bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hierarchical Region-level DecouplingKnowledge Distillation for Semantic Segmentation
Authors: Yu Xiangchun , Liu Huofa , Zhang Dingwen , Wu Jianqing , Zheng Jian 
Published: July 2024

Abstract

Region-level distillation can facilitate the transfer of salient region information for each channel, but computing Kullback-Leibler (KL) divergence for the entire soft probability map can significantly limit the function of Non-target Class Knowledge Distillation (NCKD). To address the aforementioned issue, we propose the Region-level Decoupling Knowledge Distillation (RDKD), and the simple and efficient RDKD implicitly decouples region-level distillation into Target Region Knowledge Distillation (TRKD) and Non-target Region Knowledge Distillation (NRKD), which ensures the effective transfer of the region-level dark knowledge present in both TRKD and NRKD. In order to progressively integrate global information, we further propose the Hierarchical Region-level Decoupling Knowledge Distillation (HRDKD), which gradually aggregates global information through a simple average pooling operation, thereby facilitating the distillation of multi-scale semantic information. Extensive experiments are conducted on six benchmark datasets: Cityscapes, Pascal VOC, ADE20k and COCO Stuff164k for natural images, and Synapse and FLARE22 for medical images. The experimental and visualization results demonstrate that our proposed distillation methods achieve state-of-the-art (SOTA) performance without the need for introducing auxiliary modules in the corresponding semantic segmentation tasks.
Optimal design of water distribution network considering the uncertainty and correlation of nodal demands
Authors: Hou Benwei , Zhou Baojin , Wu Shan 
Published: July 2024

Abstract

In the least-cost design of water distribution network (WDN), it is crucial to consider the uncertainties in nodal demands to improve the reliability and robustness of the design. Meanwhile, the nodal demands with similar consumption pattern may behaves notable correlations. This paper proposes a multi-objective optimal design model of WDN considering the uncertainty and correlation of nodal demands, which takes pipe diameter as the design variable aiming to maximize the service reliability of WDN and minimize the design cost of pipes. To deal with the probabilistic problem in the optimization model, the reliability of WDN is expressed as the probability of all user nodes in the WDN simultaneously satisfying the minimum water pressure. The reliability is evaluated by the Monte Carlo method, where the independent uncertain samples of nodal demands are firstly generated by Latin hypercube sampling according to their probabilistic characteristics, then the correlated uncertain samples are transformed from the independent samples through the inverse-orthogonal and inverse-Nataf transformation. The influence of uncertainty and correlation of nodal demands on the optimal design schemes is investigated by its application in two WDNs. In comparison with the result of independent uncertain cases, the correlation of uncertain nodal demands results in a greater increment of design cost by 8%~15%. In addition, the correlations of nodal demands do not always result in higher design costs. The cost corresponding to the partial inter-group correlation among user nodes with similar pattern of water demands is lower than that corresponding to independent cases.
Macrophage junctional adhesion molecule-like protein promotes NLRP3 inflammasome activation in the development of atherosclerosis
Authors: Yang Janmin , cui huiliang , Cheng Cheng , Xue Fei , xie lin , wu zhenguo , liu li , Lu Hanlin , Zhang Cheng , zhang Wencheng 
Published: July 2024

Abstract

Inflammation plays a crucial role in the progression of atherosclerosis. Junctional adhesion molecule-like protein (JAML), a type-I transmembrane glycoprotein, activates downstream signaling pathways. However, the precise role of macrophage-derived JAML in inflammation and atherosclerosis remains unclear. This study aimed to generate mice with macrophage-specific deletion or overexpression of JAML, with the focus of assessing its impact on macrophage function and elucidating its regulatory mechanism in atherosclerosis. High-throughput data screening was employed to investigate JAML expression in atherosclerosis, and macrophage-specific JAML-knockout and transgenic mice models were utilized to examine the effects of JAML on atherosclerosis. Furthermore, the role of JAML was assessed using Oil Red O staining, RNA-sequencing analysis, and co-immunoprecipitation techniques. Increased JAML expression was observed in macrophages from both mice and patients with atherosclerosis. Macrophage-specific JAML deletion attenuated atherosclerosis and inflammation, whereas macrophage-specific JAML overexpression exacerbated these conditions. Mechanistically, JAML deletion inhibited lipopolysaccharide (LPS)-induced inflammation by decreasing nuclear translocation of pyruvate kinase M2 (PKM2) and PKM2/p65 complex formation, which consequently suppressed the nuclear factor kappa B (NF-κB) pathway and NLRP3 inflammasome activation. Taken together, these findings demonstrate that macrophage-expressed JAML facilitates the progression of atherosclerosis by activating the NF-κB pathway and NLRP3 inflammasome through nuclear migration and phosphorylation of PKM2. Notably, our study revealed a novel mechanism for the regulation of NLRP3 inflammasome activation in atherosclerosis. Therefore, targeting JAML may be an effective treatment strategy for atherosclerosis, a condition characterized by chronic inflammation.
Genetic prediction of the causal relationship between systemic lupus erythematosus and interstitial lung disease: a two-sample Mendelian randomized study
Authors: Liu Yi , Song Xuejiao , Yang Yue , Yang Jungang , Zuo Xianbo , Cui Yong 
Published: July 2024

