bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware
Authors: Shivaramu Banumathi Chandini , Basavegowda Rajendra Ajjipura
Published: July 2024

The classification and analysis of malicious software (malware) has reached a huge development in the systems associated with the internet. The malware exploits the system information and takes off the important information of the user without any intimation. Moreover, the malware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect obfuscated and packed malware. To overcome these problems, the hybridized grasshopper optimization and cuckoo search (GOA-CSA) algorithm is proposed. The effective features are selected by the GOA-CSA algorithm which eases the process of classifying the malware. This research also utilized long short-term memory (LSTM)-softsign classifier to classify the malware. The malware samples are collected from the VXHeavens dataset which consists of malware samples from various software. The proposed model performance is estimated by using the performance metrics like accuracy, sensitivity, recall, and F1-score. The model attained better accuracy of 98.95% when the model is compared with other existing models.
Handover management in vehicle communication: applications, techniques, issues, and challenges: a review
Authors: Qasim Hamzah Hadi , Zainol Abidin Husna , Afzal Che Abdullah Syahrul
Published: July 2024

Vehicle-to-everything (V2X) communication is an emerging technology that facilitates communication among vehicles and numerous environmental entities. However, it encounters certain challenges throughout the handover process. This study analyses the challenges and complexity of managing handover, specifically in maintaining uninterrupted connection and meeting the service criteria outlined in the 3GPP 5G new radio (NR) standard. Various applications of V2X technology that require handover management are explored, such as vehicle safety and traffic management, enhanced driver assistance, and autonomous driving. Furthermore, this paper illuminates the most recent developments in V2X communication, highlighting the significance of efficient handover management, resolving technical issues, based on the full potential of the use of V2X apps that contribute to the establishment of a transportation ecosystem that is characterized by enhanced safety, increased intelligence, and improved connectivity. This paper can be used as a starting point for thinking about how to improve C-V2X communication.
Genetic programming in machine learning based on the evaluation of house affordability classification
Authors: Masrom Suraya , Baharun Norhayati , Mohamad Razi Nor Faezah , Abd Rahman Abdullah Sani , Mohammad Nor Hazlina , Sarkam Nor Aslily
Published: July 2024

One of the big challenges in machine learning is difficulty of achieving high accuracy in a short completion time. A more difficulties appeared when the algorithm needs to be used for solving real dataset from the survey-based data collection. Imbalance dataset, insufficient strength of correlations, and outliers are common problems in real dataset. To accelerate the modelling processes, automated machine learning based on meta-heuristics optimization such as genetic programming (GP) has started to emerge and is gaining popularity. However, identifying the best hyper-parameters of the meta-heuristics’ algorithm is the critical issue. This paper demonstrates the evaluation of GP hyper-parameters in modeling machine learning on house affordability dataset. The important hyper-parameters of GP are population size (PS), that has been observed with different setting in this research. The machine learning with GP was used to predict house affordability among employers with transport expenditure and job mobility as some of the attributes. The results from testing that run on hold-out samples show that GP machine learning can reach to 70% accuracy with split ratio 0.2 and GP PS 30. This research contributes to the advancement of automated machine learning techniques, offering potential for faster and more accurate real survey-based datasets.
An efficient hybrid LC-S compensation topology for wireless power transfer system
Authors: Irshad Talha , Nauman Malik , Abas Pg Emeroylariffion
Published: July 2024

Wireless power transfer (WPT) system is gaining prominence for charging various applications, including electric vehicles, biomedical implants, smartphones, and network sensors. Efficient compensation networks are essential to minimize leakage inductance. The performances of single-element compensation topologies are compared based on different coupling and loading conditions, revealing that series-series (SS) and series-parallel (SP) topologies exhibit lower peak efficiencies of 85% and 90%, respectively, than parallel-series (PS) and parallel-parallel (PP) topologies, having 99% and 98.5% peak efficiency, respectively; with efficiencies deteriorating under different loading and coupling conditions. To address these shortcomings, a hybrid inductor-capacitor capacitor (LC-S) compensation topology is proposed, outperforming the single element topologies and maintaining over 95% efficiency under varying loading and coupling conditions. A slight efficiency drop to 82% due to frequency splitting was observed with low load resistance. In terms of the output characteristics, the LC-S topology achieved 1.2 W. These results showcase the potential of the LC-S topology for enhancing WPT system efficiency.
Observer-based single phase robustness load frequency sliding mode controller for multi-area interconnected power systems
Authors: Nguyen Cong-Trang , Trong Hien Chiem , Phan Van-Duc
Published: July 2024

In multi-area interconnected power systems (MAIPS), all the plant state’s measurement is stiff due to the lack of a device or the cost of the sensor is expensive. To solve this restriction, a novel sliding mode control technique- based load frequency controller (LFC) is investigated for MAIPS where the estimation states of the system is utilized fully in the switching surface and controller. Initially, a single-phase switching function is suggested to dismiss the reaching phase in traditional sliding mode control (TSMC) approach. Secondly, the MAIPS’s unmeasurable variables is estimated by using the suggested observer tool. Next, a new single phase robustness load frequency sliding mode controller (SPRLFSMC) for the MAIPS is established based on the support of the observer instrument and output data only. The entire plant’s stability is ensured through the Lyapunov theory. Even though the plant’s variables are not measured, the obtained results in the simulation display that the frequency remains in the nominal domain under load instabilities on the MAIPS. The simulation results for a three-area interconnected electricity plant verify the preeminence of the anticipated SPRLFSMC over other current controllers with respect to settling time and overshoot.
Control system development for monitoring nutrition of curly mustard plants in horizontal NFT hydroponic based-IoT
Authors: Rusdiyana Liza , Suhariyanto Suhariyanto , Sampurno Bambang , Ardiyanti Pratama Tania
Published: July 2024

