Molecular dynamics simulations of lipid composition and its impact on structural and dynamic properties of skin membrane.

The stratum corneum (SC) plays the most important role in the absorption of topical and transdermal drugs. In this study, we developed a multi-layered SC model using coarse-grained molecular dynamics (CGMD) simulations of ceramides, cholesterol, and fatty acids in equimolar proportions, starting from two different initial configurations. In the first approach, all ceramide molecules were initially in the hairpin conformation, and the membrane bilayers were pre-formed. In the second approach, ceramide molecules were introduced in either the hairpin or splayed conformation, with the lipid molecules randomly oriented at the start of the simulation. The aim was to evaluate the effects of lipid chain length on the structural and dynamic properties of SC. By incorporating ceramides and fatty acids of different chain lengths, we simulated the SC membrane in healthy and diseased states. We calculated key structural properties including the thickness, normalized lipid area, lipid tail order parameters, and spatial ordering of the lipids from each system. The results showed that systems with higher ordering and structural integrity contained an equimolar ratio of ceramides (chain length of 24 carbon atoms), fatty acids with chain lengths ≥ of 20 carbon atoms, and cholesterol. In these systems, strong apolar interactions between the ceramide and fatty acid long acyl chains restricted the mobility of the lipid molecules, thereby maintaining a compact lipid headgroup region and high order in the lipid tail region. The simulations also revealed distinct flip-flop mechanisms for cholesterol and fatty acid within the multi-layered membrane. Cholesterol is mostly diffused through the tail-tail interface region of the membrane and could flip-flop in the same bilayer. In contrast, fatty acids flip-flopped between adjacent leaflets of two bilayers in which the tails crossed the thinner headgroup region of the membrane. To conclude, our SC model provides mechanistic insights into lipid mobility and is flexible in its design and composition of different lipids, enabling studies of varying skin conditions.

Road crash dynamics in Malaysia: Analysis of trends and patterns.

Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014-2022.

Evaluating the impact of occupational noise exposure on workplace fatal and nonfatal injuries in the U.S. (2006-2020).

OBJECTIVES: This study assessed the relationship between occupational noise exposure and the incidence of workplace fatal injury (FI) and nonfatal injury (NFI) in the United States from 2006 to 2020. It also examined whether distinct occupational and industrial clusters based on noise exposure characteristics demonstrated varying risks for FI and NFI. METHODS: An ecological study design was utilized, employing data from the U.S. Bureau of Labor Statistics for FI and NFI and demographic data, the U.S. Census Bureau for occupation/industry classification code lists, and the U.S./Canada Occupational Noise Job Exposure Matrix for noise measurements. We examined four noise metrics as predictors of FI and NFI rates: mean Time-Weighted Average (TWA), maximum TWA, standard deviation of TWA, and percentage of work shifts exceeding 85 or 90 dBA for 619 occupation-years and 591 industry-years. K-means clustering was used to identify clusters of noise exposure characteristics. Mixed-effects negative binomial regression examined the relationship between the noise characteristics and FI/NFI rates separately for occupation and industry. RESULTS: Among occupations, we found significant associations between increased FI rates and higher mean TWA (IRR: 1.06, 95% CI: 1.01-1.12) and maximum TWA (IRR: 1.10, 95% CI: 1.07-1.14), as well as TWA exceedance (IRR: 1.04, 95% CI: 1.01-1.07). Increased rates of NFI were found to be significantly associated with maximum TWA (IRR: 1.06, 95% CI: 1.04-1.09) and TWA exceedance (IRR: 1.03, 95% CI: 1.01-1.05). In addition, occupations with both higher exposure variability (IRR with FI rate: 1.49, 95% CI: 1.23-1.80; IRR with NFI rate: 1.40, 95% CI: 1.14-1.73) and higher level of sustained exposure (IRR with FI rate: 1.27, 95% CI: 1.12-1.44; IRR with NFI rate: 1.21, 95% CI: 1.05-1.39) were associated with higher rates of FI and NFI compared to occupations with low noise exposure. Among industries, significant associations between increased NFI rates and higher mean TWA (IRR: 1.05, 95% CI: 1.02-1.08) and maximum TWA (IRR: 1.06, 95% CI: 1.04-1.08) were observed. Unlike the occupation-specific analysis, industries with higher exposure variability and higher sustained exposures did not display significantly higher FI/NFI rates compared to industries with low exposure. CONCLUSIONS: The results suggest that occupational noise exposure may be an independent risk factor for workplace FIs/NFIs, particularly for workplaces with highly variable noise exposures. The study highlights the importance of comprehensive occupational noise assessments.