Abstract

Background
Systemic lupus erythematosus (SLE) and interstitial lung disease (ILD) are often associated with clinical practice, and their co-pathogenesis and clinical features have been reported. However, genetic causality between SLE and ILD remains unclear.
Methods
We searched public genome-wide association study (GWAS) databases for SLE and ILD data, and appropriate single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs). Inverse-variance weighting (IVW) was used as the main analysis method, and the weighted media and weighted models were supplemented by verification. The Cochran Q test was used to evaluate SNPs heterogeneity. The horizontal pleiotropy of SNPs was tested using the Mendelian randomization (MR) Egger intercept test. Using the method of leave-one-out sensitivity analysis method, we evaluated whether MR is affected by a single SNP.
Results
IVW results showed that SLE significantly increased the risk of ILD (OR = 1.095, P = 0.023). The results obtained using the other two methods were consistent with the IVW. The MR-PRESSO and MR-Egger intercept tests showed that there was no heterogeneity or horizontal pleiotropy among the IVs. The leave-one-out method confirms the robustness of the results.
Conclusion
Our results showed a genetic prediction of the causal relationship between SLE and ILD. These results provide a valuable contribution to future clinical research on SLE-ILD.
Contrasting the performance of mainstream Large Language Models in Radiology Board Examinations
Authors: Wei Boxiong , Zhang Xiumei , Shao Yuhong , Sun Xiuming , Chen Luzeng 
Published: July 2024

Abstract

Objective
This study evaluates the performance of mainstream Large Language Models, including GPT-4, Claude, Bard, Tongyi Qianwen, and Gemini Pro, in radiology board exams.
Methods
A comparative analysis of 150 multiple-choice questions from radiology board exams without images was conducted. Models were assessed on accuracy in text-based questions categorized by cognitive levels and medical specialties using chi-square tests and ANOVA.
Results
GPT-4 achieved the highest accuracy (83.3%), significantly outperforming others. Tongyi Qianwen also performed well (70.7%). Performance varied across question types and specialties, with GPT-4 excelling in both lower-order and higher-order questions, while Claude and Bard struggled with complex diagnostic questions.
Conclusion
GPT-4 and Tongyi Qianwen show promise in medical education and training. The study emphasizes the need for domain-specific training datasets to enhance large models' effectiveness in specialized fields like radiology.
Sequential Testing with Xpert MTB/RIF assay for Diagnosis of Tuberculous Meningitis in Maharaj Nakorn Chiang Mai University Hospital
Authors: Winichakoon Poramed , Watcharasaksilp Kanokwan , Butphet Sunisa , Wongworapat Kanlaya , Pantip Chansom , Khamnoi Phadungkiat , Supparatpinyo Khuanchai , Salee Parichat 
Published: July 2024