Agricultural technology with a hydroponic system provides an alternative for farmers and communities who have limited land. This research aims to make innovations with a hydroponic monitoring system that can be done remotely via the internet that combines 2 systems, namely horizontal technique and nutrient film technique (NFT). The sample used in this study was curly mustard seeds. To combine the 2 systems, researchers designed a hydroponic prototype system using internet of things (IoT) in the form of smart hydroponics in the Blynk application. This research uses literature studies for research reference and flowcharts to regulate the flow of the program to be researched. The results showed that by using the IoT and the Blynk application, owners can monitor the nutrient content and pH of curly mustard greens remotely. The system automatically controls nutrients and pH according to the desired settings. In the growth control system of mustard curly, the use of smart hydroponics is proven to be better. Harvestable plants at the age of 34 days. Unlike the conventional system, the harvest period is at the age of 40–45 days. Therefore, smart hydroponics is more efficient because it shortens the harvesting time and saves labor.
Portable internet of things-based soil nutrients monitoring for precision and efficient smart farming
Authors: Hartono Rudi , Maulana Yoeseph Nanang , Aji Purnomo Fendi , Asri Safi'ie Muhammad , Alim Tri Bawono Sahirul
Published: July 2024

This paper describes the design and implementation of a portable internet of things (IoT)-based system for online monitoring of soil nutrients, specifically nitrogen (N), phosphorus (P), and potassium (K), to improve precision and efficiency in smart farming. The main goal is to use IoT technology to analyze soil conditions on-site and provide advice about fertilization and soil management. The system measures soil nutrient levels using field-based sensors, such as an NPK probe, and transmits data over a wireless sensor network. The research comprises a quantitative evaluation of the performance of the IoT system using various sensors. An analysis of variance (ANOVA) was used to compare the accuracy of the IoT device with industrial soil nutrient measurement equipment, demonstrating differences in P and K values but not in N values. This disparity points to certain areas where the accuracy of the P and K measurements in the IoT system should be improved. This IoT-based soil nutrient monitoring system highlights the potential of smart farming technology to boost agricultural output, optimize resource consumption, and support sustainable farming practices. The system's portability and online data availability provide farmers with exact soil condition information, allowing them to make more efficient and intelligent farming decisions.
Enhancing classification in high-dimensional data with robust rMI-SVM feature selection
Authors: Chin Fung Yuen , Goh Yong Kheng
Published: July 2024

Dealing with high-dimensional datasets presents notable challenges for classification modelling, primarily due to complexity and susceptibility to overfitting. Traditional feature selection methods frequently struggle to guarantee improved classification performance by including more features. Instead, they often rely on utilising the entire feature set. To address these challenges, a robust feature selection algorithm known as ranked mutual information for support vector machines (rMI-SVM) has been introduced. This approach mitigates the risk of overfitting by selecting features that augment the classification model with additional information, thereby ensuring enhanced performance as more features are selected. rMI-SVM can accommodate datasets with missing values regardless of data linearity as it does not require additional parameters or preset the number of features needed. The proposed method offers a solution to the challenges posed by high-dimensional data, and explicitly identifies the optimal number of features required for a classification model, thus circumventing the necessity of using the full feature set. These findings are supported by receiver operating characteristic (ROC) curves, which highlight the effectiveness of rMI-SVM in outperforming existing baselines and delivering a superior classification model performance.
Comparative analysis of machine learning approaches in Kazakh banknote classification
Authors: Sadyk Ualikhan , Yerzhan Makhambet , Baimukashev Rashid , Turan Cemil
Published: July 2024

Nowadays, smartphones seamlessly blend into every aspect of our lives, including as handheld assistants for individuals with disabilities. Therefore, this research addresses the need for a robust system that can classify Kazakh banknotes. By capitalizing on the availability of smartphones and the ability to integrate detectors with classifiers this study introduces classifiers of Kazakh banknote images specifically designed for banknotes ranging from 500 KZT to 20,000 KZT. It compares traditional and hybrid machine learning (ML) approaches, utilizing a dataset of diverse banknote images, aiming for both lightweight and high accuracy. Competitive performance is demonstrated by the traditional approach, enhanced by thoughtful feature engineering. The hybrid approach, utilizing features from a pre-trained ResNet-18 model, showcases remarkable accuracy and robustness. Evaluation metrics reveal significant achievements, with the traditional approach attaining 94.00% accuracy and the hybrid approach excelling at 99.11%. Model stacking, combining classifiers from both approaches, outperforms individual classifiers, achieving 95.00% and 99.55% accuracy for the traditional and hybrid ML approaches, respectively. Our methodology’s comparable outcome in classifying Thai banknotes and coffee beans roasting levels demonstrates their versatility in image classification tasks that rely on color differentiation, showcasing the potential beyond banknote recognition.
Windows operating system malware detection using machine learning
Authors: Hilabi Rawabi , Abu-Khadrah Ahmed
Published: July 2024

