bluuebunny/crossref_metadata_2025 huggingface dataset:
First Case of VIM-1-like-Producing Pseudomonas putida Bacteremia in an Oncohematological Pediatric Patient in Italy
Authors: Cortazzo Venere , Agosta Marilena , Gaspari Stefania , Vrenna Gianluca , Lucignano Barbara , Onori Manuela , Di Ruscio Valentina , Mancinelli Livia , Domo Danielle , Perno Carlo Federico , Bernaschi Paola
Published: June 2023

Bacterial infections caused by multidrug-resistant (MDR) Gram-negatives are of great concern worldwide, as they are frequently associated with high mortality and morbidity rates. To date, two cases of VIM-2 metallo-β-lactamase (MBL)-producing Pseudomonas putida bacteremia have been ever reported in France and Spain between 2004 and 2010. Here, we present the first case of VIM-1-like-producing P. putida isolated in blood culture collected from an oncohematological pediatric patient admitted to Bambino Gesù Children’s Hospital (IRCCS) in Rome, Italy.
Finite Element Model Updating Using Resonance–Antiresonant Frequencies with Radial Basis Function Neural Network
Authors: Zhao Haifeng , Lv Jianzhuo , Wang Zunce , Gao Tianchi , Xiong Wenhao
Published: June 2023

The modal frequencies, model shapes or their derivatives are generally used as the characteristic quantities of the objective function for the finite element model (FEM) updating. However, the measurement accuracy of the model shapes is low due to the few numbers of measurement points for actual structures, which results in a large correction error. The antiresonant frequency reflects the local information of the structure more accurately than the mode shapes, which is a good complement to the resonance frequencies. In this paper, a FEM updating using resonance and antiresonant frequencies with radial basis function (RBF) neural network is proposed. The elastic modulus, added mass, tensile stiffness and torsional stiffness are selected as the updating parameters of FEM for a cantilever beam, which were grouped by the uniform design method. The resonance and antiresonant frequencies identified from the frequency response function (FRF) obtained from corresponding FEM at only one node are taken as the characteristic quantities. The RBF neural network is adopted to construct the mapping relationships between the characteristic quantities and the updating parameters. The updated parameters are substituted into the FEM, and the FRF is obtained to verify the validity of the method. The results show that the relative errors between all the updated parameters and the target values are less than 7%, and the relative errors of the characteristic quantities in the updating frequency band are less than 3%. The proposed method can accurately reproduce the dynamic characteristics of the cantilever beam. It can be applied to the damage detection and safety evaluation of large structures which are difficult to arrange more measuring points.
Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation
Authors: Li Huan , Tang Jun , Zhou Huixin
Published: June 2023

The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized.
A Structured Recognition Method for Invoices Based on StrucTexT Model
Authors: Li Zhijie , Tian Wencan , Li Changhua , Li Yunpeng , Shi Haoqi
Published: June 2023

Invoice recognition has long been an active research direction in the field of image recognition. Existing invoice recognition methods suffer from a low recognition rate for structured invoices, a slow recognition speed, and difficulty in mobile deployment. To address these issues, we propose an invoice-structured recognition method based on the StrucTexT model. This method uses the idea of knowledge distillation to speed up the recognition speed and compress the model size without reducing the model recognition rate; this is achieved using the teacher model StrucTexT to guide the student model StrucTexT_slim. The method can effectively solve the problems of slow model recognition speed and large model size that make mobile deployment difficult with traditional methods. Experimental results show that the proposed model achieves an accuracy rate of over 94% on the SROIE and FUNSD public datasets and over 95% on the self-built structured invoice dataset. In addition, the method is 30% faster than other models (YOLOv4, LeNet-5, and Tesseract-OCR) in terms of recognition speed, while the model size is compressed by about 20%.
An Adaptive Maximum Power Point Tracker for Photovoltaic Arrays Using an Improved Soft Computing Algorithm
Authors: Chao Kuei-Hsiang , Zhang Shu-Wei
Published: June 2023

