pwc-archive/papers-with-abstracts huggingface dataset:
Sens-BERT: Enabling Transferability and Re-calibration of Calibration Models for Low-cost Sensors under Reference Measurements Scarcity
Authors: M V Narayana, Kranthi Kumar Rachvarapu, Devendra Jalihal, Shiva Nagendra S M
Date: 2023-09-23 00:00:00

Low-cost sensors measurements are noisy, which limits large-scale adaptability in airquality monitoirng. Calibration is generally used to get good estimates of air quality measurements out from LCS. In order to do this, LCS sensors are typically co-located with reference stations for some duration. A calibration model is then developed to transfer the LCS sensor measurements to the reference station measurements. Existing works implement the calibration of LCS as an optimization problem in which a model is trained with the data obtained from real-time deployments; later, the trained model is employed to estimate the air quality measurements of that location. However, this approach is sensor-specific and location-specific and needs frequent re-calibration. The re-calibration also needs massive data like initial calibration, which is a cumbersome process in practical scenarios. To overcome these limitations, in this work, we propose Sens-BERT, a BERT-inspired learning approach to calibrate LCS, and it achieves the calibration in two phases: self-supervised pre-training and supervised fine-tuning. In the pre-training phase, we train Sens-BERT with only LCS data (without reference station observations) to learn the data distributional features and produce corresponding embeddings. We then use the Sens-BERT embeddings to learn a calibration model in the fine-tuning phase. Our proposed approach has many advantages over the previous works. Since the Sens-BERT learns the behaviour of the LCS, it can be transferable to any sensor of the same sensing principle without explicitly training on that sensor. It requires only LCS measurements in pre-training to learn the characters of LCS, thus enabling calibration even with a tiny amount of paired data in fine-tuning. We have exhaustively tested our approach with the Community Air Sensor Network (CAIRSENSE) data set, an open repository for LCS.
Multi-Static ISAC in Cell-Free Massive MIMO: Precoder Design and Privacy Assessment
Authors: Isabella W. G. da Silva, Diana P. M. Osorio, Markku Juntti
Date: 2023-09-23 00:00:00
Tasks: Diversity, Integrated sensing and communication, ISAC

A multi-static sensing-centric integrated sensing and communication (ISAC) network can take advantage of the cell-free massive multiple-input multiple-output infrastructure to achieve remarkable diversity gains and reduced power consumption. While the conciliation of sensing and communication requirements is still a challenge, the privacy of the sensing information is a growing concern that should be seriously taken on the design of these systems to prevent other attacks. This paper tackles this issue by assessing the probability of an internal adversary to infer the target location information from the received signal by considering the design of transmit precoders that jointly optimizes the sensing and communication requirements in a multi-static-based cell-free ISAC network. Our results show that the multi-static setting facilitates a more precise estimation of the location of the target than the mono-static implementation.
Reinforcement Learning for Robust Header Compression under Model Uncertainty
Authors: Shusen Jing, Songyang Zhang, Zhi Ding
Date: 2023-09-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Robust header compression (ROHC), critically positioned between the network and the MAC layers, plays an important role in modern wireless communication systems for improving data efficiency. This work investigates bi-directional ROHC (BD-ROHC) integrated with a novel architecture of reinforcement learning (RL). We formulate a partially observable \emph{Markov} decision process (POMDP), in which agent is the compressor, and the environment consists of the decompressor, channel and header source. Our work adopts the well-known deep Q-network (DQN), which takes the history of actions and observations as inputs, and outputs the Q-values of corresponding actions. Compared with the ideal dynamic programming (DP) proposed in the existing works, our method is scalable to the state, action and observation spaces. In contrast, DP often suffers from formidable computational complexity when the number of states becomes large due to long decompressor feedback delay and complex channel models. In addition, our method does not require prior knowledge of the transition dynamics and accurate observation dependency of the model, which are often not available in many practical applications.
How to Differentiate between Near Field and Far Field: Revisiting the Rayleigh Distance
Authors: Shu Sun, Renwang Li, Chong Han, Xingchen Liu, Liuxun Xue, Meixia Tao
Date: 2023-09-23 00:00:00

Future wireless systems are likely to adopt extremely large aperture arrays to achieve higher throughput, wider coverage, and higher spatial resolution. Conventional wireless systems predominantly operate in the far field (FF) of the radiation source. However, as the array size increases and the carrier wavelength decreases, the near field (NF) becomes nonnegligible. Since the NF and FF differ in many aspects, it is critical to identify their corresponding regions. In this article, we first provide a comprehensive overview of the existing NF-FF boundaries, then introduce a novel NF-FF demarcation method based on effective degrees of freedom (EDoF) of the channel. Since EDoF is intimately related to channel capacity, the EDoF-based border is able to characterize key channel performance more accurately than the classic Rayleigh distance and other representative benchmarks. Furthermore, we analyze the main features of the EDoF-based NF-FF boundary, provide insights into system design, and outline the associated challenges and research opportunities.
GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes
Authors: Md Masud Rana, Duc DUy Nguyen
Date: 2023-09-23 00:00:00
Tasks: Drug Design, Drug Discovery, Graph Learning

Protein-protein interactions (PPIs) are critical for various biological processes, and understanding their dynamics is essential for decoding molecular mechanisms and advancing fields such as cancer research and drug discovery. Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Predicting the impact of mutations on binding affinity is valuable but experimentally challenging. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. Machine learning-based methods, fueled by extensive PPI datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in predicting binding affinity changes. However, accurate predictions and generalization of these models across different datasets remain challenging. Geometric graph learning has emerged as a powerful approach, combining graph theory and machine learning, to capture structural features of biomolecules. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored geometric subgraphs to extract informative features, demonstrating superior performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased predictions of GGL-PPI for both direct and reverse mutations. The findings underscore the potential of GGL-PPI in accurately predicting binding free energy changes, contributing to our understanding of PPIs and aiding drug design efforts.
NeuroCADR: Drug Repurposing to Reveal Novel Anti-Epileptic Drug Candidates Through an Integrated Computational Approach
Authors: Srilekha Mamidala
Date: 2023-09-04 00:00:00
Tasks: Drug Discovery

Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. An alternative to the traditional de novo process of drug development, repurposed drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug repurposing has been performed in silico, in which databases of drugs and chemical information are used to determine interactions between target proteins and drug molecules to identify potential drug candidates. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. The proposed method displayed a high level of accuracy, outperforming nearly all in silico approaches. NeuroCADR was performed on epilepsy, a condition characterized by seizures, periods of time with bursts of uncontrolled electrical activity in brain cells. Existing drugs for epilepsy can be ineffective and expensive, revealing a need for new antiepileptic drugs. NeuroCADR identified novel drug candidates for epilepsy that can be further approved through clinical trials. The algorithm has the potential to determine possible drug combinations to prescribe a patient based on a patient's prior medical history. This project examines NeuroCADR, a novel approach to computational drug repurposing capable of revealing potential drug candidates in neurological diseases such as epilepsy.
Using a probabilistic approach to derive a two-phase model of flow-induced cell migration
Authors: Yaron Ben-Ami, Joe M. Pitt-Francis, Philip K. Maini, Helen M. Byrne
Date: 2023-09-25 00:00:00

