bluuebunny/crossref_metadata_2025 huggingface dataset:
Selective Enrichment of Angiomirs in Extracellular Vesicles Released from Ischemic Skeletal Muscles: Potential Role in Angiogenesis and Neovascularization
Authors: Dussault Sylvie , Desjarlais Michel , Raguema Nozha , Boilard Eric , Chemtob Sylvain , Rivard Alain
Published: July 2024

MicroRNAs (miRs) regulate physiological and pathological processes, including ischemia-induced angiogenesis and neovascularization. They can be transferred between cells by extracellular vesicles (EVs). However, the specific miRs that are packaged in EVs released from skeletal muscles, and how this process is modulated by ischemia, remain to be determined. We used a mouse model of hindlimb ischemia and next generation sequencing (NGS) to perform a complete profiling of miR expression and determine the effect of ischemia in skeletal muscles, and in EVs of different sizes (microvesicles (MVs) and exosomes) released from these muscles. Ischemia significantly modulated miR expression in whole muscles and EVs, increasing the levels of several miRs that can have pro-angiogenic effects (angiomiRs). We found that specific angiomiRs are selectively enriched in MVs and/or exosomes in response to ischemia. In silico approaches indicate that these miRs modulate pathways that play key roles in angiogenesis and neovascularization, including HIF1/VEGF signaling, regulation of actin cytoskeleton and focal adhesion, NOTCH, PI3K/AKT, RAS/MAPK, JAK/STAT, TGFb/SMAD signaling and the NO/cGMP/PKG pathway. Thus, we show for the first time that angiomiRs are selectively enriched in MVs and exosomes released from ischemic muscles. These angiomiRs could be targeted in order to improve the angiogenic function of EVs for potential novel therapeutic applications in patients with severe ischemic vascular diseases.
Receptors and Signaling Pathways Controlling Beta-Cell Function and Survival as Targets for Anti-Diabetic Therapeutic Strategies
Authors: Dalle Stéphane , Abderrahmani Amar
Published: July 2024

Preserving the function and survival of pancreatic beta-cells, in order to achieve long-term glycemic control and prevent complications, is an essential feature for an innovative drug to have clinical value in the treatment of diabetes. Innovative research is developing therapeutic strategies to prevent pathogenic mechanisms and protect beta-cells from the deleterious effects of inflammation and/or chronic hyperglycemia over time. A better understanding of receptors and signaling pathways, and of how they interact with each other in beta-cells, remains crucial and is a prerequisite for any strategy to develop therapeutic tools aimed at modulating beta-cell function and/or mass. Here, we present a comprehensive review of our knowledge on membrane and intracellular receptors and signaling pathways as targets of interest to protect beta-cells from dysfunction and apoptotic death, which opens or could open the way to the development of innovative therapies for diabetes.
The Triad of Sleep, Immunity, and Cancer: A Mediating Perspective
Authors: Lanza Giuseppe , Mogavero Maria P. , Salemi Michele , Ferri Raffaele
Published: July 2024

The triadic interplay between sleep, immunity, and cancer represents a growing area of biomedical research with significant clinical implications. This review synthesizes the current knowledge on how sleep influences immune function, the immune system’s role in cancer dynamics, and the direct connections between sleep patterns and cancer risk. After a comprehensive overview of the interrelationships among these three domains, the mechanisms of sleep in immune function are described, detailing how sleep regulates the immune system, the effects of sleep duration and quality on immune responses, and the underlying molecular and cellular mechanisms. Also, the complex relationship between immunity and cancer is explored, highlighting the immune system’s role in cancer prevention and progression, immune surveillance, tumor microenvironment, and the implications of immunodeficiency and immune modulation on cancer risk. The direct connections between sleep and cancer are then described, presenting epidemiological evidence linking sleep patterns to cancer risk, biological mechanisms that influence cancer development, and the role of sleep disorders in cancer prognosis. The mediating role of sleep between immunity and cancer is highlighted, proposing hypothesized pathways, summarizing evidence from experimental and clinical studies, and evaluating the impact of sleep interventions on immune function and cancer outcomes. This review concludes by discussing the clinical implications and future directions, emphasizing the potential for sleep-based interventions in cancer prevention and treatment, the integration of sleep management in oncology and immunotherapy, and outlining a future research agenda. This agenda includes understanding the mechanisms of the sleep–immunity–cancer interplay, conducting epidemiological studies on sleep and cancer risk, assessing the impact of sleep management in cancer treatment protocols, exploring sleep and tumor microenvironment interactions, and considering policy and public health implications. Through a detailed examination of these interconnected pathways, this review underscores the critical importance of sleep in modulating immune function and cancer outcomes, advocating for interdisciplinary research and clinical strategies to harness this knowledge for improved health outcomes.
Wear Mechanism of an AlCrN-Coated Solid Carbide Endmill Cutter and Machined Surface Quality under Eco-Friendly Settings during Open Slot Milling of Tempered JIS SKD11 Steel
Authors: Trung Ly Chanh , Phuc Tran Thien
Published: July 2024