Optimization of frozen goods distribution logistics network based on k-means algorithm and priority classification.

Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods.

Comprehensive geophysical, geotechnical, and geochemical assessments of an offshore landfill in Singapore.

Comprehensive site investigation techniques, including Electrical Resistivity Tomography (ERT), Induced Polarization (IP), Multichannel Analysis of Surface Waves (MASW), and Microtremor Array Method (MAM), were integrated with geotechnical and geochemical tests of retrieved waste samples from Singapore's operational offshore landfill. The properties of landfill wastes vary widely, including shear-wave velocities 127-248 m/s, densities 1.2-2.1 Mg/m3, resistivity 3.0-25.3 Ω∙m, and chargeability 48-82 mV/V. The natural clay layer underneath was clearly delineated and effectively mitigated leachate leakage. K-means clustering of the geophysical data facilitates precise mapping of waste distribution and quantities of recoverable metals based on quantitative criteria. This study illustrates a thorough case study adopting the new site investigation and characterization paradigm for an offshore landfill, which provides insights into leachate leakage detection and evaluations of landfill mining and resource recovery.

Detecting natural gas storage microleakage based on K-means clustering under constraint of Jeffries-Matusita distance criterion using mobile LiDAR data.

Natural gas leaks alter both the spectral reflectance and the structure of surface vegetation, which can be used to indirectly monitor microleakages in gas storage facilities. However, existing methods predominantly focus on the spectral rather than structural response of stressed vegetation, and it is not clear whether structure characteristic can be used to identify natural gas stressed vegetation. In this study, the utility of mobile LiDAR in detecting vegetation structure changes due to natural gas stress was demonstrated by analyzing LiDAR data from a field experiment with bean and grass plants in their growing phase. A method utilizing the Jeffries-Matusita distance criterion constrained K-means clustering (JCKC) algorithm was proposed, which comprises three main steps: First, response of vegetation structure characteristic to natural gas stress was quantitatively analyzed at plot and pixel scales using LiDAR data. Second, the optimal set of structure characteristic parameters indicating natural gas stressed vegetation was determined using hierarchical clustering algorithm. Third, the reduced LiDAR data was clustered using K-means algorithm, and the clusters were classified under constraint of Jeffries-Matusita distance criterion to identify stressed vegetation. The results indicated natural gas stress significantly changes vegetation structure (p = 0.05), decreasing parameters like height, projected leaf area, canopy relief ratio, coefficient of variation of vegetation height, and entropy, while increasing homogeneity, contrast, and dissimilarity. The set of structure characteristic parameters based on height, homogeneity, and contrast can stably indicate natural gas stress, with Jeffries-Matusita distance values for comparing healthy and stressed vegetation samples exceeding 1.8. The proposed model achieved pixel-level identification accuracies of 98.95% for bean and 96.22% for grass, with average localization accuracies of 0.15 m and 0.12 m, respectively. This study demonstrates the potential of vegetation's structure characteristic in reflecting response to natural gas stress and monitoring natural gas storage microleakage in vegetated areas.

Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia.