Abstract

Early diagnosis and appropriate treatment are crucial in the effort to reduce morbidity and mortality from tuberculous meningitis (TBM). This cross-sectional study aimed to investigate the diagnostic performance of the Xpert MTB/RIF assay in accurately diagnosing TBM. Sixty-five CSF specimens from patients at Maharaj Nakorn Chiang Mai University Hospital, Thailand, who had subacute lymphocytic meningitis between January 2015 and March 2016 were included for analysis. By using Mycobacteria growth indicator tube (MGIT) culture as a reference standard, the sensitivity and specificity of the Xpert MTB/RIF assay were 83.33% (95% CI 57.19–98.22) and 96.23% (95% CI 87.02–99.54), respectively. There was good agreement between the Xpert MTB/RIF assay and MGIT culture of 93.85% (p < 0.001) and a kappa score of 0.796 (95% CI 0.603–0.988). By using a TBM score as a cut-off point of 6, followed by the Xpert MTB/RIF assay as the sequential testing, there was an improvement in specificity from 96.23–97.15%. This study demonstrated that the Xpert MTB/RIF assay was a fast and accurate method for detecting M. tuberculosis from centrifuged CSF specimens, with a high level of sensitivity and specificity. The diagnostic algorithm of sequential testing, combined with the TBM scoring system and the Xpert MTB/RIF assay, could improve diagnostic performance.
The Effect of Maryland's 2022 Minimum Wage Increase on Employment in the Fast-Food Industry
Authors: Broccolina Ethan , Tan Kerry 
Published: July 2024

Abstract

Economic theory suggests that increasing minimum wage could lead to a decrease in employ- ment, especially for industries most sensitive to minimum wage laws. Using Pennsylvania as a control group, we study the labor implications of Maryland’s increase in minimum wage in 2022 using a difference-in-differences approach. Although OLS regressions infer that the minimum wage hike had no effect on employment in the fast-food industry, our more rigorous fixed effects regressions indicate that the minimum wage law had a negative and statistically significant impact on employment, implying that higher minimum wage caused an increase in unemployment among fast-food workers.
JEL classifications: J20, K31
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects
Authors: Fu Da , Rong Mingfei , Huang Hao , Kim Eun-Hu , Pedrycz Witold 
Published: July 2024

Abstract

Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms. This study proposes novel dual-current neural networks (DCNN), which combine the advantages of convolutional operations and self-attention mechanisms to improve the accuracy of fine-grained image classification. The main novel design features for constructing a weakly supervised learning backbone model DCNN include (a) extracting heterogeneous data, (b) keeping the feature map resolution unchanged, (c) expanding the receptive field, and (d) fusing global representations and local features. Experimental results demonstrated that using DCNN as the backbone network for classifying certain fine-grained benchmark datasets achieved performance advantage improvements of 13.5–19.5% and 2.2–12.9%, respectively, compared to other advanced convolution or attention-based fine-grained backbones
Imaging the three human primary amygdala output tracts with age and sex characterisation across the lifespan
Authors: Nasa Anurag , Drago Thomas , Shah Ashka , Kewlani Bharti , Nagassima Katharina , Mahmoud Muhammad , Roman Elena , O'Connor Michael , O’Hora Emma , Kelly Linda , Ang Jin Zhe , Guha Arunava , Ukponu Ezije , Levins Kirk J , Connaughton Michael , Mitchell Orla , O’Hanlon Erik , O’Keane Veronica , Roddy Darren William 
Published: July 2024

Abstract

Background
- The amygdala is involved memory and emotion processing and communicates with the rest of the brain through three efferent tracts: the stria terminalis (ST), ventral amygdalofugal pathway (VAP), and anterior commissure (AC). This is the first study to isolate all three major amygdalar output pathways using novel diffusion tractography protocols with exploration of the diffusion, age, and sex characteristics of each tract.
Methods
− 64 healthy individuals aged 15–64 underwent high-resolution T1, T2, and diffusion MR brain imaging with constrained spherical deconvolution tractography. Individually generated amygdalae aided the virtual dissection of the ST, VAP and AC using novel bespoke protocols for each tract based on anatomical principles. Age and sex diffusion characteristics were explored.
Results
- The ST showed age-related decreases in fractional anisotropy (left: p = 0.00018; right: p = 0.00032), mean diffusivity (left: p = 0.0017; right: p = 0.00058), and radial diffusivity (left: p = 0.00015; right: p = 3.44E-05). The AC showed decreases in mean diffusivity (p = 0.0022) and axial diffusivity (p = 0.00015). Sex had no significant effect on the diffusion metrics apart from the right ST, showing higher fractional anisotropy in males than in females (p = 0.001).
Conclusion
- This is the first study to virtually dissect the three main output tracts of the amygdala from neuroimaging. We also show age related changes in markers of neuronal integrity with age. No sex differences were found apart from potentially more robust integrity in the right ST in males. The novel anatomically-driven and reproducible protocols for ST and VAP isolation presented may guide future investigation of the connectivity and efferent circuitry of the amygdala.
A Modular Hypoplastic Constitutive Model for Granular Soils
Authors: Pucker Tim 
Published: July 2024

