bluuebunny/crossref_metadata_2025_split huggingface dataset:
A generalized interval-valued p,q Rung orthopair fuzzy Maclaurin symmetric mean and modified regret theory based sustainable supplier selection method
Authors: Chen Shuang , Ren Jian , Ye KeTing , Li FeiYan 
Published: June 2024

Abstract
A novel interval valued p,q Rung orthopair fuzzy (IVPQ-ROF) multiple attribute group decision making (MAGDM) method for sustainable supplier selection (SSS) is proposed in this paper. This study mainly contains two research points: (1) tackling the interrelation between attributes; and (2) describing the psychological state and risk attitude of decision makers (DMs). For the first research point, we introduce the Archimedean operation rules for interval valued p,q Rung orthopair fuzzy sets (IVPQ-ROFSs), then the generalized interval valued p, q Rung orthopair fuzzy Maclaurin symmetric mean (GIVPQ-ROFMSM) operator and the generalized interval valued p, q Rung orthopair fuzzy weighted Maclaurin symmetric mean (GIVPQ-ROFWMSM) operator are defined to reflect the correlation between attributes. For the second research point, we introduce the positive ideal degree (PID) and negative ideal degree (NID) based on projection of IVPQ-ROFSs, and modified regret theory. Both of them consider the best alternative and worst alternative, so as to reflect the psychological state and risk attitude of DMs. Finally, a SSS problem is presented to manifest the effectiveness of the designed method. We also provide sensitivity analysis and comparative analysis to further demonstrate the rationality and validity of the proposed method.
Changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer’s disease
Authors: Branciamore Sergio , Gogoshin Grigoriy , Rodin Andrei S. , Myers Amanda J. 
Published: June 2024

AbstractWhile there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer’s disease (LOAD), the Apolipoprotein E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59–58.75). We sought to address whether the APOE E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing APOE E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that VGF had the greatest explanatory weight. High expression of VGF is a protective signal, even on the background of APOE E4 alleles. LOAD risk signals, considering an APOE background, include high levels of SPECC1L, HLA-DRA and RANBP3L. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.
Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud
Authors: J Reji , Nidamanuri Rama Rao 
Published: June 2024

AbstractRemote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.
Insights into the genomic and functional divergence of NAT gene family to serve microbial secondary metabolism
Authors: Boukouvala Sotiria , Kontomina Evanthia , Olbasalis Ioannis , Patriarcheas Dionysios , Tzimotoudis Dimosthenis , Arvaniti Konstantina , Manolias Aggelos , Tsatiri Maria-Aggeliki , Basdani Dimitra , Zekkas Sokratis 
Published: June 2024

AbstractMicrobial NAT enzymes, which employ acyl-CoA to acylate aromatic amines and hydrazines, have been well-studied for their role in xenobiotic metabolism. Some homologues have also been linked to secondary metabolism, but this function of NAT enzymes is not as well-known. For this comparative study, we surveyed sequenced microbial genomes to update the list of formally annotated NAT genes, adding over 4000 new sequences (mainly bacterial, but also archaeal, fungal and protist) and portraying a broad but not universal distribution of NATs in the microbiocosmos. Localization of NAT sequences within microbial gene clusters was not a rare finding, and this association was evident across all main types of biosynthetic gene clusters (BGCs) implicated in secondary metabolism. Interrogation of the MIBiG database for experimentally characterized clusters with NAT genes further supports that secondary metabolism must be a major function for microbial NAT enzymes and should not be overlooked by researchers in the field. We also show that NAT sequences can be associated with bacterial plasmids potentially involved in horizontal gene transfer. Combined, our computational predictions and MIBiG literature findings reveal the extraordinary functional diversification of microbial NAT genes, prompting further research into their role in predicted BGCs with as yet uncharacterized function.
Improving the performance of 3D image model compression based on optimized DEFLATE algorithm
Authors: Kai Xue , Yuxiang Zhang 
Published: June 2024

AbstractThis study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm’s exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.
Monarch butterfly conservation through male germplasm cryopreservation
Authors: Grula Courtney C. , Rajamohan Arun , Rinehart Joseph P. 
Published: June 2024

AbstractMonarch butterfly (Danaus plexippus L.) populations have declined in North America. The International Union for Conservation of Nature (IUCN) recently classified the species as endangered, sparking public concern and conservation efforts. Our approach to conservation is through cryopreservation of germinal cells and tissue. The goal of this study was to develop a cryopreservation protocol for monarch spermatozoa to ensure successful long-term storage. Cryopreserved sperm cells would provide a reserve of monarch germplasm, which could be utilized in the event of population loss. In this study, sperm cell bundles collected from male monarch butterflies were cryopreserved in a cryoprotective medium and stored in liquid nitrogen. To determine the post-cryopreservation sperm cell viability, a subsample of preserved sperm bundles were thawed rapidly, and their viability was qualified using a sperm live/dead stain. We are presenting a protocol to preserve and store genetic material and viable sperm bundles of the monarch butterfly. To date, this is the first report of successful cryopreservation of monarch germplasm which sets the foundation for cryostorage and could be extensible to other vulnerable lepidopterans.
Plekhm2 acts as an autophagy modulator in murine heart and cardiofibroblasts
Authors: Etzion Sharon , Hijaze Raneen , Segal Liad , Pilcha Sofia , Masil Dana , Levi Or , Elyagon Sigal , Levitas Aviva , Etzion Yoram , Parvari Ruti 
Published: June 2024

