Control Dataset Research Articles

Analysis of m6A-regulated genes and subtype classification in lupus nephritis

BackgroundLupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE). N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, the roles of m6A regulators in LN are not fully demonstrated.MethodsWe downloaded the kidney tissue transcriptome dataset of LN patients and normal controls from the GEO database and extracted the expression levels of m6A regulators. We constructed and compared Random Forest (RF) and Support Vector Machine (SVM) models, and subsequently selected featured genes to develop nomogram models. The m6A subtypes were identified based on significantly differentially expressed m6A regulators, and the m6A gene subtypes were identified based on m6A-associated differential genes, and the two m6A modification patterns were comprehensively evaluated.ResultsWe obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples. We extracted the expression levels of 20 m6A regulators. By RF analysis we identified 7 characteristic m6A regulators and constructed nomogramh models with these 7 genes. We identified two m6A subtypes based on these seven important m6A regulators, and the immune cell infiltration levels of the two subtype clusters were significantly different. We identified two more m6A gene subtypes based on m6A-associated DEGs. We calculated the m6A scores using the principal component analysis (PCA) algorithm and found that the m6A scores of m6A cluster A and gene cluster A were lower than those of m6A cluster B and gene cluster B. In addition, we found that the levels of inflammatory factors were also significantly different between m6A clusters and gene clusters.ConclusionThis study confirms that m6A regulators are involved in the LN process through different modes of action and provide new diagnostic and therapeutic targets for LN.

Accurate detection of lead malfunction from ECG-derived bipolar pacing stimulus amplitude

BackgroundOne common mode of lead failure is insulation breach, which may result in myopotential noise and device malfunction. “Pseudo-unipolarization” of bipolar pacing stimuli, as observed from a routine 12-lead electrocardiogram (ECG) due to stimulus current leak, has been observed with insulation breaches. ObjectiveWe sought to characterize this electrocardiographic finding to detect this type of lead malfunction. MethodsA total of 138 transvenous leads were analyzed, including 88 with known malfunction and 50 normal leads. The amplitude of a bipolar pacing stimulus on the ECG was recorded and compared with a control data set of newly implanted leads with bipolar stimuli normalized for output. ResultsThe malfunction group consisted of 61% right atrium and 39% right ventricle leads with mean pacing output of 2.74 V at 0.5 ms. There was a significant difference in ECG bipolar stimulus amplitudes at time of identification of failure (7.89 ± 7.56 mm/V; P < .001) compared with those of normal leads (0.86 ± 0.41 mm/V). Receiver operating characteristic curve for the prediction of lead malfunction based on absolute ECG amplitude displayed an area under the curve of 0.93 (95% CI, 0.891–0.969). When normalized for programmed stimulus output, a cutoff of 5 mm/V demonstrated a sensitivity of 91% and a specificity of 92% (area under the curve, 0.967; 95% CI, 0.938–0.996). ConclusionThe maximum amplitude of a bipolar pacing stimulus on the ECG is significantly lower in normal functioning leads compared with those with known malfunction. This simply derived variable demonstrated good accuracy at identifying lead failure due to insulation breach.

Analysis of hematology quality control using six sigma metrics.

Clinical laboratories serve a critical role in increasing the efficiency of patient care. Choosing the right test, getting trustworthy results and appropriate interpretation are of utmost importance in improving the patient's well-being. Quality management strategies should be applied in routine patient care because laboratory errors have a major impact on the quality of patient care. In sigma metrics, errors identified are quantified as percentage errors or defects per million (DPM). It aims at improving the quality control (QC) process by forming an appropriate strategy. To analyze the internal quality control (IQC) of hematology analytes using the sigma metrics method and to devise the frequency of IQC by the results of six sigma metric analysis. This study was conducted in a tertiary care center of western India. Internal quality control (IQC) data sets of five analytes- Red Blood Cell count (RBC), Hemoglobin (Hb), Hematocrit (Hct), White blood cell count (WBC), and Platelet count (PLT) were analyzed retrospectively of six months using Beckman Coulter DXH 800 hematology analyzers. The observed sigma value was >6 for Hb, TLC, and PLT, indicating excellent results and requiring no modification in IQC. The Sigma value was between 3 and 4 for RBC and Hct suggested the need for improvement in quality control (QC) processes. No analytes showed a Sigma value of <3. Sigma metrics provide a quantitative framework that helps to assess analytic methodologies and can serve as an important self-assessment tool for quality assurance in the clinical laboratory.

Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis.

Tuberculosis (TB) remains a significant global health concern, necessitating accurate diagnosis and treatment monitoring. Extracellular vesicles (EVs), including exosomes, play crucial roles in disease progression, with their associated genes serving as potential biomarkers and therapeutic targets. Leveraging publicly available RNA-Seq datasets of TB patients and healthy controls (HCs), to identify differentially expressed genes (DEGs) and their associated protein-protein interaction networks and immune cell profiles, the common EV-related DEGs were identified and validated in the GSE42830 and GSE40553 datasets. We have identified nine common EV-related DEGs (SERPINA1, TNFAIP6, MAPK14, STAT1, ITGA2B, VAMP5, CTSL, CEACAM1, and PLAUR) upregulated in TB patients. Immune cell infiltration analysis revealed significant differences between TB patients and HCs, highlighting increased proportions of various immune cells in TB patients. These DEGs are involved in crucial cellular processes and pathways related to exocytosis and immune response regulation. Notably, VAMP5 exhibited excellent diagnostic performance (AUC-0.993, sensitivity-93.8%, specificity-100%), with potential as a novel biomarker for TB. The EV-related genes can serve as novel potential biomarkers that can distinguish between TB and HCs. VAMP5, which functions in exosome biogenesis and showed significant upregulation in TB, can be targeted for therapeutic interventions and treatment outcomes.

ViTCN: Hybrid Vision Transformer with Temporal Convolution for Multi-Emotion Recognition

In Talentino, HR-Solution analyzes candidates’ profiles and conducts interviews. Artificial intelligence is used to analyze the video interviews and recognize the candidate’s expressions during the interview. This paper introduces ViTCN, a combination of Vision Transformer (ViT) and Temporal Convolution Network (TCN), as a novel architecture for detecting and interpreting human emotions and expressions. Human expression recognition contributes widely to the development of human-computer interaction. The machine’s understanding of human emotions in the real world will considerably contribute to life in the future. Emotion recognition was identifying the emotions as a single frame (image-based) without considering the sequence of frames. The proposed architecture utilized a series of frames to accurately identify the true emotional expression within a combined sequence of frames over time. The study demonstrates the potential of this method as a viable option for identifying facial expressions during interviews, which could inform hiring decisions. For situations with limited computational resources, the proposed architecture offers a powerful solution for interpreting human facial expressions with a single model and a single GPU.The proposed architecture was validated on the widely used controlled data sets CK+, MMI, and the challenging DAiSEE data set, as well as on the challenging wild data sets DFEW and AFFWild2. The experimental results demonstrated that the proposed method has superior performance to existing methods on DFEW, AFFWild2, MMI, and DAiSEE. It outperformed other sophisticated top-performing solutions with an accuracy of 4.29% in DFEW, 14.41% in AFFWild2, and 7.74% in MMI. It also achieved comparable results on the CK+ data set.

