bluuebunny/crossref_metadata_2025 huggingface dataset:
Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence
Authors: Patel Ankush U. , Shaker Nada , Mohanty Sambit , Sharma Shivani , Gangal Shivam , Eloy Catarina , Parwani Anil V.
Published: July 2022

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
Advances in the Diagnosis of Pancreatic Cystic Lesions
Authors: Pușcașu Claudia Irina , Rimbaş Mihai , Mateescu Radu Bogdan , Larghi Alberto , Cauni Victor
Published: July 2022

Pancreatic cystic lesions (PCLs) are a heterogenous group of lesions ranging from benign to malignant. There has been an increase in PCLs prevalence in recent years, mostly due to advances in imaging techniques, increased awareness of their existence and population aging. Reliable discrimination between neoplastic and non-neoplastic cystic lesions is paramount to ensuring adequate treatment and follow-up. Although conventional diagnostic techniques such as ultrasound (US), magnetic resonance imaging (MRI) and computer tomography (CT) can easily identify these lesions, assessing the risk of malignancy is limited. Endoscopic ultrasound (EUS) is superior to cross-sectional imaging in identifying potentially malignant lesions due to its high resolution and better imaging characteristics, and the advantage of allowing for cyst fluid sampling via fine-needle aspiration (FNA). More complex testing, such as cytological and histopathological analysis and biochemical and molecular testing of the aspirated fluid, can ensure an accurate diagnosis.
New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times
Authors: Piccirelli Stefania , Mussetto Alessandro , Bellumat Angelo , Cannizzaro Renato , Pennazio Marco , Pezzoli Alessandro , Bizzotto Alessandra , Fusetti Nadia , Valiante Flavio , Hassan Cesare , Pecere Silvia , Koulaouzidis Anastasios , Spada Cristiano
Published: July 2022

BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26–340) and 13 min (range 3–85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.
Temporal Trends in the Prevalence, Treatment and Outcomes of Patients with Acute Coronary Syndrome at High Bleeding Risk
Authors: Arow Ziad , Ovdat Tal , Gabarin Mustafa , Omelchenko Alexander , Shuvy Mony , Or Tsafrir , Assali Abid , Pereg David
Published: July 2022

(1) Background: High bleeding risk is associated with adverse outcomes in ACS patients. We aimed to evaluate temporal trends in treatment and outcomes of ACS patients according to bleeding risk. (2) Methods: Included were ACS patients enrolled in ACSIS surveys. Patients were divided into three groups according to enrolment period: early (2002–2004), mid (2006–2010) and recent (2012–2018). Each group was further stratified into three subgroups according to CRUSADE bleeding risk score. The primary endpoints were 30-day MACE and 1-year all-cause mortality. (3) Results: Included were 13,058 ACS patients. High bleeding risk patients were less frequently treated with guideline-based medications and coronary revascularization. They also had higher rates of 30-day MACE and 1-year all-cause mortality regardless of the enrollment period. Among patients enrolled in early period, 30-day MACE rates were 10.8%, 17.5% and 24.3% (p < 0.001) and 1-year all-cause mortality rates were 2%, 7.7% and 23.6% (p < 0.001) in the low, moderate and high bleeding risk groups, respectively. Among patients enrolled in mid period, 30-day MACE rates were 7.7%, 13.4% and 23.5% (p < 0.001) and 1-year all-cause mortality rates were 1.5%, 7.2% and 22.1% (p < 0.001) in low, moderate and high bleeding risk groups, respectively. For patients enrolled in recent period, 30-day MACE rates were 5.7%, 8.6% and 16.2%, (p < 0.001) and 1-year all-cause mortality rates were 2.1%, 6% and 22.4%, (p < 0.001) in low, moderate and high bleeding risk groups, respectively. These differences remained significant following a multivariate analysis. (4) Conclusions: The percentage of patients at high bleeding risk has decreased over the last years. Despite recent improvements in the treatment of ACS patients, high bleeding risk remains a strong predictor of adverse outcomes.
Critical Quantum Metrology in the Non-Linear Quantum Rabi Model
Authors: Ying Zu-Jian , Felicetti Simone , Liu Gang , Braak Daniel
Published: July 2022

The quantum Rabi model (QRM) with linear coupling between light mode and qubit exhibits the analog of a second-order phase transition for vanishing mode frequency which allows for criticality-enhanced quantum metrology in a few-body system. We show that the QRM including a nonlinear coupling term exhibits much higher measurement precisions due to its first-order-like phase transition at finite frequency, avoiding the detrimental slowing-down effect close to the critical point of the linear QRM. When a bias term is added to the Hamiltonian, the system can be used as a fluxmeter or magnetometer if implemented in circuit QED platforms.
Insights into Teachers’ Intercultural and Global Competence within Multicultural Educational Settings
Authors: Papadopoulou Konstantina , Palaiologou Nektaria , Karanikola Zoe
Published: July 2022