In the die and mold industry, tempered JIS SKD11 steel is selected to manufacture cold-forming dies that require an optimum balance of toughness, strength, and wear resistance. Therefore, the machinability of tempered JIS SKD11 in the milling machining process is challenging. The use of eco-friendly machining settings is intended to diminish tool wear and enhance the quality of the machined surface as well as the accuracy of the machined components. Adapting to the aforementioned factors for cold-forming dies is a pivotal issue. In this study, the machinability of tempered JIS SKD11 steel was analyzed under dry, MQL, cryogenic cooling with liquid nitrogen (LN2), and liquid carbon dioxide (LCO2) machining settings during open slot milling operations with varying input parameters, including cutting speeds and cutting feeds. An in-depth evaluation of output responses, including tool wear, surface roughness, cutting temperature in the cutting zone, and microhardness of the machined surface, was also conducted. The findings unveiled that the flank wear of the cutters and surface roughness of the machined surfaces obtained minimum values of 0.22 mm and 0.197 µm, respectively, during open slot milling operations at a cutting speed of 100 m/min and a cutting feed of 204 mm/min under cryogenic cooling with liquid carbon dioxide (LCO2). The findings from this study suggest that employing cryogenic cooling with LCO2 could serve as a viable substitute for dry, MQL, and cryogenic cooling with LN2 methods to enhance the machinability of hardened JIS SKD11 steel.
Electrochemical Recovery of N and P from Municipal Wastewater
Authors: Natsi Panagiota D. , Koutsoukos Petros G.
Published: July 2024

Phosphorus, P, is a vital element of paramount importance for both humans and for the Environment. Wastewater contains often relatively high concentrations of P which can be recovered as crystalline struvite (MgNH4PO4·6H2O, MAP). This option is quite attractive in assisting sustainable development because struvite can be used as a slow-release fertilizer. Domestic wastewater is usually high in P and nitrogen, N, but relatively poor in magnesium, Mg. It is necessary to develop low-cost solutions for the enrichment of wastewater with Mg. In the present work, sacrificial magnesium anodes were used, which dissolve upon anodic polarization, releasing sufficient magnesium for the selective precipitation of MAP. The application of constant current between two electrodes of which the anode is a low-cost magnesium cylindrical rod (4 cm2 exposed surface area) and the other a platinum cathode electrode, both immersed in ammonium phosphate solutions, without adjustment of the solution pH, was investigated. Constant current density over the range 10–100 A·m−2, between the Mg- Pt electrodes immersed in solutions of ammonium hydrogen phosphate of exactly known initial concentration, was applied using a potentiostat. In the presence of sodium chloride solutions, on the magnesium anode and in the bulk solution, Mg(OH)2 (brucite) formed because of the passivation of the Mg electrode. In dilute ammonium hydrogen phosphate solutions, the magnesium anode dissolution resulted in struvite precipitation, even at a low applied current (10 mA). Struvite crystals with an average size of 20 μm were precipitated. The behavior of the cell for the electrolyte solutions used was Faradaic as long as the surface coverage of the anode was relatively low. The anodic dissolution of Mg resulted in high pH values (pH 11) eliminating the need for alkali addition.
Diversity and Distribution of Monocot Understory Herbs during Tropical Forest Succession in Northeastern Costa Rica
Authors: Sun Jennifer W. C. , Chazdon Robin L. , Rundel Philip W.
Published: July 2024