PURPOSE: To compare the performance of MRI-based Gaussian mixture model (GMM), K-means clustering, and Otsu unsupervised algorithms in predicting sarcopenia and to develop a combined model by integrating clinical indicators. METHODS: Retrospective analysis was conducted on clinical and lumbar MRI data from 118 patients diagnosed with sarcopenia and 222 patients without the sarcopenia. All patients were randomly divided into training and validation groups in a 7:3 ratio. Regions of interest (ROI), specifically the paravertebral muscles at the L3/4 intervertebral disc level, were delineated on axial T2-weighted images (T2WI). The Gaussian mixture model (GMM), K-means clustering, and Otsu's thresholding algorithms were employed to automatically segment muscle and adipose tissues at both the cohort and case levels. Subsequently, the mean signal intensity, volumes, and percentages of these tissues were calculated and compared. Logistic regression analyses were conducted to construct models and identify independent predictors of sarcopenia. An combined model was developed by combining the optimal magnetic resonance imaging (MRI) model and clinical predictors. The performance of the constructed model was assessed using receiver operating characteristic (ROC) curve analysis. RESULTS: Age, BMI, and serum albumin were identified as independent clinical predictors of sarcopenia. The cohort-level GMM demonstrated the best predictive performance both in the training group (AUC=0.840) and validation group (AUC=0.800), while the predictive performance of the other models was lower than that of the clinical model both in the training and validation groups. After combining the cohort-level GMM with the independent clinical predictors, the AUC of the training and validation groups increased to 0.871 and 0.867, respectively. CONCLUSION: The cohort-level GMM shows potential in predicting sarcopenia, and the incorporation of independent clinical predictors further increased the performance.

Development of a footwear sizing system for diabetic feet.

The number of diabetic patients is increasing rapidly who have vulnerable feet and might be easily affected by different adversities. Since there is no available footwear sizing system for diabetic patients, manufacturers produce diabetic footwear of different sizes and fittings based on other available footwear sizing systems, which may result in inappropriate fitting. To get footwear with proper fittings, diabetic patients may go for customized or bespoke footwear based on their foot conditions, which is very costly. This study attempts to explore the foot complications of diabetic patients and categorize their feet to create a new sizing system using foot measurements from 102 male diabetic patients based on three dimensions of human feet, namely foot length, ball girth, and instep circumference. K-means data clustering is followed to categorize the data into three broad groups, namely small, medium, and large groups for footwear sizing. The developed footwear sizing system uses a sizing interval of 8 mm and a fitting interval of 6 mm. This study suggests a total of 11 sizes along with 24 different fittings for the footwear manufacturers for producing diabetic footwear. This newly developed footwear sizing system has a total of 79.41 % coverage where there are 10, 10, and 4 fittings in the small, medium, and large groups, respectively. The proposed footwear sizing system can help footwear manufacturers understand the proper size and fit of diabetic patients' feet so that they can make appropriate footwear for diabetic patients economically.

White matter microstructural and inflammation-based subgroups in bipolar disorder II depression differentiate in depressive and psychotic symptoms.

BACKGROUND: Elevated inflammation and impaired white matter (WM) microstructure have been observed in bipolar disorder (BD). The link between inflammation, WM integrity, and psychiatric symptoms in BD-II depression (BDII-D) remains unknown. We aimed to define BDII-D subgroups through the interplay of inflammation and WM microstructure, and to explore differences in psychiatric symptoms between subgroups, thus offering insight into elucidating the explanatory measures linked to BDII-D. METHODS: WM differences were compared between 146 BDII-D individuals and 151 health controls (HCs) by Tract-Based Spatial Statistics. Partial correlation with multiple comparison corrections was used to explore associations between WM, inflammation, and psychiatric symptoms. The canonical correlation analysis metrics of WM and inflammation followed by k-means clustering were used to define WM microstructural-inflammation subgroups of BDII-D. The differences in clinical profiles were compared between the subgroups. RESULTS: Compared with HCs, BDII-D showed significant WM alterations in the anterior thalamic radiation (ATR), cingulum, forceps, and inferior fronto-occipital fasciculus. In BDII-D, lower fraction anisotropy (FA) within the right ATR and cingulum were significantly associated with higher interleukin-6, while lower FA in the cingulum and lower axial diffusivity in the forceps major exhibited significant links with higher C-reactive protein. Among the subgroups identified, subgroup II characterized by elevated inflammation and impaired WM integrity displayed greater psychiatric symptoms. CONCLUSIONS: WM alterations are concentrated in emotional neurocircuits and are linked to inflammation in BDII-D. WM-inflammation subgroups exhibit distinct variations in psychiatric symptoms. Thus, WM alterations and inflammation might be an explanatory process in the pathophysiology of BDII-D.

