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.

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.

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.

Systems chemistry of peptide-assemblies for biochemical transformations.

During the billions of years of the evolutionary journey, primitive polymers, involved in proto metabolic pathways with low catalytic activity, played critical roles in the emergence of modern enzymes with remarkable substrate specificity. The precise positioning of amino acid residues and the complex orchestrated interplay in the binding pockets of evolved enzymes promote covalent and non-covalent interactions to foster a diverse set of complex catalytic transformations. Recent efforts to emulate the structural and functional information of extant enzymes by minimal peptide based assemblies have attempted to provide a holistic approach that could help in discerning the prebiotic origins of catalytically active binding pockets of advanced proteins. In addition to the impressive sets of advanced biochemical transformations, catalytic promiscuity and cascade catalysis by such small molecule based dynamic systems can foreshadow the ancestral catalytic processes required for the onset of protometabolism. Looking beyond minimal systems that work close to equilibrium, catalytic systems and compartments under non-equilibrium conditions utilizing simple prebiotically relevant precursors have attempted to shed light on how bioenergetics played an essential role in chemical emergence of complex behaviour. Herein, we map out these recent works and progress where diverse sets of complex enzymatic transformations were demonstrated by utilizing minimal peptide based self-assembled systems. Further, we have attempted to cover the examples of peptide assemblies that could feature promiscuous activity and promote complex multistep cascade reaction networks. The review also covers a few recent examples of minimal transient catalytic assemblies under non-equilibrium conditions. This review attempts to provide a broad perspective for potentially programming functionality via rational selection of amino acid sequences leading towards minimal catalytic systems that resemble the traits of contemporary enzymes.

Access to densely functionalized spirocyclopentenonyl oxindole frameworks via aza- and carbo-Piancatelli rearrangement.

A new strategy for access to spirocyclopentenonyl oxindole frameworks is disclosed. Suitably anchored furfuryl alcohol at C3 of an oxindole was used for the aza-Piancatelli rearrangement, which furnished spirocyclic aminocyclopentenone frameworks with catalytic phosphomolybdic acid. The scope of the transformation was extended to the carbo-Piancatelli rearrangement with various indole derivatives.

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).

Applying Spring Security Framework with KeyCloak-Based OAuth2 to Protect Microservice Architecture APIs: A Case Study.

In this study, we implemented an integrated security solution with Spring Security and Keycloak open-access platform (SSK) to secure data collection and exchange over microservice architecture application programming interfaces (APIs). The adopted solution implemented the following security features: open authorization, multi-factor authentication, identity brokering, and user management to safeguard microservice APIs. Then, we extended the security solution with a virtual private network (VPN), Blowfish and crypt (Bcrypt) hash, encryption method, API key, network firewall, and secure socket layer (SSL) to build up a digital infrastructure. To accomplish and describe the adopted SSK solution, we utilized a web engineering security method. As a case study, we designed and developed an electronic health coaching (eCoach) prototype system and hosted the system in the expanded digital secure infrastructure to collect and exchange personal health data over microservice APIs. We further described our adopted security solution's procedural, technical, and practical considerations. We validated our SSK solution implementation by theoretical evaluation and experimental testing. We have compared the test outcomes with related studies qualitatively to determine the efficacy of the hybrid security solution in digital infrastructure. The SSK implementation and configuration in the eCoach prototype system has effectively secured its microservice APIs from an attack in all the considered scenarios with 100% accuracy. The developed digital infrastructure with SSK solution efficiently sustained a load of (≈)300 concurrent users. In addition, we have performed a qualitative comparison among the following security solutions: Spring-based security, Keycloak-based security, and their combination (our utilized hybrid security solution), where SSK showed a promising outcome.

HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study.

Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype.

Fluorescent Microswimmers Based on Cross-ß Amyloid Nanotubes and Divergent Cascade Networks.

Shaped through millions of years of evolution, the spatial localization of multiple enzymes in living cells employs extensive cascade reactions to enable highly coordinated multimodal functions. Herein, by utilizing a complex divergent cascade, we exploit the catalytic potential as well as templating abilities of streamlined cross-ß amyloid nanotubes to yield two orthogonal roles simultaneously. The short peptide based paracrystalline nanotube surfaces demonstrated the generation of fluorescence signals within entangled networks loaded with alcohol dehydrogenase (ADH). The nanotubular morphologies were further used to generate cascade-driven microscopic motility through surface entrapment of sarcosine oxidase (SOX) and catalase (Cat). Moreover, a divergent cascade network was initiated by upstream catalysis of the substrate molecules through the surface mutation of catalytic moieties. Notably, the resultant downstream products led to the generation of motile fluorescent microswimmers by utilizing the two sets of orthogonal properties and, thus, mimicked the complex cascade-mediated functionalities of extant biology.

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.

Digital Interventions on Healthy Lifestyle Management: Systematic Review.

