bluuebunny/crossref_metadata_2025_split huggingface dataset:
PUTransGCN: identification of piRNA–disease associations based on attention encoding graph convolutional network and positive unlabelled learning
Authors: Chen Qiuhao , Zhang Liyuan , Liu Yaojia , Qin Zhonghao , Zhao Tianyi 
Published: April 2024

Abstract
Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA–disease interactions. Although there have been computational methods proposed for the detection of piRNA–disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA–disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA–disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA–disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN
A new paradigm for applying deep learning to protein–ligand interaction prediction
Authors: Wang Zechen , Wang Sheng , Li Yangyang , Guo Jingjing , Wei Yanjie , Mu Yuguang , Zheng Liangzhen , Li Weifeng 
Published: April 2024

Abstract
Protein–ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein–ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel’s generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein–ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein–ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.
Computational model for drug research
Authors: Chen Xing , Huang Li 
Published: April 2024

Abstract
This special issue focuses on computational model for drug research regarding drug bioactivity prediction, drug-related interaction prediction, modelling for immunotherapy and modelling for treatment of a specific disease, as conveyed by the following six research and four review articles. Notably, these 10 papers described a wide variety of in-depth drug research from the computational perspective and may represent a snapshot of the wide research landscape.
Life history of an evolutionary distinct aquatic caecilian amphibian (genus Potomotyphlus) in eastern Amazonia, Brazil
Authors: Araujo Elisia Clara Menezes , dos Santos-Costa Maria Cristina , Maciel Adriano Oliveira 
Published: April 2024

Abstract
Basic ecological data for caecilians are relatively lacking. This study presents the first comprehensive ecological investigation of the Amazonian aquatic caecilian Potomotyphlus kaupii, utilizing a large sample size. We collected population data over a 14-month period, and also used data of museum specimens, both from Belém, State of Pará, Brazil. Our analysis focused on sex ratio, sexual size dimorphism (SSD), female fecundity, and ecological observations. No significant differences in adult sex ratio were found. SSD was found in some characters such as circumference at midbody and body mass with females being larger than males, while males had a wider and longer cloacal disc. We provide the first determination of the approximate size at which males diverge from females in the terminal portion of the body, demonstrating an allometric growth pattern of the cloacal region. Female size showed a positive correlation with the number of ovarian eggs, indicating an important reproductive strategy. Furthermore, a novel behaviour in P. kaupii was documented. We expand the understanding of Gymnophiona biology, in particular of a fully aquatic species, and provide support for future ecological and conservation research on caecilians.
Topological disruption of low- and high-order functional networks in presbycusis
Authors: Xu Yixi , Li Xiangxiang , Yan Qi , Zhang Yao , Shang Song’an , Xing Chunhua , Wu Yuanqing , Guan Bing , Chen Yu-Chen 
Published: April 2024

Abstract
Prior efforts have manifested that functional connectivity (FC) network disruptions are concerned with cognitive disorder in presbycusis. The present research was designed to investigate the topological reorganization and classification performance of low-order functional connectivity (LOFC) and high-order functional connectivity (HOFC) networks in patients with presbycusis. Resting-state functional magnetic resonance imaging (Rs-fMRI) data were obtained in 60 patients with presbycusis and 50 matched healthy control subjects (HCs). LOFC and HOFC networks were then constructed, and the topological metrics obtained from the constructed networks were compared to evaluate topological differences in global, nodal network metrics, modularity and rich-club organization between patients with presbycusis and HCs. The use of HOFC profiles boosted presbycusis classification accuracy, sensitivity and specificity compared to that using LOFC profiles. The brain networks in both patients with presbycusis and HCs exhibited small-world properties within the given threshold range, and striking differences between groups in topological metrics were discovered in the constructed networks (LOFC and HOFC). NBS analysis identified a subnetwork involving 26 nodes and 23 signally altered internodal connections in patients with presbycusis in comparison to HCs in HOFC networks. This study highlighted the topological differences between LOFC and HOFC networks in patients with presbycusis, suggesting that HOFC profiles may help to further identify brain network abnormalities in presbycusis.
Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain
Authors: Sivagnanam S , Yeu S , Lin K , Sakai S , Garzon F , Yoshimoto K , Prantzalos K , Upadhyaya D P , Majumdar A , Sahoo S S , Lytton W W 
Published: April 2024

Abstract
When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC–NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform.
Database URL: https://www.opensciencechain.org.
Field estimation of fallen deadwood volume under different management approaches in two European protected forested areas
Authors: Rousseau Mélody , Adiningrat Devara P , Skidmore Andrew K , Siegenthaler Andjin , Wang Tiejun , Abdullah Haidi 
Published: April 2024

