bluuebunny/crossref_metadata_2025_split huggingface dataset:
Insights into vehicle conflicts based on traffic flow dynamics
Authors: Ding Shengxuan , Abdel-Aty Mohamed , Wang Zijin , Wang Dongdong 
Published: January 2024

AbstractThe utilization of traffic conflict indicators is crucial for assessing traffic safety, especially when the crash data is unavailable. To identify traffic conflicts based on traffic flow characteristics across various traffic states, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) thresholds. Different traffic states and corresponding transitions are identified with the three-phase traffic theory using high-resolution trajectory data. Meanwhile, the SSMs are mapped to the corresponding traffic states from the perspectives of time, space, and deceleration. Three models, including k-means, GMM, and Mclust, are investigated and compared to optimize the identification of traffic conflicts. It is observed that Mclust outperforms the others based on the evaluation metrics. According to the results, there is a variation in the distribution of traffic conflicts among different traffic states, wide moving jam (phase J) has the highest conflict risk, followed by synchronous flow (phase S), and free flow (phase F). Meanwhile, the thresholds of traffic conflicts cannot be fully represented by the same value through different traffic states. It reveals that the heterogeneity of thresholds is exhibited across traffic state transitions, which justifies the necessity of dynamic thresholds for traffic conflict analysis.
Global characterization of biosynthetic gene clusters in non-model eukaryotes using domain architectures
Authors: Kwon Taehyung , Hovde Blake T. 
Published: January 2024

AbstractThe majority of pharmaceuticals are derived from natural products, bioactive compounds naturally synthesized by organisms to provide evolutionary advantages. Although the rich evolutionary history of eukaryotic algal species implicates a high potential for natural product-based drug discovery, it remains largely untouched. This study investigates 2762 putative biosynthetic gene clusters (BGCs) from 212 eukaryotic algal genomes. To analyze a vast set of structurally diverse BGCs, we employed comparative analysis based on the vectorization of biosynthetic domains, referred to as biosynthetic domain architecture (BDA). By characterizing core biosynthetic machineries through BDA, we identified key BDAs of modular BGCs in diverse eukaryotes and introduced 16 candidate modular BGCs with similar BDAs to previously validated BGCs. This study provides a global characterization of eukaryotic algal BGCs, offering an alternative to laborious manual curation for BGC prioritization.
Modulating the difficulty of a visual oddball-like task and P3m amplitude
Authors: Boetzel Cindy , Stecher Heiko I. , Kasten Florian H. , Herrmann Christoph S. 
Published: January 2024

AbstractIt is often necessary to modulate the difficulty of an experimental task without changing physical stimulus characteristics that are known to modulate event-related potentials. Here, we developed a new, oddball-like visual discrimination task with varying levels of difficulty despite using almost identical visual stimuli. Gabor patches of one orientation served as frequent standard stimuli with 75% probability. Gabor patches with a slightly different orientation served as infrequent target stimuli (25% probability). Analyzing the behavioral outcomes revealed a successful modulation of task difficulty, i.e. the hard condition revealed decreased d' values and longer reaction times for standard stimuli. In addition, we recorded MEG and computed event-related fields in response to the stimuli. In line with our expectation, the amplitude of the P3m was reduced in the hard condition. We localized the sources of the P3m with a focus on those that are modulated by changes in task difficulty. The sources of P3m modulation by difficulty were found primarily in the centro-parietal regions of both hemispheres. Additionally, we found significant differences in source activity between the easy and hard conditions in parts of the pre and post-central gyrus and inferior parietal lobe. Our findings are in line with previous research suggesting that the brain areas responsible for the conventional P3m generators also contribute to a modulation by task difficulty.
Exploring the presence of oral bacteria in non-oral sites of patients with cardiovascular diseases using whole metagenomic data
Authors: Chopra Aditi , Franco-Duarte Ricardo , Rajagopal Anjale , Choowong Phannaphat , Soares Pedro , Rito Teresa , Eberhard Joerg , Jayasinghe Thilini N. 
Published: January 2024

