bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nasal myiasis caused by Musca domestica in patients with respiratory failure: a case report and literature review
Authors: Xu Hua , Fan Wen , Li Wenhua , Wang Hongfei , Dou Lin 
Published: May 2024

Abstract

Nasal myiasis is a nasal infestation caused by myiasis parasitizing on the nasal cavity, is a rare parasitic disease. The nasal cavity is close to the sinuses, eyes, and cranial cavity, once the fly larvae migrate into this location, it may lead to serious consequences. Timely and effective removal of maggots and proper treatment can effectively avoid further deterioration of the disease. Here, we describe a 55 year-old woman admitted to the intensive care unit (ICU) with severe respiratory failure. On the 4th day after admission, the patient was still unconscious and several white worms crawled out of the nasal cavity. Through identification, the worm was Musca domestica larvae. And then we performed saline irrigation under nasal endoscope and anti-inflammatory therapy on this patient to prevent intracranial infection. After treatment, the patient's symptoms were controlled, and the prognosis of the patient was good till date of 1month of follow up. We present a literature review about the report of nasal myiasis caused by Musca domestica and discussed the susceptibility factors and treatment modalities for nasal myiasis.
Numerical study of passive branches micromixer based on split-and-recombination and vortex mixing
Authors: Hsu Chih-Wen , Wang Guanghui , Ji Miaomiao 
Published: May 2024

Abstract

In this study, we presented a passive micromixer that based on the mechanism of split-and-recombination (SAR) and vortex mixing, also studied over Reynolds numbers (Re) ranging from 0.01 to 100 by numerical simulation. The main channel is 240 um wide and 100 um deep, while the total length of micromixer model is 2.49 mm. The structure of micromixer is PDMS-glass-PDMS which PDMS has micropatterns and glass has vias. At low Reynolds number, the mixing mechanism is dominated by molecular diffusion and SAR; when Re ~ 10, there is vortex mixing in cylindrical channel and the microchannel of three-dimensional 90 deg change of direction resulting in the secondary flow occurs, make mixing efficiency rising significantly. These micromixers showed at its exit with mixing efficiency higher than 90% at Re = 20, and also the lowest mixing efficiency is over 70%. Finally, an optimized method of geometry was presented which considered the size accuracy of manufacturing and fluids velocity in branches. It is useful to SAR mechanism for mixing efficiency enhancement especially when Re < 0.1, meanwhile, the pressure drop and performance index curves are just little difference than before. It provides some optimization guidelines for SAR micromixers. For Re > 5, micromixer No. 1 has better mixing efficiency, while micromixer No. 2’ has better mixing efficiency when Re < 5.
Emulating CMAQ Simulations with a Conditional U-Net: A Data-Driven Approach for PM2.5 Concentration Prediction
Authors: Lee Yohan , Park Junghyun , Kim Jinseok , Woo Jung-Hun , Lee Jong-Hyeon 
Published: May 2024

Abstract

Fine particulate matter (PM2.5) pollution poses significant health risks, necessitating accurate predictions for effective management measures for the mitigator of PM2.5. While the Community Multiscale Air Quality (CMAQ) model is widely utilized to simulate PM2.5 concentrations, its computational demands often limit its application in testing various emission reduction scenarios. This study introduces a data-driven approach to emulate CMAQ simulations through AI surrogate model with a conditional U-Net architecture. The model, trained on a dataset of CMAQ-simulated PM2.5 concentrations from across South Korea in 2013, incorporates region-specific emission source activity data as inputs. It accurately predicts hourly changes in PM2.5 concentrations, facilitating the estimation of monthly and annual average concentrations under diverse emission reduction scenarios. Demonstrating robust performance in replicating the spatial and temporal patterns of CMAQ simulations, this surrogate model significantly cuts computational costs and times. Our findings offer policymakers a practical and efficient tool for evaluating the effectiveness of emission control strategies and enhancing decision-making processes in air quality management.
Identifying comorbidity patterns of mental health disorders in community-dwelling older adults: A cluster analysis
Authors: Tobin Joshua , Black Michaela , Ng James , Rankin Debbie , Wallace Jonathan , Hughes Catherine , Hoey Leane , Moore Adrian , Wang Jinling , Horigan Geraldine , Carlin Paul , McNulty Helene , Molloy Anne M , Zhang Mimi 
Published: May 2024

