CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Field-programmable gate array-based residual amplitude modulation suppression and control for compact atomic clocks.

We designed a field-programmable gate array (FPGA) fabric to provide phase modulation techniques to lock lasers to optical frequency references. The method incorporates an active residual amplitude modulation (RAM) suppression scheme that relies on complex modulation. All the required servos to construct an optical atomic clock are incorporated into the same low-cost, commercial FPGA chip. We demonstrate a reliable, long-term RAM suppression of 60 dB with the remaining RAM level at -100 dBc and an improved stability of three decades when applied on a two-photon rubidium clock.

Evolutionary dynamics of canine kobuvirus in Vietnam and Thailand reveal the evidence of viral ability to evade host immunity.

Canine kobuvirus (CaKoV) is a pathogen associated with canine gastrointestinal disease (GID). This study examined 327 rectal swabs (RS), including 113 from Vietnam (46 healthy, 67 with GID) and 214 from Thailand (107 healthy and 107 with GID). CaKoV was detected in both countries, with prevalences of 28.3% (33/113) in Vietnam and 7.9% (17/214) in Thailand. Additionally, CaKoV was found in both dogs with diarrhea and healthy dogs. CaKoV was mainly found in puppies under six months of age (30.8%). Co-detection with other canine viruses were also observed. The complete coding sequence (CDS) of nine Vietnamese and four Thai CaKoV strains were characterized. Phylogenetic analysis revealed a close genetic relationship between Vietnamese and Thai CaKoV strains, which were related to the Chinese strains. CDS analysis indicated a distinct lineage for two Vietnamese CaKoV strains. Selective pressure analysis on the viral capsid (VP1) region showed negative selection, with potential positive selection sites on B-cell epitopes. This study, the first of its kind in Vietnam, provides insights into CaKoV prevalence in dogs of different ages and healthy statuses, updates CaKoV occurrence in Thailand, and sheds light on its molecular characteristics and immune evasion strategies.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Comparison of the Snare Loop Technique and the Hungaroring Reinforcement for Physician-Modified Endograft Fenestrations-An In Vitro Study.

BACKGROUND: We conducted an in vitro comparison of the snare loop reinforcement against a closed-loop reinforcement (Hungaroring) for physician-modified endograft (PMEG) fenestrations regarding preparation time and stability during flaring balloon dilatation. MATERIALS AND METHODS: The time to complete a PMEG fenestration with reinforcement was measured and compared between the Hungaroring and snare loop groups. The number of stitches was counted. Each fenestration was dilated using a 10 mm high-pressure, non-compliant balloon up to 21 atm in pressure, and fluoroscopic images were taken. The presence of indentation on the oversized balloon at the level of the reinforcement was evaluated at each fenestration. RESULTS: Five fenestrations were created in each group (n = 5) for a total of ten pieces. The completion time in the snare loop group was 1070 s (IQR:1010-1090) compared to 760 s (IQR:685-784) in the Hungaroring group (p = 0.008). Faster completion time was achieved by faster stitching (23.2 s/stitch (IQR 22.8-27.3) for the snare loop group and 17.3 s/stitch (IQR 17.3-20.1) for the Hungaroring group (p = 0.016). None of the fluoroscopic images of the snare loop reinforcement showed an indentation on the balloon during the overexpansion; on the contrary, the Hungaroring showed indentation in every case, even at 21 atm. CONCLUSION: Fenestrations reinforced with Hungaroring can be completed significantly faster. Furthermore, the Hungaroring resists over-dilation even at high pressures, while snare loop reinforcements dilate at nominal pressure.

Changing global epidemiology of chronic hepatitis C virus-related outcomes from 2010 to 2019: cirrhosis is the growing burden of hepatitis C virus-related disease.