Abstract
Fallen deadwood is essential for biodiversity and nutrient cycling in forest ecosystems. In modern forest management, there is growing interest in developing accurate and efficient methods for field estimation of deadwood volume due to its many benefits (e.g. carbon storage, habitat creation, erosion control). The most common methods for deadwood inventories are fixed-area sampling (FAS) and line-intersect sampling (LIS) methods. While the estimations of deadwood volume by LIS generally show results comparable to FAS estimations, active management (e.g. production forestry clearcutting, logging, and thinning activities) can impair LIS accuracy by changing local deadwood patterns. Yet, the comparison of LIS and FAS methods has typically focused on production forests where deadwood is limited and deadwood volumes are comparably low. In this study, we assessed fallen deadwood volume in two large national parks—one being a more actively managed landscape (including, e.g., selective thinning for maintaining cultural–historical values and enhancing recreational opportunities) with overall lower levels of fallen deadwood, and the other having a strict non-intervention approach with higher levels of deadwood. No significant differences between average FAS and LIS estimations of deadwood volumes were detected. Additional experimentations using simulated data under varied stand conditions confirmed these results. Although line-intersect sampling showed a slight overestimation and some variability at the individual plot level, it remains an efficient, time-saving field sampling method providing comparable results to the more laborious fixed-area sampling. Line-intersect sampling may be especially suitable for rapid field inventories where relative changes in deadwood volume rather than absolute deadwood volumes are of large interest. Due to its practicality, flexibility, and relative accuracy, line-intersect sampling may gain wider use in natural resource management to inform national park managers, foresters, and ecologists.
UNC-16 interacts with LRK-1 and WDFY-3 to regulate the termination of axon growth
Authors: Drozd Cody J , Chowdhury Tamjid A , Quinn Christopher C 
Published: April 2024

Abstract
In humans, MAPK8IP3 (also known as JIP3) is a neurodevelopmental disorder-associated gene. In Caenorhabditis elegans, the UNC-16 ortholog of the MAPK8IP3 protein can regulate the termination of axon growth. However, its role in this process is not well understood. Here, we report that UNC-16 promotes axon termination through a process that includes the LRK-1 (LRRK-1/LRRK-2) kinase and the WDFY-3 (WDFY3/Alfy) selective autophagy protein. Genetic analysis suggests that UNC-16 promotes axon termination through an interaction between its RH1 domain and the dynein complex. Loss of unc-16 function causes accumulation of late endosomes specifically in the distal axon. Moreover, we observe synergistic interactions between loss of unc-16 function and disruptors of endolysosomal function, indicating that the endolysosomal system promotes axon termination. We also find that the axon termination defects caused by loss of UNC-16 function require the function of a genetic pathway that includes lrk-1 and wdfy-3, 2 genes that have been implicated in autophagy. These observations suggest a model where UNC-16 promotes axon termination by interacting with the endolysosomal system to regulate a pathway that includes LRK-1 and WDFY-3.
N-glycoproteomic analyses of human intestinal enteroids, varying in histo-blood group geno- and phenotypes, reveal a wide repertoire of fucosylated glycoproteins
Authors: Nilsson Jonas , Rimkute Inga , Sihlbom Carina , Tenge Victoria R , Lin Shih-Ching , Atmar Robert L , Estes Mary K , Larson Göran 
Published: April 2024

Abstract
Human noroviruses, globally the main cause of viral gastroenteritis, show strain specific affinity for histo-blood group antigens (HBGA) and can successfully be propagated ex vivo in human intestinal enteroids (HIEs). HIEs established from jejunal stem cells of individuals with different ABO, Lewis and secretor geno- and phenotypes, show varying susceptibility to such infections. Using bottom-up glycoproteomic approaches we have defined and compared the N-linked glycans of glycoproteins of seven jejunal HIEs. Membrane proteins were extracted, trypsin digested, and glycopeptides enriched by hydrophilic interaction liquid chromatography and analyzed by nanoLC-MS/MS. The Byonic software was used for glycopeptide identification followed by hands-on verifications and interpretations. Glycan structures and attachment sites were identified from MS2 spectra obtained by higher-energy collision dissociation through analysis of diagnostic saccharide oxonium ions (B-ions), stepwise glycosidic fragmentation of the glycans (Y-ions), and peptide sequence ions (b- and y-ions). Altogether 694 unique glycopeptides from 93 glycoproteins were identified. The N-glycans encompassed pauci- and oligomannose, hybrid- and complex-type structures. Notably, polyfucosylated HBGA-containing glycopeptides of the four glycoproteins tetraspanin-8, carcinoembryonic antigen-related cell adhesion molecule 5, sucrose-isomaltase and aminopeptidase N were especially prominent and were characterized in detail and related to donor ABO, Lewis and secretor types of each HIE. Virtually no sialylated N-glycans were identified for these glycoproteins suggesting that terminal sialylation was infrequent compared to fucosylation and HBGA biosynthesis. This approach gives unique site-specific information on the structural complexity of N-linked glycans of glycoproteins of human HIEs and provides a platform for future studies on the role of host glycoproteins in gastrointestinal infectious diseases.
NOD2 and Crohn’s Disease Clinical Practice: From Epidemiology to Diagnosis and Therapy, Rewired
Authors: Kayali Stefano , Fantasia Stefano , Gaiani Federica , Cavallaro Lucas Giovanni , de’Angelis Gian Luigi , Laghi Luigi 
Published: April 2024

