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In the Transformer architecture, positional encoding is a vital component because it provides the model with information about the structure and position of data. In Graph Transformer, there have been attempts to introduce different positional encodings and inject additional structural information. Therefore, in terms of integrating positional and structural information, we propose a Structural and Positional Ensembled Graph Transformer (SPEGT). We developed SPEGT by noting the different properties of structural and positional encodings of graphs and the similarity of their computational processes. We have set a unified component that integrates the functionalities: (i) Random Walk Positional Encoding, (ii) Shortest Path Distance between each node, and (iii) Hierarchical Cluster Encoding. We find a problem with a well-known positional encoding and experimentally verify that combining it with other encodings can solve their problem. In addition, SPEGT outperforms previous models on a variety of graph datasets. We also show that SPEGT using unified positional encoding, performs well on structurally indistinguishable graph data through error case analysis. [Display omitted] • We combined information of graph structures to create a novel positional encoding. • The components of the new unified encoding compensate for each other's shortcomings. • Our new architecture continuously injects unified information into the attention. • Our model performs better when edge information is scarce. • The model discriminates graphs that are structurally indistinguishable. [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]

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]

[Display omitted] • A sorting strategy was proposed to improve the pyrolytic homogeneity of MSW. • An index (C) was established to quantify the sorting performance for Ea and logA. • The strategy outperformed traditional ones in pyrolytic homogeneity enhancement. • M3 model showed optimal C_Ea (1578.30 kJ/mol) and C_logA (93.11 -log min). Heterogeneity of pyrolytic parameters in municipal solid waste (MSW) significantly hinders its waste-to-energy efficiency. So far, hardly any light has been shed on current pyrolytic heterogeneity conditions or feasible pyrolytic homogeneity enhancement approaches of MSW. Accordingly, pyrolytic properties (E a and logA) of 130 MSW samples in 6 categories were collected from literature. A kinetic parameters clustering-based sorting strategy for MSW was proposed. A so-called C index was established to compare their sorting performance for E a and logA against two traditional sorting strategies (substance categorization and density clustering). Results showed that the proposed sorting strategies outperformed the traditional ones in pyrolytic homogeneity enhancement, where the optimal C_E a and C_logA reached 1578.30 kJ/mol and 93.11 -log min. Among investigated clustering methods, k-means clustering outperformed hierarchical clustering, which could be attributed to its adaptability to the sample structure. Future perspectives involving data set expansion, model framework development, and downstream technologies matching were also discussed. The index C established in this study can be used to evaluate other clustering models. [ABSTRACT FROM AUTHOR]

In this work a novel hierarchical clustering technique is proposed which can be used to find a hierarchical structure of data items in a dataset. The emphasis is on maintaining consistency of the clusters at each level in the hierarchy as well as to have a natural number of clusters at each level. We propose a method by using Cluster Number Assisted k-Means (CNAK) to find the possible natural numbers of clusters for that dataset. Next, we find the association between clusters at different levels. We propose three criteria and use them to remove the insignificant levels to obtain a final hierarchical structure. This method can be used to find the number of clusters at each of the different levels, the clusters at those levels and their association with clusters at adjacent levels. Naïve Hierarchical k-means algorithm uses a pre-determined branching factor at all the levels. In most traditional agglomerative and divisive clustering, at each different level of the hierarchical structure, exactly one node is split into several nodes for the next lower level. Either this number is the same for each parent or decided independently. Clusters with multiple parents are not allowed in traditional hierarchical clustering. However, in reality a cluster may naturally have multiple parents. Experimentation have been carried out on multiple datasets to demonstrate the effectiveness of the proposed technique. • Algorithm to find the hierarchical structure of clusters. • Automatically detect the number of levels and the number of clusters at each level. • Automatically detect association between clusters at adjacent levels. • Can handle large datasets with outliers. • Tested on simulated and real world datasets. [ABSTRACT FROM AUTHOR]

Natural hydrogen has generated great interest as a potential clean and renewable energy source. To understand the occurrence of natural hydrogen, 103 1-m deep soil gas samples were acquired near the San Andreas Fault at Jasper Ridge and Portola Valley, California, USA. The gas samples were analyzed for hydrogen, helium, carbon dioxide, light hydrocarbons, and fixed gas concentrations. Statistical data analysis was carried out to group samples, reveal their spatial distribution, and understand possible sources of the gases. High concentrations of hydrogen up to 20.3 ppmv and 17.3 ppmv occur in Jasper Ridge and Portola Valley, respectively, ∼30–35 times greater than the atmospheric concentration. Most samples with high hydrogen concentrations fall on or near faults, suggesting an origin by serpentinization or geomechanical activation of catalytic sites in minerals, although a deep-seated primordial origin cannot be excluded. Elevated concentrations of carbon dioxide resulted from aerobic microbial degradation of organic matter and elevated concentrations of light hydrocarbons likely resulted from thermal cracking of organic matter. • High concentrations of hydrogen discovered to occur in Jasper Ridge and Portola Valley near faults. • Hierarchical cluster and principal component analyses reveal spatial clustering of gas samples. • Alternating least squares regression reveals pure components and sources of gases. • Geochemical soil sampling and data analysis is a tool for natural H 2 exploration. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the effect of titanium micro particles (TiP) previously functionalized with nanoparticles doped with dexamethasone (Dex) and doxycycline (Dox), on macrophage polarization and activity. Methods: Macrophages RAW264.7 were cultured in the presence TiP loaded with dexamethasone -NPs (Dex)- and doxycycline -NPs (Dox)-, and as control, TiP with or without doped NPs. Cells were tested with and without previous bacterial lipopolysaccharide endotoxin (LPS) stimulation. Their morphology, proliferation, cytotoxicity, phenotypic change, and cytokines release were assessed by LIVE/DEAD, DNA release, metabolic activity, brightfield and scanning electron microscopy. The test Kruskall-Wallis was used for comparisons, while the cytokine expression profiles were examined by hierarchical clustering (p < 0.05). Results: Upon exposure with TiP macrophages were activated and polarized to M1, but without depicting cytotoxic effects. The particles were phagocytised, and vacuolized. When exposed to functionalised TiP with NPs(Dex) and NPs(Dox), the ratio M1/M2 was up to forty times lower compared to TiP alone. When exposed to LPS, TiP reduced cell viability in half. Functionalised TiP with NPs(Dex) inhibited the cytokine release exerted by TiP on macrophages. When macrophages were exposed to functionalised TiPs with NPs(Dex) with and without LPS, the effect of TiP on cytokine secretion was inhibited. Significance: Functionalised TiPs with NPs(Dex) and NPs(Dox) may potentially have beneficial effects on modulating titanium and LPS-related inflammatory reactions. [Display omitted] • The titanium particles were phagocytised by macrophages and vacuolized. • Titanium particles induced M1 macrophages polarization without cytotoxic effects. • Bacterial lipopolysaccharide plus titanium particles half-reduced macrophage viability. • Titanium particles doped with dexamethasone reduced the ratio M1/M2 forty times. [ABSTRACT FROM AUTHOR]

• Infomap multilayer network analysis allows cluster detection in the international trade. • In the period 1993–2017, the structure of the international trade of automotive components & parts changes. • Cross-cluster connections generated by clusters almost double at the aggregate level. • Different patterns of cross-cluster connections are observed. • Denser and more hierarchical clusters are generated by the Germany-led, US-led, China&Japan-led clusters. The paper analyses the changes that occurred over 25 years in the geography of trade in automotive parts and components. Using the Infomap multilayer clustering algorithm, we identify clusters of countries and their specific trades in the automotive international trade network, we measure the relative importance of each cluster and the interconnections between them, and we analyse the contribution of countries and of trade of components and parts in the clusters. The analysis highlights the formation of denser and more hierarchical networks generated by Germany's trade relations with EU countries and by the US preferential trade agreements with Canada and Mexico, as well as the surge of China. While the relative importance of the main clusters and of some individual countries change significantly, connections between clusters increase over time. [ABSTRACT FROM AUTHOR]

