bluuebunny/crossref_metadata_2025 huggingface dataset:
Staphylococcus Infection: Relapsing Atopic Dermatitis and Microbial Restoration
Authors: Hulme John
Published: January 2023

Atopic Dermatitis (AD) skin is susceptible to Staphylococcus aureus (SA) infection, potentially exposing it to a plethora of toxins and virulent determinants, including Panton-Valentine leukocidin (PVL) (α-hemolysin (Hla) and phenol-soluble modulins (PSMs)), and superantigens. Depending on the degree of infection (superficial or invasive), clinical treatments may encompass permanganate (aq) and bleach solutions coupled with intravenous/oral antibiotics such as amoxicillin, vancomycin, doxycycline, clindamycin, daptomycin, telavancin, linezolid, or tigecycline. However, when the skin is significantly traumatized (sheathing of epidermal sections), an SA infection can rapidly ensue, impairing the immune system, and inducing local and systemic AD presentations in susceptible areas. Furthermore, when AD presents systemically, desensitization can be long (years) and intertwined with periods of relapse. In such circumstances, the identification of triggers (stress or infection) and severity of the flare need careful monitoring (preferably in real-time) so that tailored treatments targeting the underlying pathological mechanisms (SA toxins, elevated immunoglobulins, impaired healing) can be modified, permitting rapid resolution of symptoms.
Blockchain Integration in the Era of Industrial Metaverse
Authors: Mourtzis Dimitris , Angelopoulos John , Panopoulos Nikos
Published: January 2023

Blockchain can be realized as a distributed and decentralized database, also known as a “distributed ledger,” that is shared among the nodes of a computer network. Blockchain is a form of democratized and distributed database for storing information electronically in a digital format. Under the framework of Industry 4.0, the digitization and digitalization of manufacturing and production systems and networks have been focused, thus Big Data sets are a necessity for any manufacturing activity. Big Data sets are becoming a useful resource as well as a byproduct of the activities/processes taking place. However, there is an imminent risk of cyberattacks. The contribution of blockchain technology to intelligent manufacturing can be summarized as (i) data validity protection, (ii) inter- and intra-organizational communication organization, and (iii) efficiency improvement of manufacturing processes. Furthermore, the need for increased cybersecurity is magnified as the world is heading towards a super smart and intelligent societal model, also known as “Society 5.0,” and the industrial metaverse will become the new reality in manufacturing. Blockchain is a cutting-edge, secure information technology that promotes business and industrial innovation. However, blockchain technologies are bound by existing limitations regarding scalability, flexibility, and cybersecurity. Therefore, in this literature review, the implications of blockchain technology for addressing the emerging cybersecurity barriers toward safe and intelligent manufacturing in Industry 5.0 as a subset of Society 5.0 are presented.
Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
Authors: Yang Lu , She Lu , Che Yahui , He Xingwei , Yang Chen , Feng Zixian
Published: January 2023

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.
ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
Authors: Grimaccia Francesco , Niccolai Alessandro , Mussetta Marco , D’Alessandro Giuseppe
Published: January 2023

This paper proposes an energy management system based on an Artificial Neural Network (ANN) to be integrated with the standard ISO 50001 and aims to describe the definition and the enhancement of the energy baselines by means of artificial intelligence techniques applied and tested on the real electrical absorption data of the auxiliary units of different thermal power plants in Italy. Power plant optimized operations are important both for cost and energy performance reasons with related effects on the environment in the next future energy transition scenario. The improvement of the energy baselines consists in determining more accurate consumption monitoring models that are able to track inefficiencies and absorption drifts through data analytics and Artificial Intelligence. Starting from the analysis of the energy vectors at the production site level, we performed a multi-scale analysis to define the consumption at macro areas level and finally find the most relevant consumption units within the plants. A comparison of different ANNs applied to several real power plant data was performed to model complex plant architecture and optimize energy savings with respect to pre-set thresholds according to the ISO 50001 standard procedure. The energy baselines are determined through the analysis of the data available in the power plants’ Distributed Control System (DCS), and we can identify the consumption derived from the unit’s proper operation. Based on the reported numerical simulations, improved baselines have been reached up to a 5% threshold for different plant sub-units, thus representing a relevant overall saving in terms of alert threshold definition and related control efficiency: a potential saving of about 140 MWh throughout the considered three-year dataset was obtained taking into account a cooling tower sub-unit, representing a considerable economic benefit. The results obtained highlight the neural technique efficiency in defining more accurate energy baselines and represents a valuable tool for large energy plant asset management to face relevant energy drifts observed in the last years of plant operation.
Machine Learning Pipeline for the Automated Prediction of MicrovascularInvasion in HepatocellularCarcinomas
Authors: Biondi Riccardo , Renzulli Matteo , Golfieri Rita , Curti Nico , Carlini Gianluca , Sala Claudia , Giampieri Enrico , Remondini Daniel , Vara Giulio , Cattabriga Arrigo , Cocozza Maria Adriana , Pastore Luigi Vincenzo , Brandi Nicolò , Palmeri Antonino , Scarpetti Leonardo , Tanzarella Gaia , Cescon Matteo , Ravaioli Matteo , Castellani Gastone , Coppola Francesca
Published: January 2023

