bluuebunny/crossref_metadata_2025 huggingface dataset:
Stable Cell Clones Harboring Self-Replicating SARS-CoV-2 RNAs for Drug Screen
Authors: Liu Shufeng , Chou Chao-Kai , Wu Wells W. , Luan Binquan , Wang Tony T.
Published: January 2022

SARS-CoV-2 replicon systems that have been reported up to date were unsuccessful in deriving stable cell lines harboring non-cytopathic replicons. The transient expression of viral sgmRNA or a reporter gene makes it impractical for industry-scale screening of large compound libraries using these systems.
Dispersed Crude Oil Induces Dysbiosis in the Red Snapper
Lutjanus campechanus
External Microbiota
Authors: Tarnecki Andrea M. , Miller Christelle , Sherwood Tracy A. , Griffitt Robert J. , Schloesser Ryan W. , Wetzel Dana L.
Published: January 2022

Fish skin is an immunologically active tissue. It harbors a complex community of microorganisms vital to host homeostasis as, in healthy fish, they competitively exclude pathogens found in the surrounding aquatic environment.
High Prevalence of Antibodies against Canine Parvovirus and Canine Distemper Virus among Coyotes and Foxes from Pennsylvania: Implications for the Intersection of Companion Animals and Wildlife
Authors: Kimpston Caellaigh N. , Hatke Amanda L. , Castelli Benjamin , Otto Nathan , Tiffin Hannah S. , Machtinger Erika T. , Brown Justin D. , Van Why Kyle R. , Marconi Richard T.
Published: January 2022

Canine distemper virus
(CDV) and
Canine parvovirus
(CPV) are significant health threats to domestic dogs (
Canis familiaris
) and wildlife. CDV and CPV have been identified in diverse vertebrates, including endangered wildlife species.
The effects of Pilates on health-related outcomes in individuals with increased risk of fracture: a systematic review
Authors: McLaughlin Emily Claire , Bartley Joan , Ashe Maureen C. , Butt Debra A. , Chilibeck Philip D. , Wark John D. , Thabane Lehana , Stapleton Jackie , Giangregorio Lora M.
Published: January 2022

This systematic review examined the effect of Pilates on health-related outcomes in individuals with increased fracture risk to inform the 2021 Clinical Practice Guidelines for Management of Osteoporosis and Fracture Prevention in Canada. Seven electronic databases were searched to December 2020. Studies of Pilates in men and postmenopausal women aged ≥50 years with low bone mineral density (BMD), history of fragility fracture, or moderate-high risk of fragility fracture were included. Two reviewers independently screened studies and performed risk of bias assessment. Of 7286 records and 504 full-text articles, 5 studies were included, encompassing data from 143 participants (99% female). Data were insufficient for meta-analyses. There is low-certainty evidence that Pilates improved physical functioning and health-related quality of life. The effect of Pilates on falls and BMD is uncertain. No evidence was available for the effect of Pilates on mortality, fractures, or adverse events. Overall, Pilates may improve physical functioning and quality of life. Evidence of benefits relative to harms of Pilates in people with increased fracture risk, particularly males, is limited. PROSPERO registration: CRD42019122685.

Novelty: Pilates may improve physical functioning and quality of life in women with osteoporosis. Evidence of the effect of Pilates on BMD, falls, fractures, or adverse events is limited.
Lower possession limits and shorter seasons directly reduce for-hire fishing effort in a multispecies marine recreational fishery
Authors: Trudeau Ashley , Bochenek Eleanor A. , Golden Abigail S. , Melnychuk Michael C. , Zemeckis Douglas R. , Jensen Olaf P.
Published: January 2022

Managers of recreational fisheries often rely on implicit and rarely tested assumptions regarding how fishing effort will change in response to regulations. For instance, they assume that reduced seasons will directly reduce fishing effort without producing angler behavioral adaptations to maintain fishing opportunities and harvest. Vessel trip reports from a multispecies for-hire fishery in New Jersey, USA, allowed us to empirically evaluate changes in fishing effort as overlapping seasons for four species became shorter and as possession limits decreased. We conducted focus groups with fishery stakeholders and then developed statistical models to evaluate hypotheses describing how anglers aboard for-hire vessels adapted to regulations. Fishing effort aboard charter boats remained consistent and primarily responded to the availability of “something” to harvest, suggesting that their customers are willing to substitute target species. Party boat anglers, in contrast, responded to the possession limits of black sea bass (Centropristis striata) and summer flounder (Paralichthys dentatus). Because party anglers were less willing to substitute target species, party vessel operators are likely particularly vulnerable to reductions in fishing opportunity and harvest potential.
The classification of the single traveling wave solutions to the KdV equation with higher-order nonlinearity
Authors: Yang Xuefei , Zhang Kaixuan
Published: January 2022

The Korteweg–de Vries (KdV) equation with higher-order nonlinearity is considered as the model of the wave propagation in one-dimensional nonlinear lattice. In this paper, we obtain the exact solutions to the KdV equation with higher-order nonlinearity. The complete discrimination system for polynomial method is applied to handle such a problem. All the possible exact traveling wave solutions are obtained.
Closed-form invariant solutions from the Lie symmetry analysis and dynamics of solitonic profiles for (2 + 1)-dimensional modified Heisenberg ferromagnetic system
Authors: Kumar Amit , Kumar Sachin , Kharbanda Harsha
Published: January 2022