The possible contribution of brine-derived microflora to the sensory attributes of cheese is still a rather unexplored field. In this study, 365 bacteria and 105 yeast strains isolated from 11 cheese brines were qualitatively tested for proteolytic and lipolytic activities, and positive strains were identified by sequencing. Among bacteria, Staphylococcus equorum was the most frequent, followed by Macrococcus caseolyticus and Corynebacterium flavescens. As for yeasts, Debaryomyces hansenii , Clavispora lusitaniae , and Torulaspora delbrueckii were most frequently identified. A total of 38% of bacteria and 59% of yeasts showed at least 1 of the metabolic activities tested, with lipolytic activity being the most widespread (81% of bacteria and 95% of yeasts). Subsequently 15 strains of bacteria and 10 yeasts were inoculated in a curd-based medium and assessed via headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry to determine their volatilome. After a 30-d incubation at 12°C, most strains showed a viability increase of about 2 log cfu/mL, suggesting good adaptability to the cheese environment. A total of 26 compounds were detected in the headspace, carbonyl compounds and alcohols being the major contributors to the volatile profile of the curd-based medium. Multivariate analysis was carried out to elucidate the overall differences in volatiles produced by selected strains. Principal component analysis and hierarchical clustering analysis demonstrated that the brine-related microorganisms were separated into 3 different groups, suggesting their different abilities to produce volatile compounds. Some of the selected strains have been shown to have interesting aromatic potential and to possibly contribute to the sensory properties of cheese. [ABSTRACT FROM AUTHOR]

Predicting risk of waitlist mortality and subsequent classification of lung transplant candidates has been difficult due to inter-relatedness of risk factors, differential risk across populations, and changes in relationships over time. We developed a clinically intuitive indexing system to simplify mortality risk assessment. Scientific Registry of Transplant Recipients data from February 19, 2015, to May 26, 2020 (n = 13,726) were used to estimate 3 constructs. Airway and oxygen function indices were estimated using confirmatory factor analysis and hierarchical clustering was used to derive respiratory support clusters. Cox proportional hazards regression was used to characterize event-free waitlist survival by constructs (3), age, sex, and diagnosis group. Model performance was compared to the Lung Allocation Score/Composite Allocation Score (LAS/CAS). Airway and oxygen function indices were created with substantive factor loadings forced expiratory volume (0.86), forced vital capacity (0.64), partial pressure of carbon dioxide (0.56) and PO 2 /fraction of inspired oxygen (0.83), partial pressure of oxygen (0.59), and mean pulmonary artery pressure (0.30), respectively. Four respiratory support clusters (C1: as needed O 2 , C2: continuous O 2 , C3: continuous O 2 /positive pressure ventilation (PPV), C4: PPV + extracorporeal membrane oxygenation) were identified. Constructs were used to identify patient profiles. Model area under the receiver operating characteristic curve was 0.85 [0.84, 0.87] compared to the LAS 0.92 [0.91, 0.94] at 4 weeks. Risk predictions were relatively insensitive to airway and oxygen function indices in C1 and C4 but varied across C2 and C3. Reducing the dimensionality of waitlist mortality risk offers an opportunity to identify clinical phenotypes that are more nuanced and thus more interpretable than current risk assessment provided by the LAS/CAS models. [ABSTRACT FROM AUTHOR]

• Muscle MRI reveals consistent features shared by patients with distal hereditary motor neuropathies that can be helpful for the diagnosis process. • A reticular pattern of fat replacement progressing towards fat islands is the most common finding observed across all patients. • Distal muscles of the lower legs are the most affected, especially the posterior compartment of the legs including soleus and gastrocnemius muscles. • A distal to proximal gradient of fat replacement is frequent in this cohort of patients, suggesting there is a dying-back phenomenon of axon degeneration. • Hyperintensities in STIR signal are common, following a distal to proximal gradient in many cases suggesting that there is an active process of denervation in several patients. Distal motor neuropathies (dHMN) are an heterogenous group of diseases characterized by progressive muscle weakness affecting predominantly the distal muscles of the lower and upper limbs. Our aim was to study the imaging features and pattern of muscle involvement in muscle magnetic resonance imaging (MRI) in dHMN patients of suspected genetic origin (dHMN). We conducted a retrospective study collecting clinical, genetic and muscle imaging data. Muscle MRI included T1-weighted and T2 weighted Short Tau Inversion Recovery images (STIR-T2w) sequences. Muscle replacement by fat was quantified using the Mercuri score. Identification of selective patterns of involvement was performed using hierarchical clustering. Eighty-four patients with diagnosis of dHMN were studied. Fat replacement was predominant in the distal lower leg muscles (82/84 cases), although also affected thigh and pelvis muscles. Asymmetric involvement was present in 29% of patients. The superficial posterior compartment of the leg, including the soleus and gastrocnemius muscles, was the most affected area (77/84). We observed a reticular pattern of fatty replacement progressing towards what is commonly known as "muscle islands" in 79.8%. Hyperintensities in STIR-T2w were observed in 78.6% patients mainly in distal leg muscles. Besides features common to all individuals, we identified and describe a pattern of muscle fat replacement characteristic of BICD2, HSPB1 and DYNC1H1 patients. We conclude that muscle MRI of patients with suspected dHMN reveals common features helpful in diagnosis process. [ABSTRACT FROM AUTHOR]

• Β-sitosterol content was measured first time in different organs of okra. • Bioactive compound contents varied greatly between organs and accessions of okra. • Outstanding accessions with higher levels of bioactive compounds were screened out. • Multivariate analysis of bioactive compounds distinguished and grouped clearly all accessions. • This work provided valuable information on the bioactive compounds in Iranian okra to use in breeding programs. Interest in the bioactive compounds in okra has piqued because of its nutraceuticals, therapeutic properties, and health benefits. Okra has been the subject of several studies, most of which have focused mainly on the properties and concentrations of polyphenols and polysaccharides or their distribution in one organ. Hence, the present study is the first attempt to measure β-sitosterol (BSS) in different organs (leaf, seed, and pod) of 20 accessions of okra commonly cultivated in Iran. Total phenol (TPC), flavonoid (TFC), radical scavenging capacity (AOA), total polysaccharide (TOP), protein content (PT), Total soluble sugar (TSS), proline content (PR), seed oil content, and profile of fatty acids were also assessed. The results indicated that the highest TPC, TFC, and AOA levels were recorded in the A13 (Khorasan Razavi) pods, while TOP, YP, and TSS were highest in A19 (Isfahan) pods. Moreover, PT and BSS levels were highest in A4 seeds from Sistan and Baluchestan. The oil content of okra seed ranged from 22.57% (A3, West Azerbaijan) to 42.30% (A7, Sistan and Baluchestan) among studied accessions. Linoleic acids and palmitic acid were found to be the most abundant fatty acids (42.50% to 52.15% and 27.63% to 31.8%) in all accessions. Principal component analysis and hierarchical cluster analysis confirmed that A19, A13, A4, A7, and A12 are suitable for breeding programs because of their nutritional value, including high polyphenol, oil, linoleic acid, and phytosterol contents. These findings suggest enhancing the exploitation of okra organs such as seeds for oil, PT, and BSS and pods and leaves for TPC, TFC, and TOP and provide valuable information on bioactive compounds in Iranian accessions of okra to use in breeding programs and the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production. [ABSTRACT FROM AUTHOR]

• Dendrite morphological neuron learning is based on deterministic linkage trees. • Linkage trees overcome k -means' non-reproducibility in learning dendrites' locations. • A micro genetic algorithm speeds up the automatic hyperparameter tuning of models. • A balanced fitness function favors solutions with a low error rate and few dendrites. • The proposed approach beats its counterparts related to accuracy and model complexity. The current learning approach for smooth dendrite morphological neurons (DMNs) determines dendrite parameters using k -means clustering, which is non-reproducible due to its stochastic nature, risking falling into local minima. To overcome this issue, we introduce a DMN learning approach based on a deterministic hierarchical clustering method, which builds a linkage tree for each class of patterns. In addition, a micro genetic algorithm automatically tunes the cut-off points in the linkage trees hierarchy to create suitable clusters of dendrites. The classification experiments consider 40 real-world datasets. The proposed approach outperforms three DMN models in classification performance and is quite competitive with a hybrid morphological-linear perceptron, multilayer perceptron, random forest, and support vector machine. Therefore, the proposed method is a suitable alternative for pattern classification applications. [ABSTRACT FROM AUTHOR]