Who engages in electricity conservation and to what effect after real-world, high-resolution feedback? An empirical analysis of Korean households with smart meters.

Engagement with households to fully realize the potential of demand-side solutions has attracted policy attention. The potential of feedback has been understudied, especially regarding who engages more in electricity conservation. Furthermore, most studies have been limited to the Western context, with only a few that explore Asia. This study fills these gaps by investigating changes in household hourly electricity consumption patterns after its members receive high-resolution feedback. After data balancing, we partitioned 63 households into distinct groups using K-means clustering and investigated consumption changes after the provision of high-resolution electricity feedback through a mobile application. The results indicate mixed effectiveness of feedback: some households reduced consumption by about 13 %, while others increased it between 7 % and 20 %. In addition, statistical analysis using survey responses revealed that households with greater awareness of electricity costs and a stronger interest in climate change were more receptive to feedback. Demographic and housing attributes such as age, building type, and floor count also influenced the feedback effect. The findings recommend enhancing awareness of electricity costs and climate change and developing a better understanding of individuals' challenges with changing conservation behaviors based on their demographic and housing characteristics.

Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach.

Objectives: Pupil dilation is controlled both by sympathetic and parasympathetic nervous system branches. We hypothesized that the dynamic of pupil size changes under cognitive load with additional false feedback can predict individual behavior along with heart rate variability (HRV) patterns and eye movements reflecting specific adaptability to cognitive stress. To test this, we employed an unsupervised machine learning approach to recognize groups of individuals distinguished by pupil dilation dynamics and then compared their autonomic nervous system (ANS) responses along with time, performance, and self-esteem indicators in cognitive tasks. Methods: Cohort of 70 participants were exposed to tasks with increasing cognitive load and deception, with measurements of pupillary dynamics, HRV, eye movements, and cognitive performance and behavioral data. Utilizing machine learning k-means clustering algorithm, pupillometry data were segmented to distinct responses to increasing cognitive load and deceit. Further analysis compared clusters, focusing on how physiological (HRV, eye movements) and cognitive metrics (time, mistakes, self-esteem) varied across two clusters of different pupillary response patterns, investigating the relationship between pupil dynamics and autonomic reactions. Results: Cluster analysis of pupillometry data identified two distinct groups with statistically significant varying physiological and behavioral responses. Cluster 0 showed elevated HRV, alongside larger initial pupil sizes. Cluster 1 participants presented lower HRV but demonstrated increased and pronounced oculomotor activity. Behavioral differences included reporting more errors and lower self-esteem in Cluster 0, and faster response times with more precise reactions to deception demonstrated by Cluster 1. Lifestyle variations such as smoking habits and differences in Epworth Sleepiness Scale scores were significant between the clusters. Conclusion: The differentiation in pupillary dynamics and related metrics between the clusters underlines the complex interplay between autonomic regulation, cognitive load, and behavioral responses to cognitive load and deceptive feedback. These findings underscore the potential of pupillometry combined with machine learning in identifying individual differences in stress resilience and cognitive performance. Our research on pupillary dynamics and ANS patterns can lead to the development of remote diagnostic tools for real-time cognitive stress monitoring and performance optimization, applicable in clinical, educational, and occupational settings.

Face detection based on K-medoids clustering and associated with convolutional neural networks.

Over the last several years, the COVID-19 epidemic has spread over the globe. People have become used to the novel standard, which involves working from home, chatting online, and keeping oneself clean, to stop the spread of COVID-19. Due to this, many public spaces make an effort to make sure that their visitors wear proper face masks and maintain a safe distance from one another. It is impossible for monitoring workers to ensure that everyone is wearing a face mask; automated solutions are a far better option for face mask identification and monitoring to assist control public conduct and reduce the COVID-19 epidemic. The motivation for developing this technology was the need to identify those individuals who uncover their faces. Most of the previously published research publications focused on various methodologies. This study built new methods namely K-medoids, K-means, and Fuzzy K-Means(FKM) to use image pre-processing to get the better quality of the face and reduce the noise data. In addition, this study investigates various machine learning models Convolutional neural networks (CNN) with pre-trained (DenseNet201, VGG-16, and VGG-19) models, and Support Vector Machine (SVM) for the detection of face masks. The experimental results of the proposed method K-medoids with pre-trained model DenseNet201 achieved the 97.7 % accuracy best results for face mask identification. Our research results indicate that the segmentation of images may improve the identification of accuracy. More importantly, the face mask identification tool is more beneficial when it can identify the face mask in a side-on approach.

