bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Relevance Learning for Few-Shot Object Detection
Authors: Liu Weijie , Cai Xiaojie , Wang Chong , Li Haohe , Yu Shenghao 
Published: December 2024

Abstract

Expensive bounding-box annotations have limited the development of object detection task. Thus, it is necessary to focus on more challenging task of few-shot object detection. It requires the detector to recognize objects of novel classes with only a few training samples. Nowadays, many existing popular methods adopting training way similar to meta-learning have achieved promising performance, such as Meta R-CNN series. However, support data is only used as the class attention to guide the detecting of query images each time. Their relevance to each other remains unexploited. Moreover, a lot of recent works treat the support data and query images as independent branch without considering the relationship between them. To address this issue, we propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN). By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly. Comprehensive experiments have been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features. Our code is available at https://github.com/liuweijie19980216/DRL-for-FSOD.
Efficacy of the Cross-Union Protocol in the Treatment of Congenital Tibial Pseudarthrosis: A Comparative Study
Authors: Jing Yanhui , Wang Dahui , Zhang zhiqiang , Mo Yueqiang , Ning Bo , Wu Chunxing 
Published: December 2024

Abstract

Background
Congenital Pseudarthrosis of the Tibia (CPT) is a rare pediatric condition presenting substantial challenges for orthopedic surgeons aiming to achieve bone union, with subsequent complications such as refractures being common. The aim of the present study is to evaluate the results of our intentional cross-union protocol and to compare these outcomes with those obtained from our previously used techniques.
Materials and Methods
Sixteen patients, with a mean age of 4.8 years (range: 1-13.3 years), who were treated with the intentional cross-union protocol were included in Group A. Eleven patients, with a mean age of 3.5 years (range: 1.3-7.6 years), who primarily underwent intramedullary rodding with bone graft, were included in Group B. The intentional cross-union protocol involved cross-union of the tibia and fibula, autogenous iliac bone grafting, and the insertion of bone morphogenetic protein 2 (BMP-2) aimed at achieving tibia-fibula cross-union. Retrospective evaluation of serial radiographs was conducted, and the outcomes regarding union and any subsequent complications were analyzed.
Results
A total of 27 patients with CPT were included in the study. All 16 patients (100%) in Group A successfully achieved primary union. Among the 11 patients in Group B, only 4 cases achieved primary union, with a primary healing rate of 36.4%. Seven cases with delayed healing required 2-4 revision surgeries for final healing. The time to union was significantly shorter in Group A (3.37 ± 0.64 months) compared to Group B (8.67 ± 3.0 months). The cross-sectional area of union was also significantly larger in Group A (6.71 ± 0.58 cm²) compared to Group B (1.18 ± 0.19 cm²). In Group A, there were no cases of refracture, whereas in Group B, 7 cases (77.8%) experienced refracture, indicating a statistically significant difference.
Conclusions
Our study demonstrates that the Cross-union protocol is highly effective for achieving union and preventing refracture in CPT. With such significant improvement, it has notably altered the prognosis of this challenging condition.
Level of Evidence: IV
Strategic Model for Integrating Biogas A Framework for Sustainable Energy Integration in Agro-Industries
Authors: Nahwani Ahmad , Soeprijanto Soeprijanto , Widodo Erwin 
Published: December 2024

Abstract

The framework of the methodology presented in this study is an effort to integrate and optimize the agro-industry sector, especially energy in biogas. In this study, the technique of the system in functional analysis is shown systematically to translate various energy requirements in the factory as criteria for performance and functional design to be integrated, optimized, and energy efficient. The case study results indicated that biogas power plants, with a capacity of 1.5 MW, can produce around 13,140 MWh per year. The annual return on investment (ROI) is around 37.13%. With this ROI value, the payback period is 31 months. The overall reduction of greenhouse gases is approximately 77,826 tons CO2 eq/year. The potential value of carbon trading is about USD 3,113,040 per year. This strategic model presents a novel approach by integrating biogas energy production with a customized wastewater treatment system adapted to biodigesters’ effluent characteristics. It offers a sustainable, economically feasible, and scalable solution, combining resource recovery, waste minimization, and potential for carbon trading into a unified system. The novelty of this research lies in maximizing the utility of biogas plants by efficiently treating and reusing wastewater, creating a closed-loop, zero-waste process. Future research on hybrid systems integrating Biogas power plants by focusing on efficiency optimization, economic feasibility, environmental impacts, and innovative approaches like AI and blockchain could make the hybrid system a more robust, scalable, and sustainable solution. Thus, the framework based on the results of this study finds tools that can maximize and integrate energy sources, especially biogas, in the agro-industrial sector.
Rapid Diagnosis of Membranous Nephropathy Based on Kidney Tissue Raman Spectroscopy and Deep Learning
Authors: zhu Guoqiang , shadekejiang halinuer , Zhang Xueqin , Chen Cheng , Su Mingjie , Wu Shuo , Aimaijiang Gulizere , Zhang Li , Wang Shun , Yang Wenjun , Lu Chen 
Published: December 2024

