Source Usage Research Articles

Analyzing Unimproved Drinking Water Sources and Their Determinants Using Supervised Machine Learning: Evidence from the Somaliland Demographic Health Survey 2020

Access to clean and safe drinking water is a fundamental human right. Despite global efforts, including the UN’s “Water for Life” program, a significant portion of the population in developing countries, including Somaliland, continues to rely on unimproved water sources. These unimproved sources contribute to poor health outcomes, particularly for children. This study aimed to investigate the factors associated with the use of unimproved drinking water sources in Somaliland by employing supervised machine learning models to predict patterns and determinants based on data from the 2020 Somaliland Demographic and Health Survey (SHDS). Secondary data from SHDS 2020 were used, encompassing 8384 households across Somaliland. A multilevel logistic regression model was applied to analyze the individual- and community-level factors influencing the use of unimproved water sources. In addition, machine learning models, including logistic regression, decision tree, random forest, support vector machine (SVM), and K-nearest neighbor (KNN), were compared in terms of accuracy, sensitivity, specificity, and other metrics using cross-validation techniques. This study uses supervised machine learning models to analyze unimproved drinking water sources in Somaliland, providing data-driven insights into the complex determinants of water access. This enhances predictive accuracy and informs targeted interventions, offering a robust framework for addressing water-related public health issues in Somaliland. The analysis identified key determinants of unimproved water source usage, including socioeconomic status, education, region, and household characteristics. The random forest model performed the best with an accuracy of 93.57% and an area under the curve (AUC) score of 98%. Decision tree and KNN also exhibited strong performance, while SVM had the lowest predictive accuracy. This study highlights the role of socioeconomic and community factors in determining access to clean drinking water in Somali Land. Factors such as age, education, gender, household wealth, media access, urban or rural residence, poverty level, and literacy level significantly influenced access. Local policies and resource availability also contribute to variations in access. These findings suggest that targeted interventions aimed at improving education, infrastructure, and community water management practices can significantly reduce reliance on unimproved water sources and improve the overall public health.

Olivar: towards automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 15 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer mismatches overlapping with primers and predicted PCR byproducts. We also compare Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluate Olivar on real wastewater samples and found that Olivar has up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available online as a web application at https://olivar.rice.edu and can be installed locally as a command line tool with Bioconda. Source code, installation guide, and usage are available at https://github.com/treangenlab/Olivar.

Applications of Fog Computing in Healthcare.

Fog computing is a decentralized computing infrastructure that processes data at or near its source, reducing latency and bandwidth usage. This technology is gaining traction in healthcare due to its potential to enhance real-time data processing and decision-making capabilities in critical medical scenarios. A systematic review of existing literature on fog computing in healthcare was conducted. The review included searches in major databases such as PubMed, IEEE Xplore, Scopus, and Google Scholar. The search terms used were "fog computing in healthcare," "real-time diagnostics and fog computing," "continuous patient monitoring fog computing," "predictive analytics fog computing," "interoperability in fog computing healthcare," "scalability issues fog computing healthcare," and "security challenges fog computing healthcare." Articles published between 2010 and 2023 were considered. Inclusion criteria encompassed peer-reviewed articles, conference papers, and review articles focusing on the applications of fog computing in healthcare. Exclusion criteria were articles not available in English, those not related to healthcare applications, and those lacking empirical data. Data extraction focused on the applications of fog computing in real-time diagnostics, continuous monitoring, predictive analytics, and the identified challenges of interoperability, scalability, and security. Fog computing significantly enhances diagnostic capabilities by facilitating real-time data analysis, crucial for urgent diagnostics such as stroke detection, by processing data closer to its source. It also improves monitoring during surgeries by enabling real-time processing of vital signs and physiological parameters, thereby enhancing patient safety. In chronic disease management, continuous data collection and analysis through wearable devices allow for proactive disease management and timely adjustments to treatment plans. Additionally, fog computing supports telemedicine by enabling real-time communication between remote specialists and patients, thereby improving access to specialist care in underserved regions. Fog computing offers transformative potential in healthcare, improving diagnostic precision, patient monitoring, and personalized treatment. Addressing the challenges of interoperability, scalability, and security will be crucial for fully realizing the benefits of fog computing in healthcare, leading to a more connected and efficient healthcare environment.

