bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Self-Distillation and Disentanglement Network to Enhance Visual-Semantic Feature Consistency in Generalized Zero-Shot Learning
Authors: Liu Xiaoming , Wang Chen , Yang Guan , Wang Chun hua , Long Yang , Liu Jie , Zhang Zhi yuan
Published: April 2024

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).
Changes in the Transcriptome Profile in Young Chickens after Infection with LaSota Newcastle Disease Virus
Authors: Lopes Taina SB , Nankemann Jannis , Breedlove Cassandra , Pietruska Andrea , Espejo Raiumundo , Cuadrado Camila A , Hauck Ruediger
Published: April 2024

Understanding gene expression changes in chicks after vaccination against Newcastle Disease (ND) can reveal vaccine biomarkers. There is limited data on chicks’ early immune response after ND vaccination. Two trials aimed into this knowledge gap. In experiment one, forty-two 13 days-old specific pathogen free (SPF) chicks were used. Harderian glands (Hg) and tracheas (Tc) from five birds per group were sampled at 12, 24, and 48 hours post-vaccination (hpv) to evaluate the gene transcription levels by RNA sequencing (RNA-seq) and RT-qPCR. Results of RNA-seq were compared by glmFTest, while results of RT-qPCR were compared by t-test. With RNA-seq, significant up-regulation of interferon related genes along with JAK-STAT signaling pathways regulation was observed in the Hg at 24- hpv. None of the DEGs identified by RNA-seq were positive for RT-qPCR. Experiment 2 used one-hundred and twelve SPF and commercial chickens at 1 day-old and 14 days-old. Only the commercial birds had maternal antibodies for NDV. By RNA-seq, twenty core DEGs associated with innate immunity and viral genome replication inhibition were identified. Genes previously unlinked to NDV response, such as USP41, were identified. This research present genes with potential as immunity biomarkers for vaccines, yet further investigation is needed to correlate the core gene expression with viral shedding post-vaccination.
Development of a Compartment Model to Study the Pharmacokinetics of Medical THC after Oral Administration
Authors: Mahahong Thanachok , Saleewong Teerapol
Published: April 2024

This paper presents a compartmental model aimed at investigating the pharmacokinetics of medical THC after oral administration. The model was developed to be multi-compartmental, describing the kinetics of THC and its metabolites in bodily tissues. Utilizing the law of mass action, the model was converted into ordinary differential equations (ODEs), illustrating the rate of concentration changes of THC and its metabolites in each compartment. Subsequently, we applied the nonstandard finite difference (NSFD) method to construct numerical solution schemes. These schemes were implemented in the MATLAB program, along with estimated pharmacokinetic rate constants. The results of the investigation demonstrate that the simulation curves depicting the plasma concentration-time profiles of THC and THC-OH closely resemble the actual data samples, indicating that this model accurately describes the pharmacokinetics of medical THC and its metabolites. Moreover, the model exhibits predictive capabilities regarding the pharmacokinetics of THC and its metabolites in various tissues. Consequently, the model serves as a valuable tool for enhancing our understanding of the pharmacokinetics of THC and its metabolites and for guiding adjustments to dosage and administration durations for oral medical THC products.
Unlocking glioblastoma vulnerabilities with CRISPR-based genetic screening
Authors: Fang Yitong , Li Xing , Tian Ruilin
Published: April 2024

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover genetic determinants of GBM progression and susceptibility to various therapies. Furthermore, we discuss ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.
Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal
Authors: Bhungeni Orlando , Ramjatan Ashadevi , Gebreslasie Michael
Published: April 2024

Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.
In silico Splicing Analysis of the PMS2 Gene: Exploring Alternative Molecular Mechanisms in PMS2-Associated Lynch Syndrome
Authors: Munteanu Catalin Vasile , Marian Catalin Valer , Chirita-Emandi Adela , Puiu Maria , Trifa Adrian Pavel
Published: April 2024

Lynch syndrome (LS) is one of the most common hereditary cancer syndrome in human populations, associated with germline variants in MLH1, MSH2/EPCAM, MSH6 and PMS2 genes. The advent of next generation sequencing has proven a significant impact in germline variant detection in the causative genes; however, a large proportion of patients with clinical criteria still receive uncertain or negative results. PMS2 is the least frequent reported gene, associated with up to 15% of LS cases with late-onset disease and low penetrance phenotype; however, the proportion of PMS2-LS cases is considered to be highly underestimated. In this context, we consulted public available databases (ClinVar, LOVD) for missense and intronic PMS2 variants and performed an in silico analysis of the genomic sequence. Splicing bioinformatics tools proven effective in other genes were used to assess both the wild-type DNA sequence and reported genetic variants. Splicing alterations were predicted in silico and using GTEx short-read RNA expression data. Out of the 2384 missense variants discovered, 90% were classified with uncertain significance (VUS). 4.9% of missense variants were shown to have a potential splicing consequence (DS > 0.2) using SpliceAI. As described in the original publication, SpliceAI-visual was proven effective in annotation of short intronic variants (< 50 bp). Four short intronic variants were identified using SpliceAI-visual as potentially splicing disturbing, in spite of using a lower threshold (DS > 0.1). Moreover, we observed that Splicing Regulatory Elements (SREs) may play a fundamental role in alternative splicing in PMS2 gene, ESE motifs being overrepresented in highly expressed exons 5, 11 and 14. Computational analysis performed in our study serves as a valuable filtering step for guiding further RNA sequencing experiments. Additional functional data is necessary to evaluate the significance of our findings.
Trajectories of Mental Distress Among US Women by Sexual Orientation and Racialized Group During the First Year of the COVID-19 Pandemic
Authors: Beccia Ariel L. , Zubizarreta Dougie , Austin S. Bryn , Raifman Julia R. , Chavarro Jorge E. , Charlton Brittany M.
Published: April 2024