Abstract

Membranous nephropathy (MN) is one of the most common causes of glomerular disease in clinical settings. Renal biopsy is the gold standard for diagnosing MN, but it is invasive and poses risks such as renal hemorrhage, perirenal hematoma, and infection. Furthermore, conventional light and electron microscopy techniques fail to distinguish between primary and secondary MN. The recent identification of autoantibodies against phospholipase A2 receptor (PLA2R) and thrombospondin type-1 domain-containing 7A (THSD7A) antigens has provided key insights into the pathophysiology of MN. Although immunofluorescence assays for detecting serum anti-PLA2R antibodies and glomerular PLA2R antigens are used clinically to diagnose PLA2R-related MN, only 52%-86% of PLA2R-associated MN patients exhibit positive anti-PLA2R antibodies. Recent advances in Raman spectroscopy combined with deep learning methods offer promising non-invasive diagnostic approaches. Our previous studies demonstrated the high diagnostic efficacy of Raman spectroscopy for MN using human serum and urine samples, with excellent sensitivity and specificity. In this study, we present a rapid diagnostic method for MN based on Raman spectroscopy of mouse kidney tissue combined with a CNN-BiLSTM deep learning model. The model achieved 98% accuracy, with specificity and sensitivity of 98.3%, offering a novel supplementary tool for the pathological diagnosis of MN.
Uncovering causal gene-tissue pairs and variants: A multivariable TWAS method controlling for infinitesimal effects
Authors: Zhu Xiaofeng , Yang Yihe , Lorincz-Comi Noah 
Published: December 2024

Abstract

Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariable TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariable TWAS method named Tissue-Gene pairs, direct causal Variants, and Infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci (eQTL/sQTL) from 31 tissues, TGVIS improves causal gene prioritization and enhances the biological interpretability over existing methods.
Epidemiological Characteristics of Infertility, 1990-2021, and 15-Year Forecasts for Global, China, and the United States: An Analysis Based on the Global Burden of Disease Study 2021
Authors: Feng Jiale , Wu Qingguo , Liang Yangbing , Liang Yiwen , Bin Qin 
Published: December 2024

Abstract

Background
Infertility represents a widespread public health challenge. Gaining a thorough understanding of the burden and the epidemiological patterns associated with infertility is essential for evaluating advancements in addressing this epidemic, as well as for informing policy development and program execution. This research aims to elucidate the degree of infertility across various global regions and nations, offering vital insights that can aid in policy-making efforts aimed at mitigating the infertility epidemic.
Methods
The research utilized information gathered from the Global Burden of Disease (GBD) Study conducted in 2021. This dataset encompasses statistics related to prevalence and disability-adjusted life years (DALYs), as well as age-standardized prevalence rate (ASPR) and age-standardized DALYs rate (ASDR) associated with infertility, spanning the years 1990 through 2021. Furthermore, the estimated annual percentage change (EAPC) for these figures was calculated, and provided 95% confidence intervals (CIs) for these estimates as a reference.
Results
In 2021, it was calculated that the global Age-Standardized Prevalence Rate (ASPR) for male infertility stood at 1354.76 cases per 100,000 people, with a 95% Uncertainty Interval ranging from 802.12 to 2174.77 cases per 100,000 individuals. For female infertility, the ASPR was recorded at 2764.62 per 100,000 individuals (95% UI: 1476.33-4862.57 per 100,000 individuals). Between 1990 and 2021, the EAPC in ASPR was observed to be 0.5% (95% CI: 0.36–0.64) for males and 0.7% (95% CI: 0.53–0.87) for females. In that same year, the global ASDR attributed to male infertility was 7.84 per 100,000 individuals (95% UI: 2.85–18.56 per 100,000 individuals), while for female infertility, it amounted to 15.12 per 100,000 individuals (95% UI: 5.35–36.88 per 100,000 individuals). The EAPC for ASDR linked to male and female infertility from 1990 to 2021 was assessed at 0.51% (95% CI: 0.38–0.65) and 0.71% (95% CI: 0.54–0.88), respectively. Among the 204 countries and territories in 2021, India ranked first in both the prevalence of cases and DALYs associated with male and female infertility, followed by China and Indonesia.
Conclusions
Between 1990 and 2021, the worldwide occurrence of infertility has increased, particularly in countries with low to middle incomes. Developing alternative strategies for coping with infertility and creating comprehensive management plans specific to regions experiencing a significant burden is essential.
Candida species in nephrology patients: biofilm formation, enzymatic activity, and genotyping
Authors: Nouripour-sisakht Sadegh , Jafarian Hadis , Mehrabi Sadrollah , Larki Rozina Abbasi , Rabani Mohammadreza , Gharaghani Maral 
Published: December 2024

