Limited Knowledge Research Articles

AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data.

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis.

Longitudinal effects of prenatal alcohol exposure on visual neurodevelopment over infancy.

Prenatal alcohol exposure (PAE) affects neurodevelopment in over 59 million individuals globally. Prior studies using dichotomous categorization of alcohol use and comorbid substance exposures provide limited knowledge of how prenatal alcohol specifically impacts early human neurodevelopment. In this longitudinal cohort study from Cape Town, South Africa, PAE is measured continuously-characterizing timing, dose, and drinking patterns (i.e., binge drinking). High-density electroencephalography (EEG) during a visual-evoked potential (VEP) task was collected from infants aged 8 to 52 weeks with prenatal exposure exclusively to alcohol and matched on sociodemographic factors to infants with no substance exposure in utero. First trimester alcohol exposure related to altered timing of the P1 VEP component over the first 6 months postnatally, and first trimester binge drinking exposure altered timing of the P1 VEP components such that increased exposure was associated with longer VEP latencies while increasing age was related to shorter VEP latencies (n = 108). These results suggest alcohol exposure in the first trimester may alter visual neurodevelopmental timing in early infancy. Exploratory individual-difference analysis across infants with and without PAE tested the relation between VEP latencies and myelination for a subsample of infants with usable magnetic resonance imaging (MRI) T1w and T2w scans collected at the same time point as EEG (n = 47). Decreased MRI T1w/T2w ratios (an indicator of myelin) in the primary visual cortex (n = 47) were linked to longer P1 VEP latencies. Results from these two sets of analyses suggest that prenatal alcohol and postnatal myelination may both separately impact VEP latency over infancy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction.

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI.

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.

Chronic Hepatitis B in Women of Childbearing Age and Pregnant Women in China: A Cross-Sectional Study.

Pregnant women and women of childbearing age were enrolled in our study and their knowledge about the Hepatitis B virus (HBV) and chronic hepatitis B (CHB) was evaluated. A questionnaire was distributed to every woman in the cross-sectional study. The questionnaire was answered by all participants before they received health education and advice about HBV and CHB from the doctors visited. Data collected from all answers were analyzed using the χ2 test and logistic regression models. A total of 206 pregnant women and women of childbearing age with CHB infection were enrolled in the study during their first visit to the Infectious Diseases Clinic of the Third Affiliated Hospital of Guangzhou Medical University. Some women of childbearing age (40.8%) and pregnant women with CHB infection (30.6%) still believed HBV could be transmitted through diet and/or mosquito bites. Some women of childbearing age and pregnant women with CHB infection had limited knowledge of the prevention of HBV transmission (111 of 206, 53.9%). Women with higher levels of education had more knowledge about HBV (senior middle school, P = 0.02; university, P <0.01). The majority of participants were willing to take antiviral medicine to decrease the mother-to-child transmission (MTCT) rate of HBV. Some women of childbearing age and/or pregnant women with CHB infection have relatively limited knowledge about HBV or CHB. This situation contributes to the timeliness, or lack thereof, of these women with CHB to see a doctor and receive antiviral therapy. As a result, the morbidity and mortality of HBV-related complications could increase along with the rate of MTCT of HBV.

Globalization: Migrant nurses'acculturation and their healthcare encounters as consumers of healthcare.

Globally, one of every eight nurses is a migrant, but few studies have focused on the healthcare experiences of migrant nurses (MNs) as consumers or recipients of healthcare. We address this gap by examining MNs and their acculturation, barriers to healthcare access, and perceptions of healthcare encounters as consumers. For this mixed-methods study, a convenience sample of MNs working in Europe and Israel was recruited. The quantitative component's methods included testing the reliability of scales contained within the questionnaire and using Hayes Process Model #4 to test for mediation. The qualitative component's methods included analyzing interviews with iterative inductive thematic analysis. Quantitative findings on MNs (n = 73) indicated that the association between acculturation and perception of the healthcare encounter, which MNs experienced as healthcare consumers, was mediated by barriers to healthcare access, even after adjusting for age and gender (p = 0.03). Qualitative interviews with MNs (n = 13) provided possible explanations for the quantitative findings. Even after working in the host country's healthcare system for several years, MNs reported difficulties with their healthcare encounters as healthcare consumers, not only due to their limited knowledge about the culture and healthcare resources but also due to the biased responses they received.

How perceived polarization predicts attitude moralization (and vice versa): A four-wave longitudinal study during the 2020 U.S. election.

Within structurally polarized and dynamic contexts, such as the U.S. 2020 presidential elections, the moralization of individuals' attitudes on a specific topic (e.g., climate policy) can dangerously escalate disagreements between groups into zero-sum conflict. However, limited knowledge exists regarding the factors that influence individuals' tendency to moralize their attitudes over time, and what the role of structural polarization is in this psychological process. Our objective is to test a theoretically integrative model of when and how perceived polarization is related to attitude moralization over time within the polarized context of the U.S. 2020 presidential elections and explore reciprocal feedback loops to understand the dynamic relationship between polarization and moralization over time. Our model predicts that, when repeatedly faced with outgroup expressions in the news, individuals' perceptions of polarization will predict within-person attitude moralization over time via strengthening their value-protective responses to these expressions (i.e., perceiving dyadic harm and experiencing negative moral emotions toward the outgroup). To test our model, we conducted a four-wave, 4-month longitudinal study among Biden supporters (N = 1,236) and Trump supporters (N = 617). The results of the within-person analyses generally supported the model's hypotheses across both samples and various attitude topics. Furthermore, cross-lagged structural equation models explored reciprocal influences, revealing positive feedback loops between structural polarization and attitude moralization over time. Our findings thus indicate that perceived polarization strengthens attitude moralization (and vice versa) over time-a dynamic process that helps to explain how nonmoralized conflict between groups can evolve into zero-sum conflict during periods of intense polarization. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Target identification and validation in research

Target identification is a critical step in biomedical research because it lays the groundwork for the development of new therapies and drugs. Genetic research, including genome-wide association studies (GWAS), genomic sequencing, functional genomics, and data integration, is crucial for understanding disease genetics and potential treatment targets. Transcriptomics and proteomics give data on gene and protein expression, making it easier to identify targets in dysregulated diseases. Target identification is essential for drug discovery, precision medicine, lowering medication attrition, increasing therapeutic efficacy, and, eventually, transforming patient care and drug development. Target validation is a critical stage in drug development because it verifies that revealed molecular targets play a substantial role in disease progression and are therefore suitable for treatment. It employs a range of approaches, including genetic validation, pharmacological validation, and animal model validation. Target validation assures that discovered targets are physiologically relevant, druggable, and have a direct impact on disease processes, thereby reducing pharmaceutical attrition, promoting precision medicine, and hastening therapeutic development. Historically, target identification relied on limited knowledge, typically through candidate-based techniques based on assumptions or prior observations. Target validation experiments looked into how gene knockdown or RNA interference affected illness symptoms. Genomics, proteomics, and functional genomics have all made advances in recent years, as have high-throughput screening and data integration. CRISPR-based technologies and high-throughput sequencing have assisted in the validation of targets. Single-cell validation, machine learning and artificial intelligence, advanced in vitro models like organoids, and patient-derived models will all help to make future assessments of target relevance and treatment responses more precise and individualized. These developments have the potential to dramatically revolutionize research target identification and validation.

High-quality strategies for supporting children’s problem-solving skills: a study on coding toy activities in Norwegian ECECs

ABSTRACT Recent studies have shown that playing with coding toys enhances children’s skills in STEM- subjects, especially problem-solving abilities. However, there is limited knowledge of early childhood education and care (ECEC) teachers’ roles in facilitating children’s problem-solving through coding-toy play activities and the high-quality pedagogical strategies and approaches they utilize. This study explored how ECEC teachers formed and guided pedagogical practices and influenced learning quality through digital play. The data are based on video observations of five teachers and six groups of children aged 3–5 years (three to four children per group), who each played with a coding toy for 30–60 min. Drawing on the sustained shared thinking theory, this study analyzed how teachers supported children’s problem-solving processes. The results reveal that the teachers employed several pedagogical strategies and approaches, including those for nurturing children’s curiosity, encouraging questioning, fostering investigation, and promoting conceptual exploration.

