Multivariate Comparisons Research Articles

Neuro-Immune Communication at the Core of Craving-Associated Brain Structural Network Reconfiguration in Methamphetamine Users

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347.52±99.25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0.35, P <0.01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR<0.05), with strong alterations in somatomotor regions (PFDR<0.05) and moderate alterations in high-level association networks (PFDR<0.05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

Does It Matter Whether to Use Circular or Square Plots in Forest Inventories? A Multivariate Comparison

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m2), and aerial biomass.

Efficacy and feasibility of a novel semi-facial respirator with chitosan nanoparticles on the incidence of SARS-CoV-2 infection in healthcare professionals: randomized controlled trial

BackgroundEvidence suggests that semi-facial respirators provide protection against contamination in high-risk environments, although the COVID-19 pandemic called for greater protection and viral inactivation capacity. Thus, the aim of this study was to investigate the efficacy of a novel semi-facial respirator containing chitosan nanoparticles, compared with a conventional N95 respirator on the incidence of laboratory-confirmed SARS-CoV-2 in healthcare professionals. The secondary outcomes were influenza infection, usability and comfort.MethodsRandomized controlled trial within a large public hospital (reference for COVID-19 patients) carried out between March 2021 and June 2023. We included 230 healthcare professionals exposed to SARS-Cov-2 and influenza, working in emergency departments, hospital wards, and intensive care units. Participants were assessed at baseline, after 10 days, and 21 days of follow-up. Researchers, participants, and outcome assessors were blinded to the allocated groups. Outcomes were analyzed by bivariate and multivariate comparisons using logistic regression. Crude (cOR) and adjusted odds ratios (aOR) were estimated, followed by 95% confidence intervals (CIs 95%). We adopted intention-to-treat (ITT) and complete-case (CC) analyses.ResultsBaseline characteristics were considered homogeneous between groups, and usability and comfort were reported as excellent in both groups. Non-significant differences were found for all outcomes, both in the ITT and CC analyses. The incidence of COVID-19 and influenza were, respectively, cOR: 0.96 [CI95%: 0.21–4.42] and cOR: 1.25 [CI95%: 0.34–4.62]; and aOR: 1.08 [CI95%: 0.21–5.47] and aOR: 1.11 [CI95%: 0.17–7.01].ConclusionsWe found that the incidence of SARS-Cov-2 and influenza infections were similar between the new respirator compared to the conventional respirator. Furthermore, we observed that usability and comfort were similar and considered excellent for both respirators.Trial registrationClinicaltrials.gov (NCT04490200, 29/07/2020).

Detailed multivariate comparisons of environments with mobility oriented parity

Multivariate model projections onto conditions that differ from those under which the models were calibrated can result in uncertainty owing to response extrapolation. Therefore, interpretations of models transferred to conditions not analogous to those of model calibration must be done carefully to avoid misleading conclusions. A good practice when producing model transfers is to assess the degree of dissimilarity between calibration and transfer conditions. The mobility oriented parity (MOP) metric is a tool commonly used to quantify such dissimilarities. Our study elucidated the details of MOP, and expanded the interpretability of results when transfer conditions are not analogous to those of model calibration. We implemented a more detailed characterization of non-analogous conditions previously only identified as outside calibration ranges. In addition, we quantified the sensitivity of MOP to the density and position of reference conditions in environmental space, and explored the effect of sample size on MOP results. We show that detailed characterizations of non-analogous conditions can help to determine which variables are different between the two areas and how. This information can be used to understand causes of environmental dissimilarity and to identify variables that could be excluded from ecological niche modeling owing to extrapolation risks associated with them. Our results also show that dissimilarity values calculated in comparisons offer good indicators of how reliable interpretations can be in areas with conditions not analogous to those of reference. The tools developed and implemented in this study provide a useful, quantitatively efficient means of characterizing extrapolation uncertainty in model transfer studies, a best practice when using multivariate ecological niche models to make predictions about species’ distributions. An extended version of the mobility oriented parity metric helps identify variables for which risks of model extrapolations can be expected. Distinct MOP metrics used to characterize dissimilarity show comparable general patterns in transfer areas, despite differences in values. As MOP results are influenced by background sampling size, the definition of this sample should be aimed to appropriately characterize calibration areas.

