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The Cooper test (CT) is used to assess cardiorespiratory fitness (CRF) in pre-adolescents and adolescents, although it was originally developed to assess healthy adults. The aim of this study is to examine the available scientific evidence on the reliability and criterion validity of CT when administered to pre-adolescents and adolescents. Systematic searches were performed in three electronic databases (MEDLINE/PubMed, SPORTDiscuss and Scopus). To examine reliability and validity, four separate meta-analyses were implemented, finding high heterogeneity in studies with low methodological quality. There was a paucity of research regarding absolute reliability, while studies attempting to develop or cross-validate VO2max/VO2peak equations were almost non-existent. Information on the psychometric properties of CT in pre-adolescents under 12 years of age is scarce. The findings of this review cast doubt on the usefulness of CT both in identifying CRF and in determining the impact of strategies developed to improve CRF among pre-adolescents and adolescents. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Medicine & Science of Physical Activity & Sport / Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte is the property of Revista Internacional de Medicina y Ciencias de la Actividad Fisica y del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Impedance-matched Marx generators (IMGs) are considered next generation pulsed-power drivers because of their long lifetime (> 10,000 shots), repetition rate (> 0.1-Hz), fast rise time (~ 100-ns), and high-energy-delivery efficiency (~ 90%). “TITAN” is a 14-stage IMG designed to deliver 1-TW to a 2-Ω matched load. In this paper, design, simulation, and experimental results for six stages of TITAN including its triggering system, air delivery system, and pulse shaping are presented. To achieve efficiency over 85% and maximize the capability of an IMG, synchronized triggering, reduced pre-fire rate, and pulse shaping ability are crucial. In this paper, novel engineering solutions are introduced, tested, and proven to overcome those challenges. 6-stage TITAN, powered by 102 identical bricks and 102 field-distortion-triggered gas switches, could generate ~ 600-kA and ~ 700-kV across a ~ 0.9-Ω matched load when fully charged to ± 100-kV. In these experiments, 6-stage TITAN is tested up to ± 70-kV charge voltage which delivers a peak power of 330-GW to a 1.2-Ω resistive load.

Abstract In extant biology, large and complex enzymes employ low molecular weight cofactors such as dihydronicotinamides as efficient hydride transfer agents and electron carriers for the regulation of critical metabolic processes. In absence of complex contemporary enzymes, these molecular cofactors are generally inefficient to facilitate any reactions on their own. Herein, we report short peptide-based amyloid nanotubes featuring exposed arrays of cationic and hydrophobic residues that can bind small molecular weak hydride transfer agents (NaBH4) to facilitate efficient reduction of ester substrates in water. In addition, the paracrystalline amyloid phases loaded with borohydrides demonstrate recyclability, substrate selectivity and controlled reduction and surpass the capabilities of standard reducing agent such as LiAlH4. The amyloid microphases and their collaboration with small molecular cofactors foreshadow the important roles that short peptide-based assemblies might have played in the emergence of protometabolism and biopolymer evolution in prebiotic earth.

Abstract The phenomena of Squeezing and chaos have recently been studied in the context of inflation. We apply this formalism in the post-inflationary preheating phase. During this phase, the inflaton field undergoes quasi-periodic oscillation, which acts as a driving force for the resonant growth of quantum fluctuation or particle production. Furthermore, the quantum state of the fluctuations is known to have evolved into a squeezed state. In this submission, we explore the underlying connection between the resonant growth, squeezing, and chaos by computing the Out of Time Order Correlator (OTOC) of phase space variables and establishing a relation among the Lyapunov, Floquet exponents, and squeezing parameters. For our study, we consider observationally favored α-attractor E-model of inflaton coupled with the bosonic field. After the production, the system of produced bosonic fluctuations/particles from the inflaton is supposed to thermalize, and that is believed to have an intriguing connection to the nature of chaos of the system under perturbation. We conjecture a relation between the thermalization temperature ( T ¯ SS $$ {\overline{T}}_{\textrm{SS}} $$ ) of the system and quantum squeezing, which is further shown to be consistent with the well-known Rayleigh-Jeans formula for the temperature symbolized as T ¯ RJ $$ {\overline{T}}_{\textrm{RJ}} $$ , and that is T ¯ SS ≃ T ¯ RJ $$ {\overline{T}}_{\textrm{SS}}\simeq {\overline{T}}_{\textrm{RJ}} $$ . Finally, we show that the system temperature is in accord with the well-known lower bound on the temperature of a chaotic system proposed by Maldacena-Shenker-Stanford (MSS).

