bluuebunny/crossref_metadata_2025 huggingface dataset:
Electromagnetic Switching Multiple Grasping Modes Robot Hand
Authors: Liu Siyun , Qi Qingjie , Liu Yingjie , Chai Jiamei , Sun Zuo , Ma Tianfang , Li Dan , Xian Wenhao
Published: August 2022

Giving robot hands more powerful functions has always been one of the goals pursued by scholars in this field. In this paper, an electromagnetic switching multiple grasping modes robot hand (ESMGM hand) is proposed, which integrates three typical grasping modes and therefore has versatile usage and improved performance. The switchable CPS mechanism developed in this paper integrated the parallel grasping and coupled grasping modes, which are incompatible with each other, through ingenious design. The partial effective transmission mechanism guarantees the fusion and connection to self-adaptive grasping mode from both parallel grasping mode and coupled grasping mode. Based on the above two essential mechanisms, the specific structure of the ESMGM robot hand is designed. Theoretical analyses for the kinematic and grasping forces are performed, and the results show that the ESMGM hand not only has multiple grasping functions but also has the characteristics of equitable grasping motions, adequate grasping forces, and stable grasping effects. To further verify the performance of the ESMGM hand, the prototype of the ESMGM hand is manufactured, and grasping experiments are performed. The grasping forces distribution results are consistent with the theoretical analysis results. The general grasping experiments also illustrate that the ESMGM hand has the features of fast electromagnetic switching speed, good adaptability, high stability, fast response, and broad application prospects.
Special Issue on ‘Hide and Seek of Soil Microbes—Who Is Where with Whom and Why?’
Authors: Probst Maraike , Ascher-Jenull Judith
Published: August 2022

Our question posed for and used as title of the special issue ‘Hide and Seek of Soil Microbes’–Who is Where with Whom and Why [...]
Ultra-Thin/Wide-Band Polarization Conversion Metasurface and Its Applications in Anomalous Reflection and RCS Reduction
Authors: Li Xuewen , Wang Yuxiang , Fan Jingdao , He Jiahao , Huang Xiaojun
Published: August 2022

In this paper, the design of an ultra-wideband polarizer based on a metasurface with high-performance is reported and demonstrated. The polarizer is composed of a dielectric substrate with double semicircular gap patches and a metal film. Multiple strong resonance points enable the design to convert the incident linearly polarized waves into cross-polarized waves in the 14.8–28.0 GHz range, with a fractional bandwidth of 61.7% and a corresponding polarization conversion rate (PCR) above 95%. Further simulated results show that the PCR remains above 87% in the 14.37–24.75 GHz range when the incident angle of the electromagnetic (EM) waves is between 0–30°, and the physical mechanism is explained by the surface current distribution. In addition, the gradient metasurface is designed according to the Pancharatnam–Berry phase principle to achieve anomalous reflection, and the 1-bit metasurface is coded to reduce the Radar Cross Section (RCS). The EM waves reach an anomalous reflection of −23° at 15 GHz normal incidence, and the RCS is reduced by 10 dB in the range of 15.3–28.0 GHz. These findings have potential application value in stealth and antenna design.
Quantifying Opinion Strength: A Neutrosophic Inference System for Smart Sentiment Analysis of Social Media Network
Authors: Essameldin Reem , Ismail Ahmed A. , Darwish Saad M.
Published: August 2022

The contemporary speed at which opinions move on social media makes them an undeniable force in the field of opinion mining (OM). This may cause the OM challenge to become more social than technical. This is when the process can determinately represent everyone to the degree they are worth. Nevertheless, considering perspectivism can result in opinion dynamicity. Pondering the existence of opinion dynamicity and uncertainty can provide smart OM on social media. This study proposes a neutrosophic-based OM approach for Twitter that handles perspectivism, its consequences, and indeterminacy. For perspectivism, a social network analysis (SNA) was conducted using popular SNA tools (e.g., Graphistry). An influence weighting of users was performed using an artificial neural network (ANN) based on the SNA provided output and people’s reactions to the OM analyzed texts. The initiative adoption of neutrosophic logic (NL) to integrate users’ influence with their OM scores is to deal with both the opinion dynamicity and indeterminacy. Thus, it provides new uncertainty OM scores that can reflect everyone. The OM scores needed for integration were generated using TextBlob. The results show the ability of NL to improve the OM process and accurately consider the innumerable degrees. This will eventually aid in a better understanding of people’s opinions, helping OM in social media to become a real pillar of many applications, especially business marketing.
KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph
Authors: Hu Weiwei , He Liang , Ma Hanhan , Wang Kai , Xiao Jingfeng
Published: August 2022

