Search Results (10,634)

Identification of the individuals having impaired kidney function is essential in preventing the complications of this disease. We measured 1009 metabolites at the baseline study in 10,159 Finnish men of the METSIM cohort and associated the metabolites with an estimated glomerular filtration rate (eGFR). A total of 7090 men participated in the 12-year follow-up study. Non-targeted metabolomics profiling was performed at Metabolon, Inc. (Morrisville, NC, USA) on EDTA plasma samples obtained after overnight fasting. We applied liquid chromatography mass spectrometry (LC-MS/MS) to identify the metabolites (the Metabolon DiscoveryHD4 platform). We performed association analyses between the eGFR and metabolites using linear regression adjusted for confounding factors. We found 108 metabolites significantly associated with a decrease in eGFR, and 28 of them were novel, including 12 amino acids, 8 xenobiotics, 5 lipids, 1 nucleotide, 1 peptide, and 1 partially characterized molecule. The most significant associations were with five amino acids, N-acetylmethionine, N-acetylvaline, gamma-carboxyglutamate, 3-methylglutaryl-carnitine, and pro-line. We identified 28 novel metabolites associated with decreased eGFR in the 12-year follow-up study of the METSIM cohort. These findings provide novel insights into the role of metabolites and metabolic pathways involved in the decline of kidney function. Full article

Space efficiency in Singaporean tall buildings results from a complex interplay of historical, architectural, engineering, technological, socioeconomic, and environmental factors. The city-state’s innovative and adaptive approach has enabled it to overcome the challenges associated with skyscraper construction, leading to the development of some of the most advanced and sustainable high-rise structures in the world. However, there is currently a lack of detailed analysis on space utilization in Singaporean high-rise buildings. This study addresses this gap by examining 63 cases. The main findings of this research: 1. Residential functions, central core layouts, and prismatic shapes are the most frequent. 2. Concrete material with a shear-walled frame system is the preferred structural choice. 3. Average spatial efficiency is 80%, and the core-to-GFA (Gross Floor Area) ratio averages 17%. These metrics vary from a minimum of 68% and 5% to a maximum of 91% and 32%, respectively. These insights offer valuable guidance for Singaporean construction professionals, particularly architects, helping them make informed design decisions for high-rise projects. Full article

This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling techniques that can better capture the complex interactions between ice cover, water levels, and channel geometry. (iii) Combining data from multiple sources such as satellite imagery, ground-based sensors, numerical models, and machine learning algorithms can significantly improve the accuracy and reliability of ice-jam flood forecasts. (iv) Effective decision-support tools are crucial for integrating ice-jam flood forecasts into emergency response and mitigation strategies. Full article

A clinical trial was conducted to evaluate the effect of fecal microbiota transplantation (FMT) on the canine chronic enteropathy clinical activity index (CCECAI), fecal consistency, and microbiome of dogs with tylosin-responsive enteropathy (TRE). The trial consisted of four phases: (1) screening with discontinuation of tylosin for 4 weeks, (2) inclusion with re-introduction of tylosin for 3–7 days, (3) treatment with FMT/placebo for 4 weeks, and (4) post-treatment with follow-up for 4 weeks after treatment cessation. The study found that the treatment efficacy of FMT (71.4%) was slightly higher than that of placebo (50%), but this difference was not statistically significant due to underpowering. The most abundant bacterial species detected in the fecal microbiomes of dogs with TRE before FMT or placebo treatment were Blautia hansenii, Ruminococcus gnavus, Escherichia coli, Clostridium dakarense, Clostridium perfringens, Bacteroides vulgatus, and Faecalimonas umbilicata. After FMT, the microbiomes exhibited increases in Clostridium dakarense, Clostridium paraputrificum, and Butyricicoccus pullicaecorum. The microbiome alpha diversity of TRE dogs was lower when on tylosin treatment compared to healthy dogs, but it increased after treatment in both the FMT and placebo groups. Comparisons with the stool donor showed that, on average, 30.4% of donor strains were engrafted in FMT recipients, with the most common strains being several Blautia sp., Ruminococcus gnavus, unclassified Lachnoclostridium, Collinsella intestinalis, and Fournierella massiliensis. Full article

