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Article: High Content Analysis Across Signaling Modulation Treatments for Subcellular Target Identification Reveals Heterogeneity in Cellular Response.

Biswas, Sayan

Frontiers in cell and developmental biology

2021 Volume 8, Page(s) 594750

Abstract: Cellular phenotypes on bioactive compound treatment are a result of the downstream targets of the respective treatment. Here, a computational approach is taken for downstream subcellular target identification to understand the basis of the cellular ... ...

Abstract	Cellular phenotypes on bioactive compound treatment are a result of the downstream targets of the respective treatment. Here, a computational approach is taken for downstream subcellular target identification to understand the basis of the cellular response. This response is a readout of cellular phenotypes captured from cell-painting-based light microscopy images. The readouts are morphological profiles measured simultaneously from multiple cellular organelles. Cellular profiles generated from roughly 270 diverse treatments on bone cancer cell line form the high content screen used in this study. Phenotypic diversity across these treatments is demonstrated, depending on the image-based phenotypic profiles. Furthermore, the impact of the treatments on specific organelles and associated organelle sensitivities are determined. This revealed that endoplasmic reticulum has a higher likelihood of being targeted. Employing multivariate regression overall cellular response is predicted based on fewer organelle responses. This prediction model is validated against 1,000 new candidate compounds. Different compounds despite driving specific modulation outcomes elicit a varying effect on cellular integrity. Strikingly, this confirms that phenotypic responses are not conserved that enables quantification of signaling heterogeneity. Agonist-antagonist signaling pairs demonstrate switch of the targets in the cascades hinting toward evidence of signaling plasticity. Quantitative analysis of the screen has enabled the identification of these underlying signatures. Together, these image-based profiling approaches can be employed for target identification in drug and diseased states and understand the hallmark of cellular response.
Language	English
Publishing date	2021-01-07
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2737824-X
ISSN	2296-634X
ISSN	2296-634X
DOI	10.3389/fcell.2020.594750
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Article ; Online: Hospice Care, Disease Burden Prediction, and the “Unbefriended” Patient

Biswas, Sayan

International Journal of Recent Surgical and Medical Sciences ; ISSN 2455-7420 2455-0949

2020

Abstract: Abstract The COVID-19 pandemic has created a global mayhem. Patient care has been severely affected. Patients with chronic conditions such as dialysis-dependent end stage renal diseases are finding it challenging to seek out continuous treatment. Ground ... ...

Abstract	Abstract The COVID-19 pandemic has created a global mayhem. Patient care has been severely affected. Patients with chronic conditions such as dialysis-dependent end stage renal diseases are finding it challenging to seek out continuous treatment. Ground breaking work by Dr. Ravikanth Yalamuri of Methodist TexSan Hospital is paving new pathways for tackling patient volume in hospital settings, especially in hospices. In particular, Dr. Yalamuri has provided succinct insights into empathetic caring for the unbefriended patients. These elderly orphans have major issues with cognition and require high levels of social aids and support, especially when they are admitted to the hospital or when they have a terminal illness. Dr. Yalamuri has highlighted the utility of operations research in these multifaceted disease model prediction.
Keywords	covid19
Language	English
Publisher	Georg Thieme Verlag KG
Publishing country	de
Document type	Article ; Online
DOI	10.1055/s-0040-1716803
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Hospice Care, Disease Burden Prediction, and the “Unbefriended” Patient

Biswas, Sayan

International Journal of Recent Surgical and Medical Sciences

2020 Volume 6, Issue 02, Page(s) 77–79

Abstract: The COVID-19 pandemic has created a global mayhem. Patient care has been severely affected. Patients with chronic conditions such as dialysis-dependent end stage renal diseases are finding it challenging to seek out continuous treatment. Ground breaking ... ...