Broad-leaved monocot herbs form one of the most common and diverse growth forms of Neotropical plants. Their significance and frequency of occurrence is particularly notable in the understories of tropical rainforests, where they form a dominant element. We assessed and quantified changes in the cover and diversity of understory herb communities in a chronosequence of 1 ha permanent plots established as part of a multidisciplinary study on tropical forest regeneration in the Atlantic lowlands of northeastern Costa Rica. Sampled were two young stands cleared 12 years ago, two secondary forests with 21 and 39 of years of recovery since clearance, and two stands in old-growth primary forest. Changes in species composition during succession were assessed using Chao’s Jaccard similarity index. Observed species richness ranged from 15 to 26 species in individual plots, with the greatest number of species in the 21-year intermediate-age and fewest in the young 12-year plots. Herb species sampled represented 6 families, 15 genera, and 39 species, with the Araceae contributing the largest number of species. Ten species were sampled in all six stands, while fourteen species were found exclusively in one plot. Herb density (ramets m−2) showed a hump-shade trend, with peak density in the intermediate stands and a lower level in mature and young secondary forests. Mean herb cover in 25 m2 quadrats ranged from 2.0% (young stand) to 22.7% (intermediate-age stand) and differed significantly both among stand types and among sites. Both observed and estimated species richness increased along the chronosequence as a whole, with the highest number of species in primary forest, although only slightly higher than in intermediate-age stands. Over half of the species exhibited some degree of clonal growth, with the extent of clonal spread varying among species and forest stands. Although we did not find a clear pattern between clonality and forest age, we observed a greater number of clonal patches in secondary over primary forest stands.
Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology
Authors: Kim Hajin , Lim Sewon , Park Minji , Kim Kyuseok , Kang Seong-Hyeon , Lee Youngjin
Published: July 2024

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10−2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.
Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review
Authors: Wu Chieh-Chen , Poly Tahmina Nasrin , Weng Yung-Ching , Lin Ming-Chin , Islam Md. Mohaimenul
Published: July 2024

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.
Flow-Field Inference for Turbulent Exhale Flow Measurement
Authors: Transue Shane , Lee Do-kyeong , Choi Jae-Sung , Choi Seongjun , Hong Min , Choi Min-Hyung
Published: July 2024

Background: Vision-based pulmonary diagnostics present a unique approach for tracking and measuring natural breathing behaviors through remote imaging. While many existing methods correlate chest and diaphragm movements to respiratory behavior, we look at how the direct visualization of thermal CO2 exhale flow patterns can be tracked to directly measure expiratory flow. Methods: In this work, we present a novel method for isolating and extracting turbulent exhale flow signals from thermal image sequences through flow-field prediction and optical flow measurement. The objective of this work is to introduce a respiratory diagnostic tool that can be used to capture and quantify natural breathing, to identify and measure respiratory metrics such as breathing rate, flow, and volume. One of the primary contributions of this work is a method for capturing and measuring natural exhale behaviors that describe individualized pulmonary traits. By monitoring subtle individualized respiratory traits, we can perform secondary analysis to identify unique personalized signatures and abnormalities to gain insight into pulmonary function. In our study, we perform data acquisition within a clinical setting to train an inference model (FieldNet) that predicts flow-fields to quantify observed exhale behaviors over time. Results: Expiratory flow measurements capturing individualized flow signatures from our initial cohort demonstrate how the proposed flow field model can be used to isolate and analyze turbulent exhale behaviors and measure anomalous behavior. Conclusions: Our results illustrate that detailed spatial flow analysis can contribute to unique signatures for identifying patient specific natural breathing behaviors and abnormality detection. This provides the first-step towards a non-contact respiratory technology that directly captures effort-independent behaviors based on the direct measurement of imaged CO2 exhaled airflow patterns.
Boxing Punch Detection with Single Static Camera
Authors: Stefański Piotr , Kozak Jan , Jach Tomasz
Published: July 2024

