pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Sensor Selection for Biomarker Discovery
Authors: Joshua Pickard, Cooper Stansbury, Amit Surana, Lindsey Muir, Anthony Bloch, Indika Rajapakse
Date: 2024-05-16 00:00:00

Advances in methods of biological data collection are driving the rapid growth of comprehensive datasets across clinical and research settings. These datasets provide the opportunity to monitor biological systems in greater depth and at finer time steps than was achievable in the past. Classically, biomarkers are used to represent and track key aspects of a biological system. Biomarkers retain utility even with the availability of large datasets, since monitoring and interpreting changes in a vast number of molecules remains impractical. However, given the large number of molecules in these datasets, a major challenge is identifying the best biomarkers for a particular setting Here, we apply principles of observability theory to establish a general methodology for biomarker selection. We demonstrate that observability measures effectively identify biologically meaningful sensors in a range of time series transcriptomics data. Motivated by the practical considerations of biological systems, we introduce the method of dynamic sensor selection (DSS) to maximize observability over time, thus enabling observability over regimes where system dynamics themselves are subject to change. This observability framework is flexible, capable of modeling gene expression dynamics and using auxiliary data, including chromosome conformation, to select biomarkers. Additionally, we demonstrate the applicability of this approach beyond genomics by evaluating the observability of neural activity These applications demonstrate the utility of observability-guided biomarker selection for across a wide range of biological systems, from agriculture and biomanufacturing to neural applications and beyond.
Source Identification by Consensus-Based Optimization
Authors: Jan Friedrich, Sarah Schraven, Fabian Kiessling, Michael Herty
Date: 2024-05-16 00:00:00

A consensus-based optimization (CBO) algorithm, which enables derivative and mesh-free optimization, is presented to localize a bioluminescent source. The light propagation is modeled by the radiative transfer equation approximated by spherical harmonics. The approach is investigated for a hierarchy of simplified diffusion models in simulated environments and tissue-mimicking phantoms. In simulations, the state-of-the-art diffusive approximation gives reliable results for heavily scattering media. However, higher-order models achieve better localization and more accurate source intensities for deeper sources and in the presence of artificial noise in strongly absorbing, but only moderately scattering media. In phantoms, higher-order models give lower approximation errors and the most accurate localization, even for a high scattering coefficient. These results demonstrate the potential of CBO to render higher-order models at lower computational cost while ensuring accurate localization in bioluminescence tomography.
Clearing time randomization and transaction fees for auction market design
Authors: Thibaut Mastrolia, Tianrui Xu
Date: 2024-05-16 00:00:00

Flaws of a continuous limit order book mechanism raise the question of whether a continuous trading session and a periodic auction session would bring better efficiency. This paper wants to go further in designing a periodic auction when both a continuous market and a periodic auction market are available to traders. In a periodic auction, we discover that a strategic trader could take advantage of the accumulated information available along the auction duration by arriving at the latest moment before the auction closes, increasing the price impact on the market. Such price impact moves the clearing price away from the efficient price and may disturb the efficiency of a periodic auction market. We thus propose and quantify the effect of two remedies to mitigate these flaws: randomizing the auction's closing time and optimally designing a transaction fees policy for both the strategic traders and other market participants. Our results show that these policies encourage a strategic trader to send their orders earlier to enhance the efficiency of the auction market, illustrated by data extracted from Alphabet and Apple stocks.
Lysine-Cysteine-Serine-Tryptophan Inserted into the DNA-Binding Domain of Human Mineralocorticoid Receptor Increases Transcriptional Activation by Aldosterone
Authors: Yoshinao Katsu, Jiawen Zhang, Michael E. Baker
Date: 2024-05-16 00:00:00