As multicultural classrooms appear to be the norm nowadays, giving rise to new, more particular and targeted needs which must be accommodated, teachers now need to reflect on and adjust the strategies and practices they apply in order to meet the specific learning needs of their intercultural classrooms. For this reason, teachers’ education should be re-examined, evaluated, and enriched. In this light, this original study uses a case study to explores the language views and attitudes of graduates of a Hellenic Open University Master’s international programme entitled “Language Education for Refugees and Migrants” (L.R.M.). The aim is to explore teachers’ level of intercultural competence and readiness as well as their global competences as future teachers in multilingual and multicultural settings after completing the programme. To do this, the study attempts to evaluate the programme through the lens of the personal experiences of the graduates in terms of successes, weaknesses, and strengths. To conduct the research, both quantitative and qualitative methods were applied in the form of a close-ended questionnaire and a semi-structured interview conducted in two different formats. The results indicate that the majority of the participants were confident enough to manage the challenges arising in multicultural settings after completing the program, as they felt they had developed appropriate Skills, Knowledge, and Attitudes; however, a few among them still felt uncertain in doing this. In addition, a number of different approaches and methods were found to be appropriate as used for teachers’ training, while the importance of teachers’ intercultural training was highlighted.
Auction Mechanism-Based Sectored Fractional Frequency Reuse for Irregular Geometry Multicellular Networks
Authors: Ullah Rahat , Gani Abdullah , Shiraz Muhammad , Yousufzai Imran Khan , Zaman Khalid
Published: July 2022

Modern cellular systems have adopted dense frequency reuse to address the growing amount of mobile data traffic. The system capacity is improved accordingly; however, this is at the cost of augmented Inter-Cell Interference (ICI). Recently, Fractional Frequency Reuse (FFR) has emerged as an efficient ICI management scheme in Orthogonal Frequency-Division Multiple Access (OFDMA)-based cellular systems. However, the FFR scheme that leads to optimized spectrum allocation for individual users in the irregular geometry networks is not considered in the literature. Meanwhile, in the practical wireless scenario, the users are non-cooperative and want to maximize their demands. A game-theoretic Auction Mechanism-based Sectored-FFR (AMS-FFR) scheme is proposed in this paper to optimally distribute the bandwidth resources to the individual users in the realistic multicellular network deployment. In the proposed auction mechanism, the Base Station (BS) acts as an auctioneer and is the owner of sub-carriers. The users are permitted to bid for a bundle of sub-carriers corresponding to their traffic requirements. The Monte Carlo simulation results show that the presented AMS-FFR scheme outperforms the prevailing FFR schemes in terms of achievable throughput by 65% and 46% compared to the basic FFFR and dynamic FFR-3 schemes, respectively. Moreover, the average sum rate along with the user satisfaction is significantly increased while considering a full traffic load.
Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance
Authors: Behera Trupti Mayee , Samal Umesh Chandra , Mohapatra Sushanta Kumar , Khan Mohammad S. , Appasani Bhargav , Bizon Nicu , Thounthong Phatiphat
Published: July 2022

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed.
A Spatio-Temporal Feature Trajectory Clustering Algorithm Based on Deep Learning
Authors: He Xintai , Li Qing , Wang Runze , Chen Kun
Published: July 2022

The trajectory data of aircraft, ships, and so on, can be analyzed to obtain valuable information. Clustering is the basic technology of trajectory analysis, and the feature extraction process is one of the decisive factors for clustering performance. Trajectory features can be divided into two categories: spatial features and temporal features. In mainstream algorithms, spatial features are represented by latitude and longitude coordinates. However, such algorithms are only suitable for trajectories where spatial features are tightly coupled with latitude and longitude. When the same types of trajectories are in different latitude and longitude ranges or there are transformations such as rotation, scaling, and so on, this kind of algorithm is infeasible. Therefore, this paper proposes a spatio-temporal feature trajectory clustering algorithm based on deep learning. In this algorithm, the extraction process of the trajectory spatial shape feature is designed based on image matching technology, and the extracted spatial features are combined with the trajectory temporal features to improve the clustering performance. The experimental results on simulated and real datasets show that the algorithm can effectively extract the trajectory spatial shape features and that the clustering effect of the fused spatio-temporal feature is better than that of a single feature.
A Modified Modulation Strategy for an Active Neutral-Point-Clamped Five-Level Converter in a 1500 V PV System
Authors: Chen Guodong , Yang Jiatao
Published: July 2022

