bluuebunny/crossref_metadata_2025_split huggingface dataset:
Screening and identification of critical Genes and Pathways Associated with Oxidative Stress in non-obstructive azoospermia patients: an integrative bioinformatics study
Authors: Bouk Sasan , Yari Kheirollah , Barzegari Ebrahim , Soleymani Bijan 
Published: December 2023

Abstract
Azoospermia affects nearly 1% of the entire male population, and 60% of these cases are included as non-obstructive azoospermia (NOA). For a long time, oxidative stress has been considered a controversial factor in the etiology of infertility types, including male azoospermia. In various studies, the role of reactive oxygen species, as a double-edged sword, in the normal function of sperm cells and of course in DNA damage and sperm dysfunction has been reported. However, genes related to the oxidative stress process, which are responsible for infertility disorders in men, have not been specifically investigated. In this study, the expression profile of oxidative stress genes in non-obstructive azoospermia patients was investigated, and dysregulated and differentially expressed genes were obtained using Gene Expression Omnibus (GEO) datasets. Besides the limma package, other packages and tools were used for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network of the mentioned genes. Finally, Hub genes were identified using Cytoscape and CytoHubba plug-in. Finally, 75 differentially expressed oxidative stress-related genes were identified between azoospermic and control groups. These genes were enriched in the functions and pathways related to different cellular stress and oxidative stress. As far as we know, this is the first time the key genes of oxidative stress affecting non-obstructive azoospermia have been investigated. The present study suggests the hub genes JUN, FOS, ATF3, DUSP1, MYC, and HSPA5 as possible potential biomarkers in NOA. It is hoped that our results will shed light on the dark aspects of the association between oxidative stress and azoospermia and that these findings will be used as potential therapeutic and research targets.
YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method
Authors: Xiao Hengshu , Li Junying , Liang Hong , Zhang Liuchen , Zhang Hong , Ma Erdeng , Deng Xiaopeng 
Published: December 2023

Abstract
Background: Estimating tobacco leaf yield is a crucial task. The number of leaves is directly related to yield. Therefore, it is important to achieve intelligent and rapid high-throughput statistical counting of field tobacco leaves. Unfortunately, the current method of counting the number of tobacco leaves is expensive, imprecise, and inefficient. It heavily relies on manual labor and also faces challenges of mutual shading among the field tobacco plants during their growth and maturity stage, as well as complex environmental background information. This study proposes an efficient method for counting the number of tobacco leaves in a large field based on unmanned aerial vehicle (UAV) image data. First, a UAV is used to obtain high-throughput vertical orthoimages of field tobacco plants to count the leaves of the tobacco plants. The tobacco plant recognition model is then used for plant detection and segmentation to create a dataset of images of individual tobacco plants. Finally, the improved algorithm YOLOv8 with Squeeze-and-Excitation (SE) and bidirectional feature pyramid network (BiFPN) and GhostNet (YOSBG) algorithm is used to detect and count tobacco leaves on individual tobacco plants.
Results: Experimental results show YOSBG achieved an average precision (AP) value of 93.6% for the individual tobacco plant dataset with a model parameter (Param) size of only 2.5 million (M). Compared to the YOLOv8n algorithm, the F1 (F1-score) of the improved algorithm increased by 1.7% and the AP value increased by 2%, while the model Param size was reduced by 16.7%. In practical application discovery, the occurrence of false detections and missed detections is almost minimal. In addition, the effectiveness and superiority of this method compared to other popular object detection algorithms have been confirmed.
Conclusions: This article presents a novel method for high-throughput counting of tobacco leaves based on UAV image data for the first time, which has a significant reference value. It solves the problem of missing data in individual tobacco datasets, significantly reduces labor costs, and has a great impact on the advancement of modern smart tobacco agriculture.
Knowledge, attitudes, and practices toward thyroid disorders among Jordanians
Authors: Abu-Humaidan Anas H. A. , Bdour Zain , Albdour Karam , Al-Sukhon Diala , Momani Yazan , Alaridah Nader 
Published: December 2023

