Thianthrenation-promoted photoinduced alkene difunctionalization and aryl allylation with Morita-Baylis-Hillman adducts.

An organophotoredox-catalyzed alkoxyallylation of feed-stock olefins, through thianthrenation using a Morita-Baylis-Hillman adduct as an allylating agent, is described. Site-selective addition of MeOH to an alkene-thianthrenium salt and its subsequent conversion into a nucleophilic radical species forms the basis of this unique difunctionalization strategy. The scope is also expanded into radical aryl allylation.

Role of ethanolamine utilization and bacterial microcompartment formation in Listeria monocytogenes intracellular infection.

Ethanolamine (EA) affects the colonization and pathogenicity of certain human bacterial pathogens in the gastrointestinal tract. However, EA can also affect the intracellular survival and replication of host cell invasive bacteria such as Listeria monocytogenes (LMO) and Salmonella enterica serovar Typhimurium (S. Typhimurium). The EA utilization (eut) genes can be categorized as regulatory, enzymatic, or structural, and previous work in LMO showed that loss of genes encoding functions for the enzymatic breakdown of EA inhibited LMO intracellular replication. In this work, we sought to further characterize the role of EA utilization during LMO infection of host cells. Unlike what was previously observed for S. Typhimurium, in LMO, an EA regulator mutant (ΔeutV) was equally deficient in intracellular replication compared to an EA metabolism mutant (ΔeutB), and this was consistent across Caco-2, RAW 264.7, and THP-1 cell lines. The structural genes encode proteins that self-assemble into bacterial microcompartments (BMCs) that encase the enzymes necessary for EA metabolism. For the first time, native EUT BMCs were fluorescently tagged, and EUT BMC formation was observed in vitro and in vivo. Interestingly, BMC formation was observed in bacteria infecting Caco-2 cells, but not the macrophage cell lines. Finally, the cellular immune response of Caco-2 cells to infection with eut mutants was examined, and it was discovered that ΔeutB and ΔeutV mutants similarly elevated the expression of inflammatory cytokines. In conclusion, EA sensing and utilization during LMO intracellular infection are important for optimal LMO replication and immune evasion but are not always concomitant with BMC formation.IMPORTANCEListeria monocytogenes (LMO) is a bacterial pathogen that can cause severe disease in immunocompromised individuals when consumed in contaminated food. It can replicate inside of mammalian cells, escaping detection by the immune system. Therefore, understanding the features of this human pathogen that contribute to its infectiousness and intracellular lifestyle is important. In this work we demonstrate that genes encoding both regulators and enzymes of EA metabolism are important for optimal growth inside mammalian cells. Moreover, the formation of specialized compartments to enable EA metabolism were visualized by tagging with a fluorescent protein and found to form when LMO infects some mammalian cell types, but not others. Interestingly, the formation of the compartments was associated with features consistent with an early stage of the intracellular infection. By characterizing bacterial metabolic pathways that contribute to survival in host environments, we hope to positively impact knowledge and facilitate new treatment strategies.

Emergence of a short peptide based reductase via activation of the model hydride rich cofactor.

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.

Hydrogen bonding-promoted tunable approach for access to aza-bicyclo-[3.3.0]octanes and cyclopenta[b] pyrroles.

A unified strategy is disclosed that builds on successfully engaging the aniline nitrogen of 1,3-amphoteric γ-aminocyclopentenone for a tandem annulation with electron-poor alkynes, solely assisted by the H-bonding network of HFIP. This metal-free mild strategy provides access to medicinally relevant aza-bicyclo-octanes en route to another important scaffold: cyclopenta[b]pyrrole.

Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study.

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.

Role of Ethanolamine Utilization and Bacterial Microcompartment Formation in Listeria monocytogenes Intracellular Infection.