This study examines the relationship between economic indicators and crime rates in six European countries: Lithuania, Germany, Greece, Portugal, Finland and Sweden. By examining macroeconomic factors such as GDP, security spending and per capita consumption, the study aims to understand how these variables affect crime dynamics. Using robust econometric techniques, including panel regression with fixed effects, the study identifies significant correlations and patterns. The findings reveal that the crime rate has a high degree of inertia and is significantly influenced by the previous level. Contrary to expectations, increased per capita consumption is associated with higher crime rates, which may indicate that wealthier societies are experiencing an increase in economic crime. Furthermore, higher spending on security does not necessarily reduce crime, suggesting that types of crime evolve as detection capabilities improve. This study highlights the complexity of the nexus between crime and the economy, highlighting the need for multifaceted, long-term policies to effectively combat crime and increase public safety. The results offer valuable insights for policymakers to develop comprehensive crime prevention and economic development strategies. Full article

Advergames represent a novel product placement strategy that surpasses traditional advertising methods by fostering interaction between brands and their target audiences. This study investigates the unique engagement opportunities provided by video games, focusing mainly on the ‘flow experience’, an intensified state of immersion frequently encountered by players of computer games. Such immersive experiences have the potential to significantly influence a player’s perception, offering a new avenue for advertisements to impact and engage audiences effectively. The primary objective of this research was to examine the influence of advergames on players who are deeply immersed in the gaming experience, with a specific focus on the subsequent effects on brand recognition over time. The study involved 44 software developers, who were evenly divided into two groups for the experiment. Both groups were exposed to an identical gaming environment with the task of locating a designated product within the game. However, one group interacted with an enhanced version of the game, which included additional stimuli—such as dynamic music, an engaging narrative, time constraints, a competitive leaderboard, and immersive voice acting—to intensify the gaming experience. The experiment strategically placed various products within the game, and their detectability was assessed using eye-tracking technology. Following gameplay, participants completed questionnaires that measured their experience with flow state and brand recall. The data were analyzed using the Mann–Whitney U test and correlation analysis to facilitate comparisons. The findings indicated that the product associated with the primary task achieved the highest recall rate between both groups. Furthermore, eye-tracking technology identified the areas in the game that attracted the most attention, revealing a preference for mid- and high-level placements over lower-level ones. Full article

Positive Energy Districts (PEDs) are a promising approach to urban energy transformation, aiming to optimize local energy systems and deliver environmental, social and economic benefits. However, their effectiveness and justification for investment rely on understanding the additional value they provide (additionality) in comparison to current policies and planning methods. The additionality perspective is not used yet in current evaluations of PED demonstrations and pilots. Therefore, this paper introduces the concept of additionality in the evaluation of PEDs, focusing on the additional benefits they bring and the circumstances under which they are most effective. We discuss the additionality of PEDs in addressing the challenges of climate neutrality and energy system transformation in three European cities that are funded by the European Commission’s H2020 Programme. It should be noted that given the ongoing status of these projects, the assessment is mainly based on preliminary results, as monitoring is still ongoing and quantitative results are not yet available. The paper discusses the drivers and barriers specific to PEDs, and highlights the challenges posed by technical complexities, financing aspects and social and legal restrictions. Conclusions are drawn regarding the concept of additionality and its implications for the wider development of PEDs as a response to the challenges of climate neutrality and energy system transformation in cities. We conclude that the additionality perspective provides valuable insights into the impact and potential of PEDs for societal goals and recommend this approach for use in the final evaluation of R&I projects involving PEDs using actual monitored data on PEDs. Full article

