bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepMUI: A novel method to identify malicious users on online social network platforms
Authors: Shah Akash , Varshney Sapna , Mehrotra Monica 
Published: September 2023

SummaryThe use of online social network (OSN) platforms has become an essential component of contemporary society, facilitating global connectivity, and information sharing among individuals. The proliferation of malicious users has emerged as a noteworthy obstacle, exerting a detrimental effect on the authenticity of the data disseminated through these channels. A malicious profile is created with the intention of disseminating false information, manipulating perspectives, and executing harmful actions, including phishing schemes, identity theft, and the propagation of malware. Consequently, the identification of malicious users has emerged as an essential undertaking for both OSN platforms and researchers. The objective of this study is to investigate the issue of identifying malicious users on OSN platforms. The DeepMUI model has been introduced as a new approach to identifying malicious users on OSN platforms, utilizing user profile metadata‐derived characteristics. The DeepMUI architecture is composed of long short‐term memory and convolutional neural network models. Additionally, it integrates alterations to the pooling layer to improve its overall efficacy. The experiments have demonstrated that DeepMUI exhibits promising results in the task of identifying malicious users, with greater accuracy and minimal loss compared to existing methods.
Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs
Authors: de Jong Guilherme , Powell Owen , Gorjanc Gregor , Hickey John M. , Gaynor R. Chris 
Published: September 2023

AbstractThis study evaluates the impact of genomic prediction models on selecting inbred lines as parents in hybrid breeding programs. New parents in a hybrid breeding program are typically selected from early‐stage yield trials based on general combining ability (GCA) from testcrosses. Genomic studies have largely focused on predicting hybrid performance in the late stages of the breeding pipeline and largely ignored the selection of inbred lines as parents of the subsequent breeding cycles. Here, we used stochastic simulations of a maize (Zea mays L.) hybrid breeding program for 20 years to evaluate the performance of genomic prediction models for selecting parents based on their predicted GCA. Five genomic prediction models were evaluated in terms of achieved genetic gain and heterosis under two different single nucleotide polymorphism (SNP) marker densities and the true quantitative trait loci genotypes. The results show that using high‐density SNP markers generated more genetic gain and heterosis than the low‐density SNP markers. The relative performance of genomic prediction models differed across marker scenarios. For genetic gain, we observed more differences between the models at low than high marker density. For heterosis, we observed the opposite, more differences between the models at high than low marker density. Overall, models that fitted the average or additive effects specific to each heterotic pool and dominance effects provide a better fit and hence higher genetic gain in hybrid breeding programs.
Uncovering therapeutic opportunities in the clinical development of antibody‐drug conjugates
Authors: Nieto‐Jiménez Cristina , Sanvicente Adrián , Díaz‐Tejeiro Cristina , Moreno Víctor , lopez de Sá Alfonso , Calvo Emiliano , Martínez‐López Joaquín , Pérez‐Segura Pedro , Ocaña Alberto 
Published: September 2023

AbstractIntroductionAntibody‐drug conjugates (ADCs) are a family of therapeutic agents that have demonstrated clinical activity in several indications.Material and methodsIn this article, we performed a deep analysis of their clinical landscape matched with public genomic human datasets from tumour antigen targets (TATs), to identify empty areas for clinical development.ResultsWe observed that TATs used in haematological malignancies were more specific than the ones developed in solid cancers. Those included CD19, CD22, CD30, CD33 and CD79b. In solid tumours, we identified TATs, with approved ADCs, widely expressed in non‐explored niche indications like Enfortumab vedotin (anti‐Nectin4) in lung or cervical cancer; Tisotumab vedotin (anti‐TF) in glioblastoma or pancreatic cancer; and Sacituzumab govitecan (anti‐TROP2) in pancreatic, gastric, thyroid or endometrial cancer, among others. Similarly, niche indications for ADCs in clinical development included targets for CD71, PSMA, PTK7 or CD74, in tumours like breast, lung, stomach or colon. Some of these TATs were essential for the survival of tumour cells like CD71, PSMA and PTK7.ConclusionsIn summary, our study opens the door for further evaluation of ADCs in several indications not explored before.
An opportunistic energy‐efficient dynamic self‐configuration clustering algorithm in WSN‐based IoT networks
Authors: Tumula Sridevi , Ramadevi Y. , Padmalatha E. , Kiran Kumar G. , Venu Gopalachari M. , Abualigah Laith , Chithaluru Premkumar , Kumar Manoj 
Published: September 2023