Abstract The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge. In this proof-of-concept study, we underscore the significance of semantic representation in enhancing data interoperability and facilitating advanced analytics for physical activity sensor observations. Our approach focuses on enhancing the usability of physical activity datasets by employing a medical-grade (CE certified) sensor to generate synthetic datasets. Additionally, we provide insights into ethical considerations related to synthetic datasets. The study conducts a comparative analysis between real and synthetic activity datasets, assessing their effectiveness in mitigating model bias and promoting fairness in predictive analysis. We have created an ontology for semantically representing observations from physical activity sensors and conducted predictive analysis on data collected using MOX2-5 activity sensors. Until now, there has been a lack of publicly available datasets for physical activity collected with MOX2-5 activity monitoring medical grade (CE certified) device. The MOX2-5 captures and transmits high-resolution data, including activity intensity, weight-bearing, sedentary, standing, low, moderate, and vigorous physical activity, as well as steps per minute. Our dataset consists of physical activity data collected from 16 adults (Male: 12; Female: 4) over a period of 30–45 days (approximately 1.5 months), yielding a relatively small volume of 539 records. To address this limitation, we employ various synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN), to augment the dataset with synthetic data. For both the authentic and synthetic datasets, we have developed a Multilayer Perceptron (MLP) classification model for accurately classifying daily physical activity levels. The findings underscore the effectiveness of semantic ontology in semantic search, knowledge representation, data integration, reasoning, and capturing meaningful relationships between data. The analysis supports the hypothesis that the efficiency of predictive models improves as the volume of additional synthetic training data increases. Ontology and Generative AI hold the potential to expedite advancements in behavioral monitoring research. The data presented, encompassing both real MOX2-5 and its synthetic counterpart, serves as a valuable resource for developing robust methods in activity type classification. Furthermore, it opens avenues for exploration into research directions related to synthetic data, including model efficiency, detection of generated data, and considerations regarding data privacy.

Abstract In this paper we study the shear free spherical symmetric gravitational collapse of a charged radiating star. All the physical quantities including pressure, density are regular. Energy conditions are satisfied throughout the interior of the matter configuration. The luminosity is time independent and mass is radiated linearly. The causal and non causal temperature remain greater than that of the uncharged collapsing scenario.

Abstract Background Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval. Methods This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation. Results We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching. Conclusion The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

In the proposed paper, a monolayer band-stop frequency-selective surface (FSS) is structured and analyzed for application in wideband shielding. The paper proposes an organized step-by-step method to design the frequency-selective surface (FSS) to achieve enhanced shielding effectiveness (SE) over the entire band. The suggested FSS comprises of tripole-shaped unit cells on both sides of a monolayer FR-4 substrate to offer adequate shielding over the band of 3.2–8.82 GHz which includes both C- and X-bands. The operating band of the FSS achieves ultrawideband shielding by means of 93% bandwidth with 60–100 dB shielding effectiveness. Experimental data are employed for the characteristics that are fairly well congruent to simulated results. Higher angular stability beyond 80° characterizes the proposed FSS. A wide range of potential uses for the proposed design exists, including microwave shielding, wideband filtering, as well as radar cross-section (RCS) reduction, among others. The out-of-band RCS reduction capability of the FSS is also investigated and presented in this paper.