Recently, the lexicon method has been proven to be effective for named entity recognition (NER). However, most existing lexicon-based methods cannot fully utilize common-sense knowledge in the knowledge graph. For example, the word embeddings pretrained by Word2vector or Glove lack better contextual semantic information usage. Hence, how to make the best of knowledge for the NER task has become a challenging and hot research topic. We propose a knowledge graph-inspired named-entity recognition (KGNER) featuring a masking and encoding method to incorporate common sense into bidirectional encoder representations from transformers (BERT). The proposed method not only preserves the original sentence semantic information but also takes advantage of the knowledge information in a more reasonable way. Subsequently, we model the temporal dependencies by taking the conditional random field (CRF) as the backend, and improve the overall performance. Experiments on four dominant datasets demonstrate that the KGNER outperforms other lexicon-based models in terms of performance.
Discrete-Time System Conditional Optimization Based on Takagi–Sugeno Fuzzy Model Using the Full Transfer Function
Authors: Jovanović Radiša , Zarić Vladimir , Bučevac Zoran , Bugarić Uglješa
Published: August 2022

The study proposes a novel method for synthesis of a discrete-time parallel distributed compensation (PDC) controller for the nonlinear discrete-time Takagi–Sugeno (TS) fuzzy plant model. For each of the fuzzy plant model linear subsystems, a local linear first order proportional-sum (PS) controller is designed. The algebraic technique is used in two-dimensional parameter space, utilizing the characteristic polynomial of the row nondegenerate full transfer function matrix. Each system’s relative stability is accomplished in relation to the selected damping coefficient. The supplementary criterion is the minimal value of the performance index in the form of the sum of squared errors (SSE). However, unlike the traditional technique, output error is impacted by all simultaneous actions on the system: nonzero inputs and nonzero initial conditions. The full transfer function matrix of the system allows for the treatment of simultaneous actions of the input vector and unknown unpredictable initial conditions. In order to show the improvement caused by the application of a new optimization method that considers nonzero initial conditions, a comparison of PDC controllers designed under zero and nonzero initial conditions is given, where the system in both cases starts from the same nonzero initial conditions, which is often the case in practice. The simulation and experimental results on a DC servo motor are shown to demonstrate the suggested method efficiency.
Parallelization of Array Method with Hybrid Programming: OpenMP and MPI
Authors: Velarde Martínez Apolinar
Published: August 2022

For parallelization of applications with high processing times and large amounts of storage in High Performance Computing (HPC) systems, shared memory programming and distributed memory programming have been used; a parallel application is represented by Parallel Task Graphs (PTGs) using Directed Acyclic Graphs (DAGs). For the execution of PTGs in HPC systems, a scheduler is executed in two phases: scheduling and allocation; the execution of the scheduler is considered an NP-complete combinatorial problem and requires large amounts of storage and long processing times. Array Method (AM) is a scheduler to execute the task schedule in a set of clusters; this method was programmed sequentially, analyzed and tested using real and synthetic application workloads in previous work. Analyzing the proposed designs of this method in this research work, the parallelization of the method is extended using hybrid OpenMP and MPI programming in a server farm and using a set of geographically distributed clusters; at the same time, a novel method for searching free resources in clusters using Lévy random walks is proposed. Synthetic and real workloads have been experimented with to evaluate the performance of the new parallel schedule and compare it to the sequential schedule. The metrics of makespan, waiting time, quality of assignments and search for free resources were evaluated; the results obtained and described in the experiments section show a better performance with the new version of the parallel algorithm compared to the sequential version. By using the parallel approach with hybrid programming applied to the extraction of characteristics of the PTGs, applied to the search for geographically distributed resources with Lévy random walks and applied to the metaheuristic used, the results of the metrics are improved. The makespan is decreased even when the loads increase, the times of the tasks in the waiting queue are decreased, the quality of assignments in the clusters is improved by causing the tasks with their subtasks to be assigned in the same clusters or in cluster neighbors and, finally, the searches for free resources are executed in different geographically distributed clusters, not sequentially.
Oxygenated Diesel Fuels and Their Effect on PM Emissions
Authors: Kozak Miłosław , Merkisz Jerzy
Published: August 2022

Particulate matter (PM) emitted by diesel engines is one of the most harmful components of exhaust gases, including its carcinogenic effect. Due to the widespread use of diesel engines, the health effects of PM emissions affect millions of people around the world. At the same time, diesel particulate matter is characterized by a very complicated structure and mechanisms of formation compared to other exhaust gas components. It is obvious that PM emissions should be limited by all means. This article focuses on the reduction of PM emissions with the use of oxygenated fuels. The mechanisms of oxygenated fuels influence on the soot formation process in the working process of diesel engines have been discussed. The importance of the chemical structure of oxygenated compounds for the effectiveness of PM emissions reduction was considered. The results of empirical research on the influence on PM emissions of oxygenated fuels containing 12 oxygenates from chemical groups such as glycol ethers, maleates, carbonates and butanol were analyzed. The emissions tests were undertaken on a diesel passenger car over the NEDC and FTP-75 cycles. The results showed a high potential of oxygenated fuels in PM emissions reduction, even at a low oxygenates concentration of 5% v/v; namely, PM emissions were reduced by up to 32%. According to tests results, 1% of oxygen in the fuel resulted in an average reduction of PM emissions by 7% to 10%. In the view of already limited possibilities of modifying conventional parameters of diesel fuels, the use of oxygenated compounds is a promising way to trade on the potential of fuels in PM emissions reduction.
Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
Authors: Chen Luo , Liu Zebang , Ma Mengyu
Published: August 2022

