bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diagnostic Yield of Bidirectional Endoscopy for Iron Deficiency Anemia in Young Patients
Authors: Abramowitz Binyamin R. , Saba Helena , Aytaman Ayse , DiLeo Daniel A. , Roland Bani Chander 
Published: March 2024

Abstract
Introduction: Iron deficiency is the most common cause of anemia worldwide. While bidirectional endoscopy is recognized as the standard approach for investigating IDA in men ≥ 45 years and postmenopausal women, evidence supporting the application of this approach in younger men and premenopausal women is scarce in the absence of symptoms. Our primary aim is to identify the diagnostic yield of bidirectional endoscopy in men ≤ 45 years and premenopausal women and describe the clinical characteristics of those with significant endoscopic and pathology-proven findings. Methods: We performed a retrospective chart review including patients ≤ 45 with IDA who underwent esophagogastroduodenoscopy (EGD) and/or colonoscopy at the Brooklyn VA Hospital from 2009 to 2023. Demographic, clinical, and endoscopic patient data was all collected, stratified, analyzed, and interpreted. Results: In 143 patients < 45 years with IDA (70.6% females, mean age 36.18 years, SD = 5.63), 28.6% were found to have positive upper GI findings, of which 70.3% were pathology-proven H. pylori cases. 57.9% of patients reported upper GI symptoms, while 42.9% of patients were asymptomatic. In total, 18.2% of symptomatic patients were found to have clinically significant findings on EGD as compared with 42.9% of asymptomatic patients. Additionally, 9.1% of symptomatic patients were found to have biopsy proven H. pylori-associated gastritis or duodenitis as compared with 33.9% of asymptomatic patients. Of the patients who underwent colonoscopy, 8.3% were found to have lower GI lesions. Conclusions: We found the diagnostic yield of EGD to be significantly higher than that of colonoscopy in younger IDA patients. Our findings suggest current guidelines are clinically relevant to the young patient cohort. Our study also interestingly found asymptomatic IDA patients below age 45 to have a significantly higher diagnostic yield of EGD as compared to symptomatic IDA patients within the same age cohort. We speculate that the differences in diagnostic yields may be a result of symptomatic patients being more likely to have been prescribed proton pump inhibitors or histamine receptor antagonists prior to endoscopy.
Effects of a blood-free mosquito diet (BLOODless) on fitness and gonotrophic cycle parameters of laboratory reared Anopheles gambiae sensu stricto (IFAKARA)
Authors: Mosi Faith Allan , Rutha Isaac , Velez Rita , Swai Johnson Kyeba , Mlacha Yeromin P. , Marques Joana , Silveira Henrique , Tarimo Brian B. 
Published: March 2024

Abstract
Background
The current rise of new innovative tools for mosquito control such as the release of transgenic mosquitoes carrying a dominant lethal gene and Wolbachia-based strategies necessitates a massive production of mosquitoes in the insectary. However, currently laboratory rearing depends on vertebrate blood for egg production and maintenance. This practice raises, ethical concerns, incurs logistical and cost limitations, and entails potential risk associated with pathogens transmission and blood storage. Consequently, an artificial blood-free diet emerges as an essential alternative to address these challenges. This study aims to evaluate the effects of a previously formulated artificial blood-free diet (herein referred to as BLOODless) on Anopheles gambiae sensu stricto (An. gambiae s.s; IFAKARA) gonotrophic parameters and fitness compared to bovine blood.
Methods
The study was a laboratory-based comparative evaluation of the fitness, fecundity and fertility of An. gambiae s.s (IFAKARA) reared on BLOODless versus vertebrate blood from founder generation (F0) to eighth generation (F8). A total of 1000 female mosquitoes were randomly selected from F0, of which 500 mosquitoes were fed with bovine blood (control group) and the other 500 mosquitoes were fed with BLOODless diet (experimental group). The feeding success, number of eggs per female, hatching rate and pupation rate were examined post-feeding. Longevity and wing length were determined as fitness parameters for adult male and female mosquitoes for both blood-fed and BLOODless -fed populations.
Results
While blood-fed and BLOODless-fed mosquitoes showed similar feeding success OR 1.12 (95% CI, 0.82–1.53; P = 0.488), significant difference emerged in their reproductive parameters. The mean number of eggs laid per female was significantly higher for blood-fed mosquitoes P < 0.001 whereas BLOODless-fed mosquitoes had significantly lower hatching rates OR 0.17 (95% CI, 0.14–0.22; P < 0.001). Wing length and longevity parameters were similar between both groups.
Conclusion
This study demonstrates the potential of the BLOODless diet as a viable and ethical alternative to vertebrate blood feeding for rearing An. gambiae s.s facilitating large-scale mosquito mass production in captivity. This breakthrough paves the way for more efficient and ethical studies aimed at combating malaria and other mosquito-borne diseases.
Experimental method for quantifying macroplastic fragmentation in rivers
Authors: Liro Maciej , Zielonka Anna , Mikuś Paweł 
Published: March 2024

