bluuebunny/crossref_metadata_2025 huggingface dataset:
MLPER: A Machine Learning-Based Prediction Model for Building Earthquake Response Using Ambient Vibration Measurements
Authors: Damikoukas Spyros , Lagaros Nikos D.
Published: September 2023

Deep neural networks (DNNs) have gained prominence in addressing regression problems, offering versatile architectural designs that cater to various applications. In the field of earthquake engineering, seismic response prediction is a critical area of study. Simplified models such as single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems have traditionally provided valuable insights into structural behavior, known for their computational efficiency facilitating faster simulations. However, these models have notable limitations in capturing the nuanced nonlinear behavior of structures and the spatial variability of ground motions. This study focuses on leveraging ambient vibration (AV) measurements of buildings, combined with earthquake (EQ) time-history data, to create a predictive model using a neural network (NN) in image format. The primary objective is to predict a specific building’s earthquake response accurately. The training dataset consists of 1197 MDOF 2D shear models, generating a total of 32,319 training samples. To evaluate the performance of the proposed model, termed MLPER (machine learning-based prediction of building structures’ earthquake response), several metrics are employed. These include the mean absolute percentage error (MAPE) and the mean deviation angle (MDA) for comparisons in the time domain. Additionally, we assess magnitude-squared coherence values and phase differences (Δφ) for comparisons in the frequency domain. This study underscores the potential of the MLPER as a reliable tool for predicting building earthquake responses, addressing the limitations of simplified models. By integrating AV measurements and EQ time-history data into a neural network framework, the MLPER offers a promising avenue for enhancing our understanding of structural behavior during seismic events, ultimately contributing to improved earthquake resilience in building design and engineering.
Ins and Outs of the Ankle Syndesmosis from a 2D to 3D CT Perspective
Authors: Dhont Thibaut , Huyghe Manu , Peiffer Matthias , Hagemeijer Noortje , Karaismailoglu Bedri , Krahenbuhl Nicola , Audenaert Emmanuel , Burssens Arne
Published: September 2023

Despite various proposed measurement techniques for assessing syndesmosis integrity, a standardized protocol is lacking, and the existing literature reports inconsistent findings regarding normal and abnormal relationships between the fibula and tibia at the distal level. Therefore, this study aims to present an overview of two- (2D) and three-dimensional (3D) measurement methods utilized to evaluate syndesmosis integrity. A topical literature review was conducted, including studies employing 2D or 3D measurement techniques to quantify distal tibiofibular syndesmosis alignment on computed tomography (CT) or weight-bearing CT (WBCT) scans. A total of 49 eligible articles were included in this review. While most interclass correlation (ICC) values indicate favorable reliability, certain measurements involving multiple steps exhibited lower ICC values, potentially due to the learning curve associated with their implementation. Inconclusive results were obtained regarding the influence of age, sex, and height on syndesmotic measurements. No significant difference was observed between bilateral ankles, permitting the use of the opposite side as an internal control for comparison. There is a notable range of normal and pathological values, as evidenced by the standard deviation associated with each measurement. This review highlights the absence of a consensus on syndesmotic measurements for assessing integrity despite numerous CT scan studies. The diverse measurement techniques, complexity, and inconclusive findings present challenges in distinguishing between normal and pathological values in routine clinical practice. Promising advancements in novel 3D techniques offer potential for automated measurements and reduction of observer inaccuracies, but further validation is needed.
The Influence of Texture Type and Grain Milling Degree on the Attenuation Limit, Protein Content, and Degradation in Wheat Wort
Authors: Krstanović Vinko , Habschied Kristina , Košir Iztok Jože , Mastanjević Krešimir
Published: September 2023

Wheat of medium hardness with marbled endosperm (transitional form between hard and soft wheat) in which glassy and floury zones alternate, form almost exclusively available assortment for brewing needs in Southeastern Europe. The aim of this work is to establish the influence of the grain texture and the degree of milling on the attenuation limit of wheat wort obtained from this type of wheat. Wheat worts using hard, soft transitional, or marbled endosperm texture were produced. The indicators of proteolysis, cytolysis, and amylolysis were determined, with regard to the parameter attenuation limit (AL) or fermentability. From the results for the tested parameters, it was established that despite similar starting values for the most important quality parameters, transitional wheat produces significantly different wort, both among themselves and in comparison with hard and soft wheat, and also when looking at the results for different milling degree (fine or coarse). The obtained values for the attenuation limit for transition wheat are similar or even better when compared to soft wheat, with satisfactory values for almost all examined quality parameters of wort. It can be concluded that a transitional type of wheat can be used just as well as unmalted raw material for the production of wort, as well as a raw material for malting.
Development of Rhombus Hanging Basket Walking Track Robot for Cantilever Casting Construction in Bridges
Authors: Ouyang Yuping , Huang Jiarui , Song Kaifang
Published: September 2023

