapy in polycystic kidney disease.

Mouse models of autosomal dominant polycystic kidney disease (ADPKD) show that intact primary cilia are required for cyst growth following the inactivation of polycystin-1. The signaling pathways underlying this process, termed cilia-dependent cyst activation (CDCA), remain unknown. Using translating ribosome affinity purification RNASeq on mouse kidneys with polycystin-1 and cilia inactivation before cyst formation, we identify the differential 'CDCA pattern' translatome specifically dysregulated in kidney tubule cells destined to form cysts. From this, Glis2 emerges as a candidate functional effector of polycystin signaling and CDCA. In vitro changes in Glis2 expression mirror the polycystin- and cilia-dependent changes observed in kidney tissue, validating Glis2 as a cell culture-based indicator of polycystin function related to cyst formation. Inactivation of Glis2 suppresses polycystic kidney disease in mouse models of ADPKD, and pharmacological targeting of Glis2 with antisense oligonucleotides slows disease progression. Glis2 transcript and protein is a functional target of CDCA and a potential therapeutic target for treating ADPKD.

Boron-Induced Interstitial Effects Drive Water Oxidation on Ordered Ir-B Compounds.

Interstitial filling of light atoms strongly affects the electronic structure and adsorption properties of the parent catalyst due to ligand and ensemble effects. Different from the conventional doping and surface modification, constructing ordered intermetallic structures is more promising to overcome the dissolution and reconstruction of active sites through strong interactions generated by atomic periodic arrangement, achieving joint improvement in catalytic activity and stability. However, for tightly arranged metal lattices, such as iridium (Ir), obtaining ordered filling atoms and further unveiling their interstitial effects are still limited by highly activated processes. Herein, we report a high-temperature molten salt assisted strategy to form the intermetallic Ir-B compounds (IrB1.1) with ordered filling by light boron (B) atoms. The B residing in the interstitial lattice of Ir constitutes favorable adsorption surfaces through a donor-acceptor architecture, which has an optimal free energy uphill in rate-determining step (RDS) of oxygen evolution reaction (OER), resulting in enhanced activity. Meanwhile, the strong coupling of Ir-B structural units suppresses the demetallation and reconstruction behavior of Ir, ensuring catalytic stability. Such B-induced interstitial effects endow IrB1.1 with higher OER performance than commercial IrO2, which is further validated in proton exchange membrane water electrolyzers (PEMWEs).

[Charcoal-frying process and quality markers of Sanguisorbae Radix based on hemostatic effect].

Studies have reported that the hemostatic effect of Sanguisorbae Radix(SR) is significantly enhanced after processing with charcoal. However, the standard components(tannins and gallic acid) specified in the Chinese Pharmacopeia decrease in charcoal-fried Sanguisorbae Radix(CSR), which is contrast to the enhancement of the hemostatic effect. Therefore, this study aimed to optimize the charcoal-frying process of SR based on its hemostatic efficacy and comprehensively analyze the components of SR and its processed products, thus exploring the material basis for the hemostatic effect. The results indicated that SR processed at 250 ℃ for 14 min(14-min CSR) not only complied with the description in the Chinese Pharmacopeia but also demonstrated improved blood-coagulating and blood-adsorbing effects compared with raw SR(P<0.05). Moroever, 14-min CSR reduced the bleeding time in the rat models of tail snipping, liver bleeding, and muscle injury, surpassing both raw and excessively fried SR(16 min processed) as well as tranexamic acid(P<0.05). Ellagitannin, ellagic acid, methyl gallate, pyrogallic acid, protocatechuic acid, Mg, Ca, Mn, Cu, and Zn contributed to the hemostatic effect of CSR over SR. Among these substances, ellagitannin, ellagic acid, Mg, and Ca had high content in the 14 min CSR, reaching(106.73±14.87),(34.86±4.43),(2.81±0.23), and(1.21±0.23) mg·g~(-1), respectively. Additionally, the color difference value(ΔE~*ab) of SR processed to different extents was correlated with the content of the aforementioned hemostatic substances. In summary, this study optimized the charcoal-frying process as 250 ℃ for 14 min for SR based on its hemostatic effect. Furthermore, ellagic acid and/or the powder chromaticity are proposed as indicators for the processing and quality control of CSR.

The preparation of gallic acid-grafted cationic chitosan as effective salt-tolerant flocculants.

