bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Open-source Fine-tuned Large Language Model for Radiological Impression Generation: A Multi-reader Performance Study
Authors: Serapio Adrian , Chaudhari Gunvant , Savage Cody , Lee Yoo Jin , Vella Maya , Srid Shravan , Schroeder Jamie , Liu Jonathan , Yala Adam , Sohn Jae Ho 
Published: August 2024

Abstract

Background
The impression section integrates key findings of a radiology report but can be subjective and variable. A fine-tuned open-source Large Language Model (LLM) was evaluated in its ability to generate radiological report impressions across different imaging modalities and hospitals. We sought to clinically validate an open-source fine-tuned LLM that automatically generates impressions to summarize radiology reports.
Methods
In this institutional review board-approved retrospective study, we fine-tuned an open-source LLM to generate the impression from the remainder of the radiology report. CT, US, and MRI radiology reports from Hospital 1 (n = 372716) and Hospital 2 (n = 60049), both under a single institution, were included in this study. The ROUGE score was used for automatic natural language evaluation and a reader study with five thoracic radiologists was performed for a clinical evaluation of CT chest impressions with a subspecialist baseline. We also stratified the results of the reader performance study based on the diagnosis category and the original impression length to gauge case complexity.
Results
The large language model achieved ROUGE-L scores of 46.51, 44.2, and 50.96 on the Hospital 1 dataset across the CT, US, and MRI modalities respectively. Upon external validation on the Hospital 2 independent test dataset, the model achieved ROUGE-L scores of 40.74, 37.89, and 24.61 for the same set of modalities. For the reader performance study, the model achieved overall mean scores of 3.56/4, 3.92/4, and 3.37/4, 18.29 seconds, and 12.32 words for clinical accuracy, grammatical accuracy, stylistic quality, edit time, and edit distance respectively. The LLM achieved the highest clinical accuracy ratings for acute/emergent findings. In terms of impression length, the LLM performed the best in clinical accuracy on shorter impressions.
Conclusions
We demonstrated that an open-source fine-tuned LLM can generate high-quality radiological impressions of clinical accuracy, grammatical accuracy, and stylistic quality across multiple imaging modalities and hospitals.
Cypriot Teachers’ digital skills and attitudes towards AI
Authors: Kasinidou Maria , Kleanthous Styliani , Otterbacher Jahna 
Published: August 2024

Abstract

Artificial Intelligence (AI) is now everywhere, including in the classroom. Thus, it is crucial for teachers not only to be able to use AI, but also to understand it, as they bear the responsibility of teaching the next generation with and about AI. Although there have been extensive discussions about the importance of digital skills and knowledge of AI, limited research has focused on the attitudes, digital skills, and AI literacy levels of teachers, within small populations where English is not the native language, such as Cyprus. Our study addresses this gap by adapting the General Attitudes Towards AI Scale (GAAIS) and the AI Literacy Scale (AILS) into the Greek language, and investigating the digital skills, attitudes towards AI, AI literacy, and intentions to teach and use AI among teachers in Cyprus. The Greek versions of GAAIS and AILS have been validated, demonstrating good validity and reliability.Our findings also indicate that Cypriot teachers generally possess intermediate digital skills, but maintain neutral attitudes towards AI and exhibit intermediate levels of AI literacy. Moreover, we discovered significant positive correlations between teachers' digital skills and their AI literacy, attitudes towards AI, and intentions to use and teach AI.
Using Quantum Atomics and Machine Learning to Advance Picotechnology
Authors: MacDougall Preston J. , Donthula Kiran K. 
Published: August 2024

