pwc-archive/papers-with-abstracts huggingface dataset:
Integrating time-resolved $nrf2$ gene-expression data into a full GUTS model as a proxy for toxicodynamic damage in zebrafish embryo
Authors: Florian Schunck, Bernhard Kodritsch, Wibke Busch, Martin Krauss, Andreas Focks
Date: 2024-06-18 00:00:00
Tasks: Bayesian Inference

The immense production of the chemical industry requires an improved predictive risk assessment that can handle constantly evolving challenges while reducing the dependency of risk assessment on animal testing. Integrating 'omics data into mechanistic models offers a promising solution by linking cellular processes triggered after chemical exposure with observed effects in the organism. With the emerging availability of time-resolved RNA data, the goal of integrating gene expression data into mechanistic models can be approached. We propose a biologically anchored TKTD model, which describes key processes that link the gene expression level of the stress regulator $nrf2$ to detoxification and lethality by associating toxicodynamic damage with $nrf2$ expression. Fitting such a model to complex datasets consisting of multiple endpoints required the combination of methods from molecular biology, mechanistic dynamic systems modeling and Bayesian inference. In this study we successfully integrate time-resolved gene expression data into TKTD models, and thus provide a method for assessing the influence of molecular markers on survival. This novel method was used to test whether, $nrf2$, can be applied to predict lethality in zebrafish embryos. With the presented approach we outline a method to successively approach the goal of a predictive risk assessment based on molecular data.
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability
Authors: Yijin Zhou, Yutang Ge, Xiaowen Dong, Yuguang Wang
Date: 2024-06-13 00:00:00
Tasks: Image Classification, Learning Theory, Out-of-Distribution Detection

Transformers excel in natural language processing and computer vision tasks. However, they still face challenges in generalizing to Out-of-Distribution (OOD) datasets, i.e. data whose distribution differs from that seen during training. OOD detection aims to distinguish outliers while preserving in-distribution (ID) data performance. This paper introduces the OOD detection Probably Approximately Correct (PAC) Theory for transformers, which establishes the conditions for data distribution and model configurations for the OOD detection learnability of transformers. It shows that outliers can be accurately represented and distinguished with sufficient data under conditions. The theoretical implications highlight the trade-off between theoretical principles and practical training paradigms. By examining this trade-off, we naturally derived the rationale for leveraging auxiliary outliers to enhance OOD detection. Our theory suggests that by penalizing the misclassification of outliers within the loss function and strategically generating soft synthetic outliers, one can robustly bolster the reliability of transformer networks. This approach yields a novel algorithm that ensures learnability and refines the decision boundaries between inliers and outliers. In practice, the algorithm consistently achieves state-of-the-art (SOTA) performance across various data formats.
Inverse population genetic problems with noise: inferring extent and structure of haplotype blocks from point allele frequencies
Authors: Oliver Keatinge Clay
Date: 2024-06-20 00:00:00
Tasks: Position

A haplotype block, or simply a block, is a chromosomal segment, DNA base sequence or string that occurs in only a few variants or types in the genomes of a population of interest, and that has an encapsulated or 'private' frequency distribution of the string types that is not shared by neighbouring blocks or regions on the same chromosome. We consider two inverse problems of genetic interest: from just the frequencies of the symbol types (4 base types, possible single-base alleles) at each position (point, base/nucleotide) along the string, infer the location of the left and right boundaries of the block (block extent), and the number and relative frequencies of the string types occurring in the block (block structure). The large majority of variable positions in human and also other (e.g., fungal) genomes appear to be biallelic, i.e., the position allows only a choice between two possible symbols. The symbols can then be encoded as 0 (major) and 1 (minor), or as $\uparrow$ and $\downarrow$ as in Ising models, so the scenario reduces to problems on Boolean strings/bitstrings and Boolean matrices. The specifying of major allele frequencies (MAF) as used in genetics fits naturally into this framework. A simple example from human chromosome 9 is presented.
A mapping-free NLP-based technique for sequence search in Nanopore long-reads
Authors: Tomasz Strzoda, Lourdes Cruz-Garcia, Mustafa Najim, Christophe Badie, Joanna Polanska
Date: 2024-06-20 00:00:00