Objectives. To describe longitudinal trends in the prevalence of mental distress across the first year of the COVID-19 pandemic (April 2020‒April 2021) among US women at the intersection of sexual orientation and racialized group.

Methods. Participants included 49 805 cisgender women and female-identified people from the COVID-19 Sub-Study, a cohort of US adults embedded within the Nurses’ Health Studies 2 and 3 and the Growing Up Today Study. We fit generalized estimating equation Poisson models to estimate trends in depressive and anxiety symptoms by sexual orientation (gay or lesbian, bisexual, mostly heterosexual, completely heterosexual); subsequent models explored further differences by racialized group (Asian, Black, Latine, White, other or unlisted).

Results. Relative to completely heterosexual peers, gay or lesbian, bisexual, and mostly heterosexual women had a higher prevalence of depressive and anxiety symptoms at each study wave and experienced widening inequities over time. Inequities were largest for sexual minority women of color, although confidence intervals were wide.

Conclusions. The COVID-19 pandemic may have exacerbated already-glaring mental health inequities affecting sexual minority women, especially those belonging to marginalized racialized groups. Future research should investigate structural drivers of these patterns to inform policy-oriented interventions. ( Am J Public Health. 2024;114(5):511–522. https://doi.org/10.2105/AJPH.2024.307601 )
CONTRATACIÓN ONLINE. PENSAMIENTO Y NEUTRALIDAD TECNOLÓGICA DE LOS SISTEMAS COMPUTACIONALES
Authors: Tur Faúndez Carlos
Published: April 2024

Legal transactions carried out through online applications are currently a mass phenomenon that requires individualized study. The issue is often approached from a strictly legal perspective, without taking into consideration the extraordinary impact that the underlying technology has on the legislative framework that regulates online contracting.  The aim of this paper is to propose reasonable legal answers to several of the major dogmas concerning computational thinking.  The vast majority of applications for online contracting are based on traditional algorithms optimized to increase maximum revenue for service providers. Taking this statement as a point of reference, it is appropriate to review the concept of technological neutrality and reconsider the position to be adopted by lawmakers in the face of the advance of ICTs.
LA PROTECCIÓN FRENTE A LOS ATAQUES A MARCAS EN REDES SOCIALES. HONOR DE MARCA. PRESTIGIO REPUTACIONAL. DAÑOS MORALES. ¿PUEDEN TERCERAS EMPRESAS VERTER COMENTARIOS NEGATIVOS SOBRE UNA MARCA COMUNITARIA REGISTRADA EN REDES SOCIALES?
Authors: Llobet Leca Paul
Published: April 2024

The purpose of this article is to comment briefly on the legal situation in which companies owning protected trademarks find themselves in the event of suffering attacks on their trademarks on social networks that affect their reputation and image and to comment on possible ways of protection against such behaviour.
Effects of Noncrosslinked Polyvinyl Alcohol Fluid Loss Additive on the Compressive Strength and Viscosity of Class G Cement Slurries
Authors: Lima Victor N. , Skadsem Hans J. , Souza Felipe R. , Kaneshima Takuma , Letichevsky Sonia , de Avillez Roberto R. , Silva Flavio A.
Published: April 2024

Limiting the fluid loss from the cement slurry to the adjacent formation by using additives is essential for maintaining the slurry’s water/cement ratio. The present work focuses on the effect of noncrosslinked polyvinyl alcohol additive (PVOH), a widely used fluid loss additive (FLA), on the compression strength and rheological behavior of Class G cement pastes. Results of the current study show that the PVOH surfactant characteristic and its absorptive mechanism interfere not only with the hydration process but also with the physical properties and compressive strength of cement pastes, such as porosity, permeability, and early age strength, which revealed the importance of using a defoamer when PVOH is present in the mixture. In the absence of a defoamer, the PVOH additive generates foam in the mixed cement paste samples, which results in increased porosity and reduced compressive strength of the hardened cement paste. Moreover, regarding rheology, increasing the PVOH concentration increased the effective viscosity when evaluating flow curves. Therefore, this study demonstrates a systematic method for assessing the possible effects of cement paste additives, such as PVOH and defoamer, providing a physical and mechanical approach rather than just chemical to evaluate additives’ influence on the mixtures. This method should consider different additives in combination with PVOH to test cement paste stability and to obtain specific working recipes.
BrainMNet: A Unified Neural Network Architecture for Brain Image Classification
Authors: Ghosh Sudip , _ Deepti , Gupta Shivam
Published: April 2024