Abstract Background e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. As a result of the COVID-19 pandemic, ICT has improved the quality of healthcare, the exchange of information, training of healthcare professionals and patients, and facilitated the relationship between patients and healthcare providers. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods, as well as different ICT techniques used in the care of COVID-19-infected patients. Objective The purpose of this systematic literature review is to identify the e-Health methods, associated ICTs, method implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19 pandemic. Methods The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, Science Direct, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the identified research questions. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. Results Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with the inclusion and exclusion criteria. The review identified the following eHealth methods—Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using a semantic knowledge graph (e.g., ontology model). Conclusions Various e-Health methods, related remote monitoring technologies, different approaches, information categories, the adoption of ICT tools for an automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICTs. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

Abstract Nanomotor chassis constructed from biological precursors and powered by biocatalytic transformations can offer important applications in the future, specifically in emergent biomedical techniques. Herein, cross β amyloid peptide-based nanomotors (amylobots) were prepared from short amyloid peptides. Owing to their remarkable binding capabilities, these soft constructs are able to host dedicated enzymes to catalyze orthogonal substrates for motility and navigation. Urease helps in powering the self-diffusiophoretic motion, while cytochrome C helps in providing navigation control. Supported by the simulation model, the design principle demonstrates the utilization of two distinct transport behaviours for two different types of enzymes, firstly enhanced diffusivity of urease with increasing fuel (urea) concentration and secondly, chemotactic motility of cytochrome C towards its substrate (pyrogallol). Dual catalytic engines allow the amylobots to be utilized for enhanced catalysis in organic solvent and can thus complement the technological applications of enzymes.

Abstract The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.

Abstract Protein post-translational modification by the small ubiquitin-like modifier (SUMO) regulates the stability, subcellular localization, and interactions of protein substrates with consequences on cellular responses including epithelial-mesenchymal transition (EMT). Transforming growth factor beta (TGFβ) is a potent inducer of EMT with implications for cancer invasion and metastasis. The transcriptional coregulator SnoN suppresses TGFβ-induced EMT-associated responses in a sumoylation-dependent manner, but the underlying mechanisms have remained largely unknown. Here, we find that sumoylation promotes the interaction of SnoN with the epigenetic regulators histone deacetylase 1 (HDAC1) and histone acetylase p300 in epithelial cells. In gain and loss of function studies, HDAC1 suppresses, whereas p300 promotes, TGFβ-induced morphogenetic changes associated with EMT-related events in three-dimensional multicellular organoids derived from mammary epithelial cells or carcinomas. These findings suggest that sumoylated SnoN acts via the regulation of histone acetylation to modulate EMT-related effects in breast cell organoids. Our study may facilitate the discovery of new biomarkers and therapeutics in breast cancer and other epithelial cell-derived cancers.

Abstract Electronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper introduces a novel approach that combines deep learning and semantic ontologies to generate hybrid and personalized recommendations by considering “Physical Activity” as a case study. To achieve this, we employ three methods: time-series forecasting, time-series physical activity level classification, and statistical metrics for data processing. Additionally, we utilize a naïve-based probabilistic interval prediction technique with the residual standard deviation used to make point predictions meaningful in the recommendation presentation. The processed results are integrated into activity datasets using an ontology called OntoeCoach, which facilitates semantic representation and reasoning. To generate personalized recommendations in an understandable format, we implement the SPARQL Protocol and RDF Query Language (SPARQL). We evaluate the performance of standard time-series forecasting algorithms [such as 1D Convolutional Neural Network Model (CNN1D), autoregression, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)] and classifiers [including Multilayer Perceptron (MLP), Rocket, MiniRocket, and MiniRocketVoting] using state-of-the-art metrics. We conduct evaluations on both public datasets (e.g., PMData) and private datasets (e.g., MOX2-5 activity). Our CNN1D model achieves the highest prediction accuracy of 97 $$\%$$ % , while the MLP model outperforms other classifiers with an accuracy of 74 $$\%$$ % . Furthermore, we evaluate the performance of our proposed OntoeCoach ontology model by assessing reasoning and query execution time metrics. The results demonstrate that our approach effectively plans and generates recommendations on both datasets. The rule set of OntoeCoach can also be generalized to enhance interpretability.

