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arXiv 2020年

作者： Bear, daniel M. Fan, Chaofei Mrowca, damian Li, Yunzhu Alter, Seth Nayebi, Aran Schwartz, Jeremy Fei-Fei, Li Wu, Jiajun Tenenbaum, Joshua B. Yamins, daniel L.K. Department of Psychology Stanford University United States Department of Computer Science Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States MIT CSAIL MIT Brain and Cognitive Sciences Neurosciences Ph.D. Program Stanford University United States

Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs’ success on tasks that require structured understanding of visual scenes. To overcome these limitations, we introduce the idea of "physical Scene Graphs" (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts. Bound to each node is a vector of latent attributes that intuitively represent object properties such as surface shape and texture. We also describe PSGNet, a network architecture that learns to extract PSGs by reconstructing scenes through a PSG-structured bottleneck. PSGNet augments standard CNNs by including: recurrent feedback connections to combine low and high-level image information;graph pooling and vectorization operations that convert spatially-uniform feature maps into object-centric graph structures;and perceptual grouping principles to encourage the identification of meaningful scene elements. We show that PSGNet outperforms alternative self-supervised scene representation algorithms at scene segmentation tasks, especially on complex real-world images, and generalizes well to unseen object types and scene arrangements. PSGNet is also able learn from physical motion, enhancing scene estimates even for static images. We present a series of ablation studies illustrating the importance of each component of the PSGNet architecture, analyses showing that learned latent attributes capture intuitive scene properties, and illustrate the use of PSGs for compositional scene inference. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Pleiotropic endophenotypic and phenotype effects of GABAergic neurosteroid synthesis deficiency in posttraumatic stress disorder

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Current Opinion in Endocrine and Metabolic Research 2022年 25卷 100359页

作者： Rasmusson, Ann M. Novikov, Olga Brown, Kayla d. Pinna, Graziano Pineles, Suzanne L. VA National Center for PTSD Women's Health Science Division VA Boston Healthcare System Boston 02130 MA United States Department of Psychiatry Boston University School of Medicine Boston 02118 MA United States Boston University School of Medicine Psychiatry Residency Program Boston 02118 MA United States Behavioral Neurosciences Ph.D. Program Boston University School of Medicine Boston 02118 MA United States The Psychiatric Institute Department of Psychiatry College of Medicine University of Illinois at Chicago Chicago 60612 IL United States

PTSd is associated with deficits in synthesis of progesterone metabolites such as allopregnanolone and pregnanolone that potently facilitate gamma-amino-butyric acid (GABA) effects at GABA_A receptors. These neurosteroids modulate neuronal firing rate, regional brain connectivity, and activation of amygdala-mediated autonomic nervous system, hypothalamic–pituitary–adrenal axis, and behavioral reactions to unconditioned and conditioned threat. They also play critical roles in learning and memory processes such as extinction and extinction retention and inhibit toll-like receptor activation of intracellular pro-inflammatory pathways. deficient synthesis of these neurosteroids thus may contribute to individually variable PTSd clinical phenotypes encompassing symptom severity, capacity for PTSd recovery, and vulnerability to common PTSd-comorbidities such as major depression, chronic pain, alcohol and nicotine dependence, cardiovascular disease, metabolic syndrome, reproductive disorders, and autoimmune conditions. © 2022

关键词： Allopregnanolone depression GABA Neurosteroids Pregnanolone PTSd

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Investigation of ecological risk of microplastics in peatland areas: A case study in Vietnam

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Environmental Research 2023年第1期220卷 115190页

作者： Nguyen, Minh Ky Lin, Chitsan Quang Hung, Nguyen Tri Hoang, Hong-Giang Vo, dai-Viet N. Tran, Huu-Tuan Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Faculty of Environment and Natural Resources Nong Lam University of Ho Chi Minh City Hamlet 6 Linh Trung Ward Thu Duc Dist. Ho Chi Minh City700000 Viet Nam Faculty of Medicine Dong Nai Technology University Bien Hoa Dong Nai 76100 Viet Nam Nguyen Tat Thanh University Ho Chi Minh City700000 Viet Nam Laboratory of Ecology and Environmental Management Science and Technology Advanced Institute Van Lang University Ho Chi Minh City700000 Viet Nam Faculty of Applied Technology School of Engineering and Technology Van Lang University Ho Chi Minh City700000 Viet Nam

