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  1. Article ; Online: Susceptibility to SARS-CoV-2 Infection and Immune Responses to COVID-19 Vaccination Among Recipients of Solid Organ Transplants.

    Subramanian, Vijay

    The Journal of infectious diseases

    2023  Volume 228, Issue Suppl 1, Page(s) S34–S45

    Abstract: Solid organ transplant recipients (SOTRs) are at high risk for infections including SARS-CoV-2, primarily due to use of immunosuppressive therapies that prevent organ rejection. Furthermore, these immunosuppressants are typically associated with ... ...

    Abstract Solid organ transplant recipients (SOTRs) are at high risk for infections including SARS-CoV-2, primarily due to use of immunosuppressive therapies that prevent organ rejection. Furthermore, these immunosuppressants are typically associated with suboptimal responses to vaccination. While COVID-19 vaccines have reduced the risk of COVID-19-related morbidity and mortality in SOTRs, breakthrough infection rates and death remain higher in this population compared with immunocompetent individuals. Approaches to enhancing response in SOTRs, such as through administration of additional doses and heterologous vaccination, have resulted in increased seroresponse and antibody levels. In this article, safety and immunogenicity of mRNA COVID-19 vaccines in SOTRs are explored by dose. Key considerations for clinical practice and the current vaccine recommendations for SOTRs are discussed within the context of the dynamic COVID-19 vaccination guideline landscape. A thorough understanding of these topics is essential for determining public health and vaccination strategies to help protect immunocompromised populations, including SOTRs.
    MeSH term(s) Humans ; COVID-19/prevention & control ; COVID-19 Vaccines/adverse effects ; SARS-CoV-2 ; Vaccination ; Organ Transplantation/adverse effects ; Transplant Recipients ; Immunity ; Antibodies, Viral
    Chemical Substances COVID-19 Vaccines ; Antibodies, Viral
    Language English
    Publishing date 2023-08-04
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3019-3
    ISSN 1537-6613 ; 0022-1899
    ISSN (online) 1537-6613
    ISSN 0022-1899
    DOI 10.1093/infdis/jiad152
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  2. Article ; Online: Invited Commentary: Living Donor Liver Transplant for Liver Metastasis from Colorectal Cancer.

    Subramanian, Vijay / Dhanireddy, Kiran

    Journal of the American College of Surgeons

    2023  Volume 237, Issue 2, Page(s) 244–246

    MeSH term(s) Humans ; Liver Transplantation ; Living Donors ; Liver Neoplasms/surgery ; Liver Neoplasms/secondary ; Colorectal Neoplasms/surgery ; Treatment Outcome
    Language English
    Publishing date 2023-05-05
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1181115-8
    ISSN 1879-1190 ; 1072-7515
    ISSN (online) 1879-1190
    ISSN 1072-7515
    DOI 10.1097/XCS.0000000000000738
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  3. Book ; Online: A Strong Duality Result for Constrained POMDPs with Multiple Cooperative Agents

    Khan, Nouman / Subramanian, Vijay

    2023  

    Abstract: The work studies the problem of decentralized constrained POMDPs in a team-setting where multiple nonstrategic agents have asymmetric information. Using an extension of Sion's Minimax theorem for functions with positive infinity and results on weak- ... ...

    Abstract The work studies the problem of decentralized constrained POMDPs in a team-setting where multiple nonstrategic agents have asymmetric information. Using an extension of Sion's Minimax theorem for functions with positive infinity and results on weak-convergence of measures, strong duality is established for the setting of infinite-horizon expected total discounted costs when the observations lie in a countable space, the actions are chosen from a finite space, the constraint costs are bounded, and the objective cost is bounded from below.
    Keywords Mathematics - Optimization and Control ; Electrical Engineering and Systems Science - Systems and Control
    Publishing date 2023-03-27
    Publishing country us
    Document type Book ; Online
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  4. Book ; Online: Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space

    Adler, Saghar / Subramanian, Vijay

    2023  

    Abstract: Models of many real-life applications, such as queuing models of communication networks or computing systems, have a countably infinite state-space. Algorithmic and learning procedures that have been developed to produce optimal policies mainly focus on ... ...

    Abstract Models of many real-life applications, such as queuing models of communication networks or computing systems, have a countably infinite state-space. Algorithmic and learning procedures that have been developed to produce optimal policies mainly focus on finite state settings, and do not directly apply to these models. To overcome this lacuna, in this work we study the problem of optimal control of a family of discrete-time countable state-space Markov Decision Processes (MDPs) governed by an unknown parameter $\theta\in\Theta$, and defined on a countably-infinite state space $\mathcal X=\mathbb{Z}_+^d$, with finite action space $\mathcal A$, and an unbounded cost function. We take a Bayesian perspective with the random unknown parameter $\boldsymbol{\theta}^*$ generated via a given fixed prior distribution on $\Theta$. To optimally control the unknown MDP, we propose an algorithm based on Thompson sampling with dynamically-sized episodes: at the beginning of each episode, the posterior distribution formed via Bayes' rule is used to produce a parameter estimate, which then decides the policy applied during the episode. To ensure the stability of the Markov chain obtained by following the policy chosen for each parameter, we impose ergodicity assumptions. From this condition and using the solution of the average cost Bellman equation, we establish an $\tilde O(\sqrt{|\mathcal A|T})$ upper bound on the Bayesian regret of our algorithm, where $T$ is the time-horizon. Finally, to elucidate the applicability of our algorithm, we consider two different queuing models with unknown dynamics, and show that our algorithm can be applied to develop approximately optimal control algorithms.
    Keywords Electrical Engineering and Systems Science - Systems and Control ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-06-04
    Publishing country us
    Document type Book ; Online
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  5. Book ; Online: Backward and Forward Inference in Interacting Independent-Cascade Processes