Abstract

We explore the use of machine learning to predict spectroscopic properties and interaction energies of the carbonyl groups in 225 ketones, aldehydes, imides, and amides. In the combined spirit of Density Functional Theory (DFT) and the Quantum Theory of Atoms in Molecules (QTAIM), but with an eye toward eventually using databases of transferable fragment densities, we limit the training data to small sets of descriptors (from 18 to 48 per molecule) that are based on topological features in the total charge density, ρ, and/or its Laplacian, ∇2ρ. We obtain a mean absolute error under 1% for carbonyl stretching frequencies, and just over 1% for C-13 NMR shifts. Predicting interaction energies with a model nucleophile (fluoride ion) is significantly more challenging. Mean absolute errors just over 3 kcal/mol were obtained for covalent bond formation energies. Similar mean absolute errors were obtained for much weaker van der Waals interaction energies. We also conducted a stress-test to see if our small molecule-based machine learning could predict covalent bond formation energy in a model of the active site of the E. coli enzyme, D-fructose-6-phosphate aldolase.
Exploring the complexity in the differences in speeding behavior trends among motorcycle drivers in Thailand's industrial hubs
Authors: Seefong Manlika , Wisutwattanasak Panuwat , Se Chamroeun , Theerathitichaipa Kestsirin , Jomnonkwao Sajjakaj , Champahom Thanapong , Ratanavaraha Vatanavongs , Kasemsri Rattanaporn 
Published: August 2024

Abstract

Despite considerable efforts to address road traffic disasters, the challenge of excessive speeding by drivers in industrial areas remains a primary cause of accidents in Thailand. To combat this challenge effectively, it is crucial to understand the deep-rooted factors influencing speeding behavior, particularly the risky behaviors of drivers. This study employs the Theory of Planned Behavior (TPB) and the 3Es + Es framework as a reference to examine these factors, while also considering socio-demographic data. Structural Equation Modeling (SEM) was used to investigate the factors influencing speeding behavior, revealing key findings that Engineering factors significantly increase speeding. Conversely, Attitude, Subjective Norm, and Perceived Behavioral Control, which are essential components of TPB, significantly influence the intention to exhibit safe behavior, leading to reduced speeding. Additionally, the examination of latent factors using drivers' socio-demographic data found that significant influences include age, marital status, income, driving experience, accident history, and traffic tickets. Among these factors, single drivers exhibited a reduced attitude toward performing safe behaviors, while drivers with less than five years of experience showed a decreased intention to exhibit safe behaviors. In conclusion the findings from this study could be beneficial for road safety authorities by recommending policies and empirical guidelines appropriate to the context of industrial areas in Thailand, which are prone to high accident rates. This could lead to a reduction in speeding behavior among motorcycle riders and effectively mitigate road traffic disasters.
Diurnal oscillations of amino acids dynamically associate with microbiota and resistome in the colon of pigs
Authors: Wang Hongyu , Li Yue , You Jinwei , Feng Ni , Wang Dongfang , Su Yong , Feng Xiaobo 
Published: August 2024

Abstract

Background: Nutrients are one of the key determinants of gut microbiota variation. However, the intricate associations between the amino acid (AA) profile and the dynamic fluctuations in the gut microbiota and resistome remain incompletely elucidated. Herein, we investigated the temporal dynamics of AA profile and gut microbiota in the colon of pigs over a 24-hour period, and further explored the dynamic interrelationships among AA profile, microbiota, and resistome using metagenomics and metabolomics approaches.
Results: JTK_circle analysis revealed that both the AA profile and the gut microbiota exhibited rhythmic fluctuations. With respect to the feed intake, all AAs except L-homoserine (PAdj = 0.553) demonstrated significant fluctuations. Over 50% of Lactobacillaceae, Ruminococcaceae, Clostridiaceae, and Eubacteriaceae species reached their peaks during T15 ~ T21 when 50% of Lachnospiraceae species experienced a trough. The eLSA results showed that most AAs positively correlated with Prevotellaceae species but negatively correlated with Lactobacillaceae and Lachnospiraceae species. Moreover, most of the AAs negatively correlated with the mobile genetic elements Tn916 and istA group but positively correlated with plasmids. Further partial least squares structural equation model analysis indicated that AAs affected the antibiotic resistance gene dynamics through mobile genetic elements and the gut microbiota.
Conclusions: Taken together, the AA profile and the gut microbiota exhibit robust fluctuations over a day. The AA profile can affect the gut microbiota and resistome in a direct or indirect manner. These findings may provide new insights into a potential strategy for manipulating the gut microbiota and resistome.
Tailoring Nanostructured Lipid Carriers for Efficient Delivery of Asenapine Maleate in Antipsychotic Treatment
Authors: Rao Gopal Krishna , Jamadar Saba 
Published: August 2024

