pwc-archive/papers-with-abstracts huggingface dataset:
Do conformational changes contribute to the surface plasmon resonance signal?
Authors: Daniel Dobrovodský, Carmelo Di Primo
Date: 2023-04-11 00:00:00

Surface plasmon resonance (SPR)-based biosensors are widely used instruments for characterizing molecular interactions. In theory the SPR signal depends only on mass changes for interacting molecules of same chemical nature. Whether conformational changes of interacting molecules also contribute to the SPR signal is still a subject of lively debates. Works have been published claiming that conformational changes were detected but all factors contributing to the SPR signal were not carefully considered, in addition to often using no or improper controls. In the present work we used a very well-characterized oligonucleotide, the thrombin-binding DNA aptamer (TBA), which upon binding of potassium ions folds into a two G-tetrad antiparallel G-quadruplex structure. All terms contributing to the maximal expected SPR response, Rmax, in particular the refractive index increment, RII, of both partners and the fraction of immobilized TBA target available, ca, were experimentally assessed. The resulting Rmax was then compared to the maximal experimental SPR response for potassium ions binding to TBA using appropriate controls. Regardless how the RIIs were measured, by SPR or refractometry, and how much TBA available for interacting with potassium ions was considered, the theoretical and the experimental SPR responses never matched, the former being always lower than the latter. Using a straightforward experimental model system and by thoroughly taking into account all contributing factors we therefore conclude that conformational changes can indeed contribute to the measured SPR signal.
Buffalo Genome Projects: Current Situation and Future Perspective in Improving Breeding Programs
Authors: Ahmed M. Mousbah, Hesham M. Abdullah, Waleed S. Mohammed, Ali M. El-Refy, Mohamed Helmy
Date: 2023-04-11 00:00:00

Buffaloes are farm animals that contribute to food security by providing high quality meat and milk. They can better tolerate the adverse effects of global climate change on their meat and milk production. Despite their advantages, buffaloes are heavily neglected animals with fewer studies compared to other farm animals, hence, the real potential of buffaloes has never been realized. The complete genome sequencing projects of buffaloes are essential to better understanding the buffalos biology and production since they allow scientists to identify important genes and understand how the gene networks interact to determine the critical features of buffaloes. The genome projects are also valuable for gaining better knowledge of growth, development, maintenance, and determining factors associated with increased meat and milk production. Furthermore, having access to a complete genome of high quality and comprehensive annotations provides a powerful tool in breeding programs. The current review surveyed the publicly available buffalo genome projects and studied the impact of incorporating genomic selection into the buffalo breeding program. Our survey of the publicly available buffalo genome projects showed the promise of genomic selection in developing water buffalo science and technology for food security on a global scale.
Interictal MEG abnormalities to guide intracranial electrode implantation and predict surgical outcome
Authors: Thomas W. Owen, Vytene Janiukstyte, Gerard R. Hall, Fahmida A. Chowdhury, Beate Diehl, Andrew McEvoy, Anna Miserocchi, Jane de Tisi, John S. Duncan, Fergus Rugg-Gunn, Yujiang Wang, Peter N. Taylor
Date: 2023-04-11 00:00:00
Tasks: EEG

Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone (EZ) localisation, but requires a hypothesis of which tissue is epileptogenic, guided by qualitative analysis of seizure semiology and other imaging modalities such as magnetoencephalography (MEG). We hypothesised that if quantifiable MEG band power abnormalities were sampled by iEEG, then patients' post-resection seizure outcome were better. Thirty-two individuals with neocortical epilepsy underwent MEG and iEEG recordings as part of pre-surgical evaluation. Interictal MEG band power abnormalities were derived using 70 healthy controls as a normative baseline. MEG abnormality maps were compared to electrode implantation, with the spatial overlap of iEEG electrodes and MEG abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue, and resection of the strongest abnormalities determined by MEG and iEEG explained surgical outcome. Intracranial electrodes were implanted in brain tissue with the most abnormal MEG findings in individuals that were seizure-free post-resection (T=3.9, p=0.003). The overlap between MEG abnormalities and iEEG electrodes distinguished outcome groups moderately well (AUC=0.68). In isolation, the resection of the strongest MEG and iEEG abnormalities separated surgical outcome groups well (AUC=0.71, AUC=0.74 respectively). A model incorporating all three features separated outcome groups best (AUC=0.80). Intracranial EEG is a key tool to delineate the EZ and help render patients seizure-free after resection. We showed that data-driven abnormalities derived from interictal MEG recordings have clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Finally, our predictive model of post-operative seizure-freedom, which leverages both MEG and iEEG recordings, may aid patient counselling of expected outcome.
Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power
Authors: Thomas W. Owen, Vytene Janiukstyte, Gerard R. Hall, Jonathan J. Horsley, Andrew McEvoy, Anna Miserocchi, Jane de Tisi, John S. Duncan, Fergus Rugg-Gunn, Yujiang Wang, Peter N. Taylor
Date: 2023-04-11 00:00:00
Tasks: Clustering