For enhancing the efficiency of cantilever casting construction in a bridge, a novel rhombus traveling track for a hanging basket was developed and optimized. The business-oriented software called MIDAS was applied to analyze the mechanical properties of the hanging basket with a full load and a control load. The strength and distortion of the walking mechanism are within the specified range when the maximum beam unit internal force is 149.69 MPa, which is less than the allowable stress of steel of 175 Mpa, and the anti-overturning safety factor of the hanging basket is 2.5, which meets the requirements. Through the comparative analysis of the key components and the finite element calculation, it was found that there is about a 30% redundancy in the structure performance. Therefore, further optimization of each structure was carried out, and the front elevation of the main truss of 20° was achieved. It obtained the best performance in all aspects.
Design and Analysis of a New Semiactive Hydraulic Mount for a Wide-Range Tunable Damping without Magneto-Rheological/Electric-Rheological Fluid
Authors: Zhu Yuan-Cheng , Yao Guo-Feng , Wang Min , Yu Hang , Gao Kui-Yang , Zhou Pei-Lei
Published: September 2023

A hydraulic engine mount (HEM) is an advanced tuned mass damper (TMD) system used to isolate the noise and vibration from the engine to the chassis. This paper aims to design a semiactive HEM based on the TMD model, which only tunes damping without affecting the other lumped parameters of the TMD system. Firstly, the dynamic equations of an HEM modeled as a TMD system are derived based on the linear lumped parameter model (LPM). Each lumped parameter of the HEM is analyzed to identify the relevant parameters that affect damping. Secondly, a newly designed semiactive HEM design is proposed that utilizes a helical moving plate to simultaneously control both the inertia track area and length, resulting in precise damping tuning. To illustrate the dynamic performance of the newly designed HEM, calculations are presented based on various parameters for its tunable damping range and dynamic stiffness spectrum. Additionally, to demonstrate the performance of vibration isolation, this paper determines the optimal length and dynamic stiffness of the inertia track to minimize the transmissibility. Thirdly, to reveal the superiority of the newly designed HEM over the MR fluid mount, an example is presented where the MR fluid medium is used in place of conventional hydraulic fluid in the HEM while keeping all other parameters constant. Specifically, the novel semiactive HEM employs conventional hydraulic fluid and is spiral-driven by a moving plate while the MR fluid-based HEM is controlled by an additional tunable magnetic intensity controller. The nonlinear LPM of the newly designed HEM is verified by comparing the dynamic stiffness spectrum with the experimental results in the published literature. Then, the nonlinear LPM of the MR fluid mount is established, and its dynamic stiffness spectrum is calculated and compared with that of the newly designed HEM. The results indicate that the newly designed HEM and MR fluid mount have similar ranges of dynamic stiffness control, but the newly designed HEM does not require expensive MR/ER fluid or additional continuous external energy input to regulate the dynamic stiffness. Moreover, when using inexpensive low-viscosity hydraulic fluid, the newly designed HEM can provide a wider range of dynamic stiffness control.
A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems
Authors: Grznár Patrik , Burganová Natália , Mozol Štefan , Mozolová Lucia
Published: September 2023

In the current global economy, where rapid changes and constantly shifting market demands define the competitive landscape, adaptive manufacturing systems become essential for businesses striving to remain relevant and efficient. In the context of this growing need, this study focuses on planning as a part of adaptive manufacturing system. This methodology provides a systematic framework that spans from foundational groundwork to meticulous verification and validation phases. By employing advanced simulation techniques, seamless data integration, and process optimization, this methodology ensures the smooth realization of robust and efficient adaptive manufacturing systems. A detailed case study on competency islands showcases the versatility of this approach, demonstrating its efficacy in enhancing manufacturing agility and overall performance. As a significant contribution to the field of smart manufacturing, this methodology offers a structured blueprint for the realization of adaptive manufacturing systems.
A Study of Fault Signal Noise Reduction Based on Improved CEEMDAN-SVD
Authors: Zhao Sixia , Ma Lisha , Xu Liyou , Liu Mengnan , Chen Xiaoliang
Published: September 2023

In light of the challenges posed by the complex structural characteristics and significant coupling of vibration signals in rotating machinery, this study proposes an adaptive noise reduction method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Additionally, an enhanced threshold screening Singular Value Decomposition (SVD) algorithm is introduced to address the issues pertaining to noise identification and feature extraction in the context of vibration signals from rotating machinery, which are subjected to complex noise interference. The effectiveness of the proposed approach is substantiated through the evaluation of key metrics, such as the signal-to-noise ratio (SNR), as well as the utilization of advanced signal analysis techniques, including Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). The experimental results validate the finding that the combination of the improved CEEMDAN and the enhanced threshold screening SVD algorithm effectively reduces noise interference in vibration signals from rotating machinery. This integrated denoising approach successfully preserves the informative characteristics of the vibration signals, thereby laying a foundation for the subsequent fault diagnosis of rotating machinery.
Dynamic Optimization of Construction Time-Cost for Deep and Large Foundation Pit Based on BIM Technology and Genetic Algorithm
Authors: Yu Yingxia , Han Junjia , Gu Haoyu , Yang Yi
Published: September 2023