Computer vision in sports analytics is gaining in popularity. Monitoring players’ performance using cameras is more flexible and does not interfere with player equipment compared to systems using sensors. This provides a wide set of opportunities for computer vision systems that help coaches, reporters, and audiences. This paper provides an introduction to the problem of measuring boxers’ performance, with a comprehensive survey of approaches in current science. The main goal of the paper is to provide a system to automatically detect punches in Olympic boxing using a single static camera. The authors use Euclidean distance to measure the distance between boxers and convolutional neural networks to classify footage frames. In order to improve classification performance, we provide and test three approaches to manipulating the images prior to fitting the classifier. The proposed solution achieves 95% balanced accuracy, 49% F1 score for frames with punches, and 97% for frames without punches. Finally, we present a working system for analyses of a boxing scene that marks boxers and labelled frames with detected clashes and punches.
An Exact Theory of Causal Emergence for Linear Stochastic Iteration Systems
Authors: Liu Kaiwei , Yuan Bing , Zhang Jiang
Published: July 2024

After coarse-graining a complex system, the dynamics of its macro-state may exhibit more pronounced causal effects than those of its micro-state. This phenomenon, known as causal emergence, is quantified by the indicator of effective information. However, two challenges confront this theory: the absence of well-developed frameworks in continuous stochastic dynamical systems and the reliance on coarse-graining methodologies. In this study, we introduce an exact theoretic framework for causal emergence within linear stochastic iteration systems featuring continuous state spaces and Gaussian noise. Building upon this foundation, we derive an analytical expression for effective information across general dynamics and identify optimal linear coarse-graining strategies that maximize the degree of causal emergence when the dimension averaged uncertainty eliminated by coarse-graining has an upper bound. Our investigation reveals that the maximal causal emergence and the optimal coarse-graining methods are primarily determined by the principal eigenvalues and eigenvectors of the dynamic system’s parameter matrix, with the latter not being unique. To validate our propositions, we apply our analytical models to three simplified physical systems, comparing the outcomes with numerical simulations, and consistently achieve congruent results.
Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning
Authors: Dai Yanyan , Kim Deokgyu , Lee Kidong
Published: July 2024

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.
The Distribution Range of Populus euphratica Oliv. (Salicaceae) Will Decrease Under Future Climate Change in Northwestern China
Authors: Lei Xun , Qu Mengjun , Wang Jianming , Hou Jihua , Wang Yin , Li Guanjun , Luo Meiwen , Li Zhijun , Li Jingwen
Published: July 2024

Climate change has been regarded as a primary threat to biodiversity at local, regional, and global scales. Populus euphratica Oliv. is one of the main constructive species in dryland regions and has a key role in regulating ecosystem processes and services. However, there is a knowledge gap regarding the spatial distribution of habitat suitability of P. euphratica and how it will be affected by future climate change. Based on the distribution records collected from an online database and specialized literature, we applied an optimized MaxEnt model to predict the distribution range of P. euphratica in China under four climate change scenarios (SSP126, SSP245, SSP370, and SSP585) for both current and future (2090s) conditions. We found that (1) future climate change would reduce the adaptability of P. euphratica, resulting in a significant decrease in its distribution area; (2) water availability had the most important effect on P. euphratica distribution; (3) the habitat for P. euphratica would shift northwestward and contract towards lower elevations closer to rivers in the future. These findings can provide a reference for developing long-term biodiversity conservation and management strategies in arid regions.
The Impact of Future Land Use Change on Carbon Emission and Its Optimization Strategy
Authors: Sun Yang , Zhi Junjun , Han Chenxu , Xue Chen , Zhao Wenjing , Liu Wangbing , Bao Shanju
Published: July 2024