Abstract

Thyroid disorders, despite their prevalence and possible significant complications, remain considerably underdiagnosed. Thus, awareness of thyroid disorders symptoms and risk factors is needed. This study gauged the knowledge, attitudes, and practices (KAP) of Jordanians towards thyroid disorders, using an online survey. The survey assessed knowledge of thyroid function; knowledge of risk factors, symptoms, and treatments of thyroid disorder; and finally, attitudes and practices toward thyroid disorders. The survey was completed by 579 participants, predominantly female (62.9%) and married (68.7%), with a mean age of 41.4. Participants had a mean knowledge score of 7.22 out of 14.00. Specific gaps in knowledge relevant to women’s health were recognized, where a minority recognized symptoms such as menstrual abnormalities (38.2%), and risk factors such as pregnancy (19.3%). Additionally, a minority believed that thyroid medications should not be stopped during pregnancy (31.8%). Better knowledge correlated with perceiving thyroid disorders as common, dangerous, and preventable. Factors predicting higher knowledge included being female, pursuing medical studies, and having a prior thyroid disorder diagnosis. Most participants preferred visiting an endocrinologist (75.6%) over primary care (19.3%) for thyroid-related symptoms. In conclusion, the study recommends targeted public health campaigns to enhance awareness, particularly among women and expectant mothers. Disseminating accurate information through various channels, including primary care centers, is crucial for creating an informed and proactive population regarding thyroid health.
Long-term periodic management of Phragmites australis maintains native brackish wetland plant communities
Authors: Brooks Hope , Jacobson Sylvia , Baldwin Andrew , McCormick Melissa , Kettenring Karin , Buehl Eric , Whigham Dennis 
Published: December 2023

Abstract

Complete eradication of invasive plants is often infeasible while in some cases ‘functional eradication’, the reduction of an invader to low levels with reduced ongoing management costs, is a sustainable option. Non-native Phragmites australis has challenged land managers across North America but functional eradication may yet be possible in some scenarios. Here we present data from Chesapeake Bay brackish tidal wetlands where two approaches to Phragmites management (long-term, continuous management and short-term, non-continuous management) were used. We demonstrate that the application of herbicides will lead to the establishment of native species, but long-term, continuous management is required to facilitate functional eradication of Phragmites by keeping it at low levels of occurrence and restoring native plant communities. Ultimately, historical data from sites that were sampled as part of this study indicate that if management is successful and results in functional eradication of Phragmites, then the recovering vegetation will include native species that were present before the sites were invaded by Phragmites.
Construction and validation of a prediction model for preoperative prediction of Ki-67 expression in endometrial cancer patients by apparent diffusion coefficient
Authors: Zhang Meng , Jing Mengyuan , Li Ruiping , Cao Yuntai , Zhang Shan , Guo Yuzhen 
Published: December 2023

Abstract
Objective: To investigate the feasibility of the apparent diffusion coefficient (ADC) value in the preoperative non-invasive prediction of Ki-67 expression level in patients with endometrial cancer (EC), and to construct a nomogram prdiction model by combining clinical data.
Methods: We performed the retrospective analysis of 280patients with EC who underwent preoperative magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and were confirmed by pathology after operation from January 2017 to February 2023 in our hospital. Two independent radiologists measured the ADC values (ADCmax, ADCmean, and ADCmin) of EC on the ADC image by comparing the MRI enhancement and DWI images, respectively. Potential non-linear relationships between ADC values and Ki-67 expression were assessed using a 3-node restricted cubic spline plot. The included patients were randomized into a training set (n = 186) and a validation set (n = 84). Using a combination of Logistic regression and LASSO regression results, from which the four best predictors were identified for the construction of the nomogram. The accuracy and clinical applicability of the nomogram were assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve Analysis (DCA).
Results: ADCmax, ADCmean and ADCmin were significantly correlated with Ki-67 expression levels in EC patients(P=0.001). Among them, there was a non-linear correlation between ADCmin and Ki-67 expression (Nonlinear P = 0.019); and between ADCmean and Ki-67 expression (Nonlinear P = 0.048), specifically. The final obtained ADCmax, FIGO, Chemotherapy and grade were combined to construct the nomogram. The area under the curve (AUC) values of ROC for Nomogram, ADCmax, FIGO, Chemotherapy and Grade in the training set were 0.783, 0.718, 0.579, 0.636 and 0.654, respectively. In the validation set, the AUC values for Nomogram, ADCmax, FIGO, Chemotherapy and Grade were 0.820, 0.746, 0.558, 0.542 and 0.738, respectively. In addition, the calibration curves and the DCA curves suggested a better predictive efficacy of the model.
Conclusion: A nomogram prediction model constructed on the basis of ADCmax values combined with clinical data can be used as an effective method to noninvasively assess Ki67 expression in EC patients before surgery.
Utilizing Technology for Diet and Exercise Change in Complex Chronic Conditions Across Diverse Environments (U-DECIDE): Feasibility Randomised Controlled Trial
Authors: Brown Riley C. C. , Keating Shelley E. , Jegatheesan Dev K. , Mayr Hannah L. , Barnett Amandine , Conley Marguerite M. , Webb Lindsey , Kelly Jaimon T. , Snoswell Centaine L. , Staudacher Heidi , Macdonald Graeme A. , Burton Nicola W. , Coombes Jeff S. , Campbell Katrina L. , Isbel Nicole M. , Hickman Ingrid J. 
Published: December 2023

