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Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Recent advancements in high-throughput omics techniques have enhanced our understanding of the human microbiome’s role in the development of CVDs. Although the relationship between the gut microbiome and CVDs has attracted considerable research attention and has been rapidly evolving in recent years, the role of the oral microbiome remains less understood, with most prior studies focusing on periodontitis-related pathogens. In this review, we summarized previously reported associations between the oral microbiome and CVD, highlighting known CVD-associated taxa such as Porphyromonas gingivalis, Fusobacterium nucleatum, and Aggregatibacter actinomycetemcomitans. We also discussed the interactions between the oral and gut microbes. The potential mechanisms by which the oral microbiota can influence CVD development include oral and systemic inflammation, immune responses, cytokine release, translocation of oral bacteria into the bloodstream, and the impact of microbial-related products such as microbial metabolites (e.g., short-chain fatty acids [SCFAs], trimethylamine oxide [TMAO], hydrogen sulfide [H₂S], nitric oxide [NO]) and specific toxins (e.g., lipopolysaccharide [LPS], leukotoxin [LtxA]). The processes driven by these mechanisms may contribute to atherosclerosis, endothelial dysfunction, and other cardiovascular pathologies. Integrated multi-omics methodologies, along with large-scale longitudinal population studies and intervention studies, will facilitate a deeper understanding of the metabolic and functional roles of the oral microbiome in cardiovascular health. This fundamental knowledge will support the development of targeted interventions and effective therapies to prevent or reduce the progression from cardiovascular risk to clinical CVD events. Full article

Near-infrared autofluorescence (NIA) is a non-invasive retinal imaging technique used to examine the retinal pigment epithelium (RPE) based on the autofluorescence of melanin. Melanin has several functions within RPE cells. It serves as a protective antioxidative factor and is involved in the phagocytosis of photoreceptor outer segments. Disorders affecting the photoreceptor–RPE complex result in alterations of RPE cells which are detectable by alterations of NIA. NIA allows us to detect early alterations in various chorioretinal disorders, frequently before they are ophthalmoscopically visible and often prior to alterations in lipofuscin-associated fundus autofluorescence (FAF) or optical coherence tomography (OCT). Although NIA and FAF relate to disorders affecting the RPE, the findings for both imaging methods differ and the area involved has been demonstrated to be larger in NIA compared to FAF in several disorders, especially inherited retinal dystrophies (IRDs), indicating that NIA detects earlier alterations compared to FAF. Foveal alterations can be much more easily detected using NIA compared to FAF. A reduced subfoveal NIA intensity is the earliest sign of autosomal dominant Best disease, when FAF and OCT are still normal. In other IRDs, a preserved subfoveal NIA intensity is associated with good visual acuity. So far, the current knowledge on NIA in IRD has been presented in multiple separate publications but has not been summarized in an overview. This review presents the current knowledge on NIA in IRD and demonstrates NIA biomarkers. Full article

Sugarcane is a high-impact crop used in the majority of global sugar production, with India being the second largest global producer. Understanding the timing and length of sugarcane growth stages is critical to improving the sustainability of sugarcane management. Earth observation (EO) data have been shown to be sensitive to the variation in sugarcane growth, but questions remain as to how to reliably extract sugarcane phenology over wide areas so that this information can be used for effective management. This study develops an automated approach to derive sugarcane growth stages using EO data from Landsat-8 and Sentinel-2 satellite data in the Indian state of Andhra Pradesh. The developed method is then evaluated in the State of Telangana. Normalised difference vegetation index (NDVI) EO data from Landsat-8 and Sentinel-2 were pre-processed to filter out clouds and to harmonise sensor response. Pixel-based cloud filtering was selected over filtering by scene in order to increase the temporal frequency of observations. Harmonising data from two different sensors further increased temporal resolution to 3–6 days (70% of sampled fields). To automate seasonal decomposition, harmonised signals were resampled at 14 days, and low-frequency components, related to seasonal growth, were extracted using a fast Fourier transform. The start and end of each season were extracted from the time series using difference of Gaussian and were compared to assessments based on visual observation for both Unit 1 (R² = 0.72–0.84) and Unit 2 (R² = 0.78–0.82). A trapezoidal growth model was then used to derive crop growth stages from satellite-measured phenology for better crop management information. Automated assessments of the start and the end of mid-season growth stages were compared to visual observations in Unit 1 (R² = 0.56–0.72) and Unit 2 (R² = 0.36–0.79). Outliers were found to result from cloud cover that was not removed by the initial screening as well as multiple crops or harvesting dates within a single field. These results demonstrate that EO time series can be used to automatically determine the growth stages of sugarcane in India over large areas, without the need for prior knowledge of planting and harvest dates, as a tool for improving sustainable production. Full article

