bluuebunny/crossref_metadata_2025 huggingface dataset:
How path integration abilities of blind people change in different exploration conditions
Authors: Shafique Shehzaib , Setti Walter , Campus Claudio , Zanchi Silvia , Del Bue Alessio , Gori Monica
Published: May 2024

For animals to locate resources and stay safe, navigation is an essential cognitive skill. Blind people use different navigational strategies to encode the environment. Path integration significantly influences spatial navigation, which is the ongoing update of position and orientation during self-motion. This study examines two separate things: (i) how guided and non-guided strategies affect blind individuals in encoding and mentally representing a trajectory and (ii) the sensory preferences for potential navigational aids through questionnaire-based research. This study first highlights the significant role that the absence of vision plays in understanding body centered and proprioceptive cues. Furthermore, it also underscores the urgent need to develop navigation-assistive technologies customized to meet the specific needs of users.
Case report: Lymph node metastasis of pelvic alveolar rhabdomyosarcoma diagnosed by fine needle aspiration cytology
Authors: Hu Xinyu , Huang Chenxin , Li Qiyuan , Wu Baolin , Yue Chuyun , Su Xueying
Published: May 2024

Rhabdomyosarcoma (RMS) is a common soft tissue malignant tumor, especially in young patients. Alveolar rhabdomyosarcoma (ARMS) is a subtype of RMS that is prevalent in adolescents. This malignant tumor usually develops in the extremities and can also involve the trunk, perineum, and pelvis. Now, we report a rare case of pelvic lymph node metastatic alveolar RMS in a young patient, which was determined by fine needle aspiration cytology (FNAC). To the best of our knowledge, this is the first case in which the definite diagnosis of ARMS was initially made by FNAC.
Development of an artificial intelligence-based multimodal model for assisting in the diagnosis of necrotizing enterocolitis in newborns: a retrospective study
Authors: Cui Kaijie , Changrong Shao , Maomin Yu , Hui Zhang , Xiuxiang Liu
Published: May 2024

Objective

The purpose of this study is to develop a multimodal model based on artificial intelligence to assist clinical doctors in the early diagnosis of necrotizing enterocolitis in newborns.

Methods

This study is a retrospective study that collected the initial laboratory test results and abdominal x-ray image data of newborns (non-NEC, NEC) admitted to our hospital from January 2022 to January 2024.A multimodal model was developed to differentiate multimodal data, trained on the training dataset, and evaluated on the validation dataset. The interpretability was enhanced by incorporating the Gradient-weighted Class Activation Mapping (GradCAM) analysis to analyze the attention mechanism of the multimodal model, and finally compared and evaluated with clinical doctors on external datasets.

Results

The dataset constructed in this study included 11,016 laboratory examination data from 408 children and 408 image data. When applied to the validation dataset, the area under the curve was 0.91, and the accuracy was 0.94. The GradCAM analysis shows that the model's attention is focused on the fixed dilatation of the intestinal folds, intestinal wall edema, interintestinal gas, and portal venous gas. External validation demonstrated that the multimodal model had comparable accuracy to pediatric doctors with ten years of clinical experience in identification.

Conclusion

The multimodal model we developed can assist doctors in early and accurate diagnosis of NEC, providing a new approach for assisting diagnosis in underdeveloped medical areas.
Emerging biologic frontiers for Sjogren’s syndrome: Unveiling novel approaches with emphasis on extra glandular pathology
Authors: Li Xiao Xiao , Maitiyaer Maierhaba , Tan Qing , Huang Wen Hui , Liu Yu , Liu Zhi Ping , Wen Yue Qiang , Zheng Yu , Chen Xing , Chen Rui Lin , Tao Yi , Yu Shui Lian
Published: May 2024

Primary Sjögren’s Syndrome (pSS) is a complex autoimmune disorder characterized by exocrine gland dysfunction, leading to dry eyes and mouth. Despite growing interest in biologic therapies for pSS, FDA approval has proven challenging due to trial complications. This review addresses the absence of a molecular-target-based approach to biologic therapy development and highlights novel research on drug targets and clinical trials. A literature search identified potential pSS treatment targets and recent advances in molecular understanding. Overlooking extraglandular symptoms like fatigue and depression is a notable gap in trials. Emerging biologic agents targeting cytokines, signal pathways, and immune responses have proven efficacy. These novel therapies could complement existing methods for symptom alleviation. Improved grading systems accounting for extraglandular symptoms are needed. The future of pSS treatment may involve gene, stem-cell, and tissue-engineering therapies. This narrative review offers insights into advancing pSS management through innovative biologic interventions.
Combined effects of electrical muscle stimulation and cycling exercise on cognitive performance
Authors: Ando Soichi , Ishioka Yuka , Kambayashi Sari , Kano Kosuke , Fujibayashi Mami , Costello Joseph T. , Sudo Mizuki
Published: May 2024