Due to alternative splicing in an ancestral DNA-binding domain (DBD) of the mineralocorticoid receptor (MR), humans contain two almost identical MR transcripts with either 984 amino acids (MR-984) or 988 amino acids (MR-988), in which their DBDs differ by only four amino acids, Lys,Cys,Ser,Trp (KCSW). Human MRs also contain mutations at two sites, codons 180 and 241, in the amino terminal domain (NTD). Together, there are five distinct full-length human MR genes in GenBank. Human MR-984, which was cloned in 1987, has been extensively studied. Human MR-988, cloned in 1995, contains KCSW in its DBD. Neither this human MR-988 nor the other human MR-988 genes have been studied for their response to aldosterone and other corticosteroids. Here, we report that transcriptional activation of human MR-988 by aldosterone is increased by about 50% compared to activation of human MR-984 in HEK293 cells transfected with the TAT3 promoter, while the half-maximal response (EC50) is similar for aldosterone activation of MR-984 and MR-988. Transcriptional activation of human MR also depends on the amino acids at codons 180 and 241. Interestingly, in HEK293 cells transfected with the MMTV promoter, transcriptional activation by aldosterone of human MR-988 is similar to activation of human MR-984, indicating that the promoter has a role in the regulation of the response of human MR-988 to aldosterone. The physiological responses to aldosterone and other corticosteroids in humans with MR genes containing KCSW and with differences at codons 180 and 241 in the NTD warrant investigation.
Dynamics of antibody binding and neutralization during viral infection
Authors: Zhenying Chen, Hasan Ahmed, Cora Hirst, Rustom Antia
Date: 2024-05-15 00:00:00

In vivo in infection, virions are constantly produced and die rapidly. In contrast, most antibody binding assays do not include such features. Motivated by this, we considered virions with n=100 binding sites in simple mathematical models with and without the production of virions. In the absence of viral production, at steady state, the distribution of virions by the number of sites bound is given by a binomial distribution, with the proportion being a simple function of antibody affinity (Kon/Koff) and concentration; this generalizes to a multinomial distribution in the case of two or more kinds of antibodies. In the presence of viral production, the role of affinity is replaced by an infection analog of affinity (IAA), with IAA=Kon/(Koff+dv+r), where dv is the virus decaying rate and r is the infection growth rate. Because in vivo dv can be large, the amount of binding as well as the effect of Koff on binding are substantially reduced. When neutralization is added, the effect of Koff is similarly small which may help explain the relatively high Koff reported for many antibodies. We next show that the n+2-dimensional model used for neutralization can be simplified to a 2-dimensional model. This provides some justification for the simple models that have been used in practice. A corollary of our results is that an unexpectedly large effect of Koff in vivo may point to mechanisms of neutralization beyond stoichiometry. Our results suggest reporting Kon and Koff separately, rather than focusing on affinity, until the situation is better resolved both experimentally and theoretically.
Accurate and efficient protein embedding using multi-teacher distillation learning
Authors: Jiayu Shang, Cheng Peng, Yongxin Ji, Jiaojiao Guan, Dehan Cai, Xubo Tang, Yanni Sun
Date: 2024-05-20 00:00:00
Tasks: Prediction, Protein Annotation, Protein Structure Prediction

Motivation: Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods. Results: We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ~70\% and maintains almost the same accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently.
Comparison of reaction networks of insulin signaling
Authors: Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao
Date: 2024-05-17 00:00:00

Understanding the insulin signaling cascade provides insights on the underlying mechanisms of biological phenomena such as insulin resistance, diabetes, Alzheimer's disease, and cancer. For this reason, previous studies utilized chemical reaction network theory to perform comparative analyses of reaction networks of insulin signaling in healthy (INSMS: INSulin Metabolic Signaling) and diabetic cells (INRES: INsulin RESistance). This study extends these analyses using various methods which give further insights regarding insulin signaling. Using embedded networks, we discuss evidence of the presence of a structural "bifurcation" in the signaling process between INSMS and INRES. Concordance profiles of INSMS and INRES show that both have a high propensity to remain monostationary. Moreover, the concordance properties allow us to present heuristic evidence that INRES has a higher level of stability beyond its monostationarity. Finally, we discuss a new way of analyzing reaction networks through network translation. This method gives rise to three new insights: (i) each stoichiometric class of INSMS and INRES contains a unique positive equilibrium; (ii) any positive equilibrium of INSMS is exponentially stable and is a global attractor in its stoichiometric class; and (iii) any positive equilibrium of INRES is locally asymptotically stable. These results open up opportunities for collaboration with experimental biologists to understand insulin signaling better.
Mapping Differential Protein-Protein Interaction Networks using Affinity Purification Mass Spectrometry
Authors: Prashant Kaushal, Manisha R. Ummadi, Gwendolyn M. Jang, Yennifer Delgado, Sara K. Makanani, Sophie F. Blanc, Decan M. Winters, Jiewei Xu, Benjamin Polacco, Yuan Zhou, Erica Stevenson, Manon Eckhardt, Lorena Zuliani-Alvarez, Robyn Kaake, Danielle L. Swaney, Nevan Krogan, Mehdi Bouhaddou
Date: 2024-05-15 00:00:00