This paper studies the ([Formula: see text])-dimensional modified Heisenberg ferromagnetic (MHF) system, which describes the different nonlinear dynamical structures for long water-waves of small amplitude with weakly nonlinear restoring forces and high-frequency dispersion. This MHF system arises in the propagation of the magnetization vector of the isotropic ferromagnet and biological pattern formation. By employing the Lie symmetry analysis method, infinitesimal generators, Lie point symmetries, potential vector fields, commutation relations of infinitesimal vectors, and attractive symmetry reductions are derived. Based on the two phases of Lie similarity reductions, ([Formula: see text])-dimensional MHF system is reduced to various nonlinear ordinary differential equations (ODEs). Afterwards, with the help of symbolic computation, we solve the acquired ODEs and obtain a variety of exact closed-form solutions involving arbitrary independent functions and other constant parameters. The physical features of the obtained multi-wave soliton solutions are demonstrated to analyze the impact of the involved arbitrary independent functions on the dynamics of the solitary wave solutions via three-dimensional graphics. These exact solutions are accomplished in the shapes of single solitons, doubly solitons, multi-wave solitons, elastic behavior of multisoliton, oscillating multi-solitons, curved-shaped periodic solitons, and kink-type solitons, and so on. The newly constructed results show the trustworthiness, reliability, and efficiency of the Lie symmetry technique for obtaining the invariant closed-form solutions to nonlinear governing model. Moreover, conservation laws and self-adjoint systems have been obtained by implementing Noether’s technique. By using Lie symmetry analysis, the achieved outcomes might be helpful to understand the physical formation of this model and confirm the effectiveness and authenticity of the mentioned method.
On the Categorical Connections of L-G-Filter Spaces and a Galois Correspondence with L-Fuzzy Pre-Proximity Spaces
Authors: Jose Merin , Mathew Sunil C.
Published: January 2022

The study examines the categorical connections of [Formula: see text]-G-filters with [Formula: see text]-filters and [Formula: see text]-interior operators. Besides, the relationship between [Formula: see text]-G-filters and [Formula: see text]-fuzzy pre-proximities is also explored. Indeed, the Galois correspondence between the categories of stratified [Formula: see text]-G-filter spaces and [Formula: see text]-fuzzy pre-proximity spaces is revealed in this paper.
A Framework for Calculating WCET Based on Execution Decision Diagrams
Authors: Bai Zhenyu , Cassé Hugues , De Michiel Marianne , Carle Thomas , Rochange Christine
Published: January 2022

Due to the dynamic behaviour of acceleration mechanisms such as caches and branch predictors, static Worst-case Execution Time (WCET) analysis methods tend to scale poorly to modern hardware architectures. As a result, a trade-off must be found between the duration and the precision of the analysis, leading to an overestimation of the WCET bounds. In turn, this reduces the schedulability and resource usage of the system. In this article, we present a new data structure to speed up the analysis: the
eXecution Decision Diagram
(XDD), which is an ad hoc extension of
Binary Decision Diagrams
tailored for WCET analysis problems. We show how XDDs can be used to represent efficiently execution states in a modern hardware platform. Moreover, we propose a new process to build the
Integer Linear Programming
system of the
Implicit Path Enumeration Technique
using XDD. We use benchmark applications to demonstrate how the use of an XDD substantially increases the scalability of WCET analysis and the precision of the obtained WCET.
Edge Intelligence: Concepts, Architectures, Applications, and Future Directions
Authors: Mendez Javier , Bierzynski Kay , Cuéllar M. P. , Morales Diego P.
Published: January 2022

The nameedge intelligence, also known asEdge AI, is a recent term used in the past few years to refer to the confluence of machine learning, or broadly speaking artificial intelligence, with edge computing. In this article, we revise the concepts regarding edge intelligence, such as cloud, edge, and fog computing, the motivation to use edge intelligence, and compare current approaches and analyze application scenarios. To provide a complete review of this technology, previous frameworks and platforms for edge computing have been discussed in this work to provide the general view of the basis for Edge AI. Similarly, the emerging techniques to deploy deep learning models at the network edge, as well as specialized platforms and frameworks to do so, are review in this article. These devices, techniques, and frameworks are analyzed based on relevant criteria at the network edge, such as latency, energy consumption, and accuracy of the models, to determine the current state of the art as well as current limitations of the proposed technologies. Because of this, it is possible to understand the current possibilities to efficiently deploy state-of-the-art deep learning models at the network edge based on technologies such as artificial intelligence accelerators, tensor processing units, and techniques that include federated learning and gossip training. Finally, the challenges of Edge AI are discussed in the work, as well as the future directions that can be extracted from the evolution of the edge computing and Internet of Things approaches.
Software Engineering Education for the Next Generation SEENG 2021 Workshop Report
Authors: Péraire Cécile , Krusche Stephan
Published: January 2022