Abstract Interactive programming environments are powerful tools for promoting innovative network thinking, teaching science of complexity, and exploring emergent phenomena. This paper reports on our recent development of the deterministic random walk model in NetLogo, a leading platform for computational thinking, eco-system thinking, and multi-agent cross-platform programming environment. The deterministic random walk is foundational to modeling dynamical processes on complex networks. Inspired by the temporal visualizations offered in NetLogo, we investigated the relationship between network topology and diffusion saturation time for the deterministic random walk model. Our analysis uncovers that in Erdős–Rényi graphs, the saturation time exhibits an asymmetric pattern with a considerable probability of occurrence. This behavior occurs when the hubs, defined as nodes with relatively higher number of connections, emerge in Erdős–Rényi graphs. Yet, our analysis yields that the hubs in Barabási–Albert model stabilize the the convergence time of the deterministic random walk model. These findings strongly suggest that depending on the dynamical process running on complex networks, complementing characteristics other than the degree need to be taken into account for considering a node as a hub. We have made our development open-source, available to the public at no cost at https://github.com/bravandi/NetLogo-Dynamical-Processes .

Abstract Background University students commonly received COVID-19 vaccinations before returning to U.S. campuses in the Fall of 2021. Given likely immunologic variation among students based on differences in type of primary series and/or booster dose vaccine received, we conducted serologic investigations in September and December 2021 on a large university campus in Wisconsin to assess anti-SARS-CoV-2 antibody levels. Methods We collected blood samples, demographic information, and COVID-19 illness and vaccination history from a convenience sample of students. Sera were analyzed for both anti-spike (anti-S) and anti-nucleocapsid (anti-N) antibody levels using World Health Organization standardized binding antibody units per milliliter (BAU/mL). Levels were compared across categorical primary COVID-19 vaccine series received and binary COVID-19 mRNA booster status. The association between anti-S levels and time since most recent vaccination dose was estimated by mixed-effects linear regression. Results In total, 356 students participated, of whom 219 (61.5%) had received a primary vaccine series of Pfizer-BioNTech or Moderna mRNA vaccines and 85 (23.9%) had received vaccines from Sinovac or Sinopharm. Median anti-S levels were significantly higher for mRNA primary vaccine series recipients (2.90 and 2.86 log [BAU/mL], respectively), compared with those who received Sinopharm or Sinovac vaccines (1.63 and 1.95 log [BAU/mL], respectively). Sinopharm and Sinovac vaccine recipients were associated with a significantly faster anti-S decline over time, compared with mRNA vaccine recipients (P

State-of-the-art machine learning models in drug discovery fail to reliably predict the binding properties of poorly annotated proteins and small molecules. Here, the authors present AI-Bind, a machine learning pipeline to improve generalizability and interpretability of binding predictions.

Stress is a natural human reaction to demands or pressure, usually when perceived as harmful or/and toxic. When stress becomes constantly overwhelmed and prolonged, it increases the risk of mental health and physiological uneasiness. Furthermore, chronic stress raises the likelihood of mental health plagues such as anxiety, depression, and sleep disorder. Although measuring stress using physiological parameters such as heart rate variability (HRV) is a common approach, how to achieve ultra-high accuracy based on HRV measurements remains as a challenging task. HRV is not equivalent to heart rate. While heart rate is the average value of heartbeats per minute, HRV represents the variation of the time interval between successive heartbeats. The HRV measurements are related to the variance of RR intervals which stand for the time between successive R peaks. In this study, we investigate the role of HRV features as stress detection bio-markers and develop a machine learning-based model for multi-class stress detection. More specifically, a convolution neural network (CNN) based model is developed to detect multi-class stress, namely, no stress, interruption stress, and time pressure stress, based on both time- and frequency-domain features of HRV. Validated through a publicly available dataset, SWELL–KW, the achieved accuracy score of our model has reached 99.9% (Precision = 1, Recall = 1, $F1-$ score = 1, and MCC = 0.99), thus outperforming the existing methods in the literature. In addition, this study demonstrates the effectiveness of essential HRV features for stress detection using a feature extraction technique, i.e., analysis of variance.