This paper presents an improved version of the firefly algorithm (FA) by which a maximum power point (MPP) tracker was developed to track down the global maximum power point (GMPP) of a partially shaded photovoltaic module array (PVMA). As the first step, our team developed a high-voltage step-up converter where a coupled inductor was used to store the energy so that the duty cycle can be reduced so as to raise the voltage gain. The single-peaked P-V output characteristic curve of a PV array turns out to contain multiple peaks when the array is partially shaded. As a consequence, conventional MPP trackers occasionally track down a local maximum power point (LMPP), instead of the desired GMPP, and the output power of the array falls accordingly. Therefore, an improved version of the FA is proposed as a way to ensure that the GMPP can be tracked down in a more efficient way. Using the Matlab software, the MPP tracking performance of the proposed tracker was finally simulated in five scenarios. As it turned out, the proposed converter provided a high voltage gain at a relatively low duty cycle, and the improved version of the FA outperformed the original in terms of tracking time.
The Use of De-Icing Salts in Post-Tensioned Concrete Slabs and Their Effects on the Life of the Structure
Authors: Ripa Alonso Tomás Luis , Corral Moraleda Noemí , García Alberti Marcos , Pavón Rubén Muñoz , Gálvez Jaime C.
Published: June 2023

This article expounds on the problem of the use of de-icing salts in the corrosion of steel rebars in bridge decks and their effect on post-tensioning elements. In particular, this paper focuses this problem on structures affected by an aggregate–alkali reaction and without any waterproof treatment using the example of one structure whose repair was carried out in 2020. In this structure, the internal stresses due to the aggregate–alkali reaction caused longitudinal cracks in the upper face of the deck, through which the penetration of chloride ions was concentrated, causing, finally, the brittle fracture of the steel bars and the corrosion of the prestressing elements. This article also explains some conclusions about the most probable mechanisms that resulted in the brittle fracture of the steel bars due to the extraordinary and unexpected nature of this phenomenon.
Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
Authors: Kageyama Kohei , Miyazaki Tomo , Sugaya Yoshihiro , Omachi Shinichiro
Published: June 2023

With the widespread use of smartphones, there is a surging demand for localization in indoor environments. The main challenges are the requirement of special equipment (e.g., a map database and Wi-Fi access points) and error accumulation for indoor localization. In this paper, we propose a novel collaborative indoor positioning method to reduce error accumulation. Estimated positions are corrected using the collaborator’s positions when an encounter is detected by communication based on Bluetooth Low Energy (BLE). In addition, a map is obtained by taking photos of information boards. Therefore, the proposed method needs smartphones only; other equipment is not required. We obtained an accurate localization comparison using a machine learning model. The experimental results showed that the proposed method achieved reliable encounter communication in eight facilities. The collaborative localization method successfully enhanced position estimations. Specifically, the proposed method outperformed the existing baseline method by 13.0% in accuracy of indoor positioning.
Accurate Identification of Broken Rock Mass Structure and Its Application in Stability Analysis of Underground Caverns Surrounding Rock
Authors: Sun Ning , Liu Chang , Zhang Faming , Dong Menglong , Li Zinan
Published: June 2023

In view of the broken rock mass in the surrounding rock of large underground caverns, the 3D distribution model of the joints is obtained through the on-site investigation of the joints and the digital imaging technology, and the image processing software. Based on the analysis of the mutual cutting degree of the joints and the geometric shape of the block, the concepts and calculation methods of the degree of fragmentation and the degree of bite are proposed, and the degree of rock mass fragmentation is quantitatively described. Furthermore, the multi-factor analysis method is used to establish the quality evaluation method and the standard of broken rock mass based on the degree of rock fragmentation and the degree of bite, and the failure mode and safety criterion of broken rock mass of surrounding rock are proposed. Applying the discrete element numerical analysis method, the mechanical parameters of different broken rock masses are obtained. The reinforcement analysis of the surrounding rock of different broken rock masses shows that the degree of fragmentation, the degree of bite, and the classification of surrounding rock proposed in this paper can implement precise reinforcement measures for the surrounding rock of different broken rock masses, as it provides an important theoretical basis for the surrounding rock safety of large underground caverns in the broken rock mass and has wide applicability.
Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
Authors: Wang Wei , Shang Qijie , Lu Haoyuan
Published: June 2023

Novel coronavirus disease 2019 (Corona Virus Disease 2019, COVID-19) is rampant all over the world, threatening human life and health. Currently, the detection of the presence of nucleic acid from SARS-CoV-2 is mainly based on the nucleic acid test as the standard. However, this method not only takes up a lot of medical resources but also takes a long time to achieve detection results. According to medical analysis, the surface protein of the novel coronavirus can invade the respiratory epithelial cells of patients and cause severe inflammation of the respiratory system, making the cough of COVID-19 patients different from that of healthy people. In this study, the cough sound is used as a large-scale pre-screening method before the nucleic acid test. Firstly, the Mel spectrum features, Mel Frequency Cepstral Coefficients, and VGG embeddings features of cough sound are extracted and oversampling technology is used to balance the dataset for classes with a small number of samples. In terms of the model, we designed multi-headed convolutional neural networks to predict audio samples, and adopted an early stop method to avoid the over-fitting problem of the model. The performance of the model is measured by the binary cross-entropy loss function. Our model performs well on the dataset of the AICovidVN 115M challenge that its accuracy rate is 98.1%, and on the dataset of the University of Cambridge that its accuracy rate is 91.36%.
Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle
Authors: Bragança Cássio , Souza Edson F. , Ribeiro Diogo , Meixedo Andreia , Bittencourt Túlio N. , Carvalho Hermes
Published: June 2023