Over the years, cybercriminals have become more sophisticated in manipulating network users. Malware is a popular tool they use to exploit victims, targeting valuable assets such as identities and credit cards in the realm of digital technology. Cybersecurity professionals are consistently innovating to detect malicious activities. Machine learning (ML) algorithms are now a leading method for rapidly identifying unseen malware, offering efficiency and intelligence beyond traditional approaches. In fact, attackers like to see the victims suffer from damage caused by malware. Malware can destroy devices and networks. Additionally, hackers can blackmail individuals and organizations to obtain money through ransomware. Therefore, the aim of this research is developing a new model that has the capability of detecting malwares that are targeting Windows operating systems (OS) through enhancing an existing model by deploying several ML algorithms which are extreme gradient boosting (XGB) and random forest (RF). In addition, the swarm optimization and ML applied to portable executable (SOMLAP) dataset applied in the portable executable (PE) is used for training data and testing these learning algorithms. The result achieved by XGB and RF hybrid technique accuracy was 0.966, precision 0.990 and recall was 0.918.
Handwritten Arabic words detection using Faster R-CNN in IFN/ENIT dataset
Authors: Mowaffaq AL-Taee May , Ben Hassen Neji Sonia , Frikha Mondher
Published: July 2024

Recognizing Arabic offline handwritten words still faces various challenges because of the diversity of writing styles and the overlap between the words and characters. Therefore, building an effective system to solve these challenges has always been difficult, which has led to a lack of published research in this field. This study introduces two new models to recognize handwritten Arabic words based on the Faster region-convolution neural network (Faster R-CNN). These models employ two pre-trained networks during the feature extraction phase: The visual geometry group-16 (VGG-16) network and the residual network (ResNet50) network. To help with overlapping detections and make localization more accurate, a soft non-maximum suppression (Soft-NMS) strategy is used in post-processing. Models are independently trained and tested on two groups of data from the Institut Für Nachrichtentechnik/Ecole Nationale d’Ingénieurs de Tunis (IFN/ENIT) dataset. The first group includes one word in each image, while the second contains multiple words. Test results showed that the proposed models give excellent results compared to others. The results of VGG16 and ResNet50 with the first dataset reached accuracy rates of 100% and 99.5%, respectively. Meanwhile, the accuracy of the second group reached 91.4% and 100% with VGG16 and ResNet50, respectively.
Systematic review of mobile applications in learning features to support learners living with epilepsy disorders
Authors: Utami Ika Qutsiati , Hwang Wu-Yuin , Syarief Fathurrahman , Kalvin Nicholas Juan
Published: July 2024

We systematically review mobile health applications in supporting learners living with epilepsy disorders. There are two objectives of this study i.e., assessing the existing epilepsy-related apps and providing information about some features provided by the apps. In total, 18 of 47 mobile apps that meet the final criteria were reviewed using the Mobile Application Rating Scale (MARS). We found that more than half of the apps had below-average quality and most offered only a few distinct functionalities. Six of them were deemed high quality since they met all standard criteria. In terms of self-management features, we identified several important features such as the provision of a seizure calendar (14/18, 78%), report generation (5/18, 28%), adding individual seizure occurrence and causes (9/18, 50%), and emergency alert (6/18, 34%). The majority of the apps included medication tracker (12/18, 67%), expert consultation (6/18, 34%), and educational features (10/18, 56%). Moreover, 40% of included apps have considered self-efficacy features by providing analytical support for seizure frequency, duration, occurrence distribution, and analysis. This research can make in-person support more feasible for epilepsy learners so that it helps families, caregivers, and educators to easily manage the risk and perform continuous aid. This research can also be the basis for developing more patient-centered software for epilepsy management.
Artificial intelligence-based cloud-internet of things resource management for energy conservation
Authors: Ouhame Soukaina , Hadi Moulay Youssef , Mrhari Amine , Laassar Imane
Published: July 2024

The widespread demand for hosting application services in the cloud has been fueled by the deployment of cloud data centers (CDCs) on a global scale. Furthermore, modern apps' resource needs have sharply increased, especially in industries that use a lot of data. As a result, more cloud servers have been made available, resulting in higher energy usage and, ecological problems. Large-scale data centers have been developed as a result of the rapidly increasing demand for cloud services, allowing application service providers to rent data center space for application deployment by user-required quality of service (QoS). These data centers use a lot of electricity, which raises running expenses and produces more carbon dioxide (CO2) emissions. Modern cloud computing environments must also provide QoS for their users, necessitating a trade-off between power performance, energy consumption, and service-level agreement (SLA) compliance. We present an intelligent resource management policy using enforcement learning for CDCs. The objective is to continuously consolidate and dynamically allocate virtual machines (VMs). Utilizing live migration and disabling inactive nodes to reduce power consumption in this cloud environment while maintaining service quality. To enable dynamic resource management, a better power-performance tradeoff, and significantly lower energy consumption, we integrate several artificial intelligence concepts. Based on the result the proposed approach is more efficient as compared with other techniques.
Image enhancement optimization on bright and dark spots of retinal fundus image
Authors: Mohd Sharif Nurul Atikah , Harun Nor Hazlyna , Yusof Yuhanis
Published: July 2024