Rapidly changing climate issues and increasingly severe carbon emissions are great challenges to the carbon peaking and carbon neutrality strategy. Analyzing the impact of future land use changes on carbon emissions can provide an important basis and reference for scientifically constructing a low-carbon and sustainable territorial spatial planning, as well as realizing the goal of the dual-carbon strategy. Based on land use data, agricultural production activity data, and energy consumption statistics, this study simulated the land use changes of the Yangtze River Delta region (YRDR) from 2030 to 2060 under the natural development (ND) scenario and sustainable development (SD) scenario by using the Patch-generating Land Use Simulation (PLUS) model and analyzed the impacts of future land use changes on carbon emissions. The results showed that: (1) The land use simulation results obtained by using the PLUS model under the sustainable development scenario were highly consistent with the actual land use with an OA value of 97.0%, a Kappa coefficient of 0.952, and a FoM coefficient of 0.403; (2) Based on the simulated land use under the SD scenario from 2030 to 2060, the quantity of construction land was effectively controlled, and the spatial distributions of cropland and forests were found to dominate in the north and south of the Yangtze River, respectively; (3) Anhui Province was the major contributor (accounted for 49.5%) to the net carbon absorption by cropland while Zhejiang Province was the major contributor (accounted for 63.3%) to the net carbon absorption by forest in the YRDR during the period 2020–2060 under the SD scenario; (4) Carbon emissions from construction land were the main source of carbon emissions from land use in the YRDR during the period 2020–2060 with proportions higher than 99% under both the ND and SD development scenarios. These findings underscore the urgent need for the government to take measures to balance the relationships between cropland and ecological protection and economic development, which provides a reference for the optimization of land use structure and policy formulation in the future.
Identification and Verification of the Movement of the Hidden Active Fault Using Electrical Resistivity Tomography and Excavation
Authors: Arjwech Rungroj , Hongsresawat Sutatcha , Chaisuriya Suriyachai , Rattanawannee Jetsadarat , Kanjanapayont Pitsanupong , Youngme Winit
Published: July 2024

Identifying the movement of the branches of the hidden Thakhek fault in Thailand is challenging due to the absence of evident landforms indicating an active fault. In this study, we analyzed a digital elevation model (DEM) to identify potential landforms. A 2D Electrical Resistivity Tomography (ERT) survey was conducted to locate the hidden Thakhek fault. The results reveal vivid images of resistivity contrast, interpreted as two reverse faults, with mudstone exhibiting low resistivity in the middle, flanked by thick sediment layers with higher resistivity. Three trenches were excavated perpendicular to the two interpreted reverse faults. The displacement of reverse faulting appears to have shifted mudstone over Quaternary sediments, with vertical offsets revealed in trenches NWY-1, NWY-2, and NWY-3. This movement could be identified as a positive flower structure. Additionally, lakes are identified as a negative flower structure along the traces. These features result from strike-slip strains under a locally appropriate compressional and extensional environment in a shearing strike-slip fault.
A Unique Conditions Model for Landslide Susceptibility Mapping
Authors: De Smedt Florimond , Kayastha Prabin
Published: July 2024

Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models.
Risk Factors Associated with Urothelial Bladder Cancer
Authors: Alouini Souhail
Published: July 2024

Background: Urothelial bladder carcinoma (UBC) is the most frequent histologic form of bladder cancer, constituting 90% of the cases. It is important to know the risk factors of UBC to avoid them and to decrease its recurrence after treatment. The aim of this review was to provide an overview of the risk factors associated with UBC incidence. Methods: A comprehensive literature search from 2012 to 2024 was carried out in databases such as PubMed, Google Scholar, and Medline with potential keywords such as “bladder cancer”, “urothelial bladder cancer”, “incidence of urothelial bladder cancer worldwide”, “mortality rate of bladder cancer”, “incidence according to gender”, “treatment for bladder cancer”, and “risk factors of bladder cancer”. Smoking tobacco was comprehended to be the major risk factor for UBC. Smoke from tobacco products contains polycyclic aromatic hydrocarbons (PAHs) and aromatic amines such as 4-aminobiphenyl, which are known to cause UBC. Smoking-related bladder cancer mortality ranks just second to smoking-related lung cancer mortality. For non-smokers, pollution became a major risk factor associated with UBC. Polycyclic aromatic hydrocarbons (PAHs) are linked to many cancers, especially to UBC. Indoor and outdoor pollution generates VOCs (volatile organic compounds) and PAHs. Small-particle matter < 2.5 is linked to UBC and lung cancers. Drinking chlorinated water is linked to UBC. Also, swimming in chlorinated pools that produce trihalomethanes increases the risk of many cancers, and especially of bladder cancer. Occupational exposure to carcinogens, specifically aromatic amines, is a significant UBC risk factor. It has been estimated that approximately 20% of all UBCs may be linked to this type of exposure, primarily in industrial settings that treat dye, paint, petroleum chemicals, and metal. The other risk factors included genetics, diet, and medical conditions. Alcohol, consumption of processed meat and whole milk, and higher intakes of selenium and vitamins A and E also contribute to the development of UBC. Further, chemotherapeutic agents, oral hypoglycemic drugs, and radiation therapy are positively associated with UBC. Conclusions: The significance of the initial prevention of UBC must be emphasized, and especially programs for quitting cigarettes should be encouraged and supported. However, smoking is not the only risk factor for UBC. For non-smokers, other risk factors should be investigated. Air and water pollution are linked to UBC. Indoor and outdoor pollution should be more controlled. Patients and people should be informed of the risk of drinking chlorinated water and swimming in chlorinated pools.
Distribution of Private Dental Healthcare Facilities in Riyadh City: A GIS-Based Approach
Authors: Alrejaye Najla S. , Alonazi Faisal H. , Alonazi Zaid M. , Alobaidi Rahf S. , Alsaleh Asma B. , Alshami Alanoud A. , Alshamrani Sultan A. , Kaithathara Seena T.
Published: July 2024