Railways are one of the most important means of transportation, allowing people and goods to move quickly, environmentally beneficially, and efficiently over long distances. To ensure safe and reliable operations, regular condition-based assessments of trains and track are of paramount importance. Drive-by methodologies, which utilize data collected by onboard monitoring systems as the vehicle travels over the track, have gained popularity as an economically viable strategy for monitoring extensive track networks as well as vehicles traveling on them. This paper presents a critical review of these methodologies applied to railway tracks and vehicles. It assesses research on track irregularities, rail conditions, and the condition of rail supporting elements, highlighting important early developments and recent papers that provide insights into future practical applications. Additionally, the paper explores works related to global vehicle condition evaluation, focusing on the identification of suspension and wheelset element damage and also discussing challenges towards commercial application. The findings suggest that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. By leveraging drive-by assessments, railway operators can optimize maintenance efforts, detect faults early, predict remaining component life, evaluate track performance, monitor vehicle health, and make informed decisions based on data analysis. Finally, a comprehensive conclusion summarizes the achievements thus far and provides perspectives for forthcoming developments. The future practical applications of drive-by methodologies discussed in this review have the potential to revolutionize railway track and vehicle assessments, leading to safer and more efficient railway operations in the days to come.
The Hypoplastic Constitutive Model for Sandy Soil Considering the Rotation of the Principal Stress Axis
Authors: Li Xuefeng , Fan Guowei , He Yuqi
Published: June 2023

Considering the influence of fabric on the critical state of sandy soil, an anisotropic hypoplastic constitutive model is developed by introducing the anisotropic critical state line that takes into account the rotation of the principal stress axes. With the introduction of new anisotropic state variables defined by the joint invariants of the stress tensor and fabric tensor, the critical state equation of sandy soil is established to describe the effects of three factors, namely, anisotropic parameters, stress states, and the relationship between principal stresses and fabric directions, on the critical state. The mechanical response of sandy soil under different deposition angles can be described by considering the rotation of principal stresses relative to the fabric. The application range of Wu et al.’s isotropic hypoplastic model (2017) is extended by incorporating the effect of principal stress rotation on the stress–strain relationship of sandy soil. Based on a series of Toyoura sand plane strain tests, the effects of void ratio, confining pressure, and principal stress axis rotation angle on anisotropic strength and deformation characteristics are simulated under low confining pressure. Furthermore, a comparison with Wu’s transversely isotropic hypoplastic model (1998) is made regarding their simulation performances. The proposed model exhibits a balanced performance when simulating the variation of anisotropy in both strength and deformation with respect to the rotation angle, without being overestimated within a certain range of rotation angles. The prediction results demonstrate, to a certain degree, the validity and effectiveness of the proposed model.
E-Archeo Project: The 3D Reconstruction of the Roman Villae in Sirmione and Desenzano (Brescia, Italy)
Authors: Basso Patrizia , Bianchi Barbara , Bursich Daniele , Delbarba Nicola , Marinello Alessandra , Soriano Fiammetta
Published: June 2023

The e-Archeo project, commissioned from ALES S.p.A.by the Ministry of Culture (MIC), aims to valorise the multimedia experience of eight Italian archaeological sites. This paper discusses the University of Verona’s contribution to this project, which focuses on the virtual reconstruction of two Roman villas located in Sirmione and Desenzano (Lombardy). This paper outlines the 3D survey methodologies and scientific back-end approach using Extended Matrix. The architectural and decorative reconstruction process for each site is elucidated, providing a comprehensive understanding of the process followed. Furthermore, the University developed a narrative to accompany virtual visits. One of the main project outputs was e-Archeo 3d, a virtual reality web app that allows remote and on-site use.
Analysis of the Development Mechanisms of a Polar Low over the Norwegian Sea Simulated with the Canadian Regional Climate Model
Authors: Moreno-Ibáñez Marta , Laprise René , Gachon Philippe
Published: June 2023