Ethanolamine (EA) affects the colonization and pathogenicity of certain human bacterial pathogens in the gastrointestinal tract. However, EA can also affect the intracellular survival and replication of host-cell invasive bacteria such as Listeria monocytogenes (LMO) and Salmonella enterica serovar Typhimurium ( S. Typhimurium). The EA utilization ( eut) genes can be categorized as regulatory, enzymatic, or structural, and previous work in LMO showed that loss of genes encoding functions for the enzymatic breakdown of EA inhibited LMO intracellular replication. In this work, we sought to further characterize the role of EA utilization during LMO infection of host cells. Unlike what was previously observed for S. Typhimurium, in LMO, an EA regulator mutant ( ΔeutV) was equally deficient in intracellular replication compared to an EA metabolism mutant ( ΔeutB ), and this was consistent across Caco-2, RAW 264.7 and THP-1 cell lines. The structural genes encode proteins that self-assemble into bacterial microcompartments (BMCs) that encase the enzymes necessary for EA metabolism. For the first time, native EUT BMCs were fluorescently tagged, and EUT BMC formation was observed in vitro, and in vivo. Interestingly, BMC formation was observed in bacteria infecting Caco-2 cells, but not the macrophage cell lines. Finally, the cellular immune response of Caco-2 cells to infection with eut mutants was examined, and it was discovered that ΔeutB and ΔeutV mutants similarly elevated the expression of inflammatory cytokines. In conclusion, EA sensing and utilization during LMO intracellular infection are important for optimal LMO replication and immune evasion but are not always concomitant with BMC formation.

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach.

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.

Dual enzyme-powered chemotactic cross ß amyloid based functional nanomotors.

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 ß 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.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.

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.

Fe-Catalyzed Hydroallylation of Unactivated Alkenes with Vinyl Cyclopropanes.

Catalytic, reductive C-C bond formation between alkenes and vinyl cyclopropane (VCP) through hydrogen atom transfer (MHAT) is developed. Despite VCP's use as probes in radical-clock experiments, translation of this manifold into synthetic methods for accessing elusive C-C bonds remains largely unexplored. This work represents the first foray into this front where the high chemoselectivity of MHAT for alkene over VCP was pivotal for realizing the strategy. This method exhibits a broad scope, high functional group tolerance, and useful applications.

Temporal Release of Cell Cycle Regulator α-Lipoic Acid: An NIR-Light (Two-Photon) Activatable Quinoxaline-Based Nano-Prodrug Delivery System.

The regulation of the cell cycle has recently opened up a new research perspective for cancer treatment. So far, no effort has been made for temporal control of cell cycles using a photocleavable linker. Presented herein is the first report of regulation of disrupted cell cycles through the temporal release of a well-known cell cycle regulator α-lipoic acid (ALA), enabled by a newly designed NIR-active quinoxaline-based photoremovable protecting group (PRPG). The suitable quinoxaline-based photocage of ALA (tetraphenylethelene conjugated) has been formulated as fluorescent organic nanoparticles (FONs) and used effectively as a nano-DDS (drug delivery system) for better solubility and cellular internalization. Fascinatingly, the enhanced TP (two-photon) absorption cross section of the nano-DDS (503 GM) signifies its utility for biological applications. Using green light, we have successfully controlled the time span of cell cycles and cell growth of skin melanoma cell lines (B16F10) by the temporal release of ALA. Further, in silico studies and PDH activity assay supported the observed regulatory behavior of our nano-DDS with respect to photoirradiation. Overall, this approach expands the research path toward a futuristic photocontrolled toolbox for cell cycle regulation.

An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology.

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[Formula: see text], while the MLP model outperforms other classifiers with an accuracy of 74[Formula: see text]. 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.

Improving the generalizability of protein-ligand binding predictions with AI-Bind.

Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.

Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.

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 real-time 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.

Cross ß Amyloid Nanotubes Demonstrate Promiscuous Catalysis in a Chemical Reaction Network via Co-option.

In Darwin's warm pond rich with nutrients, lesser number of early catalytic machineries with modest capabilities were able to demonstrate promiscuity by catalyzing diverse biochemical transformations important for protometabolism. Herein, we report catalytically promiscuous amyloid-based short peptide assemblies that could concomitantly catalyse three metabolically important yet orthogonal reactions. The surface exposed catalytic dyads featuring lysines and imidazoles were utilized for C=N condensation via dynamic covalent linkages and modulation of protonation events, respectively. Further, the peptide assemblies could promiscuously catalyse hydrolysis as well as retro-aldol reactions, that could be co-opted to facilitate C=N bond formation, either by a feedforward-driven reaction network or by replenishing depleted substrates. The catalytic diversity of short peptide based promiscuous ß-sheet folds suggests their possible role in promoting the protometabolic network in early earth.