BACKGROUND: Chronic infection with hepatitis C virus (HCV) has a long-term impact on hepatic consequences. A comprehensive evaluation of the global burden of HCV-related health outcomes can help to develop a global HCV prevention and treatment program. METHODS: We used the 2019 Global Burden of Disease (GBD) Study to comprehensively investigate burden and temporal trends in incidence, mortality and disability-adjusted life-years (DALYs) of HCV-related diseases, including liver cancer and cirrhosis and other liver diseases across 264 countries and territories from 2010 to 2019. RESULTS: Globally, there were 152 225 incident cases, 141 811 deaths and approximately 2.9 million DALYs because of HCV-related liver cancer, and 551 668 incident cases, 395 022 deaths and about 12.2 million DALYs because of HCV-related cirrhosis in 2019. Worldwide, during the 2010-2019 period, liver cancer incidence declined, however, there was a 62% increase in cirrhosis incidence. In 2019, the Eastern Mediterranean was the region with the highest rates of incidence and mortality of both liver cancer and cirrhosis. Africa was the region with the fastest-growing trend of incidence of cirrhosis in the 2010-2019 period [annual percentage change (APC) = 2.09, 95% confidence interval (CI): 1.93-2.25], followed by the Western Pacific region (APC = 1.17, 95% CI: 1.09-1.22). Americas were the only region observing increased trends in liver cancer and cirrhosis mortality (APC = 0.70 and 0.12, respectively). We identified three patterns of temporal trends of mortality rates of liver cancer and cirrhosis in countries that reported HCV treatment rates. CONCLUSION: Urgent measures are required for diagnosis, treatment and research on HCV-related cirrhosis at global, regional and country levels, particularly in Africa, the Western Pacific and the Eastern Mediterranean.

Superhydrophobic Array Devices for the Enhanced Formation of 3D Cancer Models.

During the metastatic cascade, cancer cells travel through the bloodstream as circulating tumor cells (CTCs) to a secondary site. Clustered CTCs have greater shear stress and treatment resistance, yet their biology remains poorly understood. We therefore engineered a tunable superhydrophobic array device (SHArD). The SHArD-C was applied to culture a clinically relevant model of CTC clusters. Using our device, we cultured a model of cancer cell aggregates of various sizes with immortalized cancer cell lines. These exhibited higher E-cadherin expression and are significantly more capable of surviving high fluid shear stress-related forces compared to single cells and model clusters grown using the control method, helping to explain why clustering may provide a metastatic advantage. Additionally, the SHArD-S, when compared with the AggreWell 800 method, provides a more consistent spheroid-forming device culturing reproducible sizes of spheroids for multiple cancer cell lines. Overall, we designed, fabricated, and validated an easily tunable engineered device which grows physiologically relevant three-dimensional (3D) cancer models containing tens to thousands of cells.

Time-Resolved Evaluation of L-Dopa Metabolism in Bacteria-Host Symbiotic System and the Effect on Parkinson's Molecular Pathology.

The gut microbiome influences drug metabolism and therapeutic efficacy. Still, the lack of a general label-free approach for monitoring bacterial or host metabolic contribution hampers deeper insights. Here, a 2D nuclear magnetic resonance (NMR) approach is introduced that enables real-time monitoring of the metabolism of Levodopa (L-dopa), an anti-Parkinson drug, in both live bacteria and bacteria-host (Caenorhabditis elegans) symbiotic systems. The quantitative method reveals that discrete Enterococcus faecalis substrains produce different amounts of dopamine in live hosts, even though they are a single species and all have the Tyrosine decarboxylase (TyrDC) gene involved in L-dopa metabolism. The differential bacterial metabolic activity correlates with differing Parkinson's molecular pathology concerning alpha-synuclein aggregation as well as behavioral phenotypes. The gene's existence or expression is not an indicator of metabolic activity is also shown, underscoring the significance of quantitative metabolic estimation in vivo. This simple approach is widely adaptable to any chemical drug to elucidate pharmacomicrobiomic relationships and may help rapidly screen bacterial metabolic effects in drug development.