Polar lows (PLs) are maritime mesoscale cyclones associated with severe weather. They develop during marine cold air outbreaks near coastlines and the sea ice edge. Unfortunately, our knowledge about the mechanisms leading to PL development is still incomplete. This study aims to provide a detailed analysis of the development mechanisms of a PL that formed over the Norwegian Sea on 25 March 2019 using the output of a simulation with the sixth version of the Canadian Regional Climate Model (CRCM6/GEM4), a convection-permitting model. First, the life cycle of the PL is described and the vertical wind shear environment is analysed. Then, the horizontal wind divergence and the baroclinic conversion term are computed, and a surface pressure tendency equation is developed. In addition, the roles of atmospheric static stability, latent heat release, and surface heat and moisture fluxes are explored. The results show that the PL developed in a forward-shear environment and that moist baroclinic instability played a major role in its genesis and intensification. Baroclinic instability was initially only present at low levels of the atmosphere, but later extended upward until it reached the mid-troposphere. Whereas the latent heat of condensation and the surface heat fluxes also contributed to the development of the PL, convective available potential energy and barotropic conversion do not seem to have played a major role in its intensification. In conclusion, this study shows that a convection-permitting model simulation is a powerful tool to study the details of the structure of PLs, as well as their development mechanisms.
High-Performance High-Nickel Multi-Element Cathode Materials for Lithium-Ion Batteries
Authors: Tian Xinyong , Guo Ruiqi , Bai Ying , Li Ning , Wang Xinran , Wang Jiantao , Wu Chuan
Published: June 2023

With the rapid increase in demand for high-energy-density lithium-ion batteries in electric vehicles, smart homes, electric-powered tools, intelligent transportation, and other markets, high-nickel multi-element materials are considered to be one of the most promising cathode candidates for large-scale industrial applications due to their advantages of high capacity, low cost, and good cycle performance. In response to the competitive pressure of the low-cost lithium iron phosphate battery, high-nickel multi-element cathode materials need to continuously increase their nickel content and reduce their cobalt content or even be cobalt-free and also need to solve a series of problems, such as crystal structure stability, particle microcracks and breakage, cycle life, thermal stability, and safety. In this regard, the research progress of high-nickel multi-element cathode materials in recent years is reviewed and analyzed, and the progress of performance optimization is summarized from the aspects of precursor orientational growth, bulk phase doping, surface coating, interface modification, crystal morphology optimization, composite structure design, etc. Finally, according to the industrialization demand of high-energy-density lithium-ion batteries and the challenges faced by high-nickel multi-element cathode materials, the performance optimization direction of high-nickel multi-element cathode materials in the future is proposed.
Molecular Structure-Based Prediction of Absorption Maxima of Dyes Using ANN Model
Authors: Tomar Neeraj , Rani Geeta , Dhaka Vijaypal Singh , Surolia Praveen K. , Gupta Kalpit , Vocaturo Eugenio , Zumpano Ester
Published: June 2023

The exponentially growing energy requirements and, in turn, extensive depletion of non-restorable sources of energy are a major cause of concern. Restorable energy sources such as solar cells can be used as an alternative. However, their low efficiency is a barrier to their practical use. This provokes the research community to design efficient solar cells. Based on the study of efficacy, design feasibility, and cost of fabrication, DSSC shows supremacy over other photovoltaic solar cells. However, fabricating DSSC in a laboratory and then assessing their characteristics is a costly affair. The researchers applied techniques of computational chemistry such as Time-Dependent Density Functional Theory, and an ab initio method for defining the structure and electronic properties of dyes without synthesizing them. However, the inability of descriptors to provide an intuitive physical depiction of the effect of all parameters is a limitation of the proposed approaches. The proven potential of neural network models in data analysis, pattern recognition, and object detection motivated researchers to extend their applicability for predicting the absorption maxima (λmax) of dye. The objective of this research is to develop an ANN-based QSPR model for correctly predicting the value of λmax for inorganic ruthenium complex dyes used in DSSC. Furthermore, it demonstrates the impact of different activation functions, optimizers, and loss functions on the prediction accuracy of λmax. Moreover, this research showcases the impact of atomic weight, types of bonds between constituents of the dye molecule, and the molecular weight of the dye molecule on the value of λmax. The experimental results proved that the value of λmax varies with changes in constituent atoms and types of bonds in a dye molecule. In addition, the model minimizes the difference in the experimental and calculated values of absorption maxima. The comparison with the existing models proved the dominance of the proposed model.
Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox
Authors: Thakur Nirmalya
Published: June 2023

