bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the neutral atmosphere inversion results of multi-system GNSS occultation in Yunyao Satellite Constellation
Authors: Yue Hengyi , Fu Naifeng , Li Fenghui , Cheng Yan , Wu Mengjie , Guo Peng , Dong Wenli , Hu Xiaogong
Published: August 2024

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time-resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS and Galileo multi-system occultation profile products on a daily basis. This study independently calculates the precise orbit of LEO satellites using a separate system and inverses the neutral atmospheric refractive index profile product. The precision of the orbit product was evaluated by comparing carrier phase residuals and overlapping orbits. Simultaneously, the global distribution, local time distribution, and detection depth of the constellation were statistically analyzed. BDS has the highest number of contours and the best detection capability among the three GNSS systems.The refractive index profile product was also compared with the ECWMF ERA5 product from ECWMF. Near the top of the occultation, the standard deviation in the refractive index is higher for BDS than GPS and GLONASS. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services for applications such as weather forecasting. With the successful establishment of the global BDS-3 network, the accuracy of space signals has been further improved, making the full-link occultation products of BDS superior to those of GPS and GLONASS.
Prediction and evaluation of the risk of Stroke-associated pneumonia using an artificial neural network model
Authors: Wang Ting , Li Chunbiao , Yuan Linli , You Min , Yuan Juan , Peng Sijing , Liu Yi , Yang Min , Fan Yaling , Tong Qinsi , Li Dajin
Published: August 2024

Objective
This study developed a predictive model for the risk of stroke-associated pneumonia (SAP) based on an advanced artificial neural network (ANN) model.
Methods
Using clinical and laboratory data collected from a cohort of 456 hospital patients from July 2017 to April 2022, we constructed ANN and logistic regression (LR) models. The models were trained on a randomly selected group of 292 patients, and subsequent model validation and testing were carried out on two separate sets of 82 patients each. The predictive performances of both models were evaluated using a comprehensive range of statistical indices.
Results
During dataset partitioning, the 24 variables across the training, validation, and test sets displayed no significant discrepancies. The prediction performance of the ANN model was better than that of the LR model. When applied to the test cohort, the ANN model had a sensitivity of 83.53% and a specificity of 85.18%. Comparative analysis revealed discernible discrepancies between the performance indexes of the ANN and LR models. Based on the receiver operating characteristic curve, the ANN model showed robust ability to identify SAP, with an area under the curve value of 0.920. The principal independent predictors in the model were serum albumin, activities of daily living score, hemoglobin level, and hypersensitive C-reactive protein level.
Conclusions
The developed ANN model demonstrates promising predictive capability for assessing the risk of SAP. However, further verification with larger and more diverse datasets is needed to confirm its utility as a tool for clinical prediction.
Efficient tetracycline degradation using arbon quantum dot-modified TiO 2 @LaFeO 3 hollow core-shell photocatalysts
Authors: Hao Pengcheng , Shi Rui , Wang Xuanhang , Zhang Juan , Li Bo , Wang Jing , Liu Bo , Liu Yayuan , Qiao Xin , Wang Zhongzhi
Published: August 2024

Efficient harnessing of solar energy presents a significant challenge in environmental cleanup efforts. This study developed a highly effective carbon quantum dots-modified hollow core-shell CQDs@TiO2@LaFeO3 (CDs-TLFO) heterojunction photocatalyst. Structural analysis confirmed that nanosheets were loaded with CQDs, forming a TLFO structure with close interconnections and a hollow core-shell design. Photocatalytic experiments revealed that CDs-TLFO degraded tetracycline hydrochloride (TC) 2.02 times faster than TLFO alone, and significantly outperformed h-TiO2 and LaFeO3 (11.28 and 2.78 times, respectively). This enhancement is attributed to CQDs acting as electron acceptors with upconversion properties, enhancing the separation of e–-h+ pairs and boosting visible light absorption. Integration of CQDs onto the TLFO surface created numerous active sites and enhanced visible light absorption. LC-MS analysis identified intermediates and proposed potential TC degradation pathways, shedding light on the photocatalytic mechanism. This research introduces promising avenues for environmental cleanup and offers a straightforward, energy-efficient, and environmentally friendly method for producing CDs-TLFO heterojunction materials with superior photocatalytic capabilities.
Animal pollination shapes fruits market features, seeds functional traits and modulates their chemistry
Authors: Pioltelli Emiliano , Guzzetti Lorenzo , Biella Paolo , Sala Davide , Copetta Andrea , Mussano Paolo , Galimberti Andrea , Labra Massimo
Published: August 2024