Polo-like-kinase-1 (PLK-1) is a serine/threonine kinase that regulates the cell cycle and acts as an oncogene in multiple cancers, including oral squamous cell carcinoma (OSCC). The loss of PLK-1 can inhibit growth and induce apoptosis, making it an attractive therapeutic target in OSCC. We evaluated the efficacy of PLK-1 inhibitors as novel, targeted therapeutics in OSCC. PLK-1 inhibition using BI6727 (volasertib) was found to affect cell death at low nanomolar concentrations in most tested OSCC cell lines, but not in normal oral keratinocytes. In cell lines resistant to volasertib alone, pre-treatment with radiotherapy followed by volasertib reduced cell viability and induced apoptosis. The combinatorial efficacy of volasertib and radiotherapy was replicated in xenograft mouse models. These findings highlight the potential of adding PLK-1 inhibitors to adjuvant therapy regimens in OSCC.

Groundwater quality is one of the major concerns. Quality of the groundwater directly impacts human health, growth of plants and vegetables. Due to the severe impacts of inadequate water quality, it is imperative to find a swift and economical solution. Water quality prediction may help us to manage water resources properly. The present study has been carried out considering thirty-seven water sample data points form the Pindrawan tank command area of Raipur district, Chhattisgarh, India. A total of nineteen physicochemical parameters were measured, out of which seventeen parameters were used to compute the weight-based groundwater quality index (WQI). In this present work, the primary goal is to identify the most effective parameters for WQI prediction. Out of the seventeen parameters tested, the Mann—Whitney—Wilcoxon (MWW) statistical test has revealed that five parameters Fe, Cr, Na, Ca, and Mg hold a strong statistical significance in distinguishing between drinkable and non-drinkable water. Out of these five parameters, Cr is the only parameter that maintains a different range of values for drinkable water and non-drinkable water. To validate the efficiency of these statistically significant parameters, machine learning techniques like Artificial Neural Networks (ANN) and Logistic Regression (LR) were used. The experimental results clearly demonstrate that out of all the seventeen parameters tested, utilizing only Cr yields remarkably high classification accuracy. ‘Cr’ achieved an accuracy of 91.67% using artificial neural networks. This is much higher than the accuracy of 66.67% obtained using a parameter set with all seventeen parameters. The proposed methodology achieved good accuracy when classifying water samples into drinkable and non-drinkable water using only one parameter, ‘Cr’.

A study using mathematical modeling has been conducted to analyze how both man-made and natural sources of contaminants affect various layers of an aquifer-aquitard system. The xy-, yz-, and zx-plane have been used to depict the locations where the natural sources of contaminant occur on the xz- and yz-plane, and where the man-made sources occur, on the xy-plane. It is assumed that the sources occurring in different planes are constant, while the velocity of groundwater flow has been considered only along the x-axis. A three-dimensional advection dispersion equation (ADE) has been used to accurately model the flow of groundwater and contaminants through a porous medium. Three distinct sources exert their influence on three separate planes throughout the entire duration of this study, thus making it possible to model these sources using initial conditions. This study presents a profile of contaminant concentration in space and time when constant sources are located on different planes. Some physical assumptions have been considered to make the model relatable to real-world phenomena. Often, finding stability conditions for numerical solutions becomes difficult, so an unconditionally stable solution is more appreciable. The homotopy analysis method (HAM), a method known for its unconditional stability, has been used to solve a three-dimensional mathematical model (ADE) along with its initial conditions. Man-made sources show more impact than equal-strength natural sources in the aquifer-aquitard system.

