bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors Influencing Foraminiferal Assemblages in Coastal Sediments of the Cross River Estuary, Southeast Nigeria, Gulf of Guinea
Authors: EMEKA VICTORIA INYANG , EMEKA CHIMEZIE NDUNAGUM , ITAM ASUKWO ESSIEN , NYONG EYO ETIM 
Published: May 2024

Abstract

The Cross River estuary in Nigeria, a tropical meso-tidal estuary, empties into the Atlantic Ocean through the Gulf of Guinea. With a major navigation channel for maritime transportation, the estuary also provides commercial fishing, sand mining, and seafood trading. Sediment samples were collected at water depths of 1–12 m. From 55 samples collected, benthic foraminiferal specimens were found in only 12, representing three agglutinated species (Arenoparrella mexicana, Miliammina fusca, and Arenoparrella sp.) and one calcareous hyaline species (Ammonia sp.). Cluster analyses revealed two distinct sample clusters, one with two subgroups. Group 1a, four samples from < 10.4 m depth, were fine-grained sands containing Arenoparrella mexicana and Ammonia sp. Bottom waters were brackish, with low pH and high concentrations of dissolved oxygen (< 11 mg/l). Group 1b (4 samples), from muddy to very fine-grained sediments from shallow (< 2 m), brackish water with low pH and low dissolved oxygen, included all four species. The Group 2 assemblage, dominated by Arenoparrella mexicana, was also found in muddy to very fine-grained sediment at ≤ 5.5 m, in brackish waters with variable pH and low dissolved oxygen (< 2.5 mg/l). Arenoparrella mexicana was the most widely distributed species, while M. fusca was limited to organic-rich muddy sediments. Foraminiferal distributions correlated with environmental parameters including salinity, pH, and heavy metal concentrations. The study underscores the impact of anthropogenic activities on estuarine ecosystems, resulting in low foraminiferal density and diversity. These findings contribute valuable insights for monitoring environmental changes in the Cross River Estuary.
Reduced Sialylation of Airway Mucin Impairs Mucus Transport by Altering the Biophysical Properties of Mucin
Authors: Harris Elex S. , McIntire Hannah J. , Mazur Marina , Schulz-Hildebrandt Hinnerk , Leung Hui Min , Tearney Guillermo J , Krick Stefanie , Rowe Steven M. , Barnes Jarrod W. 
Published: May 2024

Abstract

Mucus stasis is a pathologic hallmark of muco-obstructive diseases, including cystic fibrosis (CF). Mucins, the principal component of mucus, are extensively modified with hydroxyl (O)-linked glycans, which are largely terminated by sialic acid. Sialic acid is a negatively charged monosaccharide and contributes to the biochemical/biophysical properties of mucins. Reports suggest that mucin sialylation may be altered in CF; however, the consequences of reduced sialylation on mucus clearance have not been fully determined. Here, we investigated the consequences of reduced sialylation on the charge state and conformation of the most prominent airway mucin, MUC5B, and defined the functional consequences of reduced sialylation on mucociliary transport (MCT). Reduced sialylation contributed to a lower charged MUC5B form and decreased polymer expansion. The inhibition of total mucin sialylation de novo impaired MCT in primary human bronchial epithelial cells and rat airways, and specific α-2,3 sialylation blockade was sufficient to recapitulate these findings. Finally, we show that ST3 beta-galactoside alpha-2,3-sialyltransferase (ST3Gal1) expression is downregulated in CF and partially restored by correcting CFTR via Elexacaftor/Tezacaftor/Ivacaftor treatment. Overall, this study demonstrates the importance of mucin sialylation in mucus clearance and identifies decreased sialylation by ST3Gal1 as a possible therapeutic target in CF and potentially other muco-obstructive diseases.
NTS-YOLO：a nocturnal traffic sign detection method based on improved YOLOv5
Authors: Zhang Yongchuan , Guo Mengqi , He Yong , Xia Jun , Geng Xuelai , Zou Tao , Ding Rui 
Published: May 2024