In this study, we experimentally addressed the impact of different pollination treatments on the morphological, reproductive, and chemical traits of fruits and seeds of two crop species, the wild strawberry (Fragaria vesca L.) and cowpea (Vigna unguiculata L. Walp.). Multiple flowers from each plant were exposed to different pollination treatments: i) self pollination, ii) hand cross pollination and iii) open pollination. Both the crops were positively affected by open pollination in terms of morpho-chemical parameters concerning the marketability (e.g., 35% decrease in sugar/acid ratio in open pollinated strawberries compared to the autogamous ones) and the seed germination rate as a proxy of reproduction efficiency (e.g., the almost complete absence of seed abortion in the open pollination treatment). Remarkably, the pollination treatment also strongly influenced the phytochemical composition. Open-pollinated strawberries exhibited a higher relative concentration of compounds endowed with nutraceutical properties such as anthocyanins, ellagic acid derivatives, and flavonoids. At the same time, cowpea seeds displayed higher concentrations of anti-nutrients in the self pollination treatments, such as saponins, compared to the open and hand cross pollinated seeds. This study suggests the presence of a link between the pollination mechanism, market quality, plant reproduction and chemical properties of fruits and seeds, supporting the intricate interplay between pollinators, plants, and human nutrition and highlighting the crucial importance of animal pollination in ecological and dietary contexts.
From Infection to Inflammation: A Transcriptome-Wide Analysis of CHIKV Impact on Primary Human Skeletal Muscle Cells
Authors: Adnan Nur Amelia , Kalam Nida , Komarasamy Thamil Vaani , Hasan Syed Sharifah , Balasubramaniam Vinod RMT
Published: August 2024

Chikungunya virus (CHIKV) is a single-stranded RNA virus belonging to the Togaviridae family and Alphavirus genus. It causes chikungunya fever, transmitted by Aedes mosquitoes, and is characterized by severe fever, joint pain, rash, headaches, and muscle pain lasting days to weeks. In some cases, chronic arthritis resembling rheumatoid arthritis can develop post-infection. Currently, there's no specific antiviral treatment for CHIKV, with supportive care being the main treatment approach. Chronic arthritis management includes pain relief and preventing joint damage with NSAIDs, among other medications. The role of inflammatory cytokines and chemokines in CHIKV infection, which contribute to symptom severity, parallels inflammatory arthritis, suggesting shared pathology. Our study used human primary muscle cells to explore CHIKV infection and its effects, observing a rise in proinflammatory cytokines 24 hours post-infection. This supports the connection between CHIKV infection and arthritis symptoms. The research established these cells as susceptible to CHIKV, with peak viral levels at 24 hours post-infection. RNA sequencing (RNA-Seq) was employed to analyze gene expression changes due to CHIKV, revealing gene alterations associated with skeletal and muscle disorders, immune response, cell growth and death, and viral replication. This study enhances understanding of CHIKV's pathogenic mechanisms using a clinically relevant human cell model, providing the groundwork for future research into CHIKV’s interactions with host cells during different infection stages.
Early Diagnosis of Mild Autism Spectrum Disorder and Its Relationship With Self-efficacy Beliefs
Authors: Cerqueira H. F , Santana S. M , Cerqueira C. A
Published: August 2024

Objective: To establish a correlation between perceived self-efficacy and early diagnosis of mild ASD, emphasizing the importance of early screening and diagnostic feedback in childhood for fostering individual development and societal adaptation.
Method: The sample was recruited through extensive outreach among mental health professionals in the private mental health network in Recife, Northeast Brazil. Inclusion criteria required participants to have suspected or diagnosed mild Autism Spectrum Disorder (ASD) with an Intelligence Quotient (IQ) above 80. Exclusion criteria included failure to confirm the diagnosis using the ADOS-2 test (Autism Diagnostic Observation Schedule, Second Edition). Data was collected using a sociodemographic questionnaire. Thirty-four participants of both genders, aged 11 to 55, were selected. Additionally, the General Self-Efficacy Scale (GSE) was administered. Two participants were excluded due to insufficient ADOS-2 diagnostic scores. Cronbach's alpha was calculated to assess the instrument's internal consistency. The correlation between variables (perceived self-efficacy and age at diagnosis) was analyzed using Spearman's and Kendall's correlation coefficients, along with respective statistical significance tests.
Results: Kendall's and Spearman's correlation coefficients both indicated a significant positive correlation.
Conclusion: As hypothesized, early diagnosis of ASD correlates positively with higher perceived general self-efficacy in adulthood.
An integrated generalized chordal picture fuzzy distance measure with its application to evaluate teaching quality
Authors: Wang Weiwei , Cao Guo , Shen Lixiang
Published: August 2024

Deepening the integration of industry and education (IIE) in classroom teaching has important strategic significance for the connotative development of higher education. The classroom teaching quality assessment is an important route to improve the level of the IIE and then promote the quality of talent training. Considering that picture fuzzy sets (PFSs) is much more efficient in comparison with fuzzy sets at handling the uncertainty in decision-making problems, this paper firstly applies PFSs to depict the indeterminacy and inaccuracy information in teaching quality assessment (TQA) process. To begin with, the related definitions of PFSs are all profiled successively. Then, we attempt to propose an integrated assessment method with the combined compromise solution (CoCoSo) and Taxonomy method (TM) to handle the TQA problem with PFSs. In this approach, we define a new cumulative method based on TM to aggregate individual opinion into group opinion. We also develop a weighting strategy based on the CRiteria Importance Through Intercriteria Correlation approach (CRITIC) to evaluate the significance of experts with PFSs. For this, we define a novel generalized chordal picture fuzzy (PF) distance measure that considers the marginal impacts of degree of refusal membership, which has a strong capacity of differentiation. Then, we evaluate its superiority and stability through some experiment comparisons. Again, a final ranking method is presented with the CoCoSo approach and the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Furthermore, we apply a case study of the TQA to demonstrate the implementation of the newly proposed PF-CRITIC-SWARA-TM -CoCoSo method. The results obtained from the sensitivity analysis validate that the option “Dr. Tang” consistently achieves the highest rank and is independent of variations of balancing factor and weight information of experts and criteria. Finally, a comparison is implemented to confirm the robustness and reliability of the suggested integrated framework.
Cervical cancer prediction using machine learning models based on blood routine analysis
Authors: Su Jie , Lu Hui , Zhang RuiHuan , Cui Na , Chen Chao , Si Qin , Song Biao
Published: August 2024

