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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: 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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Vagus nerve stimulation (VNS) represents a long-term adjunctive treatment option in patients with difficult-to-treat depression (DTD). Anti-inflammatory effects have been discussed as a key mechanism of action of VNS. However, long-term investigations in real-world patients are sparse. In this naturalistic observational study, we collected data on cytokines in peripheral blood in n = 6 patients (mean age 47.8) with DTD and VNS treatment at baseline and at 6 months follow-up. We have identified clusters of peripheral cytokines with a similar dynamic over the course of these 6 months using hierarchical clustering. We have investigated cytokine changes from baseline to 6 months as well as the relationship between the cytokine profile at 6 months and long-term response at 12 months. After 6 months of VNS, we observed significant correlations between cytokines (p < 0.05) within the identified three cytokine-pairs which were not present at baseline: IL(interleukin)-6 and IL-8; IL-1β and TNF-α; IFN-α2 and IL-33. At 6 months, the levels of all the cytokines of interest had decreased (increased in non-responders) and were lower (5–534 fold) in responders to VNS than in non-responders: however, these results were not statistically significant. VNS-associated immunomodulation might play a role in long-term clinical response to VNS. [ABSTRACT FROM AUTHOR]

This study investigates event-based rainfall characteristics in Sinai (Egypt) using hourly precipitation data from the Global Satellite Mapping of Precipitation (GSMaP). A hierarchical cluster analysis of a 19-year dataset (2003–2021) identified five regions in Sinai. Distinct storms were identified using a minimum inter-event time of 5 h. The analysis of storm characteristics revealed that rainfall events in Sinai last from 1.7 to 3.6 h, with a mean storm volume of 6.4 mm. Rainfall intensity ranges from 1.7 to 4 mm/h, and the average dry period duration is 34 days. The northern region has the highest frequency of storms (25 events/year). The Weibull distribution was found to fit the best for all rainfall characteristics except for intensity, which was best represented by the generalized extreme value distribution. This study provides valuable insights about rainfall events in Sinai that can be applied to improve flood mitigation strategies and water resources management. [ABSTRACT FROM AUTHOR]

Coherency detection in power system is vital step for controlled‐islanding. Model‐based slow‐coherency method is the traditional method of detecting coherent areas, which suffers from computational burdens. With the implementation of a wide‐area measurement system (WAMS) in power systems, coherent areas can be detected early after a disturbance that helps to ensure quick separation or remedial action. In this study, a novel Synchrophasor‐based coherency identification method has been proposed with bus voltage angle measurement. A new algorithm is proposed based on the first and second derivatives of the bus voltage phase angle using hierarchical clustering method for coherency detection. The proposed approach leverages hierarchical clustering method and the dynamics reflected in the bus voltage phase angle behavior to achieve automatic and accurate coherency identification. Real time simulation results demonstrate the ability of the proposed algorithm to detect the coherency with three consecutive PMU data from all the system buses and showcase coherent area boundaries. Case studies show robustness of the proposed method and is least affected by the latency in communication network compared to conventional methods. The proposed algorithm has been mathematically formulated and evaluated with different benchmark systems in OPAL‐RT real‐time simulation environment with HYPERSIM platform. [ABSTRACT FROM AUTHOR]

