bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large Language Models take on the AAMC Situational Judgment Test: Evaluating Dilemma-Based Scenarios
Authors: Cadiente Angelo , Chen Jamie , Kasselman Lora , Pilkington Bryan 
Published: July 2024

Abstract

The adoption of technology in medical education, including the use of situational judgment tests (SJTs), reflects the field's evolving nature. The Association of American Medical Colleges (AAMC) Situational Judgment Test (SJT) is understood to be the gold standard to assess pre-professional competencies in prospective medical students. Thus, the impact of such an exam is extensive and students’ abilities to engage with it can deeply impact the future of health professions – in particular, medical – education. This study aims to evaluate the performance of large language models (LLMs) in interpreting and responding to the AAMC SJT to understand the opportunities for its use and the obstacles that might exist. The study utilized the 2021 AAMC SJT practice exam, querying the LLMs to rate the effectiveness of various behavioral responses to each scenario. Both raw and adjusted scores were calculated in accordance with AAMC’s guidelines. The sentiment of each solution was analyzed using Monkeylearn Sentiment Analyzer. Statistical analysis involved descriptive statistics, logistic regression, and the calculation of Fleiss Kappa for inter-model agreement. LLMs at various stages of development were compared, with the idea that improvements might occur, a finding which would be of interest to educators using AI tools to evaluate SJTs, to prospective health professions trainees interested in preparing for such exams, and practicing professionals who aim to improve or fine tune their social intelligence-related skills. Our findings demonstrate that ChatGPT-4.0 (raw score: 58.6%, adjusted score: 71.2%) outperformed ChatGPT-3.5 (raw score: 42.4%, adjusted score: 61.6%) and Bard (raw score: 37.6%, adjusted score: 58.1%) with all LLMs tending towards making the same inaccuracies. Sentiment analysis revealed no significant individual relationship with accuracy, although a collective trend was noted. Furthermore, increased solution count in scenarios correlated with decreased accuracy across all models combined but these results fell short of statistical significance. Ultimately, our study contributes to understanding the capabilities and limitations of LLMs in the context of medical education assessments. ChatGPT-4.0's performance indicates a significant advancement in LLMs’ ability to interpret and respond to complex social situations.
Retrieving Ground Displacements by Tightly Integrating Inertial Instruments with GNSS
Authors: Lee S. W. , Yun S. H. , Chang C. W. , Kim J. R. , Tapley B. D. 
Published: July 2024

Abstract

Traditional methods of calculating near-field seismic displacements using the recordings of strong motion accelerometers require a correction procedure to remove baseline offsets that may be caused by rotation and tilting motions of the ground. The baseline correction is, however, subjective and likely to remove low-frequency components of the seismic signals. We employ a new method based on the tightly coupled integration of accelerometers, gyroscopes and GNSS, which does not require the baseline correction. To investigate how well different retrieval methods account for the effects of the rotation and tilts on the displacements, synthetic case studies are conducted using the accelerometer recordings obtained from actual earthquakes. Results showed that the method employed can provide a clear improvement over the traditional methods when the accelerometer recordings are affected by the rotation and tilting motions of the ground. Also the method was capable of recovering permanent displacements partly because it ingests GNSS measurements that allow direct observation of the ground displacements and also partly because it does not experience loss of information induced by the baseline correction.
Applying Large Language Models to Enhance Dialogue and Communication Analysis for Adaptive Team Training
Authors: Spain Randall , Min Wookhee , Kumaran Vikram , Pande Jay , Saville Jason , Lester James 
Published: July 2024