Successful epilepsy surgery depends on localising and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation, and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesising a greater overlap in seizure-free patients. Thirty-four individuals with refractory focal epilepsy underwent pre-surgical resting-state interictal MEG recording. Fourteen individuals were totally seizure free (ILAE 1) after surgery and 20 continued to have some seizures post-operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k-means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC=0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (i) a data-driven framework to validate current hypotheses of the epileptogenic zone localisation or (ii) to guide further investigation.
Object Rigidity: Competition and cooperation between motion-energy and feature-tracking mechanisms and shape-based priors
Authors: Akihito Maruya, Qasim Zaidi
Date: 2023-04-11 00:00:00

Why do moving objects appear rigid when projected retinal images are deformed nonrigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes lead to specific states of visual perception and to transitions between the states.
Green synthesis of silver nanoparticles using Curcuma longa flower extract and antibacterial activity
Authors: Kamal Kishor Rajak, Pavan Pahilani, Harsh Patel, Bhavtosh Kikani, Rucha Desai, Hemant Kumar
Date: 2023-04-10 00:00:00
Tasks: X-Ray Diffraction (XRD)

Silver nanoparticles (AgNP's) possess inherent biological potentials that have obliged an alternative, eco-friendly, sustainable approach to "Green Synthesis." In the present study, we synthesized Green Silver Nanoparticles (GAgNP's) using Curcuma longa L. (C. longa) flower extract as a reducing and capping agent. The synthesized GAgNP's were characterized using UV-Visible spectroscopy, X-ray diffraction (XRD), and High-resolution transmission electron microscopy (HR-TEM), which confirmed their homogeneity and physical characteristics. The GAgNP's were found to contain crystalline silver through XRD, and the particles were confirmed to be homogeneous and spherical with a size of approximately 5 nm, as evidenced by UV-Visible spectroscopy, XRD, and HR-TEM. In addition, the biological potential of GAgNP's was evaluated for their antibacterial activities. GAgNP's showed significant activity and formed different sizes of inhibition zones against all selected bacteria: Mycobacterium smegmatis (M. smegmatis) (26 mm), Mycobacterium phlei (M. phlei), and Staphylococcus aureus (S. aureus) (22 mm), Staphylococcus epidermidis (S. epidermidis) and Klebsiella pneumoniae (K. pneumoniae) (18 mm), and Escherichia coli (E. coli) (13 mm). The MIC value of GAgNP's was found to be between 625 ug/mL-39.06 ug/mL for different microbes tested. With further research, the green synthesis of GAgNP's using C. longa flower extracts could lead to the development of effective antibacterial treatments in the medical field.
Emergence simulation of cell-like morphologies with evolutionary potential by virtual molecular interactions
Authors: Takeshi Ishida
Date: 2023-04-10 00:00:00

This study explores the emergence of life through a simulation model approach. The model "Multi-set chemical lattice model" is a model that allows virtual molecules of multiple types to be placed in each lattice cell on a two-dimensional space. This model is capable of describing a wide variety of states and interactions in a limited number of lattice cell spaces, such as diffusion, chemical reaction, and polymerization of virtual molecules. This model is also capable of describing a wide variety of states and interactions even in the limited lattice cell space of 100 x 100 cells. Furthermore it was considered energy metabolism and energy resources environment. It was able to reproduce the "evolution" in which a certain cell-like shapes adapted to the environment survives under conditions of decreasing amounts of energy resources in the environment. This enabled the emergence of cell-like shapes with the four minimum cellular requirements: boundary, metabolism, replication, and evolution, based solely on the interaction of virtual molecules.
NutriFD: Proving the medicinal value of food nutrition based on food-disease association and treatment networks
Authors: Wanting Su, Dongwei Liu, Feng Tan, Lun Hu, Pengwei Hu
Date: 2023-04-10 00:00:00
Tasks: Nutrition

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.
Uncovering the effect of RNA polymerase steric interactions on gene expression noise: analytical distributions of nascent and mature RNA numbers
Authors: Juraj Szavits-Nossan, Ramon Grima
Date: 2023-04-11 00:00:00

The telegraph model is the standard model of stochastic gene expression, which can be solved exactly to obtain the distribution of mature RNA numbers per cell. A modification of this model also leads to an analytical distribution of the nascent RNA numbers. These solutions are routinely used for the analysis of single-cell data, including the inference of transcriptional parameters. However, these models neglect important mechanistic features of transcription elongation, such as the stochastic movement of RNA polymerases and their steric interactions. Here we construct a model of gene expression describing promoter switching between inactive and active states, binding of RNA polymerases in the active state, their stochastic movement including steric interactions along the gene, and their unbinding leading to a mature transcript that subsequently decays. We derive the steady-state distributions of the nascent and mature RNA numbers in two important limiting cases: constitutive expression and slow promoter switching. We show that RNA fluctuations are suppressed by steric interactions between RNA polymerases, and that this suppression can even lead to sub-Poissonian fluctuations; these effects are most pronounced for nascent RNA and less prominent for mature RNA, since the latter is not a direct sensor of transcription. We find a relationship between the parameters of our microscopic mechanistic model and those of the standard models that ensures excellent consistency in their prediction of the first and second RNA number moments over vast regions of parameter space, encompassing slow, intermediate, and rapid promoter switching, provided the RNA number distributions are Poissonian or super-Poissonian. Furthermore, we identify the limitations of inference from mature RNA data, specifically showing that it cannot differentiate between highly distinct RNA polymerase traffic patterns on a gene.
FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
Authors: Alireza Rahimpour, Navid Fallahinia, Devesh Upadhyay, Justin Miller
Date: 2023-03-31 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Collision Avoidance, Decoder, motion prediction, Trajectory Prediction

