bluuebunny/crossref_metadata_2025_split huggingface dataset:
Are 6 more accurate than 4? The delivery mode and its influence on postpartum depression and PTSD.
Authors: Beck-Hiestermann Franziska Marie Lea , Hartung Lisa Kathrin , Richert Nadine , Miethe Sandra , Wiegand-Grefe Silke 
Published: November 2023

Abstract
Background
Empirical evidence shows that 2–6% of all women develop post-traumatic stress disorder (PTSD) and about 10–15% postpartum depression (PPD) after childbirth. This study examines PTSD and PPD in the context of delivery mode, focusing 4 different caesarean section modes, elective primary (Grade 4), medically indicated primary (Grade 3), secondary from relative indication (Grade 2) and emergency secondary caesarean section (Grade 1), compared to vaginal and assisted vaginal delivery (AVD). The impact of the six subdivided delivery mode categories on the level of postpartum depression and PTSD will be investigated using common predictors (for postpartum depression psychological treatment before childbirth, Fear of childbirth, Planning of pregnancy, social support after childbirth and for PTSD psychological treatment before childbirth, fear of childbirth, induction of labor, birth debriefing and social support after childbirth).
Methods
Between four weeks and two years postpartum, 1223 women were surveyed once via an anonymous online questionnaire on demographic and gynecological data, type of delivery, PTSD (PCL-5) and PPD (EPDS).
Results
For both psychiatric disorders, ANOVA revealed significant differences between delivery mode and PPD and PTSD. With weak effects for PPD and medium to strong effects for PTSD. Post-hoc tests showed increased levels of postpartum depression for all caesarean section types compared to both vaginal delivery types. For PTSD, secondary relative indication (Grade 2), emergency secondary caesarean section (Grade 1) and assisted vaginal delivery (AVD) were associated with elevated levels of PTSD. Regression analysis revealed the delivery mode as a significant predictor of EPDS- (medium effect size) and PCL-5-Score (medium to high effect size).
Limitation:
Delivery was assumed to be the traumatic event; previous traumas were not recorded. The definition of the delivery mode was reported subjectively and not medically confirmed.
Conclusion
The study highlights the influence of delivery modes on the mental health of postpartum mothers: different modes influence different postpartum disorders in different ways. However, the definition of the delivery mode was only stated subjectively and not medically confirmed. Further research should investigate which aspects of the different delivery modes influence maternal mental health and how the perception of the birth may depend on a specific delivery mode.
Network Analysis and Molecular Mapping for Alzheimer’s Disease to Reveal the Drug Targets of Arbutin
Authors: Uvarajan Deenathayalan , Ravikumar Manish , Durairaj Brindha 
Published: November 2023

Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that causes the degradation of brain cells. AD is the most common causative factor of dementia that leads to cognitive decline and loss of independence. Cholinesterase inhibitors and N-methyl-D- aspartate (NMDA) antagonists are currently available drugs to treat AD related symptoms. Several studies have proved that arbutin is found to be beneficial in treating various diseases thereby modulating its brain targets which further helps to reduce AD’s side effects. The present research is mainly focused to study the molecular pathways and to determine the mechanism of action of arbutin to mitigate AD using a system pharmacology approach. Bioinformatics tools are explored to identify arbutin's therapeutic targets for AD, including Cytoscape for network analysis, ShinyGo for gene ontology enrichment, and AutoDock for docking molecules. In a Cytoscape network, the Maximal Clique Centrality (MCC) algorithm of the CytoHubba plugin was used to determine the top ten hub genes. Out of 411 targets for arbutin and 395 targets for AD, 37 targets were selected and shared through the data filtering process. The biological activities of these 37 genes include post-translational regulation of the phosphorus metabolic process, response to abiotic stimulus, regulation of cell population proliferation, regulation of programmed cell death and response to oxygen-containing compounds. The top 10 enriched pathways were selected for future study from 284, including AD, cancer pathways, MAPK signaling, Diabetic cardiomyopathy and proteoglycans in cancer. Our results proved that arbutin can reduce the possibility of developing AD by modulating the activity of primary pathways groups, including its pharmacological mechanism of action in AD, revealing its ten therapeutic targets.
Multimodal Feature Fusion Machine Learning for Predicting Chronic Injury Induced by Engineered Nanomaterials
Authors: Li Xuehua 
Published: November 2023