Background: The dental healthcare private sector in Riyadh city has been growing rapidly over the past few years; however, there is a lack of information on the accessibility and spatial distribution of private dental healthcare facilities (PDHFs) in the area. This study aimed to evaluate the spatial distribution of PDHFs in Riyadh city in relation to population density in each sub-municipality. Methods: The current information regarding the number, location, and operability of PDHFs in Riyadh city was obtained from the Ministry of Health. A total of 632 operating PDHFs were included with the precise location plotted on Quantum Geographic Information System software (version 3.32.1, Essen, Germany) using Google Earth. Four levels of buffer zones—1 km, 3 km, 5 km, and >5 km—were determined. The population statistics and mean monthly individual income per district were gathered from Zadd.910ths. Microsoft Excel (version 16.0, Microsoft, Redmond, WA, USA) and RStudio software (version 4.1.3, Posit Software, PBC, Boston, MA, USA) were used for additional data analysis. Results: There was an overall ratio of one PDHF per 9958 residents in Riyadh city. Olaya and Maather sub-municipalities had the largest PDHF-to-population ratios: (1:4566) and (1:4828), respectively. Only 36.3% of the city’s total area was within a 1 km buffer zone from a PDHF. There was an overall weak positive correlation between the number of PDHFs and the total area in each sub-municipality (r = 0.29), and the distribution of PDHFs was uneven corresponding to the area (G* = 0.357). Conclusions: There was an uneven distribution of PDHFs in Riyadh city. Some areas were underserved while others were overserved in several sub-municipalities. Policy-makers and investors are encouraged to target underserved areas rather than areas with significant clustering to improve access to care.
PON1 Status in Relation to Gulf War Illness: Evidence of Gene–Exposure Interactions from a Multisite Case–Control Study of 1990–1991 Gulf War Veterans
Authors: Steele Lea , Furlong Clement E. , Richter Rebecca J. , Marsillach Judit , Janulewicz Patricia A. , Krengel Maxine H. , Klimas Nancy G. , Sullivan Kimberly , Chao Linda L.
Published: July 2024

Background: Deployment-related neurotoxicant exposures are implicated in the etiology of Gulf War illness (GWI), the multisymptom condition associated with military service in the 1990–1991 Gulf War (GW). A Q/R polymorphism at position 192 of the paraoxonase (PON)-1 enzyme produce PON1192 variants with different capacities for neutralizing specific chemicals, including certain acetylcholinesterase inhibitors. Methods: We evaluated PON1192 status and GW exposures in 295 GWI cases and 103 GW veteran controls. Multivariable logistic regression determined independent associations of GWI with GW exposures overall and in PON1192 subgroups. Exact logistic regression explored effects of exposure combinations in PON1192 subgroups. Results: Hearing chemical alarms (proxy for possible nerve agent exposure) was associated with GWI only among RR status veterans (OR = 8.60, p = 0.014). Deployment-related skin pesticide use was associated with GWI only among QQ (OR = 3.30, p = 0.010) and QR (OR = 4.22, p < 0.001) status veterans. Exploratory assessments indicated that chemical alarms were associated with GWI in the subgroup of RR status veterans who took pyridostigmine bromide (PB) (exact OR = 19.02, p = 0.009) but not RR veterans who did not take PB (exact OR = 0.97, p = 1.00). Similarly, skin pesticide use was associated with GWI among QQ status veterans who took PB (exact OR = 6.34, p = 0.001) but not QQ veterans who did not take PB (exact OR = 0.59, p = 0.782). Conclusion: Study results suggest a complex pattern of PON1192 exposures and exposure–exposure interactions in the development of GWI.
Utilizing Multi-Class Classification Methods for Automated Sleep Disorder Prediction
Authors: Dritsas Elias , Trigka Maria
Published: July 2024