With the development of 1500 V photovoltaic (PV) systems in recent decades, multilevel inverters such as the five-level inverter have gained much attention for their higher equivalent output frequency and low semiconductor devices’ voltage stress. Among five-level inverters, the active neutral-point-clamped five-level (ANPC-5L) inverter is very competitive due to its simple structure and control methods. However, with its conventional commutation strategy, the topology of the ANPC five-level converter has the security risk of overvoltage in the power device when switching to dead time under special conditions, which affects the reliability and safety of the switch state switching process. In this paper, this issue is analyzed in detail and a modified commutation strategy is proposed. Meanwhile, a novel soft start-up method adopted to an ANPC-5L inverter is also proposed. A prototype is also set up to analyze the issue of traditional switching commutation strategies and to verify the effectiveness of the proposed commutation strategy and the soft start-up method.
Two Efficient Sparse Fourier Algorithms Using the Matrix Pencil Method
Authors: Li Bin , Hou Xueqing , Jiang Zhikang , Chen Jie
Published: July 2022

The research on efficient computation of sparse signals by various Sparse Fast Fourier Transform (sFFT) algorithms has always been a hot topic in the direction of signal processing. The algorithms can decrease the sampling and running complexity by taking advantage of the signal’s inherent characteristics that a large number of signals are sparse in the frequency domain. The sFFT algorithm is generally divided into two stages: the first stage is bucketization. The process is to divide N frequencies into B buckets through the filter. The main filters used are the flat window filter and the aliasing filter. The second stage is the spectrum recovery. The process is to successfully locate the position of the large frequency in each bucket and successfully calculate the amplitude. Among these steps, the most difficult and time-consuming step is to successfully locate the position of the large value frequency. For the sFFT algorithm based on a flat window filter, the voting method used in sFFT1.0 and sFFT2.0 algorithms should use many rounds, so these two algorithms are time-consuming and indeterministic, while the phase estimation method used in sFFT3.0 and sFFT4.0 algorithms has medium robustness. For sFFT algorithms based on an aliasing filter, the Prony method used in sFFT-DT1.0 algorithm is only applicable to noiseless signals, while the enumeration method used in the sFFT-DT2.0 algorithm has high complexity and poor robustness. In view of the performance of the old methods, new and more efficient methods are needed to achieve spectrum recovery. The spectrum restoration can be converted to estimating the complex amplitudes and attenuation coefficient in the model of the sum of complex exponentials. The matrix pencil method is a standard technique for mode frequency identification. Therefore, we propose the sFFT5.0 algorithm and sFFT-DT3.0 algorithm using the matrix pencil method to do spectrum recovery. These two algorithms are low computational complexity and strong robustness and have achieved good results in the actual comparative test.
Overview of the EEG-Based Classification of Motor Imagery Activities Using Machine Learning Methods and Inference Acceleration with FPGA-Based Cards
Authors: Majoros Tamás , Oniga Stefan
Published: July 2022

In this article, we provide a brief overview of the EEG-based classification of motor imagery activities using machine learning methods. We examined the effect of data segmentation and different neural network structures. By applying proper window size and using a purely convolutional neural network, we achieved 97.7% recognition accuracy on data from twenty subjects in three classes. The proposed architecture outperforms several networks used in previous research and makes the motor imagery-based BCI more efficient in some applications. In addition, we examined the performance of the neural network on a FPGA-based card and compared it with the inference speed and accuracy provided by a general-purpose processor.
The Influence of Movable Water on the Gas-Phase Threshold Pressure Gradient in Tight Gas Reservoirs
Authors: Zhu Weiyao , Zou Guodong , Liu Yuwei , Liu Wenchao , Pan Bin
Published: July 2022

Threshold pressure gradient (TPG) is a key parameter determining the pore-scale fluid dynamics. In tight gas reservoirs, both gas and water exist in the porous rock, and the existing water can be divided into irreducible and movable water. However, how movable water saturation will influence TPG has not yet been investigated. Therefore herein, nuclear magnetic resonance (NMR) and high-pressure mercury intrusion (HPMI) experiments were performed to determine pore-scale water distribution, movable water saturation, and pore throat distribution in the core plugs. Subsequently, the air bubble method was used to measure TPG as a function of movable water saturation and permeability inside tight gas core plugs, finding that TPG increased from 0.01 MPa/m to 0.25 MPa/m with the movable saturation increased from 2% to 35%. Finally, a semi-empirical model was derived to describe the correlation between TPG, movable water saturation, and permeability, which performed better than previous models in the literature. These insights will advance the fundamental understanding of TPG in tight gas reservoirs and provide useful guidance on tight gas reservoirs development.
Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach
Authors: Hegazy Hanem I. , Tag Eldien Adly S. , Tantawy Mohsen M. , Fouda Mostafa M. , TagElDien Heba A.
Published: July 2022

Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability. This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to detect the presence and locations of FDIAs in real-time measurements with precise locational detection accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus system in the MATPOWER package. Extensive testing has shown that the proposed approach effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large systems under various attack conditions. In addition, this work provides a customized loss function for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a modest advantage in some aspects. First, our mechanism outperforms benchmark models because the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs fewer iterations for training and reaching the optimal model. More specifically, our approach is less complex and more scalable than benchmark algorithms.
Field Study and Multimethod Analysis of an EV Battery System Disassembly
Authors: Rosenberg Sonja , Huster Sandra , Baazouzi Sabri , Glöser-Chahoud Simon , Al Assadi Anwar , Schultmann Frank
Published: July 2022

In the coming decades, the number of end-of-life (EoL) traction battery systems will increase sharply. The disassembly of the system to the battery module is necessary to recycle the battery modules or to be able to use them for further second-life applications. These different recovery paths are important pathways to archive a circular battery supply chain. So far, little knowledge about the disassembling of EoL batteries exists. Based on a disassembly experiment of a plug-in hybrid battery system, we present results regarding the battery set-up, including their fasteners, the necessary disassembly steps, and the sequence. Upon the experimental data, we assess the disassembly duration of the battery system under uncertainty with a fuzzy logic approach. The results indicate that a disassembling time of about 22 min is expected for the battery system in the field study if one worker conducts the process. An estimation for disassembling costs per battery system is performed for a plant in Germany. Depending on the plant capacity, the disassembling to battery module level is associated with costs between EUR 80 and 100 per battery system.
Assessment of Plant Origin By-Products as Lightweight Aggregates for Bio-Composite Bounded by Starch Binder
Authors: Vitola Laura , Gendelis Stanislavs , Sinka Maris , Pundiene Ina , Bajare Diana
Published: July 2022

Thermal insulation bio-composites made of plant origin by-products as bio-aggregates are one of the ways to decrease the impact of the building and construction sector on CO2 emissions. In this study, three bio-aggregates were analysed for their potential use in the production of bio-composites with potato starch binder. Technologically important properties, such as particle size, shape and compacted bulk density, as well as properties of the resulting bio-composites were identified. The main characteristics of the aggregates are relatively similar: density of 80–100 kg/m3, thermal conductivity of 0.042–0.045 W/m∙K, specific heat capacity of 1240–1330 J/g∙K, kinetic water absorption from 456–584%. This leads to similar basic properties of the produced bio-composites: density around 200 kg/m3, thermal conductivity 0.053–0.062 W/m∙K, specific heat capacity 1250–1450 J/kg∙K, with a difference in compressive strength ranging from 0.2 to 0.8 MPa. Created starch binder and agricultural by-product filler materials could be used in the production of boards where strength is required, for example, envelope and wind barrier boards, and thermal insulation boards under floors.
Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation
Authors: Yamamoto Hiroki , Kondoh Junji , Kodaira Daisuke
Published: July 2022

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy.
Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems
Authors: Watanabe Rodrigo Bairros , Ando Junior Oswaldo Hideo , Leandro Paulo Gabriel Martins , Salvadori Fabiano , Beck Marlon Felipe , Pereira Katiane , Brandt Marcelo Henrique Manzque , de Oliveira Fernando Marcos
Published: July 2022

In this paper, an algorithm for the maximum extraction of energy generated by photovoltaic (PV) systems was presented. The tracking of the global maximum point of the system is complex due to the non-linearity of the current-voltage (I-V) characteristic curve of the photovoltaic modules, as they vary according to the temperature and solar irradiation in the module. To obtain the best energy efficiency in these systems, it is important that the generation is delivering the maximum power available through the arrangement. In order to solve such problems, in this work an efficient MPPT-FA method was proposed, which showed good traceability when compared to traditional methods. Most traditional MPPT techniques are not able to find the global maximum point to extract the maximum power provided by the PV system. Finally, the Firefly Metaheuristic MPPT method proved to be robust against several partial shading scenarios. Simulations were presented to demonstrate the effectiveness of the proposal when compared to the traditional MPPT-PO method.
Microbial and Planktonic Community Characteristics of Eriocheir sinensis Culture Ponds Experiencing Harmful Algal Blooms
Authors: Gao Jiancao , Shen Lei , Nie Zhijuan , Zhu Haojun , Cao Liping , Du Jinliang , Dai Fei , Xu Gangchun
Published: July 2022