Identification of HDAC9 and ARRDC4 as potential biomarkers and targets for treatment of type 2 diabetes

We aimed to identify the key potential insulin resistance (IR)-related genes and investigate their correlation with immune cell infiltration in type 2 diabetes (T2D). The GSE78721 dataset (68 diabetic patients and 62 controls) was downloaded from the Gene Expression Omnibus database and utilized for single-sample gene set enrichment analysis. IR-related genes were obtained from the Comparative Toxicology Genetics Database, and the final IR-differentially expressed genes (DEGs) were screened by intersecting with the DEGs obtained from the GSE78721 datasets. Functional enrichment analysis was performed, and the networks of the target gene with microRNA, transcription factor, and drug were constructed. Hub genes were identified based on a protein–protein interaction network. Least absolute shrinkage and selection operator regression and Random Forest and Boruta analysis were combined to screen diagnostic biomarkers in T2D, which were validated using the GSE76894 (19 diabetic patients and 84 controls) and GSE9006 (12 diabetic patients and 24 controls) datasets. Quantitative real-time polymerase chain reaction was performed to validate the biomarker expression in IR mice and control mice. In addition, infiltration of immune cells in T2D and their correlation with the identified markers were computed using CIBERSORT. We identified differential immune gene set regulatory T-cells in the GSE78721 dataset, and T2D samples were assigned into three clusters based on immune infiltration. A total of 2094 IR-DEGs were primarily enriched in response to endoplasmic reticulum stress. Importantly, HDAC9 and ARRDC4 were identified as markers of T2D and associated with different levels of immune cell infiltration. HDAC9 mRNA level were higher in the IR mice than in control mice, while ARRDC4 showed the opposite trend. In summary, we discovered potential vital biomarkers that contribute to immune cell infiltration associated with IR, which offers a new sight of immunotherapy for T2D.

PHARE: a bioinformatics pipeline for compositional profiling of multiclonal Plasmodium falciparum infections from long-read Nanopore sequencing data.

The emergence of drug-resistant clones of Plasmodium falciparum is a major public health concern, and the ability to detect and track the spread of these clones is crucial for effective malaria control and treatment. However, in endemic settings, malaria infected people often carry multiple P. falciparum clones simultaneously making it likely to miss drug-resistant clones using traditional molecular typing methods. Our goal was to develop a bioinformatics pipeline for compositional profiling in multiclonal P. falciparum samples, sequenced using the Oxford Nanopore Technologies MinION platform. We developed the 'Finding P. falciparum haplotypes with resistance mutations in polyclonal infections' (PHARE) pipeline using existing bioinformatics tools and custom scripts written in python. PHARE was validated on three control datasets containing P. falciparum DNA of four laboratory strains at varying mixing ratios. Additionally, the pipeline was tested on clinical samples from children admitted to a paediatric hospital in the Central African Republic. The PHARE pipeline achieved high recall and accuracy rates in all control datasets. The pipeline can be used on any gene and was tested with amplicons of the P. falciparum drug resistance marker genes pfdhps, pfdhfr and pfK13. The PHARE pipeline helps to provide a more complete picture of drug resistance in the circulating P. falciparum population and can help to guide treatment recommendations. PHARE is freely available under the GNU Lesser General Public License v.3.0 on GitHub: https://github.com/Fippu/PHARE.

RPA3-UMAD1 rs12702634 and rheumatoid arthritis–associated interstitial lung disease in European ancestry

Abstract Objective Recently, a genome-wide association study identified an association between RA-associated interstitial lung disease (ILD) and RPA3-UMAD1 rs12702634 in the Japanese population, especially for patients with a usual interstitial pneumonia (UIP) pattern. We aimed to replicate this association in a European population and test for interaction with MUC5B rs35705950. Methods In this genetic case–control association study, patients with RA and ILD and controls with RA and no ILD were included from France, the USA and the Netherlands. Only cases and controls from European genetic ancestries determined by principal components analysis were included in the analyses. RA was defined by the 1987 ACR or 2010 ACR/EULAR criteria and ILD by chest high-resolution CT scan, except in the control dataset from the Netherlands, where the absence of ILD was determined by chart review. Patients were genotyped for RPA3-UMAD1 rs12702634 and MUC5B rs35705950. Associations were tested using logistic regression adjusted for sex, age at RA onset, age at ILD onset or at certified absence of ILD, tobacco smoking status and country of origin. Results Among the 883 patients included, 322 were RA-ILD cases (36.5%). MUC5B rs35705950 was strongly associated with RA-ILD in all datasets {combined adjusted odds ratio [OR] 2.9 [95% CI 2.1, 3.9], P = 1.1 × 10−11. No association between RPA3-UMAD1 rs12702634 and RA-ILD was observed [combined OR 1.2 (95% CI 0.8, 1.6), P = 0.31. No interaction was found between RPA3-UMAD1 rs12702634 and MUC5B rs35705950 (P = 0.70). Conclusion Our findings did not support a contribution of RPA3-UMAD1 rs12702634 to the overall RA-ILD susceptibility in the European population.