BACKGROUND: Digital interventions have tremendous potential to improve well-being and health care conveyance by improving adequacy, proficiency, availability, and personalization. They have gained acknowledgment in interventions for the management of a healthy lifestyle. Therefore, we are reviewing existing conceptual frameworks, digital intervention approaches, and associated methods to identify the impact of digital intervention on adopting a healthier lifestyle. OBJECTIVE: This study aims to evaluate the impact of digital interventions on weight management in maintaining a healthy lifestyle (eg, regular physical activity, healthy habits, and proper dietary patterns). METHODS: We conducted a systematic literature review to search the scientific databases (Nature, SpringerLink, Elsevier, IEEE Xplore, and PubMed) that included digital interventions on healthy lifestyle, focusing on preventing obesity and being overweight as a prime objective. Peer-reviewed articles published between 2015 and 2020 were included. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and a framework for an evidence-based systematic review. Furthermore, we improved the review process by adopting the Rayyan tool and the Scale for the Assessment of Narrative Review Articles. RESULTS: Our initial searches identified 780 potential studies through electronic and manual searches; however, 107 articles in the final stage were cited following the specified inclusion and exclusion criteria. The identified methods for a successful digital intervention to promote a healthy lifestyle are self-monitoring, self-motivation, goal setting, personalized feedback, participant engagement, psychological empowerment, persuasion, digital literacy, efficacy, and credibility. In this study, we identified existing conceptual frameworks for digital interventions, different approaches to provide digital interventions, associated methods, and execution challenges and their impact on the promotion of healthy lifestyle management. CONCLUSIONS: This systematic literature review selected intervention principles (rules), theories, design features, ways to determine efficient interventions, and weaknesses in healthy lifestyle management from established digital intervention approaches. The results help us understand how digital interventions influence lifestyle management and overcome the existing shortcomings. It serves as a basis for further research with a focus on designing, developing, testing, and evaluating the generation of personalized lifestyle recommendations as a part of digital health interventions.

Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions With Information and Communication Technology: Systematic Review.

BACKGROUND: We systematically reviewed the literature on human coaching to identify different coaching processes as behavioral interventions and methods within those processes. We then reviewed how those identified coaching processes and the used methods can be utilized to improve an electronic coaching (eCoaching) process for the promotion of a healthy lifestyle with the support of information and communication technology (ICT). OBJECTIVE: This study aimed to identify coaching and eCoaching processes as behavioral interventions and the methods behind these processes. Here, we mainly looked at processes (and corresponding models that describe coaching as certain processes) and the methods that were used within the different processes. Several methods will be part of multiple processes. Certain processes (or the corresponding models) will be applicable for both human coaching and eCoaching. METHODS: We performed a systematic literature review to search the scientific databases EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MDPI, Google Scholar, and PubMed for publications that included personal coaching (from 2000 to 2019) and persuasive eCoaching as behavioral interventions for a healthy lifestyle (from 2014 to 2019). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework was used for the evidence-based systematic review and meta-analysis. RESULTS: The systematic search resulted in 79 publications, including 72 papers and seven books. Of these, 53 were related to behavioral interventions by eCoaching and the remaining 26 were related to human coaching. The most utilized persuasive eCoaching methods were personalization (n=19), interaction and cocreation (n=17), technology adoption for behavior change (n= 17), goal setting and evaluation (n=16), persuasion (n=15), automation (n=14), and lifestyle change (n=14). The most relevant methods for human coaching were behavior (n=23), methodology (n=10), psychology (n=9), and mentoring (n=6). Here, "n" signifies the total number of articles where the respective method was identified. In this study, we focused on different coaching methods to understand the psychology, behavioral science, coaching philosophy, and essential coaching processes for effective coaching. We have discussed how we can integrate the obtained knowledge into the eCoaching process for healthy lifestyle management using ICT. We identified that knowledge, coaching skills, observation, interaction, ethics, trust, efficacy study, coaching experience, pragmatism, intervention, goal setting, and evaluation of coaching processes are relevant for eCoaching. CONCLUSIONS: This systematic literature review selected processes, associated methods, strengths, and limitations for behavioral interventions from established coaching models. The identified methods of coaching point toward integrating human psychology in eCoaching to develop effective intervention plans for healthy lifestyle management and overcome the existing limitations of human coaching.

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study.

BACKGROUND: Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This "proof-of-concept" study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. OBJECTIVE: The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. METHODS: We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of "Semantic Sensor Network Ontology" and "Systematized Nomenclature of Medicine-Clinical Terms" to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based "Jena Framework" (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the "HermiT 1.4.3.x" ontology reasoner available in Protégé 5.x. RESULTS: The proposed ontology has been implemented for the study case "obesity." However, it can be extended further to other lifestyle diseases. "UiA eHealth Ontology" has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with "Owl Viz," and the formal representation has been used to infer a participant's health status using the "HermiT" reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. CONCLUSIONS: This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

Complex Cascade Reaction Networks via Cross ߆Amyloid Nanotubes.