Even from infancy, a human’s day-life alternates from a period of wakefulness to a period of sleep at night, during the 24-hour cycle. Sleep is a normal process necessary for human physical and mental health. A lack of sleep makes it difficult to control emotions and behaviour, reduces productivity at work, and can even increase stress or depression. In addition, poor sleep affects health; when sleep is insufficient, the chances of developing serious diseases greatly increase. Researchers in sleep medicine have identified an extensive list of sleep disorders, and thus leveraged Artificial Intelligence (AI) to automate their analysis and gain a deeper understanding of sleep patterns and related disorders. In this research, we seek a Machine Learning (ML) solution that will allow for efficient classification of unlabeled instances as being Sleep Apnea, Insomnia or Normal (subjects without a specific sleep disorder) by assessing the performance of two well-established strategies for multi-class classification tasks: the One-Vs-All (OVA) and One-Vs-One (OVO). In the context of the specific strategies, two well-known binary classification models were assumed, Logistic Regression (LR) and Support Vector Machines (SVMs). Both strategies’ validity was verified upon a dataset of diverse information related to the profiles (anthropometric data, sleep metrics, lifestyle and cardiovascular health factors) of potential patients or individuals not exhibiting any specific sleep disorder. Performance evaluation was carried out by comparing the weighted average results in all involved classes that represent these two specific sleep disorders and no-disorder occurrence; accuracy, kappa score, precision, recall, f-measure, and Area Under the ROC curve (AUC) were recorded and compared to identify an effective and robust model and strategy, both class-wise and on average. The experimental evaluation unveiled that after feature selection, 2-degree polynomial SVM under both strategies was the least complex and most efficient, recording an accuracy of 91.44%, a kappa score of 84.97%, precision, recall and f-measure equal to 0.914, and an AUC of 0.927.
Assessment of Published Papers on the Use of Machine Learning in Diagnosis and Treatment of Mastitis
Authors: Bourganou Maria V. , Kiouvrekis Yiannis , Chatzopoulos Dimitrios C. , Zikas Sotiris , Katsafadou Angeliki I. , Liagka Dimitra V. , Vasileiou Natalia G. C. , Fthenakis George C. , Lianou Daphne T.
Published: July 2024

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. ‘Machine learning’ and ‘mastitis’ were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful.
Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches
Authors: Abbasi Maryam , Bernardo Marco V. , Váz Paulo , Silva José , Martins Pedro
Published: July 2024

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.
Nationwide Inventory of Mosquitoes and the Distribution of Invasive Aedes (Stegomyia) albopictus (Skuse, 1894) on the Islands of Sao Tome and Principe in Central Africa
Authors: Yen Tsai-Ying , Cheng Chien-Fu , Tseng Lien-Fen , Carvalho Ronalg Mendes Costa d’ Assunção , Tsai Kun-Hsien
Published: July 2024