Biokinetics modelling of lycopene-producing E. coli fermentation using PAT methodology

Process analytical technology (PAT), which espouses the use of in-line analytics to monitor critical process parameters (CPPs), critical quality attributes (CQAs) and mathematical analyses, was employed to elucidate underlying carbon-source metabolism of a lycopene-producing E. coli strain during non-induced and induced batch fermentations. In-line dielectric and Raman spectroscopies were deployed to monitor real-time culture biomass viability (X), primary substrate glucose (S), and intracellular lycopene production (P), together with off-line HPLC quantification of broth acetate (A). In-line PAT monitoring enabled identification of two distinct metabolic regimes during induced batch run with lycopene accumulation. Biokinetics mathematical models using differential-algebraic equations and Monod-type conversions were formulated to explain experimentally measured dynamic trends of the state variables X, S, A and P during the batch runs. Their optimized model parameters and seven dimensionless ratios reflected changes in concentrations or rates relative to glucose, acetate, or lycopene. The calculated rates of culture growth, glucose utilization, acetate accumulation, and lycopene production revealed salient underlying carbon metabolic shifts and transient trends that explained phenomenological observations: such as growth patterns, carbon source usage, overflow mechanism, cellular affinity or inhibitory effects, and lycopene cytotoxicity. These parameters and dimensionless ratios could be used to compare novel strains, develop in silico simulations for scale-up or troubleshoot batch-to-batch variations.

Risk Factors of Chronic Kidney Disease Among Patients Attending at Dessie Comprehensive Specialized Hospital, Dessie, Amhara Region, Northeastern Ethiopia: Unmatched Case–Control Study

Introduction: The incidence of chronic kidney disease is rising, primarily due to its asymptomatic nature of the disease and poor access to early detection and management services. In Ethiopia, little is known about the context-specific risk factors. This study aimed to identify the risk factors of chronic kidney diseases in Northeast Ethiopia, focusing on patients attending Dessie Comprehensive Specialized Hospital, in 2022. Methods: A hospital-based unmatched case-control study was employed among chronic kidney disease patients at Dessie Comprehensive Specialized Hospital, from May 10 to July 15/2022. Cases were all patients who were diagnosed with chronic kidney diseases at Dessie Comprehensive Specialized Hospital while controls were patients without chronic kidney diseases. For each case, two controls were selected using a systematic random sampling technique. A semi-structured interviewer-administered questionnaire with the support of a document review was used to collect the data. The data were entered into Epi data version 4.6 and exported to Stata version 14 software for analysis. During bivariable logistic regression analysis, variables having p-value <0.2 were entered and analyzed by multivariable logistic regression analysis to identify risk factors associated with chronic kidney diseases. Statistical differences were considered at P < 0.05, and the strength of association was assessed by adjusted odds ratio and respective confidence intervals. Results: A total of 78 cases and 156 controls were included in this study. This study revealed that factors such as being male [AOR: 2.54, 95%CI (1.125- 5.754)], the presence of hypertension [AOR: 5.33, 95%CI (2.107-13.489)], diabetic-mellitus [AOR: 3.64,95%CI (1.530- 8.671)], kidney stone [AOR: 3.91,95%CI (1.492- 10.257)], and underground water source usage [AOR: 2.63,95%CI (1.108-6.262] were statistically significantly associated factors with chronic kidney diseases. Conclusion: In our study, being male, the presence of hypertension, diabetic mellitus, diagnosis of a kidney stone, and underground water usage were independent risk factors for chronic kidney diseases. Therefore, policymakers, healthcare providers, and other stakeholders should emphasize the aforementioned factors to forestall the development and progression of the disease. Furthermore, routine urinalysis and glomerular filtration rates for all hospitalized patients with hypertension and diabetes-mellitus could help to detect chronic kidney diseases at an early stage.

Dynamic Adaptive Resource Allocation for Edge Computing in Big Data Analytics Using GBDT, DQN, and GA Algorithms

