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With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning models, in particular, is increasingly gaining momentum to accelerate the discovery of new materials. However, the black-box treatment of machine-learning methods suffers from a lack of model interpretability, as feature relevance and interactions can be overlooked or disregarded. In addition, naive approaches to model training often lead to irrelevant features being used which necessitates the need for various regularization techniques to achieve model generalization; this incurs a high computational cost. We present a feature-selection workflow that overcomes this problem by leveraging a gradient boosting framework and statistical feature analyses to identify a subset of features, in a recursive manner, which maximizes their relevance to the target variable or classes. We subsequently obtain minimal feature redundancy through multicollinearity reduction by performing feature correlation and hierarchical cluster analyses. The features are further refined using a wrapper method, which follows a greedy search approach by evaluating all possible feature combinations against the evaluation criterion. A case study on elastic material-property prediction and a case study on the classification of materials by their metallicity are used to illustrate the use of our proposed workflow; although it is highly general, as demonstrated through our wider subsequent prediction of various material properties. Our Bayesian-optimized machine-learning models generated results, without the use of regularization techniques, which are comparable to the state-of-the-art that are reported in the scientific literature. [ABSTRACT FROM AUTHOR]

We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach, we combine two dimensionality reduction algorithms (cc_analysis and encodermap) with a density-based spatial clustering algorithm (hierarchical density-based spatial clustering of applications with noise). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here, the cc_analysis algorithm is applied for the first time to molecular simulation data. The encodermap algorithm complements cc_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory while keeping a clear conformational identity of the clusters that are found. In practice, we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows us to find clusters of different densities and different degrees of structural identity. With the help of four protein systems, we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9, and Protein B. Each of these test systems poses their individual challenges to the scheme, which, in total, give a nice overview of the advantages and potential difficulties that can arise when using the proposed method. [ABSTRACT FROM AUTHOR]

Background and aims Design Setting Participants/Cases Measurements Findings Conclusions Previously identified national drinking patterns in Europe lack comparability and might be no longer be valid due to changes in economic conditions and policy frameworks. We aimed to identify the most recent alcohol drinking patterns in Europe based on comparable alcohol exposure indicators using a data‐driven approach, as well as identifying temporal changes and establishing empirical links between these patterns and indicators of alcohol‐related harm.Data from the World Health Organization's monitoring system on alcohol exposure indicators were used. Repeated cross‐sectional hierarchical cluster analyses were applied. Differences in alcohol‐attributable harm between clusters of countries were analyzed via linear regression.European Union countries, plus Iceland, Norway and Ukraine, for 2000, 2010, 2015 and 2019.Observations consisted of annual country data, at four different time points for alcohol exposure. Harm indicators were only included for 2019.Alcohol exposure indicators included alcohol per capita consumption (APC), beverage‐specific consumption and prevalence of drinking status indicators (lifetime abstainers, current drinkers, former drinkers and heavy episodic drinking). Alcohol‐attributable harm was measured using age‐standardized alcohol‐attributable Disability‐Adjusted Life Years (DALYs) lost and deaths per 100 000 people.The same six clusters were identified in 2019, 2015 and 2010, mainly characterized by type of alcoholic beverage and prevalence drinking status indicators, with geographical interpretation. Two‐thirds of the countries remained in the same cluster over time, with one additional cluster identified in 2000, characterized by low APC. The most recent drinking patterns were shown to be significantly associated with alcohol‐attributable deaths and DALY rates. Compared with wine‐drinking countries, the mortality rate per 100 000 people was significantly higher in Eastern Europe with high spirits and ‘other’ beverage consumption [ β^$$ \hat{\beta} $$ = 90, 95% confidence interval (CI) = 55–126], and in Eastern Europe with high lifetime abstainers and high spirits consumption ( β^$$ \hat{\beta} $$ = 42, 95% CI = 4–78).European drinking patterns appear to be clustered by level of beverage‐specific consumption, with heavy episodic drinkers, current drinkers and lifetime abstainers being distinguishing factors between clusters. Despite the overall stability of the clusters over time, some countries shifted between drinking patterns from 2000 to 2019. Overall, patterns of drinking in the European Union seem to be stable and partly determined by geographical proximity. [ABSTRACT FROM AUTHOR]

BACKGROUND: It has recently become increasingly evident that banana projects in Uganda need to consider consumer preferences as part of the breeding process to increase the acceptability of new cultivars. A trained panel used quantitative descriptive analysis (QDA) as a tool to assess the sensory characteristics of 32 cooking bananas (matooke). The aim was to investigate which sensory characteristics best describe matooke. RESULTS: Fourteen descriptors were generated. The preferred attributes of matooke were high‐intensity yellow color, homogeneous distribution of yellow color, good matooke aroma, highly moldable by touch, moist and smooth in the mouth. Analysis of variance revealed significant differences in the yellowness, homogeneity of color, firmness, moistness, smoothness, matooke aroma, hardness, and moldability across the genotypes (P < 0.05). Principal component analysis (PCA) showed strong positive correlations between yellowness and homogeneity of the color (R = 0.92). Smoothness in the mouth and moldability by touch were strongly and positively correlated (R = 0.88). Firmness in the mouth was well predicted by hardness to touch (R2 = 0.85). The matooke samples were ranked into two sensory clusters by agglomerative hierarchical clustering (AHC). CONCLUSION: The study showed attribute terms that could be used to describe matooke and also revealed that QDA may be used as a tool during the assessment and selection of new cooking banana hybrids to identify relevant sensory attributes because of its ability to discriminate among the banana hybrids. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research. [ABSTRACT FROM AUTHOR]