Abstract

A new hypoplastic constitutive model with a modular structure is presented for granular soils. The modular structure allows the application of the constitutive model with very little material information under restriction of the soil effects to be reproduced. The more material information available, the better the stress-strain behavior of the material can be represented. The basic model and six modules are presented that allow to model soil phenomena like barotropy, pyknotropy, load history and small strain stiffness. Laboratory tests are simulated to show the performance of the constitutive model.
Identification of Novel Therapeutic Targets for Head and Neck Squamous Cell Carcinoma Through Bioinformatics Analysis
Authors: Kim Yeon Soo , Jeong Eun-jeong , Kim Eunjeong 
Published: July 2024

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with limited therapeutic options. Using publicly available datasets, we identified the WD repeat domain 54 (WDR54) gene as a potential therapeutic target in HNSCC. Gene Expression Profiling Interactive Analysis version 2 (GEPIA2) was used to identify genes differentially overexpressed in HNSCC. Our results showed that WDR54, a member of the WD40 repeat domain family, was overexpressed in HNSCC tumor samples. Analysis of three Gene Expression Omnibus datasets showed that WDR54 was overexpressed in tumor samples. Using the UALCAN database, we showed that WDR54 expression in patients with HNSCC at different tumor stages gradually increased with disease progression. We confirmed the association between WDR54 and metastasis using TNMplot.com. WDR54 was overexpressed in metastatic samples compared to that in normal and tumor samples. Kaplan–Meier analysis showed that patients with high WDR54 levels had a poorer prognosis. Additionally, WDR54 expression was correlated with the epidermal growth factor receptor, which is frequently overexpressed in HNSCC. Our findings suggest that WDR54 is a promising biomarker and therapeutic target in HNSCC.
Digital Survey-based Tracing of COVID-19 Over the Early Pandemic: A Comprehensive Geospatial and Symptomatic Analysis in Lebanon
Authors: Bassim Youssef , Abdelrahman Abir , Douglas Gavin M. , Iaaly Amal , Daou Patrick , Finianos Mayda , Hassan Rim , Nassif Ibrahim , Greige Layal , Dib Liza , Chaar Mira El 
Published: July 2024

Abstract

Background
Early in the COVID-19 pandemic, an application utilizing Geographic Information Systems technology was launched to monitor the virus's spread in Lebanon. Initiated in response to the first reported case, this initiative tracked infection levels, evaluated public health measures, and explored the interaction between the virus's spread and demographic factors.
Methods
The application relied heavily on survey data gathered from users, enabling a crowd-sourced approach to trace the virus spread during the early stages of the pandemic. The accuracy of this approach can now be evaluated by comparing the application results to well-documented global trends observed during the same period.
Results
Data from approximately 11,000 individuals revealed critical trends in the virus's spread, including the disproportionate impact on specific demographics and the effectiveness of public health interventions. Statistical analysis confirmed the increased risk posed by certain behaviors and conditions. Our study corroborates global findings regarding early COVID-19 characteristics, particularly the significance of taste and smell loss as key symptoms, and highlights the impact of demographic factors on disease spread.
Conclusions
This study demonstrates the integration of digital solutions with epidemiological methods. The knowledge gained illustrates the practical application of digital health technologies in managing public health crises and provides a framework for addressing future challenges.
Machine Learning-Based Prediction Models for Postoperative Delirium:A Systematic Review and Meta-Analysis
Authors: Tu Yingying , Zhu Haoyuan , Zhang Xiaozhen , Huang Shaoyi , Tu Wenyi 
Published: July 2024

