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Article ; Online: Key players of immunosuppression in epithelial malignancies: Tumor-infiltrating myeloid cells and γδ T cells.

Tamuli, Baishali / Sharma, Sakshi / Patkar, Meena / Biswas, Subir

Cancer reports (Hoboken, N.J.)

2024 Volume 7, Issue 5, Page(s) e2066

Abstract: Background: The tumor microenvironment of solid tumors governs the differentiation of otherwise non-immunosuppressive macrophages and gamma delta (γδ) T cells into strong immunosuppressors while promoting suppressive abilities of known immunosuppressors ...

Abstract	Background: The tumor microenvironment of solid tumors governs the differentiation of otherwise non-immunosuppressive macrophages and gamma delta (γδ) T cells into strong immunosuppressors while promoting suppressive abilities of known immunosuppressors such as myeloid-derived suppressor cells (MDSCs) upon infiltration into the tumor beds. Recent findings: In epithelial malignancies, tumor-associated macrophages (TAMs), precursor monocytic MDSCs (M-MDSCs), and gamma delta (γδ) T cells often acquire strong immunosuppressive abilities that dampen spontaneous immune responses by tumor-infiltrating T cells and B lymphocytes against cancer. Both M-MDSCs and γδ T cells have been associated with worse prognosis for multiple epithelial cancers. Conclusion: Here we discuss recent discoveries on how tumor-associated macrophages and precursor M-MDSCs as well as tumor associated-γδ T cells acquire immunosuppressive abilities in the tumor beds, promote cancer metastasis, and perspectives on how possible novel interventions could restore the effective adaptive immune responses in epithelial cancers.
MeSH term(s)	Humans ; Tumor Microenvironment/immunology ; Lymphocytes, Tumor-Infiltrating/immunology ; Myeloid-Derived Suppressor Cells/immunology ; Intraepithelial Lymphocytes/immunology ; Neoplasms, Glandular and Epithelial/immunology ; Neoplasms, Glandular and Epithelial/pathology ; Immune Tolerance ; Animals ; Tumor-Associated Macrophages/immunology ; Receptors, Antigen, T-Cell, gamma-delta/metabolism ; Receptors, Antigen, T-Cell, gamma-delta/immunology ; Myeloid Cells/immunology
Chemical Substances	Receptors, Antigen, T-Cell, gamma-delta
Language	English
Publishing date	2024-05-03
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ISSN	2573-8348
ISSN (online)	2573-8348
DOI	10.1002/cnr2.2066
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Antibodies target intracellular oncodrivers through PIGR-mediated transcytosis.

Biswas, Subir / Anadon, Carmen M / Conejo-Garcia, Jose R

Genes and immunity

2023 Volume 25, Issue 1, Page(s) 85–86

MeSH term(s)	Antibodies ; Immunoglobulin A ; Transcytosis
Chemical Substances	Antibodies ; Immunoglobulin A
Language	English
Publishing date	2023-12-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2060566-3
ISSN	1476-5470 ; 1466-4879
ISSN (online)	1476-5470
ISSN	1466-4879
DOI	10.1038/s41435-023-00239-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Effects of massively multiplayer online role-playing games on psychological health and inter-personal relationships of the male youth

Mukherjee Soumabrata / Mallick Akash / Biswas Subir

Anthropological Review, Vol 84, Iss 2, Pp 163-

A cross-sectional study in sub-urban Kolkata, West Bengal, India

2021 Volume 179

Abstract: Gaming related experiences and addictive behaviours are emerging causes of adverse psycho-social health such as depression, anxiety, in adolescents and young adults around the world. Nonetheless, there remains a dearth of information relating to ... ...

Abstract	Gaming related experiences and addictive behaviours are emerging causes of adverse psycho-social health such as depression, anxiety, in adolescents and young adults around the world. Nonetheless, there remains a dearth of information relating to adolescents and young adults in India, a country which has millions of gamers.
Keywords	gaming addiction ; problematic gaming experience ; psychological health ; social relationship ; adolescents ; india ; Anthropology ; GN1-890
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	Lodz University Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation.

Roy, Avirup / Dutta, Hrishikesh / Griffith, Henry / Biswas, Subir

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 7

Abstract: A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation ...

