Piper chaba Stem Extract Facilitated the Synthesis of Iron Oxide Nanoparticles as an Adsorbent to Remove Congo Red Dye.

In this study, a straightforward, eco-friendly, and facile method for synthesizing iron oxide nanoparticles (IONPs) utilizing Piper chaba steam extract as a reducing and stabilizing agent has been demonstrated. The formation of stable IONPs coated with organic moieties was confirmed from UV-vis, FTIR, and EDX spectroscopy and DLS analysis. The produced IONPs are sufficiently crystalline to be superparamagnetic having a saturation magnetization value of 58 emu/g, and their spherical form and size of 9 nm were verified by XRD, VSM, SEM, and TEM investigations. In addition, the synthesized IONPs exhibited notable effectiveness in the removal of Congo Red (CR) dye with a maximum adsorption capacity of 88 mg/g. The adsorption kinetics followed pseudo-second-order kinetics, meaning the adsorption of CR on IONPs is mostly controlled by chemisorption. The adsorption isotherms of CR on the surface of IONPs follow the Langmuir isotherm model, indicating the monolayer adsorption on the homogeneous surface of IONPs through adsorbate-adsorbent interaction. The IONPs have revealed good potential for their reusability, with the adsorption efficiency remaining at about 85% after five adsorption-desorption cycles. The large-scale, safe, and cost-effective manufacturing of IONPs is made possible by this environmentally friendly process.

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels.

With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals' smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

Sequencing-based fine-mapping and in silico functional characterization of the 10q24.32 arsenic metabolism efficiency locus across multiple arsenic-exposed populations.

Inorganic arsenic is highly toxic and carcinogenic to humans. Exposed individuals vary in their ability to metabolize arsenic, and variability in arsenic metabolism efficiency (AME) is associated with risks of arsenic-related toxicities. Inherited genetic variation in the 10q24.32 region, near the arsenic methyltransferase (AS3MT) gene, is associated with urine-based measures of AME in multiple arsenic-exposed populations. To identify potential causal variants in this region, we applied fine mapping approaches to targeted sequencing data generated for exposed individuals from Bangladeshi, American Indian, and European American populations (n = 2,357, 557, and 648 respectively). We identified three independent association signals for Bangladeshis, two for American Indians, and one for European Americans. The size of the confidence sets for each signal varied from 4 to 85 variants. There was one signal shared across all three populations, represented by the same SNP in American Indians and European Americans (rs191177668) and in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LD patterns, minor allele frequency (MAF) (e.g., rs12573221 ~13% in Bangladesh ~0.2% among American Indians), and/or heterogeneity in effect sizes across populations likely contributed to the apparent population specificity of the additional identified signals. One of our potential causal variants influences AS3MT expression and nearby DNA methylation in numerous GTEx tissue types (with rs4919690 as a likely causal variant). Several SNPs in our confidence sets overlap transcription factor binding sites and cis-regulatory elements (from ENCODE). Taken together, our analyses reveal multiple potential causal variants in the 10q24.32 region influencing AME, including a variant shared across populations, and elucidate potential biological mechanisms underlying the impact of genetic variation on AME.

Plantar pressure alterations associated with increased BMI in young adults.