With the increasingly fierce market competition, optimizing the construction time-cost of deep and large foundation pit construction projects has become one of the key factors for construction enterprises to remain invincible. By dynamically optimizing the time-cost, the optimal time corresponding to the lowest engineering cost can be found. Based on a comprehensive transportation hub project, considering the time value of capital and the reward and punishment of construction time, a time-cost dynamic optimization model is constructed. Relying on BIM technology, the feasibility of construction plan is analyzed from both qualitative and quantitative perspectives, and the project parameters and resource information is obtained accurately and quickly. Using the MATLAB program, time-cost optimization based on genetic algorithm is carried out, and the static and dynamic optimization results are compared. The results show that the dynamic optimization scheme reduces the total cost by 1.68% while reducing the total construction time by 8.47%. The dynamic optimization scheme extends the construction period by 2 days while reducing the total cost by 89,500 yuan compared to static optimization. The peak value of the fund demand curve before and after optimization has decreased from 128,000 yuan to 127,000 yuan. The time-cost dynamic optimization, considering the time value of capital is more in line with engineering reality, and the optimization results are more reliable and accurate. BIM technology can accurately and quickly obtain project parameters and resource information, solving problems such as complex processes in super deep excavation and huge engineering data statistics. The genetic algorithm can efficiently and accurately search for the optimal solution within the global domain. This study combines BIM technology with the genetic algorithm to solve the dynamic optimization problem of construction period cost for deep and large foundation pits. The research results of this study provide a theoretical reference for the optimization of schedule cost for similar projects.
Efficient Removal of Representative Chemical Agents by Rapid and Sufficient Adsorption via Magnetic Graphene Oxide Composites
Authors: Wu Jina , Qu Gang , Yan Long , Wang Ruixue , Guo Peiwen , Yang Yang , Li Xiaosen
Published: September 2023

Chemical agents pose a significant threat to social security, highlighting the crucial role of representative chemical agents adsorption in ensuring the safety our environment. This study explored the application of Magnetic Graphene Oxide Nanoplatelet Composites (MGONCs) in adsorbing the representative chemical agents such as Lewisite (L), O-ethyl S-2-diisopropylaminoethyl methylphosphonothiolate (VX), Sarin (GB), and Soman (GD). MGONCs were synthesized through a physical blending method, with the combination of graphene oxide (GO) and Fe3O4 nanoparticles at a mass ratio of 1:1. Optimization of the adsorption process involved investigating the effects of contact time, temperature, and adsorbent dosage. Remarkably, the adsorption rate of L and VX exceeded 99% when the dosage of MGONCs was 2.5 mg, with a contact time of 30 s at room temperature. Furthermore, GB and GD achieved maximum adsorption rates after a contact time of 20 min, with the dosages of MGONCs at 10 mg and 20 mg, respectively. Characterization of the magnetic composite was accomplished through XRD, TEM, VSM, FTIR, TGA, and BET analyses. Kinetical analysis revealed that the adsorption mechanism of GB and GD on MGONCs followed pseudo-second-order (PSO) kinetics, exhibiting a high regression coefficient. The calculated qe values were 0.103125 mg/g and 0.081349 mg/g, respectively. This research demonstrated the feasibility of utilizing MGONCs as highly efficient adsorbents for representative chemical agents, particularly in on-site sampling scenarios.
Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
Authors: Paik Juryon , Baek Seung-June , Kim Jun-Wan , Ko Kwangho
Published: September 2023

The South Korean residential real estate market is influenced by both the traditional dynamics of demand and supply and external factors such as housing policies and macroeconomic conditions. Considering the proportion of housing assets in individual wealth, market fluctuations can have significant implications. While previous studies have utilized variables such as GDP growth rate, patent issuance, and birth rate, and employed models such as LSTM and ARIMA for housing price predictions, many have overlooked the influence of local factors. In particular, there has been insufficient investigation into the impact of subway stations and living social overhead capital facilities on housing prices, especially in metropolitan areas. This study seeks to bridge this gap by analyzing the usage trends of subway stations, evaluating the impact of living social overhead capital facilities on housing values, and deriving the optimal machine learning model for price predictions near subway stations. We compared and analyzed a total of eight machine learning regression models, including Linear Regression, Decision Tree, Random Forest, LightGBM, Ridge, Lasso, Elastic Net, and XGBoost, all of which are popular regression models, especially in the context of machine learning and data science. Through comparative analysis of these machine learning techniques, we aim to provide insights for more rational housing price determinations, thereby promoting stability in the real estate market.
The Application Status and Trends of Machine Vision in Tea Production
Authors: Yang Zhiming , Ma Wei , Lu Jinzhu , Tian Zhiwei , Peng Kaiqian
Published: September 2023