This study aims to investigate the distribution and ecological risk assessment of microplastics (MPs) in peatland areas located in Long An province, Vietnam's Mekong delta. In general, polyvinyl chloride (60.7%), polyethylene (25.8%), and polypropylene (11.9%) were the most abundant polymers determined in the thirty sediment samples. The hazard index (HI) remarked a level of III for MPs contamination in Tan Thanh and Thanh Hoa districts. The pollution load index (PLI) and potential ecological risk index (RI) indicated that the contamination risk of MPs polymer types in the studied sites is relatively high. According to PLI values, MPs levels of peatlands in Tan Thanh and Thanh Hoa are high and moderate, respectively, while the peatland sediments in duc Hue district are less contaminated. Furthermore, ecological risk indexes in the peatland areas were relatively high, with PLIoverall (level III);HIoverall (level V), and RIoverall (extreme danger). Hence, this study proposed a SWOT framework for challenges of MPs pollution in order to manage peatlands appropriately and minimize ecological risks. As a result, several practical strategies and appropriate approaches have been recommended to reduce microplastics towards a circular economy. These findings provide the initial quantitative assessment insights into hazard levels and ecological impacts of MPs in Vietnam's Mekong delta peatlands. © 2022 Elsevier Inc.

关键词： Risk assessment

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CoAl2O4 nanoparticles modified carbon nanofibers as high-efficiency bifunctional electrocatalyst: An efficient electrochemical aqueous asymmetric supercapacitors and non-enzymatic electrochemical sensors

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Journal of Alloys and Compounds 2023年 931卷 1页

作者： Chinnapaiyan, Sathishkumar das, Himadri Tanaya Chen, Shen-Ming Govindasamy, Mani Alshgari, Razan A. Fan, Chi-Hsuan Huang, Chi-Hsien Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei10608 Taiwan Centre for Advanced Materials and Applications Utkal University Vani vihar Odisha Bhubaneswar751004 India Department of Materials Engineering Ming Chi University of Technology New Taipei City243303 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan Chemistry Department College of Science King Saud University Riyadh11451 Saudi Arabia Korea University of Technology and Education Chungcheongnam-do Cheonan-si31253 Korea Republic of Division of Rheumatology Allergy and Immunology Chang Gung Memorial Hospital Taoyuan33378 Taiwan Research Center for Intelligent Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan

The thriving energy demand of the gradually increasing population and modernized life style requires development in energy storage devices for usage in commercial electronic devices. In this particular work, we have explored electrochemical performances of bimetallic oxide CoAl2O4 and tuned its performance by decorating it on the carbon nanofibers. The CoAl2O4/CNFs are synthesized by deep eutectic solvents based hydrothermal method. The synthesized CoAl2O4 nanoparticle was found electrochemically active and the incorporation of CNFs end up boosting, its electrochemical performances as battery-type electrode. The obtained nanocomposites were characterized by various techniques. The electrochemically active CoAl2O4/CNFs exhibited 2.2-fold higher charge storage capacity than CoAl2O4. The CoAl2O4/CNFs exhibited specific capacity as high as 132 C/g at a constant applied current density of 1 A/g in 1 M KOH. The asymmetric device fabricated with CoAl2O4/CNFs electrodes exhibited excellent energy density of 20 Wh/kg at power density of 3000 W/kg. The CoAl2O4/CNFs electrode long-term stability has been minutely observed;the high Coulombic efficiency of 90% and high rate of capacity retention 95 %. In this work, an enzyme-free electrochemical sensor based on CoAl2O4/CNFs composite was developed for the detection of luteolin (LUT). Further modification of CoAl2O4/CNFs composite can improve the high surface area and catalytic capability of the composites. Under the optimal detection conditions, the prepared sensor has good linearity in the detection of LUT within range 30 nM − 73.5 μM with a limit of detection (LOd) of 0.0124 μM (12.4 nM). The stability and selectivity of the sensor is high, and can be used to detect LUT in various real samples. © 2022 Elsevier B.V.

关键词： Electrochemical sensors

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deciding the closure of inconsistent rooted triples is NP-complete 29

Deciding the closure of inconsistent rooted triples is NP-co...