The purpose of this study was to investigate whether a combination of electrical muscle stimulation (EMS) and cycling exercise is beneficial for improving cognitive performance. Eighteen participants (7 females and 11 males) performed a Go/No-Go task before and 2 min after i) cycling exercise (EX), ii) a combination of EMS and cycling (EMS + EX) and iii) a control (rest) intervention in a randomized controlled crossover design. In the EX intervention, the participants cycled an ergometer for 20 min with their heart rate maintained at ∼120 beats·min-1. In the EMS + EX intervention, the participants cycled an ergometer simultaneously with EMS for 20 min, with heart rate maintained at ∼120 beats·min-1. In the Control intervention, the participants remained at rest while seated on the ergometer. Cognitive performance was assessed by reaction time (RT) and accuracy. There was a significant interaction between intervention and time (p = 0.007). RT was reduced in the EX intervention (p = 0.054, matched rank biserial correlation coefficient = 0.520). In the EMS + EX intervention, RT was not altered (p = 0.243, Cohen’s d = 0.285) despite no differences in heart rate between the EX and EMS + EX interventions (p = 0.551). RT was increased in the Control intervention (p = 0.038, Cohen’s d = −0.529). These results indicate that combining EMS and cycling does not alter cognitive performance despite elevated heart rate, equivalent to a moderate intensity. The present findings suggest that brain activity during EMS with cycling exercise may be insufficient to improve cognitive performance when compared to exercise alone.
Fine mapping and candidate gene analysis of CRA8.1.6, which confers clubroot resistance in turnip (Brassica rapa ssp. rapa)
Authors: Wei Xiaochun , Xiao Shixiong , Zhao Yanyan , Zhang Luyue , Nath Ujjal Kumar , Yang Shuangjuan , Su Henan , Zhang Wenjing , Wang Zhiyong , Tian Baoming , Wei Fang , Yuan Yuxiang , Zhang Xiaowei
Published: May 2024

Clubroot disease poses a significant threat to Brassica crops, necessitating ongoing updates on resistance gene sources. In F2 segregants of the clubroot-resistant inbred line BrT18-6-4-3 and susceptible DH line Y510, the genetic analysis identified a single dominant gene responsible for clubroot resistance. Through bulk segregant sequencing analysis and kompetitive allele-specific polymerase chain reaction assays, CRA8.1.6 was mapped within 110 kb (12,255–12,365 Mb) between markers L-CR11 and L-CR12 on chromosome A08. We identified B raA08g015220.3.5C as the candidate gene of CRA8.1.6. Upon comparison with the sequence of disease-resistant material BrT18-6-4-3, we found 249 single-nucleotide polymorphisms, seven insertions, six deletions, and a long terminal repeat (LTR) retrotransposon (5,310 bp) at 909 bp of the first intron. However, the LTR retrotransposon was absent in the coding sequence of the susceptible DH line Y510. Given the presence of a non-functional LTR insertion in other materials, it showed that the LTR insertion might not be associated with susceptibility. Sequence alignment analysis revealed that the fourth exon of the susceptible line harbored two deletions and an insertion, resulting in a frameshift mutation at 8,551 bp, leading to translation termination at the leucine-rich repeat domain’s C-terminal in susceptible material. Sequence alignment of the CDS revealed a 99.4% similarity to Crr1a, which indicate that CRA8.1.6 is likely an allele of the Crr1a gene. Two functional markers, CRA08-InDel and CRA08-KASP1, have been developed for marker-assisted selection in CR turnip cultivars. Our findings could facilitate the development of clubroot-resistance turnip cultivars through marker-assisted selection.
Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization
Authors: V. Pandiyaraju , Kumar A. M. Senthil , Praveen Joe I. R. , Venkatraman Shravan , Kumar S. Pavan , Aravintakshan S. A. , Abeshek A. , Kannan A.
Published: May 2024

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.
Combined transcriptome and metabolome analyses reveal the effects of selenium on the growth and quality of Lilium lancifolium
Authors: Jiang Xiaogang , Zhou Wuxian , Li Darong , Wang Hua , Yang Yuying , You Jinwen , Liu Haihua , Ai Lunqiang , Zhang Meide
Published: May 2024