Abstract

Direct field measurements of macroplastic fragmentation during its transport in rivers are unavailable, and there is no method to perform such measurements. Recent theoretical works have hypothesised that river channels may be hotspots of macroplastic fragmentation. Here, we propose a methodology for quantifying riverine macroplastic fragmentation by conducting repeated measurements of tagged macroplastic items' mass before and after their transport in the river. A 52-65-day experimental test of the proposed methodology allowed us to provide the first quantification of fragmentation of 1-liter PET bottles during their transport in a mountain river channel. We calculated the mass loss of tracked bottles (n=43), ranging from 0.025 grams/year (0.07%/year) to 1.0 gram/year (3%/year), with a median of 0.26±0.04 grams/year (0.78%/year), and the rate of bottle surface degradation, ranging from -0.29 μm/year to -11.88 μm/year (median = 3.77±0.43 μm/year). These results suggest that the total fragmentation time for a PET bottle under conditions represented by our experiment (low to medium flow) ranges from 33.63 years to 332.81 years (median = 128.92±31.07 years). Our methodology can be flexibly adapted to quantify macroplastic fragmentation in various types of rivers and other environments where macroplastic is transported
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization
Authors: Garouani Moncef , Ahmad Adeel , Bouneffa Mourad 
Published: March 2024

Abstract

Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparame-ters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.
Optimally Configured Generative Adversarial Networks to Distinguish Real and AI- Generated Human Faces
Authors: G Kalaimani , G Kavitha , Chinnaiyan Selvan , Mylapalli Srikanth 
Published: March 2024

Abstract

Artificial Intelligence (AI) has come a long way in the last several years, especially in terms of producing human-like faces with deep-fake technology. However, the challenge lies in accurately distinguishing between real and AI-generated human faces. As the applications of such technology continue to expand, the need for robust classification methods becomes crucial to ensure ethical and responsible use. Existing Generative Adversarial Networks (GANs) produce increasingly realistic synthetic faces, making it difficult for traditional methods to differentiate between real and generated faces. This poses potential risks in various domains, including security, identity verification, and misinformation. The primary objective of this research is to design an optimally configured GAN capable of distinguishing between real and generated faces and to develop a robust classifier that accurately classifies human faces as either real or generative. The results showcase the effectiveness of the optimally configured GAN model in achieving high accuracy, reaching 95%, in distinguishing between real and AI-generated faces across state-of-the-art techniques. The research contributes to the ethical deployment of AI technologies, safeguards security applications, strengthens identity verification systems, combats misinformation, and fosters public trust in the era of advanced AI.
Network activity analysis for arbitrary production structures and technologies: Generic approach with application to network DEA
Authors: Dyckhoff Harald , Souren Rainer 
Published: March 2024

Abstract

Today, production usually takes place in complex networks. An important question is how the efficiency of the whole network is related to that of its units. Respective research on this topic has been strongly growing over the past decades, as a rule using methods of data envelopment analysis that are known as “network DEA”. However, there is a lack of theoretical foundation that allows clear statements to be made for arbitrary network structures and general, possibly non-convex or even discrete production technologies. This paper develops an activity analytic approach for modelling such general production networks and measuring their efficiency. Based on work of Koopmans and embedding it into a broader framework the approach is generic as it requires rather weak premises with regard to production technology and allows the network to be simply composed from its units as subsystems. It is shown that the relationship between the efficiency of a network activity and that of the subsystems and units depends strongly on the extent of which the individual production units are free to choose their input and output quantities, i.e. whether the network is loose or tied. Especially in cases where flows of intermediate products are constrained (instead of freely disposable), the explicit modelling of their overproduction helps to analyse their influence on efficiency scores. It is furthermore shown that calculating an overall efficiency score for a decision-making unit as average of individual scores of network units is inappropriate in any case.
Optimizing Insurance Fraud Claim Detection through Machine Learning: A Comprehensive Approach for Improved Fraud Detection
Authors: . Aayush 
Published: March 2024