In brain-related diseases, including Brain Tumours and Alzheimer's, accurate and timely diagnosis is crucial for effective medical intervention. Current state-of-the-art (SOTA) approaches in medical imaging predominantly focus on diagnosing a single brain disease at a time. However, recent research has uncovered intricate connections between various brain diseases, realizing that treating one condition may lead to the development of others. Consequently, there is a growing need for accurate diagnostic systems addressing multiple brain-related diseases. Designing separate models for different diseases, however, can impose substantial overhead. To tackle this challenge, our paper introduces BrainMNet, an innovative neural network architecture explicitly tailored for classifying brain images. The primary objective is to propose a single, robust framework capable of diagnosing a spectrum of brain-related diseases. The paper comprehensively validates BrainMNet's efficacy, specifically in diagnosing Brain tumours and Alzheimer's disease. Remarkably, the proposed model workflow surpasses current SOTA methods, demonstrating a substantial enhancement in accuracy and precision. Furthermore, it maintains a balanced performance across different classes in the Brain tumour and Alzheimer's dataset, emphasizing the versatility of our architecture for precise disease diagnosis. BrainMNet undergoes an ablation study to optimize its choice of the optimal optimizer, and a data growth analysis verifies its performance on small datasets, simulating real-life scenarios where data progressively increases over time. Thus, this paper signifies a significant stride toward a unified solution for diagnosing diverse brain-related diseases.
Prediction of insulin resistance and non-alcoholic fatty liver disease using serum uric acid and related markers in children and adolescents
Authors: Song Kyungchul , Choi Youngha , Yang Hyejin , Jeon Soyoung , Cho Kyoung Won , Kim Seo Jung , Kim Sujin , LEE MYEONGSEOB , Suh Junghwan , Chae Hyun Wook , Kim Ho-Seong
Published: April 2024

Objective To investigate the relationship between serum uric acid (Uacid) and related parameters with insulin resistance (IR) and non-alcoholic fatty liver disease (NAFLD)and their potential as predictors of IR and NAFLD in children and adolescents
Methods The data of 1,648 participants aged 10–18 years was analyzed using nationwide survey. Logistic regression analysis was performed with IR and NAFLD as dependent variables, and odds ratios (ORs) and 95% confidence intervals (CIs) were computed for tertiles 2 and 3 of each parameter in comparison to tertile 1, which served as the reference group. Receiver operating characteristic (ROC) curves were generated to assess predictability of the parameters for IR and NAFLD.
Results Hyperuricemia, IR, and NAFLD were significantly associated each other. All Uacid and related markers showed continuous increase in ORs and 95% CIs across the tertiles for IR and NAFLD (all p < 0.001). In ROC curve, all Uacid and related markers demonstrated significant predictability for IR and NAFLD. Overall, Uacid combined with obesity indices showed higher ORs and AUC compared to Uacid alone. Uacid-body mass index (BMI) standard deviation score presented the largest AUC for IR. For NAFLD, Uacid-BMI and Uacid-waist-to-height ratio showed the largest AUC.
Conclusions Uacid combined with obesity indices are robust markers for prediction of IR and NAFLD in children and adolescents, which was superior to Uacid. Uacid and related markers have potential as simple markers which does not require fasting for screening of IR and NAFLD in children and adolescents
SARS-CoV-2 RNA-dependent RNA Polymerase is Linked to Mitochondrial Dysfunction in Mouse Heart
Authors: Naka Kazuhito , Ooshima Akira , Sakaguchi Takemasa , Sotomaru Yusuke , Koike Kanae , Sakamoto Naoya , Ishikawa Tomo-o , Kim Seong-Jin , Sawa Masaaki
Published: April 2024