Background: The poor chemo-response and high DNA methylation of ovarian clear cell carcinoma (OCCC) have attracted extensive attentions. Recently, we revealed the mutational landscape of the human kinome and additional cancer-related genes and found deleterious mutations in ARID1A, a component of the SWI/SNF chromatin-remodeling complex, in 46% of OCCC patients. The present study aims to comprehensively investigate whether ARID1A loss and genome-wide DNA methylation are co-regulated in OCCC and identify putative therapeutic targets epigenetically regulated by ARID1A. Methods: DNA methylation of ARID1Amt/ko and ARID1Awt OCCC tumors and cell lines were analyzed by Infinium MethylationEPIC BeadChip. The clustering of OCCC tumors in relation to clinical and mutational status of tumors were analyzed by hierarchical clustering analysis of genome-wide methylation. GEO expression profiles were used to identify differentially methylated (DM) genes and their expression level in ARID1Amt/ko vs ARID1Awt OCCCs. Combining three pre-ranked GSEAs, pathways and leading-edge genes epigenetically regulated by ARID1A were revealed. The leading-edge genes that passed the in-silico validation and showed consistent ARID1A-related methylation change in tumors and cell lines were regarded as candidate genes and finally verified by bisulfite sequencing and RT-qPCR. Results: Hierarchical clustering analysis of genome-wide methylation showed two clusters of OCCC tumors. Tumor stage, ARID1A/PIK3CA mutations and TP53 mutations were significantly different between the two clusters. ARID1A mutations in OCCC did not cause global DNA methylation changes but were related to DM promoter or gene-body CpG islands of 2004 genes. Three pre-ranked GSEAs collectively revealed the significant enrichment of EZH2- and H3K27me3-related gene-sets by the ARID1A-related DM genes. 13 Leading-edge DM genes extracted from the enriched gene-sets passed the expression-based in-silico validation and showed consistent ARID1A-related methylation change in tumors and cell lines. Bisulfite sequencing and RT-qPCR analysis showed promoter hypermethylation and lower expression of IRX1, TMEM101 and TRIP6 in ARID1Amt compared to ARID1Awt OCCC cells, which was reversed by 5-aza-2′-deoxycytidine treatment. Conclusions: Our study shows that ARID1A loss is related to the differential methylation of a number of genes in OCCC. ARID1A-dependent DM genes have been identified as key genes of many cancer-related pathways that may provide new candidates for OCCC targeted treatment. [ABSTRACT FROM AUTHOR]

Traditional methods for evaluating decision-making provide valuable insights yet may fall short in capturing the complexity of this cognitive capacity, often providing insufficient for the multifaceted nature of decisions. The Kalliste Decision Task (KDT) is introduced as a comprehensive, ecologically valid tool aimed at bridging this gap, offering a holistic perspective on decision-making. In our study, 81 participants completed KDT alongside established tasks and questionnaires, including the Mixed Gamble Task (MGT), Iowa Gambling Task (IGT), and Stimulating & Instrumental Risk Questionnaire (S&IRQ). They also completed the User Satisfaction Evaluation Questionnaire (USEQ). The results showed excellent usability, with high USEQ scores, highlighting the user-friendliness of KDT. Importantly, KDT outcomes showed significant correlations with classical decision-making variables, shedding light on participants' risk attitudes (S&IRQ), rule-based decision-making (MGT), and performance in ambiguous contexts (IGT). Moreover, hierarchical clustering analysis of KDT scores categorized participants into three distinct profiles, revealing significant differences between them on classical measures. The findings highlight KDT as a valuable tool for assessing decision-making, addressing limitations of traditional methods, and offering a comprehensive, ecologically valid approach that aligns with the complexity and heterogeneity of real-world decision-making, advancing research and providing insights for understanding and assessing decision-making across multiple domains. [ABSTRACT FROM AUTHOR]

Clustering is an important task in biomedical science, and it is widely believed that different data sets are best clustered using different algorithms. When choosing between clustering algorithms on the same data set, reseachers typically rely on global measures of quality, such as the mean silhouette width, and overlook the fine details of clustering. However, the silhouette width actually computes scores that describe how well each individual element is clustered. Inspired by this observation, we developed a novel clustering method, called SillyPutty. Unlike existing methods, SillyPutty uses the silhouette width for individual elements as a tool to optimize the mean silhouette width. This shift in perspective allows for a more granular evaluation of clustering quality, potentially addressing limitations in current methodologies. To test the SillyPutty algorithm, we first simulated a series of data sets using the Umpire R package and then used real-workd data from The Cancer Genome Atlas. Using these data sets, we compared SillyPutty to several existing algorithms using multiple metrics (Silhouette Width, Adjusted Rand Index, Entropy, Normalized Within-group Sum of Square errors, and Perfect Classification Count). Our findings revealed that SillyPutty is a valid standalone clustering method, comparable in accuracy to the best existing methods. We also found that the combination of hierarchical clustering followed by SillyPutty has the best overall performance in terms of both accuracy and speed. Availability: The SillyPutty R package can be downloaded from the Comprehensive R Archive Network (CRAN). [ABSTRACT FROM AUTHOR]

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics. [ABSTRACT FROM AUTHOR]

Since the Internet is a breeding ground for unconfirmed fake news, its automatic detection and clustering studies have become crucial. Most current studies focus on English texts, and the common features of multilingual fake news are not sufficiently studied. Therefore, this article uses English, Russian, and Chinese as examples and focuses on identifying the common quantitative features of fake news in different languages at the word, sentence, readability, and sentiment levels. These features are then utilized in principal component analysis, K-means clustering, hierarchical clustering, and two-step clustering experiments, which achieved satisfactory results. The common features we proposed play a greater role in achieving automatic cross-lingual clustering than the features proposed in previous studies. Simultaneously, we discovered a trend toward linguistic simplification and economy in fake news. Furthermore, fake news is easier to understand and uses negative emotional expressions in ways that real news does not. Our research provides new reference features for fake news detection tasks and facilitates research into their linguistic characteristics. [ABSTRACT FROM AUTHOR]

Foeniculum vulgare (Fennel) oils have an essential function in the food and therapeutic industries. The study's purpose was to describe how different environmental circumstances and locations in Egypt affected the fennel oil yields and compositions. To assess the growth, yield, and essential oil constituents of F. vulgare at four differing agro-ecological regions in Egypt (El-Minia Governorate, Bahariya Oasis, El-Sharkia Governorate, and Aswan Governorate) at two different planting dates, in-field study was performed throughout the seasons 2019/2020 and 2020/2021. According to the data, there are highly significant differences between locations and sowing dates; the second date (15th November) is the best date for planting fennel plants. Plants grown in the Bahariya Oasis location had the maximum oil yield in mL/plant and L/acre, whereas the Aswan location had the highest percentage of EO. Eight EO samples underwent GC-MS analysis and chemometric analysis, including principal component analysis (PCA) and agglomerative hierarchical clustering (AHC), yielding identification of a total of 22 compounds. EOs from plants grown in various environments varied significantly in terms of their chemical composition, mainly components' concentrations. The main components were identified as estragole (66.62-89.73%), D-limonene (5.76-17.04%), and fenchone (2.02-9.23%). In samples gathered on November 15, 2020-2021, at the Bahariya Sekem Farm, anethole was found at a high concentration (12.79%), despite the compound's absence or minimal presence in the other EOs. According to the current study findings, modulation of the cultivation and ecological circumstances can be used for the improvement of funnel EO. [ABSTRACT FROM AUTHOR]

Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a "component disorder" of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities. [ABSTRACT FROM AUTHOR]

Conservation Agriculture (CA) is actively promoted as an alternative farming system that combines environmental, economic, and social sustainability. Three pillars define CA: (i) minimum mechanical soil disturbance, (ii) permanent soil organic cover, and (iii) species diversification. The local context, constraints, and needs of the farmers influence the translation of the pillars into practices. Currently, there is no method for categorizing this diversity of CA practices, which hampers impact assessment, understanding of farmer choices and pathways, stakeholder communication, and policymaking. This paper presents a systematic method to identify and categorize the diversity of CA practices at the regional level, anchored in the three pillars and based on practices implemented by CA farmers. The classification method is grounded on the intersection of an archetypal analysis and a hierarchical clustering analysis. This method was used to study CA practices in Wallonia, Belgium, based on a survey of practices in a sample of 48 farmers. Combining the two clustering methods increases the proportion of classified farmers while allowing for the distinction between three CA-types with extreme and salient practices, and two intermediate CA-types comprising farmers whose practices fall between these references. The study reveals that three explanatory factors influence the implementation of CA practices in Wallonia: (i) the proportion of tillage-intensive crops and (ii) temporary grasslands in the crop sequence, and (iii) the organic certification. These factors lead to trade-offs that hinder the three pillars of CA from being fully implemented simultaneously. This new classification method can be replicated in other regions where CA is practiced, by adapting input variables according to context and local knowledge. [ABSTRACT FROM AUTHOR]

In this paper, a simple and efficient high‐performance liquid chromatography method was established to analyze the chemical composition fingerprint of Centellae Herba (CHA) and determine five triterpenoid active components in CHA, namely, asiaticoside B (AB), madecassoside, asiaticoside, madecasic acid, and asiaticoic acid. The separation of the compounds was carried out on an XD‐C18 column (250 mm × 4.6 mm, 5 µm), the wavelength of the ultraviolet detector was set to 205 nm, and the mobile phase was composed of acetonitrile −0.05% phosphoric acid, 2 mmol/L β‐cyclodextrin aqueous solution, and gradient elution at the flow rate of 1.0 mL/min. A general chromatographic fingerprint consisting of 20 characteristic peaks of 18 batches of CHA samples was established, and the samples were classified and evaluated by similarity analysis, hierarchical cluster analysis, and principal component analysis combined with multivariate econometric analysis. In quantitative analysis, all calibration curves showed good linear regression in the test range (r > 0.999), the average recovery was 96.12%–104.63%, and the relative standard deviations of repeatability and stability were less than 2.00%. The results show that the method is accurate and effective and can be used for the comprehensive quality control of CHA. [ABSTRACT FROM AUTHOR]

Background: Early recognition of dementia like Alzheimer's disease is crucial for disease diagnosis and treatment, and existing objective tools for early screening of cognitive impairment are limited. Objective: To investigate age-related behavioral indicators of dual-task cognitive performance and gait parameters and to explore potential objective markers of early cognitive decline. Methods: The community-based cognitive screening data was analyzed. Hierarchical cluster analysis and Pearson correlation analysis were performed on the 9-item subjective cognitive decline (SCD-9) scores, walking-cognitive dual-task performance, walking speed, and gait parameters of 152 participants. The significant differences of indicators that may related to cognitive decline were statistically analyzed across six age groups. A mathematical model with age as the independent variable and motor cognition composite score as the dependent variable was established to observe the trend of motor cognition dual-task performance with age. Results: Strong correlation was found between motor cognitive scores and SCD and age. Gait parameters like the mean value of ankle angle, the left-right difference rate of ankle angle and knee angle and the coefficient of variation of gait cycle showed an excellent correlation with age. Motor cognition scores showed a decreasing trend with age. The slope of motor cognition scores with age after 50 years (k = –1.06) was six times higher than that before 50 years (k = –0.18). Conclusions: Cognitive performance and gait parameters in the walking-cognitive dual-task state are promising objective markers that could characterize age-related cognitive decline. [ABSTRACT FROM AUTHOR]

We examine the connectedness of aerospace and defense companies and their relation to measures of geopolitical risk. With hierarchical clustering, we find stable and localized company clusters. Increasing geopolitical risk leaves these clusters intact but strengthens inter-cluster connectedness. Focusing on intraday data, we find that for most companies, instantaneous news arrival in form of jumps impacts realized volatility significantly. Further, we show that different measures of geopolitical uncertainty (GPR and COVOL) have differing impact on short-term predictions of intraday volatility, underlying the importance to distinguish between different sources of uncertainty. We provide evidence that investors react instantaneously to increases in geopolitical risk with some persistence of these shocks. The COVOL index holds significant informational content for short- and medium-term predictions of realized volatility of global aerospace and defense companies. • Aerospace and defense companies form localized clusters. • Increasing geopolitical risk increases the intercluster connectedness. • Different measures of geopolitical risks (GPR and COVOL) affect realized volatility predictions. • Instantaneous news arrival in form of volatility jumps are causally related to changes in geopolitical risk. • We provide evidence that investors react instantaneously to increases in geopolitical risk. [ABSTRACT FROM AUTHOR]

Wireless sensors networks (WSN) become more popular in recent years. For their efficient use, it is necessary to define energy-efficient routing protocols. In WSN, it sometimes arrives that some sensors have items that belong or not to them. The aim being to send each item to its real destination. This problem is known as the permutation routing problem. Some sensors may fail during the routing process. These particular cases must not prevent the operation of routing as a whole. Thus, it becomes important to ensure the fault-tolerance of the routing mechanism. In this paper we propose an energy-efficient and fault-tolerant permutation routing protocol for WSN. The proposed PEFTOSPRO protocol is realized in four stages. Firstly, we partition the sensors in cliques within which Cluster Heads (CH) are elected. Secondly, we achieve the hierarchical clustering of CH. Thirdly, we route items to their belonging cliques. And finally, we route each item to it real destination within all the cliques. We use the wake and sleep technique and the fault-tolerance in the data routing process in order to save the sensors' energy. Simulations show that the wake and sleep technique and the fault tolerance management during the data routing process help to save the sensors' energy, and by doing so, prolong the lifetime of the WSN. PEFTOSPRO is a suited protocol to solve the permutation routing problem in a multi-hop environment for wireless sensor networks, because it ensures that sensors efficiently use their energy and takes into account the fault tolerance. [ABSTRACT FROM AUTHOR]