Abstract
Background
Diet and exercise are important components of treatment for complex chronic conditions, however access to allied health support is limited. When available, support is often siloed and fragmented. Digital health incorporating patient choice may help to align health care services with preferences and goals. This study evaluated the implementation of a ubiquitously accessible patient-centred digital health diet and exercise service.
Methods
U-DECIDE was a single-centre, 26-week randomised controlled trial set in kidney and liver disease clinics in a tertiary hospital in Brisbane, Australia. Participants were adults with a complex chronic condition referred for dietetic consultation with at least one feature of the metabolic syndrome. All participants received a dietary consultation, an activity monitor and usual care. Intervention participants were offered one text message per week and access to additional digital health options (increased text message frequency, nutrition app, exercise app, group-based diet and/or exercise video consultations). The primary outcome of feasibility was determined by safety (study-related serious adverse events: SRSAEs), recruitment (≥ 50% eligible patients), retention (≥ 70%), exposure uptake (≥ 75% of intervention group had greater access to health professional contact than comparator) and video consultation adherence (≥ 80% attendance). Secondary outcomes included process evaluation metrics and clinical effectiveness.
Results
Of 67 participants (intervention n = 33, comparator n = 34), 37 (55%) were men, median (IQR) age was 51 (41–58) years. The most chosen digital health options were the nutrition app (n = 29, 88%) and exercise video consultations (n = 26, 79%). Only one participant chose no additional digital health options. The intervention group had no SRSAEs. The study exceeded targets for recruitment (52%), retention (81%) and exposure uptake (94%). Video consultation adherence was 42%. Engagement across digital health options was inconsistent.
Conclusions
Digital health options incorporating patient choice were feasible and can be offered to people with complex chronic disease as a service model option.
Trial registration
Australia and New Zealand Trials Register: Trial Registration Number: ACTRN12620001282976. Registered 27th November 2020.
Towards online training for RL-Based Query Optimizer
Authors: Ramadan Mohamed , El-Kilany Ayman , Mokhtar Hoda M. O. , Sobh Ibrahim 
Published: December 2023

Abstract

Join query optimization aims to find the best join order for tables in a query, which is critical for query processing performance. Recently, reinforcement learning models have been proposed to solve the challenges existing with query processing and join query optimization. However, changes in the data distribution can turn the trained reinforcement learning models into obsolete models, resulting in longer execution times. In this paper, we propose a new training strategy in order to extend the existing reinforcement learning models and improve their adaptation when the data distribution changes. The experiments show that the proposed strategy has a significant benefit in decreasing training time for the models given the changes in the data distribution.
Impact of time from diagnosis to chemotherapy on prognosis in advanced pancreatic cancer
Authors: Nishida Tsutomu , Sugimoto Aya , Hosokawa Kana , Masuda Haruka , Okabe Satoru , Fujii Yoshifumi , Nakamatsu Dai , Matsumoto Kengo , Yamamoto Masashi , Fukui Koji 
Published: December 2023

Abstract

Purpose
Due to the aggressive nature and poor prognosis of advanced pancreatic cancer (PC), prompt initiation of treatment is critical. We investigated the effect of the survival time interval between cancer diagnosis and initiation of chemotherapy in patients with advanced PC.
Methods
In this retrospective, single-center study, consecutive patients with advanced PC between April 2013 and March 2022 were analyzed. Data were extracted from the electronic medical records of patients who received chemotherapy for metastatic, locally advanced, or resectable PC or who received chemotherapy due to either being intolerant of or declining surgery. Chemotherapy followed clinical practice guidelines. We compared overall survival between two groups: the early waiting time (WT) group (WT ≤ 30 days from diagnosis to chemotherapy initiation) and the elective WT group (WT ≥ 31 days). Prognostic factors, including biliary drainage, were considered. The impact of WT on survival was assessed by univariate and multivariate analyses with Cox proportional hazard models. A 1:1 propensity score matching (PSM) approach balanced bias, accounting for significant poor prognosis factors, age and sex.
Results
The study involved 137 patients. Overall survival exhibited no statistically significant difference between the early and elective WT groups (207 and 261 days, P = 0.2518). Univariate and multivariate analyses identified poor performance status and metastasis presence as predictors of worse prognosis. This finding persisted post PSM (275 and 222 days, P = 0. 8223).
Conclusions
Our study revealed that initiating chemotherapy within 30 days of diagnosis, as opposed to more than 30 days later, does not significantly affect treatment efficacy.
Purification and characterization of a novel anticoagulant peptide from protein hydrolysate of Eupolyphaga sinensis Walker
Authors: Li Xidong , Zhuang Xuefei , Li Huifen , Pan Linmei , Liu Rui , Zhu Huaxu , Tang Zhishu 
Published: December 2023