The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.
Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning
Authors: Dalmau Ramon , Attia Jonathan , Gawinowski Gilles
Published: September 2023

Accurate prediction of traffic demand and airport capacity plays a crucial role in minimising ground delays and airborne holdings. This paper focuses on the latter aspect. Adverse weather conditions present significant challenges to airport operations and can substantially reduce capacity. Consequently, any predictive model, regardless of its complexity, should account for weather conditions when estimating the airport capacity. At present, the sole shared platform for airport capacity information in Europe is the EUROCONTROL Public Airport Corner, where airports have the option to voluntarily report their capacities. These capacities are presented in tabular form, indicating the maximum number of hourly arrivals and departures for each possible runway configuration. Additionally, major airports often provide a supplementary table showing the impact of adverse weather in a somewhat approximate manner (e.g., if the visibility is lower than 100 m, then arrival capacity decreases by 30%). However, these tables only cover a subset of airports, and their generation is not harmonised, as different airports may use different methodologies. Moreover, these tables may not account for all weather conditions, such as snow, strong winds, or thunderstorms. This paper presents a machine learning approach to learn mapping from weather conditions and runway configurations to the 99th percentile of the delivered throughput from historical data. This percentile serves as a capacity proxy for airports operating at or near capacity. Unlike previous attempts, this paper takes a novel approach, where a single model is trained for several airports, leveraging the generalisation capabilities of cutting-edge machine learning algorithms. The results of an experiment conducted using 2 years of historical traffic and weather data for the top 45 busiest airports in Europe demonstrate better alignment in terms of mean pinball error with the observed departure and arrival throughput when compared to the operational capacities reported in the EUROCONTROL Public Airport Corner. While there is still room for improvement, this system has the potential to assist airports in defining more reasonable capacity values, as well as aiding airlines in assessing the impact of adverse weather on their flights.
Special Issue Editorial: Long-Term Research on the Quality of Air and the Trends of Its Variability
Authors: Golobokova Liudmila P.
Published: September 2023

Long-term observations are integral to encouraging research of atmospheric composition, the climate, and human health, and thus, filling some gaps in scientific knowledge [...]
Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model
Authors: Zhang Binzhe , Duan Min , Sun Yufan , Lyu Yatong , Hou Yali , Tan Tao
Published: September 2023

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control.
How Well Does Weather Research and Forecasting (WRF) Model Simulate Storm Rashmi (2008) Itself and Its Associated Extreme Precipitation over the Tibetan Plateau at the Same Time?
Authors: An Pengchao , Li Ying , Ye Wei , Fan Xiaoting
Published: September 2023

Northward tropical cyclones over the Bay of Bengal (BoB TCs) often interact with atmospheric circulation, transporting large amounts of water vapor to the Tibetan Plateau (TP), causing extreme precipitation. The BoB surrounded by land on three sides and the complex topography of the TP bring challenges to implementing numerical simulation in these regions. However, the scarcity of data in the two areas makes it necessary to find a technological process to perform practicable numerical simulations on the BoB TC and its induced extreme precipitation to carry out further research. In this study, the WRF 3.9.1 is used to perform many simulation experiments on a northward BoB TC Rashmi (2008) from 24 to 27 October 2008 associated with a record-breaking extreme precipitation on the TP, indicating that the selection of the simulation region, the source of initial-boundary conditions, and the cumulus convection schemes are three important factors influencing the results. We examined and compared the simulation of Rashmi with 10 experiments that were generated by combining The Final Operational Global Analysis (FNL) reanalysis data and the European Centre for Medium-Range Weather Forecasting 5(th) generation reanalysis (ERA5) data as initial-boundary conditions with five cumulus convection schemes. Most of the experiments can predict Rashmi and precipitation in the TP to a certain degree, but present different characteristics. Compared with FNL, the ERA5 performs well regarding Rashmi’s intensity and thermal structure but overestimates Rashmi’s moving speed. For the extreme precipitation in the TP, experiments suffice to reproduce the heavy rainfall (>25 mm/day) in the TP, with TS and ETS scores above 0.3 and most HSS scores greater than 0.4. The optimal experiments of three stations with extreme precipitation deviated from the actual precipitation by less than 15%. The ERA5 TDK scheme is recommended as the optimal solution for balancing the simulation of Rashmi and its extreme precipitation in the TP.
Responses of Extreme Climates in South Asia under a G6sulfur Scenario of Climate Engineering
Authors: Wang Jingrong , Zhang Zhihua , Crabbe M. James C. , Das Lipon Chandra
Published: September 2023