Abstract
Insurance fraud is a growing concern, prompting proactive measures through advanced machine learning techniques. This research focuses on constructing a predictive model for distinguishing genuine and fraudulent auto insurance claims. The dataset, comprising 1,000 instances and 40 attributes, covers customer demographics, policy details, incidents, and financial data. Early fraud detection is crucial for financial loss mitigation and maintaining insurance system integrity. The study employs data preprocessing to handle missing values and features XGBoost importance, variance thresholding, and correlation analysis for enhanced model interpretability. The machine learning model integrates nine algorithms, with a hard-voting ensemble of Logistic Regression and XGBoost demonstrating competitive accuracy, reaching 83.0%. Results highlight Linear Discriminant Analysis as the leading classifier, achieving 84% accuracy. The ensemble approach achieves 83.0% accuracy with a notable precision of 91%, showcasing the strength of combining diverse models. The study emphasizes the significance of preprocessing, feature selection, and ensemble learning for fraud detection optimization. The refined model achieves a minimal Brier loss of 0.00054, indicating minimal discrepancies in predicted probabilities and actual outcomes in binary classification. Exploration of principal component analysis (PCA) with multiple linear regression reveals a trade-off between model simplicity and performance. Retaining 32 components preserves 95% of variance, achieving a balance at 0.7967, while keeping 35 components reaches the highest value of 0.9991, showcasing dimensionality reduction's potential to capture nearly all the data variance.
Improving colorectal cancer in Alberta, Canada: A qualitative study of patients and close contacts’ perceptions on diagnosis following an emergency department presentation
Authors: Botey Anna Pujadas , Watson Ashley J. , Robson Paula J. 
Published: March 2024

Abstract

Background: Colorectal cancer (CRC) is globally the third most prevalent cancer and a leading cause of cancer-related deaths. In Alberta, Canada, a significant portion of CRC diagnoses occur following emergency department (ED) presentations. Gaps remain in understanding patient’s perspectives on CRC diagnosis after an ED visit. The aim of this study was to examine the experiences and perspectives of a group of patients diagnosed with CRC subsequent to an ED visit in Alberta and their close contacts.
Methods: We conducted a qualitative study using in-depth, semi-structured interviews with patients diagnosed with CRC after an ED visit at the Rockyview General Hospital, Calgary, and their close contacts, from November 2022 to June 2023. Interviews focused on symptom recognition, healthcare interactions, and the decision-making process leading to an ED visit. They were conducted in-person or over the phone, and analysed using thematic analysis.
Results: Eighteen participants (12 patients and 6 close contacts) were interviewed, revealing four main themes: 1) variability in symptom recognition and interpretation; 2) inconsistencies in primary care consultations; 3) factors influencing decision-making leading to an ED visit; and 4) recommendations for expedited diagnosis outside of EDs.
Conclusion: The findings highlight the complexity of the diagnostic journey for CRC patients in Alberta, pointing to significant gaps in symptom recognition, primary care responsiveness, and public awareness. Implementing targeted educational initiatives for both the public and healthcare providers may help to optimize early detection. Future research should focus on exploring novel interventions to address the identified barriers to timely CRC diagnosis.
REACT: Recognize Every Action Everywhere All At Once
Authors: Chappa Naga VS Raviteja , Nguyen Pha , Dobbs Page Daniel , Luu Khoa 
Published: March 2024

Abstract

Group Activity Recognition (GAR) is a fundamental problem in computer vision, with diverse applications in sports video analysis, video surveillance, and social scene understanding. Unlike conventional action recognition, GAR aims to classify the actions of a group of individuals as a whole, requiring a deep understanding of their interactions and spatiotemporal relationships. To address the challenges in GAR, we present REACT (Recognize Every Action Everywhere All At Once), a novel architecture inspired by the transformer encoder-decoder model explicitly designed to model complex contextual relationships within videos, including multi-modality and spatio-temporal features.Our architecture features a cutting-edge Vision-Language Encoder block for integrated temporal, spatial, and multi-modal interaction modeling. This component efficiently encodes spatiotemporal interactions, even with sparsely sampled frames, and recovers essential local information. Our Action Decoder Block refines the joint understanding of text and video data, allowing us to precisely retrieve bounding boxes, enhancing the link between semantics and visual reality. At the core, our Actor Fusion Block orchestrates a fusion of actor-specific data and textual features, striking a balance between specificity and context.Our method outperforms state-of-the-art GAR approaches in extensive experiments, demonstrating superior accuracy in recognizing and understanding group activities. Our architecture's potential extends to diverse real-world applications, offering empirical evidence of its performance gains. This work significantly advances the field of group activity recognition, providing a robust framework for nuanced scene comprehension.
No-Reference Image Quality Assessment Based on Human Visual System and Dual-Branch Multi-Level Residual Network
Authors: QIU Xianwei 
Published: March 2024