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29th International Symposium on Algorithms and Computation, ISAAC 2018

作者： Johnson, Matthew P. Department of Computer Science Lehman College Ph.D. Program in Computer Science Graduate Center City University of New York United States

ISBN: (纸本)9783959770941

Interpreting three-leaf binary trees or rooted triples as constraints yields an entailment relation, whereby binary trees satisfying some rooted triples must also thus satisfy others, and thence a closure operator, which is known to be polynomial-time computable. This is extended to inconsistent triple sets by defining that a triple is entailed by such a set if it is entailed by any consistent subset of it. determining whether the closure of an inconsistent rooted triple set can be computed in polynomial time was posed as an open problem in the Isaac Newton Institute's "phylogenetics" program in 2007. It appears (as NC4) in a collection of such open problems maintained by Mike Steel, and it is the last of that collection's five problems concerning computational complexity to have remained open. We resolve the complexity of computing this closure, proving that its decision version is NP-Complete. In the process, we also prove that detecting the existence of any acyclic B-hyperpath (from specified source to destination) is NP-Complete, in a significantly narrower special case than the version whose minimization problem was recently proven NP-hard by Ritz et al. This implies it is NP-hard to approximate (our special case of) their minimization problem to within any factor. © Matthew P. Johnson

关键词： Computational complexity

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Identifying learning rules from neural network observables

arXiv

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arXiv 2020年

作者： Nayebi, Aran Srivastava, Sanjana Ganguli, Surya Yamins, daniel L.K. Neurosciences Ph.D. Program Stanford University United States Department of Computer Science Stanford University United States Department of Applied Physics Stanford University United States Department of Psychology Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States

The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem. Simulating idealized neuroscience experiments with artificial neural networks, we generate a large-scale dataset of learning trajectories of aggregate statistics measured in a variety of neural network architectures, loss functions, learning rule hyperparameters, and parameter initializations. We then take a discriminative approach, training linear and simple non-linear classifiers to identify learning rules from features based on these observables. We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula. We identify the statistics of each observable that are most relevant for rule identification, finding that statistics from network activities across training are more robust to unit undersampling and measurement noise than those obtained from the synaptic strengths. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities on the order of several hundred units, frequently measured at wider intervals over the course of learning, may provide a good basis on which to identify learning rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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Electronic structures of NANOMET-based nanocrystalline soft magnetic Alloys: Magnetic properties

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physica B: Condensed Matter 2025年 714卷

作者： Jihye Park Minyeong Choi Hyunkyung Lee Haein Choi-Yim Yang-Ki Hong Seong-Gon Kim C. Heath Turner Department of Applied Physics Sookmyung Women's University Seoul 04310 South Korea Hyundai Motor Company 150 HyundaiYeonguso-ro Namyang-eup Hwaseong-si Gyeonggi-do 18280 South Korea Department of Electrical and Computer Engineering and Materials Science Ph.D. Program The University of Alabama Tuscaloosa AL 35487 USA Department of Physics and Astronomy Mississippi State University Mississippi State MS 39762 USA Department of Chemical and Biological Engineering The University of Alabama Tuscaloosa AL 35487 USA

We developed new nanocrystalline Fe-Co-B-P-Cu soft magnetic alloys and studied their magnetic properties by calculating electronic structures using first-principles calculations based on density functional theory within the local spin density approximation and the Brillouin function. The supercell Fe-B-P-Cu was equilibrated at 3000 K for 12 ps using ab initio molecular dynamics (AIMd). Consequently, Fe, B, P, and Cu atoms are distributed randomly in an amorphous matrix formed by cooling it to 0 K. The ferromagnetic bcc α -(Fe 50 Co 50 ) crystallites were formed in the amorphous matrix by annealing the mixture at 300 K and cooling to 0 K. This resulted in the formation of nanocrystalline (Fe, Co)-B-P-Cu soft magnetic alloys. The ferromagnetic bcc α -Fe(Co) crystallites, embedded in an amorphous matrix, were confirmed experimentally for the first time. The experiment involved quickly cooling and heating a melted mixture of Fe, Co, B, Fe 3 P, and Cu to create both amorphous ribbons and crystallites. The Fe-Co-B-P-Cu alloy designed for the experiment has a saturation magnetization ( μ 0 M S ) of over 1.9 T (T) at 0 K and ranging from 1.87 to 1.92 T at 300 K. The magnetocrystalline anisotropy constant (K) decreased significantly to 142 J/m 3 from 5.2 × 104 J/m 3 with an increase in the number of embedded crystallites from one to five, indicating that the alloy became magnetically soft. At 300 K, the experimental μ 0 M S ranged from 1.79 to 1.89 T. Additionally, as the concentration of Co increased, the Curie temperature (T C ) also increased and reached about 800 °C at 3.5 at.%. The study revealed that the average grain size of the synthesized Fe-Co-B-P-Cu alloys was 29 nm. The experimental μ 0 M S was slightly lower than the calculated value for all designed Fe-Co-B-P-Cu alloys, indicating that the recrystallization process was not entirely controlled. The experimental results confirmed that the designed alloy system has achieved the target saturation magnetization of o

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PROBLEM STRUCTURES IN THE THEORY ANd PRACTICE OF SUPERIORIZATION

arXiv

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arXiv 2019年

作者： Herman, Gabor T. Computer Science Ph.D. Program The Graduate Center City University of New York New YorkNY10016 United States

The purpose of this short paper is to identify the mathematical essence of the superiorization methodology. This methodology has been developed in recent years while attempting to solve specific application-oriented problems. Consequently, superiorization is often presented using the terminology of such problems. A more general approach is provided here by discussing ideas related to superiorization in terms of an abstract mathematical concept, referred to as a problem structure.15A29, 65F22, 65K10, 90C26 Copyright © 2019, The Authors. All rights reserved.

