bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unveiling Hidden Carcinomas: nonenhanced CT-Based Radiomics Model Enhances PTC Detection in Hashimoto's Thyroiditis Running title: CT radiomics identifies carcinoma in HT
Authors: Peng Yun , Huang Kaiyao , Gong Zijian , Liu Wenying , Peng Jidong , Gong Lianggeng 
Published: September 2024

Abstract

Background: Hashimoto's thyroiditis (HT) is a common benign thyroid disease that often coexists with papillary thyroid carcinoma (PTC). Owing to the diffuse changes in the thyroid caused by HT, PTCs can be challenging to detect using conventional imaging modalities such as ultrasound and CT. The aim of this study is to develop a radiomics model that uses nonenhancedCT (NECT) to predict the presence of PTC in the patients with HT, thereby improving early diagnostic accuracy.
Materials and Methods: This retrospective study included pathologically confirmed HT patients with or without PTC who underwent NECT scans within 30 days before surgery from January 2017 to April 2023 at the Second Affiliated Hospital of Nanchang University (Hospital I) or Ganzhou People's Hospital (Hospital II). Radiomic features were extracted using PyRadiomics. Interclass correlation coefficient, Pearson correlation and LASSO analyses were conducted to reduce the dimensionality of the radiomicfeatures. Five machine learning algorithms, including logistic regression, naive Bayes, support vector machine, k-nearest neighbor, and multilayer perceptron (MLP) classifiers, were employed to develop and validate the prediction models based on the remaining features.
Results: A total of 130 patients, 89 from Hospital I and 41 from Hospital II, were included. Six features with nonzero coefficients were retained by the LASSO algorithm for inclusion in the machine learning models. The MLP model performed the best in the external validation cohort, with an area under the curve of 0.783, a sensitivity of 64.29%, and a specificity of 92.31%.
Conclusion: A radiomics model based on NECT can identify PTCs in patients with HT and has the potential to enhance early diagnosis and intervention for these patients.
Enhancing Crack Resistance and Tensile Strength of Clay with Polypropylene Fiber and Enzyme Admixtures: Laboratory Investigation and Recommendation
Authors: Dhanalakshmi Krishnan , Jose Prakash Arul 
Published: September 2024

Abstract

In expansive clay, the development of fissures during desiccation is a natural occurrence. The life of geoinfrastructures, especially hydraulic barriers, can be greatly extended by reducing desiccation-induced fissures. Enhancing soil characteristics through the use of additives is an important strategy for reducing the formation of desiccation cracks and their impact. In this work, experimental findings for a nylon fiber and enzyme-based product modified expanding clay are presented. Desiccation cracking experiments with various fiber concentrations and constant enzyme dosages were conducted. Three-point bending beam tests were conducted to evaluate the modified clay's tensile strength. The impacts of cracks were reduced because the additives, fiber, and enzyme were able to considerably change the patterns of cracks. In addition, compared to untreated soil, the addition of enzyme alone enhanced tensile strength by about 50%, whereas the combined action of both fiber and enzyme raised tensile strength by around 100%. The inquiry makes a case for the possibility of the inclusion of fiber-enzymes to reduce desiccation cracks based on measurements of fracture patterns and other changed clay characteristics. To identify the ideal dosage demands for each addition and examine the results of potential interactions between the fiber and enzyme, more research must be done.
Comparison of seven Dutch emergency departments using standardized mortality ratios in subgroups of patients categorized by presenting complaints; an observational multicenter study
Authors: Raven Wouter , Candel Bart GJ , Wali Nabila , Gaakeer Menno , Avest Ewoud Ter , Sir Ozcan , Lameijer Heleen , Hessels Roger , Reijnen Resi , Nickel Christian , De Jonge Evert , Zwet Erik , de Groot Bas 
Published: September 2024