The cryo-EM structure of ASK1 reveals an asymmetric architecture allosterically modulated by TRX1

Apoptosis signal-regulating kinase 1 (ASK1) is a crucial stress sensor, directing cells toward apoptosis, differentiation, and senescence via the p38 and JNK signaling pathways. ASK1 dysregulation has been associated with cancer and inflammatory, cardiovascular, and neurodegenerative diseases, among others. However, our limited knowledge of the underlying structural mechanism of ASK1 regulation hampers our ability to target this member of the MAP3K protein family towards developing therapeutic interventions for these disorders. Nevertheless, as a multidomain Ser/Thr protein kinase, ASK1 is regulated by a complex mechanism involving dimerization and interactions with several other proteins, including thioredoxin 1 (TRX1). Thus, the present study aims at structurally characterizing ASK1 and its complex with TRX1 using several biophysical techniques. As shown by cryo-EM analysis, in a state close to its active form, ASK1 is a compact and asymmetric dimer, which enables extensive interdomain and interchain interactions. These interactions stabilize the active conformation of the ASK1 kinase domain. In turn, TRX1 functions as a negative allosteric effector of ASK1, modifying the structure of the TRX1-binding domain and changing its interaction with the tetratricopeptide repeats domain. Consequently, TRX1 reduces access to the activation segment of the kinase domain. Overall, our findings not only clarify the role of ASK1 dimerization and inter-domain contacts but also provide key mechanistic insights into its regulation, thereby highlighting the potential of ASK1 protein-protein interactions as targets for anti-inflammatory therapy.

Understanding Maternal Nutrition Insights from Pregnant Women in Bamenda, Cameroon

The foundation for good physical health is good nutritional status. Nutritional status of pregnant women is directly affected by the foods they eat and their nutrient content. Nutritional knowledge is positively associated with diet quality. The aim of this study was to assess nutritional status and nutrition education knowledge of pregnant women attending antenatal clinics in Bamenda Health District (BHD). &#x0D; Study Design: This study was carried conducted in BHD in the North West Region of Cameroon, with pregnant women aged 13 to 49 years recruited from public hospitals.&#x0D; Place and Duration of Study: BHD including (Bamenda Regional Hospital, Azire Integrated Health centre, Atuakom Integrated Health Centre, Ntambag Integrated Health Centre) between April 2022 to September 2022.&#x0D; Methodology: This was a cross sectional study carried out in four health facilities BHD. Random sampling technique was done to target the sample size of 354 pregnant women. A questionnaire was developed to collect socio-demographic information and clinical records of the pregnant women. Anthropometric parameters, biochemical and dietary assessments of the women were determined using standard techniques. Data collected was analysed by MS excel and SPSS Statistics version 20.0. Results were considered at significance level P ≤ 0.05. &#x0D; Results: The mean age of the study population was 26.4±5.5years and the average intake of servings of meals per day was suboptimal in the consumption of vegetables (2±1.9), fruits (0.6± 1.3) and milk (0.4 ±0.6) food groups. Women consumed diets that met less than 50% of energy needs. There exists a significant difference in mean energy requirements 1828.8 ± 152.9 Kcal compared to mean energy intake 1192.0 ± 344.6 Kcal which is below the Recommended Daily Allowances (RDA) of 2400 kcal for pregnant women. Ignorance still existed in dietary knowledge and 18.1% of the women were anaemic. &#x0D; Conclusion: There was an overall suboptimal approach to dietary intake among the pregnant women, inadequate food intake, poor food choices and limited healthy dietary knowledge. Thus pregnant women in BHD still need more access to information on dietary information.

Predictors of severity and prolonged hospital stay of viral acute respiratory infections (ARI) among children under five years in Burkina Faso, 2016–2019