Abstract

Accurate traffic sign recognition is one of the core technologies of intelligent driving systems, which face multiple challenges such as insufficient light and shadow interference at night. In this paper, we improve the YOLOv5 model for small, fuzzy, and partially occluded traffic sign targets at night and propose a high-precision nighttime traffic sign recognition method, "NTS-YOLO". The method firstly preprocesses the traffic sign dataset by adopting an unsupervised nighttime image enhancement method to improve the image quality under low-light conditions; secondly, it introduces the Convolutional Block Attention Module (CBAM) attentional mechanism, which focuses on the shape of the traffic sign by weighting the channel and spatial features inside the model and color to improve the perception under complex background and uneven illumination conditions; finally, the Optimal Transport Assignment (OTA) loss function is adopted to optimize the accuracy of predicting the bounding box and thus improve the performance of the model by comparing the difference between two probability distributions, i.e., minimizing the difference. In order to evaluate the effectiveness of the method, 154 samples of typical traffic signs containing small targets and fuzzy and partially occluded traffic signs with different lighting conditions in nighttime conditions were collected, and the data samples were subjected to CBMA, OTA, and a combination of the two methods, respectively, and comparative experiments were conducted with the traditional YOLOv5 algorithm. The experimental results show that "NTS-YOLO" achieves significant performance improvement in nighttime traffic sign recognition, with a mean average accuracy improvement of 0.95% for target detection of traffic signs and 0.17% for instance segmentation.
GenAI based Digital Twins aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis based Anomaly Detection of Wearable Data
Authors: Kamruzzaman Methun , Salinas Jorge , Kolla Hemanth , Sale Kenneth , Balakrishnan Uma , Poorey Kunal 
Published: May 2024

Abstract

In an age where pandemics are getting more frequent, early detection of outbreaks is crucial for effective intervention. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we develop a framework for early infection detection in individuals like COVID-19. Despite the availability of data from recent pandemics, substantial gaps remain, hindering method development. To bridge this gap, we utilize Wasserstein Generative Adversarial Networks (WGANs) to generate realistic wearable data, augmenting our dataset for training. Subsequently, we use these real and synthetic datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to real-world counterparts, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.
The role relationship played by immune cells and circulating inflammatory proteins in NAFLD
Authors: Chi Zhenfei , Zhang Yanyun , Bai Xuesong , Zhao Baoying , Zhang Zhe 
Published: May 2024

Abstract

Objective
The mechanisms by which immune cells and inflammatory factors influence Non-Alcoholic Fatty Liver Disease (NAFLD) remain unclear. This study employs Mendelian randomization (MR) to investigate the relationship between immune cells, inflammatory factors, and NAFLD, as well as the proportion of their mutual mediation effects on NAFLD.
Methods
This study utilizes MR analysis, examining the causal relationship between 731 immune cell phenotypes, 91 circulating inflammatory proteins, and NAFLD. The data are sourced from publicly available data in the GWAS Catalog. The research process consists of two steps, analyzing them through the assessment of their mediating effects. To obtain reliable results, MR analysis necessitates the fulfillment of three fundamental assumptions. In the selection of instrumental variables, SNPs are screened, requiring significant associations with the exposure factors and no association with the outcomes. Statistical analyses employ methods such as IVW, WM, and MR-Egger to evaluate the causal relationship between exposure and outcomes. Sensitivity analyses are conducted, examining heterogeneity and horizontal pleiotropy.
Results
Ultimately, among the 731 immune cell phenotypes, 21 phenotypes are found to have a causal relationship with NAFLD, with 6 circulating inflammatory protein phenotypes playing intermediary roles. Among the 91 circulating inflammatory protein phenotypes, 7 inflammatory factor phenotypes are found to have a causal relationship with NAFLD, with 5 immune cell phenotypes playing intermediary roles.
Conclusion
Immune cells and circulating inflammatory proteins play a crucial role in NAFLD, and our study may provide new insights for the diagnosis and treatment of NAFLD in the future.
A microbiological analysis of Bacterial Adherence around Different suture Material at impacted 3rd molar site- a Cross sectional Study
Authors: Lenka S. P , Sahoo Swagata , Nayak Susmita , Bej Ananya 
Published: May 2024