Fungi-sensitized individuals have unique profiles where Alt a 1 dominates promoting response to grass, ragweed and cat allergens.

Introduction: The aim of our work was to determine comprehensively the sensitization profile of patients hypersensitive to fungal allergenic components in the Ukrainian population, identifying features of their co-sensitization to allergens of other groups and establishing potential relationships between causative allergens and their ability to provoke this hypersensitivity. Methods: A set of programs was developed using Python and R programming languages, implementing the K-means++ clustering method. Bayesian networks were constructed based on the created clusters, allowing for the assessment of the probabilistic interplay of allergen molecules in the sensitization process of patients. Results and discussion: It was found that patients sensitive to fungi are polysensitized, with 84.77% of them having unique allergological profiles, comprising from 2 to several dozen allergens from different groups. The immune response to Alt a 1 may act as the primary trigger for sensitization to other allergens and may contribute to a high probability of developing sensitivity to grasses (primarily to Phl p 2), ragweed extract, and the Amb a 1 pectate lyase, as well as to pectate lyase Cry j 1 and cat allergen Fel d 1. Individuals polysensitized to molecular components of fungi were often sensitive to such cross-reactive molecules as lipocalins Fel d 4 and Can f 6, as well. Sensitivity to Ambrosia extract which dominated in the development of sensitization to ragweed pollen indicating the importance of different allergenic components of this plant's pollen. This hypothesis, along with the assumption that Phl p 2 may be the main trigger for sensitivity to grasses in patients with Alternaria allergy, requires further clinical investigation.

[Delineation of water ecological restoration zoning from a multi-dimensional perspective: A case study in Hechi, a typical karst region]. / 多维视角下水生态修复分区划定­­ä»¥å ¸åž‹å–€æ–¯ç‰¹åœ°åŒºæ²³æ± 市为例.

Water ecological restoration zoning, which involves articulating goals for restoring water ecosystems upwards and guiding the spatial layout of restoration projects downwards, is key to achieving systematic restoration of water resource elements. There are many challenges in water ecological restoration zoning, including disparate hierarchical systems, incomplete indicators, and vague boundaries. With Guangxi Hechi, a karst ecologically fragile region, as a case, we developed a multidimensional zoning system framework based on "watershed natural unit-dominant ecological function-ecological stress risk". The first-level zoning employed river systems and geomorphic types as indicators and delineated the sub-watershed unit as the boundary. The second-level zoning adopted a "top-down" division method to clarify the goal of water ecological restoration based on watershed natural geography and select three indicators (water conservation, biodiversity, and landscape cultural services) for evaluation. We used the K-means clustering method to identify dominant ecological functions in spatial units, with the sub-watershed unit demarcating second-level zoning boundaries. The third-level zoning was the specific implementation unit for ecological restoration projects. We used three indicators (soil erosion, flooding risk, and human interference) to characterize water ecosystem risk from external coercion, and defined the third-level zoning. We delineated 11 primary water ecological zones, four secondary zones, and three tertiary zones. Synthesizing tertiary zoning results accounted for spatial differentiation characteristics of watershed natural geography, dominant ecological functions, and ecological coercion risks, and combining sub-watershed and township administrative units determined zoning boundaries, water ecological restoration zoning was comprehensively classified into five categories and 32 sub-ecological zones. Corresponding ecological restoration strategies were proposed based on zoning and classification.

Methanogenic Potential of Sewer Microbiomes and Its Implications for Methane Emission.

