pwc-archive/papers-with-abstracts huggingface dataset:
TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs
Authors: Junkai Ji, Wei Mao, Feng Xi, Shengyao Chen
Date: 2023-09-15 00:00:00
Tasks: Quantization

Direction of arrival (DOA) estimation employing low-resolution analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing problem, particularly with the rise in popularity of large-scale arrays. The substantial quantization distortion complicates the extraction of signal and noise subspaces from the quantized data. To address this issue, this paper introduces a novel approach that leverages the Transformer model to aid the subspace estimation. In this model, multiple snapshots are processed in parallel, enabling the capture of global correlations that span them. The learned subspace empowers us to construct the MUSIC spectrum and perform gridless DOA estimation using a neural network-based peak finder. Additionally, the acquired subspace encodes the vital information of model order, allowing us to determine the exact number of sources. These integrated components form a unified algorithmic framework referred to as TransMUSIC. Numerical results demonstrate the superiority of the TransMUSIC algorithm, even when dealing with one-bit quantized data. The results highlight the potential of Transformer-based techniques in DOA estimation.
Efficient Rotating Synthetic Aperture Radar Imaging via Robust Sparse Array Synthesis
Authors: Wei Zhao, Cai Wen, Quan Yuan, Rong Zheng
Date: 2023-09-14 00:00:00

Rotating Synthetic Aperture Radar (ROSAR) can generate a 360$^\circ$ image of its surrounding environment using the collected data from a single moving track. Due to its non-linear track, the Back-Projection Algorithm (BPA) is commonly used to generate SAR images in ROSAR. Despite its superior imaging performance, BPA suffers from high computation complexity, restricting its application in real-time systems. In this paper, we propose an efficient imaging method based on robust sparse array synthesis. It first conducts range-dimension matched filtering, followed by azimuth-dimension matched filtering using a selected sparse aperture and filtering weights. The aperture and weights are computed offline in advance to ensure robustness to array manifold errors induced by the imperfect radar rotation. We introduce robust constraints on the main-lobe and sidelobe levels of filter design. The resultant robust sparse array synthesis problem is a non-convex optimization problem with quadratic constraints. An algorithm based on feasible point pursuit and successive convex approximation is devised to solve the optimization problem. Extensive simulation study and experimental evaluations using a real-world hardware platform demonstrate that the proposed algorithm can achieve image quality comparable to that of BPA, but with a substantial reduction in computational time up to 90%.
Balance Measures Derived from Insole Sensor Differentiate Prodromal Dementia with Lewy Bodies
Authors: Masatomo Kobayashi, Yasunori Yamada, Kaoru Shinkawa, Miyuki Nemoto, Miho Ota, Kiyotaka Nemoto, Tetsuaki Arai
Date: 2023-09-11 00:00:00
Tasks: Diversity

Dementia with Lewy bodies is the second most common type of neurodegenerative dementia, and identification at the prodromal stage$-$i.e., mild cognitive impairment due to Lewy bodies (MCI-LB)$-$is important for providing appropriate care. However, MCI-LB is often underrecognized because of its diversity in clinical manifestations and similarities with other conditions such as mild cognitive impairment due to Alzheimer's disease (MCI-AD). In this study, we propose a machine learning-based automatic pipeline that helps identify MCI-LB by exploiting balance measures acquired with an insole sensor during a 30-s standing task. An experiment with 98 participants (14 MCI-LB, 38 MCI-AD, 46 cognitively normal) showed that the resultant models could discriminate MCI-LB from the other groups with up to 78.0% accuracy (AUC: 0.681), which was 6.8% better than the accuracy of a reference model based on demographic and clinical neuropsychological measures. Our findings may open up a new approach for timely identification of MCI-LB, enabling better care for patients.
Exact description of limiting SIR and SEIR dynamics on locally tree-like graphs
Authors: Juniper Cocomello, Kavita Ramanan
Date: 2023-09-16 00:00:00