Offline supervised learning v.s. online direct policy optimization: A comparative study and a unified training paradigm for neural network-based optimal feedback control

This work is concerned with solving neural network-based feedback controllers efficiently for optimal control problems. We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimization. Albeit the training part of the supervised learning approach is relatively easy, the success of the method heavily depends on the optimal control dataset generated by open-loop optimal control solvers. In contrast, direct policy optimization turns the optimal control problem into an optimization problem directly without any requirement of pre-computing, but the dynamics-related objective can be hard to optimize when the problem is complicated. Our results underscore the superiority of offline supervised learning in terms of both optimality and training time. To overcome the main challenges, dataset and optimization, in the two approaches respectively, we complement them and propose the Pre-train and Fine-tune strategy as a unified training paradigm for optimal feedback control, which further improves the performance and robustness significantly. Our code is accessible at https://github.com/yzhao98/DeepOptimalControl.

The direct and indirect effects of cash transfer program on the consumption of nutrients: Evidence from Kenya

AbstractHow does the receipt of a cash transfer impact consumption of nutrients, vitamins, and minerals in households? To answer this question, we use a randomized controlled trial dataset from Hunger Safety Net Program (HSNP) with 9,246 households spread across the four districts (Turkana, Marsabit, Wajir, and Mandera) of Kenya. In the experiment, HSNP treated households received a bi‐monthly cash transfer of about United States of America Dollar (USD) 20 relative to households in control sub‐locations. Using difference in‐difference specification, we find that HSNP poor beneficiary households in treated households increased (by approximately 96%, 50%, and 61%) the consumption of vitamins A, C, and beta carotene, respectively compared to those in control sub‐locations. Moreover, HSNP non‐poor, non‐beneficiary households residing in treated sub‐locations increased (by about 70% and 46%) the consumption of vitamin A and Beta carotene, respectively compared to those in control sub‐locations. In addition, HSNP‐poor beneficiary households in treated sub‐locations sourced most of their nutrients, vitamins, and minerals from the market. We rule out alternative pathways that could potentially increase consumption and conclude that a rise in consumption amongst HSNP non‐poor, non‐beneficiary households is due to sharing of HSNP transfer amongst social network members.

Using equivalence tests in higher tier studies of honey bees under the revised EFSA Bee Guidance-How?

The proposed use of equivalence tests instead of difference tests in the revised guidance on the risk assessment of plant protection products for bees is a reasonable approach given an adverse effect was observed in the lower tier studies, using the hypothesis that there is a risk as the null hypothesis places the burden to prove the opposite on the other side. However, some uncertainties regarding the application of equivalence tests in field studies are discussed in the present study. Here, we compare equivalence and difference testing methods using a control dataset of a honey bee field effect study conducted in northern Germany in 2014. Half of the 48 colonies were assigned to a hypothetical test item group, and the colony strength data were analyzed using t-tests, a generalized linear mixed model (GLMM), and the corresponding equivalence tests. The data reflected the natural variability of honey bee colonies, with initially approximately 12 000 adult bees. Although the t-test and GLMM confirmed that 24 + 24 colonies are sufficient to show "no adverse effect," the equivalence tests of the t-test and GLMM were not able to reject the null hypothesis and classified at least some of the assessments as "high risk," indicating a power that was too low. Based on this, different operating options to reduce the variability are discussed. One possible option, which may provide a more realistic application of equivalence to avoid false high risk, is to consider the lower confidence interval of the control as a baseline and use GLMMs. With this option, we demonstrate a relatively acceptable probability to prove that no high risk for initially similar groups can be achieved. Further studies with different numbers of colonies are still needed to develop and validate the suggested approach. Integr Environ Assess Manag 2024;20:1496-1503. © 2024 SETAC.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis.