The Profile of the Italian Centers for Cognitive Disorders and Dementia in the Context of New Drugs in Alzheimer's Disease.

The wait for the upcoming disease-modifying therapies (DMT) for Alzheimer's disease in Europe is raising questions about the preparedness of national healthcare systems to conduct accurate diagnoses and effective prescriptions. In this article, we focus on the current situation in Italy. The primary goal is to propose a profile of the Italian Centers for Cognitive Disorders and Dementias (CCDDs) that could be taken into consideration by regional and autonomous provincial authorities when deciding on the prescribing centers for DMT. Based on responses to a national survey on CCDDs in Italy, we identified the CCDDs that meet the requirements for effective prescription: 1) Multidisciplinary team; 2) Minimum Core Test for the neuropsychological assessment; 3) PET, CSF, and Brain MRI assessments. Univariate and multivariate comparisons were conducted between CCDDs that met the criteria and the others. Only 10.4% of CCDDs met the requirements for effective DMT prescription, mainly located in Northern Italy. They are also characterized by longer opening hours, a higher number of professionals, a university location, and a higher frequency of conducting genetic tests, and could potentially result in prescribing centers. The findings suggest that the Italian national healthcare system may benefit from further enhancements to facilitate the effective prescription of DMTs. This could involve initiatives to reduce fragmentation, ensure adequate resources and equipment, and secure sufficient funding to support this aspect of healthcare delivery.

Altitude is correlated with body size differences among Cotesia flavipes (Hymenoptera: Braconidae) wasps collected in two mountain ranges

Living at high altitudes impose physiological and ecological challenges to which species may respond altering their body size, body proportions, and the shape of their body parts. Despite the importance of this topic for understanding the origin of species diversity, little attention has been invested in this phenomenon at the populational level. This paper study the relationship between altitude and body size, body proportions, and forewing shape venation of two populations of the parasitoid wasp Cotesia flavipes. Wasps were collected from Diatraea spp. larvae from sugarcane crops in two Colombian mountain ranges that cover between 600 m and 2143 m of altitude. Linear measurements of different body regions and geometric morphometrics of the forewing were subject to multivariate comparisons and allometric analyses to assess variation and to compare trends between ranges. Central (600 m to 1704 m) and Eastern Cordillera (877 m to 2143 m) populations showed different trends between body size and altitude. Allometric trends were not uniform within or between populations nor between structures. The allometric slopes of five body measurements from a single altitude differed from these from its own mountain range suggesting that body size trends along the cordilleras are a consequence of altitude and not of intrinsic body resource allocation processes. Wing shape between populations differed; however, these changes were poorly related to altitude. In agreement with recent studies in other groups, the observed allometric and wing shape differences between the two C. flavipes populations could be a plasticity response to altitude with interesting implications for posterior genetic differentiation.

Acumulación y degradación de solutos en bayas Vitis vinifera L. en climas contrastantes

Grape ripening is a complex process conditioned by climatic factors, which influence the evolution of solutes and define its composition. The objective of the study was to evaluate the behavior of the red Tannat variety in contracting climatic situations, considering the dynamics of berry growth, the rate of accumulation and reduction of solutes during ripening, and their composition at harvest. The trial was installed in two regions, warm and temperate climate, in rainfed commercial vineyards, during 2018 and 2019. Primary berry composition at ripening and secondary composition at harvest were determined. Multivariate analysis and comparison of means (Fisher’s LSD test) were performed. The climatic type (region) conditions the evolution of solutes at ripening, but their interaction with annual conditions determines berry composition. The annual variability is explained by rainfall, with a direct influence on the duration of the cycle and the ripening, size, and composition of the berry. In warm climates, there is greater sensitivity to the ripening year effect, with a strong influence on the rapid phase. In temperate climates, the greater stability between years results from a thermal condition more favorable to ripening processes.