Background: Microvascular invasion (MVI) is a necessary step in the metastatic evolution of hepatocellular carcinoma liver tumors. Predicting the onset of MVI in the initial stages of the tumors could improve patient survival and the quality of life. In this study, the possibility of using radiomic features to predict the presence/absence of MVI was evaluated. Methods: Multiphase contrast-enhanced computed tomography (CECT) images were collected from 49 patients, and the radiomic features were extracted from the tumor region and the zone of transition. The most-relevant features were selected; the dataset was balanced, and the presence/absence of MVI was classified. The dataset was split into training and test sets in three ways using cross-validation: the first applied feature selection and dataset balancing outside cross-validation; the second applied dataset balancing outside and feature selection inside; the third applied the entire pipeline inside the cross-validation procedure. Results: The features from the tumor areas on CECT showed both the portal and the arterial phases to be the most predictive. The three pipelines showed receiver operating characteristic area under the curve (ROC AUC) scores of 0.89, 0.84, and 0.61, respectively. Conclusions: The results obtained confirmed the efficiency of multiphase CECT and the ZOT in detecting MVI. The results showed a significant difference in the performance of the three pipelines, highlighting that a non-rigorous pipeline design could lead to model performance and generalization capabilities that are too optimistic.
Research on the Prediction of Insertion Resistance of Wheel Loader Based on PSO-LSTM
Authors: Chen Yanhui , Shi Gang , Jiang Heng , Zheng Te
Published: January 2023

Insertion resistance is the resistance caused by a pile to a wheel loader when the latter inserts into the pile. It is significant to clarify the insertion resistance to avoid wheel slippage, increase additional energy consumption, and protect the wheel loader during the insertion process. To address the problem that current methods cannot accurately obtain the insertion resistance magnitude and insertion resistance variation trend, we propose a composite model based on the particle swarm optimization (PSO) algorithm and the long short-term memory (LSTM) neural network. Firstly, the Pearson correlation coefficient method is used to test the parameters related to insertion resistance. Following this, the hyperparameters in the LSTM are optimized by PSO. Finally, different proportions of training sets are set in PSO-LSTM and compared with LSTM. The experimental data are selected from gravel sample groups and sand sample groups consisting of insertion depths of 600 mm, 800 mm, and 1000 mm. The results show that PSO-LSTM has higher prediction accuracy, better robustness, stability, and generalization ability compared with LSTM. In PSO-LSTM, when the proportion of the training set is 80%, the average relative errors are 2.28%, 1.57%, and 1.53% for the gravel sample group and 1.14%, 0.71%, and 0.60% for the sand sample group.
Development and Application of Methodology for Quantification of Overbreaks in Hard Rock Tunnel Construction
Authors: Berisavljević Zoran , Bajić Dragoljub , Jovičić Vojkan
Published: January 2023

A methodology for determining overbreaks in hard rock tunnel construction using the drill-and-blast technique is presented in this paper. The methodology was developed for and applied to crystalline medium- to thick-bedded limestone, but it can be used in any jointed hard rock mass. Overbreaks are inevitable in hard rock tunnelling in a low-confinement environment (shallow tunnels up to several hundred meters deep) as a result of wedge failures along unfavourably oriented discontinuities caused by blasting. It is widely accepted in engineering practise that overbreaks will be inevitable even if smooth contour blasting is applied. If not controlled, overbreaks can result in extreme financial and time costs; and determining, predicting, and mitigating them is the key to successful tunnel construction in hard rock. Technological overbreaks, which are caused by the inappropriate use of drill-and-blast excavation, are not easily distinguished from the inevitable overbreaks dictated by the geological conditions with which they interfere and overlap. A methodology was developed with the aim of distinguishing the two causes of overbreaks, which can be applied in any phase of tunnel construction for evaluation or mitigation. The analysis of key inputs, including geological face mapping, shear strength tests along discontinuities of the rock mass, and their spatial orientation relative to tunnel advancement and survey overbreak measurements, is presented in this paper. Due to the stochastic and statistical nature of the problem, a probabilistic concept was also applied as part of the method so that the probability of failure around unprotected tunnel sections could be determined. The so-called stability criterion is introduced to distinguish between stable and unstable sections in terms of the probabilistic safety factor. The quantification of overbreaks, including the threshold value distinguishing technological from geological overbreaks, is proposed. The application of the methodology, demonstrated on an 8.1 km long section of a 12 km long pressure tunnel in hard rock, is presented in the paper.
A Wind-Turbine-Tower-Climbing Robot Prototype Operating at Various Speeds and Payload Capacity: Development and Validation
Authors: Padrigalan Kathleen Ebora , Liu Jui-Hung
Published: January 2023