Abstract

Background
Comparison of emergency departments (EDs) becomes more important, but differences are difficult to interpret because of the heterogeneity of the ED population regarding reason for ED presentation. The aim of this study was two-fold: First to compare patient characteristics (including diagnoses) across 7 EDs. Secondly, to compare Standardized Mortality Ratios (SMRs) across 7 EDs and in subgroups of ED patients categorized by presenting complaints (PCs).
Methods
Observational multicenter study including all consecutive visits of 7 EDs. Patient characteristics, including PCs as part of triage systems, and SMRs (observed divided by expected mortality) per ED and for the most common PCs (PC-SMRs) were compared across EDs and presented as funnel plots. The expected mortality was calculated with a prediction model, which was developed using multivariable logistic regression in the overall population and for PCs separately. Demographics, disease severity, diagnoses, proxies for comorbidity and complexity, and PCs (overall population only) were incorporated as covariates.
Results
We included 693,289 ED visits, of which 1.9% died. Patient characteristics varied markedly among EDs. Expected mortality was similar in prediction models with or without diagnoses as covariate. SMRs differed across EDs, ranging from 0.80 to 1.44. All EDs had SMRs within the 95%-Confidence Intervals of the funnel plot apart from one ED, which had an higher than expected SMR. However, PC-SMRs showed more variation and more EDs had SMRs falling outside the funnel, either higher or lower than expected. The ranking of SMRs across EDs was PC-dependent and differences across EDs are present only for specific PC-SMRs, such as in "dyspnea" and "feeling unwell”.
Conclusion
In summary, patient characteristics and mortality varied largely across Dutch EDs, and expected mortality across EDs is well assessed in PC subgroups without adjustment for final diagnoses. Differences in SMRs across EDs are PC-dependent. Future studies should investigate reasons of the differences in PC-SMRs across EDs and whether PC-targeted quality improvement programs can improve outcomes.
Leveraging transcriptome SRAs for virus detection in wild and colony populations of triatomines (Hemiptera: Reduviidae: Triatominae)
Authors: Bourke Brian Patrick , de Oliveira Jader , Ergunay Koray , Linton Yvonne-Marie 
Published: September 2024

Abstract

Triatomines are infamous as vectors of the parasite Trypanosoma cruzi, the causative agent of Chagas disease. However, climate-driven range expansion and urbanization adaptation of Triatomine populations coupled with their highly diverse feeding strategies (vertebrate haematophagy, kleptohaematophagy, and coprophagy) and has elevated interest in Triatomines as potential arboviral vectors. Information on the Triatomine virome is highly scant, with prior records including only eight insect-specific viruses: Triatoma virus (TrV) and Rhodnius prolixus viruses 1–7. Here we leverage publicly available transcriptome datasets to assess viral diversity in 122 wild and colony kissing bugs representing eight species from six countries. In total, six viruses were detected (including Rhodnius prolixus viruses 4–6), and TrV was detected in almost half of all screened Triatomines. TrV is reported in Triatoma brasiliensis and in the genus Mepraia (M. gajardoi, M. spinolai, M. parapatrica) for the first time, and this effort has vastly expanded the publicly available genomic resources of TrV, adding 39 genomes to the single genome currently available on GenBank. Furthermore, two additional viruses—Meccus longipennis virus 1 and Drosophila melanogaster Nora virus—are herein reported from kissing bugs for the first time. Meccus longipennis virus 1 was detected in Triatoma infestans from Argentina, Brazil, Chile, and Peru, and Drosophila melanogaster Nora virus was found in T. infestans from Argentina. Our results illustrate the advantage and utility of low-cost transcriptome data mining for the discovery of known and novel arboviruses in Triatomines, and other potential insect vectors.
Morphometric Analysis for Prioritizing Critical Areas of Urpash Watershed in Sindh Catchment of the Lesser Himalayas Using RS and GIS Approach
Authors: Attar Mohmmad Idrees , Naseer Sameena , Pandey Yogesh , Khan Junaid Nazir , Bangroo Shabir Ahmad , Khan Zubair Ahmad , Tantray Adil Majeed , Khan Afzal Husain 
Published: September 2024