Abstract

Asenapine Maleate (ASPM), is the atypical antipsychotic agent used for treating various psychological disorders such as schizophrenia and acute mania associated with bipolar disorder. However, the drug's therapeutic potential is intricately linked to its solubility, bioavailability, stability, and ability to be delivered specifically to the central nervous system. The aim of the study was to design, develop, and optimize Nanostructured Lipid Carriers (NLCs) encapsulating Asenapine maleate to improve the therapeutic efficacy. The development utilized a systematic approach; various formulation parameters such as lipid composition, surfactant concentration, and entrapment efficiency were meticulously optimized by employing quality-by-design principles. The optimized ASPM-loaded NLCs demonstrated a particle size of 97.6 ± 2.48 nm, zeta potential of −20mV, and entrapment efficiency of 96.74%, respectively. ASPM-loaded NLC showed initially rapid release (20%) accompanied by sustained release (75%) over 24 hr with 22-fold improvement in oral bioavailability with significantly reduced locomotor count induced by L-DOPA-carbidopa in comparison with the pure drug in Sprague-Dawley rats. Furthermore, in-vitro and in-vivo results underscored the enhanced permeability through the blood-brain barrier with superior pharmacokinetics for the NLC. Collectively, this research elucidates the potential of optimized NLCs as robust drug delivery system for asenapine maleate, offering a promising avenue for advancing therapeutic results in the management of psychosis.
Application of knowledge distillation method with dynamic adjustment of temperature parameters in pest classification
Authors: Wang Linan , Zhao Hongmin 
Published: August 2024

Abstract

In recent years, the output of China's four major crops has declined due to pests and diseases. This situation poses a serious challenge to food security. Therefore, timely detection and prevention of diseases is essential. First, we use data enhancement techniques to augment the data to improve the generalization ability of the model. Secondly, to reduce the model parameters and facilitate the deployment at the terminal, we use the knowledge distillation method. Finally, a method of dynamically adjusting the parameter T according to the loss value (DYTKD) is proposed to improve the performance of the model further. The experiment shows that knowledge distillation can reduce the number of parameters while making the accuracy of the student model as close as possible to the teacher model 98.94%. Meanwhile, data augmentation can also improve the accuracy of the model by 6.83%. Compared with the basic knowledge distillation method, the accuracy of DYTKD was increased by 1.3% without changing the student network and other parameters, and the accuracy of pest identification and classification was effectively improved. Among 1342 pest pictures, 1221 were correctly identified and accurately classified. Our codes are available at https://github.com/wln130221/DYTKD.
Research on the Improved BM3D Denoising Algorithm for Remote Sensing Images Based on Context Aggregation Network
Authors: ZHOU YING , Ren Chao , Shao Hongjuan , Liang Yueji , Huang Liangke 
Published: August 2024

Abstract

Denoising remote sensing images is important for their subsequent use and research. To meet the demands of image denoising in strong noise environments, this study proposes an improved BM3D remote sensing image denoising network (CBMNet) based on a context aggregation network. Initially, the original image is read, and the image outputted by BM3D filtering is stored. Following this, random patches extracted from the original image are fed into the network alongside the image processed through BM3D filtering to extract data, which is then input into the network. Subsequently, multi-scale CAN layers are established to calculate the l_2 loss function between the standard output of the BM3D filter and the network response post-processing the input image using the CAN network. Ultimately, the CBMNet network is trained to approximate the BM3D filtering operator. The experimental results indicate that, both in terms of subjective visual assessment and objective evaluation metrics, the proposed method outperforms the classical BM3D algorithm as well as the Wiener, mean, and Gaussian filtering denoising methods in removing strong Gaussian noise from remote sensing images. Additionally, the proposed method better preserves image edge details and texture information, resulting in clearer image outputs. This has significant reference value and practical utility for subsequent application research in remote sensing imagery. These results bear substantial reference significance and practical utility for subsequent applications in remote sensing imagery research.
The self-organized structure of glioma oncostreams and the disruptive role of passive cells.
Authors: Barberis Lucas , Condat Carlos A. , Faisal Syed M. , Lowenstein Pedro R. 
Published: August 2024

