Tracking the rapid spread of infectious diseases such as COVID-19 in communities poses a significant global health challenge, mainly because traditional testing methods struggle to detect asymptomatic carriers and effectively manage outbreaks. To address this, we present a scalable, networked molecular testing framework for wastewater-based epidemiology that, for the first time, integrates portable nucleic acid testing (NAT) devices with long-range wireless communication and cloud-based data management. Each battery-powered node can perform Reverse Transcription - Loop-mediated Isothermal Amplification (RT-LAMP), a rapid nucleic acid amplification method, on-site directly on raw wastewater samples for SARS-CoV-2 detection. A node executes up to eight independent tests on a microfluidic cartridge, monitoring fluorescence signals from the RT-LAMP reactions in real time and transmitting the results via long-range radio (LoRa) to a gateway, which uploads structured data to a NoSQL cloud database. An interactive dashboard enables bidirectional communication with nodes and provides spatiotemporal visualization of both live and historical data, including hotspot identification through geotagging and mapping of metrics such as 7-day positivity rates. Rigorous system testing with four nodes under varied transmission scenarios confirmed a packet delivery ratio exceeding 99%, ensuring accurate and reliable reconstruction of time-series RT-LAMP curves. By integrating NAT with IoT communication and cloud analytics, this system supports unattended, long-term wastewater monitoring and can provide public health officials with timely, actionable insights. This framework demonstrates the potential of IoT-enabled molecular testing to advance decentralized surveillance beyond conventional laboratory-based workflows.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has become an important method for characterizing cellular heterogeneity, revealing more biological insights than the bulk RNA-seq. The surge in scRNA-seq data across multiple individuals calls for efficient and statistically powerful methods for differential expression (DE) analysis that addresses individual-level biological variability. RESULTS: We introduced DiSC, a method for conducting individual-level DE analysis by extracting multiple distributional characteristics, jointly testing their association with a variable of interest, and using a flexible permutation testing framework to control the false discovery rate (FDR). Our simulation studies demonstrated that DiSC effectively controlled the FDR across various settings and exhibited high statistical power in detecting different types of gene expression changes. Moreover, DiSC is computationally efficient and scalable to the rapidly increasing sample sizes in scRNA-seq studies. When applying DiSC to identify DE genes potentially associated with COVID-19 severity and Alzheimer's disease across various types of peripheral blood mononuclear cells and neural cells, we found that our method was approximately 100 times faster than other state-of-the-art methods and the results were consistent and supported by existing literature. While DiSC was developed for scRNA-seq data, its robust testing framework can also be applied to other types of single-cell data. We applied DiSC to cytometry by time-of-flight data, DiSC identified significantly more DE markers than traditional methods. AVAILABILITY AND IMPLEMENTATION: The R software package "SingleCellStat" is freely available on CRAN (https://cran.r-project.org/web/packages/SingleCellStat/index.html) and GitHub (https://github.com/Lujun995/DiSC). The replication code for reproducing the analyses in this study is publicly accessible at https://github.com/Lujun995/DiSC_Replication_Code. The scRNA-seq expression matrix and metadata utilized in our simulations and analyses can be retrieved from https://cells.ucsc.edu/autism/rawMatrix.zip, https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30, and https://covid19.cog.sanger.ac.uk/submissions/release1/haniffa21.processed.h5ad.

BACKGROUND: Metabolic syndrome has become a major public health challenge worldwide. The association between metabolic syndrome and DNA methylation is of great research interest. RESULTS: We constructed a binomial model to investigate the association between a metabolic syndrome index and DNA methylation in the Normative Aging Study. We applied the Iterative Sure Independence Screening (ISIS) method with elastic net penalty to DNA methylation levels at 484,548 CpG markers from 659 human subjects, and demonstrated that the screening step in ISIS can significantly improve the performance of the elastic net. CONCLUSION: The proposed method identifies four CpGs which can be mapped to two biologically relevant and functional genes. Identification of significant CpG markers may potentially have practical implications for disease prevention and treatment.

BACKGROUND: RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates. RESULTS: In this paper, we present MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, we show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables. The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff. CONCLUSION: Our results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables.

BACKGROUND: DNA methylation is a widely studied epigenetic phenomenon; alterations in methylation patterns influence human phenotypes and risk of disease. As part of the Atherosclerosis Risk in Communities (ARIC) study, the Illumina Infinium HumanMethylation450 (HM450) BeadChip was used to measure DNA methylation in peripheral blood obtained from ~3000 African American study participants. Over 480,000 cytosine-guanine (CpG) dinucleotide sites were surveyed on the HM450 BeadChip. To evaluate the impact of technical variation, 265 technical replicates from 130 participants were included in the study. RESULTS: For each CpG site, we calculated the intraclass correlation coefficient (ICC) to compare variation of methylation levels within- and between-replicate pairs, ranging between 0 and 1. We modeled the distribution of ICC as a mixture of censored or truncated normal and normal distributions using an EM algorithm. The CpG sites were clustered into low- and high-reliability groups, according to the calculated posterior probabilities. We also demonstrated the performance of this clustering when applied to a study of association between methylation levels and smoking status of individuals. For the CpG sites showing genome-wide significant association with smoking status, most (~96%) were seen from sites in the high reliability cluster. CONCLUSIONS: We suggest that CpG sites with low ICC may be excluded from subsequent association analyses, or extra caution needs to be taken for associations at such sites.

We report a ZrO2-based resistive memory composed of a thin Cu doped ZrO2layer sandwiched between Pt bottom and Cu top electrode. The Cu/ZrO2:Cu/Pt shows excellent nonpolar resistive switching behaviors, such as free-electroforming, high ON/OFF resistance ratio (106), fast Set/Reset speed (50 ns/100 ns), and reliable data retention (>10 years). The temperature-dependent switching characteristics show that a metallic filamentary channel is responsible for the low resistance state. Further analysis reveals that the physical origin of this metallic filament is the nanoscale Cu conductive filament. On this basis, we propose that the set process and the reset process stem from the electrochemical reactions in the filament, in which a thermal effect is greatly involved.