Decision trees are a widely used machine learning technique due to their ease of interpretation and construction. This method allows domain experts to learn from raw data, but they cannot include their prior knowledge in the analysis due to its automatic nature, which implies minimal human intervention in its computation. Conversely, interactive visualization methods have proven to be effective in gaining insights from data, as they incorporate the researcher’s criteria into the analysis process. In an effort to combine both methodologies, we have developed a tool to manually build decision trees according to subsequent visualizations of data mapping after applying linear discriminant analysis in combination with Star Coordinates in order to analyze the importance of each feature in the separation. The nodes’ information contains data about the features that can be used to split and their cut-off values, in order to select them in a guided manner. In this way, it is possible to produce simpler and more expertly driven decision trees than those obtained by automatic methods. The resulting decision trees reduces the tree size compared to those generated by automatic machine learning algorithms, obtaining a similar accuracy and therefore improving their understanding. The tool developed and presented here to manually create decision trees in a guided manner based on the subsequent visualizations of the data mapping facilitates the use of this method in real-world applications. The usefulness of this tool is demonstrated through a case study with a complex dataset used for motion recognition, where domain experts built their own decision trees by applying their prior knowledge and the visualizations provided by the tool in node construction. The resulting trees are more comprehensible and explainable, offering valuable insights into the data and confirming the relevance of upper body features and hand movements for motion recognition. Full article

Background: Hepatitis C virus (HCV) infection presents a significant public health challenge globally, particularly in high-burden countries like Nigeria, where an estimated 2.4 million individuals are living with HCV. HCV self-testing (HCVST) can potentially bridge the significant diagnosis gap and help individuals to determine their HCV status in the privacy of their homes. It offers a solution to overcome barriers related to stigma and limited access to healthcare. In Nigeria, Self-testing for hepatitis C has only been implemented in a pilot research context. This study aimed to assess stakeholder and community awareness and perceptions of HCVST in Nigeria. The findings will provide insights that could inform effective policies and future scale-up programs for HCV control. Methods: A cross-sectional descriptive study was conducted using an online social media survey administered through SurveyMonkey. The survey was disseminated across social media platforms and groups between October–November 2023. Participants included Nigerians (both health professionals and non-health professionals) aged 18 years or older residing in any of the 36 states and the Federal Capital Territory (FCT). Data collected include sociodemographic characteristics, awareness and perceptions of HCVST, and perceived benefits and barriers. Results: Of 321 respondents, 94% perceived HCVST as highly important. While 77% of respondents knew about HIVST, only 58% had prior knowledge of HCVST. The analysis also showed that healthcare workers had greater awareness of HIV self-testing (82.3%) compared to non-healthcare workers (50.0%). Most respondents (88%) were highly likely to recommend HCVST and perceived it as a cost-effective alternative to traditional testing. Key perceived benefits included increased disease detection and control (67%), improved access to testing (21%), and reduced stigma (11%). In the unadjusted model, geographical zone (Southern Nigeria: cOR = 0.49, 95% CI: 0.30–0.77, p = 0.002), work experience (more than 20 years: cOR = 2.79, 95% CI: 1.11–8.07, p = 0.039), and prior awareness of HIV self-testing (cOR = 5.24, 95% CI: 3.00–9.43, p < 0.001) were significant predictors of HCVST awareness. However, in the adjusted model, only prior awareness of HIV self-testing remained significant (aOR = 4.77, 95% CI: 2.62–8.94, p < 0.001). Conclusions: The strong support for HCVST among stakeholders in Nigeria highlights its potential to enhance HCV control, especially within the broader context of infectious diseases like STIs. The greater awareness of HIV self-testing among healthcare workers compared to non-healthcare workers indicates the need for targeted awareness campaigns for non-healthcare populations. Addressing these awareness gaps, leveraging lessons from HIVST, and using existing infrastructure will be crucial. Prioritizing public education, outreach, and effective linkage to care will drive the impact of HCVST in achieving HCV elimination goals and position it as a model for expanding similar STI interventions in Nigeria. Full article