Little is known about specific viral factors responsible for the pathogenesis and pathophysiology of long COVID. Here we describe a conditional knock-in (cKI) mouse strain inducibly expressing Nsp12, an essential component of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Induction of Nsp12 translation was dependent on co-treatment with inhibitors of the integrated stress response in vitro and in vivo. We show that Nsp12 has a biologically significant link to mitochondrial dysfunction in vivo. In vitro, ectopic Nsp12 expression suppressed mitochondrial function in primary lung epithelial cells isolated from Nsp12 cKI mice. This functionality was physiologically relevant because, although ectopic Nsp12 expression in mouse lungs did not induce pneumonia, it did decrease mitochondrial activity in the hearts of Nsp12 cKI mice over the short and long terms. Administration in vivo of an RdRp inhibitor, EIDD-2801, restored mitochondrial function in cardiomyocytes of Nsp12 cKI mice. Our data demonstrate that SARS-CoV-2 RdRp activity in the lungs leads to cardiac mitochondrial dysfunction in vivo, generating a phenotype resembling aspects of long COVID in humans. Therapeutic targeting of SARS-CoV-2 RdRp may thus represent a novel means of preventing or mitigating intense fatigue and/or myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)-like disease caused by mitochondrial dysfunction in long COVID patients.
Comparative analysis of machine learning and time series models for cotton yield prediction in major growing districts of Karnataka
Authors: N THIMMEGOWDA M , H MANJUNATHA M , HUGGI LINGARAJ , V SOUMYA D , R JAYARAMAIAH , S SATISHA G , L NAGESHA
Published: April 2024

Background
Cotton is one of the most important commercial crop after food crops, especially in countries like India, where it’s grown extensively under rainfed condition. Because of its usage in multiple industries, such as textile, medicine and automobile industries, it has greater commercial importance. Cotton cultivation demands intensive management due to its explorative nature. The crop's performance is greatly influenced by prevailing weather dynamics. As climate change awareness grows, assessing how weather changes affect crop performance is essential. Crop models are a prominent tool for this purpose. Similarly, many techniques are vague and crop models are the dominant ones.
Results
Present study on statistical and machine learning models were compared to assess their ability to predict cotton yield across major producing districts based on long term (1990–2023) dataset on yield and weather factors. The results revealed superior performance of machine learning models such as Artificial Neural Networks (ANN) as they are iteratively trained and evaluated for higher accuracy and found that errors were within acceptable limit i.e., ± 10% and the actual and forecasted yields were in excellent agreement at both F1 and F2 stage and statistically evaluated for RMSE, nRMSE and EF, it showed good results having nRMSE value less than 10 per cent and considered as excellent for eight out of ten districts at F1 and seven districts at F2 stage because of ability of machine learning models such as ANNs to consider intricate interactive effects of weather factors. Furthermore, the tested ANN model was used to assess the importance of the dominant weather factor influencing evaluate crop performance in each district. Specifically, the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum temperature had major influence on cotton yield in most of the yield predicted districts. These difference highlighted the differential interactions of weather factors in each district, which in turn affected the crop productivity.
Conclusions
Outcomes of the study aid in understanding the weather-related yield variability and planning crop management practices and in achieving yield sustainability under changing climatic scenarios of rainfed condition in India.
Prevalence and associated factors of malnutrition among patients undergoing surgery at Saint Peter Hospital, Addis Ababa, Ethiopia,2023
Authors: Haile Robel Gemechu , Haile Abel Teklit , Habtewolde Abel Worku , Debebe Zelalem , Getu Gubala , tiruneh bitewlign , mengiste helina
Published: April 2024

Background: Malnutrition is an imbalance between food intake and needs. It is a clinical condition with multifactorial etiology, resulting in several disorders like immune suppression, increased susceptibility to infections, increased drug intolerance, and death.
Objective: To assess the prevalence and associated factors of malnutrition among patients undergoing surgery at Saint Peter Hospital, Addis Ababa, Ethiopia, 2023.
Methods: an institution based cross-sectional study was designed to investigate the prevalence and associated factors of malnutrition among adult patients undergoing surgery
Results: During the preoperative period, the study found that 12.5% of patients were undernourished, while 30% were overnourished. Following surgery, the prevalence of undernutrition decreased to 10%, while overnutrition remained high at 29.2%. Furthermore, a statistically significant difference in hospital length of stay was observed between patients with normal nutritional status and those with undernutrition postoperatively. The mean difference between these groups was 2.65812 (95% CI = -3.07882 to -2.23742), with a p-value of less than .001. Specifically, patients with normal nutritional status had an average stay of less than 2.66 days longer than those with undernutrition.
Assessment of Genetic Diversity of Indigenous Chicken Ecotypes in Selected Areas of Tanzania
Authors: LUCAS RHODA , KASANGA CHRISTOPHER JACOB , MWEGA ELISA DANIEL , ACKREY SISSA GOTIFRED
Published: April 2024