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

Racial/ethnic differences in the bullying victimization-suicidality link among LGBQ high school students in the United States.

Although the relationship between bullying victimization and suicidal behaviors of lesbian, gay, bisexual, transgender, and questioning (i.e., unsure of their sexual orientation) students has been well documented in research, few studies have focused on how bullying victimization might be related to suicidal behaviors among youth with intersectional identities. This study examines associations between bullying victimization and suicidal behaviors across racial/ethnic groups in a sample of lesbian, gay, bisexual, and questioning (LGBQ) students. Data for this cross-sectional study were derived from the Center for Disease Control and Prevention's Youth Risk Behavior Survey combined data set (2003-2019), with a sample of 95,603 students who identified as LGBQ. Analyses included descriptive statistics and logistic regression. We found that homophobic bullying victimization was associated with higher odds of suicidal ideation and plans among the total sample and Black and Hispanic students. School-based bullying victimization was associated with higher odds of suicidal ideation, plans, and attempts among white and Hispanic students and higher suicidal ideation among multiracial-non-Hispanic students. Cyberbullying victimization was not associated with suicidal behavior among Asian students, but it was associated with all forms of suicidal behavior among youth of other racial/ethnic identities. Addressing bullying victimization and suicidality with culturally relevant, evidence-based violence prevention strategies is critical.

Abstract A040: Cellular ecosystem of breast tumor microenvironment in Chinese breast cancer patients

Abstract Background: Current evidence has demonstrated the important role of tumor microenvironment (TME) in cancer initiation and progression. However, the dynamic interactions among various cells and their communities in TME remain largely unknown. Recently, EcoTyper, was developed to discover different cell states and their ecosystems in silico using bulk gene expression data. The application of this tool in the Pan-Cancer analysis identified distinct cell states and ecotypes (co-association patterns between cell states) that act as strong classifiers for cancer types and subtypes. In this study, we aimed to discover the TME ecosystems in breast tumors from an East Asian population and associate them with well annotated clinical features and breast cancer (BC) risk factors. Methods: We applied the EcoTyper framework to tumor RNA-Seq data from 242 Hong Kong BC patients to identify carcinoma ecotypes (CEs) and associated them with intrinsic subtype (PAM50), driver gene mutations (based on whole genome/exome sequencing analyses), and well-known BC risk factors. Additionally, we used The Cancer Genome Atlas BC (TCGA-BRCA) data, which was part of the initial EcoTyper Pan-Cancer analysis, as a quality control and comparison dataset. The TCGA patients were predominantly of European ancestry and had similar age distribution (median=58 years) compared to HK patients (median=60 years). The distribution of PAM50 subtype was also similar between the two datasets, with HK having a slightly higher proportion of luminal A (38% vs. 33% in TCGA) and a lower proportion of basal (11% vs. 20% in TCGA). Results: Among all ten ecotypes, the frequency of each CE was very similar between HK and TCGA. In both datasets, the prevalence of these CEs varied by tumor subtype. CE1 and CE6 were enriched in luminal A and rare in basal, while CE9 was rarely found in luminal A but showed high prevalence in HER2-enriched and basal subtypes. CE7 was enriched in luminal B (especially in HK, where among tumors with CE7, 82% of them were luminal B), CE10 was enriched in HER2-enriched (especially in HK, where 41% of tumors with CE10 were HER2-enriched), and CE2 was enriched for basal subtype. CE8 was enriched for luminal A in HK and luminal B in TCGA. The presence of CE2, CE9, or CE10 was associated with higher prevalence of TP53 somatic mutations, particularly in HK. Among the BC risk factors examined, we did not find any statistically significant associations with any CEs, probably due to the small sample size. The work of assessing the associations with the abundance of each cell state and CE is currently ongoing. Conclusions: We replicated cellular ecosystems and their associations with tumor subtypes and driver gene mutations in an East Asian population, demonstrating its generalizability and potential utility in refining BC subtypes. Next, we plan to extend the analysis to paired adjacent normal tissue to further assess the role of these ecosystems in shaping TME heterogeneity and tumor evolution. Citation Format: Hela Koka, Xin Li, Difei Wang, Difei Wang, Kristine Jones, Kristine Jones, Aurelie Vogt, Aurelie Vogt, Bin Zhu, Amy Hutchinson, Amy Hutchinson, Belynda Hicks, Belynda Hicks, Priscilla Ming Yi Lee, Lap Ah Tse, Xiaohong R Yang. Cellular ecosystem of breast tumor microenvironment in Chinese breast cancer patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A040.