Studies suggest an interplay between maternal metabolome and mental health. We investigated the association of maternal serum metabolome at pregnancy with anxiety scores during pregnancy and throughout the first year postpartum. A prospective cohort of Brazilian women collected 119 serum metabolome at pregnancy (28–38 weeks) and anxiety scores measured by the State-Trait Anxiety Inventory (STAI) at pregnancy (n = 118), 1 (n = 83), 6 (n = 68), and 12 (n = 57) months postpartum. Targeted metabolomics quantified metabolites belonging to amino acids (AA), biogenic amines/amino acid-related compounds, acylcarnitines, lysophosphatidylcholines, diacyl phosphatidylcholines, alkyl:acyl phosphatidylcholines, non-hydroxylated and hydroxylated sphingomyelins [SM(OH)], and hexoses classes. Linear mixed-effect models were used to evaluate the association of metabolites and STAI scores. Hierarchical clustering and principal component analyses were employed to identify clusters and metabolites, which drove their main differences. Multiple comparison-adjusted p -values (q -value) ≤ 0.05 were considered significant. AA (β = −1.44) and SM(OH) (β = −1.49) classes showed an association with STAI scores trajectory (q- value = 0.047). Two clusters were identified based on these classes. Women in cluster 2 had decreased AA and SM(OH) concentrations and higher STAI scores (worse symptoms) trajectory (β = 2.28; p -value = 0.041). Isoleucine, leucine, valine, SM(OH) 22:1, 22:2, and 24:1 drove the main differences between the clusters. The target semiquantitative metabolome analysis and small sample size limited our conclusions. Our results suggest that AA and SM(OH) during pregnancy play a role in anxiety symptoms throughout the first year postpartum. • Amino acids (AA) and hydroxylated sphingomyelins [SM (OH)] classes showed associations with anxiety scores trajectory. • Two clusters were identified based on AA and SM (OH) concentrations. • Women in cluster 2 had decreased AA and SM (OH) and higher anxiety scores (worse symptoms) trajectory. • Isoleucine, leucine, valine, SM (OH) 22:1, 22:2, and 24:1 drove the main differences between the clusters. [ABSTRACT FROM AUTHOR]

Heterocyclic compounds such as pyrrole and pyrrolidine derivatives have a broad spectrum of biological activity, being widely used as pharmacophore to design novel bioactive compounds. In this work, sixteen pyrrole and pyrrolidine derivatives were synthesized and evaluated for their growth inhibitory activity on two human cancer cells lines: breast cancer MDA‐MB‐231 and chronic myelogenous leukemia (K562). Eight compounds showed activity against at least one tumor cell line (IC50=49.15–195.9 μM). The compounds were tested in non‐cancerous human lung fibroblasts WI‐26VA4 to evaluate their selectivity index. In addition, Hierarchical Cluster Analysis (HCA) studies were carried out in attempt to establish a structure‐activity relationship. [ABSTRACT FROM AUTHOR]

Laser-inscribed graphene (LIG), initially developed for graphene supercapacitors, has found widespread use in sensor research and development, particularly as a platform for low-cost electrochemical sensing. However, batch-to-batch variation in LIG fabrication introduces uncertainty that cannot be adequately tracked during manufacturing process, limiting scalability. Therefore, there is an urgent need for robust quality control (QC) methodologies to identify and select similar and functional LIG electrodes for sensor fabrication. For the first time, we have developed a statistical workflow and an open-source hierarchical clustering tool for QC analysis in LIG electrode fabrication. The QC process was challenged with multi-operator cyclic voltammetry (CV) data for bare and metalized LIG. As a proof of concept, we employed the developed QC process for laboratory-scale manufacturing of LIG-based biosensors. The study demonstrates that our QC process can rapidly identify similar LIG electrodes from large batches (n ≥ 36) of electrodes, leading to a reduction in biosensor measurement variation by approximately 13% compared to the control group without QC. The statistical workflow and open-source code presented here provide a versatile toolkit for clustering analysis, opening a pathway toward scalable manufacturing of LIG electrodes in sensing. In addition, we establish a data repository for further study of LIG variation. [ABSTRACT FROM AUTHOR]

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies. Author summary: As is known, the phenotype of cancer is closely related to gene expression in the human body. In the era of precision medicine, an increasing amount of anti-cancer drug response data is urgently needed for individualized therapy. Measurements with wet-experiments are time-consuming and costly, and computational methods are necessary. Existing methods mainly focused predominantly on linear or nonlinear relationships between cells and drugs for prediction. However, it has been shown that both linear and nonlinear relationships could contribute to more precise response prediction. Here, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to better use both linear and nonlinear relationships and the experimental results show that it outperforms state-of-the-art algorithms. Our results suggest that drugs showing similar response levels tend to target similar signaling pathways while cell lines sharing similar response patterns tend to come from the same tissue subtype. The analysis of experimental results may benefit medical decision-making. [ABSTRACT FROM AUTHOR]