The 3rd International Workshop on Software Engineering Education for the Next Generation was held remotely on May 24, 2021. The workshop was an integral component of the Joint Track on Software Engineering Education and Training at the 43rd International Conference on Software Engineering. It specifically supported the general theme of "Educating the Next Generation of Software Engineers". Building on its predecessors, the workshop used a highly interactive format, structured around eight short paper presentations to generate discussion topics, an activity to select the most interesting topics, and structured breakout sessions to allow participants to discuss those topics in detail. Participants presented the results of the breakout sessions using mind maps.
Display-Size Dependent Effects of 3D Viewing on Subjective Impressions
Authors: Miyashita Yamato , Sawahata Yasuhito , Sakai Akihiro , Harasawa Masamitsu , Hara Kazuhiro , Morita Toshiya , Komine Kazuteru
Published: January 2022

This paper describes how the screen size of 3D displays affect the subjective impressions of 3D-visualized content. The key requirement for 3D displays is the presentation of depth cues comprising binocular disparities and/or motion parallax; however, the development of displays and production of content that include these cues leads to an increase in costs. Given the variety of screen sizes, it is expected that 3D characteristics are experienced differently by viewers depending on the screen size. We asked 48 participants to evaluate the 3D experience when using three different-sized stereoscopic displays (11.5, 55, and 200 inches) with head trackers. The participants were asked to score presented stimuli on 20 opposite-term pairs based on the semantic differential method after viewing each of six stimuli. Using factor analysis, we extracted three principal factors:
power
, related to strong three-dimensionality, real, etc.;
visibility
, related to stable, natural, etc.; and
space
, related to agile, open, etc., which had proportions of variances of 0.317, 0.277, and 0.251, respectively; their cumulation was 0.844. We confirmed that the three different-sized displays did not produce the same subjective impressions of the 3D characteristics. In particular, on the small-sized display, we found larger effects on power and space impressions from motion parallax (η
2
= 0.133 and 0.161, respectively) than for the other two sizes. We found degradation of the visibility impressions from binocular disparities, which might be caused by artifacts from stereoscopy. The effects of 3D viewing on subjective impression depends on the display size, and small-sized displays offer the largest benefits by adding 3D characteristics to 2D visualization.
Toward Adversary-aware Non-iterative Model Pruning through

D

ynamic

N

etwork

R

ewiring of DNNs
Authors: Kundu Souvik , Fu Yao , Ye Bill , Beerel Peter A. , Pedram Massoud
Published: January 2022

We present a dynamic network rewiring (DNR) method to generate pruned deep neural network (DNN) models that both are robust against adversarially generated images and maintain high accuracy on clean images. In particular, the disclosed DNR training method is based on a unified constrained optimization formulation using a novel hybrid loss function that merges sparse learning with robust adversarial training. This training strategy dynamically adjusts inter-layer connectivity based on per-layer normalized momentum computed from the hybrid loss function. To further improve the robustness of the pruned models, we propose DNR++, an extension of the DNR method where we introduce the idea of sparse parametric Gaussian noise tensor that is added to the weight tensors to yield robust regularization. In contrast to existing robust pruning frameworks that require multiple training iterations, the proposed DNR and DNR++ achieve an overall target pruning ratio with only a single training iteration and can be tuned to support both irregular and structured channel pruning. To demonstrate the efficacy of the proposed method under the no-increased-training-time “free” adversarial training scenario, we finally present FDNR++, a simple yet effective training modification that can yield robust yet compressed models requiring training time comparable to that of an unpruned non-adversarial training. To evaluate the merits of our disclosed training methods, experiments were performed with two widely accepted models, namely VGG16 and ResNet18, on CIFAR-10 and CIFAR-100 as well as with VGG16 on Tiny-ImageNet. Compared to the baseline uncompressed models, our methods provide over 20× compression on all the datasets without any significant drop of either clean or adversarial classification performance. Moreover, extensive experiments show that our methods consistently find compressed models with better clean and adversarial image classification performance than what is achievable through state-of-the-art alternatives. We provide insightful observations to help make various model, parameter density, and prune-type selection choices and have open-sourced our saved models and test codes to ensure reproducibility of our results.
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge
Authors: Ganesan Vinod , Kumar Pratyush
Published: January 2022

Massively parallel systolic arrays and resource-efficient depthwise separable convolutions are two promising hardware and software techniques to accelerate DNN inference on the edge. Interestingly, their combination is inefficient: Computational patterns of depthwise separable convolutions do not exhibit a rhythmic systolic flow and lack sufficient data reuse to saturate systolic arrays. In this article, we formally analyse this inefficiency and propose an efficient operator, an optimal hardware dataflow, and a superior training methodology towards alleviating this. The efficient operator, called
Fully-Separable Convolutions (FuSeConv)
,

1

is a drop-in replacement for depthwise-separable convolutions. FuSeConv generalizes factorization of convolution fully along their spatial and depth dimensions. The resultant computation is systolic and efficiently maps to systolic arrays. The optimal hardware dataflow, called
Spatial-Tiled Output Stationary
(ST-OS)
, maximizes the efficiency of FuSeConv on systolic arrays. It maps independent convolutions to rows of the systolic array to maximise resource-utilization with negligible VLSI overheads.
Neural Operator Scaffolding (NOS)
scaffolds the training of FuSeConv operators by distilling knowledge from the more expensive depthwise separable convolution operation. This bridges the accuracy gap between FuSeConv networks and networks with depthwise-separable convolutions. Additionally, NOS can be combined with
Neural Architecture Search (NAS)
to trade off latency and accuracy.