Abstract

Adaptive training environments that can analyze team communication content and provide remediation to facilitate team coordination offer great potential for enhancing adaptive training systems for teams. Developing computational models that can perform robust team communication analytics based on small datasets is challenging. Large language models (LLMs) offer significant potential to address these challenges and enhance dialogue act classification performance using zero-shot and few-shot learning. This paper evaluates LLMs against previous state-of-the-art methods, with an emphasis on dialogue act recognition performance and error analysis for identifying frequently misclassified instances. Results from a small team communication dataset indicate that zero-shot LLMs, particularly those utilizing GPT-4 and refined through robust prompt engineering, achieve significant classification performance improvements in dialogue act recognition compared to previous state-of-the-art transformer-based models fine-tuned with team communication data. Error analysis shows that the prompt refinements, especially those aimed at clarifying confusion between dialogue acts, result in superior recall rates for challenging dialogue act labels by effectively handling complex dialogue scenarios and ambiguities within communication data. Our transformer-based framework demonstrates its effectiveness in achieving high accuracy rates in dialogue act recognition with minimal training data, underscoring its potential to enhance team training programs by providing adaptive feedback. This approach paves the way for developing AI-enabled training systems that can adapt to the dynamic communication styles of different teams.
Characteristics of Thermo-stable Serine Peptidase Vpr from Endophytic Bacillus cereus strain InaCC-B1657
Authors: Hastuty Aerma , Mangunwardoyo Wibowo , Suhartono Maggy Thenawijaya , Rahmani Nanik , Ferdian Pamungkas Rizky , Hidayat Iman 
Published: July 2024

Abstract

Bacterial endophytes are a promising source of novel fibrinolytic enzymes with unique metabolic pathways and desirable characteristics that may not be present from conventionally explored sources. This study aimed to characterize fibrinolytic enzymes from selected endophytic bacteria isolated from papaya (Carica papaya L.) leaves and the genes encoding the enzymes. A strain BFP1 (InaCC-B1657) that showed the highest fibrinolytic activity was identified as Bacillus cereus based on the phylogenetic analysis of the 16S rRNA sequence. The enzyme exhibited optimum activity at 50°C and pH 7.0, and remained stable until 80°C and pH 6–10 for 24 h. The assay of metal ions and inhibitors on the fibrinolytic enzyme activity found that adding Cu2+ stimulated, while Fe2+ reduced the activity. PMSF and TPCK inhibited the enzyme activity, while adding EDTA and EGTA increased the activity. These suggest that the fibrinolytic enzymes belong to the serine protease group. Of the 21 proteases/peptidases determined from the 5,257,484 base pairs (bp) genome, minor extracellular protease Vpr and S8 family peptidase genes were found related to the fibrinolytic enzyme activity. The Vpr gene has a molecular weight of 98.5 kDa. The subtilase domain (peptidase S8 family) and the catalytic triad subtilase active sites (Asp204, His237, and Ser531) were detected. A prediction of physicochemical characteristics of the Vpr gene showed that the enzyme is hydrophilic and exhibited alkali-halo stability and thermo-stability over a broad range of temperatures.
Research on Text Sentiment Analysis of Dual-channel Hybrid Neural Network Based on LERT
Authors: Ai Peng , Liu Qicheng 
Published: July 2024

Abstract

In current text sentiment analysis tasks, existing pre-trained language models are limited in fully grasping the intrinsic language features and deeply comprehending complex language structures. Classic neural network models struggle to adequately capture the semantic aspects of text. To address these challenges, this study introduces a novel dual-channel hybrid neural network approach for text sentiment analysis, leveraging the LERT model. This approach initially utilizes the advanced pre-trained language model LERT to generate dynamic word vectors from the text and subsequently captures both local and global semantic characteristics through a parallel dual-channel feature extraction layer. The features that have been extracted are merged and inputted into the fully connected layer, followed by the application of the softmax function to classify emotions. Results from experiments demonstrate that compared with other sentiment analysis models, the proposed model LDB-Net performs better in overall performance, validating the effectiveness of the proposed method.
Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode
Authors: Du Xiangjun , Yu Li , Wang Jun , Yang Dengjie , Zheng Yao , Wang Yimin 
Published: July 2024