Multiscale manifold learning approaches enable the characterization of cellular heterogeneity toward a more comprehensive understanding of immune responses. Graph-based hierarchical clustering identifies relationships within and between cellular populations on a manifold. Topological data analysis enables continuous coarse graining while quantifying the nature and stability of data features and groupings. Diffusion condensation defines a diffusion-analogous notion of topological data analysis with useful applications to immunological data sets. Learning multiscale patient features enables patient comparison and prediction of clinical outcomes. In recent years, the vast amount of immunological data generated has resulted in an improved understanding of cellular heterogeneity. However, interrogating the multiscale structure of this heterogeneity is a less recognized aspect of single cell analysis. There are myriad computational methods that aim to characterize this structure using key principles from manifold learning and topological data analysis. Ultimately, these methods uncover structure at multiple resolutions, revealing more comprehensive insights into cellular behaviors and responses. Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity. [ABSTRACT FROM AUTHOR]

Individuals with acute venous thromboembolism (VTE) constitute a heterogeneous group of patients with diverse clinical characteristics and outcome. To identify endotypes of individuals with acute VTE based on clinical characteristics at presentation through unsupervised cluster analysis and to evaluate their molecular proteomic profile and clinical outcome. Data from 591 individuals from the Genotyping and Molecular phenotyping of Venous thromboembolism (GMP-VTE) project were explored. Hierarchical clustering was applied to 58 variables to define VTE endotypes. Clinical characteristics, three-year incidence of thromboembolic events or death, and acute-phase plasma proteomics were assessed. Four endotypes were identified, exhibiting different patterns of clinical characteristics and clinical course. Endotype 1 (n = 300), comprising older individuals with comorbidities, had the highest incidence of thromboembolic events or death (HR [95 % CI]: 3.76 [1.96–7.19]), followed by endotype 4 (n = 127) (HR [95 % CI]: 2.55 [1.26–5.16]), characterised by men with history of VTE and provoking risk factors, and endotype 3 (n = 57) (HR [95 % CI]: 1.57 [0.63–3.87]), composed of young women with provoking risk factors, vs. reference endotype 2 (n = 107). The reference endotype was constituted by individuals diagnosed with PE without comorbidities, who had the lowest incidence of the investigated endpoint. Differentially expressed proteins associated with the endotypes were related to distinct biological processes, supporting differences in molecular pathophysiology. The endotypes had superior prognostic ability compared to existing risk stratifications such as provoked vs unprovoked VTE and D-dimer levels. Four endotypes of VTE were identified by unsupervised phenotype-based clustering that diverge in clinical outcome and plasmatic protein signature. This approach might support the future development of individualized treatment in VTE. [Display omitted] • Unsupervised clustering of clinical features identified four endotypes in acute VTE. • Endotypes differed in recurrence of VTE, arterial events, death and bleeding. • Plasma protein profiling by immuno-PCR suggests pathomechanistic diversity. • The endotyping outperformed traditional tools for VTE risk stratification. [ABSTRACT FROM AUTHOR]

• Spectrally resolved DNI was tested for an entire year in Beijing. • The spectra can be represented in five-dimensional data by an autoencoder. • Hierarchical clustering was adopted to obtain ten reference spectra in the case. • Novel local reference spectra can characterize solar resources throughout the year. • The superiority of the new reference spectra over ASTM AM1.5D was verified. The established reference spectrum (ASTM AM1.5) based on mid-latitudes is still the most frequently used standard in design and simulation in various fields worldwide, however, the solar spectrum varies with climate and time, which significantly affects the evaluations of solar energy utilization. This study proposes a novel reference spectrum analysis method to establish several local reference spectra, in this case, based on 147,374 spectra in Beijing, which can characterize solar resources throughout the year. The dataset consists of spectra of direct normal irradiance, and a deep learning autoencoder is adopted to extract the intrinsic features of each spectrum for clustering. 10 clusters are finally divided by agglomerative hierarchical clustering, and 10 local reference spectra were identified from each cluster by correlation analysis. The results show that the shape of new reference spectra shifts to the short wave as the irradiance increases, and the average photon energy increases from 1.069 eV to 1.456 eV. The new local reference spectra are compared with the standard spectrum AM1.5D to show the influence of shape variations, and it reveals 61% maximum differences among photovoltaic applications. The superiority verified new reference spectra could be further used to optimize local solar technologies design and guide photovoltaic material development. [ABSTRACT FROM AUTHOR]

Studies across schizophrenia (SZ) and bipolar disorder (BD) indicate common transdiagnostic neurocognitive subgroups. However, existing studies of patients with long-term illness precludes insight into whether impairments result from effects of chronic illness, medication or other factors. This study aimed to investigate whether neurocognitive subgroups across SZ and BD can be demonstrated during early illness stages. Data from overlapping neuropsychological tests were pooled from cohort studies of antipsychotic-naïve patients with first-episode SZ spectrum disorders (n = 150), recently diagnosed BD (n = 189) or healthy controls (HC) (n = 280). Hierarchical cluster analysis was conducted to examine if transdiagnostic subgroups could be identified based on the neurocognitive profile. Patterns of cognitive impairments and patient characteristics across subgroups were examined. Patients could be clustered into two, three and four subgroups, of which the three-cluster solution (with 83% accuracy) was selected for posthoc analyses. This solution revealed a subgroup covering 39% of patients (predominantly BD) who were cognitively relatively intact , a subgroup of 33% of patients (more equal distributions of SZ and BD) displaying selective deficits, particularly in working memory and processing speed, and a subgroup of 28% (mainly SZ) with global impairments. The globally impaired group exhibited lower estimated premorbid intelligence than the other subgroups. Globally impaired BD patients also showed more functional disability than cognitively relatively intact patients. No differences were observed across subgroups in symptoms or medications. Neurocognitive results can be understood by clustering analysis with similar clustering solutions occurring across diagnoses. The subgroups were not explained by clinical symptoms or medication, suggesting neurodevelopmental origins. • Across schizophrenia and bipolar disorder, patients could be clustered according to their neurocognitive profile already at early illness stages. • We identified 2-, 3- and 4-cluster solutions, of which the 3-cluster solution was deemed optimal given its high accuracy and detailed information. • In the 3-cluster solution, patients were divided into a relatively intact (39%), selectively impaired (33%), or globally impaired (28%) subgroup. • 'Globally impaired' subgroup (mainly SZ) presented with more functional impairments and lower premorbid intelligence. [ABSTRACT FROM AUTHOR]

Over the last decade, low-cost and legacy carriers have evolved their respective business models, leading to business model "hybridization". However, the economic performance impacts of such convergence remain underinvestigated. We therefore evaluate whether airline business model hybridization has led to homogeneous economic performance in Europe. Using 2019 data for 25 European airlines, we implement hierarchical clustering based on principal components. We show that regardless of the analysis level (i.e., the number of clusters into which we group airlines), low-cost and legacy carriers remain distinct groups with specific economic performance characteristics (e.g., volume, revenue, cost and profitability). That is, despite increasing hybridization, the economic performance of airline business models is not homogeneous; overall, low-cost carriers remain more profitable (with lower costs and more affordable fares) than legacy carriers. • We evaluate whether airline business model hybridization has led to homogeneous economic performance in Europe. • We use 2019 data for 25 European airlines to implement hierarchical clustering based on principal components. • We show that low-cost and legacy carriers remain distinct groups with specific characteristics. • Despite increasing hybridization, the economic performance of airline business models is not homogeneous. • Overall, low-cost carriers remain more profitable (with lower costs and more affordable fares) than legacy carriers. [ABSTRACT FROM AUTHOR]

• We attempted to identify subgroup of drivers who text while driving (TWD). • Cluster analysis was conducted on perceived risk and frequency of TWD. • Subgroup were compared in levels of trait impulsivity and impulsive decision making. • Males who believe TWD is risky yet frequently engage in it showed greater impulsivity. • Implications for developing effective intervention strategies are discussed. Introduction: The present study attempted to provide a proof-of-concept of usefulness of cluster analysis for identifying distinct and practically meaningful subgroups of drivers who differed in their perceived risk and frequency of texting while driving (TWD). Method: Using a hierarchical cluster analysis, which involves sequential steps in which individual cases are merged together one at a time based on their similarities, the study first attempted to identify distinct subgroups of drivers who differed in their perceived risk and frequency of TWD. To further evaluate the meaningfulness of the subgroups identified, the subgroups were compared in terms of levels of trait impulsivity and impulsive decision making for each gender. Results: The study identified the following three distinct subgroups: (a) drivers who perceive TWD as risky but frequently engage in TWD; (b) drivers who perceive TWD as risky and infrequently engage in TWD; and (c) drivers who perceive TWD as not so risky and frequently engage in TWD. The subgroup of male, but not female, drivers who perceive TWD as risky but frequently engage in TWD showed significantly higher levels of trait impulsivity, but not impulsive decision making, than the other two subgroups. Discussion: This is the first demonstration that drivers who frequently engage in TWD can be categorized into two distinct subgroups that differ in terms of the perceived risk of TWD. Practical applications: For drivers who perceived TWD as risky yet frequently engage in TWD, the present study suggests that different intervention strategies may be needed for each gender. [ABSTRACT FROM AUTHOR]