Edge computing in big data refers to processing and analysing data closer to its source, reducing latency and bandwidth usage. It leverages devices at the network edge to perform computations, making real-time analytics feasible. This distributed approach improves efficiency and enables faster decision-making, critical for applications like IoT, autonomous vehicles, and healthcare. The research proposes an innovative approach that harnesses three machine learning algorithms Gradient Boosting Decision Trees (GBDT), Deep Q-Network (DQN), and Genetic Algorithm (GA) to enable dynamic adaptive resource allocation within edge computing environments tailored for big data analytics. GBDT enhances classification accuracy by sequentially refining predictions through decision trees, accommodating heterogeneous data types and yielding high prediction accuracy crucial for dynamic edge environments. The GA evaluates resource allocation strategies represented as chromosomes within a population, selecting promising solutions as parents for the next generation and generating diverse offspring through crossover and mutation operations to discover optimal solutions. DQN facilitates intelligent resource allocation by iteratively refining Q-values based on experiences gathered during interactions with the environment, utilizing a neural network to determine optimal actions for a given state, thereby enhancing performance and efficiency in edge computing environments. This integrated approach ensures flexible resource allocation and fortified capabilities for big data analytics within edge computing environments. The research underscores GBDT as the most promising algorithm for resource allocation in edge computing environments, owing to its exceptional performance in resource utilization, scalability, and accuracy. This nuanced understanding of algorithmic behaviour in dynamic settings offers invaluable insights for optimizing resource allocation strategies, thereby enhancing the efficiency and effectiveness of edge computing systems in handling big data analytics tasks.

Traditional and Social Media Usage Associated With COVID-19 Vaccine Uptake in Sapporo, Japan.

This study aimed to determine the relationship between specific information source usage and uptake of the coronavirus disease 2019 (COVID-19) vaccine. We analyzed 3348 participants aged 20 to 65 years who were not diagnosed with COVID-19 in a case-control study in Sapporo, Japan. The most prevalent information source on COVID-19 was television (TV; 87.8%), followed by online news sites (74.3%), newspapers (38.7%), websites of public institutions (30.9%), and families (29.7%). Multivariate logistic regression showed that the adjusted odds ratios of incompletion of second vaccinations for users of TV and newspaper to gather COVID-19 information were 0.31 and 0.32, respectively, whereas those for users of books, commercial video sites, Facebook, and "personal blog or bulletin board system" were 3.34, 2.22, 2.36, and 4.81, respectively. Social media use among older or male participants was associated with lower vaccine uptake.

Beyond headlines: A qualitative analysis of environmental reporting in bangladeshi print media

Environmental degradation poses significant challenges to Bangladesh's sustainable development, given its dense population and vulnerability to climate change. Media, particularly print journalism, plays a crucial role in addressing these challenges by raising awareness and fostering public discourse. This study examines environmental reporting in Bangladeshi print media, focusing on depth, source diversity, and long-term impact. Through qualitative analysis and interviews, it evaluates coverage depth, source credibility, and societal outcomes. Findings reveal a mix of superficial and investigative reporting, with varied source usage. While challenges like resource constraints and censorship exist, opportunities for improvement through training, investigative focus, source diversification, and ethical reporting practices are identified. Enhancing environmental journalism's quality and impact can promote public awareness, policy discourse, and societal change, contributing to Bangladesh's environmental sustainability.

Enhanced VOC emission with increased temperature contributes to the shift of O3-precursors relationship and optimal control strategy

Assessing the impact of anthropogenic volatile organic compounds (VOCs) on ozone (O3) formation is vital for the management of emission reduction and pollution control. Continuous measurement of O3 and the major precursors was conducted in a typical light industrial city in the YRD region from 1 May to 25 July in 2021. Alkanes were the most abundant VOC group, contributing to 55.0% of TVOCs concentration (56.43 ± 21.10 ppb). OVOCs, aromatics, halides, alkenes, and alkynes contributed 18.7%, 9.6%, 9.3%, 5.2% and 1.9%, respectively. The observational site shifted from a typical VOC control regime to a mixed regime from May to July, which can be explained by the significant increase of ROx production, resulting in the transition of environment from NOx saturation to radical saturation with respect to O3 production. The optimal O3 control strategy should be dynamically changed depending on the transition of control regime. Under NOx saturation condition, minimizing the proportion of NOx in reduction could lead to better achievement of O3 alleviation. Under mixed control regime, the cut percentage gets the top priority for the effectiveness of O3 control. Five VOCs sources were identified: temperature dependent source (28.1%), vehicular exhausts (19.9%), petrochemical industries (7.2%), solvent & gasoline usage (32.3%) and manufacturing industries (12.6%). The increase of temperature and radiation would enhance the evaporation related VOC emissions, resulting in the increase of VOC concentration and the change of ROx circulation. Our results highlight determination of the optimal control strategies for O3 pollution in a typical YRD industrial city.

The altered TBI fecal microbiome is stable and functionally distinct.