The development of control technology on wind turbine application robots has played an integral role in facilitating the digitization of inspection and maintenance in the wind energy industry. This paper presents a wind-turbine-climbing robot that determines the service lifespan of the wind turbine components subject to its payload capacity. The model has four rubber wheels, as the driving mechanism for its locomotion is being supported by a Bowden cable as a winding mechanism for its adhesion. The design further incorporates an Arduino microcontroller, distance sensors, motors, and a step motor to form its electromechanical structure. The overall capability of the robot has been analyzed through its kinematics and dynamics. Practical indoor experiments using a wind turbine tower mockup have been conducted for the validation of the various speeds and payload capacity of the prototype. The results indicate the effectiveness of its driving and winding mechanism to climb at the various speeds and with or without a payload. The advantage of the operations of its mechanism conformed with the wind turbine application robots.
An Operator Training Simulator to Enable Responses to Chemical Accidents through Mutual Cooperation between the Participants
Authors: Lee Junseo , Ma Byungchol
Published: January 2023

Research in the training simulation sector to improve the realism and immersive experience of operator training simulators (OTSs) entails combining cutting-edge technologies such as virtual reality (VR) and augmented reality (AR). Although most of the existing studies has been about troubleshooting training, research into the response to chemical accidents through mutual cooperation between the participants has been insufficient. Therefore, we developed an immersive OTS that can facilitate mutual cooperation. Training processes to educate trainees in general chemical facilities were selected, while changes that can occur in facilities during an accident and the corresponding responses in various scenarios were used as the training content. A communication system that relays information between the worksite and the control room was implemented using a distributed control system (DCS) and AR technology. We installed a pilot plant and developed a DCS, thereby establishing an infrastructure that allows the boardman and field operator to cooperate during accident scenarios. Furthermore, we developed an OTS that allowed trainees to learn prompt and accurate responses to chemical accidents through operation of the actual equipment. The training effect of the OTS was found to be approximately 4.5 times better than traditional training methods. It is, therefore, anticipated that the developed OTS will minimize losses or damage caused by chemical accidents.
Influence of Urea Content in Deep Eutectic Solvents on Thermoplastic Starch Films’ Properties
Authors: Zdanowicz Magdalena
Published: January 2023

The goal of the study was to prepare deep eutectic solvents (DESs) with different urea (U) contents and apply them as potato-starch plasticizers to investigate the influence of various DES compositions on the physicochemical properties of thermoplastic starch (TPS) obtained via thermocompression. As hydrogen bond acceptors, quaternary ammonium compounds, choline chloride (CC) and betaine (B-anhydrous and monohydrate) were used. The molar ratios of CC or B to U were 1:2, 1:3, 1:4 and 1:5. Before starch processing, the DESs were thermally characterized (DSC, TGA). The increase in U content in the eutectics led to higher phase-transition temperatures and lower thermal stability. The influence of the DESs on thermocompressed TPS mechanical (tensile test) and thermal–mechanical (DMTA) properties, morphology (XRD and FTIR), sorption/dissolution behavior and surface contact angle was investigated. The mechanical tests revealed that the increase in U led to higher elongation at break and a highly amorphous structure. The FTIR results indicated that the starch underwent some carbamation derivatization with the presence of B. The DESs with high U content plasticized starch effectively; therefore, preliminary extrusion tests for starch were performed with selected CC and B-based DES with the molar ratio of 1:5.
Torsional Capacity Prediction of Reinforced Concrete Beams Using Machine Learning Techniques Based on Ensembles of Trees
Authors: Bernardo Diana S. O. , Bernardo Luís F. A. , Imran Hamza , Ribeiro Tiago P.
Published: January 2023

For the design or assessment of framed concrete structures under high eccentric loadings, the accurate prediction of the torsional capacity of reinforced concrete (RC) beams can be critical. Unfortunately, traditional semi-empirical equations still fail to accurately estimate the torsional capacity of RC beams, namely for over-reinforced and high-strength RC beams. This drawback can be solved by developing accurate Machine Learning (ML) based models as an alternative to other more complex and computationally demanding models. This goal has been herein addressed by employing several ML techniques and by validating their predictions. The novelty of the present article lies in the successful implementation of ML methods based on Ensembles of Trees (ET) for the prediction of the torsional capacity of RC beams. A dataset incorporating 202 reference RC beams with varying design attributes was divided into testing and training sets. Only three input features were considered, namely the concrete area (area enclosed within the outer perimeter of the cross-section), the concrete compressive strength and the reinforcement factor (which accounts for the ratio between the yielding forces of both the longitudinal and transverse reinforcements). The predictions from the used models were statistically compared to the experimental data to evaluate their performances. The results showed that ET reach higher accuracies than a simple Decision Tree (DT). In particular, The Bagging Meta-Estimator (BME), the Forests of Randomized Trees (FRT), the AdaBoost (AB) and the Gradient Tree Boosting (GTB) reached good performances. For instance, they reached values of R2 (coefficient of determination) in the range between 0.982 and 0.990, and values of cvRMSE (coefficient of variation of the root mean squared error) in the range between 10.04% and 13.92%. From the obtained results, it is shown that these ML techniques provide a high capability for the prediction of the torsional capacity of RC beams, at the same level of other more complicated ML techniques and with much fewer input features.
Recent Trends on Innovative Robot Designs and Approaches
Authors: Carbone Giuseppe , Laribi Med Amine
Published: January 2023