Under global warming scenarios, extreme climate events in South Asia will occur more frequently which will seriously threaten the safety of local residents. South Asia faces dual pressures of the obligation of carbon emissions reduction globally and the demand for a better life for huge populations. Stratospheric Aerosol Injection (SAI) climate engineering provides a potential solution to this dilemma. We compared the evolution of 12 climate extreme indices under historical scenarios, two future scenarios (SSP245, SSP585) and an implementation scenario of SAI climate engineering (G6sulfur). We showed that the intensity and frequency of extreme climates under a G6sulfur scenario would be significantly higher than those under historical scenarios, and that the difference in extreme climates under three scenarios (SSP245, SSP585, and G6sulfur) would be widely varying, with some indices being considerably mitigated while others would reflect a worse set of circumstances than would be the case without SAI climate engineering. Therefore, SAI climate engineering is not an effective tool to mitigate future climate extremes in South Asia under global warming scenarios.
Vestibular Migraine Therapy: Update and Recent Literature Review
Authors: Zanandrea Laura , Colombo Bruno , Filippi Massimo
Published: September 2023

Vestibular migraine (VM) is a neurological condition that causes vertigo and headache. It is considered the most common cause of episodic vertigo. However, specific treatments are missing, and medications currently used in VM are borrowed from migraine therapy. A comprehensive practical review of the literature assessing the evidence for abortive and preventive interventions in adults with VM was published in 2022, providing practical recommendations about VM treatment. The aim of our paper is to provide an updated overview of the current state of the art of VM treatment, illustrating new evidence available in this field. Along with traditional pharmacological preventive therapies, medications targeting the CGRP pathways have recently been investigated in terms of treatment effect in VM patients, with encouraging results. Also, there is new evidence of the efficacy of non-pharmacological interventions. However, the overall evidence base for VM treatment remains sparse.
Functional Surface Coating to Enhance the Stability of LiNi0.6Mn0.2Co0.2O2
Authors: Xie Yingying , Li Matthew , Li Jiantao , Huang Xiaozhou , Cai Jiyu , Yang Zhenzhen , Nguyen Hoai , Shaik sulaiman Baasit ali , Karami Niloofar , Chernova Natalya A. , Upreti Shailesh , Prevel Brad , Wang Feng , Chen Zonghai
Published: September 2023

Parasitic reactions are responsible for continuous performance loss during the normal operation and storage of lithium-ion batteries, particularly for those using nickel-rich cathode materials. Among many contributors, residual Li2CO3 on the surface of nickel-rich cathodes plays a detrimental role in promoting parasitic reactions, and hence accelerates the performance loss of those cathode materials. In this work, a wet impregnation process was utilized to convert the detrimental Li2CO3 and LiOH impurities into a beneficial functional surface coating comprising phosphates. Specifically, hydro-phosphates were used as the functional surface modification agents to mitigate the detrimental effect of surface residuals. The best electrochemical performance was achieved by modifying LiNi0.6Mn0.2Co0.2O2 with a diluted dihydro-phosphate solution (pKa = 7.2), while the metal cation had a negligible impact on the electrochemical performance. This work provides a cheap and simple method for enabling the high performance of nickel-rich cathodes.
Systematic Myostatin Expression Screening Platform for Identification and Evaluation of Myogenesis-Related Phytogenic in Pigs
Authors: Ou Bor-Rung , Hsu Ming-Hua , Haung Ling-Ya , Lin Chuan-Ju , Kuo Li-Li , Tsai Yu-Ting , Chang Yu-Chia , Lin Wen-Yuh , Huang Tsung-Chien , Wu Yun-Chu , Yeh Jan-Ying , Liang Yu-Chuan
Published: September 2023

Skeletal muscle growth in livestock impacts meat quantity and quality. Concerns arise because certain feed additives, like beta-agonists, may affect food safety. Skeletal muscle is a specialized tissue consisting of nondividing and multinucleated muscle fibers. Myostatin (MSTN), a protein specific to skeletal muscle, is secreted and functions as a negative regulator of muscle mass by inhibiting the proliferation and differentiation of myoblasts. To enhance livestock muscle growth, phytogenic feed additives could be an alternative as they inhibit MSTN activity. The objective of this study was to establish a systematic screening platform using MSTN activity to evaluate phytogenics, providing scientific evidence of their assessment and potency. In this study, we established a screening platform to monitor myostatin promoter activity in rat L8 myoblasts. Extract of Glycyrrhiza uralensis (GUE), an oriental herbal medicine, was identified through this screening platform, and the active fractions of GUE were identified using a process-scale liquid column chromatography system. For in vivo study, GUE as a feed additive was investigated in growth-finishing pigs. The results showed that GUE significantly increased body weight, carcass weight, and lean content in pigs. Microbiota analysis indicated that GUE did not affect the composition of gut microbiota in pigs. In summary, this established rodent myoblast screening platform was used to identify a myogenesis-related phytogenic, GUE, and further demonstrated that the active fractions and compounds inhibited MSTN expression. These findings suggest a novel application for GUE in growth performance enhancement through modulation of MSTN expression. Moreover, this well-established screening platform holds significant potential for identifying and assessing a diverse range of phytogenics that contribute to the process of myogenesis.
Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI
Authors: Meng Lu , Yang Chuanhao
Published: September 2023