Biocatalytic reaction networks integrate complex cascade transformations via spatial localization of multiple enzymes confined within the cellular milieu. Inspired by nature's ingenuity, we demonstrate that short peptide-based cross-ß amyloid nanotubular hybrids can promote different kinds of cascade reactions, from simple two-step, to multistep, to complex convergent cascades. The compartmentalizing ability of paracrystalline cross-ß phases was utilized to colocalize sarcosine oxidase (SOX) and hemin as an artificial peroxidase. Further, the catalytic potential of the amyloid nanotubes with ordered arrays of imidazoles were used as hydrolase mimic. The SOX-hemin amyloid nanohybrids featuring a single extant enzyme could integrate different logic networks to access complex digital designs with the help of three concatenated AND gates and biologically relevant stimuli as inputs.

Covalent Catalysis by Cross ß Amyloid Nanotubes.

The binding pockets of extant enzymes feature precise positioning of amino acid residues that facilitate multiple complex transformations exploiting covalent and non-covalent interactions. Reversible covalent anchoring is extensively used as an efficient tool by Nature for activating modern enzymes such as esterases and dehydratases and also for proteins like opsins for the complex process of visual phototransduction. Here we construct paracrystalline amyloid surfaces through the self-propagation of short peptides which offer binding pockets exposed with arrays of imidazoles and lysines. As covalent catalysis is utilized by modern-day enzymes, these homogeneous amyloid nanotubes exploit Schiff imine formation via the exposed lysines to efficiently hydrolyze both activated and inactivated esters. Controls where lysines were mutated with charged residues accessed similar morphologies but did not augment the rate. The designed amyloid microphases thus foreshadow the generation of binding pockets of advanced proteins and have the potential to contribute to the development of functional materials.

Exponential Corrections to Black Hole Entropy.

Using the quasilocal properties alone we show that the area spectrum of a black hole horizon must be discrete, independent of any specific quantum theory of gravity. The area spectrum is found to be half-integer spaced with values 8πγℓ_{p}^{2}j where j∈N/2. We argue that if microstate counting is carried out for quantum states residing on the horizon only, correction of exp(-A/4ℓ_{p}^{2}) over the Bekenstein-Hawking area law must arise in black hole entropy.

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and "obesity/overweight" is one of the consequences. "Obesity and overweight" are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The "World Health Organization" (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as "Center for Disease Control and Prevention (CDC)" and "National Institute for Clinical Excellence (NICE)" guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in "Kaggle" and "University of California, Irvine (UCI) database", and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting "obesity and overweight" as a study case in the future? (c) why have we used the existing "Kaggle" and "UCI" datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death.

"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

Designed Negative Feedback from Transiently Formed Catalytic Nanostructures.

Highly dynamic and complex systems of microtubules undergo a substrate-induced change of conformation that leads to polymerization. Owing to the augmented catalytic potential at the polymerized state, rapid hydrolysis of the substrate is observed, leading to catastrophe, thus realizing the out-of-equilibrium state. A simple synthetic mimic of these dynamic natural systems is presented, where similar substrate induced conformational change is observed and a transient helical morphology is accessed. Further, augmented catalytic potential of these helical nanostructures leads to rapid hydrolysis of the substrate providing negative feedback on the stability of the nanostructures and realization of an out-of-equilibrium state. This simple system, made from amino acid functionalized lipids, demonstrates a substrate-induced self-assembled state, where the fuel-to-waste conversion leads to the temporal presence of helical nanostructures.

Global mapping of MtrA-binding sites links MtrA to regulation of its targets in Mycobacterium tuberculosis.

Mycobacterium tuberculosis employs two-component systems (TCSs) for survival within its host. The TCS MtrAB is conserved among mycobacteria. The response regulator MtrA is essential in M. tuberculosis. The genome-wide chromatin immunoprecipitation (ChIP) sequencing performed in this study suggested that MtrA binds upstream of at least 45 genes of M. tuberculosis, including those involved in cell wall remodelling, stress responses, persistence and regulation of transcription. It binds to the promoter regions and regulates the peptidoglycan hydrolases rpfA and rpfC, which are required for resuscitation from dormancy. It also regulates the expression of whiB4, a critical regulator of the oxidative stress response, and relF, one-half of the toxin-antitoxin locus relFG. We have identified a new consensus 9 bp loose motif for MtrA binding. Mutational changes in the consensus sequence greatly reduced the binding of MtrA to its newly identified targets. Importantly, we observed that overexpression of a gain-of-function mutant, MtrAY102C, enhanced expression of the aforesaid genes in M. tuberculosis isolated from macrophages, whereas expression of each of these targets was lower in M. tuberculosis overexpressing a phosphorylation-defective mutant, MtrAD56N. This result suggests that phosphorylated MtrA (MtrA-P) is required for the expression of its targets in macrophages. Our data have uncovered new MtrA targets that suggest that MtrA is required for a transcriptional response that likely enables M. tuberculosis to persist within its host and emerge out of dormancy when the conditions are favourable.