ING1 is a chromatin targeting subunit of the Sin3a histone deacetylase (HDAC) complex that alters chromatin structure to subsequently regulate gene expression. We find that ING1 knockdown increases expression of Twist1, Zeb 1&2, Snai1, Bmi1 and TSHZ1 drivers of EMT, promoting EMT and cell motility. ING1 expression had the opposite effect, promoting epithelial cell morphology and inhibiting basal and TGF-β-induced motility in 3D organoid cultures. ING1 binds the Twist1 promoter and Twist1 was largely responsible for the ability of ING1 to reduce cell migration. Consistent with ING1 inhibiting Twist1 expression in vivo, an inverse relationship between ING1 and Twist1 levels was seen in breast cancer samples from The Cancer Genome Atlas (TCGA). The HDAC inhibitor vorinostat is approved for treatment of multiple myeloma and cutaneous T cell lymphoma and is in clinical trials for solid tumours as adjuvant therapy. One molecular target of vorinostat is INhibitor of Growth 2 (ING2), that together with ING1 serve as targeting subunits of the Sin3a HDAC complex. Treatment with sublethal (LD25-LD50) levels of vorinostat promoted breast cancer cell migration several-fold, which increased further upon ING1 knockout. These observations indicate that correct targeting of the Sin3a HDAC complex, and HDAC activity in general decreases luminal and basal breast cancer cell motility, suggesting that use of HDAC inhibitors as adjuvant therapies in breast cancers that are prone to metastasize may not be optimal and requires further investigation.

Abstract Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-term damage. Automatic health coaching system may help people manage a healthy lifestyle with continuous health state monitoring and personalized recommendation generation with machine learning (ML). This study proposes a semantic ontology model to annotate the ML-prediction outcomes and personal preferences to conceptualize personalized recommendation generation with a hybrid approach. We use a transfer learning approach to improve ML model training and its performance, and an incremental learning approach to handle daily growing data and fit them into the ML models. Furthermore, we propose a personalized activity recommendation algorithm for a healthy lifestyle by combining transfer learning, incremental learning, the proposed semantic ontology model, and personal preference data. For the overall experiment, we use public and private activity datasets collected from healthy adults (n = 33 for public datasets; n = 16 for private datasets). The standard ML algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity, medium physical activity, and vigorous physical activity serve as input for the classification models. We first use publicly available Fitbit datasets to build the initial classification models. Subsequently, we re-use the pre-trained ML classifiers on the private MOX2-5 dataset using transfer learning. We test several standard algorithms and select the best-performing model with optimized configuration for our use case by empirical testing. We find that DecisionTreeClassifier with a criterion "entropy” outperforms other ML classifiers with a mean accuracy score of 97.50% (F1 = 97.00, precision = 97.00, recall = 98.00, MCC = 96.78) and 96.10% (F1 = 96.00, precision = 96.00, recall = 96.00, MCC = 96.10) in Fitbit and MOX2-5 datasets, respectively. Using transfer learning, the DecisionTreeClassifier with a criterion "entropy" outperforms other classifiers with a mean accuracy score of 97.99% (F1 = 98.00, precision = 98.00, recall = 98.00, MCC = 96.79). Therefore, the transfer learning approach improves the machine learning model performance by ≈ 1.98% for defined datasets and settings on MOX2-5 datasets. The Hermit reasoner outperforms other reasoners with an average reasoning time of 1.1–2.1 s, under defined settings in our proposed ontology model. Our proposed algorithm for personalized recommendations conceptualizes a direction to combine the classification results and personal preferences in an ontology for activity eCoaching. The proposed method of combining machine learning technology with semantic rules is an invaluable asset in personalized recommendation generation. Moreover, the semantic rules in the knowledge base and SPARQL (SPARQL Protocol and RDF Query Language) query processing in the query engine helps to understand the logic behind the personalized recommendation generation.