    Khan, Nouman / Mu, Kangle / Moharrami, Mehrdad / Subramanian, Vijay

    A Scalable and Convergent Message-Passing Approach

    2023  

    Abstract: We study the problems of estimating the past and future evolutions of two diffusion processes that spread concurrently on a network. Specifically, given a known network $G=(V, \overrightarrow{E})$ and a (possibly noisy) snapshot $\mathcal{O}_n$ of its ... ...

    Abstract We study the problems of estimating the past and future evolutions of two diffusion processes that spread concurrently on a network. Specifically, given a known network $G=(V, \overrightarrow{E})$ and a (possibly noisy) snapshot $\mathcal{O}_n$ of its state taken at (a possibly unknown) time $W$, we wish to determine the posterior distributions of the initial state of the network and the infection times of its nodes. These distributions are useful in finding source nodes of epidemics and rumors -- $\textit{backward inference}$ -- , and estimating the spread of a fixed set of source nodes -- $\textit{forward inference}$. To model the interaction between the two processes, we study an extension of the independent-cascade (IC) model where, when a node gets infected with either process, its susceptibility to the other one changes. First, we derive the exact joint probability of the initial state of the network and the observation-snapshot $\mathcal{O}_n$. Then, using the machinery of factor-graphs, factor-graph transformations, and the generalized distributive-law, we derive a Belief-Propagation (BP) based algorithm that is scalable to large networks and can converge on graphs of arbitrary topology (at a likely expense in approximation accuracy).
    Keywords Computer Science - Social and Information Networks ; Mathematics - Statistics Theory ; Statistics - Machine Learning
    Publishing date 2023-10-29
    Publishing country us
    Document type Book ; Online
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  6. Article ; Online: Living Donor Liver Transplant for Hepatocellular Carcinoma.

    Subramanian, Vijay / Chapman, William

    Annals of surgical oncology

    2019  Volume 26, Issue 6, Page(s) 1594–1595

    MeSH term(s) Carcinoma, Hepatocellular/pathology ; Carcinoma, Hepatocellular/surgery ; Humans ; Liver Neoplasms/pathology ; Liver Neoplasms/surgery ; Liver Transplantation/methods ; Living Donors ; Treatment Outcome
    Language English
    Publishing date 2019-04-01
    Publishing country United States
    Document type Editorial
    ZDB-ID 1200469-8
    ISSN 1534-4681 ; 1068-9265
    ISSN (online) 1534-4681
    ISSN 1068-9265
    DOI 10.1245/s10434-019-07250-w
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  7. Article ; Online: Size Matching Deceased Donor Livers: The Tampa General Measurement System.

    Hogen, Rachel / Smith, Thasya / Jowers, Bethany / Kumar, Ambuj / Buggs, Jacentha / Chavarriaga, Alejandro / Singhal, Ashish / Reino, Diego / Subramanian, Vijay / Dhanireddy, Kiran

    Transplantation proceedings

    2024  Volume 56, Issue 2, Page(s) 348–352

    Abstract: Background: No reliable or standardized system exists for measuring the size of deceased donor livers to determine whether they will fit appropriately into intended recipients.: Methods: This retrospective, single-center study evaluated the efficacy ... ...

    Abstract Background: No reliable or standardized system exists for measuring the size of deceased donor livers to determine whether they will fit appropriately into intended recipients.
    Methods: This retrospective, single-center study evaluated the efficacy of Tampa General Hospital's size-matching protocol for consecutive, deceased donor liver transplantations between October 2021 and November 2022. Our protocol uses cross-sectional imaging at the time of organ offer to compare the donor's right hepatic lobe size with the recipient's right hepatic fossa. Outcomes were analyzed, including large-for-size syndrome, small-for-size syndrome, early allograft dysfunction, primary nonfunction, graft survival, and patient survival.
    Results: We included 171 patients in the study. The donor liver physically fit in all the patients except one whose pretransplant imaging was outdated. One patient (0.6%) had large-for-size syndrome, none had small-for-size syndrome, 15 (10%) had early allograft dysfunction, and none had primary nonfunction. There were 11 (7%) patient deaths and 11 (7%) graft failures.
    Conclusion: Our measurement system is fast and effective. It reliably predicts whether the donor liver will fit in the intended recipient and is associated with low rates of early allograft dysfunction.
    MeSH term(s) Humans ; Liver Transplantation/methods ; Retrospective Studies ; Living Donors ; Liver/diagnostic imaging ; Transplantation, Homologous ; Graft Survival ; Treatment Outcome
    Language English
    Publishing date 2024-02-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 82046-5
    ISSN 1873-2623 ; 0041-1345
    ISSN (online) 1873-2623
    ISSN 0041-1345
    DOI 10.1016/j.transproceed.2024.01.006
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  8. Book ; Online: Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning

    Kao, Hsu / Subramanian, Vijay

    2021  

    Abstract: Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length. The challenge increases greatly in the ... ...