Should we really use graph neural networks for transcriptomic prediction?

The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.

Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils.

Randomized controlled trials have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. Given the urgent need for better therapies for glioblastoma, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future glioblastoma trials. In this article, we provide an in-depth appraisal of the use of external control data in the context of neuro-oncology trials. We describe several clinical trial designs with particular attention to how external information is utilized and address common fallacies that may lead to inappropriate adoptions of external control data. Using 2 completed glioblastoma trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control data set. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate. While this approach may hold promise in generating informative data in certain settings, this sense of optimism should be tampered with a healthy dose of skepticism due to a myriad of design and analysis challenges articulated in this review. Importantly, careful planning is key to its successful implementation.

RFGR: Repeat Finder for Complete and Assembled Whole Genomes and NGS Reads.

Repetitive DNA sequences cause genomic instability and are important genetic markers. Identification of repeats is a critical step in genome annotation and analysis. On the other hand, repeats also pose a technical challenge for genome assembly and alignment programs using NGS data. RFGR is a comprehensive tool that can find exact repetitive sequences in complete genomes and assembled genomes, as well as NGS reads of prokaryotes. For complete genomes, RFGR uses a suffix trees to find seed repeats of repetitive sequences of fixed length with indels. For assembled genomes, RFGR uses a modified Bowtie aligner to find seed repeats of exact repetitive sequences in the contigs/ scaffolds, which are then extended to maximal repeats. The repeats are classified and for repeats near a gene, RFGR reports the gene as well. For the control dataset of E. coli UTI89 and E. coli K12, RFGR reports 35,141 and 49,352 repeats, respectively. For NGS reads, RFGR uses the frequency of the repetitive k-mers to determine FASTQ reads containing repetitive sequences and removes them from the dataset. An E. coli K12 NGS dataset pre-processed using RFGR, on comparison with the original dataset, gives an improved assembly. The N50 value improves by 22.86% with a decrease in size of the assembly graph by nearly 50%. Thus, with RFGR, we achieve a better assembly with reduced computation. RFGR can be improved in terms of the length of the minimum repeat found, extending to find approximate repeats and to be applicable to Eukaryotes as well.

A comprehensive dataset of luminescence chronologies and environmental proxy indices of loess-paleosol deposits across Asia

Loess-paleosol sequences have been used in Asia to study climate and environmental changes during the Quaternary. The scarcity of age control datasets and proxy indices analysis data for Asian loess has limited our understanding of loess depositional processes and the reconstruction of paleoclimatic changes from loess-paleosol records. In this study, we present a dataset that includes 1785 quartz optically stimulated luminescence ages and 1038 K-feldspar post-infrared infrared stimulated luminescence ages from 128 loess-paleosol sequences located in different regions of Asia. We generate 38 high-resolution age-depth models of loess records based on the provided datasets. We provide data on 12,365 grain size records, 14,964 magnetic susceptibility records, 2204 CaCO3 content records, and 3326 color reflection records. This dataset contains the most detailed and accurate chronologies and proxy index data for loess records in Asia yet published. It provides fundamental data for understanding the spatial-temporal variations in loess depositional processes and climatic changes across the continent during the mid-late Quaternary.