Proteins congregate into complexes to perform fundamental cellular functions. Phenotypic outcomes, in health and disease, are often mechanistically driven by the remodeling of protein complexes by protein coding mutations or cellular signaling changes in response to molecular cues. Here, we present an affinity purification mass spectrometry (APMS) proteomics protocol to quantify and visualize global changes in protein protein interaction (PPI) networks between pairwise conditions. We describe steps for expressing affinity tagged bait proteins in mammalian cells, identifying purified protein complexes, quantifying differential PPIs, and visualizing differential PPI networks. Specifically, this protocol details steps for designing affinity tagged bait gene constructs, transfection, affinity purification, mass spectrometry sample preparation, data acquisition, database search, data quality control, PPI confidence scoring, cross run normalization, statistical data analysis, and differential PPI visualization. Our protocol discusses caveats and limitations with applicability across cell types and biological areas.
Could a Computer Architect Understand our Brain?
Authors: Valentin Puente-Varona
Date: 2024-05-21 00:00:00
Tasks: Descriptive, ERP, Hippocampus, speech-recognition, Speech Recognition

This paper presents a highly speculative model encompassing the cortex, thalamus, and hippocampus of the mammalian brain. While the majority of computational neuroscience models are founded upon empirical evidence, this model is predicated upon a hardware proposal for a machine learning accelerator. Such a device was designed to perform a specific task, such as speech recognition. The design process employed the principles and techniques typically used by computer architects in the design of devices such as processors. However, it also sought to maintain plausibility with biological systems in accordance with the current understanding of the mammalian brain. In the course of our research, we have identified a functional framework that may help to fill the gaps in current neuroscience, thereby facilitating the explanations for many elusive cognitive-level effects. This paper does not describe the device itself or the rationale behind the design decision, but instead, it presents a concise description of the derived model. In brief, the model provides a functional definition of the cortical column and its structural definition by the minicolumns. It also offers a descriptive model for the corticothalamic and corticostriatal loops, a functional proposal for the hippocampal complex, and a simplified view of the brainstem circuitry involved in auditory processing. The proposed model appears to provide an explanation for a number of cognitive phenomena, including some ERP effects, bottom-up and top-down attention, and the relationship between phenomena such as the cocktail party effect, anterograde and retrograde amnesia following hippocampal complex damage, and so forth.
Implementing feature binding through dendritic networks of a single neuron
Authors: Yuanhong Tang, Shanshan Jia, Tiejun Huang, Zhaofei Yu, Jian K. Liu
Date: 2024-05-21 00:00:00

A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma. Experimental and computational investigations have revealed various modes of integration operations that include linear, superlinear, and sublinear summation. Interestingly, distinct neuron types exhibit diverse patterns of dendritic integration contingent upon the spatial distribution of dendrites. The functional implications of these specific integration modalities remain largely unexplored. In this study, we employ the Purkinje cell as a model system to investigate these intricate questions. Our findings reveal that Purkinje cells (PCs) generally exhibit sublinear summation across their expansive dendrites. The degree of sublinearity is dynamically modulated by both spatial and temporal input. Strong sublinearity necessitates that the synaptic distribution in PCs be globally scattered sensitive, whereas weak sublinearity facilitates the generation of complex firing patterns in PCs. Leveraging dendritic branches characterized by strong sublinearity as computational units, we demonstrate that a neuron can adeptly address the feature-binding problem. Collectively, these results offer a systematic perspective on the functional role of dendritic sublinearity, providing inspiration for a broader understanding of dendritic integration across various neuronal types.
Alterations of electrocortical activity during hand movements induced by motor cortex glioma
Authors: Yihan Wu, Tao Chang, Siliang Chen, Xiaodong Niu, Yu Li, Yuan Fang, Lei Yang, Yixuan Zong, Yaoxin Yang, Yuehua Li, Mengsong Wang, Wen Yang, Yixuan Wu, Chen Fu, Xia Fang, Yuxin Quan, Xilin Peng, Qiang Sun, Marc M. Van Hulle, Yanhui Liu, Ning Jiang, Dario Farina, Yuan Yang, Jiayuan He, Qing Mao
Date: 2024-05-20 00:00:00