SummaryThe demand for the Internet of Things (IoT) has significantly increased in the current scenario; specific sectors that require IoT include industrial automation, home control, health care applications, military and surveillance applications, habitat monitoring, and nanoscopic sensor applications. The use of optimal wireless sensor networks (WSNs) in transmission techniques has resulted in their involvement. A WSN is made up of thousands of randomly distributed sensor nodes that sense and transmit environmental data such as temperature, pressure, humidity, light, and sound. One of the most important requirements when using these sensor nodes is energy. As a result, it has become a major area of research in recent years; additionally, several design techniques and protocols have been presented in the last decade, particularly for IoT‐based applications. As a result, the systemization of an energy‐optimized WSN in dynamic functional conditions with automatic self‐configuration of sensor nodes is a critical goal. This paper proposed an opportunistic energy‐efficient dynamic self‐configuration routing (OEDSR) algorithm for IoT‐based applications. Initially, the optimal route to the base station (BS) is calculated by using the residual energy and mobility factor of the sensor nodes obtained through a routing tree model based on graph theory. To reduce the number of connections, an optimal path is determined based on dynamic cluster generation through a hierarchical tree architecture. Finally, the network‐related parameters, such as throughput, delay and packet delivery ratio (PDR), are compared with the peer existing routing protocols to depict the efficiency of the OEDSR protocol.
An ultra‐wideband dual circularly polarized slot antenna with high isolation
Authors: Chen Wei Hao , Pan Jian Wei , Wu Duo‐Long 
Published: September 2023

SummaryAn ultra‐wideband dual circularly polarized (CP) slot antenna with high isolation is presented. Two L‐shaped probes are on the upper layer, both a ladder strip outside and a bent strip in the center on the bottom layer. The L‐shaped probes and the ladder strip result in four impedance resonance modes, and the bent strip moderates real and imaginary impedances to achieve an ultrawide impedance bandwidth. Furthermore, two L‐shaped strips on the ladder strip make two linear polarized Modes 1 and 2 into two CP modes at the low‐ and mid‐frequency bands. Meanwhile, the bent strip combined with the L‐shaped probes creates the third CP resonance mode at the high‐frequency band. The cutting corner and bent slots on the ladder strip change the amplitude and the phase of the electric fields Ex and Ey to realize ultra‐wideband CP radiation. In addition, the port–port isolation is enhanced because the currents are concentrated on the bent strip, and the coupling current path is cut off with the bent slot. The fabricated antenna achieves a −10‐dB impedance bandwidth of 111% (1.7–5.94 GHz), a 3‐dB axial ratio bandwidth of 101% (1.7–5.2 GHz), and a port–port isolation of better than 20 dB. Besides, our antenna obtains a high peak gain of 8.1 dBi and a lower profile of 0.004 λL, where λL is the lowest frequency wavelength of the operating bands in vacuum. These performances make the proposed design competitive for wireless communications.
Dimensional clinical phenotyping using post‐mortem brain donor medical records: post‐mortem RDoC profiling is associated with Alzheimer's disease neuropathology
Authors: Vogelgsang Jonathan , Dan Shu , Lally Anna P. , Chatigny Michael , Vempati Sangeetha , Abston Joshua , Durning Peter T. , Oakley Derek H. , McCoy Thomas H. , Klengel Torsten , Berretta Sabina 
Published: September 2023

AbstractIntroductionTransdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post‐mortem work, as assessments of phenotypic concepts need to rely on existing records.MethodsWe adapted well‐validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post‐mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures.ResultsOur results confirm an association of EHR‐derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes.DiscussionThis proof‐of‐concept study supports the validity of NLP‐based methodologies to obtain quantitative measures of RDoC clinical domains from post‐mortem EHR. The associations may accelerate post‐mortem brain research beyond classical case–control designs.
Trends in the application of remote sensing in blue carbon science
Authors: Araya‐Lopez Rocio , de Paula Costa Micheli Duarte , Wartman Melissa , Macreadie Peter I. 
Published: September 2023