The study of groundwater samples from Patna, which is in the middle Ganga River basin in the state of Bihar, was done to evaluate the hydrogeochemical nature of groundwater and figure out the controlling factors that affect its ionic chemistry. This study's groundwater samples were taken seasonally from 20 Patna, Bihar locations from 2019 to 2020 and analyzed for their physical and chemical properties using APHA's standard methods. Multivariate statistical techniques, such as Correlation matrix, Principal Component Analysis and Hierarchical cluster analysis, were carried out on the analytical variables, followed by interpreting the groundwater's geochemical nature using Durov Plot, Piper plot, ion-scatter plots and Gibb's diagram. The results obtained were compared with water quality standards prescribed by the Bureau of Indian Standards. The study revealed that groundwater's alkaline nature and water's total hardness varied from hard to very hard categories. Total dissolved solids range from 158 to 575 mg/l, where 31.66% of samples have exceeded the desirable TDS limit for drinking water. The abundance of anions was observed in decreasing order (HCO3− > Cl− > SO42−) during all seasons and cations as Ca2+ > Na+ > Mg2+ > K+ in pre-monsoon and post-monsoon, whereas in monsoon season, it was observed as Mg2+ > Na+ > Ca2+ > K+. Ca2+–Mg2+–HCO3− type water was revealed as the significant hydrogeochemical facie, and the reverse ion exchange process was observed to be the governing factor in 77% of the total groundwater samples. Gibbs's diagram showed rock–water dominance as a major factor in groundwater ionic composition during all seasons. [ABSTRACT FROM AUTHOR]

This article offers an alternative approach to measuring the distance between parties. It is aimed to introduce the Manhattan metric and hierarchical cluster analysis. These analytical tools measure differences based on parties' voting in parliament rather than ideological distance (polarisation). Through these tools, I delineate distances between parties in the Czech Republic after 2017 and reflect on the purpose of pre-electoral 'anti-Babiš' coalitions emerging in October 2020. The analysis shows that incentives to form alliances were primarily strategic rather than a sign of any pre-existing trend towards cooperation in parliament. [ABSTRACT FROM AUTHOR]

The emergence of conflict is a complex issue with numerous drivers and interactions playing a role. Exploratory dimension-reduction techniques can reveal patterns of association in such complex data. In this study, an existing dataset was reanalyzed using factor analysis for mixed data to visualize the data in two-dimensional space to explore the conditions associated with high levels of conflict. The first dimension was strongly associated with resilience index, control of corruption, income, income inequality, and regime type, while the second dimension was strongly associated with oil production, regime type, conflict level, political terror level, and water stress. Hierarchical clustering from principal components was used to group the observations into five clusters. Country trajectories through the two-dimensional space provided examples of how movement in the first two dimensions reflected changes in conflict, political terror, regime type, and resilience index. These trajectories correspond to the evolution of themes in research on conflict, particularly in terms of considering the importance of climate or environmental variables in stimulating or sustaining conflict. Understanding conditions associated with high conflict can be helpful in guiding the development of future models for prediction and risk assessment. [ABSTRACT FROM AUTHOR]

Untargeted metabolomics investigations have characterized metabolic disturbances associated with various diseases in domestic cats. However, the pre-analytic stability of serum metabolites in the species is unknown. Our objective was to compare serum metabolomes from healthy cats stored at -20°C for up to 12 months to samples stored at -80°C. Serum samples from 8 adult, healthy cats were stored at -20°C for 6 months, -20°C for 12 months, or -80°C for 12 months. Untargeted liquid chromatography-mass spectrometry was used to generate serum metabolite profiles containing relative abundances of 733 serum metabolites that were compared among storage conditions. Unsupervised analysis with principal component analysis and hierarchical clustering of Euclidian distances revealed separation of samples from individual cats regardless of storage condition. Linear mixed-effects models identified 75 metabolites that differed significantly among storage conditions. Intraclass correlation analysis (ICC) classified most serum metabolites as having excellent (ICC ≥ 0.9; 33%) or moderate (ICC 0.75–0.89; 33%) stability, whereas 13% had poor stability (ICC < 0.5). Biochemicals that varied significantly among storage conditions and classified with poor stability included glutathione metabolites, amino acids, gamma-glutamyl amino acids, and polyunsaturated fatty acids. The benzoate; glycine, serine and threonine; tryptophan; chemical (xenobiotics); acetylated peptide, and primary bile acid sub pathways were enriched among highly stable metabolites, whereas the monohydroxy fatty acid, polyunsaturated fatty, and monoacylglycerol sub-pathways were enriched among unstable metabolites. Our findings suggest that serum metabolome profiles are representative of the cat of origin, regardless of storage condition. However, changes in specific serum metabolites, especially glutathione, gamma-glutamyl amino acid, and fatty acid metabolites were consistent with increased sample oxidation during storage at -20°C compared with -80°C. By investigating the pre-analytic stability of serum metabolites, this investigation provides valuable insights that could aid other investigators in planning and interpreting studies of serum metabolomes in cats. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to assess the impact of immunosuppression management on coronavirus disease 2019 (COVID-19) outcomes. Methods: We performed a single-center retrospective study in a cohort of 358 lung transplant recipients (LTx) over the period from March 2020 to April 2022. All included symptomatic patients had at least one positive SARS-CoV-2 rt-PCR. We used a composite primary outcome for COVID-19 including increased need for oxygen since the hospital admission, ICU transfer, and in-hospital mortality. We assessed by univariate and multivariate analyses the risk factors for poor outcomes. Results: Overall, we included 91 LTx who contracted COVID-19. The COVID-19 in-hospital mortality rate reached 4.4%. By hierarchical clustering, we found a strong and independent association between the composite poor outcome and the discontinuation of at least one immunosuppressive molecule among tacrolimus, cyclosporine, mycophenolate mofetil, and everolimus. Obesity (OR = 16, 95%CI (1.96; 167), p = 0.01) and chronic renal failure (OR = 4.6, 95%CI (1.4; 18), p = 0.01) were also independently associated with the composite poor outcome. Conversely, full vaccination was protective (OR = 0.23, 95%CI (0.046; 0.89), p = 0.047). Conclusion: The administration of immunosuppressive drugs such as tacrolimus, cyclocporine or everolimus can have a protective effect in LTx with COVID-19, probably related to their intrinsic antiviral capacity. [ABSTRACT FROM AUTHOR]