Abstract

Understanding patterns of mental health in older people, how it is measured, and how it is affected by associated risk factors is of growing importance as life expectancy increases worldwide. Here, we aimed to explore typologies of mental health using a clustering method and determine how the identified clusters differ in terms of the prominence and severity of depression and anxiety. Our analysis hinged on the Trinity Ulster Department of Agriculture study dataset, encompassing 5186 participants with a mean age of 74.0. This dataset comprises five distinct feature sets, encompassing mental health (Center for Epidemiologic Studies Depression Scale [CES-D] and Hospital Anxiety and Depression Scale [HADS]), cognitive and neuro-psychological function, illness diagnoses and medical prescription history, lifestyle and nutritional attainment, as well as physical well-being. We perform cluster analysis on each feature set independently and subsequently elucidate the interrelations among the clusterings across the various feature sets. We uncovered a five-group typology: Group 1, mild depressive symptoms and no symptoms of anxiety; Group 2, acute depression and anxiety; Group 3, less severe but persistent depression and anxiety symptoms; Group 4, symptoms of anxiety with no depressive symptoms; and Group 5, no symptoms of either depression or anxiety. Each group is associated with distinct socio-demographic, physical, and cognitive characteristics, allowing a deeper understanding of the risk factors associated with each syndrome profile. The characteristics of the groups were explored using descriptive statistics and confirmatory analysis. Analysis of a follow-up study performed at a median of 5.4 years finds that the identified typology is robust longitudinally. The co-clustering methodology also provides partitions of the questions in the CES-D and HADS scales. Our results suggest that data-driven techniques can discern different mental health typologies in older adults, distinguished by the number and severity of issues they experience.
A DOM-structural Cohesion Analysis Approach for Segmentation of Modern Web Pages
Authors: Huynh Hieu , Le Tri , Nguyen Vu , Nguyen Tien 
Published: May 2024

Abstract

Web page segmentation is a fundamental technique applied in information retrieval systems to enhance web crawling tasks and information extraction. Its purpose is to gain deep insights from crawling results and extract the main content of a webpage by disregarding the irrelevant regions. Over time, several solutions have been proposed to address the segmentation problem using different approaches and learning strategies. Among these, the structural cue, which is a characteristic of the DOM tree, is widely utilized as a primary factor in segmentation models. In this paper, we propose a novel technique for web page segmentation using DOM-structural cohesion analysis. Our approach involves generating blocks that represent groups of DOM subtrees with similar tag structures. By analyzing the cohesion within each generated block and comparing detailed information such as types, attributes, and visual cues of web page elements, we can effectively maintain or reconstruct the segmentation layout. Additionally, we employ the Canny algorithm to optimize the segmentation result by reducing redundant spaces, resulting in a more correct segmentation. We evaluate the effectiveness of our approach using a dataset of 1,969 web pages. The approach achieves 64% on the FB3 score, surpassing existing state-of-the-art methods. The proposed DOM-structural cohesion analysis has the potential for improving web page segmentation and its various applications.
Structural, thermal and conductivity studies on Tamarind Gum based Magnesium ion conducting polymer membrane for Energy storage applications
Authors: P Saranya , D Vanitha , K Sundaramahalingam , M Nandhinilakshmi , N Vijayakumar V 
Published: May 2024

Abstract

Solid Polymer Electrolyte (SPE) based on Tamarind Gum (TG) and Magnesium nitrate is synthesized by solution casting technique. The amorphous behaviour is observed by the X-ray Diffraction (XRD) analysis and the degree of crystallinity is calculated by XRD deconvolution spectra. The complex nature is confirmed by Fourier Transform Infrared (FTIR) analysis. Using FTIR deconvolution spectra, the percentage of free ions can be calculated. Glass transition temperature (Tg) is observed by Differential Scanning Calorimetry (DSC). The higher ionic conductivity (σ) of 1.97×10− 4 S/cm is observed for the sample with 1g of tamarind gum and 0.5g of magnesium nitrate (4 TMN). The conduction mechanism shows that sample 4 TMN obeys the Quantum Mechanical Tunnelling model (QMT) and Overlapping Large Polaron Tunnelling (OLPT) model. The prepared SPEs follow the Arrhenius behaviour, and the minimum activation energy (Ea) is observed for the sample 4 TMN as 0.207 eV. The lowest relaxation time (τ) is noticed as 3.46×10− 7 s for 4 TMN by tangent spectra. The transference number of ions (tion) is calculated by Wagner’s polarization method. The primary battery is fabricated by using the sample 4TMN and the Open Circuit Voltage (OCV) of 2.01 V is observed.
Ecological Epidemiology Insights into Clonorchiosis Endemicity in Southern China and Northern Vietnam: A Comprehensive Machine Learning Analysis
Authors: Zheng Jin-xin , Zhu Hui‐Hui , Xia Shang , Qian Men‐Bao , Bergquist Robert , Nguyen Hung Manh , Zhou Xiao‐Nong 
Published: May 2024