Multivariate comparison of taxonomic, chemical and operational data from 80 different full-scale anaerobic digester-related systems

BackgroundThe holistic characterization of different microbiomes in anaerobic digestion (AD) systems can contribute to a better understanding of these systems and provide starting points for bioengineering. The present study investigates the microbiome of 80 European full-scale AD systems. Operational, chemical and taxonomic data were thoroughly collected, analysed and correlated to identify the main drivers of AD processes.ResultsThe present study describes chemical and operational parameters for a broad spectrum of different AD systems. With this data, Spearman correlation and differential abundance analyses were applied to narrow down the role of the individual microorganisms detected. The authors succeeded in further limiting the number of microorganisms in the core microbiome for a broad range of AD systems. Based on 16S rRNA gene amplicon sequencing, MBA03, Proteiniphilum, a member of the family Dethiobacteraceae, the genus Caldicoprobacter and the methanogen Methanosarcina were the most prevalent and abundant organisms identified in all digesters analysed. High ratios for Methanoculleus are often described for agricultural co-digesters. Therefore, it is remarkable that Methanosarcina was surprisingly high in several digesters reaching ratios up to 47.2%. The various statistical analyses revealed that the microorganisms grouped according to different patterns. A purely taxonomic correlation enabled a distinction between an acetoclastic cluster and a hydrogenotrophic one. However, in the multivariate analysis with chemical parameters, the main clusters corresponded to hydrolytic and acidogenic microorganisms, with SAOB bacteria being particularly important in the second group. Including operational parameters resulted in digester-type specific grouping of microbes. Those with separate acidification stood out among the many reactor types due to their unexpected behaviour. Despite maximizing the organic loading rate in the hydrolytic pretreatments, these stages turned into extremely robust methane production units.ConclusionsFrom 80 different AD systems, one of the most holistic data sets is provided. A very distinct formation of microbial clusters was discovered, depending on whether taxonomic, chemical or operational parameters were combined. The microorganisms in the individual clusters were strongly dependent on the respective reference parameters.Graphical

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across measures

Introduction Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging measures and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between measures. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox (https://github.com/neuralabc/mvcomp (https://github.com/neuralabc/mvcomp)). The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI measures or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. Methods &amp; Results The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI measure (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Discussion Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Rediscovery, systematics and conservation of an enigmatic freshwater crayfish (Parastacidae) from the Australian monsoon tropics

AbstractFreshwater wetlands are among the world's most valuable ecosystems, supporting diverse biota and critical ecological services, yet these habitats have suffered extensive and pervasive anthropogenic disturbance. Northern Australia represents a rare example of a relatively unmodified, vast wetland habitat. The freshwater crayfishes of the region are poorly documented, with one enigmatic species, the nutcracker yabby Cherax nucifraga, described from a single individual sampled opportunistically from the stomach of a predatory fish. Here we report on the rediscovery of C. nucifraga from a relatively limited distribution in semi‐permanent coastal freshwater wetlands. Field studies were conducted to inform natural resource management and conservation. Genome skimming to recover mitogenomes, 18S–28S and histone sequences demonstrated a sister relationship with two congeners from the tropics, and moderate molecular genetic substructure was apparent within C. nucifraga between mainland and Melville Island locations. Cherax nucifraga is characterised by the presence of a strawberry‐coloured soft patch on the outer margin of the claw, uniquely present in both mature males and females. Meristic and multivariate morphometric comparisons are made with the co‐occurring redclaw Cherax quadricarinatus and the allopatric whiteclaw yabby Cherax bicarinatus, with a summary of diagnostic traits developed into a visual guide and key. Programs to further understand ecology, threats and traditional ecological knowledge will help to inform the future conservation management of the species in the face of increasing development and environmental change to northern Australian coastal freshwater wetlands. Specific conservation actions include identifying and protecting refuge habitats and preventing incursions by other Cherax species.

Higher odds of meniscectomy compared with meniscus repair in a young patient population with increased neighbourhood disadvantage

ObjectivesTo investigate the impact of demographic and socioeconomic factors on the management of isolated meniscus tears in young patients and to identify trends in surgical management of meniscus tears based...