Abstract

Morphometric analysis using RS and GIS provides a cost and time-effective solution for prioritizing soil and water conservation practices. Therefore, a study was conducted to estimate various linear, areal and relief morphological parameters to prioritize the erosion-prone areas using the RS and GIS approach of an ecologically fragile and vulnerable Urpash watershed. The morphometric analysis revealed the presence of 32 streams ranging from 1st to 3rd orders, covering a total area of 21.37 km2. Results of parameters like Re, Rf, and Rc indicated an elongated shape of the watershed, suggesting lower runoff and erosion potential alongside higher sub-soil permeability. Small Dd, Fs, and Di values indicated that the Urpash watershed is at a heightened risk of experiencing flooding, gully erosion and landslides due to its inability to quickly disperse surface runoff. The priority order obtained using a compound parameter approach was WS3 > WS4 > WS5 > WS6 > WS2 > WS1.
Assessment of the impact of shielding gas flow rate on residual stresses in GMAW-deposited weld beads using LCR wave technique
Authors: Grzybowski Igor Felipe , de Almeida Diego Tolotti , Scheuer Cristiano José , Junior Paulo Pereira , Junior Auteliano Antunes dos Santos , Buenos Alexandre Aparecido 
Published: September 2024

Abstract

Welding is a widely employed manufacturing process in the industry for permanently joining pieces. Particularly in fusion welding processes, the high temperatures generate residual stresses upon process completion, rendering the welded and thermally affected region highly susceptible to failures. This occurs because such residual stresses overlay externally applied stresses. Therefore, precise determination of residual stresses in the welded joint becomes fundamental to assessing the actual forces borne by the component and ensuring its operational safety, thus averting unforeseen failures. It is pertinent to point out that welding parameters influence not only the quality and properties of the weld beads but also the magnitude of residual stresses. Among the various process-influencing parameters, the shielding gas flow rate has received minimal attention in the literature, remaining unexplored in terms of its overall impact. To address this gap, this study evaluated the impact of shielding gas flow rate on residual stresses in AWS ER70S-6 weld beads deposited on DIN EN 10025-2 S275JR steel plates. The investigation utilized Gas Metal Arc Welding (GMAW) and the non-destructive ultrasonic method of Longitudinal Critically Refracted (LCR) waves for stress measurement. Weld beads were deposited via robotic operation, using shielding gas flow rates of 12, 15, and 20 l/min, while keeping other welding parameters constant. Longitudinal profiles of residual stress distribution were obtained for each specimen. The results revealed a significant impact of gas flow rate on the residual stress profile in the weld beads, with the highest values observed for the specimen welded with a gas flow rate of 15 l/min. This study strongly emphasizes the critical importance of evaluating the influence of operational parameters in the welding process on residual stresses in the welded joint, considering their significant impact on the structural integrity of the joined components.
Sign Language Recognition using Modified Deep Learning Network and Hybrid Optimization: A Hybrid Optimizer (HO) based optimized CNNSa-LSTM Approach
Authors: Baihan Abdullah , Alutaibi Ahmed I. , Sharma Sunil Kumar 
Published: September 2024

Abstract

A speech impairment limits a person’s capacity for oral and auditory communication. A great improvement in communication between the deaf and the general public would be represented by a real-time sign language detector. Recent studies have contributed to promising progress in motion and gesture identification processes using DL methods and computer vision. But the development of static and dynamic sign language recognition models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on sign language recognition using a modified deep learning (DL) and hybrid optimization approach. With the Visual Geometry Group 16 (VGG16), spatial and geometric-based features are extracted, and motion features are extracted via the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a convolutional neural network (CNN), self-attention (SA), and long-short-Term memory (LSTM) to identify sign language. A hybrid optimizer (HO) is proposed using the hippopotamus optimization algorithm (HOA) and the pathfinder algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy, sensitivity, specificity, word error rate (WER), sign error rate (SER), and normalized discounted cumulative gain (NDCG) as well. The proposed model has recorded the highest accuracy of 98.7%.
Synthetic studies on the tetrasubstituted D-ring of cystobactamids to potent terephthalic acid antibiotics
Authors: Broenstrup Mark , Stappert Moritz , Kohnhäuser Daniel , Seedorf Tim , Coetzee Janetta , Rox Katharina , Fuchs Hazel , Cirnski Katarina , Leitner Christian , Herrmann Jennifer , Kirschning Andreas , Müller Rolf 
Published: September 2024