Patients who suffer a traumatic brain injury (TBI) often experience chronic and sometimes debilitating sequelae. Recent reports have illustrated both acute and long-term dysbiosis of the gastrointestinal microbiome with significant alterations in composition and predicted functional consequences. Working with participants from past research, metagenomic stability of the TBI- associated fecal microbiome (FMB) was evaluated by custom qPCR array comparing a fecal sample from 2015 to one collected in 2020. Metatranscriptomics identified differently expressed bacterial genes and biochemical pathways in the TBI FMB. Microbiota that contributed the largest RNA amounts identified a set of core bacteria most responsible for functional consequences of the TBI FMB. A remarkably stable FMB metagenome with significant similarity (two-tail Spearman nonparametric correlation p < 0.001) was observed between 2015 and 2020 fecal samples from subjects with TBI. Comparing the 2020 TBI FMB metagenome to FMBs from healthy controls confirmed and extended the dysbiotic genera and species. Abundance differences between average TBI and healthy FMBs revealed Bacteroides caccae, B. uniformis, Blautia spp., Collinsella spp., Dialister spp., and Ordoribacter spp. were significantly different. Functionally, the Parabacteroides genus contributed the highest percentage of RNA sequences in control FMBs followed by the Bacteroides genus as the second highest contributor. In the TBI FMB, the Corynebacterium genus contributed the most RNA followed by the Alistipes genus. Corynebacterium and Pseudomonas were distinct in the top 10 contributing genera in the TBI FMB while Parabacteroides and Ruminococcus were unique to the top 10 in controls. Comparing RNA profiles, TBI samples had ∼1.5 fold more expressed genes with almost 700 differently expressed genes (DEGs) mapped to over 100 bacterial species. Bioinformatic analysis associated DEGs with pathways led identifying 311 functions in the average TBI FMB profile and 264 in the controls. By average profile comparison, 30 pathways had significantly different abundance (p < 0.05, t-test) or were detected in >80% of the samples in only one of the cohorts (binary distinction). Functional differences between TBI and healthy control FMBs included amino acid metabolism, energy and carbon source usage, fatty acid metabolism, bacterial cell wall component production and nucleic acid synthesis and processing pathways. Together these data shed light on the functional consequences of the dysbiotic TBI FMB decades after injury.

RefseqR: an R package for common computational operations with records on RefSeq collection

Abstract Summary We introduce refseqR, an R package that offers a user-friendly solution, enabling common computational operations on RefSeq entries (GenBank, NCBI). The package is specifically designed to interact with records curated from the RefSeq database. Most importantly, the interoperability and integration with several Bioconductor objects allow connections to be applied to other projects. Availability and implementation The package refseqR is implemented in R and published under the MIT open-source license. The source code, documentation, and usage instructions are available on CRAN (https://CRAN.R-project.org/package=refseqR).

Biofilm biosynthesis of Priestia megaterium M1VB5 strain Muc Son Thanh Hoa paper mill

Introduction: Strain M1VB5, isolated from slime in paper machine pipes, is studied for its role in slime formation. This slime, comprised of microorganisms, binds and adheres to pipes, potentially leading to clogs, process inefficiencies, and machinery contamination. Methods: We investigated the biological characteristics and gene sequencing of M1VB5, focusing on its biofilm biosynthesis capabilities under varying sucrose concentrations, temperatures, pH levels, and time periods. Results: M1VB5 exhibited adaptability across a broad range of conditions, thriving at temperatures between 15-55°C and pH levels from 5 to 9, with up to 7% salinity tolerance. Capable of utilizing diverse carbon sources, M1VB5 efficiently biosynthesized extracellular enzymes, including CMCase, cellulase, xylanase, amylase, protease, and chitinase. Identified as Priestia megaterium (Gram-positive), it demonstrated significant biofilm formation, producing 36.4 g/L EPS and forming a 53.73 g/100g biofilm on PVC at a sucrose concentration of 100 g/L. Optimal biofilm production was observed at pH 8, with 28.40 g/L EPS and a biofilm mass of 59.61 g/100g PVC, and at 37°C, where EPS and biofilm weights reached 23.60 g/L and 63.78 g/100g PVC, respectively. The strain maintained steady biofilm production from day 4 to 14. Conclusion: M1VB5's versatile carbon source usage, wide temperature and pH range tolerance, and diverse enzyme production facilitate its growth and development. Its significant biofilm and EPS production capabilities contribute to its dominance in slime formations, offering insights for managing slime in industrial paper mill environments.