The southwest coast, specifically the Khulna region of Bangladesh, has seen a substantial increase in the production of dried fish, involving marginalized coastal people. This study uses a mixed methods approach and the sustainable livelihood approach (SLA) to assess these fish-drying communities’ socioeconomic characteristics, ways of living, and adaptability. Due to their lower literacy, irregular wages, and labor-intensive employment, the research outcomes indicated that the communities engaged in the drying process were economically disadvantaged. Male workers exhibited a relatively higher participation rate compared to females. However, it was observed that females had less power over their wages and earned less than USD 2.74–3.65 per day compared to males at USD 3.65–5.48 per day. Even though there were a lot of opportunities for employment, the survey showed that very few vendors, manufacturers, and laborers regarded themselves as financially independent. To cope with various impacts and obstacles, off-season earnings, a variety of fish species, drying facilities, dealer associations, and social relationships were crucial for dried-fish processors, workers, and traders. The research suggests implementing suitable measures to diversify alternative sources of income and emphasizes the importance of fostering strong collaboration among the communities, local management authorities, and the government. With regard to dry-fish approaches, these steps are essential for ensuring long-term sustainability and improving community resilience among coastal communities. Full article

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain. Full article

Additive manufacturing (AM) methods have been gaining momentum because they provide vast design and fabrication possibilities, increasing the accessibility of state-of-the-art hardware through recent developments in user-friendly computer-aided drawing/engineering/manufacturing (CAD/CAE/CAM) tools. However, in comparison to the conventional manufacturing methods, AM processes have some disadvantages, including the machining precision and fabrication process times. The first issue has been mostly resolved through the recent advances in manufacturing hardware, sensors, and controller systems. However, the latter has been widely investigated by researchers with different toolpath planning perspectives. As a contribution to these investigations, the present study proposes a toolpath planning method for AM, which aims to provide highly continuous yet distance-optimized solutions. The approach is based on the utilization of the signed distance field (SDF), clustering, and minimization of toolpath distances among cluster centroids. The method was tested on various geometries with simple closed curves to complex geometries with holes, which provides effective toolpaths, e.g., with relative distance reduction percentages up to 16.5% in comparison to conventional rectilinear infill patterns. Full article

Breast cancers can appear and progress rapidly, necessitating more frequent monitoring outside of hospital settings to significantly reduce mortality rates. Recently, there has been considerable interest in developing techniques for portable, user-friendly, and low-cost breast tumor monitoring applications, enabling frequent and cost-efficient examinations. Microwave technique-based breast cancer detection, which is based on differential dielectric properties of malignant and healthy tissues, is regarded as a promising solution for cost-effective breast tumor monitoring. This paper presents the development process of the first proof-of-concept of a breast tumor monitoring vest which is based on the microwave technique. Two unique vests are designed and evaluated on realistic 3D human tissue phantoms having different breast density types. Additionally, the measured results are verified using simulations carried out on anatomically realistic voxel models of the electromagnetic simulations. The radio channel characteristics are evaluated and analyzed between the antennas embedded in the vest in tumor cases and reference cases. Both measurements and simulation results show that the proposed vest can detect tumors even if only 1 cm in diameter. Additionally, simulation results show detectability with 0.5 cm tumors. It is observed that the detectability of breast tumors depends on the frequency, antenna selection, size of the tumors, and breast types, causing differences of 0.5–30 dB in channel responses between the tumorous and reference cases. Due to simplicity and cost-efficiency, the proposed channel analysis-based breast monitoring vests can be used for breast health checks in smaller healthcare centers and for user-friendly home monitoring which can prove beneficial in rural areas and developing countries. Full article