We present an end-to-end description of the formation of globular clusters (GCs) combining a treatment for their formation and dynamical evolution within galaxy haloes with a state-of-the-art semi-analytic simulation of galaxy formation. Our approach allows us to obtain exquisite statistics to study the effect of the environment and assembly history of galaxies, while still allowing a very efficient exploration of the parameter space. Our reference model, including both efficient cluster disruption during galaxy mergers and dynamical friction of GCs within the galactic potential, accurately reproduces the observed correlation between the total mass in GCs and the parent halo mass. A deviation from linearity is predicted at low-halo masses, which is driven by a strong dependence on morphological type: bulge-dominated galaxies tend to host larger masses of GCs than their later-type counterparts. While the significance of the difference might be affected by resolution at the lowest halo masses considered, this is a robust prediction of our model and a natural consequence of the assumption that cluster migration into the halo is triggered by galaxy mergers. Our model requires an environmental dependence of GC radii to reproduce the observed low-mass mass distribution of GCs in our Galaxy. At GC masses |$\gt 10^6\, {\rm M}_\odot$|⁠ , our model predicts fewer GCs than observed, due to an overly aggressive treatment of dynamical friction. Our model reproduces well the metallicity distribution measured for Galactic GCs, even though we predict systematically younger GCs than observed. We argue that this adds further evidence for an anomalously early formation of the stars in our Galaxy. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) is an increasingly useful method for detecting pelagic animals in the ocean but typically requires large water volumes to sample diverse assemblages. Ship-based pelagic sampling programs that could implement eDNA methods generally have restrictive water budgets. Studies that quantify how eDNA methods perform on low water volumes in the ocean are limited, especially in deep-sea habitats with low animal biomass and poorly described species assemblages. Using 12S rRNA and COI gene primers, we quantified assemblages comprised of micronekton, coastal forage fishes, and zooplankton from low volume eDNA seawater samples (n = 436, 380–1800 mL) collected at depths of 0–2200 m in the southern California Current. We compared diversity in eDNA samples to concurrently collected pelagic trawl samples (n = 27), detecting a higher diversity of vertebrate and invertebrate groups in the eDNA samples. Differences in assemblage composition could be explained by variability in size-selectivity among methods and DNA primer suitability across taxonomic groups. The number of reads and amplicon sequences variants (ASVs) did not vary substantially among shallow (<200 m) and deep samples (>600 m), but the proportion of invertebrate ASVs that could be assigned a species-level identification decreased with sampling depth. Using hierarchical clustering, we resolved horizontal and vertical variability in marine animal assemblages from samples characterized by a relatively low diversity of ecologically important species. Low volume eDNA samples will quantify greater taxonomic diversity as reference libraries, especially for deep-dwelling invertebrate species, continue to expand. [ABSTRACT FROM AUTHOR]

The study of the expansion potential of artificial oases based on remote sensing data is of great significance for the rational allocation of water resources and urban planning in arid areas. Based on the spatio-temporal relationship between morphogenetic landform types and the development of artificial oases in Xinjiang, this study explored the development pattern of artificial oases in the past 30 years by using trend analysis and centroid migration analysis, constructing a series of landform–artificial oasis change indices, and investigating the suitability of different landforms for the development of artificial oases based on geomorphological location by adopting a hierarchical clustering method. The following conclusions are drawn: (1) From 1990 to 2020, the area of artificial oases in the whole territory continued to increase, with significant expansion to the south from 2005 to 2010. (2) Six categories of landform types for artificial oasis development were created based on the clustering results. Of these, 7.39% and 6.15% of the area's geomorphological types belonged to the first and second suitability classes, respectively. (3) The optimal scale for analyzing the suitability of landforms for the development of artificial oases over the past 30 years in the whole area was 8 km, which could explain more than 96% of the changes in the growth of artificial oases. The distribution of landforms of first- and second-class suitability within the 8 km buffer zone of an artificial oasis in the year 2020 was 10.55% and 9.90%, respectively, and landforms of first-class suitability were mainly concentrated in the near plain side of the urban agglomerations located on the northern and southern slopes of the Tianshan Mountains, and the urban agglomerations at the southern edge of Altai Mountains. This study quantified the potential of different geomorphological types for the development of artificial oases and provided a basis for site selection in future artificial oasis planning and urban construction. [ABSTRACT FROM AUTHOR]

Chronic rhinosinusitis (CRS) is a complex syndrome with various inflammatory mechanisms resulting in different patterns of inflammation that correlate with the clinical phenotypes of CRS. Our aim was to use detected IL-1, IL-4, IL-6, IL-7, IL-8, IL-10, IL-12, Ki 67, HBD-2, HBD-3, and LL-37 to classify specific inflammatory endotypes in chronic rhinosinusitis with the tissue of nasal polyps (CRSwNP). Samples from 35 individuals with primary and recurrent CRSwNP were taken during surgery. The tissues were stained for the previously mentioned biomarkers immunohistochemically. A hierarchical cluster analysis was performed. The clinical parameters were compared between clusters. Five clusters had significantly different biomarkers between groups. There were no significant differences in the clinical parameters, except for the Lund–Mackay score, which was significantly higher in cluster 4 compared to that of cluster 1 (p = 0.024). Five endotypes of (CRSwNP) are characterized by different combinations of type 1, type 2, and type 3 tissue inflammation patterns. In the Latvian population, endotypes associated with neutrophilic inflammation or a combination of neutrophilic inflammation and type 2 inflammation are predominant. Increased proliferation marker Ki 67 values are not associated with more severe inflammation in the tissue samples of chronic rhinosinusitis with nasal polyps. [ABSTRACT FROM AUTHOR]