The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods.
Three Dimensional Lifetime-Multiplex Tomography Based on Time-Gated Capturing of Near-Infrared Fluorescence Images
Authors: Umezawa Masakazu , Miyata Keiji , Okubo Kyohei , Soga Kohei
Published: August 2022

We report a computed tomography (CT) technique for mapping near-infrared fluorescence (NIRF) lifetime as a multiplex three-dimensional (3D) imaging method, using a conventional NIR camera. This method is achieved by using a time-gated system composed of a pulsed laser and an NIR camera synchronized with a rotatable sample stage for NIRF-CT imaging. The fluorescence lifetimes in microsecond-order of lanthanides were mapped on reconstructed cross-sectional and 3D images, via back-projection of two-dimensional projected images acquired from multiple angles at each time point showing fluorescence decay. A method to select slopes (the observed decay rates in time-gated imaging) used for the lifetime calculation, termed as the slope comparison method, was developed for the accurate calculation of each pixel, resulting in reduction of image acquisition time. Time-gated NIRF-CT provides a novel choice for multiplex 3D observation of deep tissues in biology.
Regimes in the Response of Photomechanical Materials
Authors: Guo Tianyi , Svanidze Anastasiia , Zheng Xiaoyu , Palffy-Muhoray Peter
Published: August 2022

Photomechanical materials perform mechanical work in response to illumination. Photoisomerization-based photomechanical materials may operate in different regimes depending on the intensity of the illuminating light. We examine the photoresponse of liquid crystalline azo-acrylate networks and show that a material property, the characteristic intensity of the material, defines the boundaries between different regimes. Asymptotic analysis indicates that whereas at low relative light levels, photostress is proportional to intensity, at high levels, it is proportional to fluence. Model predictions are in good agreement with the experimental results.
Onset and Progression of Infection Based on Viral Loads in Rhesus Macaques Exposed to Zika Virus
Authors: Triplett Cheryl , Dufek Sally , Niemuth Nancy , Kobs Dean , Cirimotich Christopher , Mack Karla , Sanford Daniel
Published: August 2022

Outbreaks of Zika virus (ZIKV) have resulted in a call by global health advocates for increased surveillance and research with aggressive measures to combat ZIKV infections. There is no licensed ZIKV vaccine yet available, but a number of vaccine candidates are in development. Advancement of promising vaccine candidates to licensure may rely upon the development and use of well-characterized preclinical models developed based on the essential elements of an animal model as outlined in the U.S. FDA “Product Development Under the Animal Rule: Guidance for Industry”. Further, in the absence of adequate clinical cases to support a more traditional approval pathway based on clinical efficacy, regulatory approval could be based upon human safety data and use of a well-characterized animal model to evaluate vaccine efficacy. This report summarizes a statistical analysis that characterizes the progression of ZIKV infection in Rhesus macaques (RMs) with respect to viral load using available data on twenty-six (26) RMs from three (3) studies that were exposed to ZIKV and were not immunized with a ZIKV vaccine. Progression of infection was characterized by time to detection of viral RNA in serum (RT-qPCR) or time to positive viremia (plaque assay). Viral RNA was detected via RT-qPCR as early as day 1 post-infection and was undetectable for all animals by day 7. Viremia also was indicated by plaque assay as early as day 1 and was undetectable for all animals by day 5. Viral RNA was detected in all animals following exposure, while viremia was not observed in all animals. No significant differences in viral loads measured by either RT-qPCR or plaque assay were observed across sex, age, or study. Neither sex nor age were significant predictors of either time to detection of viral RNA or time to positive viremia following exposure to ZIKV. Progression of viral load, which is studied since infection is largely asymptomatic in both RMs and humans, is similar in RMs and humans with positive presentation ranging from 1 to 7 days post-infection and clearance by day 10. Overall, due to consistency of the model across sexes and ages and similarity to the infection profile in humans, it is concluded that the RM model of ZIKV infection is a well-characterized model for use for evaluation of ZIKV countermeasures.
Impacts of Soil Moisture and Fertilizer on N2O Emissions from Cornfield Soil in a Karst Watershed, SW China
Authors: Wei Lai , Liu Xiaolong , Qin Caiqing , Xing Wencong , Gu Yongbo , Wang Xiaoxia , Bai Li , Li Jun
Published: August 2022