The sewer system, despite being a significant source of methane emissions, has often been overlooked in current greenhouse gas inventories due to the limited availability of quantitative data. Direct monitoring in sewers can be expensive or biased due to access limitations and internal heterogeneity of sewer networks. Fortunately, since methane is almost exclusively biogenic in sewers, we demonstrate in this study that the methanogenic potential can be estimated using known sewer microbiome data. By combining data mining techniques and bioinformatics databases, we developed the first data-driven method to analyze methanogenic potentials using a data set containing 633 observations of 53 variables obtained from literature mining. The methanogenic potential in the sewer sediment was around 250-870% higher than that in the wet biofilm on the pipe and sewage water. Additionally, k-means clustering and principal component analysis linked higher methane emission rates (9.72 ± 51.3 kgCO2 eq m-3 d-1) with smaller pipe size, higher water level, and higher potentials of sulfate reduction in the wetted pipe biofilm. These findings exhibit the possibility of connecting microbiome data with biogenic greenhouse gases, further offering insights into new approaches for understanding greenhouse gas emissions from understudied sources.

Target Fitting Method for Spherical Point Clouds Based on Projection Filtering and K-Means Clustered Voxelization.

Industrial computed tomography (CT) is widely used in the measurement field owing to its advantages such as non-contact and high precision. To obtain accurate size parameters, fitting parameters can be obtained rapidly by processing volume data in the form of point clouds. However, due to factors such as artifacts in the CT reconstruction process, many abnormal interference points exist in the point clouds obtained after segmentation. The classic least squares algorithm is easily affected by these points, resulting in significant deviation of the solution of linear equations from the normal value and poor robustness, while the random sample consensus (RANSAC) approach has insufficient fitting accuracy within a limited timeframe and the number of iterations. To address these shortcomings, we propose a spherical point cloud fitting algorithm based on projection filtering and K-Means clustering (PK-RANSAC), which strategically integrates and enhances these two methods to achieve excellent accuracy and robustness. The proposed method first uses RANSAC for rough parameter estimation, then corrects the deviation of the spherical center coordinates through two-dimensional projection, and finally obtains the spherical center point set by sampling and performing K-Means clustering. The largest cluster is weighted to obtain accurate fitting parameters. We conducted a comparative experiment using a three-dimensional ball-plate standard. The sphere center fitting deviation of PK-RANSAC was 1.91 µm, which is significantly better than RANSAC's value of 25.41 µm. The experimental results demonstrate that PK-RANSAC has higher accuracy and stronger robustness for fitting geometric parameters.

Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma.

PURPOSE: The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm3 (group_1mm) and 3 × 3 × 3 mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. RESULTS: Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. CONCLUSIONS: A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes.

Relationship between changes in the triglyceride glucose-body mass index and frail development trajectory and incidence in middle-aged and elderly individuals: a national cohort study.

BACKGROUND: Insulin resistance is linked to an increased risk of frailty, yet the comprehensive relationship between the triglyceride glucose-body mass index (TyG-BMI), which reflects weight, and frailty, remains unclear. This relationship is investigated in this study. METHODS: Data from 9135 participants in the China Health and Retirement Longitudinal Study (2011-2020) were analysed. Baseline TyG-BMI, changes in the TyG-BMI and cumulative TyG-BMI between baseline and 2015, along with the frailty index (FI) over nine years, were calculated. Participants were grouped into different categories based on TyG-BMI changes using K-means clustering. FI trajectories were assessed using a group-based trajectory model. Logistic and Cox regression models were used to analyse the associations between the TyG-BMI and FI trajectory and frail incidence. Nonlinear relationships were explored using restricted cubic splines, and a linear mixed-effects model was used to evaluate FI development speed. Weighted quantile regression was used to identify the primary contributing factors. RESULTS: Four classes of changes in the TyG-BMI and two FI trajectories were identified. Individuals in the third (OR = 1.25, 95% CI: 1.10-1.42) and fourth (OR = 1.83, 95% CI: 1.61-2.09) quartiles of baseline TyG-BMI, those with consistently second to highest (OR = 1.49, 95% CI: 1.32-1.70) and the highest (OR = 2.17, 95% CI: 1.84-2.56) TyG-BMI changes, and those in the third (OR = 1.20, 95% CI: 1.05-1.36) and fourth (OR = 1.94, 95% CI: 1.70-2.22) quartiles of the cumulative TyG-BMI had greater odds of experiencing a rapid FI trajectory. Higher frail risk was noted in those in the fourth quartile of baseline TyG-BMI (HR = 1.42, 95% CI: 1.28-1.58), with consistently second to highest (HR = 1.23, 95% CI: 1.12-1.34) and the highest TyG-BMI changes (HR = 1.58, 95% CI: 1.42-1.77), and those in the third (HR = 1.10, 95% CI: 1.00-1.21) and fourth quartile of cumulative TyG-BMI (HR = 1.46, 95% CI: 1.33-1.60). Participants with persistently second-lowest to the highest TyG-BMI changes (ß = 0.15, 0.38 and 0.76 respectively) and those experiencing the third to fourth cumulative TyG-BMI (ß = 0.25 and 0.56, respectively) demonstrated accelerated FI progression. A U-shaped association was observed between TyG-BMI levels and both rapid FI trajectory and higher frail risk, with BMI being the primary factor. CONCLUSION: A higher TyG-BMI is associated with the rapid development of FI trajectory and a greater frail risk. However, excessively low TyG-BMI levels also appear to contribute to frail development. Maintaining a healthy TyG-BMI, especially a healthy BMI, may help prevent or delay the frail onset.