The dynamical robustness of networks in the presence of noise is of utmost fundamental and applied interest. In this work, we explore the effect of parametric noise on the emergence of synchronized clusters in diffusively coupled Chaté–Manneville maps on a branching hierarchical structure. We consider both quenched and dynamically varying parametric noise. We find that the transition to a synchronized fixed point on the maximal cluster is robust in the presence of both types of noise. We see that the small sub-maximal clusters of the system, which coexist with the maximal cluster, exhibit a power-law cluster size distribution. This power-law scaling of synchronized cluster sizes is robust against noise in a broad range of coupling strengths. However, interestingly, we find a window of coupling strength where the system displays markedly different sensitivities to noise for the maximal cluster and the small clusters, with the scaling exponent for the cluster distribution for small clusters exhibiting clear dependence on noise strength, while the cluster size of the maximal cluster of the system displays no significant change in the presence of noise. Our results have implications for the observability of synchronized cluster distributions in real-world hierarchical networks, such as neural networks, power grids, and communication networks, that necessarily have parametric fluctuations. [ABSTRACT FROM AUTHOR]

Background: Pigeonpea is second most important pulse crop of India after chickpea and it is necessary to identify its high yielding and stable genotypes to feed the increasing population of country. Methods: The present study was laid down in a randomized block design with three replications during kharif season of 2016-2019 at GBPUAT, Pantnagar using twenty genotypes of pigeonpea with an aim to identify the high yielding and stable genotypes. The hierarchical cluster analysis (HCA) based on mean seed yield and Average of Sum of Ranks (ASR) of all measures (Parametric and nonparametric) was used in present study. Result: The pooled ANOVA revealed the presence of significant differences among genotypes, environments and G x E interaction effects. The results of hierarchical cluster analysis indicated the genotypes PA 622 (yield=1774.85 kg/ha, ASR=2.00), PA 620 (yield= 1579.92 kg/ha, ASR=2.18), UPAS 120 (yield=1268.57 kg/ha, ASR=2.87), PA 626 (yield=1571.40 kg/ha, ASR=5.56), PUSA 992 (yield= 1331.17 kg/ha, ASR=5.68) and PA 628 (yield= 1271.50 kg/ha, ASR=7.06) as most stable and high yielding and hence these genotypes can be recommended for pigeonpea improvement programmes. [ABSTRACT FROM AUTHOR]

This study adopts an inductive approach to comprehensively examine the various factors influencing customer experience and satisfaction within Istanbul's rapidly evolving accommodation-sharing economy, with a particular focus on Airbnb. The research involves an extensive analysis of a large dataset comprising 508,746 Airbnb reviews collected from Istanbul, marking a significant endeavor in understanding the nuances of customer preferences and expectations in this domain. The process begins with a thorough preprocessing of the textual data to ensure clarity and relevance in the information analyzed. Following this, the study employs Latent Dirichlet Allocation (LDA), a sophisticated statistical model, to identify and extract 32 distinct topics from the user-generated content. These topics, embedded within the reviews, provide a rich source of insights into the guest experience. The extracted topics are systematically categorized into several key dimensions, offering a structured framework for analysis. These dimensions include detailed assessments made by guests, locational attributes of the accommodations ranging from central urban areas to more peripheral locations, and both the tangible and intangible aspects of the Airbnb listings. Additionally, the study examines the management practices of the hosts and the overall quality of service, factors that are crucial in shaping guest satisfaction. Each of these dimensions serves as a lens through which the intricate aspects of customer experience in the shared accommodation sector can be understood and evaluated. To explore deeper into the intricate relationships among these topics, the study employs hierarchical Ward Clustering. This statistical technique is instrumental in revealing the complex interplay and subtle connections between the various topics, playing a vital role in elucidating the multifaceted nature of customer experience in the peer-to-peer accommodation context. The analysis aims to provide a comprehensive and layered understanding of the determinants that shape guest experiences in Istanbul's Airbnb sector. By offering a detailed, multi-faceted perspective on the drivers of customer satisfaction, this study significantly contributes to the body of knowledge in the field, enhancing the understanding of key factors that influence guest experiences and satisfaction in the dynamic and diverse landscape of Istanbul's accommodation-sharing economy. [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]