We study the Susceptible-Infected-Recovered (SIR) and the Susceptible-Exposed-Infected-Recovered (SEIR) models of epidemics, with possibly time-varying rates, on a class of networks that are locally tree-like, which includes sparse Erd\H{o}s-R\`enyi random graphs, random regular graphs, and other configuration models. We identify tractable systems of ODEs that exactly describe the dynamics of the SIR and SEIR processes in a suitable asymptotic regime in which the population size goes to infinity. Moreover, in the case of constant recovery and infection rates, we characterize the outbreak size as the unique zero of an explicit functional. We use this to show that a (suitably defined) mean-field prediction always overestimates the outbreak size, and that the outbreak sizes for SIR and SEIR processes with the same initial condition and constant infection and recovery rates coincide. In contrast, we show that the outbreak sizes for SIR and SEIR processes with the same time-varying infection and recovery rates can in general be quite different. We also demonstrate via simulations the efficacy of our approximations for populations of moderate size.
What comes next? response times are affected by mispredictions in a stochastic game
Authors: Paulo Roberto Cabral-Passos, Antonio Galves, Jesus Enrique Garcia, Claudia Domingues Vargas
Date: 2023-09-18 00:00:00

Acting as a goalkeeper in a video-game, a participant is asked to predict the successive choices of the penalty taker. The sequence of choices of the penalty taker is generated by a stochastic chain with memory of variable length. It has been conjectured that the probability distribution of the response times is a function of the specific sequence of past choices governing the algorithm used by the penalty taker to make his choice at each step. We found empirical evidence that besides this dependence, the distribution of the response times depends also on the success or failure of the previous prediction made by the participant. Moreover, we found statistical evidence that this dependence propagates up to two steps forward after the prediction failure.
Analysis of a stochastic SIR model with media effects
Authors: Jiaxun Li, Yanni Xiao
Date: 2023-09-15 00:00:00

In this study, we investigate a stochastic SIR model with media effects. The uniqueness and the existence of a global positive solution are studied. The sufficient conditions of extinction and persistence of the disease are established. We obtain the basic reproduction number $R_0^S$ for stochastic system, which can act as the threshold given small environmental noise. Note that large noise can induce the disease extinction with probability of 1, suggesting that environmental noises can not be ignored when investigating threshold dynamics. Further, inclusion of media induced behaviour changes does not affect the threshold itself, which is similar to the conclusion of the deterministic models. However, numerical simulations suggest that media impacts induce the disease infection decline.
Topological and geometric analysis of cell states in single-cell transcriptomic data
Authors: Tram Huynh, Zixuan Cang
Date: 2023-09-14 00:00:00
Tasks: Clustering

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data where the clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions which often rely on prior clustering results. Present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently displays complex high-dimensional structures. Here, we present a tool, scGeom for exploiting the multiscale and multidimensional structures in scRNA-seq data by inspecting the geometry via graph curvature and topology via persistent homology of both cell networks and gene networks. We demonstrate the utility of these structural features for reflecting biological properties and functions in several applications where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and developmental potency of cells. We additionally illustrate that the structural characteristics can improve the classification of cell types.
Current and future directions in network biology
Authors: Marinka Zitnik, Michelle M. Li, Aydin Wells, Kimberly Glass, Deisy Morselli Gysi, Arjun Krishnan, T. M. Murali, Predrag Radivojac, Sushmita Roy, Anaïs Baudot, Serdar Bozdag, Danny Z. Chen, Lenore Cowen, Kapil Devkota, Anthony Gitter, Sara Gosline, Pengfei Gu, Pietro H. Guzzi, Heng Huang, Meng Jiang, Ziynet Nesibe Kesimoglu, Mehmet Koyuturk, Jian Ma, Alexander R. Pico, Nataša Pržulj, Teresa M. Przytycka, Benjamin J. Raphael, Anna Ritz, Roded Sharan, Yang shen, Mona Singh, Donna K. Slonim, Hanghang Tong, Xinan Holly Yang, Byung-Jun Yoon, Haiyuan Yu, Tijana Milenković
Date: 2023-09-15 00:00:00
Tasks: Data Integration, Diversity

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology.
Application of Novel PACS-based Informatics Platform to Identify Imaging Based Predictors of CDKN2A Allelic Status in Glioblastomas
Authors: Niklas Tillmanns, Jan Lost, Joanna Tabor, Sagar Vasandani, Shaurey Vetsa, Neelan Marianayagam, Kanat Yalcin, E. Zeynep Erson-Omay, Marc von Reppert, Leon Jekel, Sara Merkaj, Divya Ramakrishnan, Arman Avesta, Irene Dixe de Oliveira Santo, Lan Jin, Anita Huttner, Khaled Bousabarah, Ichiro Ikuta, MingDe Lin, Sanjay Aneja, Bernd Turowski, Mariam Aboian, Jennifer Moliterno
Date: 2023-09-18 00:00:00
Tasks: Prognosis

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64% vs. 30%, p=0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI:1.5-12.1; p<0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.
Epileptic seizure forecasting with long short-term memory (LSTM) neural networks
Authors: Daniel E. Payne, Jordan D. Chambers, Anthony Burkitt, Mark J. Cook, Levin Kuhlman, Dean R. Freestone, David B. Grayden
Date: 2023-09-18 00:00:00
Tasks: EEG

Objective: Forecasting epileptic seizures can reduce uncertainty for patients and allow preventative actions. While many models can predict the occurrence of seizures from features of the EEG, few models incorporate changes in features over time. Long Short-Term Memory (LSTM) neural networks are a machine learning architecture that can display temporal dynamics due to the recurrent connections. In this paper, we used LSTMs to monitor changes in EEG features over time to improve the accuracy of seizure forecasts and to alter the time window of the forecast. Methods: Long-term intracranial EEG recordings from eight patients from the NeuroVista dataset were used. A Fourier transform of 1-minute segments of EEG was fed into a Convolutional Neural Network (CNN). The outputs from the CNN were input to three different LSTM models at different time intervals: 1 minute, 1 hour and 1 day. The LSTM model outputs were used to predict seizure onset within a time window. The prediction and start of the time window were separated by the same length of time as the window. Window sizes tested included 2, 4, 10, 20 and 40 minutes. Results and Conclusion: Our model forecast seizure onsets well above a random predictor. Compared to other models using the same dataset, our model performed better for some patients and worse for others. Monitoring the change in EEG features over time allowed our model to produce good results over a range of different window sizes, which is an improvement on previous models and raises the possibility of altering the forecast to meet individual patient needs. Furthermore, a window size of 40 minutes provides a potential intervention time of 40 minutes, which is the first time an intervention time of more than 5 minutes have been forecast using long-term EEG recordings.
Evolution of cooperation in deme-structured populations on graphs
Authors: Alix Moawad, Alia Abbara, Anne-Florence Bitbol
Date: 2023-09-18 00:00:00

Understanding how cooperation can evolve in populations despite its cost to individual cooperators is an important challenge. Models of spatially structured populations with one individual per node of a graph have shown that cooperation, modeled via the prisoner's dilemma, can be favored by natural selection. These results depend on microscopic update rules, which determine how birth, death and migration on the graph are coupled. Recently, we developed coarse-grained models of spatially structured populations on graphs, where each node comprises a well-mixed deme, and where migration is independent from division and death, thus bypassing the need for update rules. Here, we study the evolution of cooperation in these models in the rare migration regime, within the prisoner's dilemma. We find that cooperation is not favored by natural selection in these coarse-grained models on graphs where overall deme fitness does not directly impact migration from a deme. This is due to a separation of scales, whereby cooperation occurs at a local level within demes, while spatial structure matters between demes.
Where do free-ranging dogs rest? A population level study reveals hidden patterns in resting site choice
Authors: Sourabh Biswas, Kalyan Ghosh, Kaushikee Sarkar, Anindita Bhadra
Date: 2023-09-16 00:00:00

Free-ranging dogs (FRDs) in human-dominated areas encounter obstacles such as noise, pollution, limited food sources, and anthropogenic disturbance while resting. Since FRDs have survived as a population in India, as in many other parts of the Global South for centuries, they provide a unique opportunity to study adaptation of animals to the human-dominated urban landscape. We documented factors impacting resting behaviour and site preferences in three states of India, for 284 dogs, leading to 6047 observations over 3 years. 7 physical parameters of the resting sites, along with the biological factors like mating and pup-rearing and time of day affected their choice of resting sites. The frequency-rank distribution of the unique combinations in which the parameters were selected followed a Power law distribution, which suggests underlying biological reasons for the observed preferences. Further, 3 of these parameters showed maximum consistency of choice in terms of the sub-parameters selected, explaining 30% of the observations. FRDs prefer to rest close to their resource sites within the territory, at a place that enabled maximum visibility of the surroundings. They chose such sites in the core of the territory for sleeping. At other times, they chose such sites away from the core, and were less restive, thus allowing for immediate response in case of intrusion or threat. They generally avoided anthropogenic disturbance for sleeping, and preferred areas with shade.Incorporating these aspects into urban management plans can promote human-dog cooperation and reduce situations of conflict. We envisage more inclusive urban areas in the future, that can allow for co-existence of the humans and their oldest companions in the commensal relationship that has been maintained for hundreds of generations of dogs in this part of the world.
The Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design
Authors: Chase Armer, Hassan Kane, Dana Cortade, Dave Estell, Adil Yusuf, Radhakrishna Sanka, Henning Redestig, TJ Brunette, Pete Kelly, Erika DeBenedictis
Date: 2023-09-18 00:00:00
Tasks: Benchmarking