Abstract

Oncostreams are self-organized structures formed by spindle-like, elongated, self-propelled cells recently described in glioblastomas and especially in gliosarcomas. Cells within these structures either move as large clusters in one main direction, flocks, or as linear, intermingling collections of cells advancing in opposite directions, streams. Round, passive cells are also observed, either inside or segregated from the oncostreams. Here we generalize a recently formulated particle-field approach to investigate the genesis and evolution of these structures, first showing that, in systems consisting only of identical self-propelled cells, both flocks and streams emerge as self-organized dynamic configurations. Flocks are the more stable configurations, while streams are transient and usually originate in collisions between flocks. Stream degradation is easier at low self-propulsion speeds. In systems consisting of both motile and passive cells, the latter block stream formation and accelerate their degradation and flock stabilization. Since the flock appears to be the most effective invasive structure, we thus argue that a phenotype mixture (motile and passive cells) may favor glioblastoma invasion. Oncostreams embody the spontaneous break of symmetry that results from the interplay between speed, shape, and steric repulsion.
Enriching the physical and mechanical properties of paper sheets by covering their surfaces with coats containing novel core-shell pigment
Authors: Adel Abeer M. , Ahmed Nivin M. , Kader Amal H. Abdel , Al-Shemy Mona K. , El-Sabour Mona A. 
Published: August 2024

Abstract

This study focuses on the development of coating films composed of the biodegradable poly (lactic acid) (PLA) combined with metal oxide TiO2/SiO2 core-shell pigments for enhancing the properties of paper sheets used in packaging, besides overcoming the limitations of the individual materials. The objective of this work is to investigate the impact of the different pigment concentrations on the structural, mechanical and barrier properties of the coated paper sheets. Coated papers with biodegradable materials are essential for improving the different properties, such as density, surface gloss, smoothness, and moisture resistance. In addition, core-shell pigments can offer enhanced properties by combining the different properties of more than one component together; such as titanium and silicon oxides, which can provide availability and low cost. The relevant physical and chemical properties for the prepared coating composites and the altered paper sheets were evaluated using XRD, XRF, TEM, SEM, EDX, FTIR, CA and TGA. The findings can contribute to the progress of sustainable and functional packaging materials, highlighting the importance of core-shell pigments in improving their performance.
Efficacy analysis of different approaches of percutaneous transforaminal endoscopic local anesthesia in the treatment of L5- S1 disc herniation
Authors: Feng Dongwei , Jian Wei , Zhang Yaxin , Jiao Baoliang , Wu Weize , Ding Jianan , Wang Yunhe , Jia Heping , Li Zhaowei 
Published: August 2024

Abstract

Background: Both transforaminal and interlaminar approaches are effective methods for the treatment of L5-S1 disc herniation. Few studies have compared the efficacy and complications of two approaches under local anesthesia in the treatment of L5-S1 disc herniation
Methods:
From May 2018 to July 2021, 72 patients with L5-S1 disc herniation were randomized to the transforaminal endoscopic lumbar discectomy (TELD, n = 36) or interlaminar endoscopic lumbar discectomy (IELD, n = 36). Both procedures were performed under 1% lidocaine local anesthesia. The baseline data, puncture time, radiation time, operative time unde the endoscope, total operation time, hospitalization time, bed rest time, and complications were compared between the two groups. In addition, Likert five-point scale was used for evaluation of patients experience to local anesthesia and tolerance to re-operation was assessed as well. The efficacy of surgery was assessed according to Visual Analogue Scale (VAS), Oswestry Disability Index (ODI) and modified MacNab score.
Results: All patients with an average follow-up of 27.5 months. Compared to the IELD group, the TELD group had a longer puncture time (15.31±2.53>5.11±1.67, P < 0.001) and radiation time (8.44±2.01>4.61±1.27, P < 0.001) but a shorter total operative time(52.22±5.44<58.53±0.69, P=0.001) , operative time unde the endoscope(36.92±5.79<53.42±9.60, P < 0.001) and lower VAS scores for intraoperative back pain (P < 0.001) and leg pain (P<0.001). The postoperative VAS score and ODI score in both groups were significantly lower than those before operation(P<0.001), and there was no significant difference for each data collection time between the two groups (P > 0.05). The postoperative survey showed that the satisfaction rate of TELD group was higher than that of IELD group(P=0.026), TELD group was superior to IELD group in tolerance to re-operation(P =0.007). According to MacNab criteria, the excellent and good rate rates of TELD group and IELD group were 93.3% and 90.0%, and the difference was not statistically significant (P > 0.05).
Conclusion:
For L5/S1 DH, Both TELD and IELD can achieve good clinical efficacy under local anesthesia. Compared with PETD, PEID had lower puncture time, total operation time, and radiation exposure, but higher incidence of intraoperative low back pain and patients satisfactory rates less.
Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation
Authors: Farhadi Sasan , Tatullo Samuele , Ferrian Francesco 
Published: August 2024