The hardware-software co-design of FuSeConv with ST-OS achieves a significant speedup of 4.1-9.25× with state-of-the-art efficient networks for the ImageNet dataset. The parameter efficiency of FuSeConv and its significant superiority over depthwise-separable convolutions on systolic arrays illustrates their promise as a strong solution on the edge. Training FuSeConv networks with NOS achieves accuracy comparable to the depthwise-separable convolution baselines. Further, by combining NOS with NAS, we design networks that define state-of-the-art models improving on both accuracy and latency for computer vision on systolic arrays.
Inflammatory Caspases: Toward a Unified Model for Caspase Activation by Inflammasomes
Authors: Ross Connie , Chan Amy H. , von Pein Jessica B. , Maddugoda Madhavi P. , Boucher Dave , Schroder Kate
Published: January 2022

Inflammasomes are inflammatory signaling complexes that provide molecular platforms to activate the protease function of inflammatory caspases. Caspases-1, -4, -5, and -11 are inflammatory caspases activated by inflammasomes to drive lytic cell death and inflammatory mediator production, thereby activating host-protective and pathological immune responses. Here, we comprehensively review the mechanisms that govern the activity of inflammatory caspases. We discuss inflammatory caspase activation and deactivation mechanisms, alongside the physiological importance of caspase activity kinetics. We also examine mechanisms of caspase substrate selection and how inflammasome and cell identities influence caspase activity and resultant inflammatory and pyroptotic cellular programs. Understanding how inflammatory caspases are regulated may offer new strategies for treating infection and inflammasome-driven disease.
Research on State of Health for the Series Battery Module Based on the Weibull Distribution
Authors: Zhao Qian , Jiang Haobin , Chen Biao , Wang Cheng , Xu Shanzhen , Zhu Jianhui , Chang Lv
Published: January 2022

The state of health (SOH) of the battery module is one of the important parameters in the battery management system. Accurately grasping the SOH of the battery module can provide the basis for its detection and diagnosis. In this work, the series battery module SOH is taken as the research object. Firstly, based on the characteristics of the series battery module, a two-parameter Weibull distribution is selected as the module failure data distribution form. Secondly, linearize the reliability function of the Weibull distribution and preprocess the module failure data. The least square method is used to identify the unknown parameters of the linear equation and carry out correlation analysis and model verification. The result shows the correlation coefficient

ρ

X
,
Y

=
0.9725
,

indicating that the variables X and Y are significantly correlated. The selected model is tested by the Kolmogorov-Smirnov (K–S) method, and the K-S test statistic D achieves the maximum value

D

max

=
0.0371
,

which is much smaller than the Dc = 0.301 obtained by checking the K-S D critical value table. In the reliability analysis, the failure data are evenly distributed on both sides of the reliability function, indicating that the selected model can well reflect the SOH transformation trend of the series battery module.
Electrode Alignment: Ignored but Important Design Parameter in Assembling Coin-Type Full Lithium-Ion Cells
Authors: Kim Nayeon , Byun Seoungwoo , Jin Dahee , Dzakpasu Cyril Bubu , Park Sun Ho , Lee Hyungjin , Hong Seung-Tae , Lee Yong Min
Published: January 2022

Electrode alignment is one of design parameters that must be carefully controlled for reliable full cells with limited lithium ion inventory. Especially, since punched disk-type cathodes and anodes are movable during assembling coin-type cells, the misalignment of electrodes cannot be completely prevented. Furthermore, this misalignment is not only mixed with other defects but also sometimes leads to better electrochemical characteristics. To systematically unveil this ignored but important parameter, herein, we fabricate coin-type LiNi0.6Mn0.2Co0.2O2/graphite full cells with different electrode alignments and evaluate them to figure out any noticeable changes in their electrochemical properties. As frequently reported, the misaligned cell shows lower specific discharge capacity and initial coulombic efficiency than the well-aligned one due to an irreversible Li plating on the coin cell bottom during the first charging process. However, we have not recognized the misaligned cell can exhibit a smaller low-frequency semicircle in the AC impedance spectra and lower DC-IRs at lowly charged states than those of the well-aligned cell because of the less lithiated state of the misaligned cathode. Thus, to exclude data from misaligned full cells, it is necessary to verify the electrode alignment even after the cell evaluation process.
Electrodeposited Transition Metal Dichalcogenides for Use in Hydrogen Evolution Electrocatalysts
Authors: Strange Lyndi E. , Garg Sourav , Kung Patrick , Ashaduzzaman Md , Szulczewski Gregory , Pan Shanlin
Published: January 2022