Glioma cells can reshape functional neuronal networks by hijacking neuronal synapses, leading to partial or complete neurological dysfunction. These mechanisms have been previously explored for language functions. However, the impact of glioma on sensorimotor functions is still unknown. Therefore, we recruited a control group of patients with unaffected motor cortex and a group of patients with glioma-infiltrated motor cortex, and recorded high-density electrocortical signals during finger movement tasks. The results showed that glioma suppresses task-related synchronization in the high-gamma band and reduces the power across all frequency bands. The resulting atypical motor information transmission model with discrete signaling pathways and delayed responses disrupts the stability of neuronal encoding patterns for finger movement kinematics across various temporal-spatial scales. These findings demonstrate that gliomas functionally invade neural circuits within the motor cortex. This result advances our understanding of motor function processing in chronic disease states, which is important to advance the surgical strategies and neurorehabilitation approaches for patients with malignant gliomas.
The Projective Wave Theory of Consciousness
Authors: Robert Worden
Date: 2024-05-20 00:00:00

Neural theories of consciousness face three difficulties: (1) The selection problem: how are those neurons which cause consciousness selected, from all the other neurons which do not? (2) the precision problem: how do neurons hold a detailed internal model of 3D space, as the origin of our spatial conscious experience? and (3) the decoding problem: how are the many distorted neural representations of space in the brain decoded, to give our largely undistorted conscious experience of space? These problems can all be addressed if the brains internal model of local 3D space is held not in neurons, but in a wave excitation (holding a projective transform of Euclidean space), and if the wave is the source of spatial consciousness. Such a wave has not yet been detected in the brain, but there are good reasons why it has not been detected; and there is indirect evidence for a wave, in the mammalian thalamus, and in the central body of the insect brain. The resulting projective wave theory of consciousness gives good agreement with the spatial form of our consciousness. It has a positive Bayesian balance between the complexity of its assumptions and the data it accounts for; this gives a basis to believe it.
Online Mental Stress Detection Using Frontal-channel EEG Recordings in a Classroom Scenario
Authors: Chi-Yuan Chang, Chieh Hsu, Ying Choon Wu, Siwen Wang, Darin Tsui, Tzyy-Ping Jung
Date: 2024-05-18 00:00:00
Tasks: Binary Classification, EEG, Mental Stress Detection

Objective: To investigate the effects of different approaches to EEG preprocessing, channel montage selection, and model architecture on the performance of an online-capable stress detection algorithm in a classroom scenario. Methods: This analysis used EEG data from a longitudinal stress and fatigue study conducted among university students. Their self-reported stress ratings during each class session were the basis for classifying EEG recordings into either normal or elevated stress states. We used a data-processing pipeline that combined Artifact Subspace Reconstruction (ASR)and an Independent Component Analysis (ICA)-based method to achieve online artifact removal. We compared the performance of a Linear Discriminant Analysis (LDA) and a 4-layer neural network as classifiers. We opted for accuracy, balanced accuracy, and F1 score as the metrics for assessing performance. We examined the impact of varying numbers of input channels using different channel montages. Additionally, we explored different window lengths and step sizes during online evaluation. Results: Our online artifact removal method achieved performance comparable to the offline ICA method in both offline and online evaluations. A balanced accuracy of 77% and 78% in an imbalanced binary classification were observed when using the 11-frontal-channel LDA model with the proposed artifact removal method. Moreover, the model performance remained intact when changing the channel montage from 30 full-scalp channels to just 11 frontal channels. During the online evaluation, we achieved the highest balanced accuracy (78%) with a window length of 20 seconds and a step size of 1 second. Significance: This study comprehensively investigates the deployment of stress detection in real-world scenarios. The findings of this study provide insight into the development of daily mental stress monitoring.
Comparative prospects of imaging methods for whole-brain mammalian connectomics
Authors: Logan Thrasher Collins, Todd Huffman, Randal Koene
Date: 2024-05-17 00:00:00

Mammalian whole-brain connectomes are a foundational ingredient for holistic understanding of brains. Indeed, imaging connectomes at sufficient resolution to densely reconstruct cellular morphology and synapses represents a longstanding goal in neuroscience. Mouse connectomes could soon come within reach while human connectomes remain a more distant yet still worthy goal. Though the technologies needed to reconstruct whole-brain connectomes have not yet reached full maturity, they are advancing rapidly. Close examination of these technologies may help plan connectomics projects. Here, we quantitatively compare imaging technologies that have potential to enable whole-brain mammalian connectomics. We perform calculations on electron microscopy (EM) techniques and expansion light-sheet fluorescence microscopy (ExLSFM) methods. We consider techniques that have sufficient resolution to identify all synapses and sufficient speed to be relevant for whole mammalian brains. We offer this analysis as a resource for those considering how to organize efforts towards imaging whole-brain mammalian connectomes.