Lilium lancifolium Thunb (L. lancifolium) is an important medicinal and edible plant with outstanding functionality for selenium (Se) biofortification. However, the molecular response of L. lancifolium to exogenous Se has not been fully elucidated. In this study, the effects of different levels of Se on L. lancifolium growth and quality were explored by transcriptome, metabolome and biochemical analyses. The results showed that the total Se and organic Se content in L. lancifolium bulbs increased with increasing Se dosage (0–8.0 mmol/L). Moreover, Se stimulated the growth of L. lancifolium at low level (2.0 mmol/L) but showed an inhibitory effect at high levels (≥4.0 mmol/L). Metabolomic and biochemical analyses revealed that the bulb weight and the content of amino acid, soluble sugar, and soluble protein were significantly increased in the 2.0 mmol/L Se treatment compared with those in the control (0 mmol/L Se). Transcriptome and metabolome analyses revealed that the significant upregulation of the GPD1, GPAT and ADPRM genes promoted glycerophospholipid accumulation. Additionally, the significantly upregulated glyA and downregulated asnB, nadB, thrA and SAT genes coordinate to the regulation of amino acid biosynthesis. The significantly upregulated SUS, bgl B, BAM, and SGA1 genes were involved in soluble sugar accumulation under Se treatment. In summary, this study identified the optimal Se concentration (2.0 mmol/L), which significantly improved the growth and nutritional quality of L. lancifolium and contributed to understanding the combined effects of Se treatment on the expression of genes and the accumulation of metabolites in L. lancifolium bulbs.
Role of parental educational level as psychosocial factor in a sample of inpatients with anorexia nervosa and bulimia nervosa
Authors: Bevione Francesco , Martini Matteo , Longo Paola , Toppino Federica , Musetti Alessandro , Amodeo Laura , Abbate-Daga Giovanni , Panero Matteo
Published: May 2024

Introduction

Evidence on parental educational level (PEL) as a risk factor for Eating Disorders (EDs) is mixed, and no study has assessed its role in relation to the compliance and outcomes of treatments in EDs. Further, no study differentiated from the educational level of mothers and fathers, nor considered the possible mediation of perfectionism in fostering EDs.

Methods

A clinical sample of 242 first-ever admitted inpatients with EDs provided information on PEL and completed the following questionnaires: the Eating Disorder Examination Questionnaire (EDE-Q) and the Frost Multidimensional Perfectionism Scale (F-MPS). Clinicians also provided information on the Hamilton Rating Scale for Anxiety (HAM-A) and the Hamilton Rating Scale for Depression (HAM-D) for each participant.

Results

Individuals with high PEL (whether mothers, fathers, or both parents) showed significantly higher scores on depressive symptoms and lower on parental criticism, were younger, had an earlier age of onset, had fewer years of illness, more were students and employed, and fewer had offspring. Individuals with fathers or both parents with high educational levels suffered more from Anorexia Nervosa rather than Bulimia Nervosa, had a longer length of stay during the current hospitalization, had less dietary restraint, and had higher personal standards. Individuals with mothers with high educational levels showed a lower rate of previous substance or alcohol addiction. Personal standards partially mediated the relationship between higher PEL and lower dietary restraint.

Discussion

PEL emerged to be a twofold psychosocial risk factor, being associated with higher depressive symptoms and a longer length of stay, but also with a shorter duration of illness and better scholar and working involvement. Higher PEL was related to higher personal standards but not to global perfectionism. Patterns of eating psychopathology emerged based on the high PEL of mothers or fathers.
Traditional Chinese medicine health literacy among rural older adults: a cross-sectional study
Authors: Zhang Huizhong , Zhang Yan , Yan Yuwen , Li Xizheng , Tian Yutong
Published: May 2024

Background

The purpose of this study was to understand the current status of traditional Chinese medicine (TCM) health literacy among rural older adults people and its influencing factors.

Methods

This study used a random number table method to select one prefecture from Henan Province, China and used a convenience sampling method to select 200 rural older adults who met the inclusion criteria in a township in northern Henan from March to April 2023. A cross-sectional survey was conducted using a general information questionnaire and a traditional Chinese medicine health literacy questionnaire, and the influencing factors of rural older adults were analyzed using univariate analysis of variance (ANOVA) and multiple linear regression.

Results

The total TCM health literacy score of 200 rural older adults people was 84.14 ± 6.709. One-way ANOVA revealed that six factors, including education level, marital status, type of occupation, presence of family members engaged in medical-related work, main economic sources, and monthly income, influenced the TCM health literacy score of rural older adults people (p < 0.05). Multiple linear regression analysis revealed that education level, occupation type, and the presence of family members engaged in medical-related work were the factors influencing the TCM health literacy of rural older adults.