BACKGROUND: Despite evidence suggesting that excess weight is linked to gait alterations and foot disorders, its effect on peak plantar pressure (PPP) variability and complexity during walking remains poorly understood. RESEARCH QUESTION: This study aimed to examine the influence of overweight (BMI ≥ 25) on the dynamic PPP distribution during gait using traditional and nonlinear dynamic measures in young college students. METHODS: Fifty-two overweight (BMI >25, average 29.3 ± 4.02) and sixty-four control college students (BMI<25, 21.7 ± 1.76) aged 18-25 years, walked across a Tekscan gait assessment system at their preferred speed. A t-test or a Mann Whitney U test was used for analysis, subject to data normality. Kinematic, kinetic, spatiotemporal, and GaitEn (sample entropy of 2D spatial PPP maps) for window lengths (m=2) at various filtering levels (r) were used to explore the impact of BMI on PPP alterations. RESULTS AND SIGNIFICANCE: The overweight group exhibited significantly higher mean PPP. The PPP under the forefoot region was also significantly higher for the overweight group as compared to the heel. The mean GaitEn values of overweight and control groups were found significantly different at r = (0.7-0.8) x STD, where GaitEn of the control group was relatively higher, which indicates better gait performance as compared to the overweight group in alignment with previous studies. A significant correlation of GaitEn with STD of PPP was revealed for the overweight group only, suggesting that overweight could significantly change the regularity or the complexity of the PPP series. Although no spatiotemporal parameters (stride length, step length, step width) were significantly affected by the increased BMI, GaitEn dynamic measure, along with spatiotemporal (decrease in gait velocity and cadence with increased BMI), and kinetic measures (increased maximum forces and plantar pressure with increased BMI), were significantly affected by overweight, indicating the feasibility of assessing the impact of increased BMI using pressure platforms in clinical settings.

Enhancement of electrocatalytic abilities toward CO2 reduction by tethering redox-active metal complexes to the active site.

Tethering metal complexes, like [Ru(bpy)2Cl2] (bpy = 2,2'-bipyridine), which are redox-active at low reduction potentials and have the ability to transfer electrons to another complex, to a [Ni(cyclen)]2+ electrocatalyst enhanced the reduction of CO2 to CO at low overpotentials. The [Ni(cyclen)]2+ electrocatalyst was modified by tethering redox-active metal complexes via 4-methylpyridyl linkers. The redox-active metal complexes were reduced after CO2 bound to the active site. In controlled potential electrolysis (CPE) experiments in 95 : 5 (v/v) CH3CN/H2O, [{([Ru]pic)4cyclen}NiCl]5+ ([Ru]+ = {Ru(bpy)2Cl}+; pic = 4-methylpyridyl) could be used to reduce CO2 into CO at a turnover frequency (TOF) of 708 s-1 with a faradaic efficiency (FE) of 80% at an onset potential of -1.60 V vs. NHE. At the same time, this electrocatalyst was active at an onset potential of -1.25 V vs. NHE, which is the reduction potential of one of the bpy ligands of the [Ru]+ moieties, with FE = 84% and TOF = 178 s-1. When the electrocatalysis was performed using [bn4cyclenNiCl]Cl (bn = benzyl) without tethered redox-active metal complexes, the TOF value was determined to be 8 s-1 with FE = 77% at an onset potential of -1.45 V vs. NHE. The results show that tethering redox-active metal complexes significantly improves the electrocatalytic activities by lowering the potential needed to reduce CO2.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings. Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included. Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations.

Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the context of social interactions requires a novel dataset, and K-EmoCon is such a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. Distinct from previous datasets, it includes emotion annotations from all three available perspectives: self, debate partner, and external observers. Raters annotated emotional displays at intervals of every 5 seconds while viewing the debate footage, in terms of arousal-valence and 18 additional categorical emotions. The resulting K-EmoCon is the first publicly available emotion dataset accommodating the multiperspective assessment of emotions during social interactions.

Genetic Determinants of Reduced Arsenic Metabolism Efficiency in the 10q24.32 Region Are Associated With Reduced AS3MT Expression in Multiple Human Tissue Types.