• Factors that increase the success of road safety target are examined. • Agglomerative hierarchical clustering approach is used for time-series data. • Ambitious target is less likely to be achieved. • Optimal target duration depends on socio-economic characteristics. Introduction: Setting quantified road safety targets has been recognized as a best practice to eliminate road fatalities by international organizations such as the Organisation for Economic Co-operation and Development (OECD). Previous studies have examined the relationship between setting quantified road safety targets and road fatality reduction. However, little attention has been paid to the association between the targets' characteristics and their successes under certain socioeconomic conditions. Method : This study aims to fill this gap by identifying the quantified road safety targets that are the most achievable. Specifically, using panel data on the OECD countries' quantified road safety targets, this study develops a fixed effects model to determine the specific characteristics (i.e., target duration and level of ambition) of an optimal target to make it as achievable as possible for OECD countries. Results : The study finds that a significant association exists between target duration, level of ambition, and target achievement, with targets that have lower levels of ambition having higher achievements. Moreover, different groups of OECD countries carry different characteristics (e.g., target duration) that concern their most achievable targets. Conclusions : The findings suggest that, in terms of duration and level of ambition, OECD countries' target setting should establish their own socioeconomic development conditions. This provides government officials, policymakers, and practitioners with useful references for the future quantified road safety target settings that are the most likely to be achieved. [ABSTRACT FROM AUTHOR]

Anxiety disorders are a prevalent and severe problem that are often developed early in life and can disrupt the daily lives of affected individuals for many years into adulthood. Given the persistent negative aspects of anxiety, accurate and early assessment is critical for long term outcomes. Currently, the most common method for anxiety assessment is through point-in-time measures like the GAD-7. Unfortunately, this survey and others like it can be subject to recall bias and do not fully capture the variability in an individual's day-to-day symptom experience. The current work aims to evaluate how point-in-time assessments like the GAD-7 relate to daily measurements of anxiety in a teenage population. To evaluate this relationship, we leveraged data collected at four separate three week intervals from 30 teenagers (age 15–17) over the course of a year. The specific items of interest were a single item anxiety severity measure collected three times per day and end-of-month GAD-7 assessments. Within this sample, 40 % of individuals reported clinical levels of generalized anxiety disorder symptoms at some point during the study. The first component of analysis was a visual inspection assessing how daily anxiety severity fluctuated around end-of-month reporting via the GAD-7. The second component was a between-subjects comparison assessing whether individuals with similar GAD-7 scores experienced similar symptom dynamics across the month as represented by latent features derived from a deep learning model. With this approach, similarity was operationalized by hierarchical clustering of the latent features. The aim clearly indicated that an individual's daily experience of anxiety varied widely around what was captured by the GAD-7. Additionally, when hierarchical clustering was applied to the three latent features derived from the (LSTM) encoder (r = 0.624 for feature reconstruction), it was clear that individuals with similar GAD-7 outcomes were experiencing different symptom dynamics. Upon further inspection of the latent features, the LSTM model appeared to rely as much on anxiety variability over the course of the month as it did on anxiety severity (p < 0.05 for both mean and RMSSD) to represent an individual's experience. This work serves as further evidence for the heterogeneity within the experience of anxiety and that more than just point-in-time assessments are necessary to fully capture an individual's experience. • The daily experience of Anxiety can fluctuate widely around what is typically reported via standard assessments. • Neural networks are capable of representing daily reports of anxiety over the course of a month in a reduced feature space. • These representations rely as much on anxiety variation as they do severity. • Individuals who report similar levels of anxiety on standard assessments can have very different day-to-day experiences. [ABSTRACT FROM AUTHOR]

• 15 Phenolic compounds from different families were identified. • Chaaibi and Zeitoun el ouaha cultivar olive oils were characterized by the highest content of tocopherols. • Regregui cultivar olive oil was the most abundant in phenolic compounds. • Ajmi olive fruits have the highest oil content (51.4%), followed by Chemlali and Zarrazi varieties. To enrich the Tunisian olive oil production, six minor and rare Tunisian olive cultivars were investigated and compared to the two main varieties (Chemlali and Chaaibi). This is the first evaluation of such minor varieties. Phenolic compounds profile, fatty acids composition and tocopherol compounds have been investigated in the resulting oils. The analysis of extra virgin olive oils from the minor varieties showed that all the quality parameters fall within the limits established for the extra virgin olive oils (EVOO) In particular, the olives of Regregui and Zeitoun el ouaha varieties have high content of phenolic compounds in their oils (510.65 and 354.43 mgkg−1, respectively). Moreover, the olive oils Chaaibi and Zeitoun el ouaha were characterized by the highest content of tocopherols (320.30 mgkg−1 and 264.12 mgkg−1, respectively). The olive oil of the Regregui cultivar was the richest in dialdehydic form of decarboxymethyl oleuropein aglycone (DDOA) (187.10 mgkg−1), tyrosyl acetate (106.62 mgkg−1), dialdehydic form of decarboxymethyl ligstroside aglycone (DDLA) (57.21mgkg−1) and plastochromano-8 (P8) (9.28mgkg−1). Our results showed that Ajmi oil and Zeitoun el ouaha were characterized by the highest content of apigenin (2.12 mgkg−1 and 2.49 mgkg−1, respectively). Finally, the olive Oueslati and El Guim olive oils contained the highest content of β-tocopherol (4.11mgkg−1 and 3.42 mgkg−1, respectively). In addition, with the principal component analysis (PCA) and hierarchical cluster analysis (HCA) the varieties can be differentiated from each other with regard to their phenol, tocopherol and fatty acid compositions. Thus, these compounds appear to be a useful tool for distinguishing between the monovarietal oils investigated. These varieties can be selected by Tunisian olive growers for large-scale plantation in the future [ABSTRACT FROM AUTHOR]

It is extremely important to analyze the contaminative behaviors of Perfluoroalkyl acids (PFAAs) due to their serious threats to urban environments which are closely related to humans. Current study aimed to explore the distribution, source apportionment and ecological risk assessment of PFAAs in surface water from Shijiazhuang, China. The concentrations of ∑PFAAs ranged from 19.5 to 125.9 ng/L in the investigation area. Perfluorobutanesulfonic acid (PFBS) and perfluoropentanoic acid (PFPeA) were the predominant contaminants (mean value: 14.3 ng/L and 16.6 ng/L, respectively). The distribution of PFAAs according to geospatial analysis and hierarchical clustering analysis (HCA) showed that higher levels of ∑PFAAs were detected in the southern surface water of Shijiazhuang and there was a stepwise decrease from the wet season to the dry season. Furthermore, based on source apportionment, the dominant potential sources were found to be wastewater treatment plant (WWTP) effluents and industrial discharge. The risk quotients (RQs) revealed low ecological risks of all PFAAs for aquatic organisms in Shijiazhuang surface water. Collectively, this study provided basic data for regulatory strategies for controlling PFAA pollutions in urban surface water. Graphical Abstract [Display omitted] [ABSTRACT FROM AUTHOR]