Abstract

Novel scaffolds for broad-spectrum antibiotics are rare and in strong demand because of the increase in antimicrobial resistance. The cystobactamids, discovered from myxobacterial sources, have a unique hexapeptidic scaffold with five arylamides and possess potent, resistance-breaking properties. This study investigates the role of the central D-ring pharmacophore in cystobactamids, a para-aminobenzoic acid (PABA) moiety that is additionally substituted by hydroxy and isopropoxy functions. We varied the two oxygenated substituents and replaced both amide connectors with bioisosters. Synthetic routes were developed that included metal-mediated aromatic functionalization or heterocycle formations, leading to 19 novel analogues. The antibiotic efficacy of all analogues was determined against bacteria from the ESKAPE pathogen panel. While the replacement and the repositioning of hydroxy and isopropoxy substituents was not advantageous, exchanging PABA by terephthalic acid amides led to the highly potent analogue 42 with broad-spectrum activity, insensitivity towards AlbD-mediated degradation and promising pharmacokinetic properties in mice. The study highlights the steep structure-activity relationships in the tetrasubstituted D-ring and a surprisingly favorable reversion of the amide connecting C and D.
SRREN:Self-aware and Relation-aware Recurrent Evolution Network for Temporal Knowledge Graph Reasoning
Authors: Zhao Laibin , Ratnavelu Kuru , ALDharhani Ghassan Saleh 
Published: September 2024

Abstract

Knowledge Graph (KG) reasoning for predicting incomplete KGs has been extensively studied. However, the Temporal Knowledge Graph (TKG), which consists of KGs at various timestamps, still has significant potential for improvement in forecasting future facts. A comprehensive understanding of historical facts is crucial for accurately predicting future events. For the same timestamp, the concurrent facts have structural dependencies, and the facts have a certain order in adjacent times. At the same time, for different timestamps, the attribute features of KGs in the current timestamp have a strong correlation with the attribute features of KGs in the historical timestamp. To effectively capture and comprehend these relational features and enhance the accuracy of TKG predictions , we propose a new recursive algorithm, the Self-aware and Relation-aware Recurrent Evolution Network (SRREN). SRREN learns an evolutionary representation of time-stamped entities and relations by recursively modeling the sequence of KGs. Specifically, for evolutionary units, structural dependencies are captured through relationship awareness. At each timestamp within the TKG, the sequential character of the facts is captured by a self-aware mechanism of regression modeling and a recursive component of the gate. In addition, the addition of entity static attributes can also achieve better entity representation. Experiments on six benchmark datasets show that SRREN can improve the accuracy of predicting future facts, and its performance is better than most other current models.
Interactions across Tritrophic Levels: Legume Host Plants, Insect Pest Callosobruchus maculatus, and Parasitoid Dinarmus basalis
Authors: Hamdi Soumaya Haouel , Abdelkader Nada , Boushih Emna , Jemâa Jouda Mediouni Ben 
Published: September 2024

Abstract

In an attempt to explore the tritrophic interactions among legume host plants and an insect pest C. maculatus, and an insect parasitoid D. basalis were used in this study. C. maculatus strains favoring seeds rich in protein and carbohydrate exhibit optimal physical performance in terms of weight and size, as well as biological parameters such as longevity, adult emergence and fertility. Additionally, fecundity and egg fertility of C. maculatus females correlate with larval protein and carbohydrate intake, as well as food quality. Parasitoids, crucial for controlling host densities, rely on substantial protein, lipid, and carbohydrate intake for survival and reproductive success. Focused on correlation between life history parameters of adult’s parasitoids and the host biochemical composition. Larvae L4 sugar content significantly affects parasitoid adults, with a notable positive relationship between sugar content in C. maculatus larvae and D. basalis sex ratio. This study is the first to investigate the relationships between the biochemical composition of C. maculatus L4 larvae and the biological and demographic performances of its parasitoid D. basalis. Our results show that the macronutrient content of plants and pest plays a crucial role in determining the tritrophic interactions.
Correlation Between Electrodes Surface State and Dynamic Resistance During Resistance Spot Welding of 5182 Aluminum Alloy
Authors: Nikitin Alexander , Turabov Dashqin , Ermilova Evgeniia , Evdokimov Anton , Ossenbrink Ralf , Seidlitz Holger 
Published: September 2024