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes Natural Language Processing (NLP) techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach. The training dataset consisted of RNASeq data from 6 samples. Having tested multiple NLP models, the best configuration analyses the entire sequence and uses a word length of 3 base pairs with one-word neighbor on each side. For the considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and NPV 99.25%, compared to minimap2's performance in a cross-validation scenario. Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to 98.15%. Obtained NLP model, validated on an external independent genome sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced dictionary. The salmon-estimated read counts differed from the classical approach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one. We conclude that for long Oxford Nanopore reads, an NLP-based approach can successfully replace classical mapping in case of emergency. The developed NLP model can be easily retrained to identify selected transcripts and/or work with various long-read sequencing techniques. Our results of the study clearly demonstrate the potential of applying techniques known from classical text processing to nucleotide sequences and represent a significant advancement in this field of science.
Self-Attention in Transformer Networks Explains Monkeys' Gaze Pattern in Pac-Man Game
Authors: Zhongqiao Lin, Yunwei Li, Tianming Yang
Date: 2024-06-20 00:00:00
Tasks: Decision Making

We proactively direct our eyes and attention to collect information during problem solving and decision making. Understanding gaze patterns is crucial for gaining insights into the computation underlying the problem-solving process. However, there is a lack of interpretable models that can account for how the brain directs the eyes to collect information and utilize it, especially in the context of complex problem solving. In the current study, we analyzed the gaze patterns of two monkeys playing the Pac-Man game. We trained a transformer network to mimic the monkeys' gameplay and found its attention pattern captures the monkeys' eye movements. In addition, the prediction based on the transformer network's attention outperforms the human subjects' predictions. Importantly, we dissected the computation underlying the attention mechanism of the transformer network, revealing its layered structures reflecting a value-based attention component and a component that captures the interactions between Pac-Man and other game objects. Based on these findings, we built a condensed attention model that is not only as accurate as the transformer network but also fully interpretable. Our results highlight the potential of using transformer neural networks to model and understand the cognitive processes underlying complex problem solving in the brain, opening new avenues for investigating the neural basis of cognition.
Association of neighborhood disadvantage with cognitive function and cortical disorganization in an unimpaired cohort
Authors: Apoorva Safai, Erin Jonaitis, Rebecca E Langhough, William R Buckingham, Sterling C. Johnson, W. Ryan Powell, Amy J. H. Kind, Barbara B. Bendlin, Pallavi Tiwari
Date: 2024-06-19 00:00:00

Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods.
Extended error threshold mechanism in {\it quasispecies} theory via population dynamics
Authors: Hermano Velten, Carlos Felipe Pinheiro, Alcides Castro e Silva
Date: 2024-06-20 00:00:00

We investigate Eigen's model for the evolution of the genetic code of microorganisms using a novel method based on population dynamics analysis. This model, for a given number of offspring, determines long-term survival as a function of the "genetic" information length and copy error probability. There exists a maximum threshold for the quantity of information that can be consistently preserved through the process of evolution within a population of perfectly replicating sequences, meaning no errors are allowed. With our formula, we expand upon the traditional error threshold formula of Eigen's theory and introduce a new expression for general cases where the self-reproduction process allows up to any integer number of copying errors per digit per replication step.
A game of life with dormancy
Authors: Daniel Henrik Nevermann, Claudius Gros, Jay T. Lennon
Date: 2024-06-19 00:00:00

The factors contributing to the persistence and stability of life are fundamental for understanding complex living systems. Organisms are commonly challenged by harsh and fluctuating environments that are suboptimal for growth and reproduction, which can lead to extinction. Species often contend with unfavorable and noisy conditions by entering a reversible state of reduced metabolic activity, a phenomenon known as dormancy. Here, we develop Spore Life, a model to investigate the effects of dormancy on population dynamics. It is based on Conway's Game of Life, a deterministic cellular automaton where simple rules govern the metabolic state of an individual based on the metabolic state of its neighbors. For individuals that would otherwise die, Spore Life provides a refuge in the form of an inactive state. These dormant individuals (spores) can resuscitate when local conditions improve. The model includes a parameter alpha that controls the survival probability of spores, interpolating between Game of Life (alpha = 0) and Spore Life (alpha = 1), while capturing stochastic dynamics in the intermediate regime (0 < alpha < 1). In addition to identifying the emergence of unique periodic configurations, we find that spore survival increases the average number of active individuals and buffers populations from extinction. Contrary to expectations, the stabilization of the population is not the result of a large and long-lived seed bank. Instead, the demographic patterns in Spore Life only require a small number of resuscitation events. Our approach yields novel insight into what is minimally required for the emergence of complex behaviors associated with dormancy and the seed banks that they generate.
Network-community analysis of cellular senescence
Authors: Alda Sabalic, Victoria Moiseeva, Andres Cisneros, Oleg Deryagin, Eusebio Perdiguero, Pura Muñoz-Canoves, Jordi Garcia-Ojalvo
Date: 2024-06-19 00:00:00
Tasks: Community Detection, feature selection