BackgroundViruses are the leading etiology of acute respiratory infections (ARI) in children. However, there is limited knowledge on drivers of severe acute respiratory infection (SARI) cases involving viruses. We aimed to identify factors associated with severity and prolonged hospitalization of viral SARI among children < 5 years in Burkina Faso.MethodsData were collected from four SARI sentinel surveillance sites during October 2016 through April 2019. A SARI case was a child < 5 years with an acute respiratory infection with history of fever or measured fever ≥ 38 °C and cough with onset within the last ten days, requiring hospitalization. Very severe ARI cases required intensive care or had at least one danger sign. Oropharyngeal/nasopharyngeal specimens were collected and analyzed by multiplex real-time reverse-transcription polymerase chain reaction (rRT-PCR) using FTD-33 Kit. For this analysis, we included only SARI cases with rRT-PCR positive test results for at least one respiratory virus. We used simple and multilevel logistic regression models to assess factors associated with very severe viral ARI and viral SARI with prolonged hospitalization.ResultsOverall, 1159 viral SARI cases were included in the analysis after excluding exclusively bacterial SARI cases (n = 273)very severe viral ARI cases were common among children living in urban areas (AdjOR = 1.3; 95% CI: 1.1–1.6), those < 3 months old (AdjOR = 1.5; 95% CI: 1.1–2.3), and those coinfected with Klebsiella pneumoniae (AdjOR = 1.9; 95% CI: 1.2–2.2). Malnutrition (AdjOR = 2.2; 95% CI: 1.1–4.2), hospitalization during the rainy season (AdjOR = 1.71; 95% CI: 1.2–2.5), and infection with human CoronavirusOC43 (AdjOR = 3; 95% CI: 1.2-8) were significantly associated with prolonged length of hospital stay (> 7 days).ConclusionYounger age, malnutrition, codetection of Klebsiella pneumoniae, and illness during the rainy season were associated with very severe cases and prolonged hospitalization of SARI involving viruses in children under five years. These findings emphasize the need for preventive actions targeting these factors in young children.

Elucidating the Mechanism of Metabolism of Cannabichromene by Human Cytochrome P450s.

Cannabichromene (CBC) is a nonpsychoactive phytocannabinoid well-known for its wide-ranging health advantages. However, there is limited knowledge regarding its human metabolism following CBC consumption. This research aimed to explore the metabolic pathways of CBC by various human liver cytochrome P450 (CYP) enzymes and support the outcomes using in vivo data from mice. The results unveiled two principal CBC metabolites generated by CYPs: 8'-hydroxy-CBC and 6',7'-epoxy-CBC, along with a minor quantity of 1″-hydroxy-CBC. Notably, among the examined CYPs, CYP2C9 demonstrated the highest efficiency in producing these metabolites. Moreover, through a molecular dynamics simulation spanning 1 μs, it was observed that CBC attains stability at the active site of CYP2J2 by forming hydrogen bonds with I487 and N379, facilitated by water molecules, which specifically promotes the hydroxy metabolite's formation. Additionally, the presence of cytochrome P450 reductase (CPR) amplified CBC's binding affinity to CYPs, particularly with CYP2C8 and CYP3A4. Furthermore, the metabolites derived from CBC reduced cytokine levels, such as IL6 and NO, by approximately 50% in microglia cells. This investigation offers valuable insights into the biotransformation of CBC, underscoring the physiological importance and the potential significance of these metabolites.

Legacy Mercury Re-emission and Subsurface Migration at Contaminated Sites Constrained by Hg Isotopes and Chemical Speciation.

The re-emission and subsurface migration of legacy mercury (Hg) are not well understood due to limited knowledge of the driving processes. To investigate these processes at a decommissioned chlor-alkali plant, we used mercury stable isotopes and chemical speciation analysis. The isotopic composition of volatilized Hg(0) was lighter compared to the bulk total Hg (THg) pool in salt-sludge and adjacent surface soil with mean ε202HgHg(0)-THg values of -3.29 and -2.35‰, respectively. Hg(0) exhibited dichotomous directions (E199HgHg(0)-THg = 0.17 and -0.16‰) of mass-independent fractionation (MIF) depending on the substrate from which it was emitted. We suggest that the positive MIF enrichment during Hg(0) re-emission from salt-sludge was overall controlled by the photoreduction of Hg(II) primarily ligated by Cl- and/or the evaporation of liquid Hg(0). In contrast, O-bonded Hg(II) species were more important in the adjacent surface soils. The migration of Hg from salt-sludge to subsurface soil associated with selective Hg(II) partitioning and speciation transformation resulted in deep soils depleted in heavy isotopes (δ202Hg = -2.5‰) and slightly enriched in odd isotopes (Δ199Hg = 0.1‰). When tracing sources using Hg isotopes, it is important to exercise caution, particularly when dealing with mobilized Hg, as this fraction represents only a small portion of the sources.

Improving protocols for α-synuclein seed amplification assays: analysis of preanalytical and analytical variables and identification of candidate parameters for seed quantification.