Mining and analysis of the big data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of tweets related to Ebola, E-Coli, Dengue, Human Papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson’s, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as “catalysts” for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both of these viruses. None of the prior works in this field analyzed tweets focusing on both COVID-19 and MPox simultaneously. To address this research gap, a total of 61,862 tweets that focused on MPox and COVID-19 simultaneously, posted between 7 May 2022 and 3 March 2023, were studied. The findings and contributions of this study are manifold. First, the results of sentiment analysis using the VADER (Valence Aware Dictionary for sEntiment Reasoning) approach shows that nearly half the tweets (46.88%) had a negative sentiment. It was followed by tweets that had a positive sentiment (31.97%) and tweets that had a neutral sentiment (21.14%), respectively. Second, this paper presents the top 50 hashtags used in these tweets. Third, it presents the top 100 most frequently used words in these tweets after performing tokenization, removal of stopwords, and word frequency analysis. The findings indicate that tweets in this context included a high level of interest regarding COVID-19, MPox and other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that compares the contributions of this paper with 49 prior works in this field is presented to further uphold the relevance and novelty of this work.
Integrated Manufacturing of Suspended and Aligned Nanofibrous Scaffold for Structural Maturation and Synchronous Contraction of HiPSC-Derived Cardiomyocytes
Authors: Liu Lingling , Xu Feng , Jin Hang , Qiu Bin , Yang Jianhui , Zhang Wangzihan , Gao Qiang , Lin Bin , Chen Songyue , Sun Daoheng
Published: June 2023

Electrospun nanofiber constructs represent a promising alternative for mimicking the natural extracellular matrix in vitro and have significant potential for cardiac patch applications. While the effect of fiber orientation on the morphological structure of cardiomyocytes has been investigated, fibers only provide contact guidance without accounting for substrate stiffness due to their deposition on rigid substrates (e.g., glass or polystyrene). This paper introduces an in situ fabrication method for suspended and well aligned nanofibrous scaffolds via roller electrospinning, providing an anisotropic microenvironment with reduced stiffness for cardiac tissue engineering. A fiber surface modification strategy, utilizing oxygen plasma treatment combined with sodium dodecyl sulfate solution, was proposed to maintain the hydrophilicity of polycaprolactone (PCL) fibers, promoting cellular adhesion. Human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CMs), cultured on aligned fibers, exhibited an elongated morphology with extension along the fiber axis. In comparison to Petri dishes and suspended random fiber scaffolds, hiPSC-CMs on suspended aligned fiber scaffolds demonstrated enhanced sarcomere organization, spontaneous synchronous contraction, and gene expression indicative of maturation. This work demonstrates the suspended and aligned nano-fibrous scaffold provides a more realistic biomimetic environment for hiPSC-CMs, which promoted further research on the inducing effect of fiber scaffolds on hiPSC-CMs microstructure and gene-level expression.
IntiCom-DB: A Manually Curated Database of Inter-Tissue Communication Molecules and Their Communication Routes
Authors: Xiong Changxian , Zhou Yiran , Han Yu , Yi Jingkun , Pang Huai , Zheng Ruimao , Zhou Yuan
Published: June 2023

Inter-tissue communication (ITC) is critical for maintaining the physiological functions of multiple tissues and is closely related to the onset and development of various complex diseases. Nevertheless, there is no well-organized data resource for known ITC molecules with explicit ITC routes from source tissues to target tissues. To address this issue, in this work, we manually reviewed nearly 190,000 publications and identified 1408 experimentally supported ITC entries in which the ITC molecules, their communication routes, and their functional annotations were included. To facilitate our work, these curated ITC entries were incorporated into a user-friendly database named IntiCom-DB. This database also enables visualization of the expression abundances of ITC proteins and their interaction partners. Finally, bioinformatics analyses on these data revealed common biological characteristics of the ITC molecules. For example, tissue specificity scores of ITC molecules at the protein level are often higher than those at the mRNA level in the target tissues. Moreover, the ITC molecules and their interaction partners are more abundant in both the source tissues and the target tissues. IntiCom-DB is freely available as an online database. As the first comprehensive database of ITC molecules with explicit ITC routes to the best of our knowledge, we hope that IntiCom-DB will benefit future ITC-related studies.
Understanding Snail Mucus Biosynthesis and Shell Biomineralisation through Genomic Data Mining of the Reconstructed Carbohydrate and Glycan Metabolic Pathways of the Giant African Snail (Achatina fulica)
Authors: Nualnisachol Pornpavee , Chumnanpuen Pramote , E-kobon Teerasak
Published: June 2023