AbstractBlue carbon ecosystems (BCEs), such as mangroves, saltmarshes, and seagrasses, are increasingly recognized as natural climate solutions. Evaluating the current extent, losses, and gains of BCEs is crucial to estimating greenhouse gas emissions and supporting policymaking. Remote sensing approaches are uniquely suited to assess the factors driving BCEs dynamics and their impacts at various spatial and temporal scales. Here, we explored trends in the application of remote sensing in blue carbon science. We used bibliometric analysis to assess 2193 published papers for changes in research focus over time (1990 – June 2022). Over the past three decades, publications have steadily increased, with an annual growth rate of 16.9%. Most publications focused on mangrove ecosystems and used the optical spaceborne Landsat mission, presumably due to its long‐term, open‐access archives. Recent technologies such as LiDAR, UAVs, and acoustic sensors have enabled fine‐scale mapping and monitoring of BCEs. Dominant research topics were related to mapping and monitoring natural and human impacts on BCEs, estimating vegetation and biophysical parameters, machine and deep learning algorithms, management (including conservation and restoration), and climate research. Based on corresponding author affiliations, 80 countries contributed to the field, with United States (27.2%), China (15.0%), Australia (7.5%), and India (6.0%) holding leading positions. Overall, our results reveal the need to increase research efforts for seagrasses, saltmarshes, and macroalgae, integrate technologies, increase the use of remote sensing to support carbon accounting methodologies and crediting schemes, and strengthen collaboration and resource sharing among countries. Rapid advances in remote sensing technology and decreased image acquisition and processing costs will likely enhance research and management efforts focused on BCEs.
Underground mining magnifies drought impacts in an adjacent protected raised bog
Authors: Kohv Marko , Paat Raul , Lõhmus Asko , Jõeleht Argo 
Published: September 2023

AbstractStable and high water levels are crucial for wetland ecosystems, impacting their state, vegetation and conservation. To make informed conservation decisions, it is essential to consider the full range of ecosystem‐level threats associated with artificially lowered water levels. This becomes particularly challenging when dealing with diffuse and indirect impacts, such as ombrotrophic bogs near underground mining activities that extract groundwater. In this study, we monitored water tables using automatic pressure loggers over an 11‐year period in a protected hemiboreal bog and in drained peatland surrounding it in northeastern Estonia. The bog is located within the groundwater cone of depression caused by an adjacent oil‐shale mine, approximately 60–70 m deep. By comparing monitoring points along the bog‐drained forest gradient and analysing time series data, we were able to distinguish the influences of climate and mining activities on water levels in the bog. Our findings indicate that mining activities have clear effects on deeper bedrock aquifers and the bog itself, exacerbating the impacts of recent drought events. The dynamics of the hydraulic heads at different monitoring points reveal local infiltration ‘windows’ within the bog, likely resulting from underlying fracture zones in the bedrock. Considering the escalating intensity of climate change, mires' vulnerability to these combined impacts is expected to increase. Therefore, adopting a precautionary and forward‐thinking conservation strategy is crucial when addressing ongoing or future drainage and mining activities near protected wetlands.
A fluorescent reporter model for the visualization and characterization of TDC
Authors: Fiore Alessandra , Sala Eleonora , Laura Chiara , Riba Michela , Nelli Maria , Fumagalli Valeria , Oberrauch Federico , Mangione Marta , Cristofani Claudia , Provero Paolo , Iannacone Matteo , Kuka Mirela 
Published: September 2023

AbstractTDC are hematopoietic cells that combine dendritic cell (DC) and conventional T‐cell markers and functional properties. They were identified in secondary lymphoid organs (SLOs) of naïve mice as cells expressing CD11c, major histocompatibility molecules (MHC)‐II, and the T‐cell receptor (TCR). Despite thorough characterization, a physiological role for TDC remains to be determined. Unfortunately, using CD11c as a marker for TDC has the caveat of its upregulation on different cells, including T cells, upon activation. Here, we took advantage of Zbtb46‐GFP reporter mice to explore the frequency and localization of TDC in different tissues at steady state and upon viral infection. RNA sequencing analysis confirmed that TDC sorted from Zbtb46‐GFP mice have a gene signature that is distinct from conventional T cells and DC. In addition, this reporter model allowed for identification of TDC in situ not only in SLOs but also in the liver and lung of naïve mice. Interestingly, we found that TDC numbers in the SLOs increased upon viral infection, suggesting that TDC might play a role during viral infections. In conclusion, we propose a visualization strategy that might shed light on the physiological role of TDC in several pathological contexts, including infection and cancer.
Assessment of the bioactive lipid profiles of oil extracted from Tunisian table olive cultivars (Barouni, Besbassi, and Marsaline cvs.)
Authors: Sakouhi Faouzi , Chérif Ammar , Saadi Chaima , Boukhchina Sadok 
Published: September 2023