Diagnosing diabetic retinopathy (DR) based on features that appear on fundus images is currently conducted through an eye exam by an ophthalmologist. Tracking DR progression manually is time-consuming and keen for a high-skill person. As the technology offered in industrial revolution (IR) 4.0, namely artificial intelligence, is shown to help in the medical diagnosis process, this study proposes an image enhancement algorithm based on a hybrid of contrast enhancement (CE) and particle swarm optimization (PSO). The proposed method incorporates contrast adjustment on the bright and dark region of LAB color space where the bright and dark region is initially segmented using K-mean PSO. 100 retinal fundus images are used for training and testing purposes. The proposed method undergoes qualitative and quantitative evaluation with a comparison between the two methods. The result indicates that the performance of the proposed method is more acceptable as compared to another two methods.
Optimization of mechanical properties of Al7150/Si3N4/C composites using artificial neural network
Authors: Zakaulla Mohamed , Pasha Younus
Published: July 2024

The study aims to investigate and predict the effect of reinforcements such as silicon nitride (Si3N4) and graphene (C) in aluminum 7150 matrix. Al7150/Si3N4/C hybrid composite is fabricated by a stir casting technique and subsequently T6 heat treated for applications such as body stringers, spar chords, seat tracks, and stringers of wing surfaces of aircraft. A feedforward propagation multilayer neural network was developed for modeling and prediction of hardness, tensile strength, and tensile elongation. The results show that the addition of fillers and T6 heat treatment enhances the mechanical properties of the Al7150/Si3N4/C composite. The artificial neural network (ANN) model suggested for Al7150 composites demonstrates beneficial results when compared to experimental measurements. The prediction model, which has a mean absolute percentage error of 0.64%, 0.3%, and 2.49% for hardness, tensile strength, and tensile elongation can accurately predict the effect of reinforcement contents and T6 heat treatment on mechanical properties of Al7150/Si3N4/C composites.
Comparing 5G indoor wireless technologies: optical vs ultraviolet communication
Authors: Zainal Izzah Hazirah , Mutalip Zaiton Abdul , Yusuf Haziezol Helmi Mohd , Hassan Nurmala Irdawaty , Rihawi Zeina
Published: July 2024

Wireless communication technologies have become one of the biggest technologies that are considered suitable for radio frequency in various types of applications, including indoor applications. This study investigates the differences between optical wireless communication (OWC) and ultraviolet wireless communication (UVC). Several parameters are simulated, including bit error rate (BER), signal-to-noise ratio (SNR), data rate, and received power, to compare the performance of each technology in various scenarios. Owing to its wide bandwidth, license-free frequency spectrum, and low signal attenuation at low carrier frequencies, the OWC is suitable for radio frequency applications in many situations, including indoor environments. The simulation results show that UVC outperformed OWC in terms of BER and SNR. Moreover, UVC has a higher data transmission rate of up to 1.1 Gbps, making it a suitable technology for 5G communication.
Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm
Authors: Razali Nur Syifa Nasyrah , Yasin Zuhaila Mat , Dahlan Nofri Yenita , Noor Siti Zaliha Mohammad , Ahmad Nurfadzilah , Hassan Elia Erwani
Published: July 2024

An energy storage system called a battery energy storage system (BESS) collects energy from various sources, builds up that energy, and then stores it in rechargeable batteries for future use. The battery's electrochemical energy can be discharged and supplied to buildings such as residences, electric cars, and commercial and industrial buildings. The advantages of utilizing BESSs, such as minimizing energy loss, improving voltage profile, peak shaving, and increasing power quality, may be reduced if incorrect decisions about the appropriate position and capacity for BESSs are chosen. Furthermore, the optimal position and size for BESSs are critical since deploying a BESS at every bus, particularly in an extensive network, is not a cost-effective option, and installing oversized BESSs would result in higher investment expenses. Hence, this study suggests a proficient method for identifying the most suitable position and the sizes of BESS to save costs. The grasshopper optimization algorithm (GOA) and evolutionary programming (EP) were employed to address the optimization challenge on the IEEE 69-bus distribution test system. The goal of the optimization is to minimize the overall cost. The findings indicate that the GOA has strong resilience and possesses a superior capacity for optimizing cost reduction in comparison to EP.
Google Play review analysis of digital mobile applications of Islamic microfinance institutions
Authors: Alam Azhar , Ratnasari Ririn Tri , Nurrochman Danang Dwi , Rahmawati Estina
Published: July 2024

The study aims to analyze user reviews of Islamic microfinance mobile applications to evaluate their effectiveness for micro, small, and medium-sized enterprises (MSMEs). Using the netnography method, this study collected and analyzed 4,131 reviews from the Google Play Store, focusing on applications with ratings above 4.5. The data was categorized into positive and negative reviews. Key findings indicate that users appreciate 50.42% of positive reviews expressed satisfaction and motivation to advance, 28.90% praised the ease of transactions via the application, and 20.68% appreciated the practical benefits in any payment, while with frequent errors (25.53%), issues with activation codes (21.28%), and transaction failures (17.02%) being the most common complaints. The study recommends improving technical reliability to enhance user satisfaction. Future research should explore user experiences in more diverse digital environments. This research contributes to understanding user perceptions and strengthening the development of Islamic microfinance applications.
Statistical analysis of agarwood oil chemical compound exists in four species of Aquilaria
Authors: Zaidi Amir Hussairi , Al-Hadi Anis Hazirah ‘Izzati Hasnu , Huzir Siti Mariatul Hazwa Mohd , Yusoff Zakiah Mohd , Ismail Nurlaila , Almisreb Ali Abd , Tajuddin Saiful Nizam , Taib Haji Mohd Nasir
Published: July 2024