The effect of preanalytical and analytical factors on the α-synuclein (α-syn) seed amplification assay's (SAA) performance has not been fully explored. Similarly, there is limited knowledge about the most suitable assay protocol and kinetic parameters for misfolded α-syn seed quantification. We studied the effect of centrifugation, repeated freeze-thaw cycles (up to seven), delayed freezing, detergent addition, and blood contamination on the performance of the cerebrospinal fluid (CSF) α-syn SAA real-time quaking-induced conversion (RT-QuIC). Moreover, we analysed the inter- and intra-plate variability, the recombinant protein batch effect, and the RT-QuIC parameters' variability when multiple samples were run in controlled conditions. Finally, we evaluated the assay potential of quantifying α-syn seed by assessing kinetic curves in serial CSF dilutions. Among tested preanalytical variables, a≥0.01 % blood contamination and adding detergents significantly affected the RT-QuIC kinetic parameters and the number of positive replicates. Increasing the number of replicates improved result reproducibility. The number of positive replicates in serially diluted CSF samples improved discrimination between samples with high and low seeding activity, and the time to threshold (LAG) was the most reliable kinetic parameter in multiple experiment settings. Preanalytical variables affecting α-syn RT-QuIC performance are limited to blood contamination and detergent addition. The number of positive replicates and the LAG are the most reliable variables for quantifying α-syn seeding activity. Their consistent measurement in serial dilution experiments, especially when associated with an increased number of sample replicates, will help to develop the α-syn RT-QuIC assay further into a quantitative test.

How healthcare systems are experienced by autistic adults in the United Kingdom: A meta-ethnography.

Autistic adults are more likely to experience mental and physical health difficulties, and yet can find it difficult to get the support that they need. A meta-ethnographic approach was used to review the existing research on autistic adults' experiences of accessing healthcare. Four databases were searched for qualitative and mixed-method studies which looked at the experiences of autistic adults who did not also have a learning disability when using healthcare services in the United Kingdom. Fifteen papers met the criteria to be included, and seven steps were used to analyse the information and develop new themes. Three main themes were identified: Professionals' lack of knowledge can be damaging, Need to reduce processing demands and Adaptation to improve engagement. This review highlights how damaging misdiagnosis, inappropriate treatment, overwhelming environments and systems that are difficult to access can have on the well-being of autistic adults. Limited knowledge and understanding about autism knowledge among healthcare professionals along with autistic adult's own communication and sensory differences indicate that there is a need for improved training developed with autistic adults and adaptations.

Commentary] When ‘Ignorance is Bliss’ in Medicine

Treatment of diseases can be simple as well as effective with only limited medical knowledge, provided the physician is connected with the ‘pure consciousness’ within oneself, which is also the basis of everything in the universe. The ‘inner self’ or ‘pure consciousness’ has refreshing and healing qualities. This healing ‘inner self’ is within the conscious reach of every individual in the waking state, through the practice of meditation. The physician connected to his ‘inner self’ can help the patient to reach a more blissful state of consciousness, thus facilitating healing of all types of diseases. While mind-body therapies are patient-dependent self-help strategies, the physician who is connected with the ‘inner self’, can directly help to initiate the healing process. Positivity is manifested by the physician regardless of whether the medical knowledge he utilizes is ‘simple’ or the more complex ‘scientific’. There may be ‘ignorance’ of complex ‘scientific knowledge’, but there is ‘wisdom’ of being connected with the source of all healing.

Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts.

Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

Exploring Structured Motor Play for Preschoolers with Autism: Listening to Parents’ Voices

ABSTRACT Many children with autism could benefit from adult support during motor play. The purpose of this study was to explore parents’ perspectives on the importance of structured motor activities and how they might support the motor development of their young children with autism. Interviews were conducted with six parents of preschoolers with autism to understand their perceptions about structured motor play. Results revealed that parents: (a) valued structured motor activities, but many did not implement them at home; (b) had limited knowledge about motor development; (c) were uncertain about how to find motor resources; and (d) would like teachers to lead structured motor activities at school and send home ideas for parents to build on these activities. The findings provide insights about partnering with parents to support motor development, ideas for curriculum development, and suggestions for professional development for teachers. Implications for research and practice are discussed.