Introduction: National Comprehensive Cancer Network (NCCN) recommendations for adjuvant radiation therapy (ART) use are similar for High Risk and Very High Risk cutaneous squamous cell carcinoma (cSCC) with negative post-surgical margins. Although studies report reductions in disease progression following ART treatment, ART use is likely inconsistent when guided by available risk factors. This study evaluated the association of ART with clinical risk factors in ART-treated and untreated patients and showed the clinical utility of the 40-gene expression profile (40-GEP) for guiding ART. Methods: A multicenter study of 954 patients was conducted with institutional review board (IRB) approval. The 40-GEP test was performed using primary tumor tissue from patients with either a minimum of 3 years of follow-up or a documented regional or distant metastasis. Unsupervised hierarchical cluster analysis identified patterns of clinical risk factors for ART-treated patients, then identified untreated patients with matching risk factor profiles. Results were cross-referenced to 40-GEP test results to determine utility of the test to guide ART. Results: Analysis demonstrated inconsistent implementation of ART for eligible patients. Cluster analysis identified four patient profiles based on clusters of risk factors and, notably, matching profiles in ART-treated and untreated patients. Further, the analysis identified patients who received but could have deferred ART on the basis of 40-GEP test result and biologically low risk of metastasis, and untreated patients who likely would have benefitted from ART on the basis of their 40-GEP test result. Conclusions: ART guidance is not determined by the presence of specific clinicopathologic factors, with treated and untreated patients sharing the same risk factor profiles. cSCC risk determination based on NCCN recommendations for clinical factor assessment results in inconsistent use of ART. Including tumor biology-based prognostic information from the 40-GEP refines risk and identifies patients who are most appropriate and likely to benefit from ART, and those that can consider deferring ART. [ABSTRACT FROM AUTHOR]

Over the past several decades, several air pollution prevention measures have been developed in response to the growing concern over air pollution. Using models to anticipate air pollution accurately aids in the timely prevention and management of air pollution. However, the spatial-temporal air quality aspects were not properly taken into account during the prior model construction. In this study, the distance correlation coefficient (DC) between measurements made in various monitoring stations is used to identify appropriate correlated monitoring stations. To derive spatial-temporal correlations for modeling, the causality relationship between measurements made in various monitoring stations is analyzed using Transfer Entropy (TE). This work explores the process of identifying a piecewise affine (PWA) model using a larger dataset and suggests a unique hierarchical clustering-based identification technique with model structure selection. This work improves the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing Kullback-Leibler (KL) Divergence as the dissimilarity between clusters for handling clusters with arbitrary shapes. The number of clusters is automatically determined using a cluster validity metric. The task is formulated as a sparse optimization problem, and the model structure is selected using parameter estimations. Beijing air quality data is used to demonstrate the method, and the results show that the proposed strategy may produce acceptable forecast performance. [ABSTRACT FROM AUTHOR]

Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the "decomposition–reconstruction–integration" approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning. [ABSTRACT FROM AUTHOR]