Abstract

Background: Clonorchis sinensis, the liver fluke responsible for clonorchiosis, poses significant public health challenges in Southern China and Northern Vietnam. Understanding its transmission dynamics is crucial for effective public health interventions.
Objectives: This study employs an ecological epidemiological approach, integrating environmental, climatic, and socio-cultural factors, to model and predict the transmission patterns of C. sinensis in these regions.
Methods: Leveraging machine learning techniques, we analyzed data from systematic literature reviews and national health surveys conducted between 2000 and 2018. Environmental factors, climate variables, and socio-cultural practices, notably raw fish consumption, were examined to assess their impact on the endemic of C. sinensis.
Results: Our analysis identifies raw fish consumption as a crucial determinant of C. sinensis transmission. The study revealed that 54.9% of counties in Guangxi Province and 31.7% of provincial-level divisions in Vietnam documented raw fish consumption, correlating with higher infection probabilities. Notably, logistic regression models achieved an area under the curve (AUC) of 0.941, demonstrating high predictive accuracy. Environmental comparisons showed significant differences between two places, with Vietnam showed a higher annual mean temperature (Bio1: 23.37°C vs. 20.86°C), greater temperature seasonality (Bio4: 464.92 vs. 609.33), and more annual precipitation (Bio12: 1731.64mm vs. 1607.56mm) compared to Guangxi, all factors contributing to varying levels of endemicity. These spatial analyses identified key high-risk areas, particularly along the China-Vietnam border, highlighting zones requiring targeted public health interventions.
Conclusion: This study underscores the interplay of ecological and socio-cultural factors in the transmission of clonorchiosis. The predictive models developed offer valuable insights for public health strategies, emphasizing the need for regional cooperation in disease control and prevention. Our approach demonstrates the potential of integrating diverse data sources in ecological epidemiology to address complex public health challenges.
Causal discovery of hydroclimatic drivers influencing water quality in a large lake
Authors: Shukla Rohit , Boegman Leon , Kumar Pankaj 
Published: May 2024

Abstract

Harmful algal blooms (HABs) are a threat to ecosystem services, with adverse economic and public health impacts. Large-scale climate processes will influence local environmental conditions, potentially favoring HAB formation through complex, nonlinear interactions. This study employs explainable (i.e., SHAP) machine learning to give insight on the predictions and causal analysis (i.e., PCMCI) to identify the relationships between climate indices, physical drivers, and the resultant Chlorophyll-a (CHL) concentrations in western Lake Erie. Our causal analysis revealed that runoff and water temperature directly affect CHL but also act to mediate the impacts of the Arctic Oscillation on influencing CHL. Moreover, our explainable analysis further confirmed this by identifying runoff as the main driving factor, followed by water temperature. The study highlights that water quality in the basin is subject to confounding effects resulting from interactions between global atmospheric circulation patterns and local hydro-meteorological factors, expanding HAB forecasting beyond synoptic scale meteorology.
Volumetric estimation of groundwater reserve in Akwa Ibom State University main campus, southern Nigeria using surficial resistivity measurements
Authors: Ekanem Aniekan Martin 
Published: May 2024

Abstract

Recent changes in climatic conditions and the resulting changes in demands for potable water call for a thorough understanding of groundwater resources. In this research, the usability of the electrical resistivity technique in the volumetric estimation of groundwater reserve is demonstrated. Sixteen (16) vertical electrical soundings were made at predetermined locations in Akwa Ibom State University campus, southern Nigeria with the main aim of delineating the hydrostratigraphic units and estimating the groundwater reserve in the area. Three to four geoelectric layers comprising poorly sorted continental sands with minor clay intercalations have been identified in the study site. Groundwater extraction in the area takes place in the second and third layers, based on location, at depths ranging from 0.7 to 84.7 m. The aquifer system is shown to have a mean effective porosity of 0.32, mean specific yield of 0.23 and mean specific retention of 0.09. The total aquifer volume is estimated to be 1.65 x 108 m3 while the effective water volume reserve in the area is 5.3 x 107 m3. The usable groundwater volume releasable by the aquifer during pumping is shown to be 71.9 % of the effective volumetric groundwater reserve while the fractional volume retainable by the aquifer is 28.1 %. This, by implication is an indication that the area has good groundwater potential for sustainability of water wells. The regression equations derived in this study can be used for easy and rapid prediction of aquifer porosity and specific yield/retention from surface resistivity data in regions with similar geological characteristics. These outcomes are very auspicious, particularly in devising sustainable groundwater development and exploitable schemes to meet current and future needs of the university community.
The Effect of Combinations of Antibiotics and Natural Products on the Antimicrobial Resistance of Staphylococcus aureus and Pseudomonas aeruginosa
Authors: Eladl Afaf , Attia Rasha , Abdullatif Hemat K. , El-Ganiny Amira M. 
Published: May 2024

Introduction/Background
The steadily increasing bacterial resistance to existing antimicrobial drugs is a significant issue, hence, it is imperative to look out for new approaches to bacterial therapy Occasionally, effective inhibitory action is not produced when antibiotics are used alone. To overcome this problem, a combination of drugs is often used. One approach to treat infectious diseases is the use of a combination of antibiotics together with plant extracts or phytochemicals. For patients with serious infections caused by pathogens resistant to drugs, combination therapy is beneficial and useful.