Abstract

This study determines the relationship between the state of the electrodes surface and dynamic resistance during the resistance spot welding of aluminum alloy 5182. For this purpose, dynamic resistance values for each welded spot were analyzed and a correlation between these values and the changing electrode surface state throughout the welding process was examined. The resistance is influenced by the roughness of the electrode surface, which changes during the welding process due to the gradual accumulation of Cu/Al phases. Based on the findings of this study, resistance measurements can be effectively utilized to correlate with electrode state.
Effects of Management Practices and Socio-physical Factors on Perennial Plant Diversity of Agroforestry Systems of Gedeo landscapes, Southern Ethiopia
Authors: Lemma Sileshi , Asfaw Zebene , Tolera Motuma , Teshome Akalu 
Published: September 2024

Abstract

This study investigates the influence of different agroforestry systems, management practices, and socio-physical factors on plant diversity and richness in Gedeo indigenous agroforestry systems in southern Ethiopia. It analyzed 286 sample plots (10m x 10m), collecting data on both woody and non-woody perennial species. Insights into management practices and socio-physical conditions were gathered through surveys, focus groups, and interviews. A total of 78 plant species were identified in the study area. The Coffee-Fruit-tree system showed the highest species richness (10 species per plot) and the highest Shannon (1.482) and Simpson (0.731) diversity indices. In contrast, the Coffee-Enset-tree and Enset-Tree systems had lower species richness (6 species per plot) and fewer stems (20 to 23 per plot). The study found significant differences in species diversity and abundance across elevations, with highland farms having the lowest values (p < 0.001). Plots tilled once a year showed the highest diversity, richness, and abundance, while those ploughed three times a year had the lowest. Weeding once or twice a year did not significantly affect diversity indices, but weeding three times a year reduced them. Wealthier households had lower perennial plant species richness compared to middle-class and poor households. The prevalence of economically focused plants had a detrimental effect on species diversity and richness (p < 0.001), whereas selective tree removal had a positive impact on both. Additionally, the age of the household head and higher altitudes were associated with lower species diversity and richness (p < 0.001). Increased frequency of tillage and weeding by slashing also led to reductions in species diversity and richness. The distance from home to the main market negatively influenced species diversity and richness (p = 0.004), and altitude had a negative effect on both species richness and diversity.
Dual Metal-Organic-Frameworks Mediated Resonance Energy Transfer for Ultrasensitive Electrochemiluminescence Immunosensing
Authors: Wang Chun , Cheng Gaoxing , Li Guixin , Liu Jianbo 
Published: September 2024

Abstract

In this paper, an electrochemiluminescent (ECL) biosensor for carcinoembryonic antigen (CEA) based on electrochemiluminescence resonance energy transfer (ECL-RET) was designed. Tris(2,2′-bipyridine)ruthenium(II) (Ru(bpy)32+) was used as an energy donor for ECL-RET, and an Au nanoparticle-modified MOF framework (AuCoFe MOF) was used as an energy receptor for ECL-RET. The ECL emission spectra of Ru(bpy)32+ were in the range of 550 nm to 680 nm, and a zinc oxalate MOF encapsulating Ru(bpy)32+ (Ru@ Zn oxalate MOF) encapsulating Ru(bpy)32+ was prepared. The UV-vis absorption spectrum of AuCoFe MOF ranges from 280 nm to 700 nm and overlaps with emission spectra of Ru@Zn oxalate MOF, which is critical for RET. The AuCoFe MOF-Ab2 bioconjugate, target CEA antigen, the Ru@Zn oxalate MOF-Ab1 bioconjugate together forms a sandwich structure, resulting in quenching of the ECL signal of Ru@Zn oxalate MOF by AuCoFe MOF. Under the optimized experimental conditions, the ECL-RET sensor exhibited excellent analytical performance in CEA detection with a linear range of 1.0×10− 13 to 1.0×10− 8 mg mL− 1, the minimum limit of detection of it is 1.4×10− 14 mg mL− 1 (S/N = 3), and its recoveries of spiked samples ranging from 99.1–100.7%. The developed sensor has excellent stability, reproducibility and specificity and is suitable for the detection of CEA in human serum and has the potential to provide sensitive detection of other biomarkers of diseases.