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The visual evaluation of data derived from screening and optimization experiments in the development of new analytical methods poses a considerable time investment and introduces the risk of subjectivity. This study presents a novel approach to processing such data, based on factor analysis of mixed data and hierarchical clustering — multivariate techniques implemented in the R programming language. The methodology is demonstrated in the early-stage screening and optimization of the chromatographic separation of 15 structurally diverse drugs that affect the central nervous system, using a custom R Language script. The presented explorative approach enabled the identification of key parameters affecting the separation and significantly reduced the time required to evaluate the comprehensive dataset from the screening experiments. Based on the data analysis results, the optimal combination of stationary phase and mobile phase composition was selected, considering retention, overall resolution, and peak shape of compounds. Additionally, compounds vulnerable to changes in selected chromatographic conditions were identified. As a complement to the presented R Language script, a web-based application ChromaFAMDeX has been developed to offer an intuitive interface that enhances the accessibility of the used statistical methods. Accompanying the publication, the R script and the link to the standalone application are provided, enabling replication and adaptation of the methodology. [ABSTRACT FROM AUTHOR]

Abstract Key message Despite been grown under the same climate, oak species are able to correlate with looser, but still identifiable, leaf morphological syndromes, composed by morphological traits with an ecological role in their respective macroclimates. Context Environmental restrictions imposed by climate have been shown to modulate leaf morphology. A reduction of leaf area in hot and dry climates reduces overheating because of a thinner boundary layer. Lobed shapes enhance hydraulic conductivity and faster cooling. Elongated leaves drain more quickly under high precipitation. Trichomes may help to reduce the effects of excessive sun exposure in hot and dry environments. Leaf mass per area (LMA) increases in stressful environments. Aim To assess the influence of global climate and clade on oak leaf syndromes comprising morphological traits with ecological roles. Methods We analyzed seven morphological traits in 141 oak species grown in a botanical garden, characterized into five macroclimates, and explored the partial effects of clade and climate. Results We found significant associations between macroclimate and every morphological trait measured. Temperate species tend to have large, obovate, lobed, malacophyllous leaves. Species occurring in dry habitats usually present small, rounded, pubescent, sclerophyllous leaves. Warm and wet climates induce the development of slender, lanceolate, glabrous leaves with an acuminate apex. Conclusion The functional roles of the different morphological traits are partially confirmed in genus Quercus as a response to the different macroclimates, where different leaf syndromes can be distinguished.

Evidence suggests that both craniometric and cranial morphoscopic (MMS) traits elucidate information about cranial phenotypic variation and are appropriate proxies of genetic variation. Yet, the types of variation underlying the expression of craniometric and MMS traits are unknown. Recent data sets of matched skeletal metric and MMS data enable a holistic exploration into the cranial phenotype. Subsequently, the current study strived to provide a better understanding of cranial data used to measure human variation in biological anthropology. Two contemporary U.S. samples were pooled to increase sample size and diversity. Following down-sampling for balanced representation of reported biological males and females, the final sample comprised 310 individuals. Twenty- five interlandmark distances and 11 MMS traits were used in numerous analyses: polychoric correlation, mutual information, mixed factor analysis, and factor analysis of mixed data. No demographic information besides reported biological sex was retained in the analyses. The results consistently indicate that having information about one data type does not provide certainty of another data type, even when the variables are analogous (i.e., nasal breadth and nasal aperture width). Findings reassert that skeletal variables should be analyzed jointly rather than independently to best capture the cranial phenotype. The results also highlight the differential influence of biological variables, such as sexual dimorphism, on the two types of cranial data. As data availability increases and additional matched data- type comparisons can be conducted, we will continue to gain a better understanding of the complexities surrounding skeletal phenotypic variation, evolutionary theory, and population affinity. [ABSTRACT FROM AUTHOR]