Multiparametric analysis of the specific immune response against SARS-CoV-2

Background SARS-CoV-2, which causes COVID-19, has killed more than 7 million people worldwide. Understanding the development of postinfectious and postvaccination immune responses is necessary for effective treatment and the introduction of appropriate antipandemic measures. Objectives We analysed humoral and cell-mediated anti-SARS-CoV-2 immune responses to spike (S), nucleocapsid (N), membrane (M), and open reading frame (O) proteins in individuals collected up to 1.5 years after COVID-19 onset and evaluated immune memory. Methods Peripheral blood mononuclear cells and serum were collected from patients after COVID-19. Sampling was performed in two rounds: 3-6 months after infection and after another year. Most of the patients were vaccinated between samplings. SARS-CoV-2-seronegative donors served as controls. ELISpot assays were used to detect SARS-CoV-2-specific T and B cells using peptide pools (S, NMO) or recombinant proteins (rS, rN), respectively. A CEF peptide pool consisting of selected viral epitopes was applied to assess the antiviral T-cell response. SARS-CoV-2-specific antibodies were detected via ELISA and a surrogate virus neutralisation assay. Results We confirmed that SARS-CoV-2 infection induces the establishment of long-term memory IgG+ B cells and memory T cells. We also found that vaccination enhanced the levels of anti-S memory B and T cells. Multivariate comparison also revealed the benefit of repeated vaccination. Interestingly, the T-cell response to CEF was lower in patients than in controls. Conclusion This study supports the importance of repeated vaccination for enhancing immunity and suggests a possible long-term perturbation of the overall antiviral immune response caused by SARS-CoV-2 infection.

Retail Demand Forecasting: A Multivariate Approach and Comparison of Boosting and Deep Learning Methods

Retail demand forecasting is a highly intricate and multilevel problem as it may include numerous products, under multiple categories, and potentially sold in many different stores. Forecasts may also process products in isolation or collectively so as to detect mutual correlations. In technical terms, retail demand forecasting may be broken down to a multivariate time series forecasting problem where the time axis is prevalent and introduces added complexity which necessitates proper usage of advanced methodologies with machine/deep learning backgrounds. Popular boosting regressors, such as XGBoost and LightGBM, are excellent machine learning candidates with extensive bibliographic backgrounds covering multiple scientific fields and applications including generic multivariate time series forecasts. The Temporal Convolutional Network and the Temporal Fusion Transformer are recently proposed deep neural network architectures that found success in specific multivariate scenarios but are yet to be tested in a variety of related fields such as retail demand forecasting. Therefore, within this paper, these novel in-the-field models are compared to the aforementioned boosting approaches alongside popular statistical univariate methods, namely Exponential Smoothing and SARIMA. To properly compare the selected models and attempt to generalize their usability, two separate datasets are analyzed and forecasted; an item sales dataset with 500 time series and a category sales dataset with 540 time series. The findings suggest that the deep learning solutions are the better predictors, with the Transformer model surpassing the Boosting solutions by up to 16.8% sMAPE decrease and the statistical approaches by up to 28.6% decrease, further substantiating the notion that deep learning techniques are exceptionally promising in handling large-scale, non-linear and outlier data whilst suggesting that retail demand forecasting does benefit from multivariate approaches and the application of advanced machine learning methods.

Multivariate stochastic comparisons of sequential order statistics with non-identical components

Multivariate stochastic comparisons of sequential order statistics with non-identical components

Climatic variability in the Armenian Highlands as the backdrop to hominin population dynamics 50–25 ka