The giant African snail (Order Stylommatophora: Family Achatinidae), Achatina fulica (Bowdich, 1822), is the most significant and invasive land snail pest. The ecological adaptability of this snail involves high growth rate, reproductive capacity, and shell and mucus production, driven by several biochemical processes and metabolism. The available genomic information for A. fulica provides excellent opportunities to hinder the underlying processes of adaptation, mainly carbohydrate and glycan metabolic pathways toward the shell and mucus formation. The authors analysed the 1.78 Gb draft genomic contigs of A. fulica to identify enzyme-coding genes and reconstruct biochemical pathways related to the carbohydrate and glycan metabolism using a designed bioinformatic workflow. Three hundred and seventy-seven enzymes involved in the carbohydrate and glycan metabolic pathways were identified based on the KEGG pathway reference in combination with protein sequence comparison, structural analysis, and manual curation. Fourteen complete pathways of carbohydrate metabolism and seven complete pathways of glycan metabolism supported the nutrient acquisition and production of the mucus proteoglycans. Increased copy numbers of amylases, cellulases, and chitinases highlighted the snail advantage in food consumption and fast growth rate. The ascorbate biosynthesis pathway identified from the carbohydrate metabolic pathways of A. fulica was involved in the shell biomineralisation process in association with the collagen protein network, carbonic anhydrases, tyrosinases, and several ion transporters. Thus, our bioinformatic workflow was able to reconstruct carbohydrate metabolism, mucus biosynthesis, and shell biomineralisation pathways from the A. fulica genome and transcriptome data. These findings could reveal several evolutionary advantages of the A. fulica snail, and will benefit the discovery of valuable enzymes for industrial and medical applications.
Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression
Authors: Liao Chen-Mao , Su Chuan-Tsung , Huang Hao-Che , Lin Chih-Ming
Published: June 2023

Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 497 patients with stage 3–5 chronic kidney disease (CKD) who were treated at the ward of Taipei Veterans General Hospital from January 2006 to 2019 in Taiwan. The patients underwent 3-year-long follow-up sessions for clinical measurements, which occurred every 3 months. Three time-dependent survival models, namely the Cox proportional hazard model (Cox PHM), random survival forest (RSF), and an artificial neural network (ANN), were used to process patient demographics and laboratory data for predicting progression to renal failure, and important features for optimal prediction were evaluated. The individual prediction of CKD progression was validated using the Kaplan–Meier estimation method, based on patients’ true outcomes during and beyond the study period. The results showed that the average concordance indexes for the cross-validation of the Cox PHM, ANN, and RSF models were 0.71, 0.72, and 0.89, respectively. RSF had the best predictive performances for CKD patients within the 3 years of follow-up sessions, with a sensitivity of 0.79 and specificity of 0.88. Creatinine, age, estimated glomerular filtration rate, and urine protein to creatinine ratio were useful factors for predicting the progression of CKD patients in the RSF model. These results may be helpful for instantaneous risk prediction at each follow-up session for CKD patients.
Exploring the Therapeutic Potential of Cannabinoid Receptor Antagonists in Inflammation, Diabetes Mellitus, and Obesity
Authors: Vasincu Alexandru , Rusu Răzvan-Nicolae , Ababei Daniela-Carmen , Neamțu Monica , Arcan Oana Dana , Macadan Ioana , Beșchea Chiriac Sorin , Bild Walther , Bild Veronica
Published: June 2023

Recently, research has greatly expanded the knowledge of the endocannabinoid system (ECS) and its involvement in several therapeutic applications. Cannabinoid receptors (CBRs) are present in nearly every mammalian tissue, performing a vital role in different physiological processes (neuronal development, immune modulation, energy homeostasis). The ECS has an essential role in metabolic control and lipid signaling, making it a potential target for managing conditions such as obesity and diabetes. Its malfunction is closely linked to these pathological conditions. Additionally, the immunomodulatory function of the ECS presents a promising avenue for developing new treatments for various types of acute and chronic inflammatory conditions. Preclinical investigations using peripherally restricted CBR antagonists that do not cross the BBB have shown promise for the treatment of obesity and metabolic diseases, highlighting the importance of continuing efforts to discover novel molecules with superior safety profiles. The purpose of this review is to examine the roles of CB1R and CB2Rs, as well as their antagonists, in relation to the above-mentioned disorders.
Molecular Research on Genes Involved in Metabolic Diseases
Authors: Lara-Hernandez Francisco , Alvarez Luis , Chaves Javier , Garcia-Garcia Ana-Barbara
Published: June 2023