Conclusion

The TCM health literacy level of rural older adults people is at the lower to middle level, and health educators should strengthen the publicity and education of TCM healthcare knowledge for rural older adults people to improve their TCM health literacy level and thus enhance their quality of life.
Dynamic and Integrated Security Model in Inter Cloud for Image Classification
Authors: Shah Ansar Munir , Abid Itrat
Published: May 2024

Cloud computing has transformed software and database accessibility, utilizing the Internet and server hosting. However, security risks arise, including malware attacks and website hacking. To address these challenges, deep learning models like ResNet50 have been developed. Trained on encrypted images, ResNet50 enhances the speed and accuracy of image recognition, enabling the identification of hidden data without decryption. Despite inter-cloud communication issues, cloud servers prioritize data security, user privacy, and integrity maintenance. The ResNet50 model exhibits impressive performance, achieving 99.5%accuracy and precision-recall scores of 99.5% and 99.5% using the ImageNet Dataset. Cloud computing offers significant advantages, but data security remains a critical concern. Encrypted image recognition powered by deep learning models offers efficient and private solutions. Cloud providers continually strive to improve inter-cloud communication, ensuring comprehensive protection for data and system integrity. The remarkable capabilities of ResNet50 highlight its potential in encrypted image analysis tasks.
Navigating Autonomy, Competence, and Relatedness: Insights from Middle Managers in Norway
Authors: Spieler Kristin Severinsen
Published: May 2024

Middle managers play a pivotal role in bridging the gap between senior leadership and employees, often navigating competing demands and pressures. This study investigates experiences of autonomy, competence, and relatedness among middle managers serving as department heads in the University and University College (UUC) sector in Norway. The study adopts a qualitative approach in the form of semi-structured interviews with six participants. The findings underscore the significance of autonomy, trust, and support in facilitating the effective execution of middle managers’ roles as executive and inclusive leaders. Autonomy emerges as crucial, which aligns with the principles of the Nordic work–life model. Furthermore, the study highlights the importance of internal motivation and the support provided by the immediate leadership in enhancing middle managers’ performance. Personal competence in one’s subject areas and relatedness emerge as key factors ensuring employee confidence and fostering a positive work environment. The implications of these findings suggest that nurturing autonomy, competence, and relatedness may mitigate the perceived stress associated with being a middle manager in the UUC sector. By addressing these fundamental needs, organisations can potentially enhance the well-being and effectiveness of middle managers, ultimately contributing to organisational success.
Numerical Calculation of Gas–Liquid Two-Phase Flow in Tesla Valve
Authors: Gong Jie , Li Guohua , Liu Ran , Wang Zijuan
Published: May 2024

In this paper, the gas–liquid two-phase flow within a Tesla valve under zero-gravity conditions is numerically studied. Based on the VOF model and the inlet two-phase separation method, the forward and reverse flow patterns and pressure drop changes in a Tesla valve at different inlet velocities were analyzed. At an inlet velocity of 0.1–0.2 m/s, the flow pattern was slug flow, the bubbles were evenly distributed in different positions in the Tesla valve, and the velocity difference between the main pipe and the arc branch pipe was small. When the inlet velocity was 0.4 m/s, the main flow pattern was annular flow, and there was a phenomenon of gas–liquid phase separation through different flow channels, which was related to centrifugal force. At an inlet velocity of 0.6–0.8 m/s, bubbly flow and slug flow coexisted, which was related to the uneven velocity. In the study range, the difference in the forward and reverse pressure drops of two-phase flow was smaller than that of single-phase flow, and the two-phase diodicity decreased first and then increased with the change in inlet velocity, reaching minimum values of 0.78 at 0.2 m/s and 1.44 at 0.8 m/s.
Lightweight-Improved YOLOv5s Model for Grape Fruit and Stem Recognition
Authors: Zhao Junhong , Yao Xingzhi , Wang Yu , Yi Zhenfeng , Xie Yuming , Zhou Xingxing
Published: May 2024

Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model’s computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model’s generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting.
Preliminary Results of the Impact of Beneficial Soil Microorganisms on Okra Plants and Their Polyphenol Components
Authors: Almuslimawi Alaa Abdulkadhim A. , László Lívia , Sahad Alhassani Leith , Yousif Ahmed Ibrahim Alrashid , Turóczi György , Posta Katalin
Published: May 2024

Okra (Abelmoschus esculentus L.) is a highly nutritious vegetable rich in vitamins, minerals, and bioactive compounds, including polyphenols, offering numerous health benefits. Despite its nutritional value, okra remains underutilized in Europe; however, its cultivation and popularity may rise in the future with increasing awareness of its advantages. In agricultural practices, beneficial soil microorganisms, such as arbuscular mycorrhizal fungi (AMF), Trichoderma spp., Streptomyces spp., and Aureobasidium spp., play crucial roles in promoting plant health, enhancing agricultural productivity together with improved crop nutritional value. This study aimed to investigate the effects of individual and combined inoculation on the polyphenol content of okra fruits, as analyzed by HPLC. Moreover, growth parameters and glutathione-S-transferase enzyme (GST) activities of okra leaves were also estimated. Tested microorganisms significantly increased the yield of okra plants except for A. pullulans strain DSM 14950 applied individually. All microorganisms led to increased GST enzyme activity of leaves, suggesting a general response to biotic impacts, with individual inoculation showing higher enzyme activity globally compared to combined treatments. According to the polyphenol compound analysis, the application of tested microorganisms held various but generally positive effects on it. Only the combined treatment of F. mosseae and Streptomyces strain K61 significantly increased the coumaric acid content, and the application of Aureobasidium strain DSM 14950 had a positive influence on the levels of quercetin and quercetin-3-diglucoside. Our preliminary results show how distinct polyphenolic compound contents can be selectively altered via precise inoculation with different beneficial microorganisms.
The Effect of Light Intensity on the Chlorogenic Acid Biosynthesis Pathway in Marsdenia tenacissima
Authors: Meng Hengling , Li Ying , Lu Bingyue , Zhang Wei , Shi Xian , Fu Hongbo , Long Guangqiang
Published: May 2024