Interstitial fluid flow is a feature of many solid tumours. In vitro Experiments have shown that such fluid flow can direct tumour cell movement upstream or downstream depending on the balance between the competing mechanisms of tensotaxis and autologous chemotaxis. In this work we develop a probabilistic-continuum, two-phase model for cell migration in response to interstitial flow. We use a kinetic description for the cell-velocity probability density function, and model the flow-dependent stimuli as forcing terms which bias cell migration upstream and downstream. Using velocity-space averaging, we reformulate the model as a system of continuum equations for the spatio-temporal evolution of the cell volume fraction and flux, in response to forcing terms which depend on the local direction and magnitude of the mechanochemical cues. We specialise our model to describe a one-dimensional cell layer subject to fluid flow. Using a combination of numerical simulations and asymptotic analysis, we delineate the parameter regime where transitions from downstream to upstream cell migration occur. As has been observed experimentally, the model predicts downstream-oriented, chemotactic migration at low cell volume fractions, and upstream-oriented, tensotactic migration at larger volume fractions. We show that the locus of the critical volume fraction, at which the system transitions from downstream to upstream migration, is dominated by the ratio of the rate of chemokine secretion and advection. Our model also predicts that, because the tensotactic stimulus depends strongly on the cell volume fraction, upstream, tensotaxis-dominated migration occurs only transiently when the cells are initially seeded, and transitions to downstream, chemotaxis-dominated migration occur at later times due to the dispersive effect of cell diffusion.
Categorization and analysis of 14 computational methods for estimating cell potency from single-cell RNA-seq data
Authors: Qingyang Wang, Zhiqian Zhai, Qiuyu Lian, Dongyuan Song, Jingyi Jessica Li
Date: 2023-09-24 00:00:00
Tasks: Benchmarking

In single-cell RNA sequencing (scRNA-seq) analysis, a key challenge is inferring hidden cellular dynamics from static cell snapshots. Various computational methods have been developed to address this, focusing on perspectives like pseudotime trajectories, RNA velocities, and estimating the differentiation potential of cells, often referred to as "cell potency." This review summarizes 14 methods for defining cell potency from scRNA-seq data, categorizing them into average-based, entropy-based, and correlation-based methods based on how they summarize gene expression levels into a potency measure. We highlight the key similarities and differences within and between these categories, offering a high-level intuition for each method. Additionally, we use unified mathematical notations to detail each method's methodology and summarize their usage complexities, including parameters, required inputs, and differences between published descriptions and software implementations. We conclude that cell potency estimation remains an open question without a consensus on the optimal approach, emphasizing the need for benchmark datasets and studies. This review aims to provide a foundation for future benchmark studies, while also addressing the broader challenge of comparing methods that infer cellular dynamics from scRNA-seq data through various perspectives, including pseudotime trajectories, RNA velocities, and cell potency.
Can the natural system of viruses reconcile the current taxonomy with an alternative classification useful to clinicians?
Authors: Alexandr A. Ezhov
Date: 2023-09-23 00:00:00

In 2022, a group of basic and clinical virologists, bioinformaticians, and evolutionary and structural biologists met in Oxford, UK, to develop a consensus on methodologies used to classify viruses. They concluded that virus taxonomy, which is hierarchical and based on evolution, is only one of many possible ways to classify viruses. This taxonomy, while satisfying the four principles they set out, faces difficulties in coordinating with other classification systems useful to clinicians, infectious disease specialists, agronomists, etc. One example discussed is the grouping of different viral strains that cause different diseases into the species Enterovirus C. Here we show that the use of a previously proposed variant of a natural virus classification system based on the use of Neural Replicator Analysis can resolve this contradiction by establishing the fine structure of the Enterovirus C species, in which strains that cause different diseases are placed in several different cells of the binomial table of viruses. A key element in enabling this is the sophisticated preprocessing of the original viral genomes using neural replicators.
SARS-CoV-2 Wastewater Genomic Surveillance: Approaches, Challenges, and Opportunities
Authors: Viorel Munteanu, Michael A. Saldana, David Dreifuss, Wenhao O. Ouyang, Jannatul Ferdous, Fatemeh Mohebbi, Jessica Schlueter, Dumitru Ciorba, Viorel Bostan, Victor Gordeev, Justin Maine Su, Nadiia Kasianchuk, Nitesh Kumar Sharma, Sergey Knyazev, Eva Aßmann, Andrei Lobiuc, Mihai Covasa, Keith A. Crandall, Nicholas C. Wu, Christopher E. Mason, Braden T Tierney, Alexander G Lucaci, Roel A. Ophoff, Cynthia Gibas, Piotr Rzymski, Pavel Skums, Helena Solo-Gabriele, Beerenwinkel Niko, Alex Zelikovsky, Martin Hölzer, Adam Smith, Serghei Mangul
Date: 2023-09-23 00:00:00