Abstract
The objective of No-Reference Image Quality Assessment (NR-IQA) is to simulate the human visual system in evaluating image quality. The integration of the advantages of deep neural networks with the characteristics of the human visual system has been proven to be highly effective and accurate in numerous studies. This paper proposes a no-reference image quality assessment method based on the human visual system and a dual-branch multi-level residual network. Initially, the image is processed into HSV images that better align with human perception and Contrast-sensitivity weighted gradient images. These are then fed into two multi-level residual networks. The first network extracts multi-level content features from the HSV image, while the second network extracts multi-level structural and textural features from the Contrast-sensitivity weighted gradient image. Subsequently, the two multi-level features are fused to enhance the feature representation, with a weight module assigning different weights to the two features. Finally, these features are mapped to a quality score. Experimental results demonstrate that, on the five public datasets of LIVE, CSIQ, TID2013, LIVEC, and KonIQ-10k, the proposed method outperforms other advanced methods in terms of PLCC and SROCC metrics.
Adapting to Pandemic Challenges: Thai Dentists’ Response to COVID-19 Infection Control
Authors: Prasansuttiporn Jutatip , Subbalekha KKeskanya , Wiriyakijja Paswach , Pisarnturakit Pagaporn Pantuwadee 
Published: March 2024

Abstract

Background
The COVID-19 pandemic has posed emotional challenges for dentists. This study aimed to evaluate the concerns, risk perceptions, and compliance with infection control practices among Thai dentists during and shortly after the COVID-19 pandemic. Insights from this assessment are intended to offer practical strategies to enhance dentists’ readiness for future outbreaks.
Methods
A questionnaire evaluating dentists’ perceptions of COVID-19 infection and precautionary measures was generated via Google Forms™ and distributed to Thai dentists during the late pandemic period (June-September 2022).
Results
Of the 467 respondents, 44.3% reported the highest concern level for infection risk when treating emergency patients potentially exposed to COVID-19. Regarding the infection risk in dental practice, 30.6% perceived the highest contracting risk, while 24.6% perceived the highest transmission risk. Notably, 49.7% expressed high confidence in the ability of the vaccine to reduce COVID-19 severity. Eighty-eight percent of the patients supported the continuation of pandemic-era precautionary measures even in the absence of disease. N95 mask usage in dental settings markedly increased during the outbreak. Many dental procedures were not adequately adapted to COVID-19 infection control measures, and personal protective equipment was insufficient. Factors influencing COVID-19 risk perception in dental treatment included gender, involvement in aerosol-generating procedures, and availability of protective equipment.
Conclusion
Thai dentists expressed significant concerns about the risk of contracting COVID-19 in their practice. The participants had strong confidence in the effectiveness of the vaccines in reducing symptom severity. The majority of the participants supported the continued implementation of pandemic-initiated precautionary measures. Sex, aerosol-generating procedures, and protective equipment availability were key factors influencing dentists’ risk perception. These insights underscore the need for improved infection control measures and resources in dental settings, both to address current concerns and to enhance preparedness for future health crises.
Mechanism of Ubiquitin Ligase Activation of Parkin by a Small Molecule Molecular Glue
Authors: Gehring Kalle , Sauvé Véronique , Stefan Eric , Croteau Nathalie , Goiran Thomas , Fakih Rayan , Bansal Nupur , Hadzipasic Adelajda , Fang Jing , Fon Edward A , Hirst Warren , Silvian Laura , Trempe Jean-François 
Published: March 2024

Abstract

Mutations in parkin and PINK1 cause early-onset Parkinson’s disease (EOPD). The ubiquitin ligase parkin is recruited to damaged mitochondria and activated by PINK1, a kinase that phosphorylates ubiquitin and the ubiquitin-like (Ubl) domain of parkin. Activated phospho-parkin then ubiquitinates mitochondrial proteins to target the damaged organelle for degradation. Here, we present the mechanism of activation of a new class of small molecule allosteric modulators that enhance parkin activity. The compounds act as molecular glues to enhance the ability of phospho-ubiquitin (pUb) to activate parkin. Ubiquitination assays and isothermal titration calorimetry were performed with the most active compound (BIO-2007817) to identify the binding site and mechanism of action. We determined the crystal structure of a closely related compound (BIO-1975900) bound to a complex of parkin and two pUb molecules. The compound binds next to pUb on RING0 and contacts both proteins, enhancing the affinity of pUb for RING0 and promoting the displacement of the catalytic Rcat domain. In organello and mitophagy assays demonstrate that BIO-2007817 rescues the activity of EOPD mutants, R42P and V56E, with defective Ubl domains. This study provides the basis for the design of improved parkin activators as potential therapeutics for EOPD.