Aquilaria, renowned for its agarwood, and valued for its aromatic wood and rich resin, finds use in cosmetics, fragrances, incense, and medicine. Identifying the agarwood-producing species among 21 species of Aquilaria is challenging. This study analyzes chemical compounds in agarwood oil from 4 Aquilaria species: Aquilaria beccariana, Aquilaria crassna, Aquilaria malaccensis, and Aquilaria subintegra using gas chromatography-flame ionization detector (GC-FID). Statistical analysis explores compound abundance, employing methods like mean and Z-score tests. This analysis summarizes those 14 compounds that are consistently present based on zero and non-zero observations, and the Z-score test highlights five chemical compounds, with three compounds appearing in both analyses. These compounds can serve as a reference for future studies on Aquilaria species and agarwood oil, enhancing classification efforts.
Conservation of structural brain connectivity in people with multiple sclerosis
Authors: Martí-Juan Gerard , Sastre-Garriga Jaume , Vidal-Jordana Angela , Llufriu Sara , Martinez-Heras Eloy , Groppa Sergiu , González-Escamilla Gabriel , Rocca Maria A. , Filippi Massimo , Høgestøl Einar A. , Harbo Hanne F. , Foster Michael A. , Collorone Sara , Toosy Ahmed T. , Schoonheim Menno M. , Strijbis Eva , Pontillo Giuseppe , Petracca Maria , Deco Gustavo , Rovira Àlex , Pareto Deborah
Published: July 2024

Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system. Structures affected in MS include the corpus callosum, connecting the hemispheres. Studies have shown that in mammalian brains, structural connectivity is organized according to a conservation principle, an inverse relationship between intra- and interhemispheric connectivity. The aim of this study was to replicate this conservation principle in subjects with MS and to explore how the disease interacts with it. A multicentric dataset has been analyzed including 513 people with MS and 208 healthy controls from seven different centers. Structural connectivity was quantified through various connectivity measures, and graph analysis was used to study the behavior of intra- and interhemispheric connectivity. The association between the intra- and the interhemispheric connectivity showed a similar strength for healthy controls (r = 0.38, p < 0.001) and people with MS (r = 0.35, p < 0.001). Intrahemispheric connectivity was associated with white matter fraction (r = 0.48, p < 0.0001), lesion volume (r = −0.44, p < 0.0001), and the Symbol Digit Modalities Test (r = 0.25, p < 0.0001). Results show that this conservation principle seems to hold for people with MS. These findings support the hypothesis that interhemispheric connectivity decreases at higher cognitive decline and disability levels, while intrahemispheric connectivity increases to maintain the balance.
Cumulative donor-specific antibody threshold predicts platelet transfusion response in HLA-alloimmunized patients
Authors: Boothby Aaron B. , Tanner Matthew K. , Alswied Abdullah , Youngs Danny , Bribiesca Rodriguez Josefine , Bikkani Thejaswi , Cha Nuri , Gernsheimer Terry , Gimferrer Idoia , Hess John R. , Sokol-Hessner Lauge , Marivada Snigdha , Nash Michael G. , Flegel Willy A. , Vassallo Ralph R. , Stroncek David F. , Tsang Hamilton C. , Panch Sandhya R.
Published: July 2024

Up to a third of patients with hemato-oncologic conditions who have received multiply transfusions develop immune-mediated platelet transfusion refractoriness. Yet factors that influence posttransfusion platelet corrected count increments (CCI) in patients with HLA-alloimmune platelet transfusion refractoriness remain less well elucidated. Recent advances in HLA antibody characterization using fluorescent bead-based platforms enable the study of donor-specific antibody (DSA) avidity (as measured by mean fluorescence intensity [MFI]) and its impact on HLA-alloimmune platelet transfusion refractoriness. In this large retrospective study of 2012 platelet transfusions among 73 HLA-alloimmunized patients, we evaluated the impact of cumulative HLA DSA-MFI alongside other donor, platelet component, and patient characteristics on CCI at 2 and 24 hours after transfusion. As part of a quality improvement initiative, we also developed and tested a computerized algorithm to optimize donor–recipient histocompatibility based on cumulative DSA-MFI and sought other actionable predictors of CCI. In multivariate analyses, cumulative HLA DSA-MFI of ≥10 000, major/bidirectional ABO-mismatch, splenomegaly, transfusion reactions, and platelet storage in additive solution negatively affected 2-hour but not 24-hour posttransfusion CCI. The DSA-MFI threshold of 10 000 was corroborated by greater antibody-mediated complement activation and significantly more CCI failures above this threshold, suggesting the usefulness of this value to inform “permissive platelet mismatching” and to optimize CCI. Furthermore, DSA-MFI decreases were deemed feasible by the computer-based algorithm for HLA-platelet selection in a pilot cohort of 8 patients (122 transfusions) evaluated before and after algorithm implementation. When HLA-selected platelets are unavailable, ABO-identical/minor-mismatched platelet concentrates may enhance 2-hour CCI in heavily HLA-alloimmunized patients with platelet transfusion refractoriness.
The gut microbiota-bile acid axis in cholestatic liver disease
Authors: sun Dayan , Xie Chuanping , Zhao Yong , Liao Junmin , Li Shuangshuang , Zhang Yanan , Wang Dingding , Hua Kaiyun , Gu Yichao , Du Jingbin , Huang Guoxian , Huang Jinshi
Published: July 2024