The reconstruction of visual stimuli from fMRI signals, which record brain activity, is a challenging task with crucial research value in the fields of neuroscience and machine learning. Previous studies tend to emphasize reconstructing pixel-level features (contours, colors, etc.) or semantic features (object category) of the stimulus image, but typically, these properties are not reconstructed together. In this context, we introduce a novel three-stage visual reconstruction approach called the Dual-guided Brain Diffusion Model (DBDM). Initially, we employ the Very Deep Variational Autoencoder (VDVAE) to reconstruct a coarse image from fMRI data, capturing the underlying details of the original image. Subsequently, the Bootstrapping Language-Image Pre-training (BLIP) model is utilized to provide a semantic annotation for each image. Finally, the image-to-image generation pipeline of the Versatile Diffusion (VD) model is utilized to recover natural images from the fMRI patterns guided by both visual and semantic information. The experimental results demonstrate that DBDM surpasses previous approaches in both qualitative and quantitative comparisons. In particular, the best performance is achieved by DBDM in reconstructing the semantic details of the original image; the Inception, CLIP and SwAV distances are 0.611, 0.225 and 0.405, respectively. This confirms the efficacy of our model and its potential to advance visual decoding research.
Transcranial Magnetic Stimulation Measures, Pyramidal Score on Expanded Disability Status Scale and Magnetic Resonance Imaging of Corticospinal Tract in Multiple Sclerosis
Authors: Rogić Vidaković Maja , Ćurković Katić Ana , Pavelin Sanda , Bralić Antonia , Mikac Una , Šoda Joško , Jerković Ana , Mastelić Angela , Dolić Krešimir , Markotić Anita , Đogaš Zoran , Režić Mužinić Nikolina
Published: September 2023

Probing the cortic ospinal tract integrity by transcranial magnetic stimulation (TMS) could help to understand the neurophysiological correlations of multiple sclerosis (MS) symptoms. Therefore, the study objective was, first, to investigate TMS measures (resting motor threshold-RMT, motor evoked potential (MEP) latency, and amplitude) of corticospinal tract integrity in people with relapsing-remitting MS (pwMS). Then, the study examined the conformity of TMS measures with clinical disease-related (Expanded Disability Status Scale—EDSS) and magnetic resonance imaging (MRI) results (lesion count) in pwMS. The e-field navigated TMS, MRI, and EDSS data were collected in 23 pwMS and compared to non-clinical samples. The results show that pwMS differed from non-clinical samples in MEP latency for upper and lower extremity muscles. Also, pwMS with altered MEP latency (prolonged or absent MEP response) had higher EDSS, general and pyramidal, functional scores than pwMS with normal MEP latency finding. Furthermore, the RMT intensity for lower extremity muscles was predictive of EDSS functional pyramidal scores. TMS/MEP latency findings classified pwMS as the same as EDSS functional pyramidal scores in 70–83% of cases and were similar to the MRI results, corresponding to EDSS functional pyramidal scores in 57–65% of cases. PwMS with altered MEP latency differed from pwMS with normal MEP latency in the total number of lesions in the brain corticospinal and cervical corticospinal tract. The study provides preliminary results on the correspondence of MRI and TMS corticospinal tract evaluation results with EDSS functional pyramidal score results in MS.
Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping
Authors: Abdelhamid Neda , Thind Rajdeep , Mohammad Heba , Thabtah Fadi
Published: September 2023

Autistic spectrum disorder (ASD) is a neurodevelopmental condition that characterises a range of people, from individuals who are not able to speak to others who have good verbal communications. The disorder affects the way people see, think, and behave, including their communications and social interactions. Identifying autistic traits, preferably in the early stages, is fundamental for clinicians in expediting referrals, and hence enabling patients to access to required healthcare services. This article investigates various ASD behavioral features in toddlers and proposes a data process using machine-learning techniques. The aims of this study were to identify early behavioral features that can help detect ASD in toddlers and to map these features to the neurodevelopment behavioral areas of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). To achieve these aims, the proposed data process assesses several behavioral features using feature selection techniques, then constructs a classification model based on the chosen features. The empirical results show that during the screening process of toddlers, cognitive features related to communications, social interactions, and repetitive behaviors were most relevant to ASD. For the machine-learning algorithms, the predictive accuracy of Bayesian network (Bayes Net) and logistic regression (LR) models derived from ASD behavioral data subsets were consistent pinpointing to the suitability of ML techniques in predicting ASD.
Tunneling Nanotube: An Enticing Cell–Cell Communication in the Nervous System
Authors: Dagar Sunayana , Subramaniam Srinivasa
Published: September 2023