Numerous genes involved in different metabolic diseases have been identified, and this number is increasing [...]
Prophylactic vs. Therapeutic Effect of Probiotics on the Inflammation Mediated by the NF-κB Pathway in Inflammatory Bowel Conditions
Authors: Najafi Saeideh , Sotoodehnejadnematalahi Fattah , Amiri Mohammad Mehdi , Pourshafie Mohammad Reza , Rohani Mahdi
Published: June 2023

Probiotic supplements consumed adequately at the proper time can affect health by modulating inflammatory pathways in gastrointestinal epithelial cells and modifying the resultant inflammatory response. The current study applied in vitro models to investigate the effectiveness of probiotics in modulating inflammatory pathways and altering inflammatory gene expression in gastrointestinal epithelial cells, with the ultimate goal of promoting probiotic consumption as a therapeutic and preventive measure for chronic inflammatory bowel conditions. HT-29 cells were treated with Gram-negative bacteria to evaluate the changes in pathways related to inflammation activities before and after treatment with a Lactobacillus spp. cocktail (L. plantarum, L. rhamnosus, L. brevis, and L. ruteri) and a Bifidobacterium spp. cocktail (B. bifidum, B. langum, and B. breve) using the real-time PCR method and ELISA for IL-1β and IL-6 as pro-inflammatory cytokines. The results showed that the expression of NF-κB signaling pathway genes and IL-1β and IL-6 cytokines increased after exposure to Gram-negative components. In contrast, all probiotic combinations significantly decreased the expression of genes and the secretion of cytokines. However, this decrease was significantly smaller in cells that underwent probiotic treatment after inflammation induction. In addition, cocktails containing combined Lactobacillus and Bifidobacterium demonstrated robust anti-inflammatory activity relative to solo cocktails. Our observations confirm that probiotic consumption could positively impact inflammatory conditions and alleviate inflammatory symptoms; they can be particularly effective as a preventive measure. Our study provides preliminary evidence to support the lifetime consumption of probiotics.
Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
Authors: Cao Qingjiao , Wang Liying , Zhao Weiguo , Yuan Zhouxiang , Liu Anran , Gao Yanfeng , Ye Runfeng
Published: June 2023

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units.
Finger Kinematics during Human Hand Grip and Release
Authors: Li Xiaodong , Wen Rongwei , Duanmu Dehao , Huang Wei , Wan Kinto , Hu Yong
Published: June 2023

A bionic robotic hand can perform many movements similar to a human hand. However, there is still a significant gap in manipulation between robot and human hands. It is necessary to understand the finger kinematics and motion patterns of human hands to improve the performance of robotic hands. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hands of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a larger dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Additionally, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by the PIP joint. Regarding the finger sequence, the thumb started to move before the four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided a kinematic reference for the design of robotic hands and thus contributes to its development.
Self-Help Groups within Nursing Homes: The Experiences of Family Caregivers in Northeastern Italy
Authors: De Vincenzo Ciro , Marian Ilenia , Piol Silvia , Keisari Shoshi , Testoni Ines
Published: June 2023

Older adults and their family caregivers experience nursing home placement as a particularly critical time of life. The present study explored the experiences of family caregivers of nursing home residents taking part in a self-help group for caregivers. The sample was composed of six caregivers of older adults residing in a nursing home in the northeast of Italy. The respondents, aged 57 to 71, were part of a self-help group set up by the facility between 2017 and 2019. In this qualitative methodological design, we applied the principles of interpretative phenomenological analysis. Two main themes emerged from the interviews: (a) challenges in constructing experience as caregivers; and (b) shared experiences as stabilizing tools. The findings highlight the importance of self-help groups in fostering the well-being of caregivers of older adults living in nursing homes. The self-help group enabled caregivers to deal with nursing home placement and the sense of guilt deriving from it; understand and accept the disabilities affecting their loved one; comprehend the experience of ambiguous loss; and learn to listen to their own needs, thus avoiding physical and emotional exhaustion.
An Analysis of Multigenerational Issues of Generation X and Y Employees in Small- and Medium-Sized Enterprises in Thailand: The Moderation Effect of Age Groups on Person–Environment Fit and Turnover Intention
Authors: Rattanapon Kanokon , Jorissen Ann , Jones Kevin Paul , Ketkaew Chavis
Published: June 2023