Approximately 140 million people worldwide are exposed to inorganic arsenic through contaminated drinking water. Chronic exposure increases risk for cancers as well as cardiovascular, respiratory, and neurologic diseases. Arsenic metabolism involves the AS3MT (arsenic methyltransferase) gene, and arsenic metabolism efficiency (AME, measured as relative concentrations of arsenic metabolites in urine) varies among individuals. Inherited genetic variation in the 10q24.32 region, containing AS3MT, influences AME, but the mechanisms remain unclear. To better understand these mechanisms, we use tissue-specific expression data from GTEx (Genotype-tissue Expression project) to identify cis-eQTLs (expression quantitative trait loci) for AS3MT and other nearby genes. We combined these data with results from a genome-wide association study of AME using "colocalization analysis," to determine if 10q24.32 SNPs (single nucleotide polymorphisms) that affect AME also affect expression of AS3MT or nearby genes. These analyses identified cis-eQTLs for AS3MT in 38 tissue types. Colocalization results suggest that the casual variant represented by AME lead SNP rs4919690 impacts expression of AS3MT in 13 tissue types (> 80% probability). Our results suggest this causal SNP also regulates/coregulates expression of nearby genes: BORCS7 (43 tissues), NT5C2 (2 tissues), CYP17A1-AS1 (1 tissue), and RP11-724N1.1 (1 tissue). The rs4919690 allele associated with decreased AME is associated with decreased expression of AS3MT (and other coregulated genes). Our study provides a potential biological mechanism for the association between 10q24.32 variation and AME and suggests that the causal variant, represented by rs4919690, may impact AME (as measured in urine) through its effects on arsenic metabolism occurring in multiple tissue types.

Correction: Ionic-caged heterometallic bismuth-platinum complex exhibiting electrocatalytic CO2 reduction.

Correction for 'Ionic-caged heterometallic bismuth-platinum complex exhibiting electrocatalytic CO2 reduction' by Takefumi Yoshida et al., Dalton Trans., 2020, 49, 2652-2660.

Ionic-caged heterometallic bismuth-platinum complex exhibiting electrocatalytic CO2 reduction.

An air-stable heterometallic Bi-Pt complex with the formula [BiPt(SAc)5]n (1; SAc = thioacetate) was synthesized. The crystal structure, natural bond orbital (NBO) and local orbital locator (LOL) analyses, localized orbital bonding analysis (LOBA), and X-ray absorption fine structure (XAFS) measurements were used to confirm the existence of Bi-Pt bonding and an ionic cage of O atoms surrounding the Bi ion. From the cyclic voltammetry (CV) and controlled potential electrolysis (CPE) experiments, 1 in tetrahydrofuran reduced CO2 to CO, with a faradaic efficiency (FE) of 92% and a turnover frequency (TOF) of 8 s-1 after 30 min of CPE at -0.79 V vs. NHE. The proposed mechanism includes an energetically favored pathway via the ionic cage, which is supported by the results of DFT calculations and reflectance infrared spectroelectrochemistry data.

Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are being sought. The active learning virtual screening method has demonstrated the ability to efficiently converge on predictive models with reduced datasets, though its applicability domain to probe identification has yet to be determined. In this article, we challenge active learning's ability to predict inhibitory bioactivity profiles of selective compounds when learning from chemogenomic features found in non-selective ligand-target pairs. Comparison of controls versus multiple molecule representations de-convolutes factors contributing to predictive capability. Experiments using the matrix metalloproteinase family demonstrate maximum probe bioactivity prediction achieved from only approximately 20% of non-probe bioactivity; this data volume is consistent with prior chemogenomic active learning studies despite the increased difficulty from chemical biology experimental settings used here. Feature weight analyses are combined with a custom visualization to unambiguously detail how active learning arrives at classification decisions, yielding clarified expectations for chemogenomic modeling. The results influence tactical decisions for computational probe design and discovery.

Urinary metals and metal mixtures in Bangladesh: Exploring environmental sources in the Health Effects of Arsenic Longitudinal Study (HEALS).