Abstract
Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and needs to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment of chemicals, predicting chronic in vivo nanotoxicity remains challenging due to the complex interactions at multiple nano-bio interfaces. Herein, we developed a multimodal feature fusion analysis framework to predict the fibrogenic potential of metal oxide nanoparticles (MeONPs). Eighty-seven features derived from multiple MeONP-lung interfaces were used to develop a machine learning-based predictive framework. We identified viability damage and cytokine (IL-1β and TGF-β1) production in macrophages and epithelial cells as key events that are closely associated with particle size, surface charge, and interactions with lysosomes. Experimental validations showed that the developed in silico model had 85% accuracy in predicting MeONP-induced lung fibrosis. The heterogeneity distribution of data points indicated good applicability of the predictive model. Our findings demonstrate the potential usefulness of this predictive model for risk assessment of nanomaterials and in assisting regulatory decision making.
Optimizing the wells location using the Machine learning algorithms, as well as optimizing the drilling time of production and injection wells in one of the reservoirs in south west of Iran
Authors: Nikravesh Hamed , Ranjbar Ali , Azin Reza 
Published: November 2023

Abstract
In this study, the optimization of the location of oil production and injection wells, the number of production and injection wells, and the economic evaluation in the water injection process has been carried out using three algorithms: PSO, GA, and HGAPSO. Optimizing the oil extraction process by optimizing well locations, the optimal number of production and injection wells, and economic evaluation can improve the performance and efficiency of the process. In this study, the optimization of the water injection process into oil reservoirs using the PSO, GA, and HGAPSO algorithms has been addressed. Additionally, economic evaluation, considering costs and profits from oil production using the best-optimized parameters, has been conducted, and the best Net Present Value (NPV) has been calculated. Initially, the optimal well location problem is modeled, taking into account reservoir characteristics and geological conditions. Using the PSO and GA algorithms, the best well location and number of water injection wells have been obtained, leading to improved efficiency and cost reduction. Then, the GA algorithm is used to combine and modify viewpoints and optimize the number and location of wells. This algorithm explores optimal solutions in the well location space. Economic evaluation is performed using the best-optimized parameters by the algorithms. Costs of water injection, oil production costs, and profits from increased oil production are considered. By calculating the best Net Present Value (NPV), optimal decisions regarding the water injection process and enhancing the performance of oil reservoirs are made. Simulation results indicate that the GA algorithm outperforms other algorithms and can be used as a robust and effective method for optimizing well locations and the number of production and injection wells in the oil injection process. Ultimately, this study assists the oil industry in making better decisions about optimizing water injection and improving the efficiency and profitability of the process using the introduced optimization algorithms.
Performance Analysis of SVD and LSB Steganography Data Hiding Techniques: 6G Network Security Survey
Authors: Sr Tarek , Eltokhy Mostafa , Abouelazm Atef E. , El-Bendary Mohsen A. M. 
Published: November 2023

Abstract
The proposed visions for the sixth generation wireless communications network indicate that it will have the ability to transmit massive amounts of data in a very short time. These visions require the sixth-generation network to have a high level of security. This paper focuses on the confidentiality and privacy of data on the sixth-generation wireless communications network. The paper provides a comparison between two techniques of data hiding and showing that may be used with modern security technologies in sixth-generation networks, namely LSBsteganography and SVD watermarking.This comparison between the two technologies is to determine the strength and reliability of each technology in hiding and showing data without affecting it during transmission over the network. This paper provides the comparison by implementing some simulation experiments using these technologies on different types of data such as images, numerical data, and text messages. The simulation results showed the strength and reliability of each technology in hiding and showing data, as well as the impact of each technology on the transmitted data and whether it caused any loss or not.
Shared and different immune molecular signatures of lung transcriptome between connective tissue disease associated and idiopathic pulmonary arterial hypertension
Authors: Huang Kangping , Qian Junyan , Yang Xinzhuang , Ding Yufang , Liu Wenhao , Zhao Jiuliang , Wang Qian , Tian Zhuang , Li Mengtao , Zeng Xiaofeng 
Published: November 2023