The use and function of robots are evolving at a fast pace, sparking interest in creative solutions within a quickly expanding potential market in cutting-edge industries with applications including service robotics, surgical and rehabilitative robotics, and assistive robotics [...]
Multi-Attribute Machine Learning Model for Electrical Motors Performance Prediction
Authors: Raia Maria Raluca , Ciceo Sebastian , Chauvicourt Fabien , Martis Claudia
Published: January 2023

Designing an electrical motor is a complex process that needs to deal with the non-linearity phenomena caused by the saturation of the iron at high magnetic field strength, the multi-physical nature of the investigated system and with requirements that may come into conflict. This paper proposes to use geometric parametric models to evaluate the multi-physical performances of electrical machines and build a machine learning model that is able to predict multi-physical characteristics of electrical machines from input geometrical parameters. The focus of this work is to accurately estimate the electromagnetic characteristics, motor losses and stator natural frequencies, using the developed machine learning model, at the early-design stage of the electrical motor, when the information about the housing is not available and to include the model in optimisation loops, to speed-up the computational time. Three individual machine learning models are built for each physics analysed, a model for the torque and back electromotive force harmonic orders, one model for motor losses and another one for natural frequencies of the mode-shapes. The necessary data is obtained by varying the geometrical parameters of 2D electromagnetic and 3D structural motor parametric models. The accuracy of different machine learning regression algorithms are compared to obtain the best model for each physics involved. Ultimately, the developed multi-attribute model is integrated in an optimisation routine, to compare the computational time with the classical finite element analysis (FEA) optimisation approach.
Rankine Active Earth Pressure of Unsaturated Filling under Earthquake
Authors: Fang Wei , Shen Hongyu , Li Wan , Wang Yiwei
Published: January 2023

The pseudo-static method was adopted to expand the application field of classical Rankine soil pressure theory to the circumstance of infinite inclined backfill acting on the retaining wall under earthquakes. The calculation formula of the active earth pressure for unsaturated soil was deduced by combining the shear strength model of unsaturated soil and the unified strength theory. The effects of matric suction, matric-suction-related friction angle and seismic coefficients on the intensity distribution of Rankine active earth pressure were evaluated, and the resultant action position was discussed. This research shows that rising seismic coefficients lead to a growth of the active earth pressure and an upward movement of the resultant action point; on the other hand, an increasing matric suction and its related friction angle causes a reduction of the active earth pressure and a downward movement of the resultant action. Examples indicate that the suggested formulas in this paper are in good agreement with those of the previous literature subjected to certain conditions. However, compared to the classic Rankine theory, the proposed method extends its application range by considering earthquake, unsaturated soil, inclined rough wall back, non-horizontal backfill surface, and the intermediate principal stress.
Cloud Computing Network Empowered by Modern Topological Invariants
Authors: Hamid Khalid , Iqbal Muhammad Waseem , Abbas Qaiser , Arif Muhammad , Brezulianu Adrian , Geman Oana
Published: January 2023

The cloud computing networks used in the IoT, and other themes of network architectures, can be investigated and improved by cheminformatics, which is a combination of chemistry, computer science, and mathematics. Cheminformatics involves graph theory and its tools. Any number that can be uniquely calculated by a graph is known as a graph invariant. In graph theory, networks are converted into graphs with workstations or routers or nodes as vertex and paths, or connections as edges. Many topological indices have been developed for the determination of the physical properties of networks involved in cloud computing. The study computed newly prepared topological invariants, K-Banhatti Sombor invariants (KBSO), Dharwad invariants, Quadratic-Contraharmonic invariants (QCI), and their reduced forms with other forms of cloud computing networks. These are used to explore and enhance their characteristics, such as scalability, efficiency, higher throughput, reduced latency, and best-fit topology. These attributes depend on the topology of the cloud, where different nodes, paths, and clouds are to be attached to achieve the best of the attributes mentioned before. The study only deals with a single parameter, which is a topology of the cloud network. The improvement of the topology improves the other characteristics as well, which is the main objective of this study. Its prime objective is to develop formulas so that it can check the topology and performance of certain cloud networks without doing or performing experiments, and also before developing them. The calculated results are valuable and helpful in understanding the deep physical behavior of the cloud’s networks. These results will also be useful for researchers to understand how these networks can be constructed and improved with different physical characteristics for enhanced versions.
Olga Albizu’s Lyrical Abstraction and the Borders of the Canvas
Authors: Flecha Vega Raquel
Published: January 2023