    Abstract Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length. The challenge increases greatly in the multi-agent reinforcement learning (MARL) setting where the transition probabilities, observation kernel, and reward function are unknown. Here, we develop a general compression framework with approximate common and private state representations, based on which decentralized policies can be constructed. We derive the optimality gap of executing dynamic programming (DP) with the approximate states in terms of the approximation error parameters and the remaining time steps. When the compression is exact (no error), the resulting DP is equivalent to the one in existing work. Our general framework generalizes a number of methods proposed in the literature. The results shed light on designing practically useful deep-MARL network structures under the "centralized learning distributed execution" scheme.
    Keywords Computer Science - Machine Learning ; Computer Science - Multiagent Systems ; Mathematics - Optimization and Control
    Subject code 006
    Publishing date 2021-10-24
    Publishing country us
    Document type Book ; Online
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  9. Book ; Online: Rarest-First with Probabilistic-Mode-Suppression

    Khan, Nouman / Moharrami, Mehrdad / Subramanian, Vijay

    2022  

    Abstract: Recent studies suggested that the BitTorrent's rarest-first protocol, owing to its work-conserving nature, can become unstable in the presence of non-persistent users. Consequently, for any provably stable protocol, many peers, at some point, would have ... ...

    Abstract Recent studies suggested that the BitTorrent's rarest-first protocol, owing to its work-conserving nature, can become unstable in the presence of non-persistent users. Consequently, for any provably stable protocol, many peers, at some point, would have to be endogenously forced to hold off their file-download activity. In this work, we propose a tunable piece-selection policy that minimizes this (undesirable) requisite by combining the (work-conserving but not stabilizing) rarest-first protocol with only an appropriate share of the (non-work conserving and stabilizing) mode-suppression protocol. We refer to this policy as ``Rarest-First with Probabilistic Mode-Suppression'' or simply RFwPMS. We study RFwPMS using a stochastic abstraction of the BitTorrent network that is general enough to capture a multiple swarm setting of non-persistent users -- each swarm having its own altruistic preferences that may or may not overlap with those of other swarms. Using Lyapunov drift analysis, we show that for all kinds of inter-swarm behaviors and all arrival-rate configurations, RFwPMS is stable. Then, using the Kingman's moment bound technique, we further show that the expected steady-state sojourn time of RFwPMS is independent of the arrival-rate in the single-swarm case (under a mild additional assumption). Finally, our simulation-based performance evaluation confirms our theoretical findings and shows that the steady-state expected sojourn time is linear in the file-size (compared to our loose estimate of a polynomial with degree 6). Overall, an improved performance is observed in comparison to previously proposed stabilizing schemes like mode-suppression (MS).
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 629
    Publishing date 2022-10-31
    Publishing country us
    Document type Book ; Online
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  10. Book ; Online: Learning-based Optimal Admission Control in a Single Server Queuing System

    Cohen, Asaf / Subramanian, Vijay G. / Zhang, Yili

    2022  

    Abstract: We consider a long-term average profit maximizing admission control problem in an M/M/1 queuing system with unknown service and arrival rates. With a fixed reward collected upon service completion and a cost per unit of time enforced on customers waiting ...

    Abstract We consider a long-term average profit maximizing admission control problem in an M/M/1 queuing system with unknown service and arrival rates. With a fixed reward collected upon service completion and a cost per unit of time enforced on customers waiting in the queue, a dispatcher decides upon arrivals whether to admit the arriving customer or not based on the full history of observations of the queue-length of the system. (Naor 1969, Econometrica) showed that if all the parameters of the model are known, then it is optimal to use a static threshold policy -- admit if the queue-length is less than a predetermined threshold and otherwise not. We propose a learning-based dispatching algorithm and characterize its regret with respect to optimal dispatch policies for the full information model of Naor (1969). We show that the algorithm achieves an $O(1)$ regret when all optimal thresholds with full information are non-zero, and achieves an $O(\ln^{1+\epsilon}(N))$ regret for any specified $\epsilon>0$, in the case that an optimal threshold with full information is $0$ (i.e., an optimal policy is to reject all arrivals), where $N$ is the number of arrivals.
    Keywords Mathematics - Optimization and Control ; Statistics - Machine Learning
    Subject code 629
    Publishing date 2022-12-21
    Publishing country us
    Document type Book ; Online
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