Multigenerational employees can evidently impact human resource management practices in terms of effective employee retention. Arguably, a high turnover intention of young employees can hinder a company’s human resource development, while a high volume of retirement of senior employees can create a skill deficit and even a labor management dilemma. This study explored how a supportive work environment can retain employees of different age groups in Thailand’s small- and medium-sized enterprises (SMEs), particularly Generation X and Y. It modeled a supportive work environment that impacts the behaviors of Generation X and Y employees, taking into consideration the relationship among factors such as person–job fit, person–group fit, person–supervisor fit, person–organization fit, person–environment fit, and turnover intention. This paper statistically analyzed a set of data drawn from an attentive survey of a total of 400 employees of SMEs in 4 populous provinces in Thailand using structural equation modeling (SEM) and multigroup analysis (MGA) with the moderation effect of generations. This paper then found that person–job fit, person–group fit, person–supervisor fit, person–organization fit, person–environment fit, and turnover intention can influence an employee’s intention to remain in his/her job. Additionally, the relationship manipulation among the aforementioned variables might influence Generation X and Y employees differently. Under the circumstances, supervisory support with less group involvement may encourage the retention of Generation Y employees, whereas a sufficient focus on job suitability could improve the retention of Generation X employees.
Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer
Authors: Chounta Stefania , Allodji Rodrigue , Vakalopoulou Maria , Bentriou Mahmoud , Do Duyen Thi , De Vathaire Florent , Diallo Ibrahima , Fresneau Brice , Charrier Thibaud , Zossou Vincent , Christodoulidis Stergios , Lemler Sarah , Letort Le Chevalier Veronique
Published: June 2023

Valvular Heart Disease (VHD) is a known late complication of radiotherapy for childhood cancer (CC), and identifying high-risk survivors correctly remains a challenge. This paper focuses on the distribution of the radiation dose absorbed by heart tissues. We propose that a dosiomics signature could provide insight into the spatial characteristics of the heart dose associated with a VHD, beyond the already-established risk induced by high doses. We analyzed data from the 7670 survivors of the French Childhood Cancer Survivors’ Study (FCCSS), 3902 of whom were treated with radiotherapy. In all, 63 (1.6%) survivors that had been treated with radiotherapy experienced a VHD, and 57 of them had heterogeneous heart doses. From the heart–dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. Sensitivity analyses were also conducted for sub-populations of survivors with spatially heterogeneous heart doses. Our results suggest that MHD and dosiomics-based models performed equally well globally in our cohort and that, when considering the sub-population having received a spatially heterogeneous dose distribution, the predictive capability of the models is significantly improved by the use of the dosiomics features. If these findings are further validated, the dosiomics signature may be incorporated into machine learning algorithms for radiation-induced VHD risk assessment and, in turn, into the personalized refinement of follow-up guidelines.
Novel Multi-Modal Therapies and Their Prognostic Potential in Gastric Cancer
Authors: Chidambaram Swathikan , Guiral Delia Cortés , Markar Sheraz Rehan
Published: June 2023

Background: Gastric cancer has a poor prognosis and involves metastasis to the peritoneum in over 40% of patients. The optimal treatment modalities have not been established for gastric cancer patients with peritoneal carcinomatosis (GC/PC). Although studies have reported favourable prognostic factors, these have yet to be incorporated into treatment guidelines. Hence, our review aims to appraise the latest diagnostic and treatment developments in managing GC/PC. Methods: A systematic review of the literature was performed using MEDLINE, EMBASE, the Cochrane Review, and Scopus databases. Articles were evaluated for the use of hyperthermic intraperitoneal chemotherapy (HIPEC) and pressurised intraperitoneal aerosolised chemotherapy (PIPAC) in GC/PC. A meta-analysis of studies reporting on overall survival (OS) in HIPEC and comparing the extent of cytoreduction as a prognostic factor was also carried out. Results: The database search yielded a total of 2297 studies. Seventeen studies were included in the qualitative and quantitative analyses. Eight studies reported the short-term OS at 1 year as the primary outcome measure, and our analysis showed a significantly higher OS for the HIPEC/CRS cohort compared to the CRS cohort (pooled OR = 0.53; p = 0.0005). This effect persisted longer term at five years as well (pooled OR = 0.52; p < 0.0001). HIPEC and CRS also showed a longer median OS compared to CRS (pooled SMD = 0.61; p < 0.00001). Three studies reporting on PIPAC demonstrated a pooled OS of 10.3 (2.2) months. Prognostic factors for longer OS include a more complete cytoreduction (pooled OR = 5.35; p < 0.00001), which correlated with a peritoneal carcinomatosis index below 7. Conclusions: Novel treatment strategies, such as HIPEC and PIPAC, are promising in the management of GC/PC. Further work is necessary to define their role within the treatment algorithm and identify relevant prognostic factors that will assist patient selection.