Aedes albopictus (Skuse, 1894), a mosquito originating in Asia, has been introduced to Africa since the 2000s. The mosquito is not only a nuisance but is capable of transmitting various arboviruses. The current study summarized our entomological surveys in the Democratic Republic of Sao Tome and Principe during 2000 to 2016. Adult mosquitoes were collected by sweep nets, human landing catches, and Centers for Disease Control (CDC) light traps, and the immatures were collected from water-filled habitats at 15 sentinel sites and reared to adulthood. Species identification was performed based on morphologic characteristics. Fragments of the cytochrome C oxidase subunit I (COI) and the Wolbachia surface protein (wsp) genes were amplified for mosquitoes collected in Principe. New records of four mosquito species were reported. Aedes albopictus was identified in 2015. The larvae were found distributed over the nation and were predominately in artificial water-holding containers (488/2698, 18.1%). The highest positive rate was observed in used tires in Príncipe (114/250, 45.6%). Mitochondrial DNA analysis revealed low genetic diversity among the invasive populations, but all tested specimens were superinfected by Wolbachia. The ability of Ae. albopictus to adapt to new environments and its involvement in disease transmission make the surveillance and control of this species particularly important.
Physico-Mechanical Properties and Bonding Performance of Graphene-Added Orthodontic Adhesives
Authors: Liu Shiyao , El-Angbawi Ahmed , Rosa Vinicius , Silikas Nick
Published: July 2024

This study aimed to assess the key physico-mechanical properties and bonding performance of orthodontic adhesives with graphene addition for bonding a fixed retainer. Transbond LR (3M) and Transbond LV (3M) with no graphene were set as the control groups. Graphene was added into LR and LV at concentrations of 0.01 wt%, 0.05 wt% and 0.1 wt%. The stickiness of the uncured samples (n = 5) and real-time degree of conversion (DC) of the samples (n = 3) were measured over a 24-h period using Fourier-transform infrared spectroscopy. The hardness and other mechanical parameters, including the Martens hardness (HM), indentation modulus (EIT), elastic index (ηIT) and creep (CIT), were measured (n = 5). To measure the shear bond strength (SBS), adhesive composites were applied using a mold to bond the retainer wire to the lingual surfaces of bovine incisors (n = 10). Fracture modes subsequent to the SBS test were examined under light microscopy. Statistical analysis was conducted using ANOVA and Tukey tests (α = 0.05). In the LR groups, the LR + 0.01 showed the highest SBS (12.6 ± 2.0 MPa) and HM (539.4 ± 17.9 N/mm2), while the LV + 0.05 (7.7 ± 1.1 MPa) had the highest SBS and the LV + 0.1 had the highest HM (312.4 ± 17.8 N/mm2) among the LV groups. The most frequent failure mode observed was adhesive fracture followed by mixed fracture. No statistical difference was found between the graphene-added groups and the control groups in terms of the EIT, ηIT and CIT, except that the CIT was significantly lower in the LR + 0.01 than in the control group. Graphene addition had no significant adverse effect on the stickiness and DC of both LR and LV.
Prevalence of Osteosarcopenic Adiposity in Apparently Healthy Adults and Appraisal of Age, Sex, and Ethnic Differences
Authors: Cvijetić Selma , Keser Irena , Boschiero Dario , Ilich Jasminka Z.
Published: July 2024

Osteosarcopenic adiposity (OSA) syndrome, the coexistence of osteoporosis, sarcopenia, and adiposity (either excess or redistributed/infiltrated), has been studied globally in different populations and regions (mostly in East Asia, less in Europe and North America), resulting in varied prevalence. We aimed to determine the prevalence of OSA in a large population of apparently healthy Caucasian adults (18–90 years) and to compare it with the prevalence reported in other studies and other ethnicities. This study included 9719 participants (6412 women and 3307 men), stratified into four age-group categories, and recruited from the general medical practices in Italy. OSA was defined based on body composition measurements using bioelectrical impedance BIA-ACC®, which enables assessment of total bone mass, muscle/lean, and adipose tissues. The overall prevalence of OSA was 21.9% in women and 14.0% in men, and it significantly increased in every subsequent age group for both women and men (p < 0.001). The OSA prevalence was not significantly different between men and women below 40 years; however, it was considerably higher in women over 40 years. Participants with OSA had a significantly lower BMI compared to those without OSA, indicating OSA is a separate disorder not necessarily related to physiological fluctuations of BMI. The prevalence in Asian populations was lower than in our sample, indicating ethnic specificity. The relatively high prevalence of OSA detected in this study’s population across the age groups suggests the necessity for its appropriate and timely identification to prevent possible clinical outcomes, including fracture, dismobility, frailty, or chronic diseases.