Abstract

Introduction:
Suture materials are medical devices that holds the open wound margins together, it transfers the mechanical stress to deeper structures, helps in wound margination and wound healing, prevents infection. They are classified as synthetic/natural, absorbable/non-absorbable, monofilament/braided, etc., but suture materials can also be the source of infection if proper precautions are not taken.
Material and methods
We present 60 cases of impacted third molar treated with transalveolar extraction. Four types of sutures, Vicryl, silk, prolene, and Ethilon, were selected and suturing done by the same operator. Patients were instructed not to use mouthwash or toothpaste for 1 week. After one week suture materials are collected and cultured in blood agar medium. They were graded on the basis of colony-forming units.
Results
The results show both gram positive and negative bacteria & aerobic and anaerobic bacteria growth around every suture. It was maximum in case of Vicryl sutures and minimum for nylon sutures.
Conclusion
All suture materials can be responsible for infection at surgical site as they have the capacity to havour bacteria. Braided sutures have more tendency for the same.
Reversal in China’s Air Quality Gains
Authors: Silver Ben , Reddington Carly , Chen Yue , Arnold Stephen 
Published: May 2024

Abstract

China’s air quality has improved rapidly since the early 2010s, when the government launched an action plan focussed on reducing fine particulate matter (PM2.5) pollution, the pollutant species associated with the largest negative health impact. Measurements from China’s monitoring network, now consisting of >2000 surface stations, have shown a rapid fall in PM2.5 concentrations consistently over the period 2014–2019, while sulphur dioxide (SO2), carbon monoxide (CO) and nitrogen dioxide (NO2) concentrations have also significantly decreased. Concurrently there has been a rapid increase in ozone concentrations, which has been partly attributed to falling particulate matter concentrations. However, air quality data from China remains difficult to access, and recent changes in China’s economic outlook mean that the future trajectory of China’s air quality continues to be highly uncertain. Here we analyse 10 years of air quality monitoring data from May 2014 to April 2024 to assess whether China’s air quality has continued to improve in recent years, in the wake of the COVID-19 lockdowns and other economic challenges. After using an improved data cleaning algorithm to remove outliers from the dataset, we use a non-linear trend fitting technique to extract underlying trends and their uncertainties. We find that the steep negative trend in PM2.5 that was observed during 2014–2019 has now reversed, and since June 2022 there has been a significant (>95% confident) positive trend at over half of China’s air quality monitoring stations. This is mirrored by the average trend of ozone, which was positive during 2014–2019, but since December 2022 there has been a significant negative trend at almost half of monitoring stations. The increase in PM2.5 exposure has the potential to worsen air quality health impacts in China, particularly given its ageing population.
The Som Kamla Amba Catchment Sub-watershed’s Artificial Neural Networks (ANN) and Water Assessment Tool (SWAT) Modelling
Authors: Ande Ravi 
Published: May 2024

Abstract

The hydraulic structures, soil conservation structures, water harvesting structures, and flood mitigation studies, and other related projects, rainfall-runoff modelling for unregulated river basins or catchments is useful. The Som Kamla Amba catchment area, which is located in the Rajasthani districts of Dungarpur and Udaipur, is the subject of the current study. Standard statistical indices like r, R2, RMSE, MAE, IA, VE, and NSE were used to evaluate the performance of monthly stream flow and rainfall forecasts created with the Soil and Water Assessment Tool (SWAT) and Artificial Neural Networks (ANN). For the Som Kamla Amba watershed, 27 years (1995–2021) of input data, including daily rainfall, maximum and minimum temperatures, relative humidity, sun radiation, and wind speed, and stream flow, were gathered. The Som Kamla Ambamba catchment's sub watersheds (W1 to W9) had their morphometric parameters examined using ASTER Dem at a spatial resolution of 30 m × 30 m in ArcGIS 10.4.1 software. It was discovered that the ANN model was more accurate and realistic at predicting rainfall. The SWAT model was then discovered to be accurate in forecasting stream flow. Hence, for catchments, basins, or watersheds with comparable hydrological characteristics, ANN and SWAT can be used for rainfall forecasting and stream modelling.
Optimizing Random Forest for Debris Flow Susceptibility Assessment: A Comparative Study of Advanced Algorithms
Authors: Chen Zhengsheng , Zhu Mulei , Zhang Qinghua , Rong Fengjuan 
Published: May 2024