In this work, gallic acid was successfully grafted onto quaternary aminated chitosan to prepare a high efficiency cationic flocculant. The mechanism of flocculation and different influencing factors were studied in detail. The prepared flocculant only needs 60 mg L-1 to achieve a 98.7% and 94.5% removal rate on methyl blue (MB) and Congo red (CR), respectively. The high removal rate (93.2%) of a CR-MB mixed dye also confirms the universality of flocculation. In addition, kaolin as a simulated suspended solid was removed at a rate of 97% in the experiment at a dosage of 3 mg L-1. A zeta potential test showed that it worked best when the potential of the flocculation system was zero; this was because an electrostatic balance was reached between the flocculant and pollutant. Importantly, the three-functional molecules can provide more possibilities to form hydrogen bonds with water molecules, which is conducive to the stretching of flocculant molecular chains in salt water. The flocculant maintained a high stability in four different salt environments and has a positive industrial application significance. Furthermore, the flocculation experiment of the actual wastewater of the printing and dyeing plant found that the dye wastewater changed obviously from turbidity to clarification, which proved the practical application potential of the flocculant. This work provides a feasible idea for the preparation of bio-based flocculants.

Bicontinuous RuO2 nanoreactors for acidic water oxidation.

Improving activity and stability of Ruthenium (Ru)-based catalysts in acidic environments is eager to replace more expensive Iridium (Ir)-based materials as practical anode catalyst for proton-exchange membrane water electrolyzers (PEMWEs). Here, a bicontinuous nanoreactor composed of multiscale defective RuO2 nanomonomers (MD-RuO2-BN) is conceived and confirmed by three-dimensional tomograph reconstruction technology. The unique bicontinuous nanoreactor structure provides abundant active sites and rapid mass transfer capability through a cavity confinement effect. Besides, existing vacancies and grain boundaries endow MD-RuO2-BN with generous low-coordination Ru atoms and weakened Ru-O interaction, inhibiting the oxidation of lattice oxygen and dissolution of high-valence Ru. Consequently, in acidic media, the electron- and micro-structure synchronously optimized MD-RuO2-BN achieves hyper water oxidation activity (196 mV @ 10 mA cm-2) and an ultralow degradation rate of 1.2 mV h-1. A homemade PEMWE using MD-RuO2-BN as anode also conveys high water splitting performance (1.64 V @ 1 A cm-2). Theoretical calculations and in-situ Raman spectra further unveil the electronic structure of MD-RuO2-BN and the mechanism of water oxidation processes, rationalizing the enhanced performance by the synergistic effect of multiscale defects and protected active Ru sites.

Tlr9 deficiency in B cells leads to obesity by promoting inflammation and gut dysbiosis.

Toll-like receptor 9 (TLR9) recognizes bacterial, viral and self DNA and play an important role in immunity and inflammation. However, the role of TLR9 in obesity is less well-studied. Here, we generate B-cell-specific Tlr9-deficient (Tlr9fl/fl/Cd19Cre+/-, KO) B6 mice and model obesity using a high-fat diet. Compared with control mice, B-cell-specific-Tlr9-deficient mice exhibited increased fat tissue inflammation, weight gain, and impaired glucose and insulin tolerance. Furthermore, the frequencies of IL-10-producing-B cells and marginal zone B cells were reduced, and those of follicular and germinal center B cells were increased. This was associated with increased frequencies of IFNγ-producing-T cells and increased follicular helper cells. In addition, gut microbiota from the KO mice induced a pro-inflammatory state leading to immunological and metabolic dysregulation when transferred to germ-free mice. Using 16 S rRNA gene sequencing, we identify altered gut microbial communities including reduced Lachnospiraceae, which may play a role in altered metabolism in KO mice. We identify an important network involving Tlr9, Irf4 and Il-10 interconnecting metabolic homeostasis, with the function of B and T cells, and gut microbiota in obesity.

TIMM17A overexpression in lung adenocarcinoma and its association with prognosis.

Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan-Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.

High burden of human papillomavirus infection among men in Guangzhou, South China: Implications for HPV vaccination strategies.