Most cellular phenotypes are genetically complex. Identifying the set of genes that are most closely associated with a specific cellular state is still an open question in many cases. Here we study the transcriptional profile of cellular senescence using a combination of network-based approaches, which include eigenvector centrality feature selection and community detection. We apply our method to cell-type-resolved RNA sequencing data obtained from injured muscle tissue in mice. The analysis identifies some genetic markers consistent with previous findings, and other previously unidentified ones, which are validated with previously published single-cell RNA sequencing data in a different type of tissue. The key identified genes, both those previously known and the newly identified ones, are transcriptional targets of factors known to be associated with established hallmarks of senescence, and can thus be interpreted as molecular correlates of such hallmarks. The method proposed here could be applied to any complex cellular phenotype even when only bulk RNA sequencing is available, provided the data is resolved by cell type.
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience
Authors: Uros Sutulovic, Daniele Proverbio, Rami Katz, Giulia Giordano
Date: 2024-06-19 00:00:00
Tasks: Efficient Exploration, Uncertainty Quantification

Robustness analysis is very important in biology and neuroscience, to unravel behavioural patterns of systems that are conserved despite large parametric uncertainties. To make studies of probabilistic robustness more efficient and scalable when addressing complex models in neuroscience, we propose an alternative to computationally expensive Monte Carlo (MC) methods by introducing and analysing the generalised polynomial chaos (gPC) framework for uncertainty quantification. We consider both intrusive and non-intrusive gPC approaches, which turn out to be scalable and allow for a fast comprehensive exploration of parameter spaces. Focusing on widely used models of neural dynamics as case studies, we explore the trade-off between efficiency and accuracy of gPC methods, and we adopt the proposed methodology to investigate parametric uncertainties in models that feature multiple dynamic regimes.
Methylglyoxal induces cardiac dysfunction through mechanisms involving altered intracellular calcium handling in the rat heart
Authors: Hélène Peyret, Céline Konecki, Christine Terryn, Florine Dubuisson, Hervé Millart, Catherine Feliu, Zoubir Djerada
Date: 2024-06-20 00:00:00

Methylglyoxal (MGO) is an endogenous, highly reactive dicarbonyl metabolite generated under hyperglycaemic conditions. MGO plays a role in developing pathophysiological conditions, including diabetic cardiomyopathy. However, the mechanisms involved and the molecular targets of MGO in the heart have not been elucidated. In this work, we studied the exposure-related effects of MGO on cardiac function in an isolated perfused rat heart ex vivo model. The effect of MGO on calcium homeostasis in cardiomyocytes was studied in vitro by the fluorescence indicator of intracellular calcium Fluo-4. We demonstrated that MGO induced cardiac dysfunction, both in contractility and diastolic function. In rat heart, the effects of MGO treatment were significantly limited by aminoguanidine, a scavenger of MGO, ruthenium red, a general cation channel blocker, and verapamil, an L-type voltage-dependent calcium channel blocker, demonstrating that this dysfunction involved alteration of calcium regulation. MGO induced a significant concentration-dependent increase of intracellular calcium in neonatal rat cardiomyocytes, which was limited by aminoguanidine and verapamil. These results suggest that the functionality of various calcium channels is altered by MGO, particularly the L-type calcium channel, thus explaining its cardiac toxicity. Therefore, MGO could participate in the development of diabetic cardiomyopathy through its impact on calcium homeostasis in cardiac cells.
Implant-to-Wearable Communication through the Human Body: Exploring the Effects of Encapsulated Capacitive and Galvanic Transmitters
Authors: Anyu Jiang, Cassandra Acebal, Brook Heyd, Trustin White, Gurleen Kainth, Arunashish Datta, Shreyas Sen, Adam Khalifa, Baibhab Chatterjee
Date: 2024-06-19 00:00:00