Previous research has found that human voice can provide reliable information to be used for gender identification with a high level of accuracy. In social psychology, perceived masculinity and femininity (masculinity and femininity rated by humans) has often been considered an important feature when investigating the influence of vocal features on social behaviours. While previous studies have characterized the acoustic features that contributed to perceivers' judgements of speakers' masculinity or femininity, there is limited research on developing a machine masculinity/femininity scoring model and characterizing the independent acoustic factors that contribute to perceivers' masculinity and femininity judgements. In this work, we first propose a machine scoring model of perceived masculinity/femininity based on the Extreme Random Forest and then characterize the independent and meaningful acoustic factors that contribute to perceivers' judgements by using a correlation matrix based hierarchical clustering method. Our results show that the machine ratings of masculinity and femininity strongly correlated with the human ratings of masculinity and femininity when we used an optimal speech duration of 7 s, with a correlation coefficient of up to.63 for females and.77 for males. Nine independent clusters of acoustic measures were generated from our modelling of femininity judgements for female voices and eight clusters were found for masculinity judgements for male voices. The results revealed that, for both genders, the F0 mean is the most important acoustic measure affecting the judgement of acoustic-related masculinity and femininity. The F3 mean, F4 mean and VTL estimators were found to be highly inter-correlated and appeared in the same cluster, forming the second most significant factor in influencing the assessment of acoustic-related masculinity and femininity. Next, F1 mean, F2 mean and F0 standard deviation are independent factors that share similar importance. The voice perturbation measures, including HNR, jitter and shimmer, are of lesser importance in influencing masculinity/femininity judgements. • We modelled femininity/masculinity ratings for 129 female/96 male voices. • Extreme Random Forest regression and correlated feature clustering were used. • F0 mean accounted for 23.80% of variance for femininity and 43.5% for masculinity. • A F3 mean/F4 mean/VTL estimators was also a prominent predictor. • Differences in feature weights for femininity and masculinity ratings are discussed. [ABSTRACT FROM AUTHOR]

Cholangiocarcinoma (CCA) is a heterogeneous and lethal malignancy, the molecular origins of which remain poorly understood. MicroRNAs (miRs) target diverse signalling pathways, functioning as potent epigenetic regulators of transcriptional output. We aimed to characterise miRNome dysregulation in CCA, including its impact on transcriptome homeostasis and cell behaviour. Small RNA sequencing was performed on 119 resected CCAs, 63 surrounding liver tissues, and 22 normal livers. High-throughput miR mimic screens were performed in three primary human cholangiocyte cultures. Integration of patient transcriptomes and miRseq together with miR screening data identified an oncogenic miR for characterization. MiR-mRNA interactions were investigated by a luciferase assay. MiR-CRISPR knockout cells were generated and phenotypically characterized in vitro (proliferation, migration, colony, mitochondrial function, glycolysis) and in vivo using subcutaneous xenografts. In total, 13% (140/1,049) of detected miRs were differentially expressed between CCA and surrounding liver tissues, including 135 that were upregulated in tumours. CCA tissues were characterised by higher miRNome heterogeneity and miR biogenesis pathway expression. Unsupervised hierarchical clustering of tumour miRNomes identified three subgroups, including distal CCA-enriched and IDH1 mutant-enriched subgroups. High-throughput screening of miR mimics uncovered 71 miRs that consistently increased proliferation of three primary cholangiocyte models and were upregulated in CCA tissues regardless of anatomical location, among which only miR-27a-3p had consistently increased expression and activity in several cohorts. FoxO signalling was predominantly downregulated by miR-27a-3p in CCA, partially through targeting of FOXO1. MiR-27a knockout increased FOXO1 levels in vitro and in vivo , impeding tumour behaviour and growth. The miRNomes of CCA tissues are highly remodelled, impacting transcriptome homeostasis in part through regulation of transcription factors like FOXO1. MiR-27a-3p arises as an oncogenic vulnerability in CCA. Cholangiocarcinogenesis entails extensive cellular reprogramming driven by genetic and non-genetic alterations, but the functional roles of these non-genetic events remain poorly understood. By unveiling global miRNA upregulation in patient tumours and their functional ability to increase proliferation of cholangiocytes, these small non-coding RNAs are implicated as critical non-genetic alterations promoting biliary tumour initiation. These findings identify possible mechanisms for transcriptome rewiring during transformation, with potential implications for patient stratification. [Display omitted] • CCA tissues are characterized by miR upregulation, increased miR biogenesis pathway expression and miR heterogeneity. • Most miRs upregulated in CCA resulted in increased proliferation when introduced into human cholangiocyte models in vitro. • MiR-27a-3p affects FoxO signalling in individuals with CCA in vitro and in vivo. • CRISPR/Cas9 nickase knockout of miR-27a abrogates tumorigenicity in vitro and in vivo. [ABSTRACT FROM AUTHOR]

The aim of this work was to evaluate whether serum cytokines levels are associated with ovarian response in IVF. 149 patients were included in a retrospective study. Cytokines IL-2, IL-4, IL- 6, IL-8, IL-10, VEGF, IFNγ, TNFα, IL-1α, IL-1β, MCP-1 and EGF were measured by sandwich immunoassay previously to ovarian stimulation. Performing hierarchical cluster analysis, we observed that the antral follicle count, the total oocytes recovered and the MII recovered are grouped in the same cluster as the cytokines IL-2-4-6-10-1α-1β, IFNγ y TNFα. Then, we found that the ratio between IL and 6 and IL-10 was higher in low responder women (2.15 versus 1.55; p = 0.035). If we establish 0.9 as a cut-off for the IL-6/IL-10, we observed that above this value the risk of having a low response to ovarian stimulation was more than 3 times greater than below this value (22.9 % versus 6.0 %; p = 0.007). Also, the ratio IL-1β/IL-4 was higher in patients with normal or suboptimal response (0.62 versus 0.34; p = 0.034) and any patient with low response had a value greater than 0.7 (p = 0.003). As a conclusion, the IL-6/IL-10 and IL-1β/IL-4 ratios showed differences between normoresponder women and patients with low ovarian response. [ABSTRACT FROM AUTHOR]

Drought is one of the major abiotic stresses leading to reduced yields and economic losses. Effective germplasm screening for drought tolerance particularly under managed water-deficit conditions is an effective way of selecting materials for advanced breeding programs. Here, 37 Triticum turgidum genotypes, including landraces, ancient and modern genotypes, along with 2 tritordeum cultivars, were subjected to water-deficit stress through the application of 10% (w/v) PEG 6000 and to re-watering treatment in controlled environment, and at the end of each treatment, several physiological and morphological traits were investigated. Our results revealed large variation in shoot and root fresh weight, proline, chlorophyll, and MDA concentration, and also in root morphological traits across the 37 genotypes. The hierarchical clustering of the physiological and morphological traits led to the identification of tolerant and sensitive genotypes to water-deficit stress and also reveals those genotypes characterized by deep-rooting and shallow-rooting systems. By integrating both datasets, three outstanding genotypes, namely Karim, Svems 20, and Svems 18 were identified as the most tolerant genotypes with deep-rooting system. On the other hand, Iride and Bulel tritordeum, were introduced as the most sensitive genotypes with shallow-rooting system. • Large variation in shoot and root fresh weight, proline, chlorophyll, and MDA concentration were recorded across the 37 genotypes of durum wheat in response to water-deficit stress and rewatering conditions. • The drought-tolerant and drought-sensitive genotypes were extracted from the hierarchical clustering of physiological traits. • Using hierarchical clustering of root traits across 37 genotypes, three main clusters were obtained representing deep-rooting, medium-depth rooting and shallow-rooting systems. • Three genotypes, namely Karim, Svems 20, and Svems 18 were introduced as the most tolerant genotypes with deep-rooting system. [ABSTRACT FROM AUTHOR]

The main goal of this analysis was to identify mortality patterns apparent when many drug classes are analyzed together. The Drug Involved Mortality database is a registry of drug terms mentioned on death certificates of all drug-related deaths in the United States. Means of total number of drugs involved and percentages of specific drug combinations were calculated. Dimensionality reduction using multiple correspondence analysis and hierarchical clustering identified clusters of drugs listed on death certificates. An average of 2.4 specific drugs were listed on death certificates in 2017. For 9 of the top 10 drugs involved, over 80% of deaths involved at least one other drug. As expected, opioid drugs and psychostimulants clustered together, but other psychoactive substances (non-opioid analgesics, sedatives, antidepressants, antipsychotics) clustered together into multi-class groups. Other drugs (e.g., acetaminophen, oxymorphone) were frequently involved in polysubstance death, but did not cluster with any other specific drug. Deaths involving illicit drugs listed fewer drugs than deaths involving prescription drugs. While individual drug substances might contribute to many deaths (e.g., fentanyl), polysubstance mortality is more common than single substance mortality. Multidimensional analyses integrating all drugs involved are useful to identify uncommon patterns of overdose and changing trends. [ABSTRACT FROM AUTHOR]