Chebulae Fructus (CF) is known as one of the richest sources of hydrolyzable tannins (HTs). In this study, ultra-performance liquid chromatography coupled with a photodiode array detector method was established for simultaneous determination of the 12 common phenolcarboxylic and tannic constituents (PTCs). Using this method, quantitative analysis was accomplished in CF and other four adulterants, including Terminaliae Belliricae Fructus, Phyllanthi Fructus, Chebulae Fructus Immaturus, and Canarii Fructus. Based on a quantitative analysis of the focused compounds, discrimination of CF and other four adulterants was successfully accomplished by hierarchical cluster analysis and principal component analysis. Additionally, the total contents of the 12 compounds that we focused on in this study were unveiled as 148.86 mg/g, 96.14 mg/g, and 18.64 mg/g in exocarp, mesocarp, and endocarp and seed of CF, respectively, and PTCs were witnessed to be the most abundant in the exocarp of CF. Noticeably, the HTs (chebulagic acid, chebulanin acid, chebulinic acid, and punicalagin) were observed to be ultimately degraded to chebulic acid, gallic acid, and ellagic acid during sunlight-drying of the fresh fruits. As a result, our study indicated that CF and its adulterants could be distinguished by the observed 12 PTCs, which were mainly distributed in the exocarp of the fruits. The HTs were prone to degrade into the three simple phenolcarboxylic acids during drying or processing, allowing us to obtain a more comprehensive understanding of the PTCs, with great significance in the improved quality of CF and related products. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to quantitatively assess diffuse chorioretinal atrophy (DCA) in pathologic myopia and establish a standardized classification system utilizing artificial intelligence. Methods: A total of 202 patients underwent comprehensive examinations, and 338 eyes were included in the study. The methodology involved image preprocessing, sample labeling, employing deep learning segmentation models, measuring and calculating the area and density of DCA lesions. Lesion severity of DCA was graded using statistical methods, and grades were assigned to describe the morphology of corresponding fundus photographs. Hierarchical clustering was employed to categorize diffuse atrophy fundus into three groups based on the area and density of diffuse atrophy (G1, G2, G3), while high myopic fundus without diffuse atrophy was designated as G0. One-way analysis of variance (ANOVA) and nonparametric tests were conducted to assess the statistical association with different grades of DCA. Results: On the basis of the area and density of DCA, the condition was classified into four grades: G0, G1 (0 < density ≤ 0.093), G2 (0.093 < density ≤ 0.245), and G3 (0.245 < density ≤ 0.712). Fundus photographs depicted a progressive enlargement of atrophic lesions, evolving from punctate-shaped to patchy with indistinct boundaries. DCA atrophy lesions exhibited a gradual shift in color from brown-yellow to yellow-white, originating from the temporal side of the optic disc and extending towards the macula, with severe cases exhibiting widespread distribution throughout the posterior pole. Patients with DCA were significantly older [34.00 (27.00, 48.00) vs 29.00 (26.00, 34.00) years], possessed a longer axial length (28.85 ± 1.57 vs 27.11 ± 1.01 mm), and exhibited a more myopic spherical equivalent [− 13.00 (− 16.00, − 10.50) vs − 9.09 ± 2.41 D] compared to those without DCA (G0) (all P < 0.001). In eyes with DCA, a trend emerged as grades increased from G1 to G3, showing associations with older age, longer axial length, deeper myopic spherical equivalent, larger area of parapapillary atrophy, and increased fundus tessellated density (all P < 0.001). Conclusions: The novel grading system for DCA, based on assessments of area and density, serves as a reliable measure for evaluating the severity of this condition, making it suitable for widespread application in the screening of pathologic myopia. [ABSTRACT FROM AUTHOR]

In 2003, the U.S. Census Bureau created a new census designation known as micropolitan statistical areas to better differentiate between metropolitan and nonmetropolitan areas. This article uses hierarchical clustering and discriminate analysis to create a typology of micropolitan areas using U.S. census data on demographic, housing, and economic characteristics. The regional concentration ratio (RCR) is then used to determine whether a cluster type is over- or underrepresented at the census division. The analysis revealed a three-cluster solution with: (1) a middle- class cluster overrepresented in the East North Central and West North Central divisions; (2) a poor, minority cluster overrepresented in the South Atlantic, East South Central, West South Central, and Mountain divisions; and (3) a high human capital cluster overrepresented in the New England, Mountain, and Pacific divisions. Of micropolitan areas, 59.8 percent were considered middle-class; 28.1 percent were poor, minority; and 12.1 percent were high human capital. The nine-cluster solution further refined cluster categories. [ABSTRACT FROM AUTHOR]

BACKGROUND: The gut microbiota is intricate and susceptible to multiple factors, with diet being a major contributor. The present study aimed to investigate the impact of four commonly used laboratory animal control diets, namely Keao Xieli's maintenance diet (KX), HFK's 1025 (HF), Research Diets' D12450B (RD), and Lab Diet's 5CC4 (LD), on the gut microbiota of mice. RESULTS: A total of 40 mice were randomly assigned to four groups, and each group was fed one of the four diets for a duration of 8 weeks. The assessment of gut microbiota was conducted using 16S rRNA sequencing both at the beginning of the study (week 0) and the end (week 8), which served as the baseline and endpoint samples, respectively. Following the 8‐week feeding period, no significant differences were observed in physiological parameters, including body weight, visceral weight, and blood biochemical indices, across the four groups. Nonetheless, relative to the baseline, discernible alterations in the gut microbiota were observed in all groups, encompassing shifts in beta‐diversity, hierarchical clustering, and key genera. Among the four diets, HF diet exhibited a significant influence on alpha‐diversity, RD diet brought about notable changes in microbial composition at the phylum level, and LD diet demonstrated an interconnected co‐occurrence network. Mantel analysis indicated no significant correlation between physiological parameters and gut microbiota in the four groups. CONCLUSION: Overall, our study demonstrated that the four control diets had a minimal impact on physiological parameters, while exerting a distinct influence on the gut microbiota after 8 weeks. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Symphytum (comfrey) genus, particularly Symphytum officinale, has been empirically used in folk medicine mainly for its potent anti‐inflammatory properties. In an attempt to shed light on the valorization of less known taxa, the current study evaluated the metabolite profile and antioxidant and enzyme inhibitory effects of nine Symphytum species. RESULTS: Phenolic acids, flavonoids and pyrrolizidine alkaloids were the most representative compounds in all comfrey samples. Hierarchical cluster analysis revealed that, within the roots, S. grandiflorum was slightly different from S. ibericum, S. caucasicum and the remaining species. Within the aerial parts, S. caucasicum and S. asperum differed from the other samples. All Symphytum species showed good antioxidant and enzyme inhibitory activities, as evaluated in DPPH (up to 50.17 mg Trolox equivalents (TE) g−1), ABTS (up to 49.92 mg TE g−1), cupric reducing antioxidant capacity (CUPRAC, up to 92.93 mg TE g−1), ferric reducing antioxidant power (FRAP, up to 53.63 mg TE g−1), acetylcholinesterase (AChE, up to 0.52 mg galanthamine equivalents (GALAE) g−1), butyrylcholinesterase (BChE, up to 0.96 mg GALAE g−1), tyrosinase (up to 13.58 mg kojic acid equivalents g−1) and glucosidase (up to 0.28 mmol acarbose equivalents g−1) tests. Pearson correlation analysis revealed potential links between danshensu and ABTS/FRAP/CUPRAC, quercetin‐O‐hexoside and DPPH/CUPRAC, or rabdosiin and anti‐BChE activity. CONCLUSIONS: By assessing for the first time in a comparative manner the phytochemical–biological profile of a considerably high number of Symphytum samples, this study unveils the potential use of less common comfrey species as novel phytopharmaceutical or agricultural raw materials. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus, tentatively named turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality and reduced marketability of commercial turkeys. To combat these issues, detecting and classifying the types of reoviruses in turkey populations is essential. This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types of turkey reoviruses and identify novel emerging variants. Additionally, it focuses on classifying variants of turkey reoviruses by leveraging various machine learning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, and deep learning algorithms, including convolutional neural networks (CNNs). The experiments use real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods. The results indicate that machine learning methods achieve an average accuracy of 92%, F1-Macro of 93% and F1-Weighted of 92% scores in classifying reovirus types. In contrast, the CNN model demonstrates an average accuracy of 85%, F1-Macro of 71% and F1-Weighted of 84% scores in the same classification task. The superior performance of the machine learning classifiers provides valuable insights into reovirus evolution and mutation, aiding in detecting emerging variants of pathogenic TARVs and THRVs. [ABSTRACT FROM AUTHOR]