Competitive dancesport (DS) performance is a multifactorial phenomenon influenced by physical and mental factors. The emotions experienced by athletes in competition are strongly linked to their sports performance. However, to our knowledge, no studies have investigated the direct relationship between performance and emotional states in DS. Consequently, the aims were four: (I) to investigate the influence of anxiety and self-confidence on DS performance; (II) to examine the influence of years of experience, prior victories, and perceived preparedness on performance outcomes; (III) to identify the optimal emotional state levels for peak performance; (IV) to investigate differences between different athletes’ levels and class. The participants were 71 Italian DS athletes divided into three groups (22 B-class, 25 C-class, 24 D-class). Before competition, they supplied demographic information about their gender, years of experience, perceived preparedness, previous winnings in the current class, followed by the completion of the Italian version of Revised Competitive State Anxiety Inventory–2 (CSAI-2R). To assess the athletes’ performance, the final classification of the competition was taken into consideration. The results showed that both overall and relative variables from the CSAI-2R significantly predicted performance outcomes (p < 0.05), although somatic anxiety did so to a lesser extent. Significant differences emerged between athletes of different classes in terms of years of experience (p = 0.000), perceived preparedness (p = 0.000), cognitive anxiety (p = 0.000) and self-confidence (p = 0.000). The optimal levels for good performance were cognitive anxiety (11.61 ± 2.27), somatic anxiety (15.77 ± 1.72) and self-confidence (15.12 ± 2.56). The findings of this study provide valuable insights into the multifactorial nature of competitive DS performance, particularly highlighting the significant role of emotional states such as anxiety and self-confidence, as well as other variables such as class, level, years of experience and perceived preparedness. Full article

There is a need to find innovative learning methods that enable accelerated learning of a foreign language. This study examined the effect of computer-assisted language learning (CALL) in acquiring a foreign language, which combines cognitive and emotional stimuli in the background. The study explored two factors related to the acquisition of a foreign language: the duration and scope of the learning process and the depth of internalization of the newly acquired language. Another objective was to assess the learning method in two learning environments, 2D and VR, to determine if the learning environment affects the learning results and leads to better vocabulary retention. One hundred native French speakers, with an average age of 47.5, participated in the study and had no prior knowledge of the newly acquired language. We randomly divided the participants into two groups (2D and VR). They studied 550 words in a new language for five days: 30 min each evening and 15 min in the morning. The post-learning test pointed out that both groups improved their vocabulary scores significantly. Approximately one month after the learning experience, we administered a knowledge retention test to 32 participants and found that the level of knowledge had been retained. Finally, background variables (e.g., gender, age, and previous knowledge of the newly acquired language) did not affect the learning results. The findings indicate that CALL, which integrates background cognitive and emotional stimuli in both learning environments, significantly accelerates learning pace, broadens the scope of newly acquired words, and ensures retention. The level of improvement observed in our study is notably higher than that reported in the literature for studies that had previously evaluated CALL and in-class language acquisition. Full article

This study explored Next Generation Science Standards (NGSS) in cross-cultural biology teaching through collaborative lesson studies involving educators from the USA and the Philippines. We employed grounded theory and examined iterative feedback processes during lesson development to refine learning exemplars. Learning exemplars validation affirmed their alignment with both NGSS and the Philippine science education frameworks, ensuring cultural relevance and educational rigor. Five key themes were identified as pivotal: retrieval of prior knowledge, fostering meaningful learning experiences, enhancing memory and retention, fostering active engagement, and cultivating critical thinking skills—integral for developing culturally responsive curricula. Moreover, students became independent learners, responsible for their learning, reflective and critical thinkers, problem solvers, inquiry-oriented, creative, collaborative communicators, modelers, data analysts, persistent, adaptable, and self-directed. Implications include enhancing educational policies to support cultural diversity and integrating cross-cultural learning exemplars to enhance global teaching practices. This study underscored the transformative potential of cross-cultural collaboration in advancing science education, fostering engaging learning environments, and preparing students for global citizenship. Full article