AbstractBioactive compounds are components extracted from biological matrices that may offer physiological health benefits and have nutritive value. The present study characterized bioactive lipid components such as fatty acids, phytosterols, policosanol, and triterpenes from table olives. The table olives were processed according to the green Spanish‐style method. The obtained results indicate that phytosterol fraction constitutes the major portion of the total unsaponifiable matter. The analysis of phytosterols showed the presence of 11 compounds, β‐sitosterol the predominant one. The policosanol composition indicated that hexacosanol, tetracosanol, octacosanol, and docosanol were the main compounds, accounting for over 85% of total policosanols. Pentacyclic triterpenes (cycloartenol and 24‐methylene cycloartenol) were found at a higher level (over 85%) compared to tetracyclic triterpenes (β‐amyrin, δ‐amyrin). These findings reveal that processed table olives contain an interesting amount of various bioactive compounds compared to marine and other floral biological matrices. Thus, processed table olives represent an interesting natural functional food that presents high stability and bioavailability of their natural bioactive ingredients.Practical Applications: Nowadays, with health food gaining popularity among consumers, the demand for natural bioactive compounds and functional food is expanding considerably all over the world. This study focuses on analyzing bioactive lipid components from processed table olives. Fruits were processed according to the green Spanish‐style method. The salt content of the brine was adjusted to the minimum sodium chloride value required by the Codex Alimentarius Comission, which is 5%, taking into consideration people suffering from hypertension. This concentration is considered the minimum amount of salt necessary to exhibit antibacterial activities. Considering the results obtained, processed table olives present one of the most valuable potential resources for bioactive lipid compounds and have a considerable ability to preserve the stability of their lipid components. Thus, the food industry in Mediterranean countries can promote processed table olives as conventional functional food with no need for any enrichment or fortification.
The changes of phospholipid degradation in sea urchin gonads (Glyptocidaris crenularis) during cold storage at 4°C
Authors: Zhou Limeijia , You Hailin , Wang Youjun , Zhao Yangmeijin , He Yuxuan , Ao Chengxiang , Liu Jingyi , Zhao Hui , Lu Hang 
Published: September 2023

AbstractSea urchin gonads (SUGs) are rich in phospholipid (PL) which is easily oxidized and hydrolyzed during cold storage. PL changes were evaluated to clarify the quality changes of sea urchins during 72 h storage at 4°C. During storage at 4°C, PL content decreased, including phosphatidylethanolamine and phosphatidylinositol content decreased significantly and lysophosphatidylcholine (LPC) was detected after 36 h storage. At the same time, phospholipase C and phospholipase D remained active, and the content of free choline and free fatty acids increased, suggesting that PL was hydrolyzed. The addition of n‐butanol inhibited the activity of phospholipases C and D, decrease of total PL content, and the production of LPC and free choline, indicating that phospholipase‐mediated hydrolysis of PL in gonads occurred during refrigeration. The addition of vitamin E inhibited lipoxygenase, reduced lipid peroxidation (POV), and thiobarbituric acid reactive substances. It indicated that lipoxygenase in SUG mediated the oxidative degradation of PL during cold storage.
Machine learning‐based peak ground acceleration models for structural risk assessment using spatial data analysis
Authors: Saleem Nadia , Mangalathu Sujith , Ahmed Bilal , Jeon Jong‐Su 
Published: September 2023

AbstractPredicting peak time‐domain ground‐motion parameters, such as peak ground acceleration (PGA), peak ground velocity, and peak ground displacement at a specific location, is challenging because of the limited number of recorded ground motions and the complexity of ground‐motion prediction equations. This study presents a novel approach that integrates a geographic information system with a spatial data analysis‐based machine learning PGA prediction model to overcome these challenges and predict PGA classes as a function of the PGA of the respective seismic stations, interstation distance of the seismic stations, and time‐average shear‐wave velocity in the upper 30 m of the target station. The proposed spatial data analysis‐based machine learning approach demonstrated the ability to generate satisfactory results in a short period. To account for the spatial dependencies of the variables, a feature selection method for spatial data using mutual information‐based feature selection was proposed, which provides a well‐prepared spatial matrix for machine learning algorithms. This study evaluates the performance of the model using various machine learning algorithms, including Random Forest, Naïve Bayes, K‐Nearest Neighbors, Decision Tree, AdaptiveBoost, Random Undersampling Boost, Extreme Gradient Boost (XGBoost), and Categorical Boost (CatBoost). Among these, XGBoost and CatBoost performed better than the other methods and yielded fairly accurate results. The models were validated using K‐Fold cross‐validation, and the Wilcoxon signed‐rank test was used for comparison. The spatial data analysis‐based machine learning models, particularly XGBoost and CatBoost, achieved high‐accuracy rates in classifying the PGA levels of 99.1% and 98.9%, respectively. Hyperparameters for the XGBoost model were tuned through GridSearchCV. Tree‐based models outperformed parametric models, indicating complex non‐linear spatial relationships, and by combining spatial feature selection with machine learning models demonstrated improved performance. Additionally, real‐time applications of spatial data analysis‐based machine learning PGA prediction models were used to estimate the seismic vulnerability of postulated concrete box‐girder bridges in San Fernando, thus providing insights into damage probabilities based on predicted PGA values.