The Chinese mitten crab, Eriocheir sinensis (H. Milne Edwards, 1853), is an economically important aquaculture species in China. It is a significantly desirable species by Chinese consumers that causes a high demand for environmentally friendly culture farming. In aiming to break through bottlenecks, i.e., “pond moss” and cyanobacteria, we investigated the microbial community and plankton composition of ponds with filamentous algae and cyanobacterial blooms. As results, we found Actinobacteria, Proteobacteria, and Bacteroidetes were dominant bacterial phyla, while Chlorophyta and Bacillariophyta were dominant phytoplankton phyla in E. sinensis ponds. Nitrospira sp., Flectobacillus sp. BAB-3569, Staphylococcus warneri, Fusarium oxysporum, Gromochytrium mamkaevae, and Rhizophydium sp. JEL317 were screened as bioindicators for harmful algal blooms. We found a close relationship between water quality parameters and the species composition of bacteria and zooplankton in the present study. Specifically, total nitrogen and total ammonia nitrogen significantly affected the bacterial community composition, while total phosphorus contributed to the phytoplankton community composition. We further indicated the potential competitive inhibition of Chlamydomonadales on the direct regulation of the control of harmful algal blooms. Finally, we suggested a combination of probiotics and microalgae, e.g., C. vulgaris, to prevent and control potential risks in the culture of E. sinensis. In conclusion, the present study deepened our understanding of harmful algal blooms in aquaculture ponds and suggested the baseline indications for the prevention and control of algal blooms.
Effect of Malondialdehyde on the Digestibility of Beef Myofibrillar Protein: Potential Mechanisms from Structure to Modification Site
Authors: Yin Yantao , Zhou Lei , Cai Jiaming , Feng Fan , Xing Lujuan , Zhang Wangang
Published: July 2022

Lipid oxidation and protein oxidation occur side by side in meat. Here, the effect of malondialdehyde (MDA), the major product of lipid oxidation, on the digestibility of beef myofibrillar proteins (MP) was studied. MP samples were incubated with 0, 0.1, 0.3, 0.5, and 0.7 mM MDA at 4 °C for 12 h and then subjected to in vitro gastrointestinal digestion. The result showed that MDA remarkably reduced the digestibility of MP (p < 0.05). MDA treatments significantly increased carbonyl and Schiff base contents in MP (p < 0.05). The microstructure observed by atomic force microscopy showed that MDA treatments resulted in the aggregation of MP. Non-reducing and reducing electrophoresis suggested the aggregation was mainly caused by covalent bonds including disulfide bond and carbonyl–amine bond. Proteomics analysis proved that the myosin tail was the main target of MDA attack, meanwhile, lysine residues were the major modification sites. Taken together, the above results imply that MDA induces protein oxidation, aggregation, and blockage of hydrolysis sites, consequently leading to the decrease in both gastric and gastrointestinal digestibility of MP.
Creating a Functional Biomimetic Cartilage Implant Using Hydrogels Based on Methacrylated Chondroitin Sulfate and Hyaluronic Acid
Authors: Schuiringa Gerke H. , Mihajlovic Marko , van Donkelaar Corrinus C. , Vermonden Tina , Ito Keita
Published: July 2022

The load-bearing function of articular cartilage tissue contrasts with the poor load-bearing capacity of most soft hydrogels used for its regeneration. The present study explores whether a hydrogel based on the methacrylated natural polymers chondroitin sulfate (CSMA) and hyaluronic acid (HAMA), injected into warp-knitted spacer fabrics, could be used to create a biomimetic construct with cartilage-like mechanical properties. The swelling ratio of the combined CSMA/HAMA hydrogels in the first 20 days was higher for hydrogels with a higher CSMA concentration, and these hydrogels also degraded quicker, whereas those with a 1.33 wt% of HAMA were stable for more than 120 days. When confined by a polyamide 6 (PA6) spacer fabric, the volumetric swelling of the combined CSMA/HAMA gels (10 wt%, 6.5 × CSMA:HAMA ratio) was reduced by ~53%. Both the apparent peak and the equilibrium modulus significantly increased in the PA6-restricted constructs compared to the free-swelling hydrogels after 28 days of swelling, and no significant differences in the moduli and time constant compared to native bovine cartilage were observed. Moreover, the cell viability in the CSMA/HAMA PA6 constructs was comparable to that in gelatin–methacrylamide (GelMA) PA6 constructs at one day after polymerization. These results suggest that using a HydroSpacer construct with an extracellular matrix (ECM)-like biopolymer-based hydrogel is a promising approach for mimicking the load-bearing properties of native cartilage.
Identification of New Toxicity Mechanisms in Drug-Induced Liver Injury through Systems Pharmacology
Authors: Moya-García Aurelio A. , González-Jiménez Andrés , Moreno Fernando , Stephens Camilla , Lucena María Isabel , Ranea Juan A. G.
Published: July 2022

Among adverse drug reactions, drug-induced liver injury presents particular challenges because of its complexity, and the underlying mechanisms are still not completely characterized. Our knowledge of the topic is limited and based on the assumption that a drug acts on one molecular target. We have leveraged drug polypharmacology, i.e., the ability of a drug to bind multiple targets and thus perturb several biological processes, to develop a systems pharmacology platform that integrates all drug–target interactions. Our analysis sheds light on the molecular mechanisms of drugs involved in drug-induced liver injury and provides new hypotheses to study this phenomenon.
Early Neonatal Cardiac Phenotype in Hurler Syndrome: Case Report and Literature Review
Authors: Pillai Nishitha R. , Ahmed Alia , Vanyo Todd , Whitley Chester B.
Published: July 2022

Mucopolysaccharidosis type I (MPS I) is a rare inherited lysosomal disorder caused by deficiency of the α-L-iduronidase enzyme, resulting in the progressive accumulation of glycosaminoglycans (GAGs), which interfere with the normal function of multiple tissues and organs. The clinical phenotype includes characteristic facial features, hepatosplenomegaly, dysostosis multiplex, umbilical and inguinal hernias, progressive cognitive deficits with corresponding hydrocephalus, and neuropathology. Untreated children do not survive into the second decade. The common cardiac phenotype seen in MPS I and other MPS types includes valve thickening and dysfunction, conduction abnormalities, coronary artery disease, and cardiomyopathy—usually seen later in the disease course. A 15-month-old ex-35-weeker who presented with cardiomyopathy and left ventricular failure at the age of three weeks is presented here. Early evaluation and diagnosis with the help of newborn screening (NBS), followed by treatment with enzyme replacement therapy (ERT) and hematopoietic stem cell transplantation (HSCT), resulted in improvement of his cardiopulmonary status. In MPS I, an early cardiac phenotype is uncommon. Based on the evidence from the literature review for early neonatal cardiac phenotype, we propose that all infants with abnormal newborn screening for MPS I should receive cardiac screening with echocardiogram and NT-proB-type natriuretic peptide (BNP) during the initial evaluation.
Differential Methylation Profile in Fragile X Syndrome-Prone Offspring Mice after in Utero Exposure to Lactobacillus Reuteri
Authors: AlOlaby Reem R. , Zafarullah Marwa , Barboza Mariana , Peng Gang , Varian Bernard J. , Erdman Susan E. , Lebrilla Carlito , Tassone Flora
Published: July 2022

Environmental factors such as diet, gut microbiota, and infections have proven to have a significant role in epigenetic modifications. It is known that epigenetic modifications may cause behavioral and neuronal changes observed in neurodevelopmental disabilities, including fragile X syndrome (FXS) and autism (ASD). Probiotics are live microorganisms that provide health benefits when consumed, and in some cases are shown to decrease the chance of developing neurological disorders. Here, we examined the epigenetic outcomes in offspring mice after feeding of a probiotic organism, Lactobacillus reuteri (L. reuteri), to pregnant mother animals. In this study, we tested a cohort of Western diet-fed descendant mice exhibiting a high frequency of behavioral features and lower FMRP protein expression similar to what is observed in FXS in humans (described in a companion manuscript in this same GENES special topic issue). By investigating 17,735 CpG sites spanning the whole mouse genome, we characterized the epigenetic profile in two cohorts of mice descended from mothers treated and non-treated with L. reuteri to determine the effect of prenatal probiotic exposure on the prevention of FXS-like symptoms. We found several genes involved in different neurological pathways being differentially methylated (p ≤ 0.05) between the cohorts. Among the key functions, synaptogenesis, neurogenesis, synaptic modulation, synaptic transmission, reelin signaling pathway, promotion of specification and maturation of neurons, and long-term potentiation were observed. The results of this study are relevant as they could lead to a better understanding of the pathways involved in these disorders, to novel therapeutics approaches, and to the identification of potential biomarkers for early detection of these conditions.
Molecular Characterization of TGF-Beta Gene Family in Buffalo to Identify Gene Duplication and Functional Mutations
Authors: Rehman Muhammad Saif-ur , Hassan Faiz-ul , Rehman Zia-ur , Ishtiaq Iqra , Rehman Saif ur , Liu Qingyou
Published: July 2022