The abstractionist paintings of Puerto Rican artist Olga Albizu (1924–2005) gained prominence in the late 1950s when her work debuted in galleries across the Americas and entered the commercial music industry with RCA and Verve records. However, existing scholarship has failed to capture the complex relationship between Albizu’s anti-commercial abstractionist aesthetic and its mass reproduction as cover art for vinyl records during the Cold War era. Returning to the canvas to explore the iconographic, formal, and aesthetic qualities of Albizu’s work within its sociohistorical post-World War II context, this study reveals Albizu’s devotion to formal borders, vivid color juxtapositions, and compositional tensions. I argue that Albizu’s practice constitutes an ongoing concern with a Modernist dialectic and ideals about subjective transformation in a postmodern world of mass culture, a message she conveyed through the material and experiential borders of the canvas. As an avowed formalist and Modernist existing between the postcolonial and postmodern worlds of San Juan and New York City, her work merits formal scrutiny. This paper will add to the diverse histories of Abstract Expressionism and mid-century Modernisms across the Americas while shedding light on an important post-war historical moment and artistic impulse that held on to anti-commercial values in an all-encompassing consumerist world.
Assessment of Antarctic Amplification Based on a Reconstruction of Near-Surface Air Temperature
Authors: Zhu Jiangping , Xie Aihong , Qin Xiang , Xu Bing , Wang Yicheng
Published: January 2023

Polar amplification has been a research focus in climate research in recent decades. However, little attention has been paid to Antarctic amplification (AnA). We have examined the variations in annual and seasonal temperature over the Antarctic Ice Sheet and its amplification based on reconstruction covering the period 2002–2018. The results show the occurrence of annual and seasonal AnA, with an AnA index greater than 1.39 with seasonal differences, and that AnA is strong in the austral winter and spring. Moreover, AnA displays regional differences, with the greatest amplification occurring in East Antarctica, with an AnA index greater than 1.51, followed by West Antarctica. AnA is always absent in the Antarctic Peninsula. In addition, amplification in East Antarctica is most conspicuous in spring, which corresponds to the obvious warming in this season; and the spring amplification signal is weakest for West Antarctica. When considering the influence of the ocean, the AnA becomes obvious, compared to when only the land is considered. Southern Annular Mode (SAM), surface pressure and westerlies work together to affect the temperature change over Antarctica and AnA; and SAM and surface pressure are highly correlated with the temperature change over East Antarctica. The picture reflects the accelerated changes in Antarctic temperature.
Editorial for the Special Issue of Axioms “Calculus of Variations, Optimal Control and Mathematical Biology: A Themed Issue Dedicated to Professor Delfim F. M. Torres on the Occasion of His 50th Birthday”
Authors: Martins Natália , Almeida Ricardo , Silva Cristiana J. , Sidi Ammi Moulay Rchid
Published: January 2023

This publication is an editorial for the Special Issue of Axioms “Calculus of Variations, Optimal Control and Mathematical Biology: A Themed Issue Dedicated to Professor Delfim F [...]
Direct Power Series Approach for Solving Nonlinear Initial Value Problems
Authors: Salah Emad , Saadeh Rania , Qazza Ahmad , Hatamleh Raed
Published: January 2023

In this research, a new approach for solving fractional initial value problems is presented. The main goal of this study focuses on establishing direct formulas to find the coefficients of approximate series solutions of target problems. The new method provides analytical series solutions for both fractional and ordinary differential equations or systems directly, without complicated computations. To show the reliability and efficiency of the presented technique, interesting examples of systems and fractional linear and nonlinear differential equations of ordinary and fractional orders are presented and solved directly by the new approach. This new method is faster and better than other analytical methods in establishing many terms of analytic solutions. The main motivation of this work is to introduce general new formulas that express the series solutions of some types of differential equations in a simple way and with less calculations compared to other numerical power series methods, that is, there is no need for differentiation, discretization, or taking limits while constructing the approximate solution.
Validation of Adapted Neutralization Assays Developed to Discriminate Anti-Rabies Virus Activity of Two Different Anti-Rabies Virus Monoclonal Antibodies Administered as a Combination
Authors: Companjen Arjen , Moore Susan , Boulanger Bruno , Kostense Stefan , Marissen Wilfred
Published: January 2023

Assessment of rabies virus (RABV) neutralizing antibodies in subjects vaccinated or injected with anti-RABV immunoglobulins is central in determination of rabies protection. The rapid fluorescent focus inhibition test (RFFIT) is used for assessment of anti-RABV activity in serum. The current anti-RABV polyclonal preparations on the market pose difficulties in production and vary in quality. RABV neutralizing monoclonal antibodies (MAbs) are being evaluated as replacements. Different anti-RABV MAbs may neutralize different RABV isolates, thus two or more MAbs directed against different epitopes on the RABV glycoprotein are needed. It is therefore important to ensure neutralizing activity against all RABV isolates in sera of subjects injected with an anti-RABV MAb product consisting of two or more MAbs. The RFFIT, utilizing CVS-11 as challenge virus, cannot discriminate between the activities of different anti-RABV MAbs. We developed and validated two RFFIT methods enabling specific assessment of two different anti-RABV MAbs (CR57 and CR4098) in using two mutant CVS-11 strains resistant to either CR57 or CR4098 neutralization. The validation results demonstrate that both RFFIT assays using MAb resistant RABV are precise, accurate, linear, specific, and stable within the linear range of 0.025 IU/mL to 1.0 IU/mL. This method design can, therefore, be used to determine MAb specific anti-RABV activity in human serum samples.
Structure and Function Analysis of Cultivated Meconopsis integrifolia Soil Microbial Community Based on High-Throughput Sequencing and Culturability
Authors: Wang Yan , Ma Qingyun , Wang Lingling , Hu Jingkuo , Xue Huiying , Han Dongfei , Xing Zhen , Ruan Zhiyong
Published: January 2023