Cholestatic liver diseases (CLD) are characterized by impaired normal bile flow, culminating in excessive accumulation of toxic bile acids. The majority of patients with CLD ultimately progress to liver cirrhosis and hepatic failure, necessitating liver transplantation due to the lack of effective treatment. Recent investigations have underscored the pivotal role of the gut microbiota-bile acid axis in the progression of hepatic fibrosis via various pathways. The obstruction of bile drainage can induce gut microbiota dysbiosis and disrupt the intestinal mucosal barrier, leading to bacteria translocation. The microbial translocation activates the immune response and promotes liver fibrosis progression. The identification of therapeutic targets for modulating the gut microbiota-bile acid axis represents a promising strategy to ameliorate or perhaps reverse liver fibrosis in CLD. This review focuses on the mechanisms in the gut microbiota-bile acids axis in CLD and highlights potential therapeutic targets, aiming to lay a foundation for innovative treatment approaches.
The pillars of the sea: strategies to achieve successful marine citizen science programs in the Mediterranean area
Authors: Coppari Martina , Roveta Camilla , Di Camillo Cristina , Garrabou Joaquim , Lucrezi Serena , Pulido Mantas Torcuato , Cerrano Carlo
Published: July 2024

Marine ecosystems are facing a dramatic loss of biodiversity worldwide, together with a widespread collapse of habitats and their functionality. In this context, Marine Citizen Science (MCS) can be a powerful tool to monitor these changes over time. The flowering of very well-structured international projects is strengthening the scientific credibility of MCS data, especially when data are collected after specifically designed training programs and shared in public user-friendly repositories. Here we present a new perspective on the use of MCS in the Mediterranean area, along with the main benefits for the stakeholders (i.e., diving centers, trainers, and policymakers) and the users (i.e., divers), resumed in three pillars: Pillar I – MCS as a tool for the site valorization; Pillar II – MCS as a new career opportunity for graduated students; Pillar III – MCS as a business opportunity for diving centers. In the frame of the Quintuple Helix Approach, for which there is a strong need of a socioecological transition of the society and economy, we show how MCS can be a win-win-win solution for all the actors involved, providing the vision for new and highly qualified job and business opportunities for the diving sector.
Genome-wide analysis and identification of Carotenoid Cleavage Oxygenase (CCO) gene family in coffee (coffee arabica) under abiotic stress
Authors: Naeem Shajiha , Wang Yuexia , Han Shiming , Haider Muhammad Zeshan , Sami Adnan , Shafiq Muhammad , Ali Qurban , Bhatti Muhammad Hamza Tariq , Ahmad Arsalan , Sabir Irfan Ali , Dong Jihong , Alam Pravej , Manzoor Muhammad Aamir
Published: July 2024

The coffee industry holds importance, providing livelihoods for millions of farmers globally and playing a vital role in the economies of coffee-producing countries. Environmental conditions such as drought and temperature fluctuations can adversely affect the quality and yield of coffee crops.Carotenoid cleavage oxygenases (CCO) enzymes are essential for coffee plants as they help break down carotenoids contributing to growth and stress resistance. However, knowledge about the CCO gene family in Coffee arabica was limited. In this study identified 21 CCO genes in Coffee arabica (C. arabica) revealing two subfamilies carotenoid cleavage dioxygenases (CCDs) and 9-cis-epoxy carotenoid dioxygenases (NCED) through phylogenic analysis. These subfamilies exhibited distribution patterns in terms of gene structure, domains, and motifs. The 21 CaCCO genes, comprising 5 NCED and 16 CCD genes were found across chromosomes. Promoter sequencing analysis revealed cis-elements that likely interact with plant stress-responsive, growth-related, and phytohormones, like auxin and abscisic acid. A comprehensive genome-wide comparison, between C. arabica and A. thaliana was conducted to understand the characteristics of CCO genes. RTqPCR data indicated that CaNCED5, CaNCED6, CaNCED12, and CaNCED20 are target genes involved in the growth of drought coffee plants leading to increased crop yield, in a conditions, with limited water availability. This reveals the role of coffee CCOs in responding to abiotic stress and identifies potential genes useful for breeding stress-resistant coffee varieties.
A single viral amino acid shapes the root system architecture of a plant host upon virus infection
Authors: Roy Brandon G. , Fuchs Marc
Published: July 2024

Background

Grapevine fanleaf virus (GFLV) is one of the most detrimental viral pathogens of grapevines worldwide but no information is available on its effect on the root system architecture (RSA) of plant hosts. We used two wildtype GFLV strains and their single amino acid mutants to assess RSA traits in infected Nicotiana benthamiana and evaluate transcriptomic changes in host root gene expression in replicated time course 3’RNA-Seq experiments. Mutations targeted the multi-functional GFLV-encoded protein 1EPol*/Sd, a putative RNA-dependent RNA polymerase and determinant of foliar symptoms in N. benthamiana plants.