AbstractPlekhm2 is a protein regulating endosomal trafficking and lysosomal distribution. We recently linked a recessive inherited mutation in PLEKHM2 to a familial form of dilated cardiomyopathy and left ventricular non-compaction. These patients’ primary fibroblasts exhibited abnormal lysosomal distribution and autophagy impairment. We therefore hypothesized that loss of PLEKHM2 impairs cardiac function via autophagy derangement. Here, we characterized the roles of Plekhm2 in the heart using global Plekhm2 knockout (PLK2-KO) mice and cultured cardiac cells. Compared to littermate controls (WT), young PLK2-KO mice exhibited no difference in heart function or autophagy markers but demonstrated higher basal AKT phosphorylation. Older PLK2-KO mice had body and heart growth retardation and increased LC3II protein levels. PLK2-KO mice were more vulnerable to fasting and, interestingly, impaired autophagy was noted in vitro, in Plekhm2-deficient cardiofibroblasts but not in cardiomyocytes. PLK2-KO hearts appeared to be less sensitive to pathological hypertrophy induced by angiotensin-II compared to WT. Our findings suggest a role of Plekhm2 in murine cardiac autophagy. Plekhm2 deficiency impaired autophagy in cardiofibroblasts, but the autophagy in cardiomyocytes is not critically dependent on Plekhm2. The absence of Plekhm2 in mice appears to promote compensatory mechanism(s) enabling the heart to manage angiotensin-II-induced stress without detrimental consequences.
Prognostic potential of CUL3 ligase with differential roles in luminal A and basal type breast cancer tumors
Authors: Pantazi Vasiliki , Miklós Vanda , Smith Paul , Oláh-Németh Orsolya , Pankotai-Bodó Gabriella , Teja Dondapati Divya , Ayaydin Ferhan , D’Angiolella Vincenzo , Pankotai Tibor 
Published: June 2024

AbstractBreast cancer is a prevalent and significant cause of mortality in women, and manifests as six molecular subtypes. Its further histologic classification into non-invasive ductal or lobular carcinoma (DCIS) and invasive carcinoma (ILC or IDC) underscores its heterogeneity. The ubiquitin–proteasome system plays a crucial role in breast cancer, with inhibitors targeting the 26S proteasome showing promise in clinical treatment. The Cullin-RING ubiquitin ligases, including CUL3, have direct links to breast cancer. This study focuses on CUL3 as a potential biomarker, leveraging high-throughput sequencing, gene expression profiling, experimental and data analysis tools. Through comprehensive analysis using databases like GEPIA2 and UALCAN, as well as TCGA datasets, CUL3's expression and its association with prognostic values were assessed. Additionally, the impact of CUL3 overexpression was explored in MCF-7 and MDA-MB-231 breast cancer cell lines, revealing distinct differences in molecular and phenotypic characteristics. We further profiled its expression and localization in breast cancer tissues identifying prominent differences between luminal A and TNBC tumors. Conclusively, CUL3 was found to be associated with cell cycle progression, and DNA damage response, exhibiting diverse roles depending on the tumor's molecular type. It exhibits a tendency to act as an oncogene in triple-negative tumors and as a tumor suppressor in luminal A types, suggesting a potential significance in breast cancer progression and therapeutic directions.
A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography
Authors: Chen Chiu-Fan , Hsu Chun-Hsiang , Jiang You-Cheng , Lin Wen-Ren , Hong Wei-Cheng , Chen I.-Yuan , Lin Min-Hsi , Chu Kuo-An , Lee Chao-Hsien , Lee David Lin , Chen Po-Fan 
Published: June 2024

AbstractIn tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection. We also collected 1150 abnormal CXRs and 627 normal CXRs for training abnormality detection. For external validation, we collected 250 CXRs from our hospital. We also compared the accuracy of the algorithm to five pulmonologists and radiological reports. In external validation, the AI algorithm showed areas under the curve (AUC) of 0.951 and 0.975 in validation dataset 1 and 2. The accuracy of the pulmonologists on validation dataset 2 showed AUC range of 0.936–0.995. When abnormal CXRs other than TB were added, AUC decreased in both human readers (0.843–0.888) and AI algorithm (0.828). When combine human readers with AI algorithm, the AUC further increased to 0.862–0.885. The TB CXR AI algorithm developed by using Google teachable machine in this study is effective, with the accuracy close to experienced clinical physicians, and may be helpful for detecting tuberculosis by CXR.
High-precision object detection network for automate pear picking
Authors: Zhao Peirui , Zhou Wenhua , Na Li 
Published: June 2024