The field of neuroscience is rapidly progressing, continuously uncovering new insights and discoveries. Among the areas that have shown immense potential in research, tunneling nanotubes (TNTs) have emerged as a promising subject of study. These minute structures act as conduits for the transfer of cellular materials between cells, representing a mechanism of communication that holds great significance. In particular, the interplay facilitated by TNTs among various cell types within the brain, including neurons, astrocytes, oligodendrocytes, glial cells, and microglia, can be essential for the normal development and optimal functioning of this complex organ. The involvement of TNTs in neurodegenerative disorders, such as Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease, has attracted significant attention. These disorders are characterized by the progressive degeneration of neurons and the subsequent decline in brain function. Studies have predicted that TNTs likely play critical roles in the propagation and spread of pathological factors, contributing to the advancement of these diseases. Thus, there is a growing interest in understanding the precise functions and mechanisms of TNTs within the nervous system. This review article, based on our recent work on Rhes-mediated TNTs, aims to explore the functions of TNTs within the brain and investigate their implications for neurodegenerative diseases. Using the knowledge gained from studying TNTs could offer novel opportunities for designing targeted treatments that can stop the progression of neurodegenerative disorders.
White Adipose Tissue Heterogeneity in the Single-Cell Era: From Mice and Humans to Cattle
Authors: Ford Hunter , Liu Qianglin , Fu Xing , Strieder-Barboza Clarissa
Published: September 2023

Adipose tissue is a major modulator of metabolic function by regulating energy storage and by acting as an endocrine organ through the secretion of adipokines. With the advantage of next-generation sequencing-based single-cell technologies, adipose tissue has been studied at single-cell resolution, thus providing unbiased insight into its molecular composition. Recent single-cell RNA sequencing studies in human and mouse models have dissected the transcriptional cellular heterogeneity of subcutaneous (SAT), visceral (VAT), and intramuscular (IMAT) white adipose tissue depots and revealed unique populations of adipose tissue progenitor cells, mature adipocytes, immune cell, vascular cells, and mesothelial cells that play direct roles on adipose tissue function and the development of metabolic disorders. In livestock species, especially in bovine, significant gaps of knowledge remain in elucidating the roles of adipose tissue cell types and depots on driving the pathogenesis of metabolic disorders and the distinct fat deposition in VAT, SAT, and IMAT in meat animals. This review summarizes the current knowledge on the transcriptional and functional cellular diversity of white adipose tissue revealed by single-cell approaches and highlights the depot-specific function of adipose tissue in different mammalian species, with a particular focus on recent findings and future implications in cattle.
Repurposing Normal Chromosomal Microarray Data to Harbor Genetic Insights into Congenital Heart Disease
Authors: Walton Nephi A. , Nguyen Hoang H. , Procknow Sara S. , Johnson Darren , Anzelmi Alexander , Jay Patrick Y.
Published: September 2023

About 15% of congenital heart disease (CHD) patients have a known pathogenic copy number variant. The majority of their chromosomal microarray (CMA) tests are deemed normal. Diagnostic interpretation typically ignores microdeletions smaller than 100 kb. We hypothesized that unreported microdeletions are enriched for CHD genes. We analyzed “normal” CMAs of 1762 patients who were evaluated at a pediatric referral center, of which 319 (18%) had CHD. Using CMAs from monozygotic twins or replicates from the same individual, we established a size threshold based on probe count for the reproducible detection of small microdeletions. Genes in the microdeletions were sequentially filtered by their nominal association with a CHD diagnosis, the expression level in the fetal heart, and the deleteriousness of a loss-of-function mutation. The subsequent enrichment for CHD genes was assessed using the presence of known or potentially novel genes implicated by a large whole-exome sequencing study of CHD. The unreported microdeletions were modestly enriched for both known CHD genes and those of unknown significance identified using their de novo mutation in CHD patients. Our results show that readily available “normal” CMA data can be a fruitful resource for genetic discovery and that smaller deletions should receive more attention in clinical evaluation.
cccDNA-Targeted Drug Screen Reveals a Class of Antihistamines as Suppressors of HBV Genome Levels
Authors: Ren Ee Chee , Zhuo Nicole Ziyi , Goh Zhi Yi , Bonne Isabelle , Malleret Benoît , Ko Hui Ling
Published: September 2023