Incubation experiments using a typical cornfield soil in the Wujiang River watershed, SW China, were conducted to examine the impacts of soil moisture and fertilizer on N2O emissions and production mechanisms. According to the local fertilizer type, we added NH4NO3 (N) and glucose (C) during incubation to simulate fertilizer application in the cornfield soil. The results showed that an increase in soil moisture and fertilizer significantly stimulated N2O emissions in cornfield soil in the karst area, and it varied with soil moisture. The highest N2O emission fluxes were observed in the treatment with nitrogen and carbon addition at 70% water-filled pore space (WFPS), reaching 6.6 mg kg−1 h−1, which was 22,310, 124.9, and 1.4 times higher than those at 5%, 40%, and 110% WFPS, respectively. The variations of nitrogen species indicated that the production of extremely high N2O at 70% WFPS was dominated by nitrifier denitrification and denitrification, and N2O was the primary form of soil nitrogen loss when soil moisture was >70% WFPS. This study provides a database for estimating N2O emissions in cropland soil in the karst area, and further helped to promote proper soil nitrogen assessment and management of agricultural land of the karst watersheds.
Analysis of TRWP Particle Distribution in Urban and Suburban Landscapes, Connecting Real Road Measurements with Particle Distribution Simulation
Authors: Kunze Miles , Feißel Toni , Ivanov Valentin , Bachmann Thomas , Hesse David , Gramstat Sebastian
Published: August 2022

This article deals with methods and measurements related to environmental pollution and analysis of particle distribution in urban and suburban landscapes. Therefore, an already-invented sampling method for tyre road wear particles (TRWP) was used to capture online emission factors from the road. The collected particles were analysed according to their size distribution, for use as an input for particle distribution simulations. The simulation model was a main traffic intersection, because of the high vehicle dynamic related to the high density of start–stop manoeuvres. To compare the simulation results (particle mass (PM) and particle number (PN)) with real-world emissions, measuring points were defined and analysed over a measuring time of 8 h during the day. Afterwards, the collected particles were analysed in terms of particle shape, appearance and chemical composition, to identify the distribution and their place of origin. As a result of the investigation, the appearance of the particles showed a good correlation to the vehicle dynamics, even though there were a lot of background influences, e.g., resuspension of dust. Air humidity also showed a great influence on the recorded particle measurements. In areas of high vehicle dynamics, such as heavy braking or accelerating, more tyre and brake particles could be found.
Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula
Authors: Kim Jong-Min , Han Hope H. , Kim Sangjin
Published: August 2022

This paper introduces methodologies in forecasting oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. We also apply Bayesian variable selection and nonlinear principal component analysis (NLPCA) for data dimension reduction. With a reduced number of important covariates, we also forecast oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. To apply real data to the proposed methods, we select monthly log returns of 2 oil prices and 74 large-cap, major S&P 500 stock prices across the period of February 2001–October 2019. We conclude that vine copula regression with NLPCA is superior overall to other proposed methods in terms of the measures of prediction errors.
An MDL-Based Wavelet Scattering Features Selection for Signal Classification
Authors: Bruni Vittoria , Cardinali Maria Lucia , Vitulano Domenico
Published: August 2022

Wavelet scattering is a redundant time-frequency transform that was shown to be a powerful tool in signal classification. It shares the convolutional architecture with convolutional neural networks, but it offers some advantages, including faster training and small training sets. However, it introduces some redundancy along the frequency axis, especially for filters that have a high degree of overlap. This naturally leads to a need for dimensionality reduction to further increase its efficiency as a machine learning tool. In this paper, the Minimum Description Length is used to define an automatic procedure for optimizing the selection of the scattering features, even in the frequency domain. The proposed study is limited to the class of uniform sampling models. Experimental results show that the proposed method is able to automatically select the optimal sampling step that guarantees the highest classification accuracy for fixed transform parameters, when applied to audio/sound signals.
Transposition Regular TA-Groupoids and Their Structures
Authors: An Xiaogang , Zhang Xiaohong
Published: August 2022

Tarski associative groupoid (TA-groupoid) is a kind of non-associative groupoid satisfying Tarski associative law. In this paper, the new notions of transposition regular TA-groupoid are proposed and their properties and structural characteristics are studied by using band and quasi-separativity. In particular, the following conclusions are strictly proved: (1) every left transposition regular TA-groupoid is a semigroup; (2) every left transposition regular TA-groupoid is the disjoint union of sub Abelian groups; and (3) a finite TA-groupoid with quasi-separativity and a finite left transposition regular TA-groupoid are equivalent.
Minimum Energy Problem in the Sense of Caputo for Fractional Neutral Evolution Systems in Banach Spaces
Authors: Ech-chaffani Zoubida , Aberqi Ahmed , Karite Touria , Torres Delfim F. M.
Published: August 2022

We investigate a class of fractional neutral evolution equations on Banach spaces involving Caputo derivatives. Main results establish conditions for the controllability of the fractional-order system and conditions for existence of a solution to an optimal control problem of minimum energy. The results are proved with the help of fixed-point and semigroup theories.
Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder–Decoder Segmentation Networks
Authors: Lee Chien-Cheng , So Edmund Cheung , Saidy Lamin , Wang Min-Ju
Published: August 2022