Research on Using K-Means Clustering to Explore High-Risk Products with Ethylene Oxide Residues and Their Manufacturers in Taiwan.

Considering the frequency of ethylene oxide (EtO) residues found in food, the health effects of EtO have become a concern. Between 2022 and 2023, 489 products were inspected using the purposive sampling method in Taiwan, and nine unqualified products were found to have been imported; subsequently, border control measures were enhanced. To ensure the safety of all imported foods, the current study used the K-means clustering method for identifying EtO residues in food. Data on finished products and raw materials with EtO residues from international public opinion bulletins were collected for analysis. After matching them with the Taiwan Food Cloud, 90 high-risk food items with EtO residues and 1388 manufacturers were screened. The Taiwan Food and Drug Administration set up border controls and grouped the manufacturers using K-means clustering in the unsupervised learning algorithm. For this study, 37 manufacturers with priority inspections and 52 high-risk finished products and raw materials with residual EtO were selected for inspection. While EtO was not detected, the study concluded the following: 1. Using international food safety alerts to strengthen border control can effectively ensure domestic food safety; 2. K-means clustering can validate the risk-based purposive sampling results to ensure food safety and reduce costs.

Assessment of 'Golden Delicious' Apples Using an Electronic Nose and Machine Learning to Determine Ripening Stages.

Consumers often face a lack of information regarding the quality of apples available in supermarkets. General appearance factors, such as color, mechanical damage, or microbial attack, influence consumer decisions on whether to purchase or reject the apples. Recently, devices known as electronic noses provide an easy-to-use and non-destructive assessment of ripening stages based on Volatile Organic Compounds (VOCs) emitted by the fruit. In this study, the 'Golden Delicious' apples, stored and monitored at the ambient temperature, were analyzed in the years 2022 and 2023 to collect data from four Metal Oxide Semiconductor (MOS) sensors (MQ3, MQ135, MQ136, and MQ138). Three ripening stages (less ripe, ripe, and overripe) were identified using Principal Component Analysis (PCA) and the K-means clustering approach from various datasets based on sensor measurements in four experiments. After applying the K-Nearest Neighbors (KNN) model, the results showed successful classification of apples for specific datasets, achieving an accuracy higher than 75%. For the dataset with measurements from all experiments, an impressive accuracy of 100% was achieved on specific test sets and on the evaluation set from new, completely independent experiments. Additionally, correlation and PCA analysis showed that choosing two or three sensors can provide equally successful results. Overall, the e-nose results highlight the importance of analyzing data from several experiments performed over a longer period after the harvest of apples. There are similarities and differences in investigated VOC parameters (ethylene, esters, alcohols, and aldehydes) for less or more mature apples analyzed during autumn or spring, which can improve the determination of the ripening stage with higher predicting success for apples investigated in the spring.