The goal of this study was to understand the effect of light intensity on the chlorogenic acid content and biosynthesis-related gene expression in Marsdenia tenacissima. In this study, M. tenacissima plants were treated with different light intensities; the chlorogenic acid content was determined by high-performance liquid chromatography; and transcriptome sequencing was performed. The amount of chlorogenic acid in the control was the highest and differed significantly from that under three different shading treatments. With a decrease in light intensity, the content of chlorogenic acid also showed a decreasing trend. A total of 1149 differentially expressed genes were identified by transcriptome sequencing, and most of the genes were down-regulated under the 90% shading treatment. A weighted gene co-expression network analysis identified the differentially expressed genes associated with light-induced chlorogenic acid biosynthesis. The different shading treatments down-regulated the expression of the chlorogenic acid biosynthesis pathway structural genes (HCTs). The MIKC family genes were the main transcription factors regulating light-induced chlorogenic acid biosynthesis, but the MYB and SBP family genes were also involved. In summary, combined physiological and transcriptome analysis, candidate structural genes, and transcription factors in the biosynthesis pathway of chlorogenic acid were identified in M. tenacissima.
Assessment of Phthalate Esters and Physiological Biomarkers in Bottlenose Dolphins (Tursiops truncatus) and Killer Whales (Orcinus orca)
Authors: Lemos Leila S. , Di Perna Amanda C. , Steinman Karen J. , Robeck Todd R. , Quinete Natalia S.
Published: May 2024

There is growing concern about the potential adverse health effects of phthalates (PAEs) on human health and the environment due to their extensive use as plasticizers and additives in commercial and consumer products. In this study, we assessed PAE concentrations in serum samples from aquarium-based delphinids (Tursiops truncatus, n = 36; Orcinus orca, n = 42) from California, Florida, and Texas, USA. To better understand the physiological effects of phthalates on delphinids, we also explored potential correlations between phthalates and the biomarkers aldosterone, cortisol, corticosterone, hydrogen peroxide, and malondialdehyde while accounting for sex, age, and reproductive stage. All PAEs were detected in at least one of the individuals. ΣPAE ranges were 5.995–2743 ng·mL−1 in bottlenose dolphins and 5.372–88,675 ng·mL−1 in killer whales. Both species displayed higher mean concentrations of DEP and DEHP. PAEs were detected in newborn delphinids, indicating transference via placenta and/or lactation. Linear mixed model results indicated significant correlations between aldosterone, month, location, status, and ΣPAEs in killer whales, suggesting that aldosterone concentrations are likely affected by the cumulative effects of these variables. This study expands on the knowledge of delphinid physiological responses to PAEs and may influence management and conservation decisions on contamination discharge regulations near these species.
Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting
Authors: Li Ximing , Liang Zhicai , Zhuang Yitao , Wang Zhe , Zhang Huan , Gao Yuefang , Guo Yubin
Published: May 2024

Fish fry counting has been vital in fish farming, but current computer-based methods are not feasible enough to accurately and efficiently calculate large number of fry in a single count due to severe occlusion, dense distribution and the small size of fish fry. To address this problem, we propose the deconvolution enhancement keypoint network (DEKNet), a method for fish fry counting that features a single-keypoint approach. This novel approach models the fish fry as a point located in the central part of the fish head, laying the foundation for our innovative counting strategy. To be specific, first, a fish fry feature extractor (FFE) characterized by parallel dual branches is designed for high-resolution representation. Next, two identical deconvolution modules (TDMs) are added to the generation head for a high-quality and high-resolution keypoint heatmap with the same resolution size as the input image, thus facilitating the precise counting of fish fry. Then, the local peak value of the heatmap is obtained as the keypoint of the fish fry, so the number of these keypoints with coordinate information equals the number of fry, and the coordinates of the keypoint can be used to locate the fry. Finally, FishFry-2023, a large-scale fish fry dataset, is constructed to evaluate the effectiveness of the method proposed by us. Experimental results show that an accuracy rate of 98.59% was accomplished in fish fry counting. Furthermore, DEKNet achieved a high degree of accuracy on the Penaeus dataset (98.51%) and an MAE of 13.32 on a public dataset known as Adipocyte Cells. The research outcomes reveal that DEKNet has superior comprehensive performance in counting accuracy, the number of parameters and computational effort.
Carcass, Egg Characteristics and Leg Bone Dimensions of Pigeons of Different Origin
Authors: Stęczny Kamil , Kokoszyński Dariusz , Włodarczyk Karol , Arpášová Henrieta , Gondek Michalina , Saleh Mohamed , Wegner Marcin , Kądziołka Kamil
Published: May 2024