The epidemiological and clinical aspects of Human Papillomavirus (HPV) infection in women have been extensively studied. However, there is a lack of information regarding HPV characteristics in males. In this study, we conducted a retrospective and observational study of 3737 consecutive male individuals attending outpatient clinics of Guangdong Women and Children Hospital from 2012 to 2023 in Guangzhou, South China, to determine the age- and genotype-specific prevalence of HPV in men. The results showed the overall prevalence of HPV among men was 42.15% (1575/3737), with variations ranging from 29.55% to 81.31% across distinct diagnostic populations. Low-risk HPV6 (15.47%), HPV11 (8.94%), and high-risk HPV52 (5.51%) were the most common types. The annual HPV prevalence decreased significantly (Z = -3.882, p < .001), ranging from 31.44% to 52.90%. 28.77% (1075/3737) of men manifested infection with a singular HPV type, predominantly identified as a low-risk type. The age-specific distribution of HPV infections revealed distinctive peaks in the < 25 y age group (47.60%, 208/437) and the 40-44 y age group (44.51%, 154/346). Notably, the positive rate of Chlamydia trachomatis was significantly higher among HPV-positive individuals in comparison to HPV-negatives (16.14% vs. 11.25%, p < .05). Our findings reveal a substantial prevalence of HPV infection among outpatient men in Guangzhou, South China. It is recommended to consider the inclusion of HPV vaccination for adolescent males in national immunization schedules, once an adequate supply of vaccines is accessible.

LLPS of FXR proteins drives replication organelle clustering for ß-coronaviral proliferation.

ß-Coronaviruses remodel host endomembranes to form double-membrane vesicles (DMVs) as replication organelles (ROs) that provide a shielded microenvironment for viral RNA synthesis in infected cells. DMVs are clustered, but the molecular underpinnings and pathophysiological functions remain unknown. Here, we reveal that host fragile X-related (FXR) family proteins (FXR1/FXR2/FMR1) are required for DMV clustering induced by expression of viral non-structural proteins (Nsps) Nsp3 and Nsp4. Depleting FXRs results in DMV dispersion in the cytoplasm. FXR1/2 and FMR1 are recruited to DMV sites via specific interaction with Nsp3. FXRs form condensates driven by liquid-liquid phase separation, which is required for DMV clustering. FXR1 liquid droplets concentrate Nsp3 and Nsp3-decorated liposomes in vitro. FXR droplets facilitate recruitment of translation machinery for efficient translation surrounding DMVs. In cells depleted of FXRs, SARS-CoV-2 replication is significantly attenuated. Thus, SARS-CoV-2 exploits host FXR proteins to cluster viral DMVs via phase separation for efficient viral replication.

Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.

In this paper, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation framework based on marginal model likelihood, and we develop an efficient computational algorithm for inference. We establish a novel high-dimensional theory for this nonlinear multivariate regression. Our theory is general, allowing for potential correlations between the binary responses. We propose a new type of nuclear norm penalty using the smooth clipped absolute deviation, filling the gap in the related non-convex penalization literature. We theoretically demonstrate that the proposed approach improves estimation accuracy by considering multiple responses jointly through the proposed estimator when the underlying coefficient matrix is low-rank and row-wise sparse. In particular, we establish the non-asymptotic error bounds, and both rank and row support consistency of the proposed method. Moreover, we develop a consistent rule to simultaneously select the rank and row dimension of the coefficient matrix. Furthermore, we extend the proposed methods and theory to a joint Ising model, which accounts for the dependence relationships. In our analysis of both simulated data and the cancer cell line encyclopedia data, the proposed methods outperform the existing methods in better predicting responses.

Transition-metal ions intercalation chemistry enabled the manganese oxides-based cathode with enhanced capacity and cycle life for high-performance aqueous zinc-ion batteries.

Aqueous zinc-ion batteries (AZIBs) employing mild aqueous electrolytes are recognized for their high safety, cost-effectiveness, and scalability, rendering them promising candidates for large-scale energy storage infrastructure. However, the practical viability of AZIBs is notably impeded by their limited capacity and cycling stability, primarily attributed to sluggish cathode kinetics during electrochemical charge-discharge processes. This study proposes a transition-metal ion intercalation chemistry approach to augment the Zn2+ (de)intercalation dynamics using copper ions as prototypes. Electrochemical assessments reveal that the incorporation of Cu2+ into the host MnO2 lattice (denoted as MnO2-Cu) not only enhances the capacity performance owing to the additional redox activity of Cu2+ but also facilitates the kinetics of Zn2+ ion transport during charge-discharge cycles. Remarkably, the resulting AZIB employing the MnO2-Cu cathode exhibits a superior capacity of 429.4 mA h g-1 (at 0.1 A g-1) and maintains 50% capacity retention after 50 cycles, surpassing both pristine MnO2 (146.8 mA h g-1) and non-transition-metal ion-intercalated MnO2 (MnO2-Na, 198.5 mA h g-1). Through comprehensive electrochemical kinetics investigations, we elucidate that intercalated Cu2+ ions serve as mediators for interlayer stabilization and redox centers within the MnO2 host, enhancing capacity and cycling performance. The successful outcomes of this study underscore the potential of transition-metal ion intercalation strategies in advancing the development of high-performance cathodes for AZIBs.