Erythritol occurs naturally in some fruits and fermented foods, and has also been used as an artificial sweetener since the 1990s. Although there have been questions and some studies regarding its potential adverse health effects, the association between serum erythritol and long-term mortality has not been evaluated. To examine the association between serum erythritol’s biochemical status and risk of overall and cause-specific mortality, a prospective cohort analysis was conducted using participants in the ATBC Study (1985–1993) previously selected for metabolomic sub-studies. The analysis included 4468 participants, among whom 3377 deaths occurred during an average of 19.1 years of follow-up. Serum erythritol was assayed using an untargeted, global, high-resolution, accurate-mass platform of ultra-high-performance liquid and gas chromatography. Cause-specific deaths were identified through Statistics Finland and defined by the International Classification of Diseases. After adjustment for potential confounders, serum erythritol was associated with increased risk of overall mortality (HR = 1.50 [95% CI = 1.17–1.92]). We found a positive association between serum erythritol and cardiovascular disease mortality risk (HR = 1.86 [95% CI = 1.18–2.94]), which was stronger for heart disease mortality than for stroke mortality risk (HR = 3.03 [95% CI = 1.00–9.17] and HR = 2.06 [95% CI = 0.72–5.90], respectively). Cancer mortality risk was also positively associated with erythritol (HR = 1.54 [95% CI = 1.09–2.19]). The serum erythritol–overall mortality risk association was stronger in men ≥ 55 years of age and those with diastolic blood pressure ≥ 88 mm Hg (p for interactions 0.045 and 0.01, respectively). Our study suggests that elevated serum erythritol is associated with increased risk of overall, cardiovascular disease, and cancer mortality. Additional studies clarifying the role of endogenous production and dietary/beverage intake of erythritol in human health and mortality are warranted. Full article

Selective laser sintering (SLS) is a bed fusion additive manufacturing technology that facilitates rapid, versatile, intricate, and cost-effective prototype production across various applications. It supports a wide array of thermoplastics, such as polyamides, ABS, polycarbonates, and nylons. However, manufacturing plastic components using SLS poses significant challenges due to issues like low strength, dimensional inaccuracies, and rough surface finishes. The operational principle of SLS involves utilizing a high-power-density laser to fuse polymer or metallic powder surfaces. This paper presents a comprehensive analysis of the SLS process, emphasizing the impact of different processing variables on material properties and the quality of fabricated parts. Additionally, the study explores the application of machine learning (ML) techniques—supervised, unsupervised, and reinforcement learning—in optimizing processes, detecting defects, and ensuring quality control within SLS. The review addresses key challenges associated with integrating ML in SLS, including data availability, model interpretability, and leveraging domain knowledge. It underscores the potential benefits of coupling ML with in situ monitoring systems and closed-loop control strategies to enable real-time adjustments and defect mitigation during manufacturing. Finally, the review outlines future research directions, advocating for collaborative efforts among researchers, industry professionals, and domain experts to unlock ML’s full potential in SLS. This review provides valuable insights and guidance for researchers in regard to 3D printing, highlighting advanced techniques and charting the course for future investigations. Full article

Microbiota and luminal components may affect epithelial integrity and thus participate in the pathophysiology of colon cancer (CC) and inflammatory bowel disease (IBD). Therefore, we aimed to determine the effects of fecal luminal factors derived from patients with CC and ulcerative colitis (UC) on the colonic epithelium using a standardized colon-derived two-dimensional epithelial monolayer. The complex primary human stem cell-derived intestinal epithelium model, termed RepliGut^® Planar, was expanded and passaged in a two-dimensional culture which underwent stimulation for 48 h with fecal supernatants (FS) from CC patients (n = 6), UC patients with active disease (n = 6), and healthy subjects (HS) (n = 6). mRNA sequencing of monolayers was performed and cytokine secretion in the basolateral cell culture compartment was measured. The addition of fecal supernatants did not impair the integrity of the colon-derived epithelial monolayer. However, monolayers stimulated with fecal supernatants from CC patients and UC patients presented distinct gene expression patterns. Comparing UC vs. CC, 29 genes were downregulated and 33 genes were upregulated, for CC vs. HS, 17 genes were downregulated and five genes were upregulated, and for UC vs. HS, three genes were downregulated and one gene was upregulated. The addition of FS increased secretion of IL8 with no difference between the study groups. Fecal luminal factors from CC patients and UC patients induce distinct colonic epithelial gene expression patterns, potentially reflecting the disease pathophysiology. The culture of colonic epithelial monolayers with fecal supernatants derived from patients may facilitate the exploration of IBD- and CC-related intestinal microenvironmental and barrier interactions. Full article

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R² = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)², consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R² = 0.96; MSE = 0.33 (µg/L)²; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. Full article