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Article: Role of Ethanolamine Utilization and Bacterial Microcompartment Formation in

Chatterjee, Ayan / Kaval, Karan Gautam / Garsin, Danielle A

bioRxiv : the preprint server for biology

2024

Abstract: 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 ... ...

Abstract	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
Language	English
Publishing date	2024-04-11
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.19.572424
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Article ; Online: Hydrogen bonding-promoted tunable approach for access to aza-bicyclo-[3.3.0]octanes and cyclopenta[

Pramanik, Sourav / Hazra, Subhadeep / Chatterjee, Ayan / Saha, Jaideep

Chemical communications (Cambridge, England)

2024

Abstract: 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 ... ...

Abstract	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[
Language	English
Publishing date	2024-04-17
Publishing country	England
Document type	Journal Article
ZDB-ID	1472881-3
ISSN	1364-548X ; 1359-7345 ; 0009-241X
ISSN (online)	1364-548X
ISSN	1359-7345 ; 0009-241X
DOI	10.1039/d4cc01065e
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Author Correction: Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation.

Chatterjee, Ayan / Pahari, Nibedita / Prinz, Andreas / Riegler, Michael

Scientific reports

2023 Volume 13, Issue 1, Page(s) 2954

Language	English
Publishing date	2023-02-20
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-30029-9
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Article ; Online: Applying Spring Security Framework with KeyCloak-Based OAuth2 to Protect Microservice Architecture APIs: A Case Study.

Chatterjee, Ayan / Prinz, Andreas

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 5

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Computer Security ; Software
Language	English
Publishing date	2022-02-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22051703
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Article: Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling.

Chatterjee, Ayan / Prinz, Andreas

JMIR medical informatics

2022 Volume 10, Issue 6, Page(s) e33847

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2022-06-23
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/33847
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Article ; Online: Revisiting and redefining return rate for determination of the precise growth status of a species.

Paul, Ayan / Chatterjee, Neelakshi / Bhattacharya, Sabyasachi

Journal of biological physics

2023 Volume 49, Issue 2, Page(s) 195–234

Abstract: Growth curve models play an instrumental role in quantifying the growth of biological processes and have immense practical applications across all disciplines. The most popular growth metric to capture the species fitness is the "Relative Growth Rate" in ...

Abstract	Growth curve models play an instrumental role in quantifying the growth of biological processes and have immense practical applications across all disciplines. The most popular growth metric to capture the species fitness is the "Relative Growth Rate" in this domain. The different growth laws, such as exponential, logistic, Gompertz, power, and generalized Gompertz or generalized logistic, can be characterized based on the monotonic behavior of the relative growth rate (RGR) to size or time. Thus, in this case, species fitness can be determined truly through RGR. However, in nature, RGR is often non-monotonic and specifically bell-shaped, especially in the situation when a species is adapting to a new environment [1]. In this case, species may experience with the same fitness (RGR) for two different time points. The species precise growth and maturity status cannot be determined from this RGR function. The instantaneous maturity rate (IMR), as proposed by [2], helps to determine the correct maturity status of the species. Nevertheless, the metric IMR suffers from severe drawbacks; (i) IMR is intractable for all non-integer values of a specific parameter. (ii) The measure depends on a model parameter. The mathematical expression of IMR possesses the term "carrying capacity" which is unknown to the experimenter. (iii) Note that for identifying the precise growth status of a species, it is also necessary to understand its response when the populations are deflected from their equilibrium position at carrying capacity. This is an established concept in population biology, popularly known as the return rate. However, IMR does not provide information on the species deflection rate at the steady state. Hence, we propose a new growth measure connected with the species return rate, termed the "reverse of relative of relative growth rate" (henceforth, RRRGR), which is treated as a proxy for the IMR, having similar mathematical properties. Finally, we introduce a stochastic RRRGR model for specifying precise species growth and status of maturity. We illustrate the model through numerical simulations and real fish data. We believe that this study would be helpful for fishery biologists in regulating the favorable conditions of growth so that the species can reach a steady state with optimum effort.
MeSH term(s)	Animals ; Growth and Development ; Models, Biological
Language	English
Publishing date	2023-03-22
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2016734-9
ISSN	1573-0689 ; 0092-0606
ISSN (online)	1573-0689
ISSN	0092-0606
DOI	10.1007/s10867-023-09628-0
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Article ; Online: AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations

Ayan Chatterjee / Nibedita Pahari / Andreas Prinz / Michael Riegler

BMC Medical Informatics and Decision Making, Vol 23, Iss 1, Pp 1-

a meta-heuristic approach

2023 Volume 28

Abstract: 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 ... ...

Abstract	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 ...
Keywords	eCoach ; Physical activity ; Autoregression ; Time-series ; Residual error minimization ; Ensemble ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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

Chatterjee, Ayan / Pahari, Nibedita / Prinz, Andreas / Riegler, Michael

BMC medical informatics and decision making

2023 Volume 23, Issue 1, Page(s) 278

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 ...

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.
MeSH term(s)	Humans ; Artificial Intelligence ; Heuristics ; Semantics ; Algorithms ; Information Storage and Retrieval
Language	English
Publishing date	2023-12-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-023-02364-4
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Article ; Online: Fe-Catalyzed Hydroallylation of Unactivated Alkenes with Vinyl Cyclopropanes.

Mondal, Biplab / Hazra, Subhadeep / Chatterjee, Ayan / Patel, Manveer / Saha, Jaideep

Organic letters

2023 Volume 25, Issue 30, Page(s) 5676–5681

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2023-07-23
Publishing country	United States
Document type	Journal Article
ISSN	1523-7052
ISSN (online)	1523-7052
DOI	10.1021/acs.orglett.3c02105
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Article: Multi Kernel Support Vector Machine for Particulate Matter Estimation

Chatterjee, Ayan / Chatterjee, Sandipan / Mukherjee, Shankarashis

Asian journal of water, environment and pollution

2022 Volume 19, Issue 5, Page(s) 83

Language	English
Document type	Article
ZDB-ID	2204521-1
ISSN	0972-9860
Database	Current Contents Nutrition, Environment, Agriculture

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