Hydrogen is a promising alternative to gasoline due to its higher energy density and ability to burn cleanly only producing H2O as a by-product. Electrolytic water splitting is an effective technique for generating molecular hydrogen. However, for hydrogen to be a viable alternative energy source to be produced from water electrolysis, affordable and durable electrocatalysts need to be developed to replace platinum. Transition metal dichalcogenides (TMDs) are a promising alternative since they are abundant, inexpensive, and have a tunable structure. There are various ways to produce TMD films including chemical and mechanical exfoliation, chemical vapor deposition (CVD), and electrodeposition. Exfoliation and CVD techniques often require a transfer of TMDs from the growth substrate to an electrode, which introduces impurities and possible defects to the film. Electrodeposition, however, provides a way to produce TMDs directly onto the electrode with excellent surface coverage. This work uses electrodeposition to produce TMD and TMD bilayer electrodes using sequential electrodeposition for electrocatalytic hydrogen evolution reaction (HER). The results presented include cost-effective deposition techniques along with enhanced proton reduction activity for the sequentially deposited bilayer TMD structure consisting of MoS2 and MoSe2, which suggests the electron transfer kinetics from the conductive glass substrate to the top-layer is enhanced with a MoS2 layer. Furthermore, the bilayer structures synthesized by sequential deposition are characterized via XPS, XPS depth-profiling, and SEM-EDS for enhanced understanding of the fabricated structure.
Corrosion of Alloy 690 in Simulated Steam Generator Crevices: Effect of Applied Potential, pH and Pb Addition
Authors: Betova Iva , Bojinov Martin , Ikäläinen Tiina , Saario Timo , Sipilä Konsta
Published: January 2022

The mechanism of general corrosion of Alloy 690 in simulated steam generator crevices is investigated by voltammetry, electrochemical impedance spectroscopy and slow strain rate tests, combined with ex-situ characterization of the oxides using X-ray photoelectron and Glow-discharge optical emission spectroscopies. Quantitative interpretation of impedance spectra with the Mixed-Conduction Model furnishes estimates of kinetic and transport parameters of the corrosion and anodic oxidation processes. The results indicate a beneficial effect of Pb addition on general corrosion regardless of crevice solution composition.
Chronic edible dosing of Δ9-tetrahydrocannabinol (THC) in nonhuman primates reduces systemic platelet activity and function
Authors: Reitsma Stéphanie E. , Lakshmanan Hari Hara Sudhan , Johnson Jennifer , Pang Jiaqing , Parra-Izquierdo Iván , Melrose Alex R. , Choi Jaewoo , Anderson Deirdre E. J. , Hinds Monica T. , Stevens Jan Frederik , Aslan Joseph E. , McCarty Owen J. T. , Lo Jamie O.
Published: January 2022

Cannabis usage has steadily increased as acceptance is growing for both medical and recreational reasons. Medical cannabis is administered for treatment of chronic pain based on the premise that the endocannabinoid system signals desensitize pain sensor neurons and produce anti-inflammatory effects. The major psychoactive ingredient of cannabis is Δ9-tetrahydrocannabinol (THC) that signals mainly through cannabinoid receptor-1 (CBr), which is also present on nonneuron cells including blood platelets of the circulatory system. In vitro, CBr-mediated signaling has been shown to acutely inhibit platelet activation downstream of the platelet collagen receptor glycoprotein (GP)VI. The systemic effects of chronic THC administration on platelet activity and function remain unclear. This study investigates the effects of chronic THC administration on platelet function using a nonhuman primate (NHP) model. Our results show that female and male NHPs consuming a daily THC edible had reduced platelet adhesion, aggregation, and granule secretion in response to select platelet agonists. Furthermore, a change in bioactive lipids (oxylipins) was observed in the female cohort after THC administration. These results indicate that chronic THC edible administration desensitized platelet activity and function in response to GPVI- and G-protein coupled receptor-based activation by interfering with primary and secondary feedback signaling pathways. These observations may have important clinical implications for patients who use medical marijuana and for providers caring for these patients.
A subset of synaptic transmission events is coupled to acetyl coenzyme A production
Authors: Jakkamsetti Vikram , Ma Qian , Pascual Juan M.
Published: January 2022

In all tissues, metabolism and excitation are coupled but the details of this relationship remain elusive. Using a brain-targeted genetic approach in mice, reduction of pyruvate dehydrogenase, a major gateway in glucose metabolism, leads to changes that affect the synaptic event charge associated primarily with large excitatory (i.e., glutamate mediated) synaptic potentials. This can be modified in the direction of normal using the alternative fuel acetate, indicating that this phenomenon depends on rapid metabolic flux.
Esketamine increases neurotransmitter releases but simplifies neurotransmitter networks in mouse prefrontal cortex
Authors: Wang Ye , Zhang Yunyun , Wang Kai , Zhu Zhenghua , Wang Dan , Yang Qianzi , Dong Hailong
Published: January 2022