In the past, studies have been conducted on the evaluation of meat traits of pigeons, but the knowledge obtained is incomplete and needs to be expanded. The purpose of this study was to obtain information on the weight and proportion of carcass elements, femur and tibia bone dimensions, and egg characteristics of meat of King breed and carrier pigeons. For this study, 16 carcasses of carrier pigeons and 16 carcasses of King pigeons were used, with 8 carcasses of males and 8 carcasses of females of each breed. Additionally, 20 eggs evaluated were from carrier pigeons and 20 eggs from King breed pigeons. The carcasses and eggs were obtained from birds that were 12 months old. The compared pigeon breeds differed (p < 0.05) significantly in terms of the weight of the eviscerated carcass with the neck; the content of neck, wings, pectoral and leg muscles in the carcass; as well as in terms of all specified dimensions of tibia and femur length and width. The origin of the pigeons had an effect (p < 0.05) on egg weight and dimensions, egg index, and the other studied egg traits, with the exception of eggshell weight and eggshell yellowness, yolk weight, yolk height, yolk diameter, and yolk index. So far, there have been no studies comparing carrier pigeons and King breed pigeons in terms of femur and tibia bone dimensions, morphological composition and egg dimensions, and egg content traits, which adds to the knowledge in this area and indicates the need for continuation in the future.
Examining the Alterations in Metabolite Constituents and Antioxidant Properties in Mountain-Cultivated Ginseng (Panax ginseng C.A. Meyer) Organs during a Two-Month Maturation Period
Authors: Lee Hee Yul , Cho Du Yong , Kim Du Hyun , Park Jong-Hwan , Jeong Jong Bin , Jeon Se Hyeon , Lee Ji Ho , Ko Eun Jeong , Cho Kye Man , Lee Jin Hwan
Published: May 2024

The current research was the first to prove the existence of fluctuations in the metabolite constituents and antioxidant properties in different organs (leaves, stems, and roots) of the mountain-cultivated ginseng (MCG) plant during a two-month maturation period. Four metabolites, including fatty acids, amino acids, ginsenosides, and phenolic phytochemicals, exhibited considerable differences in organs and maturation times with the following order: leaves > stems > roots. The predominant metabolite contents were found in leaves, with fatty acid (1057.9 mg/100 g) on 31 May, amino acid (1989.2 mg/100 g) on 13 July, ginsenosides (88.7 mg/g) on 31 May, and phenolic phytochemical (638.3 μg/g) on 31 May. Interestingly, ginsenoside content in leaves were highest, with 84.8 → 88.7 → 82.2 → 78.3 mg/g. Specifically, ginsenosides Re, Rd, and F2 showed abundant content ranging from 19.1 to 16.9 mg/g, 8.5 to 14.8 mg/g, and 9.5 to 13.1 mg/g, respectively. Phenolic phytochemicals exhibited remarkable differences in organs compared to maturation periods, with the highest total phenolic content and total flavonoid content recorded at 9.48 GAE and 1.30 RE mg/g in leaves on 31 May. The antioxidant capacities on radical, FRAP, and DNA protection differed significantly, with leaves on 31 May exhibiting the highest values: 88.4% (DPPH), 89.5% (ABTS), 0.84 OD593 nm (FRAP) at 500 μg/mL, and 100% DNA protection at 50 μg/mL. Furthermore, principal cluster analysis revealed metabolite variability as follows: ginsenoside (83.3%) > amino acid (71.8%) > phenolic phytochemical (61.1%) > fatty acid (58.8%). A clustering heatmap highlighted significant changes in metabolite components under the maturation times for each organ. Our findings suggest that MCG leaves on 31 May may be a potential source for developing nutraceuticals, offering highly beneficial components and strong antioxidants.
Analyzing the Effect of Tethered Cable on the Stability of Tethered UAVs Based on Lyapunov Exponents
Authors: Tang Zhiren , Liu Chaofeng , Jiang Hongbo , Hou Feiyu , Wang Shenglan
Published: May 2024