The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and adverse weather conditions. Collisions with large animals such as deer in low light cause significant cost and damage every year. In this paper, we propose the first AI-based method for future trajectory prediction of large animals and mitigating the risk of collision with them in low light. In order to minimize false collision warnings, in our multi-step framework, first, the large animal is accurately detected and a preliminary risk level is predicted for it and low-risk animals are discarded. In the next stage, a multi-stream CONV-LSTM-based encoder-decoder framework is designed to predict the future trajectory of the potentially high-risk animals. The proposed model uses camera motion prediction as well as the local and global context of the scene to generate accurate predictions. Furthermore, this paper introduces a new dataset of FIR videos for large animal detection and risk estimation in real nighttime driving scenarios. Our experiments show promising results of the proposed framework in adverse conditions. Our code is available online.
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data
Authors: Orazio Pinti, Assad A. Oberai
Date: 2023-03-29 00:00:00

Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of low-fidelity data. In the approach described in this paper, this is accomplished by constructing a graph Laplacian using the low-fidelity data and computing its low-lying spectrum. This spectrum is then used to cluster the data and identify points that are closest to the centroids of the clusters. High-fidelity data is then acquired for these key points. Thereafter, a transformation that maps every low-fidelity data point to its bi-fidelity counterpart is determined by minimizing the discrepancy between the bi- and high-fidelity data at the key points, and to preserve the underlying structure of the low-fidelity data distribution. The latter objective is achieved by relying, once again, on the spectral properties of the graph Laplacian. This method is applied to a problem in solid mechanics and another in aerodynamics. In both cases, this methods uses a small fraction of high-fidelity data to significantly improve the accuracy of a large set of low-fidelity data.
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
Authors: Qian Wang, Huanyi Shui, Thi Tu Trinh Tran, Milad Zafar Nezhad, Devesh Upadhyay, Kamran Paynabar, Anqi He
Date: 2023-03-31 00:00:00
Tasks: Causal Inference

In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and implementing appropriate treatments, and refining treatments if needed. The main pain-point is the lack of a systematic method to identify causal relationships, evaluate treatment effectiveness, and direct the next actionable treatment if the current treatment was deemed ineffective. This paper will show how we leverage causal Machine Learning (ML) to speed up such processes. A real-word data set collected from on-road vehicles will be used to demonstrate the proposed framework. Open challenges for vehicle quality applications will also be discussed.
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies
Authors: Eloghosa Ikponmwoba, Ope Owoyele
Date: 2023-03-29 00:00:00
Tasks: global-optimization, Multi-agent Reinforcement Learning

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each agent within the swarm is defined as its current position and function value within a design space and the agents learn to take favorable actions that maximize reward, which is based on the final value of the objective function. The proposed approach is tested on various benchmark optimization functions and compared to the performance of other global optimization strategies. Furthermore, the effect of changing the number of agents, as well as the generalization capabilities of the trained agents are investigated. The results show superior performance compared to the other optimizers, desired scaling when the number of agents is varied, and acceptable performance even when applied to unseen functions. On a broader scale, the results show promise for the rapid development of domain-specific optimizers.
DynamicDet: A Unified Dynamic Architecture for Object Detection
Authors: ZhiHao Lin, Yongtao Wang, Jinhe Zhang, Xiaojie Chu
Date: 2023-04-12 00:00:00
Proceeding: CVPR 2023 1
Tasks: Computational Efficiency, Object, Object Detection

Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However, it is challenging to design a powerful dynamic detector, because of no suitable dynamic architecture and exiting criterion for object detection. To tackle these difficulties, we propose a dynamic framework for object detection, named DynamicDet. Firstly, we carefully design a dynamic architecture based on the nature of the object detection task. Then, we propose an adaptive router to analyze the multi-scale information and to decide the inference route automatically. We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors. Last, we present a variable-speed inference strategy, which helps to realize a wide range of accuracy-speed trade-offs with only one dynamic detector. Extensive experiments conducted on the COCO benchmark demonstrate that the proposed DynamicDet achieves new state-of-the-art accuracy-speed trade-offs. For instance, with comparable accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is available at https://github.com/VDIGPKU/DynamicDet.