Data transfer using human-body communication (HBC) represents an actively explored alternative solution to address the challenges related to energy-efficiency, tissue absorption, and security of conventional wireless. Although the use of HBC for wearable-to-wearable communication has been well-explored, different configurations for the transmitter (Tx) and receiver (Rx) for implant-to-wearable HBC needs further studies. This paper substantiates the hypothesis that a fully implanted galvanic Tx is more efficient than a capacitive Tx for interaction with a wearable Rx. Given the practical limitations of implanting an ideal capacitive device, we choose a galvanic device with one electrode encapsulated to model the capacitive scenario. We analyze the lumped circuit model for in-body to out-of-body communication, and perform Circuit-based as well as Finite Element Method (FEM) simulations to explore how the encapsulation thickness affects the received signal levels. We demonstrate in-vivo experimental results on live Sprague Dawley rats to validate the hypothesis, and show that compared to the galvanic Tx, the channel loss will be $\approx$ 20 dB higher with each additional mm thickness of capacitive encapsulation, eventually going below the noise floor for ideal capacitive Tx.
Essays on Responsible and Sustainable Finance
Authors: Baridhi Malakar
Date: 2024-06-18 00:00:00

The dissertation consists of three essays on responsible and sustainable finance. I show that local communities should be seen as stakeholders to decisions made by corporations. In the first essay, I examine whether the imposition of fiduciary duty on municipal advisors affects bond yields and advising fees. Using a difference-in-differences analysis, I show that bond yields reduce by 9\% after the imposition of the SEC Municipal Advisor Rule. In the second essay, we analyze the impact of USD 40 billion of corporate subsidies given by U.S. local governments on their borrowing costs. We find that winning counties experience a 15 bps increase in bond yield spread as compared to the losing counties. In the third essay, we provide new evidence that the bankruptcy filing of a locally-headquartered and publicly-listed manufacturing firm imposes externalities on the local governments. Compared to matched counties with similar economic trends, municipal bond yields for affected counties increase by 10 bps within a year of the firm filing for bankruptcy. The final essay examines whether managers walk the talk on the environmental and social discussion. We train a deep-learning model on various corporate sustainability frameworks to construct a comprehensive Environmental and Social (E and S) dictionary. Using this dictionary, we find that the discussion of environmental topics in the earnings conference calls of U.S. public firms is associated with higher pollution abatement and more future green patents.
Robust Lambda-quantiles and extremal distributions
Authors: Xia Han, Peng Liu
Date: 2024-06-19 00:00:00

In this paper, we investigate the robust models for $\Lambda$-quantiles with partial information regarding the loss distribution, where $\Lambda$-quantiles extend the classical quantiles by replacing the fixed probability level with a probability/loss function $\Lambda$. We find that, under some assumptions, the robust $\Lambda$-quantiles equal the $\Lambda$-quantiles of the extremal distributions. This finding allows us to obtain the robust $\Lambda$-quantiles by applying the results of robust quantiles in the literature. Our results are applied to uncertainty sets characterized by three different constraints respectively: moment constraints, probability distance constraints via the Wasserstein metric, and marginal constraints in risk aggregation. We obtain some explicit expressions for robust $\Lambda$-quantiles by deriving the extremal distributions for each uncertainty set. These results are applied to optimal portfolio selection under model uncertainty.
Pricing VIX options under the Heston-Hawkes stochastic volatility model
Authors: Oriol Zamora Font
Date: 2024-06-19 00:00:00

We derive a semi-analytical pricing formula for European VIX call options under the Heston-Hawkes stochastic volatility model introduced in arXiv:2210.15343. This arbitrage-free model incorporates the volatility clustering feature by adding an independent compound Hawkes process to the Heston volatility. Using the Markov property of the exponential Hawkes an explicit expression of $\text{VIX}^2$ is derived as a linear combination of the variance and the Hawkes intensity. We apply qualitative ODE theory to study the existence of some generalized Riccati ODEs. Thereafter, we compute the joint characteristic function of the variance and the Hawkes intensity exploiting the exponential affine structure of the model. Finally, the pricing formula is obtained by applying standard Fourier techniques.
A note on robust convex risk measures
Authors: Marcelo Righi
Date: 2024-06-18 00:00:00

We study robust convex risk measures related to worst-case values under uncertainty in random variables. Our first main result characterizes the convex conjugate penalty term, which is the key to dual representations. Our second main result uses such penalty term to provide closed forms when uncertainty sets are based on closed balls under p-norms and Wasserstein distance.