The TGF-β superfamily is ubiquitously distributed from invertebrates to vertebrates with diverse cellular functioning such as cell adhesion, motility, proliferation, apoptosis, and differentiation. The present study aimed to characterize the TGF-β gene superfamily in buffalo through evolutionary, structural, and single nucleotide polymorphism (SNPs) analyses to find the functional effect of SNPs in selected genes. We detected 32 TGF-β genes in buffalo genome and all TGF-β proteins exhibited basic nature except INHA, INHBC, MSTN, BMP10, and GDF2, which showed acidic properties. According to aliphatic index, TGF-β proteins were thermostable but unstable in nature. Except for GDF1 and AMH, TGF-β proteins depicted hydrophilic nature. Moreover, all the detected buffalo TGF-β genes showed evolutionary conserved nature. We also identified eight segmental and one tandem duplication event TGF-β gene family in buffalo, and the ratio of Ka/Ks demonstrated that all the duplicated gene pairs were under selective pressure. Comparative amino acid analysis demonstrated higher variation in buffalo TGF-β gene family, as a total of 160 amino acid variations in all the buffalo TGF-β proteins were detected. Mutation analysis revealed that 13 mutations had an overall damaging effect that might have functional consequences on buffalo growth, folliculogenesis, or embryogenesis.
Genome-Wide Identification and Expression Analysis of SnRK2 Gene Family in Dormant Vegetative Buds of Liriodendron chinense in Response to Abscisic Acid, Chilling, and Photoperiod
Authors: Hussain Quaid , Zheng Manjia , Chang Wenwen , Ashraf Muhammad Furqan , Khan Rayyan , Asim Muhammad , Riaz Muhammad Waheed , Alwahibi Mona S. , Elshikh Mohamed S. , Zhang Rui , Wu Jiasheng
Published: July 2022

Protein kinases play an essential role in plants’ responses to environmental stress signals. SnRK2 (sucrose non-fermenting 1-related protein kinase 2) is a plant-specific protein kinase that plays a crucial role in abscisic acid and abiotic stress responses in some model plant species. In apple, corn, rice, pepper, grapevine, Arabidopsis thaliana, potato, and tomato, a genome-wide study of the SnRK2 protein family was performed earlier. The genome-wide comprehensive investigation was first revealed to categorize the SnRK2 genes in the Liriodendron chinense (L. chinense). The five SnRK2 genes found in the L. chinense genome were highlighted in this study. The structural gene variants, 3D structure, chromosomal distributions, motif analysis, phylogeny, subcellular localization, cis-regulatory elements, expression profiles in dormant buds, and photoperiod and chilling responses were all investigated in this research. The five SnRK2 genes from L. chinense were grouped into groups (I–IV) based on phylogeny analysis, with three being closely related to other species. Five hormones-, six stress-, two growths and biological process-, and two metabolic-related responsive elements were discovered by studying the cis-elements in the promoters. According to the expression analyses, all five genes were up- and down-regulated in response to abscisic acid (ABA), photoperiod, chilling, and chilling, as well as photoperiod treatments. Our findings gave insight into the SnRK2 family genes in L. chinense and opened up new study options.
Epigenetic Peripheral Biomarkers for Early Diagnosis of Alzheimer’s Disease
Authors: Villa Chiara , Stoccoro Andrea
Published: July 2022

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and represents the leading cause of cognitive impairment and dementia in older individuals throughout the world. The main hallmarks of AD include brain atrophy, extracellular deposition of insoluble amyloid-β (Aβ) plaques, and the intracellular aggregation of protein tau in neurofibrillary tangles. These pathological modifications start many years prior to clinical manifestations of disease and the spectrum of AD progresses along a continuum from preclinical to clinical phases. Therefore, identifying specific biomarkers for detecting AD at early stages greatly improves clinical management. However, stable and non-invasive biomarkers are not currently available for the early detection of the disease. In the search for more reliable biomarkers, epigenetic mechanisms, able to mediate the interaction between the genome and the environment, are emerging as important players in AD pathogenesis. Herein, we discuss altered epigenetic signatures in blood as potential peripheral biomarkers for the early detection of AD in order to help diagnosis and improve therapy.
Validation of Analytical Solutions for Predicting Drilled Pile Behaviour under Bi-Directional Static Load Tests
Authors: Wang Runshen , Ong Dominic E. L. , Zhou Jialin , Liu Siwei , Oh Erwin
Published: July 2022