Results

Plant infection with wildtype GFLV strain GHu and mutant GFLV strain F13 1EPolG802K, both carrying a lysine in position 802 of protein 1EPol*/Sd, resulted in a significantly lower number of root tips (-30%), and a significantly increased average root diameter (+ 20%) at 17 days post inoculation (dpi) in comparison with roots of mock inoculated plants. In contrast, the RSA of plants infected with wildtype GFLV strain F13 and mutant GFLV strain GHu 1EPolK802G, both carrying a glycine in position 802 of protein 1EPol*/Sd, resembled that of mock inoculated plants. Modifications of RSA traits were not associated with GFLV titer. Root tissue transcriptome analysis at 17 dpi indicated dysregulation of pattern recognition receptors, plant hormones, RNA silencing, and genes related to the production of reactive oxygen species (ROS). For wildtype GFLV strain GHu, RSA modifications were correlated with an abundant accumulation of ROS in the pericycle of primary roots at 7 dpi and the duration of vein clearing symptom expression in apical leaves. Dysegulation of a hypersensitive response was an overarching gene ontology found through enrichment analyses of 3’RNA-Seq data.

Conclusions

Our findings revealed the causative role of lysine in position 802 of protein 1EPol*/Sd in a novel RSA phenotype during viral infection and documented GFLV-N. benthamiana interactions at the root level based on (i) antiviral response, (ii) receptor mediated production of ROS, and (iii) hormone regulation. A correlation between above and below ground symptoms was reported for the first time in plants infected with wildtype GFLV strain GHu. Further work is warranted to test whether the modified RSA of a plant host might impact GFLV acquisition and transmission by the ectoparasitic dagger nematode Xiphinema index.
Coronavirus disease 2019-related myocarditis genes contribute to ECMO prognosis
Authors: Yan An , Zhang Ruiying , Feng Chao , Feng Jinping
Published: July 2024

Background

Acute myocardial injury, cytokine storms, hypoxemia and pathogen-mediated damage were the major causes responsible for mortality induced by coronavirus disease 2019 (COVID-19)-related myocarditis. These need ECMO treatment. We investigated differentially expressed genes (DEGs) in patients with COVID-19-related myocarditis and ECMO prognosis.

Methods

GSE150392 and GSE93101 were analyzed to identify DEGs. A Venn diagram was used to obtain the same transcripts between myocarditis-related and ECMO-related DEGs. Enrichment pathway analysis was performed and hub genes were identified. Pivotal miRNAs, transcription factors, and chemicals with the screened gene interactions were identified. The GSE167028 dataset and single-cell sequencing data were used to validate the screened genes.

Results

Using a Venn diagram, 229 overlapping DEGs were identified between myocarditis-related and ECMO-related DEGs, which were mainly involved in T cell activation, contractile actin filament bundle, actomyosin, cyclic nucleotide phosphodiesterase activity, and cytokine-cytokine receptor interaction. 15 hub genes and 15 neighboring DEGs were screened, which were mainly involved in the positive regulation of T cell activation, integrin complex, integrin binding, the PI3K-Akt signaling pathway, and the TNF signaling pathway. Data in GSE167028 and single-cell sequencing data were used to validate the screened genes, and this demonstrated that the screened genes CCL2, APOE, ITGB8, LAMC2, COL6A3 and TNC were mainly expressed in fibroblast cells; IL6, ITGA1, PTK2, ITGB5, IL15, LAMA4, CAV1, SNCA, BDNF, ACTA2, CD70, MYL9, DPP4, ENO2 and VEGFC were expressed in cardiomyocytes; IL6, PTK2, ITGB5, IL15, APOE, JUN, SNCA, CD83, DPP4 and ENO2 were expressed in macrophages; and IL6, ITGA1, PTK2, ITGB5, IL15, VCAM1, LAMA4, CAV1, ACTA2, MYL9, CD83, DPP4, ENO2, VEGFC and IL32 were expressed in vascular endothelial cells.

Conclusion

The screened hub genes, IL6, ITGA1, PTK2, ITGB3, ITGB5, CCL2, IL15, VCAM1, GZMB, APOE, ITGB8, LAMA4, LAMC2, COL6A3 and TNFRSF9, were validated using GEO dataset and single-cell sequencing data, which may be therapeutic targets patients with myocarditis to prevent MI progression and adverse cardiovascular events.
The application of health information technology for the elderly care in the emergency department: a conceptual model
Authors: Shagerdi Ghazal , Ayatollahi Haleh , Hemmat Morteza , Zeraatkar Kimia
Published: July 2024

Introduction

In the emergency departments (EDs), usually the longest waiting time for treatment and discharge belongs to the elderly patients. Moreover, the number of the ED admissions for the elderly increases every year. It seems that the use of health information technology in geriatric emergency departments can help to reduce the burden of the healthcare services for this group of patients. This research aimed to develop a conceptual model for using health information technology in the geriatric emergency department.

