This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

BACKGROUND: Childhood overweight and obesity is a global concern and has increased in Spain over the last decades. Combinations of lifestyle behaviors (i.e., diet, sleep, and sedentarism) are highly related to weight status. Therefore, this study aimed to identify lifestyle patterns among children from Madrid City, and analyze associations with the prevalence of overweight, obesity, and abdominal obesity, considering socio-economic factors.
METHODS: A cross-sectional analysis was conducted on 4545 children from the ENPIMAD study with data on diet, sleep, anthropometric, and socio-economic variables. K-means cluster analysis was used to identify lifestyle clusters, and logistic regressions were used to examine the associations between socio-economic indicators and cluster membership, and between clusters and weight status.
RESULTS: Findings show three lifestyle clusters (healthy, mixed, and unhealthy), with boys and older children more represented in the unhealthy cluster. Food insecurity and low socio-economic status were associated with unhealthier clusters in boys and girls. Children in unhealthier clusters were more likely to have obesity and abdominal obesity. However, these associations disappeared in girls after controlling for food insecurity.
CONCLUSIONS: These results provide insight into the combination of behaviors and socio-economic factors associated with childhood obesity that may aid in the design of future interventions.

Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Z\'s perceptions toward plant-based foods and diets and explore the relationship that attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet have with willingness to adopt green-eating practices. Using cross-sectional data from university students in Greece, India, and the UK, various tools were employed to determine the factors influencing youths\' consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students\' viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Z\'s willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing, and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents of plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of animal-based foods, dissatisfaction with plant-based foods\' attributes, and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a \"homogeneity\" in the factors influencing youths\' intention to adopt plant-based diets, with attitude components, meal preparation indicators, perceived barriers to eating \"green\", and personal factors, such as self-assessed knowledge of healthy eating and physical activity, being strongly associated with students\' willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths\' transition to a more sustainable lifestyle.

BACKGROUND: Recent operational criteria for treatment-resistant schizophrenia (TRS) recognized positive and negative symptoms. TRS patients may have heterogeneity in negative symptoms, but empirical data were lacking. We aimed to characterize TRS patients based on negative symptoms using cluster analysis, and to examine between-cluster differences in social functioning.
METHODS: We administered the Clinical Assessment Interview of Negative symptoms (CAINS), Brief Negative Symptom Scale (BNSS), the Positive and Negative Syndrome Scale (PANSS) and the Social and Occupational Functional Assessment (SOFAS to 126 TRS outpatients. All patients also completed the Temporal Experience of Pleasure Scale (TEPS), the Emotion Expressivity Scale (EES), and the Social Functional Scale (SFS). A two-stage hierarchical cluster analysis was performed with the CAINS, TEPS and EES as clustering variables. We validated the clusters using ANOVAs to compare group differences in the BNSS, PANSS, SOFAS and SFS.
RESULTS: Clustering indices supported a 3-cluster solution. Clusters 1 (n = 46) and 3 (n = 16) exhibited higher CAINS scores than Cluster 2 (n = 64), and were negative-symptom TRS subtypes. Cluster 1 reported lower TEPS than Cluster 3; but Cluster 3 reported lower EES than Cluster 1. Upon validation, Clusters 1 and 3 exhibited higher BNSS scores than Cluster 2, but only Cluster 1 exhibited lower SOFAS and higher PANSS general symptoms than Cluster 2. Both Clusters 1 and 3 had higher self-report functioning than Cluster 2.
CONCLUSIONS: We provided evidence for heterogeneity of negative symptoms in TRS. Negative symptoms can characterize TRS patients and predict functional outcome.

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features\' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering\'s user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters \"bundles\". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.