MilkyBase, a database of human milk composition as a function of maternal-, infant- and measurement conditions.

This study describes the development of a database, called MilkyBase, of the biochemical composition of human milk. The data were selected, digitized and curated partly by machine-learning, partly manually from publications. The database can be used to find patterns in the milk composition as a function of maternal-, infant- and measurement conditions and as a platform for users to put their own data in the format shown here. The database is an Excel workbook of linked sheets, making it easy to input data by non-computationally minded nutritionists. The hierarchical organisation of the fields makes sure that statistical inference methods can be programmed to analyse the data. Uncertainty quantification and recording dynamic (time-dependent) compositions offer predictive potentials.

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations.

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).

Activation and Regulation of NLRP3 by Sterile and Infectious Insults.

Nod-Like Receptor (NLR) is the largest family of Pathogen Recognition Receptors (PRRs) that patrols the cytosolic environment. NLR engagement drives caspase-1 activation that cleaves pro-IL-1B which then gets secreted. Released IL-1B recruits immune cells to the site of infection/injury. Caspase-1 also cleaves Gasdermin-D (GSDM-D) that forms pores within the plasma membrane driving inflammatory cell death called pyroptosis. NLRP3 is the most extensively studied NLR. The NLRP3 gene is encoded by 9 exons, where exon 1 codes for pyrin domain, exon 3 codes for NACHT domain, and Leucine Rich Repeat (LRR) domain is coded by exon 4-9. Exon 2 codes for a highly disorganized loop that connects the rest of the protein to the pyrin domain and may be involved in NLRP3 regulation. The NLRP3 inflammasome is activated by many structurally divergent agonists of microbial, environmental, and host origin. Activated NLRP3 interacts with an adaptor protein, ASC, that bridges it to pro-Caspase-1 forming a multi-protein complex called inflammasome. Dysregulation of NLRP3 inflammasome activity is a hallmark of pathogenesis in several human diseases, indicating its highly significant clinical relevance. In this review, we summarize the existing knowledge about the mechanism of activation of NLRP3 and its regulation during activation by infectious and sterile triggers.

Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling.

BACKGROUND: Automatic e-coaching may motivate individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Multiple studies have reported on uninterrupted and automatic monitoring of behavioral aspects (such as sedentary time, amount, and type of physical activity); however, e-coaching and personalized feedback techniques are still in a nascent stage. Current intelligent coaching strategies are mostly based on the handcrafted string messages that rarely individualize to each user's needs, context, and preferences. Therefore, more realistic, flexible, practical, sophisticated, and engaging strategies are needed to model personalized recommendations. OBJECTIVE: This study aims to design and develop an ontology to model personalized recommendation message intent, components (such as suggestion, feedback, argument, and follow-ups), and contents (such as spatial and temporal content and objects relevant to perform the recommended activities). A reasoning technique will help to discover implied knowledge from the proposed ontology. Furthermore, recommendation messages can be classified into different categories in the proposed ontology. METHODS: The ontology was created using Protégé (version 5.5.0) open-source software. We used the Java-based Jena Framework (version 3.16) to build a semantic web application as a proof of concept, which included Resource Description Framework application programming interface, World Wide Web Consortium Web Ontology Language application programming interface, native tuple database, and SPARQL Protocol and Resource Description Framework Query Language query engine. The HermiT (version 1.4.3.x) ontology reasoner available in Protégé 5.x implemented the logical and structural consistency of the proposed ontology. To verify the proposed ontology model, we simulated data for 8 test cases. The personalized recommendation messages were generated based on the processing of personal activity data in combination with contextual weather data and personal preference data. The developed ontology was processed using a query engine against a rule base to generate personalized recommendations. RESULTS: The proposed ontology was implemented in automatic activity coaching to generate and deliver meaningful, personalized lifestyle recommendations. The ontology can be visualized using OWLViz and OntoGraf. In addition, we developed an ontology verification module that behaves similar to a rule-based decision support system to analyze the generation and delivery of personalized recommendation messages following a logical structure. CONCLUSIONS: This study led to the creation of a meaningful ontology to generate and model personalized recommendation messages for physical activity coaching.