Key message: Despite been grown under the same climate, oak species are able to correlate with looser, but still identifiable, leaf morphological syndromes, composed by morphological traits with an ecological role in their respective macroclimates. Context: Environmental restrictions imposed by climate have been shown to modulate leaf morphology. A reduction of leaf area in hot and dry climates reduces overheating because of a thinner boundary layer. Lobed shapes enhance hydraulic conductivity and faster cooling. Elongated leaves drain more quickly under high precipitation. Trichomes may help to reduce the effects of excessive sun exposure in hot and dry environments. Leaf mass per area (LMA) increases in stressful environments. Aim: To assess the influence of global climate and clade on oak leaf syndromes comprising morphological traits with ecological roles. Methods: We analyzed seven morphological traits in 141 oak species grown in a botanical garden, characterized into five macroclimates, and explored the partial effects of clade and climate. Results: We found significant associations between macroclimate and every morphological trait measured. Temperate species tend to have large, obovate, lobed, malacophyllous leaves. Species occurring in dry habitats usually present small, rounded, pubescent, sclerophyllous leaves. Warm and wet climates induce the development of slender, lanceolate, glabrous leaves with an acuminate apex. Conclusion: The functional roles of the different morphological traits are partially confirmed in genus Quercus as a response to the different macroclimates, where different leaf syndromes can be distinguished. [ABSTRACT FROM AUTHOR]

Introduction: Determining socio-economic status (SES) can greatly help decision makers in the field of social health. Because SES can play an important role in accessing medical services or welfare amenities. We aimed to determine the SES using principal component analysis (PCA), multiple correspondence analysis (MCA), and factor analysis of mixed data (FAMD) methods. Methods: In this cross-sectional study (2023), 4448 employees aged 19 to 75 years were included to the study from Tehran University of Medical Sciences employees' cohort (TEC). Demographic variables and socioeconomic factors were considered. Considering the weaknesses of PCA and MCA methods, we calculated the SES score using PCA, MCA and FAMD methods, and the percentile of people was determined. These weaknesses include normality assumption and considering only linear relationship for PCA, inability to interpret the relationships between variables and considering each level of classification variables as a new variable for MCA. Results: We studied 4448 people (39.3% men) with mean age of 42.3 and a standard deviation of 8.7. The correlation between the percentiles obtained through PCA, MCA and FAMD methods was very high, and the highest correlation was related to the percentiles obtained through PCA and FAMD methods with a value of 0.994. The intraclass correlation coefficient value was 0.996. Also, this value was 0.996 and 0.994 in the random samples of 250 and 100 individuals from the original data, respectively. Conclusion: All of the three methods worked similarly on determining the SES and calculating the percentile of people. PCA and FAMD methods had better agreement than others. Therefore, in studies that have both quantitative and qualitative variables, the choice of analysis method depends on the opinion of the researcher. [ABSTRACT FROM AUTHOR]

Abstract Background The aim of the study was to investigate whether MwoA and MwA are different manifestations of a single disease, distinct clinical entities, or located at two poles of a spectrum. Methods In this cross-sectional study, 5438 patients from 10 hospitals in China were included: 4651 were diagnosed with migraine without aura (MwoA) and 787 with migraine with aura (MwA). We used a validated standardized electronic survey to collect multidimensional data on headache characteristics and evaluated the similarities and differences between migraine subtypes. To distinguish migraine subtypes, we employed correlational analysis, factor analysis of mixed data (FAMD), and decision tree analysis. Results Compared to MwA, MwoA had more severe headaches, predominantly affected females, were more easily produced by external factors, and were more likely to have accompanying symptoms and premonitory neck stiffness. Patients with MwA are heterogeneous, according to correlation analysis; FAMD divided the subjects into three clear clusters. The majority of the differences between MwoA and MwA were likewise seen when typical aura with migraine headache (AWM) and typical aura with non-migraine headache (AWNM) were compared. Furthermore, decision trees analysis revealed that the chaotic MwA data reduced the decision tree’s accuracy in distinguishing MwoA from MwA, which was significantly increased by splitting MwA into AWM and AWNM. Conclusions The clinical phenomics of headache phenotype varies gradually from MwoA to AWM and AWNM, and AWM is a mid-state between MwoA and AWNM. We tend to regard migraine as a spectrum disorder, and speculate that different migraine subtypes have different “predominant regions” that generate attacks.