Background and objective:
Cervical cancer is the fourth most common cancer among women globally. The key of prevention and treatment of cervical cancer is early detection, diagnosis and treatment. We aimed to develop an interpretable model to predict the risk for patients with cervical cancer based on blood routine data and used the Shapley additive interpretation (SHAP) method to explain the model and explore factors for cervical cancer.
Methods
In this paper, medical records of patients from 2013 to 2023 were collected for retrospective study. 2533 patients with cervical cancer were used as the case group, and 9879 patients with apparent healthy subjects were used as the control group. Using age, clinical diagnosis information and 22 blood cell analysis results, four different algorithm were used to construct cervical cancer prediction model.
Results
Using lasso regression and random forest method, 15 important blood routine features were finally selected from 23 features for model training. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.964, whereas RF had the poorest generalization ability (AUC = 0.907). The SHAP method reveals the top 6 predictors of cervical cancer according to the importance ranking, and the average of the PDW was recognized as the most important predictor variable.
Conclusion
In conclusion, we select the best ML based on performance and rank the importance of features according to Shapley Additive Explanation (SHAP) values. Compared to the other 4 algorithms, the results showed that the XGB had the best prediction performance for successfully predicting cervical cancer recurrence and was adopted in the establishment of the prediction model.
Blockchain-based distributed federated learning using proof of accuracy consensus
Authors: Sadegh Aghil , Bidgoly Amir Jalaly
Published: August 2024

This paper explores integrating federated learning (FL) and blockchain tech- nology, two burgeoning fields in information technology. Despite their growing popularity, both domains face significant challenges. In federated learning, the primary concern is safeguarding the integrity of the general model against client- induced compromises. Blockchain technology grapples with the need for a green mining approach through an energy-efficient consensus protocol. Our study lever- ages the strengths of each platform to mitigate the weaknesses of the other. We introduce an innovative blockchain-based FL model that eliminates the need for a central aggregator. Utilizing a green mining consensus algorithm named Proof of Accuracy (PoA), we create a competitive environment among nodes, fostering the creation of superior models. This approach ensures data integrity and model validation through a community-based consensus, resulting in a fully distributed system. This system enhances FL’s security and scalability and addresses vulner- abilities like malicious aggregators and scalability issues. Through experimental evaluations on the MNIST dataset with 20 miners, on one hand, our method enhances model accuracy to nearly 99% only after 10 blocks which is a higher point compared to FL and central learning. On the other hand, replacing Proof of Work (PoW) with PoA reduces energy consumption by nearly 30%. More- over, blockchain attacks appeared to be inapplicable, or resolvable after 6 blocks like fork attacks. After all, the introduced incentivizing mechanism lets malicious nodes get nearly zero rewards and allocates main rewards to honest nodes which is coherent with their efforts to present a superior model.
Reading the Unreadable: Synchrotron XRF Spectroscopy for Reading Salt-Encrusted Cuneiform Tablets
Authors: Surdi Mirko , Tack Pieter , Lycke Sylvia , Schoeder Sebastian , De Graef Katrien
Published: August 2024

Cuneiform tablets were a primary writing medium in the ancient Near East from the late fourth millennium BCE to the first century CE. Although these clay tablets were durable for daily use, prolonged burial over millennia has made them vulnerable to salt damage. Fluctuations in temperature and humidity cause the migration of salts to the surface of the tablets, damaging them and covering the inscriptions, making the text unreadable. Traditional preservation and restoration techniques, such as firing and tetraethyl orthosilicate (TEOS) treatments, although effective in making the text legible again, cause irreversible physicochemical alterations, compromising the historical integrity of the tablets. To address this issue, we used synchrotron radiation X-ray fluorescence (SR-XRF) spectroscopy to analyze cuneiform tablets covered by salts. This method enabled the recovery of previously unreadable texts without altering the nature of the tablets. Our findings highlight the importance of non-invasive methods for preserving and studying cuneiform tablets, maintaining their physicochemical integrity, and allowing for future research using various techniques.
Exposing factors influencing Korean leisure life satisfaction through machine learning techniques
Authors: Lee Yong-Kwan , Kim Boohyun , Kim Jinheum
Published: August 2024