In this study, we found that esketamine significantly increased the cortical concentration of multiple neurotransmitters in mice. However, esketamine dynamically simplified the overall network of cortical neurotransmitters at different behavioral states during the perianesthesia period. The concentration of 5-HT in the medial prefrontal cortex (mPFC) was highly correlated with the esketamine-increased gamma oscillation. These findings suggested that the transformation of the neurotransmitter network rather than the concentrations of neurotransmitters could be more indicative of the consciousness shift during esketamine anesthesia.
Mice with monoallelic GNAO1 loss exhibit reduced inhibitory synaptic input to cerebellar Purkinje cells
Authors: Feng Huijie , Yuan Yukun , Williams Michael R. , Roy Alex J. , Leipprandt Jeffery R. , Neubig Richard R.
Published: January 2022

This report reveals the electrophysiological mechanisms of a movement disorder animal model with monoallelic Gnao1 loss. This study illustrates the role of Gαo protein in regulating GABA release in mouse cerebellum. This study could also facilitate the discovery of new drugs or drug repurposing for GNAO1-associated disorders. Moreover, since GNAO1 shares pathways with other genes related to movement disorders, developing drugs for the treatment of GNAO1-associated movement disorders could further the pharmacological intervention for other monogenic movement disorders.
Two-Way Neural Network Chinese-English Machine Translation Model Fused with Attention Mechanism
Authors: Liang Jing , Du Minghui
Published: January 2022

This study uses an end-to-end encoder-decoder structure to build a machine translation model, allowing the machine to automatically learn features and transform the corpus data into distributed representations. The word vector uses a neural network to achieve direct mapping. Research on constructing neural machine translation models for different neural network structures. Based on the translation model of the LSTM network, the gate valve mechanism reduces the gradient attenuation and improves the ability to process long-distance sequences. Based on the GRU network structure, the simplified processing is performed on the basis of it, which reduces the training complexity and achieves good performance. Aiming at the problem that some source language sequences of any length in the encoder are encoded into fixed-dimensional background vectors, the attention mechanism is introduced to dynamically adjust the degree of influence of the source language context on the target language sequence, and the translation model’s ability to deal with long-distance dependencies is improved. In order to better reflect the context information, this study further proposes a machine translation model based on two-way GRU and compares and analyzes multiple translation models to verify the effectiveness of the model’s performance improvement.
Decreased Gray Matter Volume in the Frontal Cortex of Migraine Patients with Associated Functional Connectivity Alterations: A VBM and rs-FC Study
Authors: Cao Zhijian , Yu Wenjing , Zhang Zhengxiang , Xu Maosheng , Lin Jiangnan , Zhang Luping , Song Wenwen
Published: January 2022

Background. Resting-state functional MRI is widely used in migraine research. However, the pathophysiology and imaging markers specific for migraine pathologies are not well understood. In this study, we combined both structural and functional images to explore the concurrence and process of migraines. Methods. Thirty-four patients with a history of migraine without aura presenting during the interictal period (MwoA-DI), 10 patients with migraine without aura presenting during the acute attack (MwoA-DA), and 32 healthy controls (HCs) were recruited in this study. All participants underwent scanning via MRI. Voxel-based morphometry (VBM) and seed-based resting-state functional connectivity (rs-FC) analysis were used to detect the brain structural and associated brain functional connectivity. Results. In VBM analysis, a decrease of gray matter volume (GMV) in the middle frontal cortex was found in MwoA patients compared with HCs. The GMV of the middle frontal cortex had a negative correction with the duration of disease. In rs-FC analysis, the left middle frontal cortex (lower, VBM result) in both the MwoA-DA and the HC groups showed significantly increased functional connectivity with the left middle frontal cortex (upper) and left superior frontal cortex compared with MwoA-DI. The left middle frontal cortex (lower) in the MwoA-DI group also showed decreased functional connectivity in the left posterior cingulate cortex (PCC) compared with the HC group. The left middle frontal cortex (lower) in the MwoA-DA group demonstrated significantly increased functional connectivity in the left cerebellum lobule VI compared with the HC group. Conclusions. Our results demonstrated that the middle frontal cortex may serve as an important target in the frequency and severity of migraines due to its role in pain regulation through the default mode network, especially in the PCC. In addition, the cerebellum may modulate the pathophysiology of migraines by serving as a communication point between the cortex and the brainstem.
Development and Implementation Path of Kindergarten Stem Educational Activities Based on Data Mining
Authors: Jiang Liangfu
Published: January 2022

Early childhood education in China has given stem education constant attention and study. On the one hand, it has introduced many foreign research findings on stem education, such as curriculum practice, evaluation systems, teacher training, and so on; on the other hand, this paper investigates the localization implementation path of stem education based on the realities of kindergartens. This paper investigates the development and implementation path of kindergarten stem education activities using data mining, analyzes how the kindergarten stem education monitoring index system is developed and further improved using data mining algorithm, and determines the function path and mode of data mining algorithm in kindergarten stem education. It is expected to be used as a reference in the development and implementation of stem education and teaching activities. The development and implementation path of kindergarten stem educational activities based on data mining algorithm using data technology to realize continuous audit can not only improve the audit means and scope but also provide new research ideas for the expansion and innovation of audit work, which is useful in building a path model of kindergarten stem educational activities development and implementation.
The Effect of Autologous Adipose‐Derived Stromal Vascular Fractions on Cartilage Regeneration Was Quantitatively Evaluated Based on the 3D‐FS‐SPGR Sequence: A Clinical Trial Study
Authors: Zhang Yin , Bi Qing , Luo Junchao , Tong Yu , Yu Taihen , Zhang Qiong
Published: January 2022