Purpose: Recovery Colleges (RCs) have been implemented across England with wide variation in organisational characteristics. The purpose of this study is to describe RCs across England in terms of organisational and student characteristics, fidelity and annual spending, to generate a RC typology based on characteristics and to explore the relationship between characteristics and fidelity. Methods: All RC in England meeting criteria on recovery orientation, coproduction and adult learning were included. Managers completed a survey capturing characteristics, fidelity and budget. Hierarchical cluster analysis was conducted to identify common groupings and generate an RC typology. Results: Participants comprised 63 (72%) of 88 RC in England. Fidelity scores were high (median 11, IQR 9–13). Both NHS and strengths-focussed RCs were associated with higher fidelity. The median annual budget was £200,000 (IQR £127,000–£300,000) per RC. The median cost per student was £518 (IQR £275–£840), cost per course designed was £5,556 (IQR £3,000–£9,416) and per course run was £1,510 (IQR £682–£3,030). The total annual budget across England for RCs is an estimated £17.6 m including £13.4 m from NHS budgets, with 11,000 courses delivered to 45,500 students. Conclusion: Although the majority of RCs had high levels of fidelity, there were sufficiently pronounced differences in other key characteristics to generate a typology of RCs. This typology might prove important for understanding student outcomes and how they are achieved and for commissioning decisions. Staffing and co-producing new courses are key drivers of spending. The estimated budget for RCs was less than 1% of NHS mental health spending. [ABSTRACT FROM AUTHOR]

The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200–950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes. [ABSTRACT FROM AUTHOR]

The classical Hotelling's T 2 test and Bayesian hypothesis tests breakdown for the problem of comparing two high-dimensional population means due to the singularity of the pooled sample covariance matrices when the model dimension p exceeds the sample size n. In this paper, we develop a simple closed-form Bayesian testing procedure based on a split-and-merge technique. Specifically, we adopt the subspace clustering technique to split the high-dimensional data into lower-dimensional random spaces so that the Bayes factor can be implemented. Then we utilize the geometric mean to merge the results of the Bayesian test to obtain a novel test statistic. We carry out simulation studies to compare the performance of the proposed test with several existing ones in the literature. Finally, two real-data applications are provided for illustrative purposes. [ABSTRACT FROM AUTHOR]

Objectives: Most research in human dental age estimation has focused on point estimates of age, and most research on dental development theories has focused on morphology or eruption. Correlations between developing teeth using ordinal staging have received less attention. The effect of demographic variables on these correlations is unknown. I tested the effect of reference sample demographic variables on the residual correlation matrix using the lens of cooperative genetic interaction (CGI). Materials and Methods: The sample consisted of Moorrees et al., Journal of Dental Research, 1963, 42, 1490–1502, scores of left mandibular permanent teeth from panoramic radiographs of 880 London children 3–22.99 years of age stratified by year of age, sex, and Bangladeshi or European ancestry. A multivariate cumulative probit model was fit to each sex/ancestry group (n = 220), each sex or ancestry (n = 440), and all individuals (n = 880). Residual correlation matrices from nine reference sample configurations were compared using Bartlett's tests of between‐sample difference matrices against the identity matrix, hierarchical cluster analysis, and dendrogram cophenetic correlations. Results: Bartlett's test results were inconclusive. Cluster analysis showed clustering by tooth class, position within class, and developmental timing. Clustering patterns and dendrogram correlations showed similarity by sex but not ancestry. Discussion: Expectations of CGI were supported for developmental staging. This supports using CGI as a model for explaining patterns of variation within the dentition. Sex was found to produce consistent patterns of dental correlations, whereas ancestry did not. Clustering by timing of development supports phenotypic plasticity in the dentition and suggests shared environment over genetic ancestry to explain population differences. Research Highlights: Dental developmental correlations follow expectations of Cooperative Genetic Interaction theory.Patterning by timing of development supports phenotypic plasticity.Correlation patterns vary by sex but not ancestry: environment is more important than ancestry. [ABSTRACT FROM AUTHOR]

In recent years, energy-dissipation devices have gained significant attention in seismic protection engineering because of their effectiveness in mitigating the destructive effects of earthquakes. Inspired by an origami pattern invented by Ron Resch, in this study, we propose a novel type of energy-dissipation brace to guide the deformation process and prevent the global buckling of engineering structures. To this end, a parametric geometric model is developed, followed by exploring the effects of different geometric parameters on structural collapse modes. Subsequently, by using hierarchical clustering, the collapse modes are classified into four groups. Finally, an optimal design is introduced to improve energy absorption during quasi-static axial crushing while reducing the initial peak force. The results show specific parameters play a decisive role in determining the collapse modes of the structures. Each group is associated with a particular force-displacement curve characterized by specific properties. We demonstrate that the proposed structural design optimization process reduces the initial peak force by 15.6% without affecting the specific energy absorption. This study provides insights into the application of origami-inspired structures in the design and development of high-performance energy-dissipation braces. [ABSTRACT FROM AUTHOR]