This study examines factors influencing leisure life satisfaction (LLS) through machine learning techniques based on the data from the 2019 National Leisure Activity Survey in Korea. The results show that using machine learning techniques in identifying LLS influencing factors improves predictive power and helps detect effective leisure interventions. We also strengthen studies on mediators and moderators related to LLS and provide new insights into related research. The findings suggest that a diverse and balanced leisure repertoire is associated with greater levels of LLS, particularly in active leisure and social activities. However, the relationship between the repertoire of passive leisure activities and LLS is negative, suggesting that the optimal point for leisure activities is found between various leisure experiences and limited resources. Leisure resource availability, such as expenditure, time, facilities, and interpersonal factors, may affect LLS, but varies with age. The results provide new insights and more accurate models of the factors influencing LLS and their complex relationships.
Diagnostic stewardship and dermatology consultation in cellulitis management: a systematic literature review and meta-analysis
Authors: Lin Vivian , Callado Gustavo Yano , Pardo Isabele , Gutfreud Maria , Hsieh Mariana Kim , Pereira Andrea Mendes Rodrigues , Kwong Bernice Y. , Aleshin Maria , Holubar Marisa , Salinas Jorge , Diekema Daniel J. , Marra Alexandre R.
Published: August 2024

Importance: Cellulitis is a common skin infection often requiring antibiotic treatment. However, misdiagnosis and inappropriate antibiotic use contribute to antibiotic resistance and healthcare costs.
Objective: We aimed to evaluate the impact of dermatology consultation on treatment modification in patients with suspected cellulitis and to determine whether dermatologists’ evaluation can be used as a reference to diagnose suspected cellulitis.
Data Sources: We explored MedLine (PubMed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane CENTRAL, Web of Science, and Scopus and Embase, including publications from database inception to July 25, 2023.
Study Selection: Studies were included if they evaluated treatment modification involving the use of antibiotics of patients with suspected cellulitis with and without dermatology consultation. We excluded comments or reviews, pilot studies, and studies without a non-dermatology control group, treatment modifications, the use of antibiotics, and patients with cellulitis. Of the 49 full-text articles, 14 studies met the selection criteria.
Data Extraction and Synthesis: This systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Of five independent reviewers (GYC, IP, MG, MKH, and AMRP), two abstracted data for each article using a standardized abstraction form. We used the Downs and Black scale to evaluate study quality. Subgroup analysis was made regarding readmission rate in 30 days between two independent populations seen by a non-dermatologist physician or a dermatologist. We employed random-effect models to obtain pooled mean differences. Heterogeneity was assessed using the I-squared value.
Main Outcomes and Measures: The impact of dermatology consultation on treatment modification involving antibiotics in patients with suspected cellulitis and readmission rates in 30 days.
Results: Dermatology consultation changed initial treatment plans involving antibiotics from 47% to 96% of the time, improving diagnostic accuracy and, consequently, antibiotic stewardship of cellulitis. Dermatology consultation was associated with lower readmission rates in 30 days (pooled OR = 0.56, 95% CI: 0.25 to 1.25, I2 = 0%).
Conclusions and Relevance: Dermatology consultation in patients with suspected cellulitis may improve diagnosis and management, thereby reducing antibiotic misuse, unnecessary tests, and prolonged hospitalizations.
Clinical and Molecular Spectrum Along With Genotype-Phenotype Correlation of 25 Patients Diagnosed With 3M Syndrome: A Study from Turkey
Authors: Akalın Akçahan , Özalkak Şervan , Yıldırım Ruken , Karakaya Amine Aktar , Kolbaşı Barış , Durmuşalioğlu Enise Avcı , Kökali Funda , Demir Gizem Ürel , Ünal Edip , Atik Tahir , Kiper Pelin Özlem Şimşek , Elcioglu Nursel H
Published: August 2024

Objective: 3M syndrome is a well-known autosomal recessive skeletal genetic disorder caused by biallelic pathogenic variants in the CUL7, OBSL1, and CCDC8genes. Affected individuals exhibit profound pre- and postnatal growth retardation, distinctive facial features with normal intelligence. This study aims to provide insight into the comprehensive evaluation of clinical, laboratory, and radiological findings, expand the mutational spectrum of the disease, and establish a genotype-phenotype correlation in present cases.
Study design: A total of 25 patients from 19 unrelated families were included in the study. Genetic etiology was determined in probands through the utilization of Sanger sequencing and/or targeted gene panel analysis. The clinical, laboratory, and genetic features of all patients at admission and during follow-up were documented. Genotype-phenotype correlation was carried out in the CUL7 and OBSL1 groups.
Results: The genetic etiology was established in all patients (n = 25/25, 100%). We identified fourteen distinct variants in CUL7, OBSL1, and CCDC8 genes, with ten being novel. CUL7 variants were present in thirteen patients (n = 13/25, 52%), while OBSL1 variants were found in eleven patients (n = 11/25, 44%). No notable distinctions were found in mean birth weight, height, and standard deviation scores between the CUL7 and OBSL1 mutation groups (p>0.05). Patients with CUL7 variants exhibited significantly lower height standard deviation scores both at admission and at the last examination, as well as lower weight standard deviation scores at the last examination, compared to those with OBSL1variants (p<0.05).
Conclusion: To date, genotype-phenotype correlations have been identified in a limited number of studies. Further research involving larger cohorts is necessary to solidify these correlations.
Bifidobacterium animalis BD400 alleviates collagen-induced arthritis through branched-chain amino acids (BCAAs) and ubiquinone biosynthesis
Authors: Yang Yang , Hong Qing , Zhang Xuehong , Liu Zhenmin
Published: August 2024