Abstract

Background
The number of risk prediction models for postoperative delirium has increased yearly, but their quality and applicability in clinical practice and future research remain unclear.
Aims
This systematic review aimed to evaluate published studies on postoperative delirium risk prediction models and provide guidance for model establishment and improvement.
Methods
We searched PubMed, Embase, Cochrane Library, and Web of Science for eligible studies up to February 10, 2024. Included studies provided data for assessing the sensitivity and specificity of prediction models.
Results
We included 12 articles with 58 machine learning (ML) prediction models, covering 37,978 cases with 3,044 instances of postoperative delirium. The combined area under the receiver operating characteristic curve (AUC) for predicting postoperative delirium was 0.82 [95% CI, 0.79–0.85], with a sensitivity of 0.74 [95% CI, 0.69–0.78] and a specificity of 0.78 [95% CI, 0.73–0.82].Subgroup analysis showed that prediction models using random forests had a higher combined AUC of 0.90 [95% CI, 0.87–0.92]. Models for orthopedic surgeries and individuals aged over 60 had higher predictive value. Asian populations showed higher predictive value compared to European and American populations.
Conclusions
ML models perform well in predicting the occurrence of postoperative delirium, particularly random forest models.
Hemodynamic changes for half cover Left subclavian artery ostium during thoracic endovascular aortic repair
Authors: Li Xiaowei , Wen Zan , Yuan Xiaoming , Bi Wei 
Published: July 2024

Abstract

Some clinicians use endograft to cover half the left subclavian artery (LSA) ostium to cure some cases with insufficient landing area in thoracic endovascular aortic repair(TEVAR) treatment. So we used computational fluid dynamics (CFD) to study the hemodynamic changes on LSA, because they may cause acute thrombosis or arteriosclerosis of LSA.
Methods
The digital model of the aortic arch was established and named model A, which only included supraarch branch the LSA. By directly covering half of the LSA ostiumto simulate half cover LSA ostium as model B. All established models were imported into the Gambit grid division software for grid division and were subsequently imported into the Fluent software for hemodynamic numerical simulation and calculation. The related changes for hemodynamic parameters of LSA were analyzed and compared.
Results
Under the same aortic inlet flow, in model B, the local blood flow velocity of LSA ostium increased and whole blood flow velocity at the distal end decreased. The average wall shear stress(WSS) of the LSA was significantly decreased. Meanwhile there was an obvious turbulent flow in the LSA lumen, and the related blood flow state was disordered.
Conclusion
CFD research confirmed that the implantation of an endograft covering half the LSA ostium can cause obvious hemodynamic changes, which is likely to cause a long-term hardening or an acute thrombosis of the LSA, finally increased the risk of stroke. Once this operation is performed in some specific clinical cases for simplicityand economy, we should actively anticoagulate and follow up regularly.
Cortical thickness alternation in obsessive-compulsive disorder patients compared with healthy controls
Authors: Dong Chunyu , Zheng Hao , Shen Hailong , Wan Yu , Xu Yinghong , Li Ying , Ping Liangliang , Yu Hao , Liu Chuanxin , Cui Jian , Li Kun , Zhou Cong 
Published: July 2024

Abstract

Cortical morphological abnormalities are one of the neuropathological changes of obsessive-compulsive disorder (OCD). The surface-based morphometry (SBM) analyses have reported altered cortical thickness (CTh) among patients with OCD in numerous neuroimaging studies; however, the results have been inconsistent. We are attempting to utilize meta-analysis of whole-brain data to investigate changes in cortical thickness among individuals with OCD. This endeavor may enhance spatial accuracy in identification, thus effectively improving our recognition of OCD and facilitating the development of more precise and efficient treatment strategies. The seed-based d mapping (SDM) approach was employed to conduct a vertex-wise coordinate-based meta-analysis (CBMA) of whole-brain studies that explored CTh alteration among patients with OCD, compared with healthy controls (HCs). A systematic literature search identified 9 studies (containing 9 datasets) of CTh, including 518 patients with OCD and 449 healthy controls (HCs). Overall, patients with OCD showed decreased CTh in the left anterior cingulate / paracingulate gyri, right insula, as well as increased CTh in the left lingual gyrus, left inferior frontal gyrus, orbital part, left superior frontal gyrus, dorsolateral of the regions. Besides, the Meta-regression analyses revealed a negative correlation between age and cortical thickness of the right insula in patients with OCD. Due to the limited number of studies and sample sizes included in the final analysis, as well as partial data missing from some samples, we were unable to conduct a comprehensive subgroup analysis. Besides, the outcomes of meta-regression analyses should be interpreted cautiously due to their reliance on a limited number of studies. The meta-analysis revealed cortical thickness alterations in specific regions of the brain among individuals diagnosed with OCD. These findings contribute to a deeper understanding of the underlying mechanisms of OCD brain dysfunction and provide insights for early intervention strategies for the disorder.