Energy-efficient operation is one of the prime goals of Wireless Sensor Networks (WSNs) because of the limited battery capacity and the harshness of the environment they are deployed in. Clustering techniques with multihop communication is one such technique that is suitable to achieve this goal. These approaches suffer from load balancing and hotspot issues because of the uneven energy consumption between cluster head (CH) nodes and member nodes (MNs). The Hierarchical Layer Balanced Clustering (HLBC) approach is a clustering framework that is highly effective in comparison to the current state of the art in addressing such issues. This work aims to optimize the process of CH selection and reduce intra-cluster communication distance (IACD) in the context of HLBC. For this purpose, Multi-Objective Optimization based on Ratio Analysis (MOORA) has been employed by considering three critical attributes, viz., residual energy, node centrality, and distance to relay, along with a Shannon entropy-based attribute weighting scheme for CH selection. The modified Dijkstra-based minimum spanning tree formation technique based on energy left, load, and distance to the relay node has been presented to reduce IACD and to distribute the load on MNs evenly. The proposed HCM scheme has been analyzed corresponding to two network scenarios, each for a homogeneous and heterogeneous network, based on three performance measures: node death rate, energy consumption, and network lifetime. The proposed framework has outperformed other state-of-the-art techniques for both homogeneous and heterogeneous cases in all considered scenarios. The First Node Death (FND) of EMUC, HLBC, and the proposed HCM approach are at rounds 201, 341, and 417 for scenario 1 and 254, 309, and 382 for scenario 2, respectively, for homogeneous cases. The proposed HCM protocol has achieved a percentage increase of [22–24]% in terms of FND corresponding to homogeneous network scenarios and a [23–27]% improvement in FND corresponding to heterogeneous network scenarios. [ABSTRACT FROM AUTHOR]

Wild growing Origanum majorana L. var. tenuifolium Weston was collected from three different locations (Om1: Avtepe, Om2: Alevkayası, Om3: Kozanköy) in North Cyprus. The essential oils were hydrodistilled and chemical composition analyses were simultaneously performed by GC-FID and GC-MS. The average yields of essential oils were 5.46-8.14%. Major compounds of flowering Om1 oil were cis-sabinene hydrate (45.0%) and terpinen-4-ol (11.1%) while those of Om3 (Kozanköy) oil were cis-sabinene hydrate (33.3%), terpinen-4-ol (12.3%) and α-terpineol (11.5%). In the post-flowering stage, the major compounds for Om1 and Om3, respectively, were cis-sabinene hydrate (16.7% and 18.8%), terpinen-4-ol (18.1% and 14.5%) and α-terpineol (23.9% and 20.0%). However, the main compound of the essential oils of the flowering and post-flowering oil of Om2 was α-terpineol (76.9% and 51.6%). Statistical Principal Component (PCA) and Hierarchical Cluster Analyses (HCA) tools demonstrated the composition variations of the essential oils at different times and locations. Om1 and Om3 (HCA similarity level: 98.98%) were similar while Om2 (HCA similarity level: 46.32%) was discordant. [ABSTRACT FROM AUTHOR]

Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime in mechanically stirred tanks equipped with four vertical baffles through the characterization of pressure fluctuations. Our innovative proposal is based on force sensors strategically placed in the baffles of the mixing tank. The signals coming from the sensors are transmitted to an electronic module based on an Arduino UNO development board. In the electronic module, the pressure signals are conditioned, amplified and sent via Bluetooth to a computer. In the computer, the signals can be plotted or stored in an Excel file. In addition, the proposed system includes a moving average filtering and a hierarchical bottom-up clustering analysis that can determine the real-time flow regime (i.e., the Reynolds number, Re) in which the tank was operated during the mixing process. Finally, to demonstrate the versatility of the proposed prototype, experiments were conducted to identify the Reynolds number for different flow regimes (static, laminar, transition and turbulent), i.e., 0 ≤ R e ≤ 42,955. Obtained results were in agreement with the prevailing consensus on the onset and developed from different flow regimes in mechanically stirred tanks. [ABSTRACT FROM AUTHOR]