Fungal infections are increasingly prevalent; however, antifungal stewardship attracts little funding or attention. Previous studies have shown that knowledge of guidelines and scientific evidence regarding antifungals is poor, leading to prescribing based on personal experiences and the inherent biases this entails. We carried out a retrospective study of inpatient antifungal usage at two major hospitals. We assessed the longitudinal trends in antifungal usage and the effect of COVID-19 on antifungal prescription, alongside levels of empirical and diagnostically targeted antifungal usage. Our results showed that the longitudinal patterns of total systemic antifungal usage within the trusts were similar to national prescribing trends; however, the composition of antifungals varied considerably, even when looking exclusively at the more homogenous group of COVID-19 patients. We showed a high level of empirical antifungal use in COVID-19 patients, with neither trust adhering to international recommendations and instead appearing to follow prior prescribing habits. This study highlights the significant challenges to optimise antifungal use with prescribing behaviour largely dictated by habit, a lack of adherence to guidelines, and high rates of empirical non-diagnostic-based prescribing. Further research and resources are required to understand the impact of antifungal stewardship on improving antifungal prescribing behaviours in this setting and the effects on outcome. Full article

Background: The thyroid gland plays a crucial role in regulating metabolism and various bodily functions through hormone production. Women are particularly susceptible to thyroid disorders such as hypothyroidism and Hashimoto’s disease, with associated symptoms affecting overall well-being. Prior research has inadequately addressed the influence of dietary habits and nutritional knowledge on thyroid health, especially in women. Objective: This study aimed to evaluate the dietary habits and nutritional awareness of women aged 18–45 with diagnosed thyroid disorders, emphasizing the effects of education level on knowledge and dietary practices. Material and Methods: A cross-sectional survey was conducted with 297 women diagnosed with thyroid conditions. The survey assessed demographics, comorbidities, hydration habits, and knowledge about nutrient intake critical for thyroid health. Chi-square tests, ANOVA, and correlation analyses were performed to evaluate associations. Results: Hypothyroidism and Hashimoto’s disease were most prevalent among younger women (18–25 years). A significant association was observed between higher education and knowledge of protein and carbohydrate roles in managing thyroid health (p < 0.01). Women with higher educational backgrounds more frequently used healthier cooking methods and were more informed about beneficial nutrients, including vitamin D and omega-3. A chi-square test indicated that low water intake was significantly associated with comorbid conditions, including insulin resistance and cardiovascular disease (p < 0.01). Conclusions: Significant gaps remain in dietary knowledge, particularly concerning protein intake and nutrient–drug interactions, indicating a need for targeted dietary education. Women with higher education demonstrated greater dietary awareness, emphasizing the importance of tailored educational interventions to enhance thyroid disorder management. Full article

The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness of traffic event detection. To overcome these challenges, this paper introduces a heuristic optimal scheduling approach designed to enhance traffic event detection efficiency while operating within limited computational resources. This method leverages historical data and prior knowledge to compute a weighted event feature value for each camera, providing a quantitative measure of its detection efficiency. To optimize resource allocation, a cyclic elimination mechanism is implemented to exclude low-performing cameras, enabling the dynamic reallocation of resources to higher-performing cameras, thereby enhancing overall detection performance. Finally, the effectiveness of the proposed method is validated through a case study conducted in a representative region of a major metropolitan city in China. The results revealed a substantial improvement in traffic event detection efficiency, with increases of 40%, 28%, 17%, and 28% across different time periods when compared to the pre-optimized state. Furthermore, the proposed method outperformed existing resource scheduling algorithms in terms of average load degree, load balance degree, and higher computational resource utilization. By avoiding the common issues of resource wastage and insufficiency often found in static allocation models, this approach offers greater flexibility and adaptability in computational resource scheduling, thereby effectively addressing the practical demands of traffic anomaly detection and early warning systems. Full article