AbstractCardiovascular diseases (CVDs) encompass various conditions affecting the heart and its blood vessels and are often linked with oral microbes. Our data analysis aimed to identify oral bacteria from other non-oral sites (i.e., gut, arterial plaque and cultured blood) that could be linked with CVDs. Taxonomic profiling identified bacteria to the species level and compared with the Human Oral Microbiome Database (HOMD). The oral bacteria in the gut, cultured blood and arterial plaque samples were catalogued, with their average frequency calculated for each sample. Additionally, data were filtered by comparison with the Human Microbiome Project (HMP) database. We identified 17,243 microbial species, of which 410 were present in the HOMD database and further denominated as “oral”, and were found in at least one gut sample, but only 221 and 169 species were identified in the cultured blood and plaque samples, respectively. Of the 410 species, 153 were present solely in oral-associated environments after comparison with the HMP database, irrespective of their presence in other body sites. Our results suggest a potential connection between the presence of specific species of oral bacterial and occurrence of CVDs. Detecting these oral bacterial species in non-oral sites of patients with CVDs could help uncover the link between oral health and general health, including cardiovascular conditions via bacterial translocation.
Structural validity of the Pittsburgh Sleep Quality Index among medical students in Iran
Authors: Shadzi Mohammad Reza , Rahmanian Mahdi , Heydari Aigin , Salehi Alireza 
Published: January 2024

AbstractThe Pittsburgh Sleep Quality Index (PSQI) is a self-reported questionnaire evaluating sleep quality based on seven domains of sleep disorders. However, the factor structure of PSQI needs to be well-established among medical students. This study was conducted to establish the factor structure of PSQI among medical students. Four-hundred and four medical students completed the PSQI. Considering each PSQI component as an "observed variable," exploratory factor analysis was conducted. Six models explaining the factor structure of PSQI were compared by confirmatory factor analysis to achieve the best model. All PSQI components were loaded on three factors. The first factor included sleep duration and habitual sleep efficiency, the second was subjective sleep quality and sleep latency, and the third was sleep disturbances, sleeping medications, and daytime dysfunction (root mean square error of approximation =  < 0.01, goodness of fit index = 0.99). The differences in Bayesian information criteria and root mean square error of approximation between our best-fit model and each of the other resulted in rejection of all other Models. Besides confirming the structural validity among medical students, our findings indicated the adequacy of the 3-factor structure of PSQI, including sleep efficiency, perceived sleep quality, and daily disturbances in this population.
Inhibition of mucus secretion by niclosamide and benzbromarone in airways and intestine
Authors: Ousingsawat Jiraporn , Centeio Raquel , Reyne Nicole , McCarron Alexandra , Cmielewski Patricia , Schreiber Rainer , diStefano Gabriella , Römermann Dorothee , Seidler Ursula , Donnelley Martin , Kunzelmann Karl 
Published: January 2024

AbstractThe Ca2+ activated Cl− channel TMEM16A (anoctamin 1; ANO1) is expressed in secretory epithelial cells of airways and intestine. Previous studies provided evidence for a role of ANO1 in mucus secretion. In the present study we investigated the effects of the two ANO1-inhibitors niclosamide (Niclo) and benzbromarone (Benz) in vitro and in vivo in mouse models for cystic fibrosis (CF) and asthma. In human CF airway epithelial cells (CFBE), Ca2+ increase and activation of ANO1 by adenosine triphosphate (ATP) or ionomycin was strongly inhibited by 200 nM Niclo and 1 µM Benz. In asthmatic mice airway mucus secretion was inhibited by intratracheal instillation of Niclo or Benz. In homozygous F508del-cftr mice, intestinal mucus secretion and infiltration by CD45-positive cells was inhibited by intraperitoneal injection of Niclo (13 mg/kg/day for 7 days). In homozygous F508del-cftr rats intestinal mucus secretion was inhibited by oral application of Benz (5 mg/kg/day for 60 days). Taken together, well tolerated therapeutic concentrations of niclosamide and benzbromarone corresponding to plasma levels of treated patients, inhibit ANO1 and intracellular Ca2+ signals and may therefore be useful in inhibiting mucus hypersecretion and mucus obstruction in airways and intestine of patients suffering from asthma and CF, respectively.
Detection of sub-nmol amounts of the antiviral drug favipiravir in 19F MRI using photo-chemically induced dynamic nuclear polarization
Authors: Bernarding J. , Bruns C. , Prediger I. , Mützel M. , Plaumann M. 
Published: January 2024