Abstract

This paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n + EMBC + SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. Moreover, the model exhibits an improved Frames Per Second (FPS) value of 38.21, representing a 3.56 f/s advancement over YOLOv8n. Heatmap analysis validates the model's superiority in terms of high detection accuracy, precise fault identification, and clear fault localization. This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings.
High-throughput RNA-Seq and In-silico analysis ofglioblastoma cells treated with cold atmosphericplasma and temozolomide.
Authors: Soni Vikas , Dawson Tyson , Lin Li , Crandall Keith , Sherman Jonathan , Keidar Michael 
Published: July 2024

Abstract

Glioblastoma multiforme (GBM) is one of the most common and aggressive forms of malignant brain cancer in adults and is classified based on its isocitrate dehydrogenase (IDH) mutation. Surgery, radiotherapy, and Temozolomide (TMZ) are the standard treatment methods for GBM. Here we present a combination therapy of cold atmospheric plasma (CAP) and TMZ as a key treatment for GBM. CAP works by increasing reactive oxygen and nitrogen species (RONS) and targets the spread of the tumor. In this study, we performed the transcriptomic analysis of U-87MG cells by high throughput deep RNA-Seq analysis to quantify differential gene expression across the genome. Furthermore, we studied various signaling pathways and predicted structural changes of consequential proteins to elucidate the functional changes caused by up or down-regulation of the most altered genes. Our results demonstrate that combination treatment downregulated key genes like p53, histones, DNA damage markers, cyclins, in the following pathways: MAPK, P53, DNA damage and cell cycle. Moreover, in silico studies were conducted for further investigation to verify these results, and the combination of CAP & TMZ showed a significant antitumor effect in the GBM cells leading to apoptosis and damaged key proteins. Further studies of the impact of TMZ on gene expression, biochemical pathways, and protein structure will lead to improved treatment approaches for GBM.
Genetic Inference of On-target and Off-target Side-effects of Antipsychotic Medications
Authors: Elmore Andrew , Sadik Aws , Paternoster Lavinia , Khandaker Golam , Gaunt Tom , Hemani Gibran 
Published: July 2024

Abstract

Adverse side-effects are the primary cause of antipsychotic drug discontinuation rather than poor efficacy. It is often difficult to ascertain whether patient-reported side-effects are caused by a drug, and if so, through which mechanism. Using a novel genetic method we have devised, we investigated evidence of causality and mechanism for 105 reported side-effects of 6 commonly prescribed antipsychotic drugs which target 68 receptors. We used publicly available drug binding affinity data and GWAS summary statistics along with statistical methods including Mendelian randomization and genetic colocalization to devise a representative ‘score’ for each combination of drug, side-effect, and receptor. We show that 48 side-effects are likely caused by drug action through 30 receptors, which are mainly attributable to off-target effects (26 off-target receptors underlying 39 side effects). Of individual drugs, clozapine has the largest cumulative side effect profile (Score = 27.4, SE = 5.2), and the largest number of side-effects (n = 46). We show that two well-known side-effects for clozapine, neutropenia and weight change, are underpinned by the action of GABA and CHRM3 receptors respectively. Our novel genetic approach can map side-effects to drugs and elucidate underlying mechanisms, which could potentially inform clinical practice, drug repurposing, and pharmacological development.
Interactive effects of family history of respiratory disease with age and body mass index on the risk of chronic bronchitis: A Cross-sectional Study
Authors: Li Ming , Du Jing , Gao Yanlin , Shi Yunping , Su Tingjian , Liu Qingping , Liu Yang , Wang Ping , Wang Chao , Shao Bing , Li Gang 
Published: July 2024