Background
Rheumatoid arthritis (RA) is a common chronic and systemic autoimmune disease. Numerous clinical studies have indicated a correlation between alterations in gut microbiota and the onset and progression of RA. As a result, this research aims to restore intestinal microbiota to a healthy state through the oral administration of Bifidobacterium in the early stages with the goal of delaying the onset and progression of RA.
Results
The findings reveal that administering Bifidobacterium animalis BD400 orally led to a significant reduction in arthritis clinical scores and paw swelling thickness in CIA rats. Additionally, there was a decrease in osteo-facial fusion and calcified cartilage thickening in the knee joint. Furthermore, the oral administration of B. animalis BD400 resulted in the down-regulation of inflammatory factors TNF-α and collagenase MMP-13 in the knee joint. Levels of specific antibodies (anti-CII IgG, anti-CII IgG1, and anti-CII IgG2a) and cytokine IL-17A in serum, as well as cytokines (TNF-α and IL-1β) in the synovial fluid of B. animalis BD400-treated CIA rats, were significantly reduced (p < 0.05). The gene expression levels of intestinal barrier proteins (occludin-1, MUC-2, and ZO-1) showed a significant increase (p < 0.05) in B. animalis BD400-treated CIA rats. The oral administration of B. animalis BD400 altered the composition of intestinal microorganisms in CIA rats at the phylum and genus levels, particularly affecting the genus HT002.
Conclusions
B. animalis BD400 alleviates RA by down-regulating 2-ketobutyric acid and pyruvate in the biosynthesis of branched-chain amino acids, as well as down-regulating 4-hydroxyphenyl-pyruvate in the biosynthesis of ubiquinone and other terpenoid-quinone, laying a foundation for the RA clinical treatment of probiotics.
Impact of Bladder Size and Shape on the Accuracy of Formula Method for Bladder Volume Evaluation.
Authors: Shen Jintao , Zuo Yaqi , Song Jing , Su Zhen , Wang Shiwen , Jiang Haihong
Published: August 2024

Background
To explore the impact of bladder size and shape on the accuracy of formula method (V = 0.52 × d1 × d2 × d3) for bladder volume evaluation.
Methods
Data was retrospectively collected from 220 patients without reportable bladder diseases. CT images were imported into 3D Slicer software to measure the bladder volume VA (reference standards). Bladder volume was also measured by the formula method VB=0.52×d1×d2×d3. Results of these two methods were compared based on bladder size and shape.
Results
The bldder volume was 121.05 ± 83.59ml with the formula method, compared with 128.50 ± 82.61ml measured by 3D Slicer (P < 0.05). Patients were divided into three groups based on bladder size, the mean percentage deviations between two methods were 18.81 ± 20.82%, 3.37 ± 12.90% and 4.61 ± 10.64%, respectively. According to the bladder shape, it can be divided into 5 types. For round and triangle shape, there was no significant statistical difference in the results of two methods. For bladder shape with ellipse, rectangle and irregular shape, the volume evaluated by formula method were statistically lower. Their deviations were 9.73 ± 17.49%, 12.92 ± 9.62% and 14.35 ± 21.18%, respectively.
Conclusion
The accuracy of the formula method for estimating bladder volume is affected by bladder size and shape. Overall, the formula method tends to underestimate the bladder volume. The error of small-sized bladders was much greater than that of large-sized bladders. Furthermore, the formula method has high accuracy in measuring bladder volume with round and triangle shapes.
The kinetics of maternal and self-developed Streptococcus suis-specific antibodies
Authors: Vreman Sandra , Jansen Rutger , Bastian Mikael , Beckers Patricia , Riet Miriam van , Fijten Helmi , Fledderus Jan , de Greeff Astrid , Winkelman Helene , Stockhofe Norbert , Faba Lluis , Wisselink Henk J. , Vrieling Manouk
Published: August 2024

Background: Streptococcus suis (S. suis) infections are responsible for a large disease burden in piglets after weaning, compromising animal welfare and increasing antibiotic use. The immune gap caused by decreased maternal-derived antibodies (MDA) and insufficient acquired antibodies in weaned pigs could be a key factor for increased susceptibility to S. suis infections. To better understand this, two studies were performed. Study I evaluated the associations between sow antibodies in colostrum and serum, birth parameters (e.g., birth weight, colostrum intake and piglet growth) and the levels of S. suis-specific (serotypes 2 and 9) antibodies in one-day-old piglets from four farms. Subsequently, Study II used one of these farms to evaluate S. suis-specific and total antibody kinetics in piglets (10 litters with 6 selected piglets per litter, total n=60) from birth until10 weeks of age. Additionally, tonsil swabs from sows and piglets were taken to evaluate the S. suis tonsillar carrier status (serotypes 2 and 9) before and after weaning.
Results: High variability in serum and colostrum antibody levels was observed between and within the four farms (study I). In Study II, there was a decrease in S. suis-specific MDA after 24 hours of age, with the lowest level occurring at approximately 18/19 days of age. Afterwards, there was an increase in specific antibodies, most likely due to acquired immunity. Colostrum intake, birth weight and 24-h weight gain after birth were important parameters that were positively associated with S. suis antibody levels in piglets after birth but also affected these antibody levels at a later age. All the piglet tonsils were colonized with S. suisserotype 9 before weaning, while the prevalence of serotype 2 increased after weaning.
Conclusions: The lowest level of S. suis-specific antibodies was detected just before weaning, which contributes to piglet susceptibility to S. suis infections. Farmers and veterinarians should focus on piglets with low birth weights, late-born piglets, and/or piglets with low colostrum intake because these parameters reduce both the S. suis-specific MDA preweaning and the specific antibodies acquired postweaning.
Graves’ Disease Diagnosed Six Months After Microwave Ablation of Benign Thyroid Nodules: A case report
Authors: Gu Yunru , Chen Rui , Chen Mingming , Jiang Xiaohong , Wang Long , Huang Xiaolin
Published: August 2024