A mediation analysis framework based on variance component to remove genetic confounding effect.

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Statistical methods for assessing the effects of de novo variants on birth defects.

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder.

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

Systematic characterization of protein structural features of alternative splicing isoforms using AlphaFold 2.

Alternative splicing is an important cellular process in eukaryotes, altering pre-mRNA to yield multiple protein isoforms from a single gene. However, our understanding of the impact of alternative splicing events on protein structures is currently constrained by a lack of sufficient protein structural data. To address this limitation, we employed AlphaFold 2, a cutting-edge protein structure prediction tool, to conduct a comprehensive analysis of alternative splicing for approximately 3,000 human genes, providing valuable insights into its impact on the protein structural. Our investigation employed state of the art high-performance computing infrastructure to systematically characterize structural features in alternatively spliced regions and identified changes in protein structure following alternative splicing events. Notably, we found that alternative splicing tends to alter the structure of residues primarily located in coils and beta-sheets. Our research highlighted a significant enrichment of loops and highly exposed residues within human alternatively spliced regions. Specifically, our examination of the Septin-9 protein revealed potential associations between loops and alternative splicing, providing insights into its evolutionary role. Furthermore, our analysis uncovered two missense mutations in the Tau protein that could influence alternative splicing, potentially contributing to the pathogenesis of Alzheimer's disease. In summary, our work, through a thorough statistical analysis of extensive protein structural data, sheds new light on the intricate relationship between alternative splicing, evolution, and human disease.

Emergence and circulation of enterovirus B species in infants in southern China: A multicenter retrospective analysis.

BACKGROUND: Enteroviruses (EV) are common and can cause severe diseases, particularly in young children. However, the information of EV infection in infants in China is limited due to the vast population size and extensive geographical area of the country. Here, we conducted a retrospective multicenter analysis of available EV data to assess the current epidemiological situation in the infant population in southern China. METHODS: The study enrolled infants with suspected EV infection from 34 hospitals across 12 cities in southern China between 2019 to 2022, and the confirmation of EV was done using RT-PCR and VP1 gene sequencing. RESULTS: Out of 1221 infants enrolled, 330 (27.03%) were confirmed as EV-infected. Of these, 260 (78.79%) were newborns aged 0-28 days. The EV belonged to three species: EV-B (80.61%), EV-A (11.82%), and human rhinovirus (7.58%). Newborns were more susceptible to EV-B than older infants (p < 0.001). Within EV-B, we identified 15 types, with coxsackievirus (CV) B3 (20.91%), echovirus (E) 11 (19.70%), and E18 (16.97%) being the most common. The predominant EV types changed across different years. EV infection in infants followed a seasonal pattern, with a higher incidence from May to August. Furthermore, perinatal mother-to-child EV transmission in 12 mother-newborn pairs were observed. CONCLUSION: Our study is the first to demonstrate the emergence and widespread circulation of EV-B species, mainly CVB3, E11, and E18, in southern China, primarily affecting young infants. This research provides valuable insights for future epidemic assessment, prediction, as well as the elimination of mother-to-child transmission.

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And in fields such as healthcare, there are higher requirements for the accuracy of the generated imputation values. To train a high-performance classifier and pursue high accuracy, we attempted to resolve any potential negative impact by using a novel algorithmic approach based on the combination of multivariate imputation by chained equations and the ensemble learning method (MICEEN), which can solve the two problems simultaneously. We used multivariate imputation by chained equations to generate more accurate imputation values for the training set passed to ensemble learning to build a predictor. On the other hand, missing values were introduced into minority classes and used them to generate new samples belonging to the minority classes in order to balance the distribution of classes. On real-world datasets, we perform extensive experiments to assess our method and compare it to other state-of-the-art approaches. The advantages of the proposed method are demonstrated by experimental results for the benchmark datasets and self-collected datasets of physical fitness assessment of tumor patients with varying missing rates.