Experimental and theoretical analysis of Metal-Organic Frameworks’ energy storage performance for heating, cooling, power generation, and desalination

The study addresses the urgent need to find materials and an advanced adsorption cycle for renewable and sustainable energy sources, aiming to resolve the conflict between rapidly increasing renewable energy demand and the low efficiency of the adsorption system. The simultaneous thermal analysis machine is utilized to measure the adsorption characteristics and adsorption heat of the adsorbent. Microscopic analysis of the adsorbent's structure is conducted using a confocal microscope. Through computational analysis, this paper investigates the comprehensive performance of the advanced adsorption cycle, including adsorption refrigeration, heating, power generation, and seawater desalination capabilities. The findings indicate that the material of HKUST-1(Cu) has the highest performance in heating, cooling, power generation, and desalination. The cooling capacity, heating capacity, power generation capacity, and desalination capacity are 727.9 kJ/kg, 1431.9 kJ/kg, 14.5 kJ/kg, 0.34 kg/kg when the temperature of heating, condensation, and evaporation is about 140 °C, 30 °C, and 15 °C. The HKUST-1(Cu) and UiO-66-BDC-NH2 have the highest desalination capacity and are suitable for high and low-temperature heat source usage. The study concludes that the material of HKUST-1(Cu) possesses the potential to serve as a viable alternative energy source in the cooling, heating, power generation, and desalination system. The novelty of this work lies in the comprehensive evaluation of the advanced cycle’s performance of multiple metal–organic frameworks within the context of cooling, heating, desalination, and power generation in one adsorption system. This research goes beyond previous efforts by providing valuable insights into the advanced cycle and its comprehensive performance of cooling, heating, power generation, and desalination capabilities.

ROCCO: a robust method for detection of open chromatin via convex optimization.

Analysis of open chromatin regions across multiple samples from two or more distinct conditions can determine altered gene regulatory patterns associated with biological phenotypes and complex traits. The ATAC-seq assay allows for tractable genome-wide open chromatin profiling of large numbers of samples. Stable, broadly applicable genomic annotations of open chromatin regions are not available. Thus, most studies first identify open regions using peak calling methods for each sample independently. These are then heuristically combined to obtain a consensus peak set. Reconciling sample-specific peak results post hoc from larger cohorts is particularly challenging, and informative spatial features specific to open chromatin signals are not leveraged effectively. We propose a novel method, ROCCO, that determines consensus open chromatin regions across multiple samples simultaneously. ROCCO employs robust summary statistics and solves a constrained optimization problem formulated to account for both enrichment and spatial dependence of open chromatin signal data. We show this formulation admits attractive theoretical and conceptual properties as well as superior empirical performance compared to current methodology. Source code, documentation, and usage demos for ROCCO are available on GitHub at: https://github.com/nolan-h-hamilton/ROCCO. ROCCO can also be installed as a stand-alone binary utility using pip/PyPI.

О новом словаре-справочнике цитат для школьников (Рецензия на издание: Дунев А.И. Цикады цитат. Цитатный минимум школьника. Словарь-справочник. – Санкт-Петербург: Изд-во РГПУ им. А.И. Герцена, 2022. – 160 с.)

The article represents a review of a glossary that contains quotes from school literary fiction books. The relevance of the publication is defined by disturbing trends of modern reality: teenagers' narrow reading interests, decrease in the general index of precedence, cognitive distortion while interpreting axiologically marked content of precedent units. The essential set of glossary units contains 350 quotes chosen according to the program content of literature education at school. The choice of quotes to be included in the glossary is due not so much to a methodological tradition as to the actual existence and frequency of the quote. A glossary entry includes several zones that give information about the length of a quote, its source and possible usage. Appendixes with quotes distributed accordingly to levels of education, as well as teaching tips to create various tasks based on quotes, add practical value to the glossary. The author of the glossary rightfully argues that the utterances that have become independent pieces of culture help to understand cultural meanings on the one hand, and on the other hand, start the mechanism of word coinage that affects the linguistic personality development.

APEAR: an R package for autonomous visualization of pathway enrichment networks.

The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological themes in the experiment. Our approach enables an automated and objective overview of the data without manual and time-consuming parameter tweaking. The package aPEAR is implemented in R, published under the MIT open-source licence. The source code, documentation, and usage instructions are available on https://gitlab.com/vugene/aPEAR as well as on CRAN (https://CRAN.R-project.org/package=aPEAR).

COVID-19 vaccine-related misinformation identification among Chinese residents during a regional outbreak.