Abstract

Family history of respiratory disease (FHRD) is linked to chronic bronchitis (CB).This study aimed to investigate whether FHRD interacted with age and body mass index (BMI) to increase the risk of CB. A cross-sectional analysis was conducted on 22533 participants enrolled from the Beijing Population Health Cohort Study. Age and BMI were transformed into binary variables using cut-off points of 65 years old and 24 kg/m2, correspondingly. Logistic regression was used to examine the independent and interactive effects among FHRD, age and BMI on CB. Additive interactions between FHRD and age or BMI were further estimated using relative excess risk due to interaction (RERI), attribute proportion due to interaction (AP), and synergy index (S). FHRD was positively associated with the risk of CB (OR 2.21, 95% CI 1.92–2.55 ), which was enhanced by age ≥ 65 years old to 3.43 (95% CI 2.53–4.65) and by BMI ≥ 24 kg/ m2 to 2.99 (95% CI 2.40–3.72). There was no significant additive interaction between FHRD and age or BMI on CB. FHRD, older age and higher BMI were independently associated with increased risk of CB. Nevertheless, no interactive effects between FHRD and age or BMI were observed, and further studies were needed.
Exploring the limits of the provenience postulate: chemical and mineralogical characterization of Bronze Age ceramics from the Great Hungarian Plain
Authors: Golitko Mark , Riebe Danielle J. , Duffy Paul Regis , Parditka Györgyi , Kreiter Attila 
Published: July 2024

Abstract

Determining the provenience of archaeological objects relies on the so-called “provenience postulate,” namely, that sources of these objects are more compositionally distinct from each other than they are internally variable. For ceramics, it can be relatively straight forward in geologically heterogeneous environments to determine where vessels were produced, and whether they were traded or not. In geologically homogeneous regions, this can be far more complicated. In this study, we mineralogically and chemically compare Bronze Age ceramics (primarily Middle Bronze Age) from five archaeological sites on the Great Hungarian Plain to a large regional clay sample. The Great Hungarian Plain is comprised almost entirely of Pleistocene loess deposits, yet prior compositional studies have identified patterned variability between ceramics from different sites. Our results show that chemical variation in the region is continuous and clinal, making it difficult to strictly apply the provenience postulate to identify distinct production locations. However, we show that this clinal chemical variability can be used to make broad statements about whether most ceramics at any given site were produced relatively locally or were obtained from further distances (c. 50 km or more). We show that while production at most of our study sites was likely relatively localized, in one instance (the tell at Berettyóújfalu-Herpály-Földvár), many ceramics may have been obtained from other Bronze Age communities, including those in the Körös River drainage.
In vitro ruminal fermentation kinetics of alternative supplemental feeds formulated with by-products from food crops
Authors: Scott Mikeilah , John Eden Natalia , Jones Kegan Romelle 
Published: July 2024

Abstract

Commercial feed is an important component of ruminant production systems in Trinidad and Tobago. However, most farmers are unable to afford commercial feeds because of recent hike in prices and has resorted to available alternative feedstuffs. This study evaluated the in vitro ruminal fermentation kinetics and fermentation profile of two alternative supplemental feeds formulated from available agriculture by-products to determine their usefulness as alternatives to commercial feeds for small ruminants. Two iso-nitrogenous and iso-caloric alternative supplemental feeds were formulated with available agriculture by-products (cocoa hulls and shells, coconut meal, powdered bean, rice bran, rice hulls and corn stover). One was formulated without coconut meal (Supp.-CM) and the other without corn stover (Supp.-CS). In vitro ruminal gas production of the alternative supplemental feeds and a commercial feed was measured at 2, 4, 6, 8, 10, 12,18, 24, 36, 48, and 72 hours after incubation with buffered rumen inoculum. Gas production rate peaked 6-hours post incubation in the commercial feed and around 12–18-hours post incubation in the alternative supplemental feeds. The gas produced for the immediately soluble fraction (a) and potential degradability (PD) was highest in the commercial feed. However, gas production from the insoluble fraction (b) was similar for the commercial feed and Supp.-CS (246–262 g/kg− 1). Gas production rate constant for insoluble fraction (c), the lag phase, ruminal pH and partitioning factor (PF) did not differ between feed types. The estimated amount of methane produced from the alternative supplemental feeds (49.1 ml/g DM− 1) were significantly lower than the commercial feed (40.5–41.5 ml/g DM− 1) while organic matter digestibility (iOMD) was highest in the commercial feed (586 g/kg). Supplemental feeds from available agriculture by-products has potential to be used as alternatives to commercial concentrate feeds for ruminants in Trinidad and Tobago due to comparable fermentation efficiency (PF), fermentation of insoluble fraction and reduced estimated methane production. However, potential deleterious effects of Supp.-CM and Supp.-CS at the on-set of ruminal fermentation is concerning and may require further investigation.
Using Generative and Explainable Neural Networks to Investigate the Relationship Between Motor Cortex Activity and Animal Behavior During Motor Task Learning.
Authors: Luczak Artur , Tanabe Sean , Eckert Michael , Whishaw Ian , Tatsuno Masami 
Published: July 2024