(1) Background: The structure, function, and community interactions of soil microbial communities of cultivated Meconopsis integrifolia were characterized by studying this alpine flower and traditional endangered Tibetan medicine. (2) Methods: Soil bacteria and fungi were studied based on high-throughput sequencing technology. Bacteria were isolated using culturomics and functionally identified as IAA-producing, organic phosphorus-dissolving, inorganic phosphorus-dissolving, and iron-producing carriers. (3) Results: The dominant bacterial phyla were found to be Proteobacteria and Acidobacteria, and unclassified_Rhizobiales was the most abundant genus. Ascomycota and Mortierellomycota were the dominant fungal phyla. The bacteria were mainly carbon and nitrogen metabolizers, and the fungi were predominantly Saprotroph—Symbiotroph. The identified network was completely dominated by positive correlations, but the fungi were more complex than the bacteria, and the bacterial keystones were unclassified_Caulobacteraceae and Pedobacter. Most of the keystones of fungi belonged to the phyla Ascomycetes and Basidiomycota. The highest number of different species of culturable bacteria belonged to the genus Streptomyces, with three strains producing IAA, 12 strains solubilizing organic phosphorus, one strain solubilizing inorganic phosphorus, and nine strains producing iron carriers. (4) Conclusions: At the cost of reduced ecological stability, microbial communities increase cooperation toward promoting overall metabolic efficiency and enabling their survival in the extreme environment of the Tibetan Plateau. These pioneering results have value for the protection of endangered Meconopsis integrifolia under global warming and the sustainable utilization of its medicinal value.
Maternal Exposure to Acephate Caused Nephrotoxicity in Adult Offspring Rats Mediated by Excessive Autophagy Activation, Oxidative Stress Induction, and Altered Epithelial Sodium Channel and Na+/K+-ATPase Gene Expression
Authors: Mufti Afoua , Jalouli Maroua , Nahdi Saber , Tlili Nizar , Alqahtani Wadha , Mansour Lamjed , Alwasel Saleh , Harrath Abdel Halim
Published: January 2023

This study examined how maternal exposure to acephate—an organophosphate-based insecticide—affected the renal development in rat offspring during adulthood. Virgin female Wistar rats were randomly allocated to three groups: group 1 (control) received sterile water; groups 2 and 3 were intragastrically exposed to low (14 mg/kg) and high (28 mg/kg) doses of acephate from day 6 of pregnancy until delivery, respectively. Further, the offspring of the adult female rats were euthanized in postnatal week 8. Compared with the controls, the adult rat offspring with exposure to low and high doses of acephate exhibited elevated plasma creatinine and blood urea nitrogen levels. Additionally, immunofluorescence analysis revealed the upregulation of autophagic marker genes (Beclin-1 and LC-3) in the acephate-treated rat offspring, thereby suggesting the induction of an autophagic mechanism. Notably, the increased malondialdehyde level, decreased glutathione level, and decreased superoxide dismutase and catalase activities confirmed the ability of acephate to induce oxidative stress and apoptosis in the kidneys of the rat offspring. This may explain the renal histopathological injury detected using hematoxylin and eosin staining. Furthermore, a reverse transcription polymerase chain reaction revealed that the mRNA expression levels of the Na+/K+-ATPase and the epithelial sodium channel (ENaC) genes were significantly higher in the kidney of female offspring than that of controls owing to acephate toxicity. However, there was no significant effect of acephate on the expression of NHE3 in the treatment group compared with the control group. Overall, the present findings suggest that oxidative stress caused by prenatal exposure to acephate causes nephrotoxicity and histopathological alterations in adult rat offspring, likely by actions on renal ENaC and Na+/K+-ATPase genes as well as the autophagic markers Beclin-1 and LC-3.
Giant Panda Microhabitat Study in the Daxiangling Niba Mountain Corridor
Authors: Jia Wei , Yan Shasha , He Qingqing , Li Ping , Fu Mingxia , Zhou Jiang
Published: January 2023