Abstract	The COVID-19 pandemic has created a global mayhem. Patient care has been severely affected. Patients with chronic conditions such as dialysis-dependent end stage renal diseases are finding it challenging to seek out continuous treatment. Ground breaking work by Dr. Ravikanth Yalamuri of Methodist TexSan Hospital is paving new pathways for tackling patient volume in hospital settings, especially in hospices. In particular, Dr. Yalamuri has provided succinct insights into empathetic caring for the unbefriended patients. These elderly orphans have major issues with cognition and require high levels of social aids and support, especially when they are admitted to the hospital or when they have a terminal illness. Dr. Yalamuri has highlighted the utility of operations research in these multifaceted disease model prediction.
Keywords	unbefriended ; chronic medical condition ; comorbidity ; cognitive decline ; resource allocation
Language	English
Publishing date	2020-09-22
Publisher	Thieme Medical and Scientific Publishers Pvt. Ltd.
Publishing place	Stuttgart ; New York
Document type	Article
ISSN	2455-0949 ; 2455-7420
ISSN (online)	2455-0949
ISSN	2455-7420
DOI	10.1055/s-0040-1716803
Database	Thieme publisher's database

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Book ; Online: PRIVIC

Biswas, Sayan / Palamidessi, Catuscia

A privacy-preserving method for incremental collection of location data

2022

Abstract: With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the ... ...

Abstract	With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability (geo-ind). However, geo-ind alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geo-ind. In this paper, we focus on a mechanism based on the Blahut-Arimoto algorithm (BA) from the rate-distortion theory. We show that BA, in addition to providing geo-ind, enforces an elastic metric that mitigates the problem of isolation. Furthermore, BA provides an optimal trade-off between information leakage and quality of service. We then proceed to study the utility of BA in terms of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we de-noise the reported data by applying the iterative Bayesian update (IBU), an instance of the expectation-maximization method. It turns out that BA and IBU are dual to each other, and as a result, they work well together, in the sense that the statistical utility of BA is quite good and better than LAP for high privacy levels. Exploiting these properties of BA and IBU, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. We illustrate the soundness and functionality of our method both analytically and with experiments.
Keywords	Computer Science - Cryptography and Security ; Computer Science - Information Theory ; Mathematics - Probability
Subject code	303
Publishing date	2022-06-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Advancing Personalized Federated Learning

Galli, Filippo / Jung, Kangsoo / Biswas, Sayan / Palamidessi, Catuscia / Cucinotta, Tommaso

Group Privacy, Fairness, and Beyond

2023

Abstract: Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by ... ...

Abstract	Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minimizing a cost function over their local inputs. FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown vulnerable to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups than to others. In this paper, we address the triadic interaction among personalization, privacy guarantees, and fairness attained by models trained within the FL framework. Differential privacy and its variants have been studied and applied as cutting-edge standards for providing formal privacy guarantees. However, clients in FL often hold very diverse datasets representing heterogeneous communities, making it important to protect their sensitive information while still ensuring that the trained model upholds the aspect of fairness for the users. To attain this objective, a method is put forth that introduces group privacy assurances through the utilization of $d$-privacy (aka metric privacy). $d$-privacy represents a localized form of differential privacy that relies on a metric-oriented obfuscation approach to maintain the original data's topological distribution. This method, besides enabling personalized model training in a federated approach and providing formal privacy guarantees, possesses significantly better group fairness measured under a variety of standard metrics than a global model trained within a classical FL template. Theoretical justifications for the applicability are provided, as well as experimental validation on real-world datasets to illustrate the working of the proposed method.
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security ; Computer Science - Computers and Society ; Statistics - Machine Learning
Subject code	303
Publishing date	2023-09-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Tight Differential Privacy Blanket for Shuffle Model

Biswas, Sayan / Jung, Kangsoo / Palamidessi, Catuscia

2022

Abstract: With the recent bloom of focus on digital economy, the importance of personal data has seen a massive surge of late. Keeping pace with this trend, the model of data market is starting to emerge as a process to obtain high-quality personal information in ... ...

Abstract	With the recent bloom of focus on digital economy, the importance of personal data has seen a massive surge of late. Keeping pace with this trend, the model of data market is starting to emerge as a process to obtain high-quality personal information in exchange of incentives. To have a formal guarantee to protect the privacy of the sensitive data involved in digital economy, \emph{differential privacy (DP)} is the go-to technique, which has gained a lot of attention by the community recently. However, it is essential to optimize the privacy-utility trade-off by ensuring the highest level of privacy protection is ensured while preserving the utility of the data. In this paper, we theoretically derive sufficient and necessary conditions to have tight $(\epsilon,\,\delta)$-DP blankets for the shuffle model, which, to the best of our knowledge, have not been proven before, and, thus, characterize the best possible DP protection for shuffle models which can be implemented in data markets to ensure privacy-preserving trading of digital economy. Comment: Extended Abstract
Keywords	Computer Science - Cryptography and Security
Subject code	330
Publishing date	2022-05-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Impact of Sampling on Locally Differentially Private Data Collection

Biswas, Sayan / Cormode, Graham / Maple, Carsten

2022

Abstract: With the recent bloom of data, there is a huge surge in threats against individuals' private information. Various techniques for optimizing privacy-preserving data analysis are at the focus of research in the recent years. In this paper, we analyse the ... ...