Lung segmentation of chest X-ray (CXR) images is a fundamental step in many diagnostic applications. Most lung field segmentation methods reduce the image size to speed up the subsequent processing time. Then, the low-resolution result is upsampled to the original high-resolution image. Nevertheless, the image boundaries become blurred after the downsampling and upsampling steps. It is necessary to alleviate blurred boundaries during downsampling and upsampling. In this paper, we incorporate the lung field segmentation with the superpixel resizing framework to achieve the goal. The superpixel resizing framework upsamples the segmentation results based on the superpixel boundary information obtained from the downsampling process. Using this method, not only can the computation time of high-resolution medical image segmentation be reduced, but also the quality of the segmentation results can be preserved. We evaluate the proposed method on JSRT, LIDC-IDRI, and ANH datasets. The experimental results show that the proposed superpixel resizing framework outperforms other traditional image resizing methods. Furthermore, combining the segmentation network and the superpixel resizing framework, the proposed method achieves better results with an average time score of 4.6 s on CPU and 0.02 s on GPU.
scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms
Authors: Algabri Yousif A. , Li Lingyu , Liu Zhi-Ping
Published: August 2022

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.
Adaptation of Bacteria to Antineoplastic Agents Involves Persister Cells and Increases Resistance to Antibiotics
Authors: de Carvalho Carla C. C. R.
Published: August 2022

The increasing number of life-threatening infections observed in cancer patients has been ascribed to chemotherapy-induced neutropenia and to invasive medical procedures such as surgery and the application of catheters. In this study, it was questioned if the infections could also be favored by an increased resistance of bacteria due to the adaptation to antineoplastic agents used in chemotherapy. After exposure to several antineoplastic agents, it was observed that cells of Staphylococcus aureus, Mycobacterium vaccae, Pseudomonas aeruginosa, and Escherichia coli changed the fatty acid profile of their cellular membranes, produced exopolymeric substances, and formed aggregates that adhered to surfaces. Additionally, when exposed to high concentrations of these compounds, a persister sub-population could be identified. After adaptation to antineoplastic agents, the minimum inhibitory concentration (MIC) of several antibiotics increased considerably in the tested strains.
Effects of 6-Week Betaine Supplementation on Muscular Performance in Male Collegiate Athletes
Authors: Yang Ming-Ta , Lin Ho-Wei , Chuang Chih-Yuan , Wang Yin-Chun , Huang Bo-Huei , Chan Kuei-Hui
Published: August 2022

The purpose of this study was to investigate the effects of 6-week betaine supplementation during a preparatory period of collegiate athletes on muscular power and strength. Sixteen male collegiate athletes received 5 g/day of betaine (betaine group, n = 9) or carboxymethyl cellulose (placebo group, n = 7) for 6 weeks. All participants engaged in their regular training during the experimental period. The overhead medicine-ball throw (OMBT), countermovement jump, and maximal strength (one repetition maximum, 1-RM) on the bench press, overhead press, half squat, and sumo dead lift by the participants were assessed before and after betaine supplementation. Blood lipids were also analyzed before and after betaine supplementation. After supplementation, there were no significant differences between betaine and placebo groups on any variables. Compared to presupplementation, the performance of OMBT and 1-RM of overhead press and half squat in the betaine group had significantly improved (p < 0.05). By contrast, no significant differences were observed in the placebo group before and after supplementation. Blood analysis revealed no negative effect on blood lipid profiles. Betaine seems to be a useful nutritional strategy to improve and maintain performance during 6-week preparatory periods in collegiate athletes.
Observation of Palatal Wound Healing Process Following Various Degrees of Mucoperiosteal and Bone Trauma in a Young Rat Model
Authors: Liu Yingmeng , Zhang Shiming , Sakran Karim Ahmed , Yin Jiayi , Lan Min , Yang Chao , Wang Yan , Zeng Ni , Huang Hanyao , Shi Bing
Published: August 2022

The accidental injury or surgery on soft and hard palatal tissue has an adverse impact on normal maxillary morphology. To design a single-factor experiment that excludes other interfering factors on maxillary growth, a young rat model was established to simulate the various degrees of palatal trauma. Eight maxillary parameters were measured to evaluate the impact of palatal trauma on maxillary growth. Furthermore, the acellular dermal matrix (ADM) was applied to cure the palatal trauma and alleviate the adverse impact of bone denudation on the maxillary growth. Micro-CT scanning and histology analyses were used. One-way ANOVA with least significant difference (LSD) post-test was used to evaluate the statistical significance. The palatal trauma mainly disturbed the transverse development of the maxilla. ADM promotes mucosa healing, but there is still an inhibitory effect on maxillofacial growth.
Isolation, Physiological Characterization, and Antibiotic Susceptibility Testing of Fast-Growing Bacteria from the Sea-Affected Temporary Meltwater Ponds in the Thala Hills Oasis (Enderby Land, East Antarctica)
Authors: Akulava Volha , Miamin Uladzislau , Akhremchuk Katsiaryna , Valentovich Leonid , Dolgikh Andrey , Shapaval Volha
Published: August 2022