Background
Microwave ablation is a new minimally invasive technique for the treatment of thyroid nodules. Hyperthyroidism due to destructive thyroiditis is a known risk of MWA, but this occurs in only a minority of cases. We report a rare case of a patient diagnosed with Graves' disease 6 months after microwave ablation of a thyroid nodule.
Case presentation
On July 31, 2022, a 43-year-old male patient presented to our hospital with symptoms of pyrexia, excessive sweating, and palpitations for 15 days. History inquiry revealed that the patient had undergone microwave ablation of right-sided thyroid nodule 6 months ago in another hospital. The patient’s thyroid ultrasound suggested bilateral diffuse thyroid lesions, with a moderately echogenic mass observed on the right side of the thyroid gland, potentially indicative of thyroid nodule ablation. The patient had elevated serum thyroid hormone levels, decreased thyroid-stimulating hormone levels and positive associated thyroid antibodies. To control the symptoms of hyperthyroidism, the patient opted for oral antithyroid medication, and the patient's thyroid function returned to normal after 3 months of medication. The patient is now under regular follow-up.
Conclusions
Although microwave ablation is becoming more popular, a number of adverse effects have limited its use. The objective of our study is to analyze the causes of Graves' disease after ablation, emphasizing the importance of strictly adhering to ablation guidelines and standardizing the procedure. Further large-scale studies are necessary to predict the risk factors that affect the regression of thyroid function after ablation and to prevent postoperative abnormalities of thyroid function.
Factors Influencing on Drought Distress: A Study on Vulnerable Communities in Rural Karnataka, India
Authors: Ramesha Naika , Ramesh Jare
Published: August 2024

The present study examined the factors influencing on drought distress among vulnerable communities in rural Karnataka, India. Data was collected from both primary and secondary sources. A sample of 264 vulnerable communities was interviewed, 13 measured variables were identified, and a 5-point Likert scale was used to validate the study. The results of the study indicate that three factors contribute to drought: Drought Distress Factors (DDF), Economic Factors (EF), and Social Factors (SF). This study further revealed that vulnerable communities are most distressed by deficits because of frequent incidents of drought, high temperatures due to climate change, and losses of agricultural and income generation livelihood activities, which are leading challenges in sustaining livelihoods among vulnerable communities. Subsequently, the government should compensate for losses such as crop damage and livestock, and crop insurance can be introduced by providing skill-based training, which can be used as a livelihood to sustain vulnerable communities.
Biased Reasoning with Unbalanced Attention for Autopilot Flight Considering Anchoring Effects
Authors: Zhou Yutong , Fu Shan
Published: August 2024

The anchoring effect in psychology suggests that people may pay more attention to early information received. Cognitive anchoring effects can lead to unbalanced attention in decision-making, which is rarely considered in computational human performance and human-computer interaction (CHP \& HCI). This paper proposed biased reasoning with unbalanced attention to derive autopilot commands in flight. First, we establish a fuzzy reasoning module to compute a binary indicator of attention toward early information. Second, we devise an unequally-spaced sampling window to generate cognition weights on information at different times. Finally, we apply the binary indicator and cognition weights to derive virtual heading commands in autopilot. Simulation results demonstrate that the proposed method can simulate the anchoring effect to reflect the unbalanced attention in decision-making under various autopilot scenarios. We found that individual differences in previous impressions affect attention and reasoning, a breakthrough in CHP \& HCI. In summary, our approach represents a new route of biased cognitive reasoning due to unbalanced attention in decision-making.
A new BNiLi glass sample [Li2O-B2O3-NiO] conjugated with poly (L-alanine) modified carbon paste electrode for the electrochemical determination of linezolid
Authors: Abd-Elsabour M. , Abou-Krisha Mortaga M. , Alhamzani Abdulrahman G. , Yousef Tarek A. , Alotaibi Abdullah N. , Mohy-Eldin Mohamed S.
Published: August 2024