Abstract

Background Candiduria is a common form of candidiasis and can be asymptomatic in critically ill patients. Kidney illness patients may experience nosocomial infections, including candiduria. Detection of virulence factors in this species plays an important role in the better understanding of pathogenesis and antifungal treatment. Molecular typing investigations are important in the epidemiological interpretation of infection. This study aimed to evaluate the frequency of candiduria among nephrology patients with pathogenic factors and genotyping of Candida species. Methods Two hundred forty-five patients admitted to the nephrology department were examined for candiduria. Then, biofilm formation, enzymatic activity, and antifungal susceptibility of Candida species were tested. Finally, the DNA of C. albicans isolates was amplified for ABC genotyping. Results Overall, 44 Candida species were isolated among candiduria patients. Nakaseomyces glabratus had the highest frequency. The highest enzyme activity of Candida isolates was obtained for phospholipase (84.1%). Among Candida species, the highest rate of biofilm formation was related to C.albicans (64.7%). Candida isolates were only resistant to caspofungin (31.8%). Genotype A was the predominant genotype among C.albicans isolates. Conclusions Nakaseomyces glabratus emerged as the most frequently isolated pathogen in this study. The enzymatic activity and biofilm formation capabilities of Candida species further contribute to their pathogenicity, making them formidable pathogens in urinary tract infections.
Selective Outcome Reporting Concerning Antibiotics and Third Molar Surgery
Authors: Fagoni Thalita Guarda , Rafalovich Vanessa Cristina , Brozoski Mariana Aparecida , Deboni Maria Cristina Zindel , de Oliveira Natacha Kalline 
Published: December 2024

Abstract

Objectives
This study evaluates the selective outcome reporting (SOR) in clinical trials on antibiotic use in third molar surgeries. It explores how SOR may bias results and affect systematic reviews, potentially leading to misinterpretations of intervention efficacy.
Materials and Methods
A search was conducted on "ClinicalTrials.gov" and the "Brazilian Registry of Clinical Trials" using the terms "third molar" and "antibiotics" up to October 2024. Two independent researchers selected eligible clinical trials. Data were extracted from registered protocols and corresponding publications. Discrepancies were analysed using established criteria, and the risk of bias was assessed with Risk of Bias2.
Results
Discrepancies between protocols and publications were found in 83.3% of cases, affecting outcomes in 58.3% of studies. SOR significantly influenced results in studies with three or more discrepancies. Seventy percent of studies assess pain post-antibiotic therapy; of those, 50% found significant results. Only 30% of studies showed significant reductions in trismus and edema with antibiotic use. Risk of bias varied significantly across studies.
Conclusions
The high rate of selective reporting stresses the need for transparent studies to clarify the role of antibiotics in the perioperative period. Researchers should adhere to best clinical practices, including protocol registration, accurate sample size calculations, and precision in reporting. Journals and reviewers must prioritize transparency to reduce bias and improve research quality.
Clinical Relevance
This study emphasizes the impact of SOR in clinical trials using antibiotics in third molar surgery. Clinicians should be more cautious in reading evidence based on randomized clinical trials with SORs.
Machine Learning to classify the Depth of Reflection in STEM Student Writings
Authors: Alrashidi Huda , Ullmann Thomas 
Published: December 2024