INTRODUCTION: Environmental exposure to toxic metals and metalloids is pervasive and occurs from multiple sources. The Health Effects of Arsenic Longitudinal Study (HEALS) is an ongoing prospective study predominantly focused on understanding health effects associated with arsenic exposure from drinking water. The goal of this project was to measure a suite of elements in urine to better understand potential exposure patterns and to identify common environmental sources of exposure among this semi-rural Bangladeshi population. METHODS: In a random sample of 199 adult HEALS participants (50% female), the concentrations of 15 urinary elements (As, Ba, Cd, Co, Cs, Cu, Mn, Mo, Ni, Pb, Se, Sr, Tl, W, Zn) were assessed by Inductively-Coupled Plasma Mass Spectrometry (ICP-MS) to assess commonalities with sociodemographic characteristics and potential sources of exposure. We used principal component analysis (PCA) with varimax normalized rotations, and hierarchical cluster analysis (CA), using Ward's method with Euclidean distances, to evaluate these relationships. RESULTS: PCA and CA showed similar patterns, suggesting 6 principal components (PC) and 5 clusters: 1)PC: Sr-Ni-Cs/ CA: Sr-Ni-Co; 2) Pb-Tl/Pb-Tl-Se-Cs; 3) As-Mo-W/As-Mo-W; 4) Ba-Mn/Ba-Mn; 5) Cu-Zn/Cu-Zn-Cd; and 6) Cd. There was a strong significant association between the As-Mo-W PC/cluster and water arsenic levels (p < 0.001) and between the Cd PC and betel nut use (p = 0.003). The Sr-Ni-Cs PC was not related to any of the socio-demographic characteristics investigated, including smoking status and occupation. The first PC, Sr-Ni-Cs, explained 21% of the variability; the third PC, As-Mo-W, explained 12.5% of the variability; and the sixth PC, Cd, explained 10% of the variability. Day laborers appeared to have the highest exposure. CONCLUSIONS: Groundwater and betel nut use are likely important sources of metal and metalloid exposure in this population. These findings will guide future exposure assessment research in Bangladesh and future epidemiologic research investigating the degree to which metal mixtures play a role in disease development.

A Survey of Web-Based Chemogenomic Data Resources.

Chemogenomics is a comparatively nascent branch dealing with the effects of drugs and chemicals on molecular level systems. With the emergence of this new epoch, the quantity of data sources is also unprecedentedly increasing. Despite having a plethora of a databases, the variation in bioactivity measurement as well as bias toward specific protein studies, varied computational procedures and redundant information make data mining tedious, especially for newcomers in the field. In this chapter, we give an overview of hands-on data collection and domains of applicability from some useful Web-based chemogenomic resources that are accessible with nothing more than a Web browser. This overview can help assist users in acquiring chemogenomic datasets for their project at hand.

Enhancement of electrocatalytic abilities for reducing carbon dioxide: functionalization with a redox-active ligand-coordinated metal complex.

A binary system consisting of a ditopic planar pseudo-pincer ligand (qlca = quinoline-2-carbaldehyde (pyridine-2-carbonyl) hydrazone) coordinated to two metal centres affording [{Ru(bpy)2}(µ-qlca)NiCl2]Cl·4H2O·CH3OH (2) (bpy = 2,2'-bipyridine) is reported. The Ni2+ moiety acts as the electrocatalytic active site for CO2 reduction to CO. The turnover frequency (TOF) increased from 0.83 s-1 for [Ni(qlca)Cl2] (3) to 120 s-1 for 2, and the overpotential is 350 mV less than that for 3 due to the electronic influence of the {Ru(bpy)2}2+ moiety on the catalytic active site.

Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families.

Computational models for predicting the activity of small molecules against targets are now routinely developed and used in academia and industry, partially due to public bioactivity databases. While models based on bigger datasets are the trend, recent studies such as chemogenomic active learning have shown that only a fraction of data is needed for effective models in many cases. In this article, the chemogenomic active learning method is discussed and used to newly analyze public databases containing nuclear hormone receptor and cytochrome P450 enzyme family bioactivity. In addition to existing results on kinases and G-protein coupled receptors, results here demonstrate the active learning methodology's effectiveness on extracting informative ligand-target pairs in sparse data scenarios. Experiments to assess the domain of the applicability demonstrate the influence of ligand profiles of similar targets within the family.

Arsenic exposures alter clinical indicators of anemia in a male population of smokers and non-smokers in Bangladesh.