In the working process of the tethered unmanned aerial vehicle (UAV), there is interference from the tethered cable, which can easily lead to the instability of the UAV. To solve the above problems, a method based on the Lyapunov exponent is proposed to analyze the stability of tethered cables for tethered UAVs. The dynamics equation of the UAV platform is established using the Euler–Poincare equation. The tension formula of the tethered cable is derived from the catenary theory and the principle of micro-segment equilibrium. Based on the Lyapunov exponential method, the stability changes of the tethered UAV in the takeoff, hovering, and landing stages are simulated and analyzed in a MATLAB environment. Prototype tests are carried out to prove the correctness of the simulation model and calculation conclusions. The results show that with an increase in the density of the tethered cable, the stability of the tethered UAV tends to decrease. At the same time, stability is affected by the density of the tethered cable more often during takeoff than during landing.
Association between Biochemical Parameters, Especially Hydration Status and Dietary Patterns, and Metabolic Alterations in Polish Adults with Metabolic Syndrome
Authors: Frąckiewicz Joanna , Białkowska Agnieszka , Drywień Małgorzata Ewa , Hamulka Jadwiga
Published: May 2024

It is important to understand which factors are central to the development of metabolic syndrome as the burden of the condition increases (MetS). The aim of this study was to search for associations between the frequency of non-alcoholic beverage consumption (FFQ), blood pressure, anthropometric measurements, biochemical parameters related to health and hydration status, and the number of MetS components in 290 adults diagnosed with metabolic disorders. Blood pressure and anthropometric measurements: body weight (BW), height (H), waist circumference (WC), handgrip strength (HGS), and total body water (TBW) were measured. Blood and urine samples were collected. We observed the highest frequency of consumption of tea drinks in women with four MetS components and fruit and vegetable juices in men with five MetS components. The highest systolic blood pressure (SBP) and BMI were found with five MetS components and the lowest TBW with three MetS components in both sexes. The lowest uric acid and urea were observed in women with three MetS components, while the lowest pH with five MetS components and the highest urine-specific gravity (USG) with four MetS components were observed in men. These findings highlight the need to focus on elucidating the relationship between diet, including beverage consumption, blood pressure, anthropometric measurements and biochemical parameters, and health and hydration status in adults with metabolic disorders in further research.
A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells
Authors: Lee Jae-Hyeon , Kim Jung-Eun , Lee Eun-Ok , Lee Hyo-Jeong
Published: May 2024

Prostate cancer is frequently diagnosed and the leading cause of death in men worldwide. Prostate-specific antigen (PSA) blood tests and biopsies are the primary methods for diagnosing prostate cancer; however, their accuracy is less than 50%. Therefore, there is a need to develop diagnostic tests that minimize patient discomfort during examination and adequate biomarkers that are more accurate, sensitive, and specific for the detection of prostate cancer. This study investigated the application of metabolomics to identify biomarkers in prostate cancer biofluids. In addition, changes in prostate cancer metabolite levels induced by chrysin, a natural anticancer compound, were evaluated and compared with those in non-treated prostate cancer cells. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling was performed to investigate the differences in metabolic alterations among prostate cancer, normal prostate, and chrysin-treated prostate cancer cells. Pairwise comparisons of the extracellular fluid metabolomes were performed using principal component analysis (PCA), partial least squares–discriminant analysis (PLS-DA), and Student’s t-test. The results revealed significantly different patterns among the metabolite groups, including alcohols, amino acids, carboxylic acids, organic acids, sugars, and urea. The RWPE-1- and chrysin-treated PC-3 (PC-3 Chr) cell groups showed similar tendencies for 23 metabolites, while the groups showed significant differences from the PC-3 group. Most amino acids showed higher concentrations in PC-3 cells than in the normal cell line RWPE-1 cells and PC-3 Chr cells. Our results revealed that GC-MS might be an effective diagnostic tool to detect prostate cancer and contribute to finding new tumor markers for prostate cancer as the basis for new ideas.
Analysis and Optimization of the Performances of the Tandem Blade Radial Compressor Using the CFD
Authors: Soylemez Mustafa Erturk , Behçet Rasim , Parlak Zekeriya
Published: May 2024

Centrifugal compressors are frequently used in both military and commercial areas because they can be easily manufactured and reach high-pressure compression ratios. The factor that limits the performance and operating range of compressors is flow instability. Many ideas have been put forward for performance improvement, but tandem blade radial compressors, which do not require an extra air system, have attracted the most interest. In this study, computational fluid dynamics (CFD) analyses were carried out on various parameters of the tandem blade (TB) radial compressor, and an optimization study was carried out to find the best design using a genetic algorithm on a whole operating curve. It was investigated how these parameters affected the efficiency and total pressure ratio between the determined lower and upper limits. The numerical analyses of the optimum design obtained as a result of the iterations were carried out. As a result of the iterations, three optimum designs were obtained and numerical analysis was carried out according to one of them, and then they were compared with the results in the literature. The general agreement of computational fluid dynamics and the literature data served as a validation for the computational approach. The error rates between the numerical analysis results and the experimental results in the literature were calculated for different flow rates and were found to be 1.98% as the highest and 0.35 as the lowest. The work carried out in this article will provide a valuable reference for future advanced tandem blade compressor designs.
RQ-OSPTrans: A Semantic Classification Method Based on Transformer That Combines Overall Semantic Perception and “Repeated Questioning” Learning Mechanism
Authors: Tan Yuanjun , Liu Quanling , Liu Tingting , Liu Hai , Wang Shengming , Chen Zengzhao
Published: May 2024