Misinformation about the COVID vaccines poses a significant challenge to vaccination efforts in many countries. This study examined Chinese citizens' ability to correctly identify COVID-19 vaccine misinformation in geographic areas with and without a regional outbreak. We also investigated the associations between misinformation identification and information source usage, source trust, perceived information quality, and demographic characteristics. The online survey was conducted in four cities from June 8th to 15th, 2021 in Guangdong Province, two of which were experiencing a regional surge of COVID-19 delta variant infections, and four cities in Hunan Province, a neighboring province largely unaffected. A total of 4,479 individuals aged 18 and above completed the online questionnaire. Given survey length, those who finished the study under 5 min were excluded, resulting in a final sample of 3,800. Misinformation identification, source exposure, source trust, and perceived information quality. Results showed slightly higher levels of correct misinformation identification in surge vs. non-surge areas. Trust in official information sources was positively associated with correct misinformation identification in full sample analysis, while trust in informal sources was negatively associated with the same outcome. Perceived information quality was positively associated with correct misinformation identification in the full sample. Information providers in China should enhance the quality of the vaccine information they provide, and the Chinese public should balance their usage of different sources of information to acquire vaccine knowledge.

Olivar: automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens.

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 2 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer SNPs overlapping with primers and predicted PCR byproducts. We also compared Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluated Olivar on real wastewater samples and found that Olivar had up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available as a web application at https://olivar.rice.edu. Olivar can also be installed locally as a command line tool with Bioconda. Source code, installation guide and usage are available at https://github.com/treangenlab/Olivar.

Openness to change among COVID misinformation endorsers: Associations with social demographic characteristics and information source usage

RationaleMisinformation is a major concern for public health, with its presence and impact strongly felt in the COVID-19 pandemic. Misinformation correction has drawn strong research interest. In contrast, relatively little attention has been given to the likelihood of favorable behavioral change post correction (i.e., openness to change). ObjectiveThis study seeks to identify the characteristics of COVID misinformation endorsers who are open to adopt more self-protective behaviors after misinformation correction. MethodsCOVID misinformation endorsers (N = 1991) in a large, nationally representative survey (fielded in October and November 2021) were asked how likely they would adopt self-protective behaviors if a source they trusted debunked their prior misperceptions. Multiple linear regression estimated the relationships between openness to change and socio-demographics, health-related factors, and health information source usage patterns. ResultsLess than half of the misinformation endorsers in the sample (41.3%) indicated any openness to change. Openness to change was positively associated with minority status and negatively associated with leaning Republican in political affiliation, higher income, being currently unvaccinated or unsure about vaccination status, better health, and a greater number of misinformation items endorsed. Past-month use of government, community, and personal sources for health information positively predicted openness. The use of online media was negatively associated with openness. ConclusionsOpenness to change is not guaranteed after misinformation correction. Targeted interventions based on openness to change are needed to enhance the public health impact of misinformation correction efforts.

Hydrocarbon generation differences of shales composed of green algal and cyanobacteria: A case study of Mesozoic and Cenozoic saline lacustrine shales in Junggar Basin, NW China

The Mesozoic and Cenozoic strata in the Junggar basin developed two sets of shallow to semi-deep lacustrine shale, namely, the Cretaceous Qingshuihe Formation (K1q) and the Paleogene Anjihaihe Formation (E2-3a). Through organic petrology and scanning electron microscope (SEM) observation, it is found that the primary hydrocarbon-generating organic matter (OM) in the two sets of strata is different. The biological precursor of the E2-3a OM is mainly green algae (Pediastrum), while the precursor of K1q kerogen is mainly cyanobacteria (Oscillatoria). Then, the E2-3a green algae-rich shale and K1q cyanobacteria-rich shale were subjected to hydrous pyrolysis and kinetic analysis, respectively. The results show that the evolution modes of hydrocarbon generation of the typical shales are very different. Green algae have the characteristics of a low oil generation threshold, heavy oil quality, and no prominent oil peak, while cyanobacteria have the characteristics of late oil generation, concentrated hydrocarbon generation, and relatively light oil quality. The characteristics of oil generation can also be well reflected in the composition evolution of the crude oil components. The carbon isotope of gas, kerogen, and extracts of the E2-3a green algae-rich shale are significantly heavier than the K1q cyanobacteria-rich shale, which may be related to the living habits of their biological precursors, carbon source usage, photosynthesis efficiency, and carbon fixation efficiency.