Abstract
Background
Pulmonary arterial hypertension (PAH) is one of severe pulmonary circulatory complications and could be idiopathic or associated with connective tissue diseases (CTD). Immune mechanism has been discovered as an important mechanism in both idiopathic PAH (IPAH) and CTD-associated PAH (CTD-PAH). However, differences in immune response between IPAH and CTD-PAH need further exploration. This study compared the immune features of IPAH and CTD-PAH with integrated transcriptomics data from lung tissue.
Methods
Microarray data (GSE48149, GSE113439, and GSE117261) were downloaded from NCBI GEO. Differentially expressed genes (DEGs) were screened by limma package. Functional enrichment analysis and Gene set enrichment analysis (GSEA) were performed. Weighted gene co-expression network analysis (WGCNA) was conducted to screen immune-related module correlated with the two diseases. A specific Protein-Protein Interaction (PPI) network based on screened modules and the most representative pathway with the disease were visualized using Pathview. Potential targeted drug were selected based on selected DEGs and Drugbank database.
Results
Transcriptome data from 19 CTD-PAH patients, 46 IPAH patients and 45 healthy controls (HCs) were collected and integrated. 394 and 207 DEGs were identified in CTD-PAH and IPAH respectively and 111 shared DEGs were found. Inflammatory response were found enriched in shared DEGs, while CTD-PAH-specific pathways were more relevant to innate immune response. WGCNA analysis and following enrichment identified TNF signaling pathway correlated with CTD-PAH and Th1/Th2 differentiation pathway correlated with IPAH. Hub genes found in CTD-PAH included IL6 and CCL2, and in IPAH, CCL5 and IFNG were found to potentially play important roles. Possible drugs targeting discovered genes included adalimumab in CTD-PAH and emapalumab in IPAH.
Conclusions
This study revealed the shared and different characteristics of transcriptome between CTD-PAH and IPAH, particularly in immune response. Our results provide more basis for further research design in the pathophysiologic processes in lung and potential drug discovery.
On the Nonlinearity of the Foreperiod Effect
Authors: Chatroudi Amirmahmoud Houshmand , Mioni Giovanna , Yotsumoto Yuko 
Published: November 2023

Abstract
One of the frequently employed tasks within the implicit timing paradigm is the foreperiod task. The foreperiod is the time interval spanning from the presentation of a warning signal to the appearance of a target stimulus, during which reaction time trajectory follows time uncertainty. While the typical approach in analyzing foreperiod effects is based on linear approximations, the uncertainty in the estimation of time, expressed by the Weber fraction, implies a nonlinear trend. In this study, we analyzed the variable foreperiod reaction times from a relatively large sample (n = 109). We found that the linear regression on reaction times and log-transformed reaction times poorly fitted the foreperiod data. However, a nonlinear regression based on an exponential decay function with three distinctive parameters provided the best fit. We discussed the inferential hazards of a simplistic linear approach and demonstrated how a nonlinear formulation can create new opportunities for studies in implicit timing research, which were previously impossible.
On Categorical Approach to Reaction Systems
Authors: Kaniecki Mariusz , Mikulski Łukasz 
Published: November 2023

Abstract
In every matured theory, there is a need to investigate possible relationships between considered objects. To address this issue, it is natural to relate a category with given model of computing. Thanks to such approach, many properties are unified and simplified. In this paper, we investigate how category theory can be used to give a faithful semantics for reaction systems. In particular, we propose and discuss possible approaches to the problem of defining morphisms between reaction systems. We provide the definition of morphism that keep the behaviour of the original reaction system. Especially, some equivalences of reaction systems are reflected in terms of morphisms. For this purpose we expressed isomorphisms and sections in term of transition systems. Moreover, accelerating morphism defined in the last section give a new approach for including time in reaction system.
Numerical study of the performance of compaction grouting in structured clay using a hypoplastic model
Authors: Diao Yu , Jia Zhenyang , Zhu Pengyu , Zheng Gang , Peng Jun 
Published: November 2023

Abstract

Previous studies on compaction grouting mainly focused on reconstituted clay but did not consider the widespread structure of natural clay. Unlike reconstituted clay, the stress state and void ratio are independent in structured clay due to weathering. Thus S, the state parameter of structured clay, was proposed in this paper to more accurately describe the state of structured clay. Rs, the relative state parameter of structured clay was also proposed to normalize S. Then the numerical hypoplastic constitutive model of structured clay was implemented to evaluate the performance of compaction grouting in structured clay. It shows that the grout efficiency increases with sensitivity when Rs=0 but decreases when Rs=1. Besides, the grout efficiency and grout pressure decrease with increasing Rs. For structured clay with different values of S, there exists a certain value of Rs where the grout efficiency and grout pressure are equivalent to those of the corresponding reconstituted clay. Furthermore, the results were compared with those of heavily overconsolidated clay. It is determined that although negative pore pressure appears in both the horizontal direction and vertical direction in the structured clay when Rs=0, it only appears in the horizontal direction in heavily overconsolidated clay during grouting.
Pharmacological HIF-1 activation upregulates exosome production synergistically with adiponectin through transcriptional induction and protein stabilization of T-cadherin
Authors: Fujii Kohei , Fujishima Yuya , Kita Shunbun , Kawada Keitaro , Fukuoka Keita , Sakaue Taka-aki , Okita Tomonori , Kawada-Horitani Emi , Nagao Hirofumi , Fukuda Shiro , Maeda Norikazu , Nishizawa Hitoshi , Shimomura Iichiro 
Published: November 2023