Abstract
Crohn’s disease (CD) is a chronic inflammatory bowel disease with a multifactorial pathogenesis involving environmental and genetic factors. Since the late 20th century, the discovery of the first susceptibility gene (NOD2, previously referred to as CARD15) for CD has paved the way for further investigations into the correlations between clinical features and genetics, and its potential impact on clinical practice has fueled the research in the last 2 decades. Recent therapeutic advancements involving novel biologic drugs and small molecules have shifted inflammatory bowel disease management from a disease-centered to a patient-centric approach. To date, the role of NOD2 has not been fully understood yet. Recent data suggest that its clinical impact may be greater than currently recognized. This review overviews the most common NOD2 variants’ role in real-life clinical practice. These genetic variants increase the risk of developing the disease and can aid in tailoring diagnosis and treatment. They are associated with the stricturing phenotype and ileal involvement and increase the risk of steroid refractoriness. In the meantime, limited and inconclusive evidence exists regarding their predictive role in response to azathioprine, biologic drugs, and small molecules. Eventually, their role in increasing the risk for surgery is evident, especially in those with the L1007fs variant. If further trials will support the initial evidence reported so far, NOD2 genetic variants will emerge as possible candidates for developing precision medicine in CD.
Divisional patents: a system prone to abuse
Authors: Pantelidis Konstantinos 
Published: April 2024

Abstract
Divisional patents are derivatives to an already active patent or pending application used to separate part of the parent-patent’s subject-matter and create a new autonomous patent. They are extensively used in the pharmaceuticals sector to protect blockbuster medicines with multiple overarching patent rights, thus creating complex patent portfolios. These strategies have attracted the attention of competition enforcers and judicial bodies as potential abuses of dominant position because they aim to prevent and delay generic market entry for the patent holder to maintain their monopolistic prices. However, scrutinizing these strategies has proven challenging because the current rules are prone to exploitation, there is no unified approach to evaluating them, and the core of the issue is the much-debated interplay between IP and competition law. It will be shown that European case law varies across jurisdictions, with none of the current solutions fully addressing the issues. In the search for a functional, effective system, it is essential to understand the rationale behind patent rights, the unique features of the pharmaceuticals sector, and the arguments for and against the use of competition law to scrutinize patent strategies. On this basis, the article proposes a system which combines competition law enforcement with patent law amendments.
Sow serenity: automatic long-term measurement of lying behavior in crates and free-farrowing pens using 3D accelerometers
Authors: Knoll Maximilian , Gygax Lorenz , Hillmann Edna 
Published: April 2024

Abstract
Accelerometers are useful in analyzing lying behavior in farm animals. The effect of the farrowing system on sow lying behavior has been studied around parturition, but not long-term. In a natural environment, sows increase activity 14 d post parturition, which we expected to be also evident in housed sows when they can move freely. The objective of this study was (1) to validate the methodology to automatically measure sow lying bouts and duration with accelerometers and (2) to apply it to crated and free-farrowing sows 24-h pre-parturition until weaning. We used videos with manual behavior coding as the gold standard for validation and calculated the agreement with an intraclass correlation coefficient (ICC), which was 0.30 (95% CI: −0.10 to 0.64) for the number of lying bouts. When transitional sitting bouts were excluded from the video dataset, the ICC for lying bouts increased to 0.86 (95% CI: 0.40 to 0.95). For lying duration, the ICC was 0.93 (95% CI: 0.26 to 0.98). We evaluated the effects of housing, day relative to parturition, and time of day on lying using the accelerometer data and linear mixed models. In crated sows, the number of lying bouts increased toward parturition, peaking at about five bouts per 6 h, and decreased to almost zero bouts after parturition. Then, it increased again (P = 0.001). In free-farrowing sows, the number of lying bouts gradually decreased from a high level towards parturition and was lowest after parturition. It remained constant, as in the crated sows, until day 15, when the number of bouts increased to eight bouts on day 20 (P = 0.001). Sows in both systems were lying almost all of the time between 18:00 and 00:00 hours and on all days (P = 0.001). The crated sows showed a very similar pattern in the other three-quarters of the day with a reduced lying time before parturition, a peak after parturition, reduced lying time from days 5 to 20, and an increase again towards weaning (P = 0.001). Free-farrowing sows had a similar pattern to the crated sows from 00:00 to 06:00 hours, but without the reduction in lying time from days 5 to 20. They showed an increase in lying time toward parturition, which remained constant with a final decrease toward weaning, especially during the day (P = 0.001). This study proves the accuracy of accelerometer-based sow lying behavior classification and shows that free-farrowing systems benefit lactating sows around parturition but also towards weaning in the nest-leaving phase by facilitating activity.
Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter
Authors: Fernandez-Serantes Luis-Alfonso , Casteleiro-Roca José-Luis , Berger Hubert , Simić Dragan , Calvo-Rolle José-Luis 
Published: April 2024

Abstract
A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification.