pwc-archive/papers-with-abstracts huggingface dataset:
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Authors: Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Date: 2023-01-14 00:00:00
Tasks: Two-sample testing

Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on $n$ sample points. However, existing kernel tests either run in $n^2$ time or sacrifice undue power to improve runtime. To address these shortcomings, we introduce Compress Then Test (CTT), a new framework for high-powered kernel testing based on sample compression. CTT cheaply approximates an expensive test by compressing each $n$ point sample into a small but provably high-fidelity coreset. For standard kernels and subexponential distributions, CTT inherits the statistical behavior of a quadratic-time test -- recovering the same optimal detection boundary -- while running in near-linear time. We couple these advances with cheaper permutation testing, justified by new power analyses; improved time-vs.-quality guarantees for low-rank approximation; and a fast aggregation procedure for identifying especially discriminating kernels. In our experiments with real and simulated data, CTT and its extensions provide 20--200x speed-ups over state-of-the-art approximate MMD tests with no loss of power.
Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity Prediction
Authors: Md Masud Rana, Duc DUy Nguyen
Date: 2023-01-15 00:00:00
Tasks: Drug Design, Graph Learning

Understanding and accurately predicting protein-ligand binding affinity are essential in the drug design and discovery process. At present, machine learning-based methodologies are gaining popularity as a means of predicting binding affinity due to their efficiency and accuracy, as well as the increasing availability of structural and binding affinity data for protein-ligand complexes. In biomolecular studies, graph theory has been widely applied since graphs can be used to model molecules or molecular complexes in a natural manner. In the present work, we upgrade the graph-based learners for the study of protein-ligand interactions by integrating extensive atom types such as SYBYL and extended connectivity interactive features (ECIF) into multiscale weighted colored graphs (MWCG). By pairing with the gradient boosting decision tree (GBDT) machine learning algorithm, our approach results in two different methods, namely $^\text{sybyl}\text{GGL}$-Score and $^\text{ecif}\text{GGL}$-Score. Both of our models are extensively validated in their scoring power using three commonly used benchmark datasets in the drug design area, namely CASF-2007, CASF-2013, and CASF-2016. The performance of our best model $^\text{sybyl}\text{GGL}$-Score is compared with other state-of-the-art models in the binding affinity prediction for each benchmark. While both of our models achieve state-of-the-art results, the SYBYL atom-type model $^\text{sybyl}\text{GGL}$-Score outperforms other methods by a wide margin in all benchmarks.
Brain tumour genetic network signatures of survival
Authors: James K Ruffle, Samia Mohinta, Guilherme Pombo, Robert Gray, Valeriya Kopanitsa, Faith Lee, Sebastian Brandner, Harpreet Hyare, Parashkev Nachev
Date: 2023-01-15 00:00:00
Tasks: Diagnostic

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic, and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH- wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH- mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours, and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.
Using Fano factors to determine certain types of gene autoregulation
Authors: Yue Wang, Siqi He
Date: 2023-01-17 00:00:00

The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
Adapting Modeling and Simulation Credibility Standards to Computational Systems Biology
Authors: Lillian T. Tatka, Lucian P. Smith, Joseph L. Hellerstein, Herbert M. Sauro
Date: 2023-01-15 00:00:00
Tasks: Decision Making

Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as form of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrow domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems
Authors: V. A. Aksyuk
Date: 2023-01-17 00:00:00
Tasks: Bayesian Inference, Value prediction

Machine learning algorithms have achieved superhuman performance in specific complex domains. However, learning online from few examples and compositional learning for efficient generalization across domains remain elusive. In humans, such learning includes specific declarative memory formation and is closely associated with consciousness. Predictive processing has been advanced as a principled Bayesian framework for understanding the cortex as implementing deep generative models for both sensory perception and action control. However, predictive processing offers little direct insight into fast compositional learning or of the separation between conscious and unconscious contents. Here, propose that access consciousness arises as a consequence of a particular learning mechanism operating within a predictive processing system. We extend predictive processing by adding online, single-example new structure learning via hierarchical binding of unpredicted inferences. This system learns new causes by quickly connecting together novel combinations of perceptions, which manifests as working memories that can become short- and long-term declarative memories retrievable by associative recall. The contents of such bound representations are unified yet differentiated, can be maintained by selective attention and are globally available. The proposed learning process explains contrast and masking manipulations, postdictive perceptual integration, and other paradigm cases of consciousness research. 'Phenomenal conscious experience' is how the learning system transparently models its own functioning, giving rise to perceptual illusions underlying the meta-problem of consciousness. Our proposal naturally unifies the feature binding, recurrent processing, predictive processing, and global workspace theories of consciousness.
Deep Learning Models to Study Sentence Comprehension in the Human Brain
Authors: Sophie Arana, Jacques Pesnot Lerousseau, Peter Hagoort
Date: 2023-01-16 00:00:00
Tasks: Sentence

Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data.
Mechanisms in neurodegenerative disorders and role of non-pharmacological interventions in improving neurodegeneration and its clinical correlates: A review
Authors: Sheng Mai
Date: 2023-01-15 00:00:00

Mild cognitive impairment (MCI) leading to dementia results in a constellation of psychiatric disorders including depression, mood disorders, schizophrenia and others. With increasing age, mild cognitive impairment leads to increased disability-adjusted life-years and healthcare burden. A huge number of drug trials for the treatment of MCI associated with Alzheimer's disease have undergone failure leading to the development of drugs that could avert the progression of the disease. However, some novel non-drug-based therapies like ultrasound ablation of amyloid plaques have influenced researchers to explore the non-pharmacological modalities for the treatment of mild cognitive impairment. To compensate for neurodegenerative loss resulting in coexisting psychiatric disorders, neurofeedback therapy has also been proven to improve behavioural outcomes by inducing neuroplasticity. The aim of the current review is to highlight the pathophysiological aspects of mild cognitive impairment leading to dementia that could be addressed with no pharmacological interventions and to understand the mechanisms behind the effects of these interventions.
Continuous odor profile monitoring to study olfactory navigation in small animals
Authors: Kevin S. Chen, Rui Wu, Marc H. Gershow, Andrew M. Leifer
Date: 2023-01-14 00:00:00

Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like C. elegans and larval D. melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor's adhesion, adsorption or reemission. Here we present a method to flexibly control and precisely measure airborne odor concentration in an arena with agar while imaging animal behavior. Crucially and unlike previous methods, our method allows continuous monitoring of the odor profile during behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be accurately inferred and continuously monitored at all points in time. We use this approach to measure the precise odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations under different spatial odor landscapes.
Evolution of dispersal in advective patchy environments with varying drift rates
Authors: Shanshan Chen, Jie Liu, Yixiang Wu
Date: 2023-01-16 00:00:00

In this paper, we study a two stream species Lotka-Volterra competition patch model with the patches aligned along a line. The two species are supposed to be identical except for the diffusion rates. For each species, the diffusion rates between patches are the same, while the drift rates vary. Our results show that the convexity of the drift rates has a significant impact on the competition outcomes: if the drift rates are convex, then the species with larger diffusion rate wins the competition; if the drift rates are concave, then the species with smaller diffusion rate wins the competition.
The k-Robinson-Foulds Dissimilarity Measures for Comparison of Labeled Trees
Authors: Elahe Khayatian, Gabriel Valiente, Louxin Zhang
Date: 2023-01-14 00:00:00

Understanding the mutational history of tumor cells is a critical endeavor in unraveling the mechanisms underlying cancer. Since the modeling of tumor cell evolution employs labeled trees, researchers are motivated to develop different methods to assess and compare mutation trees and other labeled trees. While the Robinson-Foulds distance is a widely utilized metric for comparing phylogenetic trees, its applicability to labeled trees reveals certain limitations. This paper introduces the $k$-Robinson-Foulds dissimilarity measures, tailored to address the challenges of labeled tree comparison. The Robinson-Foulds distance is succinctly expressed as n-RF in the space of labeled trees with n nodes. Like the Robinson-Foulds distance, the k-Robinson-Foulds is a pseudometric for multiset-labeled trees and becomes a metric in the space of 1-labeled trees. By setting k to a small value, the k-Robinson-Foulds dissimilarity can capture analogous local regions in two labeled trees with different size or different labels.
Evaluating the sustainability of a de facto harvest strategy for British Columbia's Spot Prawn (Pandalus platyceros) fishery in the presence of environmental drivers of recruitment and hyperstable catch rates
Authors: Steven P. Rossi, Sean P. Cox, Samuel D. N. Johnson, Ashleen J. Benson
Date: 2023-01-13 00:00:00
Tasks: Management

The Spot Prawn trap fishery off the west coast of British Columbia (BC) is managed using a fixed escapement strategy that aims to prevent recruitment overfishing while maximizing expected long-term yield by closing the fishery when the catch rate of spawners, projected to the following spring, drops below 1.7 spawners per trap (the de jure rule). We develop a management strategy evaluation framework for BC's Spot Prawn fishery that examines the expected performance of the management procedure implemented in practice (the de facto rule), which was significantly more conservative than the de jure rule, usually closing the fishery when spawner catch rates were at least twice as high as specified by the de jure rule. Simulations indicate that the de facto spawner index rule using average empirical March 31st targets from 2000 to 2019 maintains most stocks near or above 0.8 BMSY with or without accounting for environmental effects and/or increasing future SST on recruitment. Abundance indices were found to be strongly hyperstable, with fishing efficiency 1.5 to 3.0 times higher under low biomass than high biomass.
Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the southeastern United States
Authors: Felipe Sanchez, Jason A. Galvis, Nicolas Cardenas, Cesar A. Corzo, Chris Jones, Gustavo Machado
Date: 2023-01-13 00:00:00

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movement of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data, and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. Sow farms were consistently categorized as high risk farm types, while downstream farms (i.e., finisher and nursery farms) had more susceptible farms within areas of significant-high relative risk. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4{\deg}C and 10{\deg}C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies at farms and areas within the distance threshold of PRRSV positive farms.
Extracting continuous sleep depth from EEG data without machine learning
Authors: Claus Metzner, Achim Schilling, Maximilian Traxdorf, Holger Schulze, Konstantin Tziridis, Patrick Krauss
Date: 2023-01-17 00:00:00
Tasks: Clustering, EEG, Electroencephalogram (EEG)

The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems. It is however unclear whether these human-defined stages can be re-discovered with unsupervised methods of data analysis, using only a minimal amount of generic pre-processing. Based on EEG data, recorded overnight from sleeping human subjects, we investigate the degree of clustering of the sleep stages using the General Discrimination Value as a quantitative measure of class separability. Virtually no clustering is found in the raw data, even after transforming the EEG signals of each thirty-second epoch from the time domain into the more informative frequency domain. However, a Principal Component Analysis (PCA) of these epoch-wise frequency spectra reveals that the sleep stages separate significantly better in the low-dimensional sub-space of certain PCA components. In particular the component $C_1(t)$ can serve as a robust, continuous 'master variable' that encodes the depth of sleep and therefore correlates strongly with the 'hypnogram', a common plot of the discrete sleep stages over time. Moreover, $C_1(t)$ shows persistent trends during extended time periods where the sleep stage is constant, suggesting that sleep may be better understood as a continuum. These intriguing properties of $C_1(t)$ are not only relevant for understanding brain dynamics during sleep, but might also be exploited in low-cost single-channel sleep tracking devices for private and clinical use.
Artificial intelligence as a gateway to scientific discovery: Uncovering features in retinal fundus images
Authors: Parsa Delavari, Gulcenur Ozturan, Ozgur Yilmaz, Ipek Oruc
Date: 2023-01-17 00:00:00
Tasks: Image Segmentation, scientific discovery, Semantic Segmentation

Purpose: Convolutional neural networks can be trained to detect various conditions or patient traits based on retinal fundus photographs, some of which, such as the patient sex, are invisible to the expert human eye. Here we propose a methodology for explainable classification of fundus images to uncover the mechanism(s) by which CNNs successfully predict the labels. We used patient sex as a case study to validate our proposed methodology. Approach: First, we used a set of 4746 fundus images, including training, validation and test partitions, to fine-tune a pre-trained CNN on the sex classification task. Next, we utilized deep learning explainability tools to hypothesize possible ways sex differences in the retina manifest. We measured numerous retinal properties relevant to our hypotheses through image segmentation to identify those significantly different between males and females. To tackle the multiple comparisons problem, we shortlisted the parameters by testing them on a set of 100 fundus images distinct from the images used for fine-tuning. Finally, we used an additional 400 images, not included in any previous set, to reveal significant sex differences in the retina. Results: We observed that the peripapillary area is darker in males compared to females ($p=.023, d=.243$). We also observed that males have richer retinal vasculature networks by showing a higher number of branches ($p=.016, d=.272$) and nodes ($p=.014, d=.299$) and a larger total length of branches ($p=.045, d=.206$) in the vessel graph. Also, vessels cover a greater area in the superior temporal quadrant of the retina in males compared to females ($p=0.048, d=.194$). Conclusions: Our methodology reveals retinal features in fundus photographs that allow CNNs to predict traits currently unknown, but meaningful to experts.