BACKGROUND: the study of the possible determinants of the rise and fall of infections can be of great relevance, as was experienced during the COVID-19 pandemic. One of the methods to understand whether determinants are simultaneous or develop through contiguity between different areas is the study of the diagnostic replication index RDt among regions.
OBJECTIVE: to introduce the analysis of RDt variability and the subsequent application of a recently introduced functional clustering method as highly useful procedures for recognizing the presence of clusters with similar trends in epidemic curves.
METHODS: within the considered period, trends in regional RDt are analyzed in detail over four different time intervals.
METHODS: to exemplify this methodology, the study of variability in the period from the end of 2021 to the beginning of 2022 may be of interest.
METHODS: the variability in the regional RDt indices is assessed by means of the correlation coefficient weighted with respect to the populations of the individual regions. The clustering procedure is applied to the time series of absolute RDt values.
RESULTS: it emerges that the periods of increasing variability in the RDt correspond to the initial growth or decrease in the number of infections, while functional clustering identifies macro-areas in which the epidemic curves have had similar trends. What caused contagions to increase seems to relate to a factor that is not specific to certain areas, with the contribution in some cases of a contagion dynamic between adjacent areas.
CONCLUSIONS: the variability in the trend of regional diagnostic replication indices, which are calculated with only a few days delay, is a further indicator for the early detection of major changes in the trend of epidemic curves. The clustering of epidemic index curves may be useful to determine whether determinants act simultaneously or by contiguity between adjacent areas.

This study describes the first detection of Ixodes ventalloi in Slovakia. Two engorged females of I. ventalloi were collected from Dunnocks (Prunella modularis) captured in eastern Slovakia. The identification of females was based on morphological and molecular 16S rRNA gene features. Phylogenetic analysis revealed a classification of the females into distinct genogroups. Moreover, comparative morphological analysis highlighted variations between the two females, particularly in the curvature of the auriculae, the shape of coxa I, and the internal spur. These findings suggest the potential for varied phenotypes of I. ventalloi correlated with their genogroups. Nonetheless, I. ventalloi population establishment within Slovakia necessitates further investigation through flagging or drag sampling.

BACKGROUND: Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions.
RESULTS: The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit\'s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.
CONCLUSIONS: In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.

OBJECTIVE: To determine if self-reported fatigue, anxiety, depression, cognitive difficulties, health-related quality of life, disease activity scores and neuropsychological battery (NB) cluster into distinct groups in patients with SLE based on symptom intensity and if they change at 1-year follow-up.
METHODS: This is a retrospective analysis of consecutive consenting patients, followed at a single centre. Patients completed a comprehensive NB, the Beck Anxiety Inventory, Beck Depression Inventory, Fatigue Severity Scale, Short-Form Health Survey Physical Component Summary and Mental Component Summary scores and the Perceived Deficits Questionnaire. Disease activity was assessed by Systemic Lupus Erythematosus Disease Activity Index 2000. Ward\'s method was used for clustering and principal component analysis was used to visualise the number of clusters. Stability at 1 year was assessed with kappa statistic.
RESULTS: Among 142 patients, three clusters were found: cluster 1 had mild symptom intensity, cluster 2 had moderate symptom intensity and cluster 3 had severe symptom intensity. At 1-year follow-up, 49% of patients remained in their baseline cluster. The mild cluster had the highest stability (77% of patients stayed in the same cluster), followed by the severe cluster (51%), and moderate cluster had the lowest stability (3%). A minority of patients from mild cluster moved to severe cluster (19%). In severe cluster, a larger number moved to moderate cluster (40%) and fewer to mild cluster (9%).
CONCLUSIONS: Three distinct clusters of symptom intensity were documented in patients with SLE in association with cognitive function. There was a lower tendency for patients in the mild and severe clusters to move but not moderate cluster over the course of a year. This may demonstrate an opportunity for intervention to have moderate cluster patients move to mild cluster instead of moving to severe cluster. Further studies are necessary to assess factors that affect movement into moderate cluster.

OBJECTIVE: To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.
METHODS: We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.
RESULTS: Five distinct clusters of accumulating LTC trajectories were identified and characterised as: \'no LTC\' (18.57%), \'single LTC\' (31.21%), \'evolving multimorbidity\' (25.82%), \'moderate multimorbidity\' (17.12%) and \'high multimorbidity\' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the \'high multimorbidity\' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the \'no LTC\' cluster.
CONCLUSIONS: The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.