Many genomic, anatomical and functional differences exist between the medullary (MTAL) and the cortical thick ascending limb of the loop of Henle (CTAL), including a higher expression of claudin-10 (CLDN10) in the MTAL than in the CTAL. Therefore, we assessed to what extent the Cldn10 gene expression is a determinant of differential gene expression between MTAL and CTAL. RNAs extracted from CTAL and MTAL microdissected from wild type (WT) and Cldn10 knock out mice (cKO) were analyzed by RNAseq. Differential and enrichment analyses (GSEA) were performed with interactive R Shiny software. Between WT and cKO MTAL, 637 genes were differentially expressed, whereas only 76 were differentially expressed between WT and cKO CTAL. Gene expression patterns and GSEA analyses in all replicates showed that WT MTAL did not cluster with the other replicates; no hierarchical clustering could be found between WT CTAL, cKO CTAL and cKO MTAL. Compared to WT replicates, cKO replicates were enriched in Cldn16, Cldn19, Pth1r, (parathyroid hormone receptor type 1), Casr (calcium sensing receptor) and Vdr (Vitamin D Receptor) mRNA in both the cortex and medulla. Cldn10 is associated with gene expression patterns, including genes specifically involved in divalent cations reabsorption in the TAL. [ABSTRACT FROM AUTHOR]

Asparagus, a vital economic contributor, is a well‐liked vegetable grown around the globe, and some secondary metabolites in its spear are beneficial to human health. Asparagus spears possess a significant quantity of nutrients and phytochemicals; however, the difference in these chemical compositions among various varieties has not been sufficiently studied. This work aimed to detect the chemical compositions of 30 varieties of asparagus and to assess them by principal component analysis (PCA). The results showed that the contents of these chemical compositions varied in varieties. Selenium (Se, 1.12–2.9 μg/100 g dry‐weight [DW]) was abundant in asparagus, with an average dry matter content of 8.25%. Free amino acids (5.60–9.98 g/100 g DW) and polyphenols (6.34–8.67 mg/g DW) were both present in high amounts, along with flavonoids (4.218–8.22 mg/g DW) and protodioscin (0.44–1.96 mg/g DW). Correlation analysis, PCA, and hierarchical cluster analysis were used to conduct a comprehensive evaluation of asparagus. Atlas, Appolo, Jinggang 111, Jingke 2, and WS‐1 were the top five varieties with comprehensive scores. This study provided valuable data for the breeding, quality improvement, processing, and utilization of asparagus varieties in the future. [ABSTRACT FROM AUTHOR]

Understanding the long-term spatiotemporal variability of precipitation at the regional scale is critical for developing flood and drought control strategies and water resource management. This study assessed the spatiotemporal variability of monthly precipitation over the Khyber Pakhtunkhwa province of Pakistan for 1998–2019 using hierarchical cluster analysis to cluster 156 Tropical Rainfall Measuring Mission grids. Statistical properties of clusters were calculated and the relationship of geographical features such as latitude, longitude, and altitude and statistical variables including standard deviation, maximum and minimum precipitation, and coefficient of variation (CV) with average precipitation was assessed. Findings showed that northeast parts received maximum precipitation while north and southern regions received less precipitation. Temporal analysis showed two clusters of rainy months (February, March, April, May, July, and August) and dry months (January, June, September, October, November, and December). The region was divided into two homogeneous precipitation regions. From January to April and November to December, cluster 1 occupied northern parts with maximum average precipitation while cluster 2 southern parts. From June to September, cluster 2 covered the northeast and southern parts with the highest average precipitation. During May, cluster 2 received the highest average precipitation in the northeast and southeast parts, whereas cluster 1 covered the northwest and southwest. In October, cluster 2 received maximum average precipitation covering the northeast. CV suggested higher temporal variability in cluster 2 (67.75–102.36)% than cluster 1 (65.82–99.55)%. Precipitation correlation showed that CV opposed the longitude and averages, whereas latitude and altitude demonstrated minimal correlations. These insights can assist decision-makers in devising suitable strategies to plan and control unexpected volumes of precipitation. [ABSTRACT FROM AUTHOR]