The sustainable usage and conservation of indigenous chickens require a genotypic characterization. This study was conducted in the Bahi and Songea districts of Tanzania. Blood samples of 100 indigenous chickens were collected and genotyped using eighteen microsatellite markers recommended by the International Society for Animal Genetics, Food and Agriculture Organization Advisory Group for Animal Genetics. Data were analyzed using GenAIEXv.6.5 software. Results showed that Bahi had a total of 117 alleles while Songea had 111 alleles. There were 6.5 alleles on average per locus for Bahi and 6.1 for Songea. The locus with the least number of alleles was MCW0078, with just 5 alleles, whereas the MCW0111F locus was highly polymorphic with 20 alleles. The mean number of different alleles (Na) was 7.250 ± 0.544 for Bahi and 7.000 ± 0.742 for Songea, with an overall average of 7.125 ± 0.515. The number of private alleles was 31 and 37 for Bahi and Songea, respectively. Genetic variation among the population was 17%, while within the population it was 83%. Nei’s genetic distance was calculated to be 0.589, and the principal of coordinate analysis (PCoA) revealed two clusters with some intermixes. At p < 0.05, divergence from Hard Weinberg equilibrium was significant in all loci. In conclusion, there was a considerable genetic variation between the two study populations hence, they are very useful in conservation programs of the indigenous chickens.
Rupture of Anterior Communicating Artery Aneurysm Is Associated With Bifurcation Angle, Aneurysm neck width, and Blood flow velocity
Authors: Li Yang , Wang Bangyue , Ge Xinbo , An Xiuhu , Li Hongwen , Jiang Yuhang , Li Tianxing , Zhao Yan , Zhang Nai , Song Yunfei , Zhang Suqin , Zhu Xiaowei , Liu Zhenbo , Hu Fuguang , Yang Xinyu
Published: April 2024

Background and Purpose：
The Anterior Communicating Artery (ACom) is one of the common sites of intracranial aneurysms (IA), and its rupture can severely affect the patient’s life and health. This study pays extensive attention to the morphological and hemodynamic characteristics of the anterior communicating artery aneurysm (ACoA) and further evaluates the causes of ACoA rupture by analyzing these factors.
Methods：
This study included patient data from January 2017 to October 2023 from the Chinese Multi-central Cerebral Aneurysm Database (CMAD), which contained imaging data for 119 cases. The relationship between clinical characteristics, morphological features, hemodynamic features, and the rupture of the ACoA was explored through traditional logistic regression models, personalized analysis of restrictive cubic spline regression (RCS) models for different factors, and correlation analysis.
Results：
In multivariable logistics regression (MLR), a history of ischemic stroke, smoking history, aneurysm neck width, and the angle between the anterior cerebral artery A1 segment and the anterior communicating artery (A1-ACom angle) are protective factors. In contrast, the diameter of the anterior communicating artery (ACom) and blood flow velocity are independent risk factors. Correlation analysis shows that there is a significant positive correlation between WFNs score and Hunt-Hess grading, and there is also a significant positive correlation between aneurysm height and aneurysm neck width, and aneurysm height and minimum wall shear stress (WSSmin). In the single-variable restricted cubic regression, the nonlinear model of aneurysm neck width shows some significance in the total threshold. There is significance between aneurysm neck width and a single covariate and all covariates.
Conclusions：
Factors such as aneurysm neck width, A1-ACom angle, the diameter of the ACom, and blood flow velocity affect the risk of rupture of the ACoA. The aneurysm neck width shows specific nonlinear characteristics in ACoA rupture.
Intraocular scatter introduced by refractive surgery may induce high-order aberrations
Authors: Zhang Zitao , Zhao Junlei , Gong Rui , Liao Meng , Yang Yanrong , Zhang Haobo , Chen Hao , Dai Yun
Published: April 2024

Purpose：To investigate whether intraocular scatter introduced by refractive surgery will induce high-order aberrations (HOAs).
Method：Intraocular scatter and HOAs (measured at a pupil diameter of 5mm) of 130 myopic eyes (67 patients) were analyzed and compared before surgery and at 1 week, 6 and 12 months after surgery, while 40 normal eyes were set as a control group. Autocorrelation analysis of intraocular scatter and HOAs were performed to confirm the variation trend of ocular scatter and HOAs, respectively. The causal relationship (based on eight criteria: temporal order, strength of association, correlation, exclusion of other explanations, reproducibility, evidence from experimental studies, biological plausibility, and specificity) between intraocular scatter changes and HOAs was analyzed.
Result：Intraocular scatter significantly increased during the refractive surgery and then gradually decreased. HOAs decrease slightly during the refractive surgery, and then gradually increased. The results about the causal relationship analysis: (1) There was a significant correlation between the changes of intraocular scatter during refractive surgery and the HOAs at 6 months after refractive surgery (correspond to the temporal order and correlation). (2) The correlations were also found between the changes of intraocular scatter at 6 months after refractive surgery and HOAs at 12 months after refractive surgery (correspond to the reproducibility). (3) The strong correlation within the experimental group did not exist in the control group (correspond to the exclusion of other explanations). (4) The proportion of individuals in the experimental and control groups who match the conclusions drawn from the analysis was calculated and used to calculate relative ratio (RR) of 4.769 (99.0% CI: 1.790~12.706). The z-test (z=4.106, p=0.000) indicates a high strength of causal association, and the proportion of matches in the experimental group is 4.769 times that of the control group. (5) The data collected in this study were based on clinical trials, which meet the requirements for evidence from experimental studies.
Conclusions：Intraocular scatter introduced by refractive surgery would induce HOAs.
Machine learning-based models predict postoperative cardiovascular and neurological complications after pneumonectomy: A 10-year retrospective observational study
Authors: Wang Yaxuan , Xie Shiyang , Liu Jiayun , Wang He , Yu Jiangang , Li Wenya , Guan Aika , Xu Shun , Cui Yong , Tan Wenfei
Published: April 2024