Abstract
Eupolyphaga sinensis Walker (ESW), an animal drug in traditional Chinese medicine, has been used clinically for thousands of years for cardiovascular disease and osteoarthritis. Many studies of ESW have reported that it had have anti-inflammatory and anti-tumor activities due to the small-molecule ingredients. However, large-molecule compounds of ESW representing significant pharmacological effects such as anti-thrombotic, anti-cancer, and anti-inflammatory have not been revealed yet. Here, a novel anticoagulant peptide (P9) containing 9 amino acids was isolated from the hydrolysate of aqueous extracts of ESW. Further, P9 synthesized by solid-phase synthesis was able to prolong APTT and TT and bind to thrombin in a mixed mode. Molecular docking and spectroscopy demonstrated that P9 was able to inhibit thrombin activity by binding to the active site of thrombin and altering the secondary structure of thrombin. In an in vivo study, P9 was able to reduce the intensity of Phenylhydrazine (PHZ)-induced cardiac staining in thrombosed zebrafish with antithrombotic activity. The results suggest that peptides originated from ESW hydrolysates could exert an anticoagulant effect, which is likely to be a potential source of bioactive peptides with anticoagulant activity.
Analysis of the effect of spray mode on coating porosity and hardness when spraying press screws by the high velocity oxy fuel method
Authors: Nguyen Tuan-Linh , Nguyen Hong Tien , Nguyen Van Thien , Khuat Duc Duong 
Published: December 2023

Porosity and coating hardness are two very important properties of the coating. In order to achieve low coating porosity and high hardness, a suitable spray mode is desired. In the particular application for press screws with the complex surface, a suitable spray mode plays a significant role in the formation of the coating properties. This paper employs the Taguchi experimental design method combined with ANOVA analysis to evaluate the impact of the spray mode on the porosity and hardness of the coating while spraying the screw surface using the High Velocity Oxy Fuel (HVOF) method. The injection material used is WC HMSP1060-00 +60 % 4070, with its main components being Nickel and Carbide Wolfram. And the press screw material is 1045 steel. The impactful parameters of the spray mode investigated and tested are the flow rate of spray (F) with a range varying from 25 g/min to 35 g/min, spray distance (D) with a range of values varying from 0.25 m to 0.35 m, and an oxygen/propane ratio (R) from 4 to 6. The analysis shows that the spray mode significantly affects the coating properties, and a suitable set of spray parameters is found to achieve low coating porosity and high coating hardness. The spray mode with the lowest porosity is achieved at a spray rate (F) of 35 g/min, a spray distance (D) of 0.3 m, and an oxygen/propane ratio (R) of 6. The interactions between D and R, as well as between F and D, are statistically significant, influencing each other's effects on porosity. However, the interaction between F and R is relatively low, indicating that changes in one parameter have less impact on porosity when the other parameter is varied. Similarly, for the highest coating hardness, the optimal spray mode includes an F of 35 g/min, D of 0.25 m, and R of 6. There is a significant interaction between F and D, while the interaction between F and R is relatively low. Notably, there is no interaction between F and R
iProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking
Authors: Shujaat Muhammad , Yoo Sunggoo , Tayara Hilal , Chong Kil To 
Published: December 2023

Background and Objective:
Gene promoters play a crucial role in regulating gene transcription
by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches,
including alignment signal and content-based methods for promoter prediction, accurately
identifying promoters remains challenging due to the lack of explicit features in their sequences.
Consequently, many machine learning and deep learning models for promoter identification have
been presented, but the performance of these tools is not precise. Most recent investigations have
concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces
cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”,
a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast
Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally,
we developed a more difficult negative set by employing promoter sequences rather than nonpromoter
regions of the genome. The newly developed negative reconstruction approach improves
classification and minimizes the amount of false positive predictions.

Methods:
To overcome the problems associated with promoter prediction, we investigate alternate
vector encoding and feature extraction methodologies. Following that, these strategies are coupled
with several machine learning algorithms and a 1-D convolutional neural network model. Our results
show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine-
learning stacking approach is excellent for accurate promoter categorization. Furthermore, we
provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions,
resulting in higher classification performance and fewer false positive predictions.

Results:
Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a
good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%,
the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient
(MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was
0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast
across other species.

Conclusion:
iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae
promoters. With advanced vector encoding techniques and a negative reconstruction approach,
it achieves improved classification accuracy and reduces false positive predictions. In addition,
it offers researchers a reliable and precise webserver to study gene regulation in diverse
organisms.