The grand challenge of protein engineering is the development of computational models that can characterize and generate protein sequences for any arbitrary function. However, progress today is limited by lack of 1) benchmarks with which to compare computational techniques, 2) large datasets of protein function, and 3) democratized access to experimental protein characterization. Here, we introduce the Protein Engineering Tournament, a fully-remote, biennial competition for the development and benchmarking of computational methods in protein engineering. The tournament consists of two rounds: a first in silico round, where participants use computational models to predict biophysical properties for a set of protein sequences, and a second in vitro round, where participants are challenged to design new protein sequences, which are experimentally measured with open-source, automated methods to determine a winner. At the Tournament's conclusion, the experimental protocols and all collected data will be open-sourced for continued benchmarking and advancement of computational models. We hope the Protein Engineering Tournament will provide a transparent platform with which to evaluate progress in this field and mobilize the scientific community to conquer the grand challenge of computational protein engineering.
DeCovarT, a multidimensional probalistic model for the deconvolution of heterogeneous transcriptomic samples
Authors: Bastien Chassagnol, Grégory Nuel, Etienne Becht
Date: 2023-09-18 00:00:00

Although bulk transcriptomic analyses have greatly contributed to a better understanding of complex diseases, their sensibility is hampered by the highly heterogeneous cellular compositions of biological samples. To address this limitation, computational deconvolution methods have been designed to automatically estimate the frequencies of the cellular components that make up tissues, typically using reference samples of physically purified populations. However, they perform badly at differentiating closely related cell populations. We hypothesised that the integration of the covariance matrices of the reference samples could improve the performance of deconvolution algorithms. We therefore developed a new tool, DeCovarT, that integrates the structure of individual cellular transcriptomic network to reconstruct the bulk profile. Specifically, we inferred the ratios of the mixture components by a standard maximum likelihood estimation (MLE) method, using the Levenberg-Marquardt algorithm to recover the maximum from the parametric convolutional distribution of our model. We then consider a reparametrisation of the log-likelihood to explicitly incorporate the simplex constraint on the ratios. Preliminary numerical simulations suggest that this new algorithm outperforms previously published methods, particularly when individual cellular transcriptomic profiles strongly overlap.
Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images
Authors: Michelle A Bartolo, Alyssa M Taylor-LaPole, Darsh Gandhi, Alexandria Johnson, Yaqi Li, Emma Slack, Isaiah Stevens, Zachary Turner, Justin D Weigand, Charles Puelz, Dirk Husmeier, Mette S Olufsen
Date: 2023-09-15 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Patient-specific computational modeling is a popular, non-invasive method to answer medical questions. Medical images are used to extract geometric domains necessary to create these models, providing a predictive tool for clinicians. However, in vivo imaging is subject to uncertainty, impacting vessel dimensions essential to the mathematical modeling process. While there are numerous programs available to provide information about vessel length, radii, and position, there is currently no exact way to determine and calibrate these features. This raises the question, if we are building patient-specific models based on uncertain measurements, how accurate are the geometries we extract and how can we best represent a patient's vasculature? In this study, we develop a novel framework to determine vessel dimensions using change points. We explore the impact of uncertainty in the network extraction process on hemodynamics by varying vessel dimensions and segmenting the same images multiple times. Our analyses reveal that image segmentation, network size, and minor changes in radius and length have significant impacts on pressure and flow dynamics in rapidly branching structures and tapering vessels. Accordingly, we conclude that it is critical to understand how uncertainty in network geometry propagates to fluid dynamics, especially in clinical applications.