Abstract

Assessing reflective writing is essential for students’ learning. There is a growing need to improve the evaluation of reflective writing skills that students can organise their thoughts into a logical structure and connect new information with existing knowledge. However, manually analysing reflective writing is time-consuming and subjective, and there is a need for automation to make the process more reliable.
This study aims to investigate the potential use of machine learning and natural language processing to enhance the assessing process of reflective writing. In order to explore this research gap. Firstly, the study investigates of seven reflection indicators in terms of Natural Language Processing (NLP) into four groups of feature extraction (n-grams, PoS n-grams, sentiment features, and WordNet-Affect features) according to Reflective Writing Framework (RWF). Secondly, the automated RWF investigates of NLP’s findings with a wide range of machine learning algorithms to detect the reflection levels in text.
The study showed that the evaluation of the automated reflective writing analysis is reliable and valid, as measured by Cohen’s κ ranged from 0.46 to 0.75 across the seven indicators and 0.40 and 0.72 for the three reflection levels, demonstrating moderate to substantial agreement.
This research sheds light on important parts that need to be considered when attempting to improve the automated assessment of reflective writing. The contribution of this research is represented by the new knowledge it unearths related to reflection and its automated assessment using machine learning techniques.
Cost-Effective 18 DOF Dexterous Hand: Fusing Force-Position Multimodal Sensing of Whole Hand with a Monocular Camera
Authors: wei Cheng , Chen Shiwei , Li Jiapeng , Deng Zhiming , Wang Peiji 
Published: December 2024

Abstract

The anthropomorphic hand plays a crucial role in human-machine interaction tasks. However, there are very few hands that realize multimodal perception with high DOF in a low-cost way. We present a dexterous hand that achieves multimodal sensing solely through a camera. The hand has 18 DOF but does not require any position or force sensors, making it low-cost and easy to manufacture. We developed an integrated base for the hand that provides both actuation and multimodal sensing information simultaneously. This includes the angles of 18 joints, 5 fingertip positions and contact forces, as well as information on object softness and texture. The core principle of sensing is that the camera can track the displacement and tension of all tendons simultaneously. By characterizing tendon properties and coupling them with the hand dynamics, we developed a multimodal sensing model. Experiments indicate that our hand has potential in multimodal sensing and dexterity.
Relationship between abdominal obesity and C-reactive protein in combination with hypertension in middle-aged and older adults: insights from the CHARLS study
Authors: Liao Yanping , Huang Meirong , Wang Harry HX , Zhao Mo 
Published: December 2024

Abstract

Background Hypertension is a global public health problem, and as good prevention and management of hypertension can effectively reduce the risk of myocardial infarction and stroke, thereby significantly reducing the health economic burden, exploring more risk factors used to identify hypertension remains necessary. Aim To investigate whether the combination of elevated hs-CRP levels and abdominal obesity increases the risk of hypertension in middle-aged and older adults. Methods This study used data from 3439 investigators aged 45 years and older who participated in the 2015 China Health and Retirement Longitudinal Study (CHARLS). Hypertension, waist circumference, and hs-CRP were measured. logistic regression adjusted for confounding variables was used to examine the association between abdominal obesity and hs-CRP under the combination with hypertension. Results The risk of hypertension was significantly increased when participants had both abdominal obesity and high hs-CRP levels. Even after adjusting for covariates, a significant risk of hypertension was still possible (OR = 1.71, 95% CI:1.33–2.20). Sex- and age-stratified analyses showed that the association between abdominal obesity and high hs-CRP levels in combination with incident hypertension was more likely to be observed in participants under 60 years of age (OR = 1.72, 95% CI:1.15–2.59) and in women (OR = 1.72, 95% CI:1.15–2.59). Conclusions The findings suggest that elevated hs-CRP levels in combination with abdominal obesity may increase the risk of hypertensive events in the middle-aged and elderly Chinese population.
Investigation of Hardware Testing Utilizing Standard 4-D Convolution and Optimized Deep Convolution Formulas
Authors: Wang Xiaoyin 
Published: December 2024

Abstract

The purpose of this study is to discuss the application and performance of the standard four-dimensional convolution calculation and optimization deep convolution formula in hardware testing. With the wide application of convolutional neural networks (CNNs) [1] in image processing, video processing and other fields, how to efficiently complete these computing tasks on resource-limited hardware platforms has become a key problem. Although standard four-dimensional convolution is widely used, its computational complexity and resource consumption limit its application in large-scale convolutional networks. For this reason, deep convolutional optimization techniques are proposed to reduce the computation and memory footprint. However, as CNN goes deeper, parameters required by convolution increase sharply, which makes the on-chip memory solution inefficient [2]. In this study, field programmable gate array (FPGA) was used as a test platform to evaluate the resource consumption difference between standard convolution and deep convolution by comparing their parameters, computing time and power consumption under different hardware conditions. Test results show that deep convolution reduces memory footprint by about 90%, computation time by about 70%, and power consumption by about 50%. Studies have shown that deep convolution performs well on resource-constrained hardware platforms, especially for low-power devices such as mobile terminals and edge computing devices. In summary, deep convolutions provide an efficient and low-power solution for modern convolutional neural network hardware implementations.Key Words: Standard Four-Dimensional Convolution, Deep Convolution, hardware testing, optimization, CNNS.