Habitat reduction and increased fragmentation are urgent issues for the survival and recovery of the giant panda (Ailuropoda melanoleuca). However, changes in the distribution and microhabitat selection of giant panda habitats in different seasons in the same region have rarely been assessed. To further understand giant panda habitat requirements, this study analyzed the giant panda habitat selection characteristics and differences using the sample data of the giant panda occurrence sites collected during 2020–2022. The results showed that the giant panda in both seasons selected medium altitudes (2000–2400 m), southeastern slopes, slopes less than 15°, taller tree layers (8–15 m) with a larger diameter at breast height (17–25 cm) and medium density (25–55%), shorter shrub layers (<4 m) with sparse density (<30%), and taller bamboo (>2 m) with high density (>35%). The giant panda microhabitat survey in the Niba Mountain corridor clarified the characteristics of suitable habitat selection for the giant panda in the corridor. The findings of the study can provide scientific references for the development of practical habitat conservation and management measures for giant pandas in the study area.
A Proteomic Analysis for the Red Seaweed Asparagopsis taxiformis
Authors: Patwary Zubaida P. , Zhao Min , Wang Tianfang , Paul Nicholas A. , Cummins Scott F.
Published: January 2023

The red seaweed Asparagopsis taxiformis is a promising ruminant feed additive with anti-methanogenic properties that could contribute to global climate change solutions. Genomics has provided a strong foundation for in-depth molecular investigations, including proteomics. Here, we investigated the proteome of A. taxiformis (Lineage 6) in both sporophyte and gametophyte stages, using soluble and insoluble extraction methods. We identified 741 unique non-redundant proteins using a genome-derived database and 2007 using a transcriptome-derived database, which included numerous proteins predicted to be of fungal origin. We further investigated the genome-derived proteins to focus on seaweed-specific proteins. Ontology analysis indicated a relatively large proportion of ion-binding proteins (i.e., iron, zinc, manganese, potassium and copper), which may play a role in seaweed heavy metal tolerance. In addition, we identified 58 stress-related proteins (e.g., heat shock and vanadium-dependent haloperoxidases) and 44 photosynthesis-related proteins (e.g., phycobilisomes, photosystem I, photosystem II and ATPase), which were in general more abundantly identified from female gametophytes. Forty proteins were predicted to be secreted, including ten rhodophyte collagen-alpha-like proteins (RCAPs), which displayed overall high gene expression levels. These findings provide a comprehensive overview of expressed proteins in A. taxiformis, highlighting the potential for targeted protein extraction and functional characterisation for future biodiscovery.
Polymeric Nanocomposite Hydrogel Scaffolds in Craniofacial Bone Regeneration: A Comprehensive Review
Authors: Bashir Maha H. , Korany Nahed S. , Farag Dina B. E. , Abbass Marwa M. S. , Ezzat Bassant A. , Hegazy Radwa H. , Dörfer Christof E. , Fawzy El-Sayed Karim M.
Published: January 2023

Nanocomposite biomaterials combine a biopolymeric matrix structure with nanoscale fillers. These bioactive and easily resorbable nanocomposites have been broadly divided into three groups, namely natural, synthetic or composite, based on the polymeric origin. Preparing such nanocomposite structures in the form of hydrogels can create a three-dimensional natural hydrophilic atmosphere pivotal for cell survival and new tissue formation. Thus, hydrogel-based cell distribution and drug administration have evolved as possible options for bone tissue engineering and regeneration. In this context, nanogels or nanohydrogels, created by cross-linking three-dimensional polymer networks, either physically or chemically, with high biocompatibility and mechanical properties were introduced as promising drug delivery systems. The present review highlights the potential of hydrogels and nanopolymers in the field of craniofacial tissue engineering and bone regeneration.
The Essentials on microRNA-Encoded Peptides from Plants to Animals
Authors: Ormancey Mélanie , Thuleau Patrice , Combier Jean-Philippe , Plaza Serge
Published: January 2023

Primary transcripts of microRNAs (pri-miRNAs) were initially defined as long non-coding RNAs that host miRNAs further processed by the microRNA processor complex. A few years ago, however, it was discovered in plants that pri-miRNAs actually contain functional open reading frames (sORFs) that translate into small peptides called miPEPs, for microRNA-encoded peptides. Initially detected in Arabidopsis thaliana and Medicago truncatula, recent studies have revealed the presence of miPEPs in other pri-miRNAs as well as in other species ranging from various plant species to animals. This suggests that miPEP numbers remain largely underestimated and that they could be a common signature of pri-miRNAs. Here we present the most recent advances in miPEPs research and discuss how their discovery has broadened our vision of the regulation of gene expression by miRNAs, and how miPEPs could be interesting tools in sustainable agriculture or the treatment of certain human diseases.
SARS-CoV-2 Induces Epithelial-Enteric Neuronal Crosstalk Stimulating VIP Release
Authors: Balasubramaniam Arun , Tedbury Philip R. , Mwangi Simon M. , Liu Yunshan , Li Ge , Merlin Didier , Gracz Adam D. , He Peijian , Sarafianos Stefan G. , Srinivasan Shanthi
Published: January 2023