Time series analysis can facilitate the detection of complex behavioral patterns and potentially provide new opportunities to assess animal welfare. The aim was to investigate whether dairy cows exhibit daily, individual patterns in activity and in area use in the barn. We predicted that behavioral patterns will be more consistent (1) within than between cows, (2) when area categorization is more specific and, thus, allows the detection of individual preferences for areas, and (3) during the night. We conducted the study at an experimental farm with 20 lactating Brown Swiss and Swiss Fleckvieh cows. The animals were housed in cubicles, and they received feed and were milked twice daily. Activity was recorded with IceTag pedometers (IceRobotics Ltd.), and area use with the SMARTBOW sensor system (Zoetis). Data were collected for 55 consecutive days and analyzed at 1-min intervals. To investigate the behavioral time series, we performed a hierarchical clustering analysis. A clustering process calculated distances between days, which were compared within and between cows based on t -tests and analyses of variance. Dendrograms of activity and area use showed that days of individual cows could not be grouped more closely together than those of different cows. A slightly better grouping was achieved with a more specific area categorization, but not during a specific time period. However, the average distances between days were always smaller within (mean ± SD; activity: 95.62 ± 76.88, lying areas: 0.14 ± 0.03, functional areas: 0.12 ± 0.01) than between cows (activity: 109.62 ± 75.33, lying areas: 0.16 ± 0.02, functional areas 0.13 ± 0.01). Considering that the time series of individual cows were slightly but always more similar compared with those between cows, and that more consistent patterns were found when the area categorization was more specific, it can be concluded that the cows exhibited weak individual preferences in area use and also weak daily individual patterns in activity and area use. Because the visual exploratory and empirical approaches used in this study do not account for variability, they do not seem to be suitable for the detection of patterns in animals that display greater plasticity in their temporal structure of activity. Thus, although determining the temporal structure of activity and area use bears the potential to assess the behavior and, in turn, for example, the physiological state and health status of cows, it does not seem to be achievable with a cluster analysis. Therefore, time series methods that account for temporal fluctuations in behavior should be further explored. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of international policies and agreements to promote the integration of renewable energy sources, mainly solar and wind, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such potential solar resource variability are generally scarce. Under this framework, the present paper proposes a methodology focused on characterizing and clustering the spatio-temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows — from short-term to long-term data — can be subsequently evaluated by using various sources of information. The Spanish territory is selected as case study, considering 22-year period data (1999–2020) and 1,936,917 observations from online satellite database. Spatial variability and geographical clustering differences are discussed and compared depending on the selected time windows, identifying relevant spatial variations for some specific months. Additionally, some years present more variability as well, in line with the sunspot peak of the solar cycles. The proposed approach gives an alternative comprehensive spatio-temporal clustering and characterization of GHI evolution, providing a suitable methodology to help the current European sustainable energy transition. • Solar irradiance resource assessment is proposed through spatio-temporal modeling. • Dynamic clustering model of global horizontal irradiance is proposed and evaluated. • Different time windows are compared from short-term to long-term data. • The Spanish area is considered as case study including two solar cycles. • 761,059 on-ground meteorological data and 1,936,917 online data are analyzed. [ABSTRACT FROM AUTHOR]

Time series analysis can facilitate the detection of complex behavioral patterns and potentially provide new opportunities to assess animal welfare. The aim was to investigate whether dairy cows exhibit daily, individual patterns in activity and in area use in the barn. We predicted that behavioral patterns will be more consistent (1) within than between cows, (2) when area categorization is more specific and, thus, allows the detection of individual preferences for areas, and (3) during the night. We conducted the study at an experimental farm with 20 lactating Brown Swiss and Swiss Fleckvieh cows. The animals were housed in cubicles, and they received feed and were milked twice daily. Activity was recorded with IceTag pedometers (IceRobotics Ltd.), and area use with the SMARTBOW sensor system (Zoetis). Data were collected for 55 consecutive days and analyzed at 1-min intervals. To investigate the behavioral time series, we performed a hierarchical clustering analysis. A clustering process calculated distances between days, which were compared within and between cows based on t -tests and analyses of variance. Dendrograms of activity and area use showed that days of individual cows could not be grouped more closely together than those of different cows. A slightly better grouping was achieved with a more specific area categorization, but not during a specific time period. However, the average distances between days were always smaller within (mean ± SD; activity: 95.62 ± 76.88, lying areas: 0.14 ± 0.03, functional areas: 0.12 ± 0.01) than between cows (activity: 109.62 ± 75.33, lying areas: 0.16 ± 0.02, functional areas 0.13 ± 0.01). Considering that the time series of individual cows were slightly but always more similar compared with those between cows, and that more consistent patterns were found when the area categorization was more specific, it can be concluded that the cows exhibited weak individual preferences in area use and also weak daily individual patterns in activity and area use. Because the visual exploratory and empirical approaches used in this study do not account for variability, they do not seem to be suitable for the detection of patterns in animals that display greater plasticity in their temporal structure of activity. Thus, although determining the temporal structure of activity and area use bears the potential to assess the behavior and, in turn, for example, the physiological state and health status of cows, it does not seem to be achievable with a cluster analysis. Therefore, time series methods that account for temporal fluctuations in behavior should be further explored. [ABSTRACT FROM AUTHOR]

Background: In current intensive care treatment, some patients with severe burns cannot be saved due to progressive organ failure. Further investigation of the pathogenesis of severe burns is needed to improve the mortality rate. In burns, inflammatory cytokines form a network that leads to an inflammatory response. Adipocytes secrete physiologically active substances (adipokines). The roles of adipokines have not been completely clarified in burn patients. This study aimed to determine the relation between serial changes of adipokines and clinical course in severely burned patients.Methods: This was a single-center, retrospective, observational study. Patients' blood samples were collected on the day of injury and around 1 week later. Adipokines (adiponectin, angiotensinogen, chemerin, CXCL-12/SDF-1, leptin, resistin, vaspin, visfatin), various inflammatory cytokines, syndecan-1 and C1 esterase inhibitor were measured.Results: Thirty-eight patients were included. Resistin levels were significantly higher in the non-survivors versus survivors on Day 1 after burn injury. Hierarchical clustering analysis showed common clusters on Day 1 and at 1 Week after burn injury (resistin, IL-6, IL-8, IL10 and MCP-1). The correlation coefficient of resistin to SOFA score at 1 Week was significant. Logistic regression analysis showed a significant relation of resistin levels on Day 1 with prognosis; the area under the ROC curve for resistin was 0.801.Conclusions: In the acute phase of burns, resistin was associated with other pro-inflammatory cytokines and was related to the severity and prognosis of major burns. [ABSTRACT FROM AUTHOR]

• There is a high correlation between nCounter and RNA-seq data analysis the same sample. • Data from nCounter analysis of FFPE tissue correlated highly with data from RNA-seq analysis of FFPE tissue and fresh frozen tissue. • nCounter makes FFPE tissue avaliable in the field of molecular analysis. Formalin-fixed paraffin-embedded (FFPE) tissue is the most common source of archived material for genomic medicine. However, FFPE tissue is suboptimal for high-throughput analyses, such as RNA sequencing, because the quality of nucleic acids in FFPE tissues is low. We compared RNA-seq with the nCounter system to evaluate use of FFPE tissue for genomic medicine. Twelve fresh frozen bladder cancer samples were analyzed by both RNA sequencing and nCounter, and matched FFPE samples, by nCounter. Gene-expression values obtained by these two platforms were compared by calculating Pearson correlation coefficients for each sample (across the set of matched genes) and for each matched gene (across the set of samples). For each sample, gene-expression levels measured by RNA sequencing highly correlated with those measured by nCounter (all Pearson's R > 0.8, P < 0.0001), as seen by hierarchical clustering. RNA sequencing results for fresh frozen tissues positively correlated with nCounter results for FFPE tissues (R ranged from 0.675 to 0.873, all P < 0.0001). Correlation and hierarchical-clustering analyses of nCounter data from the two specimens demonstrated a strong positive correlation between each group (R ranged from 0.779 to 0.977, all P < 0.0001). Our findings suggest that the nCounter system is useful for assaying archived-FFPE samples and that the gene-expression signatures obtained from FFPE samples represent those from fresh frozen tissues. [ABSTRACT FROM AUTHOR]