Abstract	With the recent bloom of data, there is a huge surge in threats against individuals' private information. Various techniques for optimizing privacy-preserving data analysis are at the focus of research in the recent years. In this paper, we analyse the impact of sampling on the utility of the standard techniques of frequency estimation, which is at the core of large-scale data analysis, of the locally deferentially private data-release under a pure protocol. We study the case in a distributed environment of data sharing where the values are reported by various nodes to the central server, e.g., cross-device Federated Learning. We show that if we introduce some random sampling of the nodes in order to reduce the cost of communication, the standard existing estimators fail to remain unbiased. We propose a new unbiased estimator in the context of sampling each node with certain probability and compute various statistical summaries of the data using it. We propose a way of sampling each node with personalized sampling probabilities as a step to further generalisation, which leads to some interesting open questions in the end. We analyse the accuracy of our proposed estimators on synthetic datasets to gather some insight on the trade-off between communication cost, privacy, and utility.
Keywords	Computer Science - Cryptography and Security
Subject code	310
Publishing date	2022-06-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response

Biswas, Sayan / Jung, Kangsoo / Palamidessi, Catuscia

2022

Abstract: Most differentially private (DP) algorithms assume a central model in which a reliable third party inserts noise to queries made on datasets, or a local model where the users locally perturb their data. However, the central model is vulnerable via a ... ...

Abstract	Most differentially private (DP) algorithms assume a central model in which a reliable third party inserts noise to queries made on datasets, or a local model where the users locally perturb their data. However, the central model is vulnerable via a single point of failure, and in the local model, the utility of the data deteriorates significantly. The recently proposed shuffle model is an intermediate framework between the central and the local paradigms where the users send their locally privatized data to a server where messages are shuffled, effacing the link between a privatized message and the corresponding user, giving a better trade-off between privacy and utility than the local model, as its privacy gets amplified without adding more noise. In this paper, we theoretically derive the strictest known bound for DP guarantee for the shuffle models with $k$-Randomized Response local randomizers. There on, we focus on the utility of the shuffle model for histogram queries. Leveraging on the matrix inversion method, which is used to approximate the original distribution from the empirical one produced by the $k$-RR mechanism, we de-noise the histogram produced by the shuffle model to evaluate the total variation distance of the resulting histogram from the true one, which we regard as the measure of utility of the privacy mechanism. We perform experiments on both synthetic and real data to compare the privacy-utility trade-off of the shuffle model with that of the central one privatized by adding the state-of-the-art Gaussian noise to each bin. Although the experimental results stay consistent with the literature that favour the central model, we see that, the difference in statistical utilities between the central and the shuffle models is very small, showing that they are almost comparable under the same level of DP.
Keywords	Computer Science - Cryptography and Security
Subject code	330
Publishing date	2022-05-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Ignition of ultra-lean premixed hydrogen/air by an impinging hot jet

Biswas, Sayan / Qiao, Li

Applied energy. 2018 Oct. 15, v. 228

2018

Abstract: The ignition characteristics of a hot turbulent jet impinging on a flat plate surrounded by an ultra-lean premixed H2/air was studied both experimentally and numerically. The hot turbulent jet was generated by burning a small quantity of stoichiometric ... ...