Background: Diarrhea is present in up to 30–50% of patients with COVID-19. The mechanism of SARS-CoV-2-induced diarrhea remains unclear. We hypothesized that enterocyte–enteric neuron interactions were important in SARS-CoV-2-induced diarrhea. SARS-CoV-2 induces endoplasmic reticulum (ER) stress in enterocytes causing the release of damage associated molecular patterns (DAMPs). The DAMPs then stimulate the release of enteric neurotransmitters that disrupt gut electrolyte homeostasis. Methods: Primary mouse enteric neurons (EN) were exposed to a conditioned medium from ACE2-expressing Caco-2 colonic epithelial cells infected with SARS-CoV-2 or treated with tunicamycin (ER stress inducer). Vasoactive intestinal peptides (VIP) expression and secretion by EN were assessed by RT-PCR and ELISA, respectively. Membrane expression of NHE3 was determined by surface biotinylation. Results: SARS-CoV-2 infection led to increased expression of BiP/GRP78, a marker and key regulator for ER stress in Caco-2 cells. Infected cells secreted the DAMP protein, heat shock protein 70 (HSP70), into the culture media, as revealed by proteomic and Western analyses. The expression of VIP mRNA in EN was up-regulated after treatment with a conditioned medium of SARS-CoV-2-infected Caco-2 cells. CD91, a receptor for HSP70, is abundantly expressed in the cultured mouse EN. Tunicamycin, an inducer of ER stress, also induced the release of HSP70 and Xbp1s, mimicking SARS-CoV-2 infection. Co-treatment of Caco-2 with tunicamycin (apical) and VIP (basolateral) induced a synergistic decrease in membrane expression of Na+/H+ exchanger (NHE3), an important transporter that mediates intestinal Na+/fluid absorption. Conclusions: Our findings demonstrate that SARS-CoV-2 enterocyte infection leads to ER stress and the release of DAMPs that up-regulates the expression and release of VIP by EN. VIP in turn inhibits fluid absorption through the downregulation of brush-border membrane expression of NHE3 in enterocytes. These data highlight the role of epithelial-enteric neuronal crosstalk in COVID-19-related diarrhea.
Breath Analysis of COVID-19 Patients in a Tertiary UK Hospital by Optical Spectrometry: The E-Nose CoVal Study
Authors: Laird Steven , Debenham Luke , Chandla Danny , Chan Cathleen , Daulton Emma , Taylor Johnathan , Bhat Palashika , Berry Lisa , Munthali Peter , Covington James A.
Published: January 2023

Throughout the SARS-CoV-2 pandemic, diagnostic technology played a crucial role in managing outbreaks on a national and global level. One diagnostic modality that has shown promise is breath analysis, due to its non-invasive nature and ability to give a rapid result. In this study, a portable FTIR (Fourier Transform Infra-Red) spectrometer was used to detect chemical components in the breath from Covid positive symptomatic and asymptomatic patients versus a control cohort of Covid negative patients. Eighty-five patients who had a nasopharyngeal polymerase chain reaction (PCR) test for the detection of SARS-CoV-2 within the last 5 days were recruited to the study (36 symptomatic PCR positive, 23 asymptomatic PCR positive and 26 asymptomatic PCR negative). Data analysis indicated significant difference between the groups, with SARS-CoV-2 present on PCR versus the negative PCR control group producing an area under the curve (AUC) of 0.87. Similar results were obtained comparing symptomatic versus control and asymptomatic versus control. The asymptomatic results were higher than the symptomatic (0.88 vs. 0.80 AUC). When analysing individual chemicals, we found ethanol, methanol and acetaldehyde were the most important, with higher concentrations in the COVID-19 group, with symptomatic patients being higher than asymptomatic patients. This study has shown that breath analysis can provide significant results that distinguish patients with or without COVID-19 disease/carriage.
Benzodiazepines and Mood Stabilizers in Schizophrenia Patients Treated with Oral versus Long-Acting Injectable Antipsychotics—An Observational Study
Authors: Miron Ana Aliana , Petric Paula Simina , Teodorescu Andreea , Ifteni Petru , Chele Gabriela , Szalontay Andreea Silvana
Published: January 2023

Schizophrenia is a chronic, invalidating, and polymorphic disease, characterized by relapses and remission periods. The main treatment option in schizophrenia are antipsychotics, administered as an oral or as a long-acting injectable (LAI) formulation. Although international guidelines rarely recommend it, mood stabilizers (MS) and/or benzodiazepines (BZD) are frequently prescribed as adjunctive therapy in schizophrenia patients for various reasons. This is an observational, cross-sectional study including stabilized schizophrenia patients. A total of 315 patients were enrolled. Of these, 77 patients (24.44%) were stabilized on LAIs and 238 (75.56%) patients on oral antipsychotics (OAP). Eighty-four patients (26.66%) had concomitant treatment with MS and 119 patients (37.77%) had concomitant benzodiazepine treatment. No statistical significance was observed in MS or BZD use between LAIs and OAPs. In total, 136 patients (43.17%) were stabilized on antipsychotic monotherapy. Our study shows that the long-term use of benzodiazepines and mood stabilizers remains elevated among stabilized schizophrenia patients, regardless of the antipsychotic formulation (oral or LAI). Patients receiving second-generation LAI antipsychotics (SGA-LAI) seem to be more likely to be stabilized on monotherapy compared to those receiving oral antipsychotics. Further randomized controlled trials are necessary in order to clarify the benefits of the current drug polypharmacy trends.