The pre-trained language model based on Transformers possesses exceptional general text-understanding capabilities, empowering it to adeptly manage a variety of tasks. However, the topic classification ability of the pre-trained language model will be seriously affected in the face of long colloquial texts, expressions with similar semantics but completely different expressions, and text errors caused by partial speech recognition. We propose a long-text topic classification method called RQ-OSPTrans to effectively address these challenges. To this end, two parallel learning modules are proposed to learn long texts, namely, the repeat question module and the overall semantic perception module. The overall semantic perception module will conduct average pooling on the semantic embeddings produced by BERT, in addition to multi-layer perceptron learning. The repeat question module will learn the text-embedding matrix, extracting detailed clues for classification based on words as fundamental elements. Comprehensive experiments demonstrate that RQ-OSPTrans can achieve a generalization performance of 98.5% on the Chinese dataset THUCNews. Moreover, RQ-OSPTrans can achieve state-of-the-art performance on the arXiv-10 dataset (84.4%) and has a comparable performance with other state-of-the-art pre-trained models on the AG’s News dataset. Finally, the results indicate that our method exhibits a superior performance compared with the baseline methods on small-scale domain-specific datasets by validating RQ-OSPTrans on a specific task scenario by using our custom-built dataset CCIPC.
Enhancing Patient Flow in Emergency Departments: A Machine Learning and Simulation-Based Resource Scheduling Approach
Authors: Kim Jae-Kwon
Published: May 2024

The efficient scheduling of resources within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources. Reducing patient wait times can enhance the operation of emergency departments and improve patient satisfaction and the quality of medical care. This study develops a simulation model using Discrete Event Simulation (DES) methodology, examining six resource scheduling policies that consider different combinations of general and senior physicians. By leveraging six scheduling policies and machine learning techniques, this model dynamically identifies the most effective scheduling policy, based on a comprehensive dataset of ED visits in South Korea. The ED simulation achieves an accuracy rate of 90% and demonstrates that our proposed integrated machine learning approach reduces average length of stay (LoS) to approximately 322.91 min, compared to 327.10 min under traditional methods. This study underscores the potential of integrating DES and machine learning to enhance resource management in EDs.
Multi-Modal Low-Data-Based Learning for Video Classification
Authors: Citak Erol , Karsligil Mine Elif
Published: May 2024

Video classification is a challenging task in computer vision that requires analyzing the content of a video to assign it to one or more predefined categories. However, due to the vast amount of visual data contained in videos, the classification process is often computationally expensive and requires a significant amount of annotated data. Because of these reasons, the low-data-based video classification area, which consists of few-shot and zero-shot tasks, is proposed as a potential solution to overcome traditional video classification-oriented challenges. However, existing low-data area datasets, which are either not diverse or have no additional modality context, which is a mandatory requirement for the zero-shot task, do not fulfill the requirements for few-shot and zero-shot tasks completely. To address this gap, in this paper, we propose a large-scale, general-purpose dataset for the problem of multi-modal low-data-based video classification. The dataset contains pairs of videos and attributes that capture multiple facets of the video content. Thus, the new proposed dataset will both enable the study of low-data-based video classification tasks and provide consistency in terms of comparing the evaluations of future studies in this field. Furthermore, to evaluate and provide a baseline for future works on our new proposed dataset, we present a variational autoencoder-based model that leverages the inherent correlation among different modalities to learn more informative representations. In addition, we introduce a regularization technique to improve the baseline model’s generalization performance in low-data scenarios. Our experimental results reveal that our proposed baseline model, with the aid of this regularization technique, achieves over 12% improvement in classification accuracy compared to the pure baseline model with only a single labeled sample.
Static Spherically Symmetric Perfect Fluid Solutions in Teleparallel F(T) Gravity
Authors: Landry Alexandre
Published: May 2024

In this paper, we investigate static spherically symmetric teleparallel F(T) gravity containing a perfect isotropic fluid. We first write the field equations and proceed to find new teleparallel F(T) solutions for perfect isotropic and linear fluids. By using a power-law ansatz for the coframe components, we find several classes of new non-trivial teleparallel F(T) solutions. We also find a new class of teleparallel F(T) solutions for a matter dust fluid. After, we solve the field equations for a non-linear perfect fluid. Once again, there are several new exact teleparallel F(T) solutions and also some approximated teleparallel F(T) solutions. All these classes of new solutions may be relevant for future cosmological and astrophysical applications.