Abstract

Random forest (RF) method is widely used in debris flow susceptibility research. The algorithm's performance can be enhanced through the careful selection of influencing factors and the optimization of RF hyperparameters. The selected study area is Xiaojin County in Sichuan Province, recognized for its frequent occurrence of debris flows, serving as a critical area for analyzing debris flow susceptibility. 12 key influencing factors of debris flows have been identified and their correlation has been analyzed. Sparrow search algorithm (SSA), genetic algorithm (GA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO) are employed for the optimization of the hyperparameters of RF, respectively. The model's performance was assessed using 5 metrics: ROC curve, confusion matrix, 10-fold cross-validation, iteration time, and convergence count. Based on the results, SSA-RF model demonstrates the highest accuracy (ACC) and area under the curve (AUC) values, with respective scores of 0.9629 and 0.98. It performs RF model by 0.06 and 0.1296, respectively. Furthermore, SSA-RF model demonstrates exceptional performance with regard to other assessment parameters. This observation suggests that RF model's performance experiences a significant enhancement following parameter optimization, thereby providing additional confirmation of the efficacy of optimization algorithms in improving RF model performance. In particular, the performance of SSA is noteworthy in this respect. The study results offer a robust scientific basis for the relevant departments in Xiaojin County and other high-risk debris flow locations to develop catastrophe mitigation and prevention strategies.
Novel genetic insights into the roles of amino acids in metabolic dysfunction-associated steatotic liver disease
Authors: Liu Jun , Chen Yuxuan , Qian Jin , Cui Ri , Demirkan Ayse , Zheng Yihu 
Published: May 2024

Abstract

Abstract
Background
The liver metabolizes amino acids, but previous studies show that amino acids have significant effects also on cardiometabolic diseases like Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). Our study investigates the genetic aspects of amino acids in MASLD, explores their causal links, accounting for obesity's influence, and investigates their potential in predicting MASLD complications

Methods
Using UK Biobank, we examined the observational associations of ten amino acids with MASLD in a cohort of 72,626 cases and 128,102 controls. We used Mendelian randomization (MR) to investigate the relationships between amino acids and MASLD, considering the impact of obesity. Survival analysis assessed the link between baseline amino acid levels and the incidence of major outcomes in 15 year follow-up.

Results
Nine amino acids showed significant association with MASLD. We observed that higher levels of genetically predicted leucine and valine were associated with an increased risk of MASLD. Among them, valine appeared to independently mediate the link between obesity and MASLD, suggesting a unique role in MASLD beyond its association with obesity. Moreover, an increased risk of genetically predicted MASLD was associated with higher phenylalanine levels. Elevated phenylalanine levels in MASLD patients were linked to increased risks of future metabolic dysfunction-associated steatohepatitis (MASH), hepatocellular carcinoma, cirrhosis, heart failure, stroke, and mortality.