Background. Numerous reports confirmed the safety and clinical efficacy of autologous adipose‐derived stromal vascular fractions (SVF), which have recently been used to treat osteoarthritis (OA). However, there is still no consensus as to whether SVF can promote cartilage regeneration. Herein, the purpose of our study was to evaluate the effectiveness of SVF versus hyaluronic acid (HA) in cartilage regeneration by establishing a cartilage model based on the three‐dimensional fat‐suppressed spoiled gradient recalled echo (3D‐FS‐SPGR) sequence. Methods. Patients with symptomatic OA were recruited in our research, who were randomized into two groups. Meanwhile, patients in Kellgren‐Lawrence (K‐L) grades 2 and 3 were distinguished in each group. In the test group, patients received SVF injections of the knee, while patients in the control group received the same dose of HA. Each patient underwent the 3D‐FS‐SPGR sequence to establish a cartilage model at baseline, 6 months, and 12 months, respectively. The cartilage was characterized into six regions, and relevant parameters of the cartilage model were counted. Clinical and radiographic scores were recorded in one‐year follow‐up. Results. In all regions, the thickness and volume of cartilage defect and the volume of healthy cartilage were improved to some extent in the test group, especially the medial femoral condyle (MF) and medial tibial condyle (MT). In grades 2 and 3, the thickness and volume of cartilage defect decreased by 0.92 ± 0.18 mm and 1.03 ± 0.23 mm and 84.00 ± 32.30 mm3 and 130.30 ± 49.56 mm3 in MF and by 0.96 ± 0.22 mm and 0.99 ± 0.14 mm and 64.18 ± 21.40 mm3 and 95.11 ± 19.93 mm3 in MT, respectively. No such phenomenon was observed in the control group. Meanwhile, the SVF‐treated knees showed significant improvement in clinical and radiographic scores at 12 months. Nevertheless, these scores of the control group became worse at 12‐month follow‐up visit. Conclusion. Taken together, this study shows that intra‐articular injection of SVF markedly improved the clinical symptoms without adverse events, thereby repairing the damaged articular cartilage through cartilage regeneration.
Isothermal Desorption Hysteretic Model for Deep Coalbed Methane Development
Authors: Tang Songlei , Zhai Hongbo , Tang Hong , Yang Feng
Published: January 2022

The adsorption/desorption mechanism of coalbed methane is significant for gas control and coalbed methane exploitation; scholars have done a lot of research on it and generally have confidence in that temperature, pressure, and moisture are central factors affecting the adsorption of coalbed methane. Considering the reduction of recovery efficiency caused by desorption hysteresis in deep coalbed methane drainage, the effects of high reservoir pressure, high gas content, and low permeability on the hysteresis index were analyzed. A desorption hysteresis model based on the combination of dual-porosity media and traditional Langmuir adsorption theory was proposed. By comparing with the four experimental data of Ma et al., the advantages of the new model in fitting desorption data were investigated. Based on the new desorption hysteresis model, the hysteresis index was calculated from the adsorption capacity and desorption capacity under the abandonment pressure. The hysteresis index under different coal sizes and adsorption pressure were calculated, and a good linear relationship was found between the adsorption pressure and the hysteresis index. Through a large number of field production data analysis, the following conclusions are drawn: as the adsorption pressure increases, the hysteresis index enhances; when the coal sample size increases, the hysteresis index also increases. Finally, by comparing experimental data from deep and shallow coal samples, the influence of desorption hysteresis on deep coalbed methane mining was explored. This paper draws the conclusion that although the gas content in deep coalbed methane is considerable, its hysteresis index is also enhanced, which makes coalbed methane development more difficult. The findings of this study can provide theoretical support for coal bed gas control and coal bed methane heat injection mining.
TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction
Authors: Zhou Ning , Shi Wenqian , Liang Renyu , Zhong Na
Published: January 2022

When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords.
Importance and Relevance of Phytochemicals Present in Galenia africana
Authors: Heredia Dario , Green Ivan , Klaasen Jeremy , Rahiman Farzana
Published: January 2022

Many people in developing countries rely primarily on medicinal plants as their main source of healthcare, particularly for the treatment of skin infections. Despite the widespread use of medicinal plants, there is a lack of literature describing the relevance and risks of exposure of the phytochemicals present. Galenia africana has been used traditionally in the form of pastes, decoctions, and lotions to treat wounds and other skin-related ailments. This is a report on the phytochemical composition of G. africana and a review on the pharmacological importance and relevance of these phytochemicals. The major groups of phytochemicals identified in G. africana extracts were aliphatics, aliphatic triterpenoids, fatty acids, flavonoids, and phenolic and tocopherol compounds. These have been found to exhibit medicinal properties, thus highlighting the need to assess the safety of G. africana for topical application. The information related to the safety of the various compounds could indicate the potential risks related to accidental intake of the extract upon topical product applications. This report concludes that the quantities of the phytochemicals present in G. africana should not cause undue risk to human health, which provides comfort to pursue future work on using and developing G. africana as a therapeutic agent.
Microencapsulation of Purple Mashua Extracts Using Andean Tuber Starches Modified by Octenyl Succinic Anhydride
Authors: Velásquez-Barreto Frank Fluker , Sánchez Carmen Eloisa Velezmoro
Published: January 2022