Abstract	The ignition characteristics of a hot turbulent jet impinging on a flat plate surrounded by an ultra-lean premixed H2/air was studied both experimentally and numerically. The hot turbulent jet was generated by burning a small quantity of stoichiometric H2/air mixture in a separate small volume called the pre-chamber. The higher pressure resulting from pre-chamber combustion pushed the combustion products into the main chamber through a small nozzle (0.75–4.5 mm in diameter) in the form of a hot turbulent jet, which then impinged on a flat plate. Six different plates with varying impinging heights and angles were used. Two important parameters controlling the impinging characteristics of the jet, the ratio of the impinging distance to the nozzle diameter, H/D and the impinging angle, θ were examined. Simultaneous high-speed Schlieren and OH∗ chemiluminescence imaging were applied to visualize the jet penetration/impinging and ignition process inside the main combustion chamber. Results illustrate the existence of two distinct types of ignition mechanisms. If the impinging distance is short and the hot turbulent jet hits the plate with high enough momentum, the temperature increases around the stagnation point and ignition starts from this impinging region. However, if the impinging distance is long, the hot turbulent jet mixes with the ambient unburned H2/air in the main chamber and ignites the mixture at the upstream from the plate. For such type of ignition, the impinging plate has a minimum role on main chamber ignition. Employing the stagnation point ignition, a leaner limit of H2/air in the main chamber was achieved. Numerical modeling of the turbulent hot jet impingement process was carried out to explain the impinging jet ignition mechanism. It was found that H/D ratio was the controlling parameter between the two ignition mechanisms. The limiting H/D ratio was found to be 21.6, below which ignition occurred via jet impingement. Unlike the H/D ratio, the impinging angle did not affect the ignition mechanism; however, it affected the main chamber burn time.
Keywords	air ; burning ; chemiluminescence ; combustion ; hydrogen ; image analysis ; mathematical models ; momentum ; temperature
Language	English
Dates of publication	2018-1015
Size	p. 954-964.
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	2000772-3
ISSN	0306-2619
ISSN	0306-2619
DOI	10.1016/j.apenergy.2018.06.102
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Development and Validation of the Chronic Subdural HematOma Referral oUtcome Prediction Using Statistics (CHORUS) Score: A Retrospective Study at a National Tertiary Center.

Biswas, Sayan / MacArthur, Joshua / Sarkar, Ved / Thompson, Helena / Saleemi, Mohammad / George, K Joshi

World neurosurgery

2022 Volume 170, Page(s) e724–e736

Abstract: Background: Chronic subdural hematoma (CSDH) is a common neurosurgical condition with an increasing rate of patient referrals. CSDH referral decision-making is a subjective clinical process, and our aim was to develop a simple scoring system capable of ... ...

Abstract	Background: Chronic subdural hematoma (CSDH) is a common neurosurgical condition with an increasing rate of patient referrals. CSDH referral decision-making is a subjective clinical process, and our aim was to develop a simple scoring system capable of acting as a decision support tool aiding referral triage. Methods: A single tertiary center retrospective case series analysis of all CSDH patient referrals from 2015 to 2020 was conducted. Ten independent variables used in the referral process were analyzed to predict the binary outcome of either accepting or rejecting the CSDH referral. Following feature selection analysis, a multivariable scoring system was developed and evaluated. Results: 1500 patient referrals were included. Stepwise multivariable logistic and least absolute shrinkage and selection operator regression identified age <85 years, the presence of headaches, dementia, motor weakness, radiological midline shift, a reasonable premorbid quality of life, and a large sized hematoma to be statistically significant predictors of CSDH referral acceptance (P <0.04). These variables derived a scoring system ranging from -9 to 6 with an optimal cut-off for referral acceptance at any score >1 (P <0.0001). This scoring system demonstrated optimal calibration (brier score loss = 0.0552), with a score >1 predicting referral acceptance with an area under the curve of 0.899 (0.876-0.922), a sensitivity of 83.838% (76.587-91.089), and a specificity of 96.000% (94.080-97.920). Conclusions: Certain patient specific clinical and radiological characteristics can predict the acceptance or rejection of a CSDH referral. Considering the precision of this scoring system, it has the potential for effectively triaging CSDH referrals.
MeSH term(s)	Humans ; Aged, 80 and over ; Retrospective Studies ; Hematoma, Subdural, Chronic/diagnostic imaging ; Hematoma, Subdural, Chronic/surgery ; Quality of Life ; Prognosis ; Referral and Consultation ; Recurrence
Language	English
Publishing date	2022-11-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2534351-8
ISSN	1878-8769 ; 1878-8750
ISSN (online)	1878-8769
ISSN	1878-8750
DOI	10.1016/j.wneu.2022.11.097
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