Comparison of HIV characteristics across 3 datasets: the Korea HIV/AIDS Cohort Study prospective, retrospective, and national reporting system.

The Korea HIV/AIDS Cohort Study has been conducted prospectively for 18 years. However, it faces limitations in representing the entire population of patients with human immunodeficiency virus (HIV) in Korea. To address these limitations and validate the study design, we analyzed characteristics across several HIV datasets. We compared epidemiological and clinical characteristics from 3 datasets: the Korea HIV/AIDS Cohort Study (dataset 1, n=1,562), retrospective cohort data (dataset 2, n=2,665), and the national HIV reporting system of the Korea Disease Control and Prevention Agency (KDCA) (dataset 3, n=17,403). The demographic characteristics of age, sex, and age at HIV diagnosis did not differ significantly across datasets. However, dataset 3 contained a higher proportion of patients diagnosed after 2008 (69.5%) than the other datasets. Regarding transmission routes, same-sex contact accounted for a greater proportion of dataset 1 (59.8%) compared to datasets 2 (20.9%) and 3 (32.6%). The proportion of patients with CD4 T-cell counts below 200/mm3 at HIV diagnosis was higher in datasets 1 (39.4%) and 2 (33.3%) compared to dataset 3 (16.3%). Initial HIV viral load measurements were not obtained for dataset 3. The Korea HIV/AIDS Cohort Study demonstrated representativeness regarding the demographic characteristics of Korean patients. Of the sources, dataset 1 contained the most data on transmission routes. While the KDCA data encompassed all HIV patients, it lacked detailed clinical information. To improve the representativeness of the Korea HIV/AIDS Cohort Study, we propose expanding and revising the cohort design and enrolling more patients who have been recently diagnosed.

Feature Engineering and Supervised Machine Learning to Forecast Biogas Production during Municipal Anaerobic Co-Digestion.

Municipalities with excess anaerobic digestion capacity accept offsite wastes for co-digestion to meet sustainability goals and create more biogas. Despite the benefits inherent to co-digestion, the temporal and compositional heterogeneity of external waste streams creates operational challenges that lead to upsets or conservative co-digestion. Given the complex microbial bioprocesses occurring during anaerobic digestion, prediction and modeling of the outcomes can be challenging, and machine learning has the potential to improve understanding and control of co-digestion processes. Biogas flows are a surrogate for process health, and here, we predicted biogas production from historical data collected by a water resource recovery facility (WRRF) during normal operation. We tested a daily lab and operational data set (n = 1089 after cleaning) and a minute-by-minute supervisory control and data acquisition (SCADA) operational data set (n = 491,761 after cleaning) to determine if forecasting biogas flow for a 24 h time horizon is feasible without collecting additional data. We found that a multilayer perceptron (MLP) neural network model outperformed tree-based and multiple linear regression models. Using a high-resolution SCADA data set for the first time, we showed that MLP neural networks could predict biogas production with an adjusted coefficient of determination (R2) of 0.78 and a mean absolute percentage error of 13.4% on a holdout test set. Adding daily laboratory analyses to the model did not appreciably improve the prediction of biogas flows. Feature engineering was essential to an accurate prediction, and 11 of the 15 most important features in the SCADA model were calculated from raw SCADA outputs. In summary, this paper demonstrates that minute-scale SCADA information collected at a municipal co-digestion facility can forecast biogas production, as a first step toward a digital twin model, without additional data collection.