This work is aimed at optimising the spray drying conditions of the phenolic extracts of purple mashua microencapsulated with octenyl succinic anhydride (OSA) Andean tuber starches. Purple mashua extracts were obtained and spray dried using native and OSA starches of yellow oca, pink oca, and yellow olluco (140°C, 4% starch). The powders obtained were analysed by encapsulation efficiency of anthocyanin (EE), total phenol content, and antioxidant capacity to select the best starch for optimisation purposes. OSA pink oca starch was selected because the obtained powder presented the highest EE. The spray drying conditions optimised were obtained using a central composite rotatable design (CCRD) and response surface methodology. The encapsulant proportion of OSA pink oca starch (2-12%) and the inlet drying temperature (IDT, 120-160°C) were used as factors of the design. The optimised spray drying condition was 160°C IDT and 2% encapsulant; this condition maximised the EE, total phenol content, antioxidant capacity, and solubility and minimised the water activity and hygroscopicity of the powder. The OSA pink oca starch could be used as an encapsulating agent of phenolic extracts because it can produce powder with high antioxidant capacity and high EE.
Comparison of Risk Assessment Strategies for Patients with Diabetes Mellitus and Stable Chest Pain: A Coronary Computed Tomography Angiography Study
Authors: Zhao Jia , Wang Shuo , Zhao Pengyu , Huo Yong , Li Chunjie , Zhou Jia
Published: January 2022

Background. To compare two risk assessment strategies to identify individuals likely to benefit from further imaging testing in patients with diabetes mellitus (DM) and stable chest pain (SCP) suspected of obstructive coronary artery disease (CAD). Methods. 602 DM patients referred to coronary computed tomography angiography (CCTA) for SCP were included. They were divided into high- and low-risk groups according to the 2016 National Institute of Health and Care Excellence guideline-determined strategy (NICE strategy) which focused on symptom evaluation and 2019 European Society of Cardiology guideline-determined strategy (ESC strategy) which was based on pretest probability (PTP) sequentially determined by the ESC-PTP estimator and risk factor-weighted clinical likelihood (RF-CL) model, respectively. The associations of clinical outcomes with risk groups and net reclassification improvement (NRI) were evaluated. Results. The NICE and ESC strategy classified 44% and 39% patients into the low-risk group, respectively. Compared to the NICE strategy, the ESC strategy indicated stronger associations between risk groups and events (hazard ratios: 4.24 versus 1.91), intensive clinical management, and a positive NRI (27.71%,

p
<
0.0001

). The application of the RF-CL model ameliorated the underestimation of risk in patients with borderline ESC-PTP, which principally account for the improvement of the ESC strategy. Conclusion. Compared to the NICE strategy, the ESC strategy seemed to be associated with greater efficiency in identifying high risk individuals in patients with DM and SCP.
Efficacy and Safety of Endoscopic Submucosal Dissection for Dysplasia in Ulcerative Colitis Patients: A Systematic Review and Meta-Analysis
Authors: Zeng Qi-Shan , Zhao Zhi-Jing , Nie Jiao , Zou Min , Yang Jia-Hui , Zhang Jin-Zhi , Gan Hua-Tian
Published: January 2022

Background and Aims. Ulcerative colitis (UC) is associated with an increased risk of colorectal cancer. Current guidelines recommend endoscopic resection if the lesion is visible with distinct margins and a complete resection can be achieved. However, submucosal fibrosis due to chronic inflammation may increase the procedural risk and reduce the complete resection rate. The aim of this study is to assess the efficacy and safety of endoscopic submucosal dissection (ESD) for dysplasia in UC patients. Materials and Methods. A systematic search of databases was performed until May 30, 2021. Studies that reported the resection rates and complication rates of ESD for dysplasia in UC patients were included. A random-effects model was used to generate conservative estimates of the prevalence of the outcome variables. All data analyses were performed using software Stata (version 15). Results. 8 studies were enrolled in the meta-analysis, with a total of 203 dysplastic lesions in 192 UC patients. The mean lesion size was 26.7 mm. About 83% of the lesions were located in the left-side colon, and 90% of the lesions were nonpolypoid, and about 71% of the lesions had submucosal fibrosis. The mean procedural time of ESD was 83 minutes. The en bloc resection rate, complete resection rate, and curative resection rate were 94%, 84%, and 81%, respectively, with a local recurrence rate of 5%. The pooled prevalence of bleeding and perforation were 8% and 6%, respectively. The rates of metachronous tumors and additional surgery after ESD were 6% and 10%, respectively. Conclusion. Despite some limitations, our study suggests that ESD is an effective and safe treatment for dysplasia in UC patients. However, randomized controlled multicenter studies with less heterogeneity and longer follow-up are needed to better assess the clinical outcomes of ESD in UC patients.