Background
Reducing postoperative cardiovascular and neurological complications (PCNC) in thoracic surgery is key for improving postoperative survival. Therefore, we aimed to investigate the independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer.
Methods
This study used data from a previous retrospective study of 16,368 lung cancer patients with American Standards Association physical status I-IV who underwent surgery. Postoperative information was collected from electronic medical records; the optimal model was analyzed and filtered using multiple machine learning models (Logistic regression, eXtreme Gradient Boosting, Random Forest, Light Gradient Boosting Machine, and Naïve Bayes). The predictive nomogram was built, and the efficacy, accuracy, discriminatory power, and clinical validity were assessed using receiver operator characteristics, calibration curves, and decision curve analysis.
Results
Multivariate logistic regression analysis showed that age, duration of surgery, intraoperative intercostal nerve block, postoperative patient-controlled analgesia, bronchial blocker, and sufentanil were independent predictors of PCNC. Random forest was identified as the optimal model with an area under the curve of 0.898 in the training set and 0.752 in the validation set, confirming the excellent prediction accuracy of the nomogram. All the net benefits of five machine learning models in the training and validation sets demonstrated excellent clinical applicability, and calibration curves also showed good agreement between the predicted and observed risks.
Conclusion
The combination of machine learning models and nomograms may contribute to the early prediction and reduction of the incidence of PCNC.
Assessment of Aflatoxin Contamination in Roasted Peanut Samples from Gondar City, Ethiopia: Risk Evaluation and Health Implications
Authors: Tibebe Dessie , Kassaw Moges , Mulugeta Marye , Kassa Yezbie , Moges Zerubabel , Yenealem Dereje , Fentie Tarekegn , Amare Agmas , Sheferaw Hailu
Published: April 2024

The study aimed to detect the level of aflatoxin contamination and risk assessment of aflatoxins. A total of eighteen roasted peanut samples were collected and analyses in different area of Gondar City. The aflatoxin was extracted from peanut with (Acetonitrile: Methanol: Water (15: 25: 60) and the analyses were performed using High-Performance Liquid Chromatography detector method. From the result, A good linearity of standard calibration was found for AFB1, AFB2, AFG1 and AFG2 at a range of 0.2–50 ppb. Regression coefficient (R2) values were >0.996. The average recoveries for spiked sample were ranged from 81.83 to 108.16% and Limit of detection and quantification ranged from (0.138 - 0.6 ppb), (0.46 - 2.0 ppb), respectively. The determined aflatoxin contamination levels in roasted peanut samples ranged from 2.6± 0.081-246.42± 0.082, 0.64± 0.012-23.41±0.016, 9.02± 0.008-156.10± 0.041, 1.49± 0.048 -19.25± 0.04 μg/kg for AFB1, AFB2, AFG1, AFG2, respectively. Moreover, the total amount of aflatoxin detected ranged from 14.05μg/Kg to 445.18μg/Kg. All the analyzed samples exceeded tolerance limit of European Union,(recommended maximum limit of 4μg/kg. Hence, US Food and Drug Administration and World Health Organization tolerance limit 20 μg/kg of aflatoxin total. The Estimated Daily Intakes (EDI) of the total aflatoxins in the peanut samples was 0.531, 0.299 and 0.229μg/Kgbw/day for children, adolescents, and adults respectively. For the Margin of Exposure (MOE), values of 753.3, 1337.8 and 1746.7 were recorded respectively. The average potency of the aflatoxins was 0.00825 ng Aflatoxins/kgbw/day and produced a population risk of 4.4 × 10-3, 2.5 × 10-3 and 1.9 × 10-3 respectively. In this study, a probable menace for cancer was projected at 0.0019− 0.0044 cancers per year per 100,000 person’s health among some community in Gondar, Ethiopia that could be harmed by aflatoxins in toxication via consumption of peanuts and products with an estimated daily intake (EDI) of 0.087 ng/kg bw/day of peanuts in a Gondar, Ethiopia population.
The potential causal association between aspirin consumption and the risk of prostate cancer incidence: a Mendelian randomization analysis
Authors: Zhu Wenliang , Lai Xiaomin , Lai Huajian , Qiu Wenfeng , Xiao Chutian , Hu Daoyuan , Yan Dong , Tao Yiran , Wang Dejuan , Qiu Jianguang
Published: April 2024