Abstract	A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 μs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles.
MeSH term(s)	Algorithms ; Computers ; Gestures ; Machine Learning ; Neural Networks, Computer
Language	English
Publishing date	2022-03-25
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/s22072514
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks

Dutta, Hrishikesh / Bhuyan, Amit Kumar / Biswas, Subir

2023

Abstract: This article explores the concepts of online protocol synthesis using Reinforcement Learning (RL). The study is performed in the context of sensor and IoT networks with ultra low complexity wireless transceivers. The paper introduces the use of RL and ... ...

Abstract	This article explores the concepts of online protocol synthesis using Reinforcement Learning (RL). The study is performed in the context of sensor and IoT networks with ultra low complexity wireless transceivers. The paper introduces the use of RL and Multi Armed Bandit (MAB), a specific type of RL, for Medium Access Control (MAC) under different network and traffic conditions. It then introduces a novel learning based protocol synthesis framework that addresses specific difficulties and limitations in medium access for both random access and time slotted networks. The mechanism does not rely on carrier sensing, network time-synchronization, collision detection, and other low level complex operations, thus making it ideal for ultra simple transceiver hardware used in resource constrained sensor and IoT networks. Additionally, the ability of independent protocol learning by the nodes makes the system robust and adaptive to the changes in network and traffic conditions. It is shown that the nodes can be trained to learn to avoid collisions, and to achieve network throughputs that are comparable to ALOHA based access protocols in sensor and IoT networks with simplest transceiver hardware. It is also shown that using RL, it is feasible to synthesize access protocols that can sustain network throughput at high traffic loads, which is not feasible in the ALOHA-based systems. The ability of the system to provide throughput fairness under network and traffic heterogeneities are also experimentally demonstrated. Comment: In International Conference on Ubiquitous Networking (UNet 2022), Springer
Keywords	Computer Science - Networking and Internet Architecture ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	303
Publishing date	2023-01-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage

Dutta, Hrishikesh / Bhuyan, Amit Kumar / Biswas, Subir

2023

Abstract: This paper proposes a Time Division Multiple Access (TDMA) MAC slot allocation protocol with efficient bandwidth usage in wireless sensor networks and Internet of Things (IoTs). The developed protocol has two primary components: a Multi-Armed Bandits ( ... ...

Abstract	This paper proposes a Time Division Multiple Access (TDMA) MAC slot allocation protocol with efficient bandwidth usage in wireless sensor networks and Internet of Things (IoTs). The developed protocol has two primary components: a Multi-Armed Bandits (MAB)-based slot allocation mechanism for collision free transmission, and a Decentralized Defragmented Slot Backshift (DDSB) operation for improving bandwidth usage efficiency. The proposed framework is decentralized in that each node finds its transmission schedule independently without the control of any centralized arbitrator. The developed mechanism is suitable for networks with or without time synchronization, thus, making it suitable for low-complexity wireless transceivers for wireless sensor and IoT nodes. This framework is able to manage the trade-off between learning convergence time and bandwidth. In addition, it allows the nodes to adapt to topological changes while maintaining efficient bandwidth usage. The developed logic is tested for both fully-connected and arbitrary mesh networks with extensive simulation experiments. It is shown how the nodes can learn to select collision-free transmission slots using MAB. Moreover, the nodes learn to self-adjust their transmission schedules using a novel DDSB framework in order to reduce bandwidth usage.
Keywords	Computer Science - Networking and Internet Architecture ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	003
Publishing date	2023-01-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Fostering Human Learning in Sequential Decision-Making

Gupta, Piyush / Biswas, Subir / Srivastava, Vaibhav

Understanding the Role of Evaluative Feedback

2023

Abstract: Cognitive rehabilitation, STEM skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of ...

Abstract	Cognitive rehabilitation, STEM skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. We also explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes.
Keywords	Computer Science - Human-Computer Interaction
Publishing date	2023-11-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Integrated assessment of flood risk in Arial Khan floodplain of Bangladesh under changing climate and socioeconomic conditions

Chyon, Md Sadiul Alam / Biswas, Subir / Mondal, M. Shahjahan / Roy, Binata / Rahman, Afeefa

Journal of Flood Risk Management. 2023 June, v. 16, no. 2 p.e12876-

2023

Abstract: In the assessment of flood risk, the future flood hazard due to climate change is often tied to the present socioeconomic conditions. This makes an implicit assumption that the drivers of risk, other than the hazard, remain constant with time. Therefore, ...