A new glass sample (BNiLi) was prepared by melt-quenching method. The physical nature of glass sample was investigated via X-ray diffraction, while the atomic structure was studied via density and infrared spectroscopy. The existence of main structure units such as BO3 and BO4 was confirmed. Furthermore, the optical absorbance was measured, and the electronic transitions of nickel ions were revealed. Optical band gap energy was estimated for BNiLi glass sample. The prepared BNiLi glass was used to modify a carbon paste electrode (CPE) with a polymer film of L-alanine (Ala). The obtained p-Ala@BNiLi/CPE was employed to determine Linezolid (LIN) by cyclic voltammetric, linear sweep voltammetric, differential pulse voltammetric and chronoamperometric methods. The proposed sensor achieve a low limit of detection(23 nM), good repeatability and high stability.The fabricated sensor was applied to detect LIN in pharmaceutical and human serum samples.
Analysis of the seismic isolation characteristics of the overall friction pendulum bearing (OFPB) of a pagoda under three types of seismic actions
Authors: He Qing , Liu Dewen , Lei Min , Li Xiaopeng , Wu Haoxuan
Published: August 2024

In this study, the seismic performance of an ancient pagoda with significant historical and cultural values in China is upgraded by using overall friction pendulum bearing (OFPB) seismic isolation technology. By implementing the OFPB seismic isolation retrofit on the pagoda and comparing and analyzing it with the unretrofitted seismic model, the response of the pagoda under the action of nine seismic waves of three types, namely, near-field earthquakes, far-field earthquakes, and common earthquakes, is investigated. The results show that the OFPB seismic isolation technique significantly reduces the seismic response of the ghats, in which the acceleration amplification factor is reduced by a maximum of 82.46%, the inter-story displacement is reduced by a maximum of 85.15%, and the base shear force is reduced by a maximum of 96.76%. In addition, the tensile damage of the ghats with OFPB seismic isolation was significantly controlled. While the model without seismic isolation has serious damage under the same seismic action and even faces the risk of collapse. The results of the study confirm that the OFPB seismic isolation technology plays a key role in improving the seismic performance of the tower, which is of great theoretical and practical significance to the protection of the tower, and provides an effective technical way for the protection of the tower in the seismic-prone areas.
Using Borda and Bargaining algorithms to prioritize potential contamination of springs
Authors: Haghizadeh Ali , Hassanvand Mahsa , kamarhei Bahram , Ghasemi Leila
Published: August 2024

The selection of an optimal decision-making method for water resource management is important and necessary. In this context, game theory makes a significant contribution to the advancement of decision-making for achieving optimal outcomes due to its high efficiency in solving multi-objective problems and offering diverse modeling techniques. In this study, the prioritization of potential contamination of springs in Khorramabad was considered, and by using the Borda and bargaining algorithms in game theory, the most effective parameters and the most critical resources in terms of potential contamination were identified. Various parameters, including pH, NO3 and etc., were collected and calculated for studied springs and entered into the algorithms based on their importance in creating pollution potential. The Landsat 8 satellite's OLI sensor with an imagery date of 2017 was utilized to generate a LU/LC map for the region. By employing the Borda scoring method, the CO3, K, and HCO3 parameters were the most effective, with scores of 232, 157.5 and 153.5, respectively. Implementing the bargaining algorithm, CO3, K, and pH parameters were the most effective, with scores of 254, 177 and 165, respectively. Finally, the prioritization of water resources was presented using both methods. In the Borda method, the Golestan and Qeyu, springs were ranked first. implementing the bargaining algorithm, the Qeyu and Dareh-Saki springs are ranked as top priorities. Which of these methods is more suitable for groundwater resource management, according to its guiding principles, depends on the preferences of the decision-makers and their goals for water resource management.
Fine-scaled habitat preference of the Yangtze finless porpoise in the Yangtze River
Authors: Zhang Ping , Yu Zhongyin , Zhang Kang , Yu Daoping , Chen Minmin
Published: August 2024

The Yangtze finless porpoise (Neophocaena asiaeorientalis) is critically endangered and suffered from extensive habitat loss and fragmentation. Knowledge on habitat preference of the Yangtze finless porpoise could assist finless porpoise and associated ecosystem-based conservation by knowing porpoises’ suitable environments and prioritizing conservation sectors. In the present study, porpoise occurrences and numbers were collected in a 30-kilometer mainstream of the Yangtze River. Environmental variables were uniformly sampled in the dry and wet seasons. The maximum entropy model (MaxEnt) was then applied to porpoise data and environmental variables to analyze the effects of environmental variables on porpoises habitat preference. Within the study area, suitable habitats of the finless porpoises accounted for 19.0% and 15.4% of the study area during the dry and wet seasons. The suitable habitat of the finless porpoises shifted from the mainstream in the dry season to the sandbar head and tributary area in the wet season. In the dry season, variables influencing finless porpoise distribution were phytoplankton biomass, flow rate and zooplankton biomass, which contributed 97.05% variation in the MaxEnt modeling. In the wet season, water depth and surface water temperature accounted for 88.45% of the porpoise distribution. Above results highlighted higher conservation priority for the finless porpoise in the dry season when fish resources are relatively scarce while porpoises mainly distribute in the mainstream. Measures including monitoring and evaluating prey resources, management of shipping routes and shipping speed should be considered.
Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data
Authors: Wang Zhicheng , Gu Ying , Huang Lindan , Liu Shuai , Chen Qun , Yang Yunyun , Hong Guolin , Ning Wanshan
Published: August 2024