Functional tricuspid regurgitation (FTR) is an independent risk factor for morbidity and mortality. New pathophysiological concepts but also new therapeutic options are justifying new knowledges for characterizing FTRs and their prognoses. Aim: To study echocardiographic criteria associated with prognosis in FTR-patients using a clustering method in two cohorts. Two hundred forty-one patients with at least severe (≥grade 3) TR were enrolled: 92 in the retrospective cohort (mean age 77.9 ± 13 years) and 149 in the prospective validation cohort. Hierarchical clustering analysis was conducted. Four parameters explained the clustering categorization according to a multinomial regression (right ventricular (RV) end-diastolic mid-cavity diameter, RV free-wall strain, right atrial (RA) volume index, RA strain; p = 0.0039). Three clusters were identified in the retrospective cohort: Cluster 1 had better right ventricular, left ventricular, and right atrial function than Cluster 2 (reduced RV and RA strain despite similar sizes). Cluster 3 included patients with severely dilated heart chambers associated to RV and RA dysfunctions. When applying the model in the validation (external) cohort, the rate of the primary endpoint (hospitalization for heart failure and/or death from any cause) was lowest in Cluster 1 (30.8% versus 48% and 58.8% in Clusters 2 and 3, respectively; p < 0.05). In FTR patients, different profiles of RV and RA remodeling are associated with different outcomes. Therefore, the diagnostic work-up in this clinical setting should include RV and RA characteristics. Under noninterventional management, the phenotype corresponding to preserved RV size and preserved RA and RV functions appears to have a better prognosis. [Display omitted] • In patients with a Functional Tricuspid Regurgitation, different profiles of RV and RA remodeling are associated with different outcomes. • Not only the right ventricular size or function are important, both should be consider. • Do not forget the right atrium. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the scale of modern industrial processes, more and more machine learning approaches using process variables for process monitoring and alarm analysis. The complex correlation of these variables makes a purely process knowledge-based variable division method unsatisfactory for process monitoring. To address this problem, a distributed process monitoring and abnormity root cause analysis model is built from a data-driven perspective. The proposed hierarchical clustering-based multicorrelation block partial least squares (HCMCB-PLS) divides the whole process into several blocks by using hierarchical clustering (HC), and the maximum information coefficient (MIC) is performed to select the correlation variables between the sub-blocks. PLS is conducted in each sub-block for process monitoring. Besides, a modified contribution-based abnormity root cause analysis strategy is developed, which uses an online distributed contribution analysis method to track the root cause variables. The effectiveness of proposed HCMCB-PLS is validated through a case study on the Tennessee-Eastman process. Comparative simulation results indicate that the HCMCB-PLS methodology outperforms other models in both industrial process monitoring and abnormity root cause analysis. • A distributed process monitoring and root cause analysis model is proposed. • Simulations of process monitoring and root cause analysis are carried out. • Simulation results verify the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Object re-identification is a retrieval task to identify the same identity, usually utilizing the ranking list to sort the features. However, most existing ranking lists only calculate the distance from the probe sample to its k -nearest neighbor samples in the gallery set but ignore the distribution of the gallery set. Meanwhile, we find that the gallery set tends to converge into different groups. When the probe lies near the boundary, its k -nearest neighbors often contain samples of different identities from the probe sample. This paper presents a Distribution Corrected Ranking List (DCRL) to correct the ranking by considering the distance between the probe sample and its nearest distribution (i.e., the cluster center). Specifically, given a probe sample, first, we cluster the gallery samples with the agglomerative hierarchical clustering method to obtain the cluster centers. Second, we compute the distance between the probe and the cluster centers. Third, the final ranking list is considered as the weighted combination of the original ranking list (i.e., the distance between the probe sample and its k -nearest neighbor in the gallery) and the distribution ranking list (i.e., the distance between the probe sample and its nearest cluster center). The proposed distribution corrected ranking list is suitable for large datasets because it does not require human intervention or labeled data. Experimental results on several benchmark re-identification datasets, such as Market-1501, DukeMTMC-reID, MSMT17, VeRi-776, and VeRi-Wild, show that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Unsupervised anomaly detection in time series remains challenging, due to the rare and complex patterns of anomalous data. Previous change point detection methods based on extreme learning machine and mutual information (ELM-MI) are potential solutions for this problem. However, the kernels in these methods are randomly initialized on test data, which imposes a constraint that these methods can only be used for offline inference. Moreover, these methods are limited in utilizing temporal contexts, and require the ensemble of multiple models to improve the robustness. To tackle these problems, we introduce a multivariate ELM-MI framework, and combine it with a dynamic kernel selection method, which performs a hierarchical clustering procedure on unlabeled training data and utilizes the clusters to determine the kernels in ELM-MI. In this way, our method can tackle the unsupervised online detection of various anomalous (e.g., point anomalies and group anomalies) and reduce the computational cost. Extensive experiments on three public datasets and our collection of real-life 4G Long-Term Evolution data demonstrate that the proposed method outperforms state-of-the-art methods in terms of effectiveness and efficiency. For demo, see this link: https://personal.ntu.edu.sg/ezplin/NC-demo.htm. [ABSTRACT FROM AUTHOR]

• Fasciculations were abundantly detected on muscle ultrasonography from the early stage of amyotrophic lateral sclerosis. • Fasciculation in the brainstem and thoracic regions has high specificity in the diagnosis of amyotrophic lateral sclerosis. • Via machine learning, we developed a muscle ultrasonography-based diagnostic tool for early-stage amyotrophic lateral sclerosis. Although fasciculation on muscle ultrasonography (MUS) is useful in diagnosing amyotrophic lateral sclerosis (ALS), its applicability to early diagnosis remains unclear. We aimed to develop and validate diagnostic models especially beneficial to early-stage ALS via machine learning. We investigated 100 patients with ALS, including 50 with early-stage ALS within 9 months from onset, and 100 without ALS. Fifteen muscles were bilaterally observed for 10 s each and the presence of fasciculations was recorded. Hierarchical clustering and nominal logistic regression, neural network, or ensemble learning were applied to the training cohort comprising the early-stage ALS to develop MUS-based diagnostic models, and they were tested in the validation cohort comprising the later-stage ALS. Fasciculations on MUS in the brainstem or thoracic region had high specificity but limited sensitivities and predictive profiles for diagnosis of ALS. A machine learning-based model comprising eight muscles in the four body regions had a high sensitivity (recall), specificity, and positive predictive value (precision) for both early- and later-stage ALS patients. We developed and validated MUS-fasciculation-based diagnostic models for early- and later-stage ALS. Fasciculation detected in relevant muscles on MUS can contribute to the diagnosis of ALS from the early stage. [ABSTRACT FROM AUTHOR]

• Cross referencing the RIKEN FANTOM5 CAGE peak data with the annotated core matrisome reveals a tissue- and cell-specific catalog of actively transcribed ECM genes. • Tracking individual ECM molecules reveals a tissue- and cell-specific signature indicative of sites of active transcription. • Clustering exposes gene subsets with positive and negative correlations in cells and tissues with mechanistic implications. The extracellular matrix (ECM) is a highly dynamic, well-organized acellular network of tissue-specific biomolecules, that can be divided into structural or core ECM proteins and ECM-associated proteins. The ECM serves as a blueprint for organ development and function and, when structurally altered through mutation, altered expression, or degradation, can lead to debilitating syndromes that often affect one tissue more than another. Cross-referencing the FANTOM5 SSTAR (Semantic catalog of Samples, Transcription initiation And Regulators) and the defined catalog of core matrisome ECM (glyco)proteins, we conducted a comprehensive analysis of 511 different human samples to annotate the context-specific transcription of the individual components of the defined matrisome. Relative log expression normalized SSTAR cap analysis gene expression peak data files were downloaded from the FANTOM5 online database and filtered to exclude all cell lines and diseased tissues. Promoter-level expression values were categorized further into eight core tissue systems and three major ECM categories: proteoglycans, glycoproteins, and collagens. Hierarchical clustering and correlation analyses were conducted to identify complex relationships in promoter-driven gene expression activity. Integration of the core matrisome and curated FANTOM5 SSTAR data creates a unique tool that provides insight into the promoter-level expression of ECM-encoding genes in a tissue- and cell-specific manner. Unbiased clustering of cap analysis gene expression peak data reveals unique ECM signatures within defined tissue systems. Correlation analysis among tissue systems exposes both positive and negative correlation of ECM promoters with varying levels of significance. This tool can be used to provide new insight into the relationships between ECM components and tissues and can inform future research on the ECM in human disease and development. We invite the matrix biology community to continue to explore and discuss this dataset as part of a larger and continuing conversation about the human ECM. An interactive web tool can be found at matrixpromoterome.github.io along with additional resources that can be found at dx.doi.org/10.6084/m9.figshare.19794481 (figures) and https://figshare.com/s/e18ecbc3ae5aaf919b78 (python notebook). [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes (GD) and maternal excess weight are common pregnancy conditions that increase the risk of future complications for both the mother and her offspring. Their consequences on neurodevelopment are widely described in the literature, but less is known concerning the potential transgenerational influence on the brain structure.Methods: We used a combination of support vectors machine and hierarchical clustering to investigate the potential presence of anatomical brain differences in a sample of 109 children aged six years, born to mothers with overweight or obesity, or to mothers diagnosed with GD during pregnancy.Results: Significant effects are visible in the brain of children born to mothers with GD associated with pregestational excess weight, especially overweight instead of obesity. No differences in children's brain were observed when considering those born to normal-weight mothers.Conclusions: Our study highlights the need for clinical attention of pregnant women at risk to develop GD, and especially those with pregestational excess weight, since this status was found to be associated with detectable transgenerational brain changes. These effects may be due to the absence of specific and individualized intervention in these mothers during pregnancy. [ABSTRACT FROM AUTHOR]