Purpose: This multicentre, collaborative, cross-sectional study aimed to explore the characteristics of subgroups based on central sensitivity syndromes (CSSs) and low back pain (LBP) severity. Furthermore, we investigated the relationship between the classified subgroups and work status among the care workers. Patients and methods: In 660 care workers, we assessed LBP intensity, pain duration, pain sites, CSS (using the central sensitization inventory-9), psychological factors (using the pain catastrophizing scale and pain self-efficacy questionnaire), and work status (interference, amount of assistance, frequency of assistance, and work environment). We used hierarchical clustering analysis to divide the participants into subgroups based on CSS and LBP severity. We further performed multiple comparison analyzes and adjusted the residuals (chi-square test) to reveal differences between clusters. Results: Care workers with LBP were divided into four subgroups (Cluster 1: no CSS and mild LBP, Cluster 2: mild CSS and severe LBP, Cluster 3: mild CSS and mild LBP, Cluster 4: severe CSS and moderate LBP). Cluster 4 tended to have a higher number of pain sites, severe pain catastrophizing, and poor pain self-efficacy. In addition, Cluster 4 showed a higher frequency of assistance and an inadequate working environment and equipment. By contrast, Cluster 2 tended to have low pain self-efficacy. In addition, Cluster 2 experienced the highest work-related interference compared with any of the subgroups. Conclusion: Our findings suggested that the severe LBP and severe CSS subgroups had common and different characteristics concerning psychological factors and work status, including interference with work. Our results may help to improve the management of care workers with LBP. [ABSTRACT FROM AUTHOR]

R-mode hierarchical clustering is a method for forming hierarchical groups of mutually exclusive subsets of variables. This R-mode cluster method identifies interrelationships between variables which are useful for variable selection and dimension reduction. Importantly, the method is based on metric elements defined on the sample space of variables. Consequently, hierarchical clustering of compositional parts should respect the particular geometry of the simplex. In this work, the connections between concepts such as distance, cluster representative, compositional biplot, and log-ratio basis are explored within the framework of the most popular R-mode agglomerative hierarchical clustering methods. The approach is illustrated in a paleoecological study to identify groups of species sharing similar behavior. [ABSTRACT FROM AUTHOR]

Bacterial communities directly influence ecological processes in the ocean, and depth has a major influence due to the changeover in primary energy sources between the sunlit photic zone and dark ocean. Here, we examine the abundance and diversity of bacteria in Monterey Bay depth profiles collected from the surface to just above the sediments (e.g., 2000 m). Bacterial abundance in these Pacific Ocean samples decreased by >1 order of magnitude, from 1.22 ±0.69 ×106 cells ml-1 in the variable photic zone to 1.44 ± 0.25 ×105 and 6.71 ± 1.23 ×104 cells ml-1 in the mesopelagic and bathypelagic, respectively. V1-V2 16S rRNA gene profiling showed diversity increased sharply between the photic and mesopelagic zones. Weighted Gene Correlation Network Analysis clustered co-occurring bacterial amplicon sequence variants (ASVs) into seven subnetwork modules, of which five strongly correlated with depth-related factors. Within surface-associated modules there was a clear distinction between a 'copiotrophic' module, correlating with chlorophyll and dominated by e.g., Flavobacteriales and Rhodobacteraceae, and an 'oligotrophic' module dominated by diverse Oceanospirillales (such as uncultured JL-ETNP-Y6, SAR86) and Pelagibacterales. Phylogenetic reconstructions of Pelagibacterales and SAR324 using full-length 16S rRNA gene data revealed several additional subclades, expanding known microdiversity within these abundant lineages, including new Pelagibacterales subclades Ia.B, Id, and IIc, which comprised 4–10% of amplicons depending on the subclade and depth zone. SAR324 and Oceanospirillales dominated in the mesopelagic, with SAR324 clade II exhibiting its highest relative abundances (17±4%) in the lower mesopelagic (300–750 m). The two newly-identified SAR324 clades showed highest relative abundances in the photic zone (clade III), while clade IV was extremely low in relative abundance, but present across dark ocean depths. Hierarchical clustering placed microbial communities from 900 m samples with those from the bathypelagic, where Marinimicrobia was distinctively relatively abundant. The patterns resolved herein, through high resolution and statistical replication, establish baselines for marine bacterial abundance and taxonomic distributions across the Monterey Bay water column, against which future change can be assessed. [ABSTRACT FROM AUTHOR]