Methods

This study was conducted in 2021. The initial conceptual model was designed based on the findings derived from the previous research phases (literature review and interview with the experts). Then, the model was examined by an expert panel (n = 7). Finally, using the Delphi technique (two rounds), the components of the conceptual model were reviewed and finalized. To collect data, a questionnaire was used, and data were analyzed using descriptive statistics.

Results

The common information technologies appropriate for the elderly care in the emergency departments included emergency department information system, clinical decision support system, electronic health records, telemedicine, personal health records, electronic questionnaires for screening, and other technologies such as picture archiving and communication systems (PACS), electronic vital sign monitoring systems, etc. The participants approved all of the proposed systems and their applications in the geriatric emergency departments.

Conclusion

The proposed model can help to design and implement the most useful information systems in the geriatric emergency departments. As the application of technology accelerates care processes, investing in this field would help to support the care plans for the elderly and improve quality of care services. Further research is recommended to investigate the efficiency and effectiveness of using these technologies in the EDs.
Risk factors for esophageal anastomotic stricture after esophagectomy: a meta-analysis
Authors: Zhong Yuan , Sun Ruijuan , Li Wei , Wang Weiqian , Che Jianpeng , Ji Linlin , Guo Bingrong , Zhai Chunbo
Published: July 2024

Background

The aim of this study was to assess the risk factors for anastomotic stricture in esophageal cancer patients undergoing esophagectomy. Esophageal anastomotic stricture is the most common long-term complication for esophagectomy. The risk factors for esophageal anastomotic stricture still remain controversial.

Methods

MEDLINE, Cochrane Library, and EMBASE were searched to identify observational studies reporting the risk factors for esophageal anastomotic stricture after esophagectomy. A meta-analysis was conducted to investigate the impact of various risk factors on esophageal anastomotic stricture. The GRADE [Grading of Recommendations Assessment, Development and Evaluation] approach was used for quality assessment of evidence on outcome levels.

Results

This review included 14 studies evaluating 5987 patients.The meta-analysis found that anastomotic leakage (odds ratio [OR]: 2.75; 95% confidence interval[CI]:2.16–3.49), cardiovascular disease [OR:1.62; 95% CI: 1.22–2.16],diabete [OR: 1.62; 95% CI: 1.20–2.19] may be risk factors for esophageal anastomotic stricture.There were no association between neoadjuvant therapy [OR: 0.78; 95% CI:0.62–0.97], wide gastric conduit [OR:0.98; 95% CI: 0.37–2.56],mechanical anastomosis [OR: 0.84; 95% CI:0.47–1.48],colonic interposition[OR:0.20; 95% CI: 0.12–0.35],and transhiatal approach[OR:1.16; 95% CI:0.81–1.64],with the risk of esophageal anastomotic stricture.

Conclusions

This meta-analysis provides some evidence that anastomotic leakage,cardiovascular disease and diabete may be associated with higher rates of esophageal anastomotic stricture.Knowledge about those risk factors may influence treatment and procedure-related decisions,and possibly reduce the anastomotic stricture rate.
Diagnostic cervical excision in patients with HPV positivity, cytological abnormalities- and preoperative cervical stenosis
Authors: Denecke Agnieszka , Emar Duaa , Hillemanns Peter , Ramachandran Dhanya , Brüning Bianca , Jentschke Matthias
Published: July 2024

Aim

There is currently no protocol for classifying patients with HPV persistence and preoperative stenosis of the cervical canal. This has a significant impact on cytology results, colposcopy results and the possibility of obtaining reliable cervical histology outcomes. Our analysis clearly shows that colposcopy and cytology underestimate the histological results in patients with limited visibility due to the presence of a type 3 transformation zone (TZ). Our analysis revealed a significant discrepancy between the colposcopy and cytology results and the histological outcomes. Insufficient colposcopy led to the underdiagnosis of dysplastic lesions in patients with a type 3 TZ and cervical stenosis. In the case of repeated cytological abnormalities and inadequate colposcopy examination, it is crucial to perform a diagnostic conization to exclude high-grade dysplastic changes and cervical carcinoma.

Methods

We conducted a retrospective analysis of 1,021 conizations performed in tertiary care hospital in Wolfsburg, Germany between 2014 and 2020. Of these surgical procedures, 89 were diagnostic conizations. In our analysis, we defined diagnostic conization as a procedure performed when there is HPV persistence and repeated cytologic abnormalities in combination with a type 3 TZ, and when it is not possible to retrieve a relevant cervical histology sample.

Results

In this period, 8.7% of all conizations were diagnostic excisions. We found histological abnormalities in 48 of 89 patients (53.9%). The histological examination of the excised cone revealed high-grade cervical intraepithelial neoplasia (CIN/HSIL) in 9 patients (10.1%) and CIN 2+ (HSIL) in 23 out of the 89 patients (25.8%). Two cases of early-stage cervical carcinoma (FIGO IA1 and FIGO IA2) were confirmed (2.3%).

Conclusion

Patients with cervical stenosis, high-risk HPV persistence and repeated cytological abnormalities are at high risk of undetected high-grade cervical dysplasia. Histologic confirmation must be ensured in this patient consultation and this can be achieved by performing diagnostic excisions.