AbstractIn biological tissues, 19F magnetic resonance (MR) enables the non-invasive, background-free detection of 19F-containing biomarkers. However, the signal-to-noise ratio (SNR) is usually low because biomarkers are typically present at low concentrations. Measurements at low magnetic fields further reduce the SNR. In a proof-of-principal study we applied LED-based photo-chemically induced dynamic nuclear polarization (photo-CIDNP) to amplify the 19F signal at 0.6 T. For the first time, 19F MR imaging (MRI) and spectroscopy (MRS) of a fully biocompatible model system containing the antiviral drug favipiravir has been successfully performed. This fluorinated drug has been used to treat Ebola and COVID-19. Since the partially cyclic reaction scheme for photo-CIDNP allows for multiple data acquisitions, averaging further improved the SNR. The mean signal gain factor for 19F has been estimated to be in the order of 103. An in-plane resolution of 0.39 × 0.39 mm2 enabled the analysis of spatially varying degrees of hyperpolarization. The minimal detectable amount of favipiravir per voxel was estimated to about 500 pmol. The results show that 19F photo-CIDNP is a promising method for the non-invasive detection of suitable 19F-containing drugs and other compounds with very low levels of the substance.
Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression
Authors: Bulant Carlos A. , Boroni Gustavo A. , Bass Ronald , Räber Lorenz , Lemos Pedro A. , García-García Héctor M. , Blanco Pablo J. 
Published: January 2024

AbstractCoronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline’s plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.
Management of acute diverticulitis in Stage 0-IIb: indications and risk factors for failure of conservative treatment in a series of 187 patients
Authors: Agnes Amedea L. , Agnes Annamaria , Di Grezia Marta , Giambusso Mauro , Savia Eleonora , Grieco Michele , Cozza Valerio , Magalini Sabina , Sganga Gabriele 
Published: January 2024

AbstractLeft-sided acute diverticulitis in WSES Stage 0-IIb preferentially undergoes conservative management. However, there is limited understanding of the risk factors for failure of this approach. The aim of this study was to investigate the factors associated with the decision to perform conservative treatment as well as the predictors of its failure. We included patients with a diagnosis of WSES diverticulitis CT-driven classification Stage 0-IIb treated in the Emergency Surgery Unit of the Agostino Gemelli University Hospital Foundation between 2014 and 2020. The endpoints were the comparison between the characteristics and clinical outcomes of acute diverticulitis patients undergoing conservative versus operative treatment. We also identified predictors of conservative treatment failure. A set of multivariable backward logistic analyses were conducted for this purpose. The study included 187 patients. The choice for operative versus conservative treatment was associated with clinical presentation, older age, higher WSES grade, and previous conservative treatment. There were 21% who failed conservative treatment. Of those, major morbidity and mortality rates were 17.9% and 7.1%, respectively. A previously failed conservative treatment as well as a greater WSES grade and a lower hemoglobin value were significantly associated with failure of conservative treatment. WSES classification and hemoglobin value at admission were the best predictors of failure of conservative treatment. Patients failing conservative treatment had non-negligible morbidity and mortality. These results promote the consideration of a combined approach including baseline patients’ characteristics, radiologic features, and laboratory biomarkers to predict conservative treatment failure and therefore optimize treatment of acute diverticulitis.
Availability of ChatGPT to provide medical information for patients with kidney cancer
Authors: Choi Joongwon , Kim Jin Wook , Lee Yong Seong , Tae Jong Hyun , Choi Se Young , Chang In Ho , Kim Jung Hoon 
Published: January 2024

AbstractChatGPT is an advanced natural language processing technology that closely resembles human language. We evaluated whether ChatGPT could help patients understand kidney cancer and replace consultations with urologists. Two urologists developed ten questions commonly asked by patients with kidney cancer. The answers to these questions were produced using ChatGPT. The five-dimension SERVQUAL model was used to assess the service quality of ChatGPT. The survey was distributed to 103 urologists via email, and twenty-four urological oncologists specializing in kidney cancer were included as experts with more than 20 kidney cancer cases in clinic per month. All respondents were physicians. We received 24 responses to the email survey (response rate: 23.3%). The appropriateness rate for all ten answers exceeded 60%. The answer to Q2 received the highest agreement (91.7%, etiology of kidney cancer), whereas the answer to Q8 had the lowest (62.5%, comparison with other cancers). The experts gave low assessment ratings (44.4% vs. 93.3%, p = 0.028) in the SERVQUAL assurance (certainty of total answers) dimension. Positive scores for the overall understandability of ChatGPT answers were assigned by 54.2% of responders, and 70.8% said that ChatGPT could not replace explanations provided by urologists. Our findings affirm that although ChatGPT answers to kidney cancer questions are generally accessible, they should not supplant the counseling of a urologist.
Optimizing classification of diseases through language model analysis of symptoms
Authors: Hassan Esraa , Abd El-Hafeez Tarek , Shams Mahmoud Y. 
Published: January 2024