The impact of Late Pleistocene climatic oscillations, volcanism and the diverse terrain of the Armenian Highlands affected hominin population dynamics and movements through the region. To test different scenarios for the period 50–25 ka regarding expansion, adaptive response, intra-population interactions and extinction, we need local on-site paleoclimatic data found in association with hominin occupations. However, this approach has been hampered by the dearth and highly uneven spatiotemporal recovery of paleoclimatic data from prehistoric sites across the region. In this study, we analyze multiple assemblages of fossil micromammals from the Late Middle Paleolithic (LMP) Lusakert-1 rockshelter (two fossil-yielding strata; <55 ka) and Early Upper Paleolithic (EUP) Aghitu-3 Cave (four fossil-yielding strata; ∼39–24 ka) using geometric morphometric-aided taxonomy, to quantify the representation of different species among the abundant and diverse arvicoline genera (Microtus and Chionomys; mean = 58% of assemblages). We combined this approach with quantitative species-level paleoclimatic reconstructions (Bioclimatic Model; 14 rodent taxa) to determine changes in habitat composition as well as temperature and precipitation shifts during the Late Middle–Early Upper Paleolithic. Taphonomic analysis was conducted to establish isotaphonomy among the assemblages. The analysis shows higher than present-day absolute temperatures for the series of occupations at both Lusakert-1 and Aghitu-3, making it unlikely that hominin activity at the sites coincided with extreme cold-climate phases. A multivariate comparison of the taxonomic abundances (genus level) across a wider sample of regional Middle Paleolithic (MP) and Upper Paleolithic (UP) cave sites (Hovk-1, Kudaro-1, Mezmaiskaya, Satsurblia and Dzudzuana) indicates the possibility of cold-period occupation at only two of the sites, Mezmaiskaya Layers 3 and 2 and Kudaro-1 Layer 3. We surmise that the manufacturers of LMP lithic industries in the Caucasus were well adapted to these challenging environments, thus undermining arguments that rapid climate change through the LMP/EUP period could alone have determined the course of population dynamics in this region.

Differential sex-related effect of left ventricular ejection fraction trajectory on the risk of mortality and heart failure readmission following hospitalization for acute heart failure: A longitudinal study.

There is limited information on the sex-specific longitudinal changes of left ventricular ejection fraction (LVEF) after an acute heart failure (AHF) hospitalization. We aimed to investigate whether LVEF trajectories over time and their impact on mortality and AHF readmission rates differ between men and women. We conducted a retrospective sex-specific analysis of longitudinal LVEF measurements (n = 9581) in 3383 patients with an index hospitalization for AHF in a single tertiary-level hospital. Statistical techniques suited for longitudinal data analysis were used. The mean age of the sample was 73.8 ± 11.2 years, and 47.9% were women. The mean LVEF was 49.4 ± 15.3%. At a median follow-up of 2.58 years (interquartile range 0.77-5.62), we registered 2197 deaths (64.9%) and 2597 AHF readmissions in 1302 (38.5%) patients. The longitudinal analysis showed that women had consistently higher LVEF values throughout the follow-up with both trajectories characterized by an early peak-approximately at 1 year-followed by decreasing values in men but a plateau in women. Multivariate between-sex comparisons across LVEF categories revealed that women had lower rates of AHF readmissions when LVEF ≤40%. On the contrary, women displayed an excess risk of AHF readmissions when LVEF >60%. A trend in the same direction was found for cardiovascular and all-cause mortality. Sex was a significant factor in determining the follow-up trajectory of LVEF and predicting differences in outcomes after an AHF admission. The findings suggest that women have a higher risk of AHF readmissions at higher LVEF values, while men have a higher risk at lower LVEF values. For all-cause and cardiovascular mortality, the same direction of the association was inferred but they were not significant.

Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation.

One of the most common forms of cancer in fair skinned populations is Non-Melanoma Skin Cancer (NMSC), which primarily consists of Basal Cell Carcinoma (BCC), and cutaneous Squamous Cell Carcinoma (SCC). Detecting NMSC early can significantly improve treatment outcomes and reduce medical costs. Similarly, Actinic Keratosis (AK) is a common skin condition that, if left untreated, can develop into more serious conditions, such as SCC. Hyperspectral imagery is at the forefront of research to develop non-invasive techniques for the study and characterisation of skin lesions. This study aims to investigate the potential of near-infrared hyperspectral imagery in the study and identification of BCC, SCC and AK samples in comparison with healthy skin. Here we use a pushbroom hyperspectral camera with a spectral range of ≈ 900 to 1600 nm for the study of these lesions. For this purpose, an ad hoc platform was developed to facilitate image acquisition. This study employed robust statistical methods for the identification of an optimal spectral window where the different samples could be differentiated. To examine these datasets, we first tested for the homogeneity of sample distributions. Depending on these results, either traditional or robust descriptive metrics were used. This was then followed by tests concerning the homoscedasticity, and finally multivariate comparisons of sample variance. The analysis revealed that the spectral regions between 900.66-1085.38 nm, 1109.06-1208.53 nm, 1236.95-1322.21 nm, and 1383.79-1454.83 nm showed the highest differences in this regard, with <1% probability of these observations being a Type I statistical error. Our findings demonstrate that hyperspectral imagery in the near-infrared spectrum is a valuable tool for analyzing, diagnosing, and evaluating non-melanoma skin lesions, contributing significantly to skin cancer research.