Abstract
Pharmacological activation of hypoxia-inducible factor 1alpha (HIF-1α), a hypoxia-responsive transcription factor, has attracted increasing attention due to its efficacy not only in renal anemia but also in various disease models. Our study demonstrated that a HIF-1 activator enhanced exosome production from cultured endothelial cells synergistically with adiponectin, an adipocyte-derived factor, through both transcriptional induction and posttranscriptional stabilization of an adiponectin binding partner, T-cadherin. Increased exosome levels were observed in wild-type mice but not in T-cadherin null mice after consecutive administration of roxadustat. Adiponectin- and T-cadherin-dependent increased exosome production may be involved in the pleiotropic effects of HIF-1 activators.
Anomalously acting agents: the deployment problem
Authors: Wenger Ingeborg , Ebel Henrik , Eberhard Peter 
Published: November 2023

Abstract
The detection of intentionally antagonistic behavior in robot swarms brings about challenges that exceed identifying merely erroneous behavior. We investigate a data-based approach to recognize antagonistic behavior in robots executing a deployment task. The task requires a robot swarm of variable size and start positions to optimally distribute within an arbitrary convex surveillance area. Combining a long short-term memory neural network and a normalizing flow, our approach learns to approximate the probability of a robot action. Thus, actions with low probability density values can be categorized as anomalous. The applicability of the proposed approach is validated on simulated runs containing benevolent, antagonistic, and erroneous robots. Both antagonistic and erroneous robots are detected with an accuracy of more than 90 percent.
Identification and functional analysis of proteins in Pomacea canaliculate gut contents: a proteomics analysis
Authors: Li Dan , Liu Jing , Hu Wei , Li Jiang , Liu Lei , Yang Hua 
Published: November 2023

Abstract
Pomacea canaliculate is an invasive pest originating from South America. It has strong adaptability to the environment and can endanger local biodiversity, agricultural production, and human health. P. canaliculate can ingest a wide variety of food by a well-developed digestive and diverse feeding mechanisms. Aiming to target the digestive system as the way to control the spread of P. canaliculate, we conducted quantitative proteomics to comprehensively elucidate the protein composition in the gut contents of P. canaliculate. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) assay suggested that 845 proteins and 5861 unique peptides were identified in the gut contents of P. canaliculate. Among of the proteins, the EF-hand domain-containing proteins are the most abundance proteins. It has been suggested that EF-hand domain-containing proteins may participate in the formation of calcareous eggshell. Additionally, we discussed the structure and function of 11 cellulases detected from the gut contents of P. canaliculate. These cellulases were divided into three groups belonging to the Glycoside hydrolase family 9, Glycoside hydrolase family 10 and Glycoside hydrolase family 45, respectively. Notably, these cellulases play an important role in the digestion of cellulose. These findings put forth novel possibilities for exploring new safe, easy and efficient control measures of P. canaliculate.
EfficientPose: A Lightweight And Efficient Model with Transformer For Human Pose Estimation
Authors: Liang Wei , Cheng Zhang , Wang Yanxia , Han Junjia 
Published: November 2023

Abstract
The current methods for human pose estimation focus on improving the accuracy of prediction results, but they overlook the significant issues of computational cost and large number of parameters in practical deployment.Although some lightweight pose estimation models have successfully in reducing the number of parameters, lightweight models typically employ smaller convolutional kernel to reduce the model size, leading to insufficient capture of contextual information.To address this issue, this paper constructs a lightweight network model EfficientPose.Specifically, to expand the receptive field and acquire richer feature information without increasing computational costs, this paper proposes the Efficient Bottleneck Block (EBB) module.Additionally, to capture global spatial dependencies and enhance the representation capability of low-resolution features, a Transformer encoder is introduced into the model.Meanwhile, to overcome the issue of excessively long training time for lightweight models, a novel iterative training strategy is proposed to fully unleash the potential of EfficientPose.To validate the effectiveness of EfficientPose model, extensive comparative experiments and ablation studies are conducted in this paper.Compared with HRNet-W48, which has the same backbone network, EfficientPose not only reduces the number of parameters by 72\% when the input image size is the same but also improves the accuracy by 0.8 and 0.9 percentage points in the validation and test sets of COCO, respectively.Experiments show that the EfficientPose model can maintain high accuracy even with a significant reduction in the number of parameters.This provides the potential for further application in real-world scenarios with limited resources.