Chronic infection with hepatitis B virus (HBV) is incurable, as the current therapeutics cannot eliminate its persistent genomic material, cccDNA. Screening systems for cccDNA-targeting therapeutics are unavailable, as low copies of cccDNA in vitro complicate detection. To address this, cccDNA copies were massively increased to levels detectable via automated plate readers. This was achieved via continuous infection in a contact-free co-culture of an HBV generator (clone F881), which stably produced clinically relevant amounts of HBV, and HBV acceptors selected to carry high cccDNA loads. cccDNA-targeted therapeutics were then identified via reduced cccDNA-specific fluorescence, taking differences in the cell numbers and viability into account. Amongst the drugs tested, the H1 antihistamine Bilastine, HBVCP inhibitors and, surprisingly, current HBV therapeutics downregulated the cccDNA significantly, reflecting the assay’s accuracy and sensitivity in identifying drugs that induce subtle changes in cccDNA levels, which take years to manifest in vivo. Bilastine was the only therapeutic that did not reduce HBV production from F881, indicating it to be a novel direct suppressor of cccDNA levels. When further assessed, only the structurally similar antihistamines Pitolisant and Nizatidine suppressed cccDNA levels when other H1 antihistamines could not. Taken together, our rapid fluorescence cccDNA-targeted drug screen successfully identified a class of molecules with the potential to treat hepatitis B.
Significance of Plasma Irisin, Adiponectin, and Retinol Binding Protein-4 Levels as Biomarkers for Obstructive Sleep Apnea Syndrome Severity
Authors: Fazlıoğlu Nevin , Uysal Pelin , Durmus Sinem , Yurt Sibel , Gelisgen Remise , Uzun Hafize
Published: September 2023

Objective: Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder that is caused by the reduction or cessation of airflow in the upper airway. Irisin, retinol-binding protein-4 (RBP-4), and adiponectin are the three significant factors in the metabolic process of the human body. The objective of this study was to investigate whether plasma irisin, RBP-4, and adiponectin levels are associated with the severity of OSAS. Methods: According to inclusion and exclusion criteria, 125 patients with OSAS and 46 healthy, gender-matched controls were included in this study. The patients were classified according to the apnea hypopnea index (AHI) as 14 mild cases (5 < AHI < 15), 23 moderate OSAS cases (15 < AHI < 30), and 88 severe OSAS cases (AHI > 30). The plasma irisin, RBP-4, and adiponectin levels were measured and compared between groups. Results: RBP-4 levels were higher in severe OSAS compared to other groups, and irisin levels were significantly lower in severe OSAS compared to other groups. There was a negative correlation between irisin and RBP-4 (r = −0.421; p < 0.001), and irisin and AHI (r = −0.834; p < 0.001), and a positive correlation between irisin and adiponectin (r = 0.240; p = 0.002). There was a negative correlation between RBP-4 and adiponectin (r = −0.507; p < 0.001) and a positive correlation between RBP-4 and AHI (r = 0.473; p < 0.001). As a predictor of OSAS, adiponectin showed the highest specificity (84.8%) and RBP-4 the highest sensitivity (92.0%). Conclusion: Circulating adiponectin, irisin, and RBP-4 may be new biomarkers in OSAS patients in addition to risk factors such as diabetes, obesity, and hypertension. When polysomnography is not available, these parameters and clinical data can be used to diagnose the disease. As a result, patients with an AHI score greater than thirty should be closely monitored for metabolic abnormalities.
KDM2A Deficiency in the Liver Promotes Abnormal Liver Function and Potential Liver Damage
Authors: Martin Matthew , Motolani Aishat , Kim Hyeong-Geug , Collins Amy M. , Alipourgivi Faranak , Jin Jiamin , Wei Han , Wood Barry A. , Ma Yao-Ying , Dong X. Charlie , Mirmira Raghavendra G. , Lu Tao
Published: September 2023

Dysregulation of metabolic functions in the liver impacts the development of diabetes and metabolic disorders. Normal liver function can be compromised by increased inflammation via the activation of signaling such as nuclear factor (NF)-κB signaling. Notably, we have previously identified lysine demethylase 2A (KDM2A)—as a critical negative regulator of NF-κB. However, there are no studies demonstrating the effect of KDM2A on liver function. Here, we established a novel liver-specific Kdm2a knockout mouse model to evaluate KDM2A’s role in liver functions. An inducible hepatic deletion of Kdm2a, Alb-Cre-Kdm2afl/fl (Kdm2a KO), was generated by crossing the Kdm2a floxed mice (Kdm2afl/fl) we established with commercial albumin-Cre transgenic mice (B6.Cg-Tg(Alb-cre)21Mgn/J). We show that under a normal diet, Kdm2a KO mice exhibited increased serum alanine aminotransferase (ALT) activity, L-type triglycerides (TG) levels, and liver glycogen levels vs. WT (Kdm2afl/fl) animals. These changes were further enhanced in Kdm2a liver KO mice in high-fat diet (HFD) conditions. We also observed a significant increase in NF-κB target gene expression in Kdm2a liver KO mice under HFD conditions. Similarly, the KO mice exhibited increased immune cell infiltration. Collectively, these data suggest liver-specific KDM2A deficiency may enhance inflammation in the liver, potentially through NF-κB activation, and lead to liver dysfunction. Our study also suggests that the established Kdm2afl/fl mouse model may serve as a powerful tool for studying liver-related metabolic diseases.