In this study, for the first time, we report the identification and characterization of culturable fast-growing bacteria isolated from the sea-affected temporary meltwater ponds (MPs) in the East Antarctica area of the Vecherny region (−67.656317, 46.175058) of the Thala Hills Oasis, Enderby Land. Water samples from the studied MPs showed alkaline pH (from 8.0 to 10.1) and highly varied total dissolved solids (86–94,000 mg/L). In total, twenty-nine bacterial isolates were retrieved from the studied MPs. The phylogenetic analysis based on 16S rRNA gene sequence similarities showed that the isolated bacteria belong to the phyla Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes and the twelve genera Pseudomonas, Shewanella, Acinetobacter, Sporosarcina, Facklamia, Carnobacterium, Arthrobacter, Brachybacterium, Micrococcus, Agrococcus, Leifsonia, and Flavobacterium. Most of the isolated bacteria were psychrotrophs and showed the production of one or more extracellular enzymes. Lipolytic and proteolytic activities were more prevalent among the isolates. Five isolates from the Actinobacteria phylum and one isolate from the Bacteroidetes phylum had strong pigmentation. Antibiotic susceptibility testing revealed that most of the isolates are resistant to at least one antibiotic, and seven isolates showed multi-resistance.
Anthropometrics, Performance, and Psychological Outcomes in Mixed Martial Arts Athletes
Authors: Camarco Nathalia Ferreira , Neto Ivo Vieira de Sousa , Ribeiro Evaldo , Andrade Anderson J. Martino
Published: August 2022

The digit ratio (2D:4D) is related to prenatal testosterone (T) and sports performance. Few investigations have explored 2D:4D in determining the potential performance of individuals in power-based sports, specifically combat sports. This study compared 2D:4D between mixed martial arts (MMA) athletes and non-athletes and investigated the association between (1) handgrip strength (HGS) and lean body mass (LBM) with 2D:4D in MMA athletes and (2) psychometric variables and 2D:4D in MMA athletes and non-athletes. In total, 122 men participated in this study (53 non-athletes, 45 professionals, and 24 amateur fighters). The 2D:4D was measured using a caliper, HGS was assessed with a dynamometer, and psychometric variables were evaluated using questionnaires. Athletes displayed significantly (p < 0.05) lower median values of right (0.95 ± 0.04) and left (0.96 ± 0.03) 2D:4D in comparison with non-athletes, (0.97 ± 0.03) and (0.99 ± 0.03), respectively. We observed that left hand 2D:4D was negatively correlated with HGS (r = −0.43; p < 0.05) and lean body mass (r = −0.49; p < 0.05) in professional athletes. Professional athletes also displayed significant differences (* p < 0.05; ** p < 0.001) in psychometric variables compared to non-athletes for (1) agreeableness **, median values (interquartile range) = −0.11 (−0.19–0.07) and 0.2 (−0.09–0.33), respectively; (2) anger **, mean ± standard error = 2.40 ± 0.12 and 2.89 ± 0.89, respectively; and (3) openness *, mean ± standard error = 0.17 ± 0.04 and −0.006 ± 0.04, respectively. However, we did not observe an association between psychometric variables or wins with 2D:4D. In conjunction with other measures, 2D:4D is valuable in determining the potential athleticism of an MMA athlete.
Protective and Curative Activities of Paenibacillus polymyxa against Zucchini yellow mosaic virus Infestation in Squash Plants
Authors: Abdelkhalek Ahmed , Al-Askar Abdulaziz A. , Elbeaino Toufic , Moawad Hassan , El-Gendi Hamada
Published: August 2022