Copyright of Revista Andaluza de Medicina del Deporte is the property of Centro Andaluza de Medicina del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Measurement(s) Concentration of biochemical compounds in human milk or/and derived quantities, like their sums or ratios. Technology Type(s) Data mining, by means of Machine Learning and targeted manual literature search within available scientific publications in the internet. Factor Type(s) Georgaphical region • Cohort size • Measurement Method • Various characteristics (including history) of mother, child, breast milk and measurement Sample Characteristic - Organism Human milk Sample Characteristic - Environment Standard birth environment Sample Characteristic - Location Various regions of the world

Abstract Background Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. Methods We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting different phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fidelity eCoach prototype. In design workshop 2, we focused on maturing the low-fidelity solution design and development for the visualization of continuous and discrete data, artificial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. Results The iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an effective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. Conclusions The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working research prototype to improve the fit between technology, end-user, and researchers. Furthermore, we performed a technological readiness study of ProHealth eCoach against standard levels set by European Union (EU).

Groundwater is being contaminated rapidly due to various anthropogenic activities and geogenic sources. In this direction, assessment of water quality analysis is the basic requirement for nurturing the human being and its evolution. The Water Quality Index (WQI) parameter has been widely used in determining water quality globally. The study aims to provide the suitability of groundwater in the specified region using the polynomial approximation method for drinking and irrigation purposes along with the computation of WQI using the conventional method. Weierstrass's polynomial approximation theorem along with longitudinal and latitudinal values has been used to evaluate the polynomial regarding various physicochemical parameters. To validate the obtained results from the present approach, groundwater quality data collected and analyzed from the Pindrawan tank area in Raipur district, Chhattisgarh, India, have been used. The result is obtained, i.e., the intermediate value of the parameters obtained correctly from the mathematical modeling, with an average error of 7%. This polynomial approximation method can also be used as the substitute of inverse modeling to determine the location of the source in the two-dimensional system. The approach output can be beneficial to administrators in making decisions on groundwater quality and gaining insight into the tradeoff between system benefit and environmental requirement. HIGHLIGHTS Approximation of contaminant concentration of groundwater for various physicochemical parameters.; Use of polynomial approximation in any geological scenario to predict contaminant concentration.; Collect data for a specific region and use the data to find the values of the constants.; Cross-verify the result with the observed value and 7% expected error is obtained.;

Copyright of International Journal of Medicine & Science of Physical Activity & Sport / Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte is the property of Revista Internacional de Medicina y Ciencias de la Actividad Fisica y del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Medicine & Science of Physical Activity & Sport / Revista Internacional de Medicina y Ciencias de la Actividad Física y del Deporte is the property of Revista Internacional de Medicina y Ciencias de la Actividad Fisica y del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Medical Things (IoMT) combines medical devices and applications that use network technologies to connect healthcare information systems (HIS). IoMT is reforming the medical industry by adopting information and communication technologies (ICTs). Identity verification, secure collection, and exchange of medical data are essential in health applications. In this study, we implemented a hybrid security solution to secure the collection and management of personal health data using Spring Framework (SF), Services for Sensitive Data (TSD) as a service platform, and Hyper-Text-Transfer-Protocol (HTTP (H)) security methods. The adopted solution (SFTSDH = SF + TSD + H) instigated the following security features: identity brokering, OAuth2, multifactor authentication, and access control to protect the Microservices Architecture Application Programming Interfaces (APIs), following the General Data Protection Regulation (GDPR). Moreover, we extended the adopted security solution to develop a digital infrastructure to facilitate the research and innovation work in the electronic health (eHealth) section, focusing on solution validation with theoretical evaluation and experimental testing. We used a web engineering security methodology to achieve and explain the adopted security solution. As a case study, we designed and implemented electronic coaching (eCoaching) prototype system and deployed the same in the developed infrastructure to securely record and share personal health data. Furthermore, we compared the test results with related studies qualitatively for the efficient evaluation of the implemented security solution. The SFTSDH implementation and configuration in the prototype system have effectively secured the eCoach APIs from an attack in all the considered scenarios. The eCoach prototype with the SFTSDH solution effectively sustained a load of (≈) 1000 concurrent users in the developed digital health infrastructure. In addition, we performed a qualitative comparison among the following security solutions: SF security, third-party security, and SFTSDH, where SFTSDH showed a promising outcome.