During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods.
Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI Signals on DTI Networks in Mild Cognitive Impairment
Authors: John Stewart Fabila-Carrasco, Avalon Campbell-Cousins, Mario A. Parra-Rodriguez, Javier Escudero
Date: 2023-09-21 00:00:00
Tasks: Time Series

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Recently, Permutation Entropy for Graph signals ($PEG$) has been proposed to extend PE to data residing on irregular domains. However, $PEG$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals \emph{at the vertex level}: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PEG$, can be discerned using our graph-based patterns. These are then validated in DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns in individual brain regions change as the disease progresses, demonstrating potential as a method of analyzing graph-signals at a granular scale.
Nutritional composition and bioactive compounds of mini watermelon genotypes in Bangladesh
Authors: Hasina Sultana, Sharmila Rani Mallick, Jahidul Hassan, Joydeb Gomasta, Md. Humayun Kabir, Md. Sakibul Alam Sakib, Mahmuda Hossen, Muhammad Mustakim Billah, Emrul Kayesh
Date: 2023-09-24 00:00:00

Given the present rising trends in changing lifestyle and consumption patterns, watermelon production has shifted from big to small-sized fruits having desirable quality attributes. Hence, analyses of fruit quality traits of mini watermelon are crucial to develop improved cultivars with enhanced nutritional compositions, consumer-preferred traits and extended storage life. In this context, fruit morphological and nutritional attributes of five mini watermelon genotypes namely BARI watermelon 1 (W1), BARI watermelon 2 (W2), L-32468 (W3), L-32236 (W4) and L-32394 (W5) were evaluated to appraise promising genotypes with better fruit quality. The evaluated genotypes expressed different levels of diversity for fruit physical qualitative traits including differences in shape, rind and flesh color and texture. The study also revealed significant variability among the genotypes regarding all observed fruit morphological and nutritional aspects as well as bioactive compounds. Among the studied genotypes, W1 stood out with the highest TSS as well as rind vitamin C and total phenolic content accompanied by higher fruit weight and thick rind. On the other hand, W3 genotype was featured with higher amount of \b{eta} carotene, total phenolic and flavonoid content in its flesh along with rind enriched with \b{eta} carotene and minerals. However, comparatively higher amount of sugar and total flavonoid content was recorded in the rind of W5 genotype. Therefore, W1 and W3 could be exploited for table purpose and using in breeding program to develop mini watermelon cultivars with more attractive fruits in terms of quality acceptance and nutritional value in Bangladesh. Furthermore, rind of BARI watermelon 1 and L-32394 could be considered as the potential cheap source of bioactive compounds to be used for dietary and industrial purpose which would decrease the solid waste in the environment.
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth
Authors: Jamie Porthiyas, Daniel Nussey, Catherine A. A. Beauchemin, Donald C. Warren, Christian Quirouette, Kathleen P. Wilkie
Date: 2023-09-23 00:00:00
Tasks: Bayesian Inference, Model Selection, parameter estimation

Mathematical models (MMs) are a powerful tool to help us understand and predict the dynamics of tumour growth under various conditions. In this work, we use 5 MMs with an increasing number of parameters to explore how certain (often overlooked) decisions in estimating parameters from data of experimental tumour growth affect the outcome of the analysis. In particular, we propose a framework for including tumour volume measurements that fall outside the upper and lower limits of detection, which are normally discarded. We demonstrate how excluding censored data results in an overestimation of the initial tumour volume and the MM-predicted tumour volumes prior to the first measurements, and an underestimation of the carrying capacity and the MM-predicted tumour volumes beyond the latest measurable time points. We show in which way the choice of prior for the MM parameters can impact the posterior distributions, and illustrate that reporting the highest-likelihood parameters and their 95% credible interval can lead to confusing or misleading interpretations. We hope this work will encourage others to carefully consider choices made in parameter estimation and to adopt the approaches we put forward herein.