The use of microbial products as natural biocontrol agents to increase a plant’s systemic resistance to viral infections is a promising way to make agriculture more sustainable and less harmful to the environment. The rhizobacterium Paenibacillus polymyxa has been shown to have strong biocontrol action against plant diseases, but its antiviral activity has been little investigated. Here, the efficiency of the culture filtrate of the P. polymyxa strain SZYM (Acc# ON149452) to protect squash (Cucurbita pepo L.) plants against a Zucchini yellow mosaic virus (ZYMV, Acc# ON159933) infection was evaluated. Under greenhouse conditions, the foliar application of the culture filtrate of SZYM either in protective or curative treatment conditions enhanced squash growth, reduced disease severity, and decreased ZYMV accumulation levels in the treated plants when compared to the non-treated plants. The protective treatment group exhibited the highest inhibitory effect (80%), with significant increases in their total soluble carbohydrates, total soluble protein content, ascorbic acid content, and free radical scavenging activity. Furthermore, a considerable increase in the activities of reactive oxygen species scavenging enzymes (superoxide dismutase, polyphenol oxidase, and peroxidase) were also found. In addition, the induction of systemic resistance with a significant elevation in the transcriptional levels of polyphenolic pathway genes (CHS, PAL, and C3H) and pathogenesis-related genes (PR-1 and PR-3) was observed. Out of the 14 detected compounds in the GC–MS analysis, propanoic acid, benzenedicarboxylic acid, tetradecanoic acid, and their derivatives, as well as pyrrolo [1,2-a] pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl) were the primary ingredient compounds in the ethyl acetate extract of the SZYM-culture filtrate. Such compounds may act as elicitor molecules that induce systemic resistance against viral infection. Consequently, P. polymyxa can be considered a powerful plant growth-promoting bacterium (PGPB) in agricultural applications as well as a source of bioactive compounds for sustainable disease management. As far as we know, this is the first time that P. polymyxa has been shown to fight viruses in plants.
Biomarkers of Frailty: miRNAs as Common Signatures of Impairment in Cognitive and Physical Domains
Authors: Dato Serena , Crocco Paolina , Iannone Francesca , Passarino Giuseppe , Rose Giuseppina
Published: August 2022

The past years have seen an increasing concern about frailty, owing to the growing number of elderly people and the major impact of this syndrome on health and social care. The identification of frail people passes through the use of different tests and biomarkers, whose concerted analysis helps to stratify the populations of patients according to their risk profile. However, their efficiency in prognosis and their capability to reflect the multisystemic impairment of frailty is discussed. Recent works propose the use of miRNAs as biological hallmarks of physiological impairment in different organismal districts. Changes in miRNAs expression have been described in biological processes associated with phenotypic outcomes of frailty, opening intriguing possibilities for their use as biomarkers of fragility. Here, with the aim of finding reliable biomarkers of frailty, while considering its complex nature, we revised the current literature on the field, for uncovering miRNAs shared across physical and cognitive frailty domains. By applying in silico analyses, we retrieved the top-ranked shared miRNAs and their targets, finally prioritizing the most significant ones. From this analysis, ten miRNAs emerged which converge into two main biological processes: inflammation and energy homeostasis. Such markers, if validated, may offer promising capabilities for early diagnosis of frailty in the elderly population.
Novel Scaffolds for Modulation of NOD2 Identified by Pharmacophore-Based Virtual Screening
Authors: Guzelj Samo , Tomašič Tihomir , Jakopin Žiga
Published: August 2022

Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) is an innate immune pattern recognition receptor responsible for the recognition of bacterial peptidoglycan fragments. Given its central role in the formation of innate and adaptive immune responses, NOD2 represents a valuable target for modulation with agonists and antagonists. A major challenge in the discovery of novel small-molecule NOD2 modulators is the lack of a co-crystallized complex with a ligand, which has limited previous progress to ligand-based design approaches and high-throughput screening campaigns. To that end, a hybrid docking and pharmacophore modeling approach was used to identify key interactions between NOD2 ligands and residues in the putative ligand-binding site. Following docking of previously reported NOD2 ligands to a homology model of human NOD2, a structure-based pharmacophore model was created and used to virtually screen a library of commercially available compounds. Two compounds, 1 and 3, identified as hits by the pharmacophore model, exhibited NOD2 antagonist activity and are the first small-molecule NOD2 modulators identified by virtual screening to date. The newly identified NOD2 antagonist scaffolds represent valuable starting points for further optimization.
Targeting Oxidative Stress Involved in Endometriosis and Its Pain
Authors: Clower Lauren , Fleshman Taylor , Geldenhuys Werner J. , Santanam Nalini
Published: August 2022

Endometriosis is a common gynecological disorder seen in women and is characterized by chronic pelvic pain and infertility. This disorder is becoming more prevalent with increased morbidity. The etiology of endometriosis remains to be fully elucidated, which will lead to improved therapeutic options. In this review, we will evaluate the biochemical mechanisms leading to oxidative stress and their implication in the pathophysiology of endometriosis, as well as potential treatments that target these processes. A comprehensive exploration of previous research revealed that endometriosis is associated with elevated reactive oxygen species and oxidation products, decreased antioxidants and detoxification enzymes, and dysregulated iron metabolism. High levels of oxidative stress contributed to inflammation, extracellular matrix degradation, angiogenesis, and cell proliferation, which may explain its role in endometriosis. Endometriosis-associated pain was attributed to neurogenic inflammation and a feed-forward mechanism involving macrophages, pro-inflammatory cytokines, and pain-inducing prostaglandins. N-acetylcysteine, curcumin, melatonin, and combined vitamin C and E supplementation displayed promising results for the treatment of endometriosis, but further research is needed for their use in this population.
Identification of the Antigens Recognised by Colorectal Cancer Patients Using Sera from Patients Who Exhibit a Crohn’s-like Lymphoid Reaction
Authors: Boncheva Viktoriya , Linnebacher Michael , Kdimati Said , Draper Hannah , Orchard Laurence , Mills Ken , O’Sullivan Gerald , Tangney Mark , Guinn Barbara-ann
Published: August 2022