Smartphone Addiction and Related Factors among Athletes.

Smartphone addiction (SA) is increasing worldwide. The aim of this study is to determine the level of SA in athletes affiliated to the Turkish Handball Federation in Izmir and to examine its relationship with factors such as sociodemographic status, health status, eating attitude, and body perception. This cross-sectional study was conducted in March-April 2021 in Izmir Province. The sample of the study consisted of 212 licensed handball athletes. The short SA scale, three-factor nutrition scale, and body perception scale were used. A chi-square test was used for bivariate comparisons and logistic regression analysis was used for multivariate comparisons. The study was completed with 202 individuals (the coverage rate was 95.3%). The prevalence of SA was found to be 27.7%. The risk of SA increased 2.49-fold (CI: 1.17-5.31, p = 0.018) in female participants, 2.01-fold (CI: 1.01-4.06, p = 0.048) in participants with alcohol use, 2.17-fold (CI: 1.04-4.58, p = 0.042) in participants with low nutritional scores, 2.65-fold (CI: 1.15-6.10, p = 0.022) in individuals with high-income status, and 2.66-fold (CI: 1.07-6.64, p = 0.036) in individuals with high body perception scale score. In total, 27.7% of the athlete sample had scores above the SA threshold. These results point out that a behavioral addiction such as SA can occur even in professionals of an activity such as sports, known for beneficial effects in terms of healthy life.

Extended multivariate comparison of 68 cluster validity indices. A review

Clustering is an unsupervised machine learning methodology widely used in several sciences to find groups of similar patterns in complex data. The results generated by clustering algorithms generally depend on user-defined input parameters such as the number of expected clusters, which can have a great impact on the homogeneity of the identified clusters.Clustering validity indices (CVIs) are an effective method for determining the optimal number of clusters that best fit the natural partition of a dataset. They do not require any underlying assumption nor a priori knowledge about the true dataset structure. Since 1965, many cluster validity indices have been proposed in the literature and used in several different applications.In this paper, the performance of 68 cluster validity indices was evaluated on 21 real-life research and simulated datasets. CVIs were compared on the same partition for each dataset, which was searched for by the k-means clustering algorithm. Multivariate chemometric methods were applied to disclose mutual relationships among the indices and to select those that are more effective in terms of accuracy and reliability.

Disparities in Liver Transplantation for Nonalcoholic Steatohepatitis in Women.

Nonalcoholic steatohepatitis (NASH) is the fastest-growing indication for liver transplantation (LT). Sex disparities among patients with cirrhosis on the LT waitlist are well known. We wanted to understand these disparities further in women with end-stage liver disease patients listed for NASH cirrhosis in a contemporary cohort. We used data from the Scientific Registry of Transplant Recipients to assess sex racial, and ethnic differences in NASH patients listed for LT. Adults transplanted from August 1997 to June 2021 were included. Inferential statistics were used to evaluate differences with univariate and multivariate comparisons, including competitive risk analysis. During the study time period, we evaluated 12 844 LT for NASH cirrhosis. Women were transplanted at a lower rate (46.5% versus 53.5%; P < 0.001) and higher model for end-stage liver disease (MELD) (23.8 versus 22.6; P < 0.001) than men. Non-White women were transplanted at a higher MELD (26.1 versus 23.1; P < 0.001) than White women and non-White male patients (26.1 versus 24.8; P < 0.001). Graft and patient survivals were significantly different ( P < 0.001) between non-White women and White women and men (White and non-White). Evaluation of LT candidates in the United States demonstrates women with NASH cirrhosis have a higher MELD than men at LT. Additional disparities exist among non-White women with NASH as they have higher MELD and creatinine at LT compared with White women. After LT, non-White women have worse graft and patient survival compared with men or White women. These data indicate that non-White women with NASH are the most vulnerable on the LT waitlist.