Abstract

Efficient prediction of fatigue life in structural components is crucial for ensur- ing their integrity and reliability, especially considering the dominant occurrence of fatigue failure in metallic structures within the industrial sectors. Conven- tional fatigue assessment methods, although theoretically established, are often time-consuming and exhibit limitations due to the intricate nature of the fatigue mechanism. Machine learning models have demonstrated significant potential for enhancing the efficiency of predictions in fatigue life. This research explores the effectiveness of ensemble learning models—boosting, stacking, and bag- ging—compared to linear regression and K-Nearest Neighbors as benchmarks. Fatigue life prediction is conducted across different notched scenarios using Incre- mental Energy Release Rate (IERR) measures in addition to the more standard stress/strain field measures. To assess the performance of the proposed models, a comprehensive set of evaluation metrics was performed, including mean square error (MSE), mean squared logarithmic error (MSLE), symmetric mean abso- lute percentage (SMAPE), and Tweedie score. The findings reveal that ensemble learning models, particularly the ensemble neural networks, stands out as a superior approach for fatigue life cycle assessment compared to other methods. Moreover, the integration of IERR in predicting fatigue life for notched-shape components indicates a promising approach for enhancing the reliability and efficiency of fatigue life predictions in real-world industrial applications.
ERCP-related Adverse Events in Pediatric Patients: A 10-Years Single-Site Review
Authors: Li Qinqin , Li Suolin , Hou Senlin , Zhang Lichao , Chen Shengxiong , Wang Jianlong , Lv Jiaxun , Wu Yuqing , Huang Qizheng , Li Yingchao 
Published: August 2024

Abstract

Purpose
This retrospective analysis aimed to assess the feasibility and safety of endoscopic retrograde cholangiopancreatography (ERCP) in pediatric patients by examining ERCP-related adverse events (AEs) occurring over a decade at a single center.
Methods
Pediatric patients under 18 years old who underwent ERCP at the Second Hospital of Hebei Medical University from 1/2013 to 11/2023 were included. ERCP-related AEs were defined according to ERCP-related adverse events: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Clinical data of patients experiencing ERCP-related AEs were obtained from electronic medical records for analysis.
Results
Over the past decade, a total of 76 pediatric patients underwent 113 ERCP procedures, including 26 patients who underwent repeat ERCP, totaling 63 procedures. There were 32 males and 44 females, with a median age of 13 years (range 3 years and 5 months–17 years and 9 months). Among all ERCP procedures, 14 (12.4%) were diagnostic and 99 (87.6%) were therapeutic, with a 100% success rate. 16 cases (14.2%) of ERCP-related AEs, all post-ERCP pancreatitis (PEP), were observed, while no other AEs defined by ESGE such as bleeding, perforation, cholangitis, cholecystitis, or sedation-related events were noted. Additionally, 23 cases (20.4%) of ERCP-related AEs not included in the ESGE definition were observed, including post-ERCP abdominal pain in 20 cases (17.7%), post-ERCP nausea and vomiting in 2 cases (1.8%), and unplanned reoperation in 1 case (0.9%). In the 26 cases of pediatric patients who underwent repeat ERCP, we observed that AEs occurred in 15 cases (57.7%) during their initial ERCP, which was much higher than the overall average level.
Conclusions
Post-ERCP abdominal pain and PEP are the most common ERCP-related AEs in pediatric patients, while severe AEs such as bleeding and perforation are rare. The incidence of AEs after initial ERCP in pediatric patients who received repeat ERCP is higher than the overall average level. Based on our center's experience, we believe that ERCP can be safely performed in children over 3 years old with biliary and pancreatic diseases and obtain reliable clinical benefits. However, active monitoring and management of ERCP-related AEs are essential to improve the clinical outcomes of pediatric ERCP.