A Crohn’s-like lymphoid reaction (CLR) is observed in about 15% of colorectal cancer (CRC) patients and is associated with favourable outcomes. To identify the immune targets recognised by CRC CLR patient sera, we immunoscreened a testes cDNA library with sera from three patients. Immunoscreening of the 18 antigens identified by SEREX with sera from normal donors showed that only the heavy chain of IgG3 (IGHG3) and a novel antigen we named UOB-COL-7, were solely recognised by sera from CRC CLR patients. ELISA showed an elevation in IgG3 levels in patients with CRC (p = 0.01). To extend our studies we analysed the expression of our SEREX-identified antigens using the RNA-sequencing dataset (GSE5206). We found that the transcript levels of multiple IGHG probesets were highly significant (p < 0.001) in their association with clinical features of CRC while above median levels of DAPK1 (p = 0.005) and below median levels of GTF2H5 (p = 0.004) and SH3RF2 (p = 0.02) were associated with improved overall survival. Our findings demonstrate the potential of SEREX-identified CRC CLR antigens to act as biomarkers for CRC and provide a rationale for their further characterization and validation.
Nanoscale Technologies in the Fight against COVID-19: From Innovative Nanomaterials to Computer-Aided Discovery of Potential Antiviral Plant-Derived Drugs
Authors: Iraci Nunzio , Corsaro Carmelo , Giofrè Salvatore V. , Neri Giulia , Mezzasalma Angela Maria , Vacalebre Martina , Speciale Antonio , Saija Antonina , Cimino Francesco , Fazio Enza
Published: August 2022

The last few years have increasingly emphasized the need to develop new active antiviral products obtained from artificial synthesis processes using nanomaterials, but also derived from natural matrices. At the same time, advanced computational approaches have found themselves fundamental in the repurposing of active therapeutics or for reducing the very long developing phases of new drugs discovery, which represents a real limitation, especially in the case of pandemics. The first part of the review is focused on the most innovative nanomaterials promising both in the field of therapeutic agents, as well as measures to control virus spread (i.e., innovative antiviral textiles). The second part of the review aims to show how computer-aided technologies can allow us to identify, in a rapid and therefore constantly updated way, plant-derived molecules (i.e., those included in terpenoids) potentially able to efficiently interact with SARS-CoV-2 cell penetration pathways.
Combined Hepatocellular-Cholangiocarcinoma: An Update on Pathology and Diagnostic Approach
Authors: Choi Joon Hyuk , Ro Jae Y.
Published: August 2022

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver carcinoma displaying both hepatocytic and cholangiocytic differentiation within the same tumor. Relative to classic hepatocellular carcinoma (HCC), cHCC-CCA has more aggressive behavior and a poorer prognosis. Though recent advances have improved our understanding of the biology underlying cHCC-CCAs, they remain diagnostically challenging for pathologists because of their morphologic and phenotypic diversity. Accurate diagnosis of cHCC-CCA is important for patient management and prognostication. Herein, we review recent updates on cHCC-CCA, focusing on tumor classification, pathology, and diagnostic approach.
Platelet-Rich Plasma in Alopecia Areata—A Steroid-Free Treatment Modality: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Authors: Meznerics Fanni Adél , Illés Kata , Dembrovszky Fanni , Fehérvári Péter , Kemény Lajos Vince , Kovács Kata Dorottya , Wikonkál Norbert Miklós , Csupor Dezső , Hegyi Péter , Bánvölgyi András
Published: August 2022

Background: Alopecia areata (AA) is a chronic autoimmune condition that can lead to a serious deterioration in patients’ quality of life. The first line of treatment in patchy AA is triamcinolone acetonide (TrA); however, the efficacy of the treatment varies greatly. Our aim was to investigate the therapeutic effects of platelet-rich plasma (PRP) in the treatment of AA. Method: We performed a systematic literature search in four databases. Randomized clinical trials (RCT) reporting on patients with AA treated with PRP were included, comparing PRP with TrA or a placebo. The primary outcome was the Severity of Alopecia Tool (SALT) score. Results: Our systematic search provided a total of 2747 articles. We identified four studies eligible for quantitative analysis. The pooled mean differences from the four studies did not exhibit a significant difference in the mean change in the SALT score when PRP and TrA groups were compared (MD =−2.04, CI: −4.72–0.65; I2 = 80.4%, p = 0.14). Conclusions: PRP is a promising topical, steroid-free treatment modality in the therapy of AA. No significant difference was found between PRP and TrA treatment; however, further high-quality RCTs are needed to further assess the efficacy of PRP treatment and strengthen the quality of evidence.