AbstractTo address the urgent need for agricultural intelligence in the face of increasing agricultural output and a shortage of personnel, this paper proposes a high precision object detection network for automated pear picking tasks. The current object detection method using deep learning does not fully consider the redundant background information of the pear detection scene and the mutual occlusion characteristics of multiple pears, so that the detection accuracy is low and cannot meet the needs of complex automated pear picking detection tasks. The proposed, High-level deformation-perception Network with multi-object search NMS(HDMNet), is based on YOLOv8 and utilizes a high-level Semantic focused attention mechanism module to eliminate irrelevant background information and a deformation-perception feature pyramid network to improve accuracy of long-distance and small scale fruit. A multi-object search non-maximum suppression is also proposed to choose the anchor frame in a combined search method suitable for multiple pears. The experimental results show that the HDMNet parameter amount is as low as 12.9 M, the GFLOPs is 41.1, the mAP is 75.7%, the mAP50 reaches 93.6%, the mAP75 reaches 70.2%, and the FPS reaches 73.0. Compared with other SOTA object detection methods, it has the transcend of real-time detection, low parameter amount, low calculation amount, high precision, and accurate positioning.
Bayesian phylogenetic analysis of Philippine languages supports a rapid migration of Malayo-Polynesian languages
Authors: King Benedict , Greenhill Simon J. , Reid Lawrence A. , Ross Malcolm , Walworth Mary , Gray Russell D. 
Published: June 2024

AbstractThe Philippines are central to understanding the expansion of the Austronesian language family from its homeland in Taiwan. It remains unknown to what extent the distribution of Malayo-Polynesian languages has been shaped by back migrations and language leveling events following the initial Out-of-Taiwan expansion. Other aspects of language history, including the effect of language switching from non-Austronesian languages, also remain poorly understood. Here we apply Bayesian phylogenetic methods to a core-vocabulary dataset of Philippine languages. Our analysis strongly supports a sister group relationship between the Sangiric and Minahasan groups of northern Sulawesi on one hand, and the rest of the Philippine languages on the other, which is incompatible with a simple North-to-South dispersal from Taiwan. We find a pervasive geographical signal in our results, suggesting a dominant role for cultural diffusion in the evolution of Philippine languages. However, we do find some support for a later migration of Gorontalo-Mongondow languages to northern Sulawesi from the Philippines. Subsequent diffusion processes between languages in Sulawesi appear to have led to conflicting data and a highly unstable phylogenetic position for Gorontalo-Mongondow. In the Philippines, language switching to Austronesian in ‘Negrito’ groups appears to have occurred at different time-points throughout the Philippines, and based on our analysis, there is no discernible effect of language switching on the basic vocabulary.
Characterization of expression patterns and dynamic relocation of Notch proteins during acrosome reaction of bull spermatozoa
Authors: Diniz Patrícia , Leites Inês , R. Batista Mariana , Torres Ana Catarina , Mateus Luísa , Lopes-da-Costa Luís , Silva Elisabete 
Published: June 2024

AbstractNotch is a conserved cell-signaling pathway involved in spermatogenesis regulation. This study firstly evaluated the presence, localization patterns, acquisition origin and relation to acrosome reaction of Notch proteins in bull sperm. Western Blot analysis detected all Notch proteins in ejaculated bull sperm, and immunostaining described their specific sperm localization. Recovery of sperm from different segments showed that Notch proteins have testicular origin (NOTCH1, NOTCH2, DLL4), are sequentially acquired during sperm maturation along epididymal transit (NOTCH3, DLL3, JAGGED1-2), or post-ejaculation (DLL1, NOTCH4). Testis NOTCH2 is ubiquitously expressed in all germ-cell lines, whereas DLL4 is expressed in round and elongated spermatids during the Golgi, Cap, Acrosome and Maturation phases. In vitro spontaneous and induced sperm acrosome reaction induce consistent sperm regional relocation of NOTCH2, DLL4 and JAGGED1, and these relocation patterns are significantly associated to sperm acrosome status. NOTCH2 and JAGGED1 are relocated from the head apical to the post-equatorial regions, whereas DLL4 is lost along with the acrosome, evidencing that sperm spatial redistribution of NOTCH2 and JAGGED1 is linked to acrosome reaction onset, whereas DLL4 loss is linked to AR completion. Overall, results prompt for a relevant Notch role in bull sperm acrosome testicular development, epididymal maturation and acrosome reaction.
A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia
Authors: Shephard Adam J. , Bashir Raja Muhammad Saad , Mahmood Hanya , Jahanifar Mostafa , Minhas Fayyaz , Raza Shan E. Ahmed , McCombe Kris D. , Craig Stephanie G. , James Jacqueline , Brooks Jill , Nankivell Paul , Mehanna Hisham , Khurram Syed Ali , Rajpoot Nasir M. 
Published: June 2024

AbstractOral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.