Source–sink zones refer to aggregated adjacent origins/destinations with homogeneous trip flow characteristics. Current relevant studies mostly detect source–sink zones based on outflow/inflow volumes without considering trip routes. Nevertheless, trip routes detail individuals' journeys on road networks and give rise to relationships among human activities, road network structures, and land-use types. Therefore, this study developed a novel approach to delineate source–sink zones based on trip route aggregation on road networks. We first represented original trajectories using road segment sequences and applied the Latent Dirichlet Allocation (LDA) model to associate trajectories with route semantics. We then ran a hierarchical clustering operation to aggregate trajectories with similar route semantics. Finally, we adopted an adaptive multi-variable agglomeration strategy to associate the trajectory clusters with each traffic analysis zone to delineating source and sink zones, with a trajectory topic entropy defined as an indicator to analyze the dynamic impact between the road network and source–sink zones. We used taxi trajectories in Xiamen, China, to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples. [ABSTRACT FROM AUTHOR]

The methanolic extracts of selected Stachys and Betonica species were analyzed by high performance thin layer chromatography (HPTLC) method using cyano bonded stationary phase and mobile phase consisted of methanol and water 45:55 (v/v) with Natural product reagent derivatization. The HPTLC method was validated (linearity, precision, accuracy, robustness, LOD, LOQ) and the validation results confirm the usefulness of the method used. The obtained images of fingerprint chromatograms presented as numerical data (using the TLC analyzer program) were next processed using Excel and SpecAlign softwares. The phytochemical similarity between analyzed plant extracts was evaluated using the similarity index (Pearson correlation coefficient) and the results were presented as hierarchical cluster analysis dendrograms and as graph for principal component analysis (PCA). The total phenolic content (TPC) by Folin-Ciocalteu method and the antioxidant activity with Ferric Ion Reducing Antioxidant Potential (FRAP) technique were determined for studied extracts using gallic acid as standards in both methods. [ABSTRACT FROM AUTHOR]

Criminal cases involving questioned documents often cause the loss of a huge amount of money in a country. Red ballpoint pen inks found on threatening notes, or any official documents are potentially encountered by a forensic document examiner as questioned documents. Identifying sources of the questioned ink entries is paramount to revealing the authenticity or sources of the document. Despite the multitude of works being devoted to discriminating pen inks by their sources, very little is focused on the volatile fractions. In this study, the volatile compositional profiles of ten types of red ballpoint pen inks as acquired by gas chromatography-flame ionization detection (GC-FID) were studied using various multivariate exploratory tools. Scores plot of principal component analysis reveals that inter-pen brand variation is higher than the inter-pen type variation. Meanwhile, hierarchical clustering analysis showed that 6 of the 10 pen types and all five pen brands could be discriminated. In conclusion, volatile organic compositions of red pen inks are useful in classifying the pen inks by brands. The outcome of the study will provide vital insight into the potential of volatile organic profiles in discriminating red pen inks sold in Malaysia. [ABSTRACT FROM AUTHOR]

Rationale: Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps. Methods: To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation. Results: In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI. Conclusions: We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI. [ABSTRACT FROM AUTHOR]

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets. [ABSTRACT FROM AUTHOR]

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants. [ABSTRACT FROM AUTHOR]

A better understanding of fog and low stratus (FLS) life cycles, in particular the typical onset and dissipation times, can help traffic safety and solar power planning. Besides its high dependence on the prevalent meteorological conditions, the FLS life cycle is further determined by the FLS type as well as the underlying geography and climate. While satellite‐based FLS data sets with high temporal and spatial resolution are available, an objective, large‐scale, satellite‐based classification of FLS life‐cycle regions, which would allow for more targeted analyses of the FLS life cycle, is lacking. In this study, a hierarchical clustering algorithm is applied to identify regions of similar relationships of the FLS life cycle to environmental conditions over central Europe. The clustering reveals major FLS life‐cycle regimes with distinct differences in FLS occurrence, temporal FLS life‐cycle patterns, and climatic characteristics. Based on regime‐specific sensitivity analyses, the relationships of the FLS life cycle with near‐ground temperature and specific humidity are identified as the most relevant input relationships for the clustering algorithm, especially in the Mediterranean region. FLS life‐cycle regimes are further presented at a regional scale, outlining the applicability of the derived FLS life‐cycle regimes to future regional process‐oriented FLS life‐cycle studies. [ABSTRACT FROM AUTHOR]

The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set within the leaf nodes and the query descriptor to rank them accordingly. Three tree search algorithms are presented to enable a trade-off between search accuracy and speed that allows for substantial gains at the expense of a slightly reduced retrieval accuracy. Our proposed method demonstrates enhancement in image retrieval speed when applied to the CLIP-based model, UNICOM, designed for category-level retrieval, as well as the CNN-based R-GeM model, tailored for particular object retrieval by validating its effectiveness across various domains and backbones. We achieve an 18-times speed improvement while preserving over 99% accuracy when applied to the In-Shop dataset, the largest dataset in the experiments. [ABSTRACT FROM AUTHOR]

This study investigated the relative influences of environmental, spatial, and historical factors, including the island‐specific history of land connectivity, on bat assemblages in the Japanese Archipelago. We collected bat distribution data from 1408 studies and assigned them to Japan's First Standard Grid (approximately 6400 km2). Japanese bat assemblages were analyzed at two scales: the entire Japanese Archipelago comprised 16 islands and exclusively the four main islands. At first, we calculated taxonomic and functional total beta diversity (βtotal) by Jaccard pairwise dissimilarity and then divided this into turnover (βrepl) and richness‐difference (βrich) components. We conducted hierarchical clustering of taxonomic beta diversity to examine the influence of the two representative sea straits, Tsugaru and Tokara, which are considered biogeographical borders. Variation partitioning was conducted to evaluate the relative effects of the three factors on the beta diversity. Clustering revealed that the Tokara Strait bordered the two major clades; however, the Tsugaru Strait did not act as a biogeographical border for bats. In the variation partitioning, shared fraction between spatial and historical factors significantly explained taxonomic and functional βtotal and taxonomic βrepl at the entire archipelago scale, but not at the four main islands scale extending only Tsugaru Strait but not Tokara Strait. Pure environmental factors significantly explained functional βtotal at both scales and taxonomic βtotal only at the four main islands scale. These results suggest that spatial and historical factors are more pronounced in biogeographical borders, primarily structuring assemblage composition at the entire archipelago scale, especially in taxonomic dimension. However, current environmental factors primarily shape the assemblage composition of Japanese bats at the main island scale. The difference in results between the two scales highlights that the primary processes governing assemblages of both dimensions depend on the quality of the dispersal barriers between terrestrial and aquatic barriers for bats. [ABSTRACT FROM AUTHOR]