Drinking water arsenic (WAs) exposure has been linked to a number of detrimental health outcomes including anemia, primarily among pregnant women. Little is known about the effects of arsenic (As) on hematological disorders among men. We have examined the role of As exposure on hematological indicators of anemia in a group of men exposed to a wide range of As in their drinking water. We conducted a cross-sectional investigation among 119 healthy men in the Health Effects of As Longitudinal Study (HEALS) cohort, in rural Bangladesh. The participants are part of an ongoing study focused on evaluating the influence of As and smoking on immune function. Samples were collected at recruitment and analyzed for water As, urinary As (UAs) and UAs metabolites to assess As exposure. Blood samples were also collected at recruitment and assayed immediately for hematological parameters. We found that increased WAs levels were associated with decreased red blood cell counts [ß=-0.13, p<0.0001] as well as hematocrit packed cell volumes [ß=-0.68, p=0.008] following adjustment for age, smoking, body mass index and polycyclic aromatic hydrocarbon-DNA adducts. Other measures of As exposure (UAs and its metabolites) demonstrated similar associations. Slightly stronger effects were observed among smokers. We also observed an effect of As on hemoglobin among smokers in relation to UAs [ß=-0.54, p<0.05]. Our analysis revealed effects of As exposure on hematological indicators of anemia in a group of healthy male smokers and non-smokers.

In silico prediction of structure and functions for some proteins of male-specific region of the human Y chromosome.

Male-specific region of the human Y chromosome (MSY) comprises 95% of its length that is functionally active. This portion inherits in block from father to male offspring. Most of the genes in the MSY region are involved in male-specific function, such as sex determination and spermatogenesis; also contains genes probably involved in other cellular functions. However, a detailed characterization of numerous MSY-encoded proteins still remains to be done. In this study, 12 uncharacterized proteins of MSY were analyzed through bioinformatics tools for structural and functional characterization. Within these 12 proteins, a total of 55 domains were found, with DnaJ domain signature corresponding to be the highest (11%) followed by both FAD-dependent pyridine nucleotide reductase signature and fumarate lyase superfamily signature (9%). The 3D structures of our selected proteins were built up using homology modeling and the protein threading approaches. These predicted structures confirmed in detail the stereochemistry; indicating reasonably good quality model. Furthermore the predicted functions and the proteins with whom they interact established their biological role and their mechanism of action at molecular level. The results of these structure-functional annotations provide a comprehensive view of the proteins encoded by MSY, which sheds light on their biological functions and molecular mechanisms. The data presented in this study may assist in future prognosis of several human diseases such as Turner syndrome, gonadal sex reversal, spermatogenic failure, and gonadoblastoma.

Genome-wide DNA methylation profiles in hepatocellular carcinoma.

UNLABELLED: Alterations in DNA methylation frequently occur in hepatocellular cancer (HCC). We have previously demonstrated that hypermethylation in candidate genes can be detected in plasma DNA before HCC diagnosis. To identify, with a genome-wide approach, additional genes hypermethylated in HCC that could be used for more accurate analysis of plasma DNA for early diagnosis, we analyzed tumor and adjacent nontumor tissues from 62 Taiwanese HCC cases using Illumina methylation arrays (Illumina, Inc., San Diego, CA) that screen 26,486 autosomal CpG sites. After Bonferroni adjustment, a total of 2,324 CpG sites significantly differed in methylation level, with 684 CpG sites significantly hypermethylated and 1,640 hypomethylated in tumor, compared to nontumor tissues. Array data were validated with pyrosequencing in a subset of five of these genes; correlation coefficients ranged from 0.92 to 0.97. Analysis of plasma DNA from 38 cases demonstrated that 37%-63% of cases had detectable hypermethylated DNA (≥ 5% methylation) for these five genes individually. At least one of these genes was hypermethylated in 87% of the cases, suggesting that measurement of DNA methylation in plasma samples is feasible. CONCLUSION: The panel of methylated genes indentified in the current study will be further tested in a large cohort of prospectively collected samples to determine their utility as early biomarkers of HCC.