AbstractThis paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization—Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.
Analysis of two layered peristaltic-ciliary transport of Jeffrey fluid and in vitro preimplantation embryo development
Authors: Ashraf Hameed , Siddique Imran , Siddiqa Ayesha , Tawfiq Ferdous M. O. , Tchier Fairouz , Zulqarnain Rana Muhammad , Rehman Hamood Ur , Bhatti Shahzad , Rehman Abida 
Published: January 2024

AbstractThe analysis of peristaltic-ciliary transport in the human female fallopian tube, specifically in relation to the growing embryo, is a matter of considerable physiological importance. This paper proposes a biomechanical model that incorporates a finite permeable tube consisting of two layers, where the Jeffrey fluid model characterizes the viscoelastic properties of the growing embryo and continuously secreting fluid. Jeffrey fluid entering with some negative pressure gradient forms the core fluid layer while continuously secreting Jeffrey fluid forms the peripheral fluid layer. The resulting partial differential equations are solved for closed-form solutions after employing the assumption of long wavelength. The analysis delineated that increasing the constant secretion velocity, Darcy number, and Reynolds number leads to a decrease in the appropriate residue time of the core fluid layer and a reduction in the size of the secreting fluid bolus in the peripheral fluid layer. Eventually, the boluses completely disappear when the constant secretion velocity exceeds 3.0 Progesterone ($$P_4$$

P
4

) and estradiol ($$E_2$$

E
2

) directly regulate the transportation of the growing embryo, while luteinizing hormone (LH) and follicle-stimulating hormone (FSH), prolactin, anti-mullerian hormone (AMH), and thyroid-stimulating hormone (TSH) have an indirect effects. Based on the number and size of blastomeres, the percentage of fragmentation, and the presence of multinucleated blastomeres two groups were formed in an in vitro experiment. Out of 50 patients, 26 (76.5%) were pregnant in a group of the good quality embryos, and only 8 (23.5%) were in a group of the bad quality embryos. The transport of growing embryo in the human fallopian tube and preimplantation development of human embryos in in vitro are constraint by baseline hormones FSH, LH, prolactin, $$E_{2}$$

E
2

, AMH, and TSH.
New colonisers drive the increase of the emerging loggerhead turtle nesting in Western Mediterranean
Authors: Luna-Ortiz Astrid , Marín-Capuz Gisela , Abella Elena , Crespo-Picazo José Luis , Escribano Fernando , Félix Guillem , Giralt Silvia , Tomás Jesús , Pegueroles Cinta , Pascual Marta , Carreras Carlos 
Published: January 2024

AbstractThe loggerhead sea turtle (Caretta caretta) is sensitive to climate change and is responding by colonising the Western Mediterranean. To understand the rapid nesting increase in recent years in Spain, we sampled 45 hatchlings from 8 nests between 2016 and 2019. We sequenced a mtDNA D‐loop region, genotyped 2291 SNPs using 2bRAD and collected data on clutch size, hatching success, and incubation duration. We confirmed that the colonisation has a Mediterranean and Atlantic mixed origin and we detected that these nests were laid by different females, except for two nests within the same season. Our results suggest that the recent increase in nesting is due to an increase in the number of colonising individuals rather than females born in the same area returning to breed. We hypothesize that this increase in the number of colonisers results from successful conservation efforts, feminisation of the populations of origin and earlier sexual maturation. However, the percentage of offspring females produced in Spain suggests that future returning individuals will aid to the settlement of the new population. These results allow defining the current status of this colonisation although future efforts are needed to detect remigrants to confirm the establishment of a resident population.