The absence of displacement monitoring data presents a challenge to real-time dam safety monitoring. This article introduces a novel method for reconstructing missing displacements, comprehensively taking into account the spatiotemporal correlation and causal effect mechanism of displacement. Firstly, adaptive weighted derivative dynamic time warping (AWDDTW) and adaptive weighted dynamic time warping (AWDTW), in conjunction with clustering algorithms, are proposed to extract spatiotemporal correlation among dam displacements. Secondly, by stacking residual blocks within the residual network and densifying the shortcut connections between residual blocks, the deep stacked residual network (DS-ResNet) is established to effectively capture the intricate mappings between displacements and various factors. Finally, using two arch dams, as examples, we simulated scenarios of continuous long-term and multiple short-term missing data to validate the new method proposed in this study. The results indicate that the proposed clustering algorithm can accurately compute the similarity between displacement sequences of different lengths and monitoring frequencies, thereby providing more precise displacement point partitioning results. Compared to models such as random forests, relevance vector machines, multilayer perceptron, ResNet and Transformer, the DS-ResNet demonstrates superior capability in identifying and extracting effective information from displacement sequences under both continuous long-term and multiple short-term missing data scenarios. Compared to methods that solely consider spatiotemporal correlation or causal effect mechanism of displacement, the proposed method can more comprehensively reflect the operating patterns of arch dams and more accurately reconstruct missing displacement values. The research presented herein offers new technical means and solution approaches for dam safety monitoring and operational management. • Both the spatiotemporal correlation and causal effect mechanism of dam displacement are considered. • Adaptive weighted derivative dynamic time warping (AWDDTW) and adaptive weighted dynamic time warping (AWDTW) are proposed. • Clustering based on AWDDTW and AWDTW enables more accurate partitioning of monitoring points. • Deep stacked residual network (DS-ResNet) can reconstruct the missing value more accurately. • Taking two dams as examples, two missing scenarios are set as research data. [ABSTRACT FROM AUTHOR]

• A pattern-based colorimetric sensor array chip for the identification of typical gas mixtures - Baijiu aroma was constructed. • Nanomaterials can adsorb gas molecules and enhance the reaction of trace gases with the sensor array. • Pattern recognition-based data analysis proved to be perfectly distinguishable between 16 different brands of commercial Baijiu. Gas mixtures are comprised of numerous complex components, making the accurate identification a continuing challenge due to the significant limitations of existing detection methods. Herein, we developed a low-cost and sensitive pattern-based colorimetric sensor array chip for the identification of typical gas mixtures - Baijiu aroma. Specifically, three nanomaterials (AuNPs, MoS 2 and ZIF-8) were prepared to adsorb gas molecules and enhance the reaction of trace gases with sensor arrays. The colorimetric sensor array chip took only 5 min to complete the recognition of Baijiu aromas and effectively avoided recognition errors caused by sommelier olfactory fatigue. Notably, the hierarchical cluster analysis (HCA) revealed no confusion or errors in the results of 80 tests across the five trials involving 16 commercial Baijius. Even fake Baijius with similar ingredients could be easily identified, demonstrating the excellent analytical capabilities of the system in Baijiu identification and its significant potential for quality control of Baijius. [ABSTRACT FROM AUTHOR]

Achieving carbon neutrality by 2050 requires evaluating and retrofitting existing buildings. However, despite the numerous studies on energy analytics, they usually focus on energy consumption patterns and motifs rather than encompassing various energy usage characteristics. This study proposes a novel symbolic hierarchical clustering for building energy analytics at the city level. It utilizes change-point model (CPM) parameters to represent building energy usage, performance, occupant behavioral characteristics. The clustering method based on the CPM parameters defines energy performance signatures (EPS) for determining their energy characteristics and as symbolic data transformation. In a case study conducted in Gangwon, South Korea, five different energy performance signatures (EPSs 1–5) showing their unique energy characteristics were determined for commercial buildings. EPS1 to 3 were classified as signatures with good performance (65.5% of all buildings) while EPS4 and 5 were classified as signatures with bad performance (34.5%). Using this EPS symbolic data, an EPS map was visualized and analyzed from various perspectives. For example, buildings that showed a continuous or overall decline in envelope performance over five years were among the oldest buildings (construction completion date closer to 1978; 7.9%). Despite poor envelope performance, buildings with lower energy usage showed a tendency for occupants to delay heating (28.4%). The proposed method can contribute to the data-driven building energy analytics in providing detailed insights into energy usage patterns, building energy performance, and occupant behavioral characteristics at the city level. The effectiveness of open-source energy data for urban building energy analysis would be improved through the proposed method. [Display omitted] • Energy performance signature (EPS)-based clustering is proposed for urban building energy analysis. • A combined data-driven five-parameter change-point model and clustering using symbolically transformed data are proposed. • A case study is conducted to analyze energy usage characteristics of commercial buildings in Gangwon, South Korea. • The EPS map from the proposed method provides information on the change in envelope performance, energy consumption pattern, and occupant behavior. [ABSTRACT FROM AUTHOR]

We aimed to identify clinically relevant clusters among patients with post-Covid-19 condition (PCC) and assess prognosis overall and within clusters. Prospective cohort study of patients with PCC attending a rehabilitation clinic. We monitored patient reported outcome measures (PROMs): EuroHIS quality of life and symptoms. Unsupervised hierarchical cluster analyses were performed to identify clusters of patients with different quantity of symptoms, and symptoms presenting together. Preliminary findings on symptom prevalence and quality of life at 12 months are reported. Among 409 patients, 70.4% were women, with an average baseline of 20.3 (SD 6.8) symptoms. Three clusters emerged based on symptom quantity, labelled by the average number of symptoms at baseline: Cluster-11 (17% of all patients), Cluster-17 (35%), and Cluster-25 (48%). Multinomial logistic regression showed female sex, multiple comorbidities predicting more symptoms. Four symptom-based clusters were defined: fatigue and cognitive complaints; pain, trouble sleeping, palpitations and other symptoms; gastrointestinal symptoms; and emotion-related symptoms. Linear regression models showed that female sex, multiple comorbidities, anxiety, use of antidepressants, BMI and smoking were among the determinants of symptom clusters. In 12-month follow-up, symptom count decreased, and quality of life improved across all clusters, with 9% having good quality of life at baseline and 33% at 12 months. Four patient clusters based on symptoms were identified in the PCC cohort. Prognosis was favorable across all clusters, with symptom reduction and improved quality of life observed. Female sex, comorbidities, BMI, and mental-health related variables predicted higher symptom burden, suggesting multifactorial origins of PCC. • Patients with post-Covid condition were studied at a rehabilitation clinic. • They had on average 20 symptoms from the list of 35 symptoms at baseline. • Four symptom clusters were defined in hierarchical clustering. • At follow up at 12 months, the patients had on average 15 symptoms. • Quality of life measured by EuroHIS improved in all symptom clusters. [ABSTRACT FROM AUTHOR]