Background
Currently, the potential link between aspirin use and the development of prostate cancer remains uncertain. The purpose of this study using two-sample Mendelian randomization (MR) was to elucidate the causal effect of aspirin use on prostate cancer (PCa) risk.
Methods
This study included pooled statistics from two Genome-Wide Association Study (GWAS), one for aspirin use (61583 cases and 50427 control) and the other for PCa of European descent (22534 cases and 270176 control). Inverse variance weighting (IVW) was used as the main method, MR-Egger method, weighted median method and weighted model method were used to evaluate the causal relationship between aspirin use and PCa risk. Cochran's Q test was used to check the heterogeneity, and the MR-Egger intercept test was used to analyze the horizontal pleotropy of the results, and a residual analysis was carried out to confirm the robustness and reliability of the results.
Results
Inverse variance weighting was used to infer that aspirin use had a protective effect on reducing the incidence of PCa (OR = 0.986, 95% CI = 0.978–0.994, P = 0.001). Sensitivity analysis showed that pleiotropy and heterogeneity were not observed. Furthermore, the remain-one analysis showed that the survey results were not significantly affected by any instrumental variable.
Conclusion
There is a causal relationship between aspirin use and the development of PCa, that is, aspirin use reduces the risk of PCa.
An Adaptive Exponentially Weighted Moving Average Based Support Vector Data Description Machine for Multivariate Process
Authors: Ahsan Muhammad , Mashuri Muhammad , Jaya Andi Indra , Rifki Kevin Agung Fernanda , Prasetya Ichwanul Kahfi , Lee Muhammad Hisyam
Published: April 2024

In recent years, modern manufacturing systems have become increasingly complex and generate a wide variety of data. This poses challenges for finding patterns and effective monitoring processes. One-class classification (OCC) is a machine learning technique suitable for finding data patterns. OCC algorithms learn a model of normal process behavior and flag deviations. This helps to detect outliers and process shifts. Support Vector Data Description (SVDD), an OCC type, was used to monitor high-dimensional data. It constructs a hypersphere around normal data and flag outliers. However, SVDD is sensitive to volatile process shifts. Adaptive Exponentially Weighted Moving Average (AEWMA) control charts address this issue. They dynamically adjust the control limits based on the process-mean changes. This study proposes a new AEWMA-based SVDD control chart (D'AEWMA) for monitoring multivariate processes. The D'AEWMA chart outperformed the traditional D'EWMA chart in detecting out-of-control signals and minimizing false alarms.
Elemental detection and mapping of rat bone matrix induced by chemoradiotherapy with confocal μ-XRF
Authors: Li Qiuxia , Chen Hongchi , Zhou Lazhen , Li Fangzuo
Published: April 2024

Breast cancer is among the most prevalent malignant tumors in women. The administration of chemotherapy and radiotherapy for breast cancer treatment can lead to amenorrhea and substantial bone loss in women. Consequently, postmenopausal women are susceptible to the morbidity and mortality risks associated with breast cancer and postmenopausal osteoporosis. In this study, we investigated the impact of chemotherapy and radiotherapy on osteoporosis in female rats using confocal microbeam X-ray fluorescence (µ-XRF) method. The female Sprague Dawley (SD) rats were categorized into three groups: the control group (G1), the chemotherapy and radiotherapy group (G2), and the radiotherapy-only group (G3). The SD rats were euthanized six weeks post chemotherapy and radiotherapy, and femur slices with a thickness of 1mm were obtained for confocal µ-XRF analysis. The results demonstrate a significant loss of calcium in the G2 and G3 groups, while the G2 group exhibited a substantial increase in Fe content compared to the G1 group. The conclusion can be drawn that the occurrence of osteoporosis is related to chemotherapy and radiotherapy, while the significant elevation in bone iron content signifies the progression of osteoporosis.
Versatile, accessible cross-platform molecular profiling of central nervous system tumors: web-based, prospective multi-center validation
Authors: Sahm Felix , Patel Areeba , Göbel Kirsten , Hinz Felix , Ille Sebastian , Dogan Helin , Lomakin Artem , Schrimpf Daniel , Göller Pauline , Ritter Michael , Kerbs Paul , Pfeifer Lisa , Schoebe Natalie , Hamelmann Stefan , Blume Christina , Bogumil Henri , Berghaus Natalie , Euskirchen Philipp , Schweizer Leonille , Hultschig Claus , Roy Nadine Van , Dorpe Jo Van , Meulen Joni Van der , Loontiens Siebe , Dedeurwaerdere Franceska , Leske Henning , Halldorsson Skarphedinn , Fox Graeme , Deacon Simon , Cahyani Inswasti , Holmes Nadine , Wibowo Satrio , Munro Rory , Martin Dan , Sharif Abid , Housley Mark , Goldspring Robert , Brandner Sebastian , Roy Somak , Hench Jürgen , Frank Stephan , Unterberg Andreas , Goidts Violaine , Jäger Natalie , Paine Simon , Smith Stuart , Herold-Mende Christel , Wick Wolfgang , Pfister Stefan , Krieg Sandro , Vik-Mo Einar , von Deimling Andreas , Jones David , Loose Matthew , Schlesner Matthias , Sill Martin
Published: April 2024