Abstract	In the assessment of flood risk, the future flood hazard due to climate change is often tied to the present socioeconomic conditions. This makes an implicit assumption that the drivers of risk, other than the hazard, remain constant with time. Therefore, such risk assessment does not provide a realistic outlook for devising plausible mitigation strategies and plans. In this study, flood risk was assessed from an integrated perspective by considering both physical hazard, and socioeconomic exposure and vulnerability—all changing with time. The flood hazard in the Arial Khan River floodplain in the southcentral Bangladesh was simulated with a two‐dimensional hydrodynamic model, and the exposure and vulnerability were projected using different statistical techniques. Principal component analysis was conducted to assign weights to the indicators of hazard, exposure, sensitivity, and adaptive capacity. The results show that the flood depth, duration, and extent would increase from the baseline to 2080s under regional concentration pathway (RCP) 2.6 and RCP 8.5 scenarios. The sensitivity and vulnerability would decrease, reflecting an improved adaptive capacity. The low‐risk areas could increase from 62% in the baseline to 85%–91% in 2080s depending on the RCPs. The approach followed can be applied elsewhere in developing countries, particularly in riverine floodplain settings.
Keywords	climate ; climate change ; floodplains ; hydrologic models ; principal component analysis ; riparian areas ; risk ; risk assessment ; risk management ; rivers ; Bangladesh
Language	English
Dates of publication	2023-06
Publishing place	Blackwell Publishing Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	2430376-8
ISSN	1753-318X
ISSN	1753-318X
DOI	10.1111/jfr3.12876
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis

Dutta, Hrishikesh / Biswas, Subir

2021

Abstract: This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP), and solved using reinforcement learning with every network node acting as a ...

Abstract	This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP), and solved using reinforcement learning with every network node acting as a distributed learning agent. The solution components are developed step by step, starting from a single-node access scenario in which a node agent incrementally learns to control MAC layer packet loads for reining in self-collisions. The strategy is then scaled up for multi-node fully-connected scenarios by using more elaborate reward structures. It also demonstrates preliminary feasibility for more general partially connected topologies. It is shown that by learning to adjust MAC layer transmission probabilities, the protocol is not only able to attain theoretical maximum throughput at an optimal load, but unlike classical approaches, it can also retain that maximum throughput at higher loading conditions. Additionally, the mechanism is agnostic to heterogeneous loading while preserving that feature. It is also shown that access priorities of the protocol across nodes can be parametrically adjusted. Finally, it is also shown that the online learning feature of reinforcement learning is able to make the protocol adapt to time-varying loading conditions. Comment: Accepted and presented in 13th International Conference on COMmunication Systems & NETworkS (COMSNETS) 2021, Bangalore 2021. IEEE, 2021
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Electrical Engineering and Systems Science - Systems and Control
Subject code	006
Publishing date	2021-02-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers

Dutta, Hrishikesh / Biswas, Subir

2021

Abstract: This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks with low-complexity wireless transceivers. The proposed framework does not rely on complex ... ...

Abstract	This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks with low-complexity wireless transceivers. The proposed framework does not rely on complex hardware capabilities such as carrier sensing and its associated algorithmic complexities that are often not supported in wireless transceivers of low-cost and low-energy IoT devices. In this framework, the access protocols are first formulated as Markov Decision Processes (MDP) and then solved using RL. A distributed and multi-Agent RL framework is used as the basis for protocol synthesis. Distributed behavior makes the nodes independently learn optimal transmission strategies without having to rely on full network level information and direct knowledge of behavior of other nodes. The nodes learn to minimize packet collisions such that optimal throughput can be attained and maintained for loading conditions that are higher than what the known benchmark protocols (such as ALOHA) for IoT devices without complex transceivers. In addition, the nodes are observed to be able to learn to act optimally in the presence of heterogeneous loading and network topological conditions. Finally, the proposed learning approach allows the wireless bandwidth to be fairly distributed among network nodes in a way that is not dependent on such heterogeneities. Via simulation experiments, the paper demonstrates the performance of the learning paradigm and its abilities to make nodes adapt their optimal transmission strategies on the fly in response to various network dynamics.
Keywords	Computer Science - Machine Learning ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Networking and Internet Architecture ; Electrical Engineering and Systems Science - Systems and Control
Subject code	006
Publishing date	2021-04-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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