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Molecular mechanics-driven graph neural network with multiplex graph for molecular structures

arXiv

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arXiv 2020年

作者： Zhang, Shuo Liu, Yang Xie, Lei Ph.D. Program in Computer Science Graduate Center City University of New York United States Department of Computer Science Hunter College City University of New York United States Helen & Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University United States

The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design. A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. These models improve their expressive power by incorporating auxiliary information in molecules while inevitably increase their computational complexity. In this work, we aim to design a GNN which is both powerful and efficient for molecule structures. To achieve such goal, we propose a molecular mechanics-driven approach by first representing each molecule as a two-layer multiplex graph, where one layer contains only local connections that mainly capture the covalent interactions and another layer contains global connections that can simulate non-covalent interactions. Then for each layer, a corresponding message passing module is proposed to balance the trade-off of expression power and computational complexity. Based on these two modules, we build Multiplex Molecular Graph Neural Network (MXMNet). When validated by the QM9 dataset for small molecules and PdBBind dataset for large protein-ligand complexes, MXMNet achieves superior results to the existing state-of-the-art models under restricted resources. Copyright © 2020, The Authors. All rights reserved.

关键词： Molecules

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diffusion Kurtosis Imaging Biomarkers Associated with Amelioration of Neuroinflammation, Gray Matter Microstructural Abnormalities, and Gut dysbiosis by Central Thalamic deep Brain Stimulation in Autistic -Like Young Rats

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NeuroImage 2025年

作者： Lalitha Palanivelu You-Yin Chen Yao-Wen Liang Ssu-Ju Li Ching-Wen Chang Yu-Ting Huang Yu-Chun Lo International Ph.D. Program in Medicine College of Medicine Taipei Medical University 7F. No. 250 Wuxing St. Xinyi Dist. Taipei city 110 Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University No.155 Sec.2 Linong St. Taipei 11221 Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University. 12F. Education and Research Building Shuang-Ho Campus No. 301 Yuantong Rd. New Taipei City 23564 Taiwan Department of Medicine Taipei Medical University No. 250 Wuxing St. Xinyi Dist. Taipei city 110 Taiwan

Autism spectrum disorder (ASd) is a neurodevelopmental condition characterized by abnormalities in brain microstructure, neuroinflammation, and social behavior deficits. In addition, children with ASd frequently exhibit irritable bowel syndrome and other gastrointestinal symptoms linked to anxiety. This study investigated if central thalamic nucleus deep brain stimulation (CTN-dBS) can improve social behavior, suppress neuroinflammation, restore brain microstructure, and reverse gut dysbiosis in the valproic acid-induced rat model of ASd by modulating the microbiota–gut–brain (MGB) axis. daily CTN-dBS for 7 days (30 min/day) enhanced neuronal density, organization, and microstructural complexity as evidenced by increases in the diffusion kurtosis imaging (dKI) metrics—mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK). These neurostructural improvements were associated with reduced astrocyte and microglial activation, two core hallmarks of neuroinflammation in ASd, and lower systemic levels of the pro-inflammatory cytokines interleukin (IL)-1β, IL-6, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α, signaling factors that may increase gut permeability and disrupt gut microbial composition. Indeed, CTN-dBS enhanced gut barrier function, promoted the proliferation of beneficial Bacteroides spp. , and improved short-chain fatty acid (SCFA) metabolism, thereby restoring normal gut acetate and butyrate levels and counteracting dysbiosis. Specific energy absorption rate and thermal effect analyses demonstrated that CTN-dBS is safe under dKI. These findings support CTN-dBS as a safe and efficacious therapeutic strategy to reduce neuroinflammation, restore gray matter circuit function, and improve gut microbial composition in ASd via MGB axis modulation. Furthermore, dKI can reveal neurobiomarkers indicative of these improvements in ASd model rats.

关键词： autism spectrum disorder social interaction central thalamic dBS microbiota–gut–brain axis neuroinflammation diffusion kurtosis imaging

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