Background
Cardiovascular disease, also known as circulation system disease, remains the leading cause of morbidity and mortality worldwide. Traditional methods for diagnosing cardiovascular disease are often expensive and time-consuming. So the purpose of this study is to construct machine learning models for the diagnosis of cardiovascular diseases using easily accessible blood routine and biochemical detection data and explore the unique hematologic features of cardiovascular diseases, including some metabolic indicators.
Methods
After the data preprocessing, 25794 healthy people and 32822 circulation system disease patients with the blood routine and biochemical detection data were utilized for our study. We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. Finally, the SHAP algorithm was used to interpret models.
Results
The circulation system disease prediction model constructed by XGBoost possessed the best performance (AUC: 0.9921; Acc: 0.9618; Sn: 0.9690; Sp: 0.9526; PPV: 0.9631; NPV: 0.9600; MCC: 0.9224; F1 score: 0.9661). Most models of distinguishing various circulation system diseases also had good performance, the model performance of distinguishing dilated cardiomyopathy from other circulation system diseases was the best (AUC: 0.9267). The model interpretation by the SHAP algorithm indicated features from biochemical detection made major contributions to predicting circulation system disease, such as potassium (K), total protein (TP), albumin (ALB), and indirect bilirubin (NBIL). But for models of distinguishing various circulation system diseases, we found that red blood cell count (RBC), K, direct bilirubin (DBIL), and glucose (GLU) were the top 4 features subdividing various circulation system diseases.
Conclusions
The present study constructed multiple models using 50 features from the blood routine and biochemical detection data for the diagnosis of various circulation system diseases. At the same time, the unique hematologic features of various circulation system diseases, including some metabolic-related indicators, were also explored. This cost-effective work will benefit more people and help diagnose and prevent circulation system diseases.
Regulatory Role of LncRNA TUG1 in Hypertrophic Scar Development through miR-627 and IGFR1 Signaling
Authors: Bai Zeming , Liang Jiulong , Han Ziyang , Wang Hongyi , Zou Rifeng , Li Zhe , Lin Feng , Kong Xiangzi , Han Dong
Published: August 2024

Hypertrophic scar (HS) is a sequela of abnormal dermal repair, marked by the excessive proliferation of fibroblasts and dermal fibrosis. While long-non-coding RNAs (LncRNAs) have emerged as crucial modulators in HS, the underlying mechanisms are yet to be fully elucidated. Our study employed DNA Microarrays to analyze differentially expressed LncRNAs in HS and identified significant upregulation of TUG1. Further analysis based on the TargetScan database revealed that TUG1 has binding sites for miR-627 and its target gene IGFR1. Quantitative Real-Time PCR (qRT-PCR) confirmed the upregulation of TUG1 and IGFR1, and downregulation of miR-627 in HS samples. Subsequent assays, including qRT-PCR, luciferase reporter gene, and Western Blot, were conducted to explore the interactions between TUG1, miR-627, and IGFR1. MTT and Transwell assays assessed the proliferative and migratory abilities of hypertrophic scar fibroblasts (HSFs). Furthermore, the rabbit ear scar model supported our findings. We discovered that upregulation of TUG1 or downregulation of miR-627 facilitated HSF proliferation and migration, elucidating a negative regulatory relationship between TUG1 and miR-627. Mechanically, TUG1 competitively binds to miR-627, thus freeing IGFR1 for upregulation. In conclusion, TUG1 knockout can inhibit HSF proliferation and migration by upregulating miR-627, which subsequently downregulates IGFR1. These findings offer novel insights for the effective treatment of HS.
A Comprehensive Hybrid Framework to Study the Impact of Dams on the Shoreline Evolution in the Downstream Estuarian Region
Authors: Palanimanickam Vengadesan , Annamalaisamy Sannasiraj Sannasi , Bhallamudi Murty
Published: August 2024

The increasing demand for fresh water has led to a proliferation of dams worldwide. While humans benefit from water storage projects, the environment faces growing pressure. Dams significantly impact flow dynamics, reducing freshwater and sediment flow. This alteration in flow results in increased tidal dominance in estuarine areas. The combined effects of changes in water and sediment flow in the river, along with heightened tidal influence, have a significant impact on downstream morpho-dynamics. Understanding the long-term impacts of dam construction on shorelines is crucial as changes in shoreline evolution can have significant ecological, economic, and social implications. The present study proposes a novel hybrid framework, which combines Range of Variability Analysis (RVA) of river discharges, hydrologic modeling for sediment trapping by dams, hydrodynamic modeling of estuaries, and digital shoreline analysis, for gaining insight into shoreline changes triggered by the construction of large dams on rivers, and identifying the factors driving these changes. To illustrate the proposed methodology, it is applied to the Narmada River estuary in central India to study the impacts of the world’s second-largest concrete dam, Sardar Sarovar Dam (SSD). The construction of SSD reduced the magnitude of the flood by more than 40% and the sediment flow by 90%. The rate of shoreline change in the estuary has reduced during the post-construction period although the shoreline length undergoing erosion has significantly increased. The analysis using the hybrid approach suggests that the decreased rate of shoreline change is most likely due to reduced monsoonal flow, while the increased extent of erodible areas can be related to decreased sediment transport.