Leishmania spp. commonly infects phagocytic cells of the immune system, particularly macrophages, employing various immune evasion strategies that enable their survival by altering the intracellular environment. In mammals, these parasites establish persistent infections by modulating gene expression in macrophages, thus interfering with immune signaling and response pathways, ultimately creating a favorable environment for the parasite’s survival and reproduction. In this study, our objective was to use data mining and subsequent filtering techniques to identify the genes that play a crucial role in the infection process of Leishmania spp. We aimed to pinpoint genes that have the potential to influence the progression of Leishmania infection. To achieve this, we exploited prior, curated knowledge from major databases and constructed 16 datasets of human molecular information consisting of coding genes and corresponding proteins. We obtained over 400 proteins, identifying approximately 200 genes. The proteins coded by these genes were subsequently used to build a network of protein–protein interactions, which enabled the identification of key players; we named this set Predicted Genes. Then, we selected approximately 10% of Predicted Genes for biological validation. THP-1 cells, a line of human macrophages, were infected with Leishmania major in vitro for the validation process. We observed that L. major has the capacity to impact crucial genes involved in the immune response, resulting in macrophage inactivation and creating a conducive environment for the survival of Leishmania parasites. Full article

The Bayesian approach offers a flexible, interpretable and powerful framework for statistical analysis, making it a valuable tool to help in making optimal decisions under uncertainty. It incorporates prior knowledge or beliefs about the parameters, which can lead to more accurate and informative results. Also, it offers credible intervals as a measure of uncertainty, which are often more interpretable than confidence intervals. Hence, the Bayesian approach is utilized to estimate the parameters, reliability function, hazard rate function and reversed hazard rate function of a new competing risks model. A squared error loss function as a symmetric loss function and a linear exponential loss function as an asymmetric loss function are employed to derive the Bayesian estimators. Credible intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are obtained. Predicting future observations is important in many fields, from finance and weather forecasting to healthcare and engineering. Thus, two-sample prediction (as a special case of the multi-sample prediction) for future observation is considered. An adaptive Metropolis algorithm is applied to conduct a simulation study to evaluate the performance of the Bayes estimates and predictors. Moreover, two applications of medical and engineering data sets are used to test and validate the theoretical results, ensuring that they are accurate, applicable to real-world scenarios and contribute to the understanding of the world and inform decision-making. Full article

Deploying and effectively utilizing wireless sensor networks (WSNs) in underwater habitats remains a challenging task. In underwater wireless sensors networks (UWSNs), the availability of a continuous energy source for communicating with nodes is either very costly or is prohibited due to the marine life law enforcement agencies. So, in order to address this issue, we present a Q-learning-based approach to designing an energy-efficient medium access control (MAC) protocol for UWSNs through collision avoidance. The main goal is to prolong the network’s lifespan by optimizing the communication methods, specifically focusing on improving the energy efficiency of the MAC protocols. Factors affecting the energy consumption in communication are adjustments to the interference ranges, i.e., changing frequencies repeatedly to obtain optimal communication; data packet retransmissions in case of a false acknowledgment; and data packet collision occurrences in the channel. Our chosen protocol stands out by enabling sensor (Rx) nodes to avoid collisions without needing extra communication or prior interference knowledge. According to the results obtained through simulations, our protocol may increase the network’s performance in terms of network throughput by up to 23% when compared to benchmark protocols depending on the typical traffic load. It simultaneously decreases end-to-end latency, increases the packet delivery ratio (PDR), boosts channel usage, and lessens packet collisions by over 38%. All these gains result in minimizing the network’s energy consumption, with a proportional gain. Full article

Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint potential bridge damage by reconstructing bridge inclination data. For an intact bridge, we selected reference cross-sections and trained multiple Backpropagation Artificial Neural Networks (BP-ANNs) to simulate transfer matrices for inclination between these base sections and other sections of the bridge. These BP-ANNs were then employed to reconstruct inclination data at the same cross-sections on a bridge with artificial damage. We demonstrated that damage localization is feasible through a comparison of the reconstructed and actual measured responses. The theoretical underpinnings of the transfer matrix and the damage localization method were initially elucidated through an analysis of the dynamics of a simplified vehicle–bridge interaction (VBI) system. A series of finite element models were constructed to substantiate the theoretical basis of the damage localization method. Additionally, a large-scale laboratory experiment was carried out to assess the practical effectiveness of the proposed method. The proposed method has been demonstrated to effectively pinpoint the location of potential structural damage. It successfully differentiates between areas in close proximity to the damage and those that are more distant. Compared to existing research, our method does not necessitate prior knowledge of factors such as mode shape functions, traffic conditions, or the constraint of inspecting with a single vehicle. This approach is anticipated to be more convenient for engineering applications, particularly in the development of online monitoring systems, due to its streamlined requirements and robust performance in identifying damage localization. Full article