The 2021 WHO classification underscores the importance of molecular data integration in Central Nervous System (CNS) tumor diagnostics. However, currently used assays have disadvantages due to technical complexity, required equipment and reagent cost, as well as lengthy turnaround times. In response to these challenges, we introduce Rapid-CNS2 and MNP-Flex. Rapid-CNS2, an adaptive sampling-based nanopore sequencing workflow, offers real-time methylation classification and DNA copy-number information within a 30-minute window, suitable for intra-operative settings, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We have prospectively validated Rapid-CNS2 in a multi-center setting on 223 samples. For even more widespread use of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 CNS tumor classes. MNP-flex achieved 92% accuracy across a global validation cohort of 78,000 samples spanning five different technologies. These innovations represent a significant advancement in CNS tumor diagnostics, making rapid, actionable molecular insights more widely and more rapidly available, which is crucial for personalized treatment strategies. Their integration streamlines the diagnostic process, broadening access to accurate molecular classification and promising improved patient outcomes in neurooncology on a global scale. MNP-Flex is available as a web-service https://mnp-flex.org and the Rapid-CNS2 workflow is available on Github.
Decisional procrastination and problematic mobile phone use: the mediation of anxiety and the moderation of 2019-nCoV traumatic experience
Authors: Chen Lili , Gao Sun-Yu , Li Jun , Chen Ling-Ge , Huang Jian-Hao
Published: April 2024

Background Decisional procrastination (DP) has an important effect on problematic mobile phone use (PMPU); however, the potential mechanisism and boundary conditions between DP and PMPU remianed to be further explored. This essay studied the mediation of anxiety between DP and PMPU and whether 2019-nCoV traumatic experience moderated the mediation process.Methods A questionnaire was used in this study. A valid sample of 798 college students reported levels of decisional procrastination, problematic mobile phone use, anxiety, and 2019-nCoV traumatic experience.Results The results indicated that decisional procrastination is positively associated with problematic mobile phone use among college students. Anxiety served as a partial mediator in the association between decisional procrastination and problematic mobile phone use. 2019-nCoV traumatic experience would positively moderated the mediating effects of anxiety between DP and PMPU. A higher degree of 2019-nCoV traumatic experience would strengthen the mediation effects of DP to PMPU through anxiety.Conclusions This study deepens our understanding of how DP affects college students' PMPU. It was found in the study that 2019-nCoV traumatic experience positively moderated the mediation effects of anxiety between DP and PMPU. These findings provide universities with a theoretical foundation for preventing PMPU among college students.
Genetic diversity and population structure analysis of Ethiopian bread wheat (Triticum eastivum L.) germplasm using SSR markers
Authors: Tiruneh Abebe , Feyissa Tileye
Published: April 2024

Background: Bread wheat, belonging to the most diverse and important family Poaceae in the plant kingdom, produces crucial edible grains. Ethiopia has been considered as a center of diversity and the second center of bread wheat domestication. Genetic diversity and population structure analyses in the Ethiopian bread wheat germplasm have enormous importance in enhancing breeding and sustainable conservation.
Methodology: 96 bread wheat germplasm were gathered from Kulumsa and Adet Research Centers, Ethiopia. The samples were taken to the ICARDA-BIGMP, Cairo, Egypt and grown at the green house, after two weeks leaf samples were collected per plant, and taken to the laboratory for DNA extraction. Data were analyzed using PowerMarker ver. 3.25, NJ, UPGMA, Structure, ver.2.3.4, and AMOVA.
Results: Genetic diversity and population structure were estimated across 96 germplasm using 7 polymorphic and informative SSRs. Varied values of diversity indices were observed across chromosomes and genomes. Higher mean values of MAF (0.67), PIC (0.34), and Nei's gene diversity (Gd) (0.36), and values of Gd (0.41) and PPL (87.28%) were signifying the presence of high genetic diversity within and among populations, respectively. AMOVA showed highly significant population differentiation for 98% variation within population letting only 2% significant variation among populations. The Structure analysis showed four populations (COV, EBWNVT, EBWYT, and EBWAT), while the UPGMA revealed 3 main population clusters, in which the EBWYT and EBWAT were the 2 sub-clusters. The NJ analysis and PCoA across 96 germplasm revealed three main clusters in each the germplasm were found inter-mixed irrespective of their breeding history and evolution likely to the Clumpak result, signifying the presence of higher admixture due to the existence of historical exchanges of seeds through informal system involving regional and nationwide farming communities in Ethiopia.
Conclusions: Sustainable utilization and conservation of rich Ethiopian bread wheat genetic resource is an irreplaceable means to cope up the recurrent climate changes and biotic stresses happening wide, and thereby able to meet the demand of bread wheat productivity for the ever-growing human population.