Conclusion
Our results suggest that valine and leucine may cause incident MASLD, presenting as potential therapeutic targets for MASLD. Alterations in phenylalanine could serve as a novel biomarker for MASLD progression and predicting major outcomes in MASLD patients.
Backstepping Adaptive Control of Water-Based Position Servo System for High-Speed On/off Valve Based on Extended Sliding Mode Observer
Authors: Ma Aixiang , Yan Xihao , Jin Zihan , Zhou Yunlin , Wei Yaqiang , Zhao Sihai 
Published: May 2024

Abstract

Most mining hydraulic systems use water-based emulsions as the transmission medium. However, the low viscosity of water-based fluids causes leakage, making proportional flow control in water hydraulic systems challenging and preventing precise positioning of hydraulic cylinders. To achieve continuous flow control in water-based fluids, this research proposes a novel backstepping adaptive controller integrated with an extended sliding mode observer (ESMO). By using additional pressure sensors, continuous proportional control of the output flow of a two-stage high-speed on/off valve (TSHSV) can be realized. The ESMO estimates the valve spool position and unmodeled disturbances based on the current in the coil winding, and system output flow is derived from the spool position. An adaptive controller adjusts the spool position to control the entire system’s flow output, achieving proportional flow control. Simulation and experimental results validate the control accuracy of the system. The results show that the system’s performance surpasses that of proportional valves in many respects.
Exploring the Causal Link Between Immune Cell Phenotypes and Asthma Through Two-Sample and Bayesian Weighted Mendelian Randomization
Authors: Kaihao Du , Lizhao Hou , Mu Qiao , Xiaoge Dong , Zhanjin Wang , Lanminghui Luo , Wei Jiang , Zhan Wang 
Published: May 2024

Abstract

Objective: Using two-sample Mendelian Randomization (MR) and Bayesian Weighted Mendelian Randomization (BWMR), this study explores the causal links between 731 immune cell phenotypes and asthma, providing useful biomarkers for potential therapeutic targets for asthma.
Methods: The study employed two-sample MR and BWMR to evaluate the causal relationships between 731 immune cell phenotypes and asthma, using large-scale Genome-Wide Association Study (GWAS) datasets to exclude confounding factors and conduct various sensitivity analyses.
Results: The study conducted an in-depth analysis of the causal relationship between 731 immune cell phenotypes and asthma across three databases (ebi, finn, and ukb). Integrating the results from IVW and BWMR across these databases, we identified CD16+ monocyte %monocyte as a protective factor against asthma, whereas CD62L- myeloid Dendritic Cell Absolute Count, CD62L- myeloid Dendritic Cell %Dendritic Cell, CD62L- CD86+ myeloid Dendritic Cell Absolute Count, and CD62L- CD86+ myeloid Dendritic Cell %Dendritic Cell were identified as risk factors.
Conclusion: Our research confirms that CD16+ monocyte %monocyte serves as a protective factor against asthma, while CD62L- myeloid Dendritic Cell Absolute Count, CD62L- myeloid Dendritic Cell %Dendritic Cell, CD62L- CD86+ myeloid Dendritic Cell Absolute Count, and CD62L- CD86+ myeloid Dendritic Cell %Dendritic Cell pose risks for asthma.
Sinoatrial Node Cells Implementation by Low Cost Digital Hardware
Authors: Naderi Ali , Ghanbarpour Gilda , Ghanbarpour Milad , Haghiri Saeed , Ahmadi Arash 
Published: May 2024

Abstract

In mammalian hearts, the Sinoatrial (SA) node cells act as the fundamental pacemaker location. The intricate patterns of SA node activity can be elucidated by a series of differential equations featuring non-linear functions. This study presents a novel approach to improve the digital representation of the SA node cell model. The results consist of reduced hardware requirements, enhanced speed and accuracy, and lowered implementation costs. The proposed method involves converting the non-linear functions found in the original model into exponential functions represented as $2^x$. This conversion gives rise to a set of mathematical equations that eliminate the need for multipliers. These equations are subsequently refined by minimizing the number of $2^x$ terms, leading to a more effective emulation of the SA model's biological characteristics. To validate the concept, the suggested model is successfully synthesized and implemented on an FPGA. The implementation outcomes demonstrate a substantial enhancement in operating frequency, showing a 3.4-fold increase compared to the original model. Moreover, there is a noticeable 47\% reduction in power consumption. The reduced hardware requirements also enable running a significantly larger number of neurons, precisely 12 times more, on a single FPGA board compared to the original model.