A bi-directional static load test (BDSLT) is one of the most effective methods for accurately estimating pile bearing capacity, in which the test pile is divided into two portions by activating the single-loading device welded along the pile shaft. BDSLT, thus, eliminates the safety concerns and space limitations imposed by the reaction system, as compared to conventional static load tests (kentledge). Based on this study’s project requirements, two loading devices (supercells) were welded along the pile shaft to provide sufficient bearing capacity under the BDSLT, and an equivalent method was applied to interpret the measured load–settlement response. Since the sacrificial loading device welded along the pile shaft cannot be re-used, BDSLTs lead to increased construction costs; however, their capacity for rapid set-up in a limited space and reliable application for long piles are benefits that easily justify their use. Therefore, researchers must understand how BDSLTs perform, especially regarding double-loading devices. As informed by site investigation, this paper validates the conventional analytical solutions regarding test piles in preliminary designs, including Alpha and Beta and semi-empirical methods. In terms of a soil stiffness reduction model, modified closed-form analytical solutions based on Randolph’s analytical method were applied to predict the load–settlement response.
A Narrative Review of Telemedicine in Latin America during the COVID-19 Pandemic
Authors: Camacho-Leon Genesis , Faytong-Haro Marco , Carrera Keila , Molero Marlyn , Melean Franhe , Reyes Yuliana , Mautong Hans , De La Hoz Ivonne , Cherrez-Ojeda Ivan
Published: July 2022

Background: The COVID-19 pandemic greatly impacted traditional health structures, posing new challenges in an unprecedented health crisis. Telemedicine services were implemented in countries with robust digital platforms to reduce hospital attendance while continuing to provide medical care. This study aims to determine how telemedicine services have been used as a tool to ensure the right to health in Latin America during the pandemic. Materials and methods: We conducted a narrative review in which words such as telemedicine, COVID-19, Latin America, access, and right to health, were searched on scientific medical datasets such as PubMed and SciELO. Additionally, we reviewed legislation in the Latin American health domain regarding the administration and transmission of digital data. Results and conclusions: Several countries have used telemedicine to reduce the saturation of healthcare systems and increase patient access. Issues such as broadband access for low-income populations and adequate legal regulations for transmitting and storing confidential data must be addressed to improve telemedicine use in Latin America.
Hard Voting Ensemble Approach for the Detection of Type 2 Diabetes in Mexican Population with Non-Glucose Related Features
Authors: Morgan-Benita Jorge A. , Galván-Tejada Carlos E. , Cruz Miguel , Galván-Tejada Jorge I. , Gamboa-Rosales Hamurabi , Arceo-Olague Jose G. , Luna-García Huizilopoztli , Celaya-Padilla José M.
Published: July 2022

Type 2 diabetes mellitus (T2DM) represents one of the biggest health problems in Mexico, and it is extremely important to early detect this disease and its complications. For a noninvasive detection of T2DM, a machine learning (ML) approach that uses ensemble classification models with dichotomous output that is also fast and effective for early detection and prediction of T2D can be used. In this article, an ensemble technique by hard voting is designed and implemented using generalized linear regression (GLM), support vector machines (SVM) and artificial neural networks (ANN) for the classification of T2DM patients. In the materials and methods as a first step, the data is balanced, standardized, imputed and integrated into the three models to classify the patients in a dichotomous result. For the selection of features, an implementation of LASSO is developed, with a 10-fold cross-validation and for the final validation, the Area Under the Curve (AUC) is used. The results in LASSO showed 12 features, which are used in the implemented models to obtain the best possible scenario in the developed ensemble model. The algorithm with the best performance of the three is SVM, this model obtained an AUC of 92% ± 3%. The ensemble model built with GLM, SVM and ANN obtained an AUC of 90% ± 3%.
Autochthonous Rose Hybrid Rosa pendulina × spinosissima Overshines Main Genotype Rosa pendulina in the Biochemical Characteristics of Their Hips
Authors: Kunc Nina , Mikulič-Petkovšek Maja , Hudina Metka , Bavcon Jože , Vreš Branko , Osterc Gregor , Ravnjak Blanka
Published: July 2022

The medicinal value of rose hips largely depends on the contents of vitamin C and flavonoids. Rose hips contain more vitamin C than most fruits and vegetables. We were particularly interested in how the bioactive substances of rose hips are inherited from main rose species. The aim of the study was to compare the contents of ascorbic acid, organic acids, and phenolics in the fruits, rose hips, of Rosa pendulina L. and the hybrid Rosa pendulina L. × spinosissima L. The contents of ascorbic acid in the studied hips were higher in R. pendulina × spinosissima than in R. pendulina. The contents of total organic acids were higher in the hips of R. pendulina × spinosissima than in the hips of R. pendulina. The contents of phenolics in the hybrid were significantly higher in flesh with skin and in seeds. The contents of cyanidin-3-glucoside were higher in R. pendulina × spinosissima. We can conclude that the contents of bioactive substances in our samples is rich and could potentially be used for human consumption. The results will help to increase transparency about which rose species provide rose hips that are the most suitable for nutritional purposes.