Materials and Methods
Seven antibiotics were obtained from a local pharmacy (gentamicin, ceftazidime, ciprofloxacin, doxycycline, amoxicillin, ceftriaxone, and azithromycin). Minimum inhibitory concentrations (MIC) were determined by broth micro-dilution method, and different antimicrobial combinations were studied on 20 Multidrug-resistant (MDR) clinical isolates (10 S. aureus and 10 P. aeruginosa). Moreover, the antibacterial activity of some volatile oils (limonene, rosemary, salvia, thymus, and black pepper), plant extracts (moringa seed, curcumin, and capsicum), and phytochemicals (thymol, and chitosan) was detected against S. aureus and P. aeruginosa isolates using broth micro-dilution method.

Results
According to our findings, ceftriaxone and ciprofloxacin or gentamicin together exhibited a substantial synergistic effect against S. aureus. Moreover, the combination of amoxicillin with ceftazidime was synergistic to reduce MIC by five to six times. Regarding MDR clinical isolates of P. aeruginosa, the combination of azithromycin with doxycycline exhibited a decrease of MIC of azithromycin by about five to sixfold. The combination of gentamicin with ceftriaxone was significant. For natural compounds, thymol, rosemary oil, curcumin, capsicum, and moringa seed extract exhibited the highest synergistic activity with the tested antibiotics against S. aureus and P. aeruginosa.

Conclusion
In conclusion, the lack of new antibiotics necessitates the improvement of existing ones. Our study shows that antibiotic combinations and antibiotic-natural plant combinations are very promising strategies for combating complex bacterial resistance.
Time Series AI Model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study
Authors: Heo Suncheol , Kang Eun-Ae , Yu Jae Yong , Kim Hae Reong , Lee Suehyun , Kim Kwangsoo , Hwangbo Yul , Park Rae Woong , Shin Hyunah , Ryu Kyeongmin , Kim Chungsoo , Jung Hyojung , Chegal Yebin , Lee Jae-Hyun , Park Yu Rang 
Published: May 2024

Abstract

Background
Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)–based time series data are rare.

Objective
In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)–based model to hospital electronic health record (EHR)–based time series data in patients who took nephrotoxic drugs using a DRN.

Methods
We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model’s contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA.

Results
This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase.

Conclusions
Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.
Geospatial Imprecision With Constraints for Precision Public Health: Algorithm Development and Validation
Authors: Harris Daniel , Delcher Chris 
Published: May 2024

Background
Location and environmental social determinants of health are increasingly important factors in both an individual’s health and the monitoring of community-level public health issues.

Objective
We aimed to measure the extent to which location obfuscation techniques, designed to protect an individual’s privacy, can unintentionally shift geographical coordinates into neighborhoods with significantly different socioeconomic demographics, which limits the precision of findings for public health stakeholders.

Methods
Point obfuscation techniques intentionally blur geographic coordinates to conceal the original location. The pinwheel obfuscation method is an existing technique in which a point is moved along a pinwheel-like path given a randomly chosen angle and a maximum radius; we evaluate the impact of this technique using 2 data sets by comparing the demographics of the original point and the resulting shifted point by cross-referencing data from the United States Census Bureau.

Results
Using poverty measures showed that points from regions of low poverty may be shifted to regions of high poverty; similarly, points in regions with high poverty may be shifted into regions of low poverty. We varied the maximum allowable obfuscation radius; the mean difference in poverty rate before and after obfuscation ranged from 6.5% to 11.7%. Additionally, obfuscation inadvertently caused false hot spots for deaths by suicide in Cook County, Illinois.

Conclusions
Privacy concerns require patient locations to be imprecise to protect against risk of identification; precision public health requires accuracy. We propose a modified obfuscation technique that is constrained to generate a new point within a specified census-designated region to preserve both privacy and analytical accuracy by avoiding demographic shifts.
El Benjamin e Imamura
Authors: Lama Bárrbara 
Published: May 2024

El presente trabajo quiere problematizar los textos de Walter Benjamin Experiencia y pobreza y El Narrador desde sus consecuencias en la concepción de barbarie. Para ello se propone el arte como lugar de levantamiento de una paradoja: el autorretrato en tanto manifestación artística que busca reafirmar la experiencia arte/vida y, en la obra de Shōhei Imamura para la película 11 ́09 ́ ́01 September 11,en que ésta exhibe su anomalía en la imposibilidad de narración de toda experiencia.