Abstract

Understanding complex relations between neuronal activity and animal behavior is one of the most crucial questions in neuroscience. Rapid advancements in Machine Learning (ML) methods offer new powerful tools that can be used to investigate highly non-linear mapping between motor cortex activity and body movements. Here, by using explainable convolutional network (ConvNet) and Generative Adversarial Networks (GAN), we show how neuronal activity can be predicted from raw videos of animal behavior, and interestingly, we show that detailed videos of behaving animals can be recreated from activity of just few selected neurons. Those analyses revealed that the predictability of behavior from neuronal activity (and vice versa) initially increases as an animal learns a new task. However, after the animal performance on a motor task achieves the required accuracy, then “coupling” between neuronal activity and behavior decreases, without degrading task performance. In summary, we show how new ML methods could be applied to provide new insights in the relation between neuronal activity and behavior.
Regulation Compliance and Suspect Identification in Formula 1 Racing- A Case Study
Authors: Samuel Stephen 
Published: July 2024

Abstract

This paper presents a novel approach for suspect identification in Formula 1
racing. The energy required to drive the vehicle and the energy available from
the power sources while the vehicle is in the Drag Reduction Systems (DRS)
zone are considered as key parameters in this approach. It uses the data from
qualification runs of about 14 cars over 20 races in the 2022 race calendar. The
proposed approach is sensitive enough to detect the changes in vehicle setting
that is sufficient enough to influence the energy demand while on a straight part
of the track. This novel approach can be used to check the compliance between
Pre and Post-race Parc-ferme checks and identify suspects violating technical
regulations during dynamic events.
Bioinformatics analysis of ATOH7 gene mutation in retinogenesis
Authors: Choudhury Shouhartha 
Published: July 2024

Abstract

Background: The ATOH7 genes are established in the retina for optical nerve formation and ganglion cell maturation. Several data and reviews suggested that ATOH7 is known to be vertebrate Math5. So, the Math5 (ATHO7) gene is revealed in the neural retina for the genesis of retina ganglion cells and ocular or optic nerves. The primary neuron in the retina has been generalized in the eyes. The neuron-specific ATOH7 from the atonal gene family plays a fundamental role in neurons during retinal cell proliferation. The evolutionary wonderful eye patterns require atonal gene response at the initial state of retinal neuron growth. Therefore, the growth of the neuron in the retina requires inherited and peripheral elements.
Objective: In this study, the objective is to investigate the Math5 gene mutation in mammalian genomes. Therefore, perform powerful bioinformatics and computational techniques and tools for the current knowledge of ATOH7 gene mutations in two different organisms’ genomes.
Results: Genome-wide observation suggested the total number of bHLH domains in Homo sapiens, Pan Troglodytes, Gorillas, Ailuropoda melanoleuca, and Mus musculus. Further, bioinformatics and comparative analysis results documented the total numbers of ATOH7 genes and their family in all organisms. Also, the conserved domain, motifs, phylogeny, chromosome location, gene network, and gene expression demonstrated that the ATHO7 gene is associated with optical nerve formation and ganglion cell maturation.
Remarks: Therefore, finding data concluded the Math5 gene plays a fundamental role during retinogenesis. So, the ATOH7 or Math5 gene specified ocular nerve formation and ganglion cell maturation.