This article seeks to determine the most influential factors on the sustainability of a pension system. To this end, an empirical analysis was conducted on a sample of 13 countries, with different characteristics, according to three criteria: the size of the pension system's assets, the old‐age dependency ratio and the success of the system according to the Mercer CFA Institute Global Pension Index. The results of this preliminary study show that countries with successful pension systems share a high rate of formal employment, good political stability, high life expectancy at birth, and a good rate of coverage by the pension system. [ABSTRACT FROM AUTHOR]

Inequalities in the equipment and use of information and communications technology (ICT) in Spanish households can lead to users being unable to access certain information or to carry out certain procedures. Accessibility to ICT is considered a social determinant of health (SDOH) because it can generate inequalities in access to information and in managing access to health services. In the face of a chronic illness such as diabetes mellitus (DM)—for which a comprehensive approach is complex and its complications have a direct impact on current healthcare systems—all the resources that patients may have are welcome. We aimed to analyze hospitalizations and amputations as direct consequences of DM among the autonomous communities of Spain (ACS) in 2019, along with socioeconomic factors related to health, including inequalities in access to ICT between territories, as well as citizens' interest in online information searches about DM. We used different databases such as that of the Ministerio de Sanidad (Spain's health ministry), Ministerio de Asuntos Económicos y transformación (Ministry of Economic Affairs and Digital Transformation), Google Trends (GT), and the Instituto Nacional de Estadística (Spain's national institute of statistics). We examined the data with R software. We employed a geolocation approach and performed multivariate analysis (specifically factor analysis of mixed data [FAMD]) to evaluate the aggregate interest in health information related to DM in different regions of Spain grounded in online search behavior. The use of FAMD allowed us to adjust the techniques of principal component analysis (PCA) and multiple correspondence analysis (MCA) to detect differences between the direct consequences of DM, citizen's interest in this non-communicable disease, and socioeconomic factors and inequalities in access to ICT in aggregate form between the country's different ACS. The results show how SDOH, such as poverty and education level, are related to the ACS with the highest number of homes that cite the cost of connection or equipment as the reason for not having ICT at home. These regions also have a greater number of hospitalizations due to DM. Given that in Spain, there are certain differences in accessibility in terms of the cost to households, in the case of DM, we take this issue into account from the standpoint of an integral approach by health policies.

Diabetes mellitus (DM) is a serious non-communicable disease (NCD) and relies on the patient being aware of their condition, proactive, and having adequate medical care. European countries healthcare models are aware of the impact of these variables. This study evaluates the impact of online health information seeking behavior (OHISB) during World Diabetes Mellitus Day (WDMD) in European countries from 2014 to 2019 by grouping countries according to the changes in citizens' search behavior, diabetes mellitus prevalence, the existence of National Health Plans (NHP), and their respective healthcare systems. We extracted data from Global Burden of Disease, Google Trends (GT), Public Health European Commission, European Coalition for Diabetes, and the Spanish Ministry of Health. First, we used the broken-line models to analyze significant changes in search trends (GT) in European Union member countries in the 30-day intervals before and after the WDMD (November 14) from 2014 to 2019. Then the results obtained were used in the second phase to group these countries by factor analysis of mixed data (FAMD) using the prevalence of DM, the existence of NHP, and health models in each country. The calculations were processed using R software (gtrendsR, segmented, Factoextra, and FactoMineR). We established changes in search trends before and after WDMD, highlighting unevenness among European countries. However, significant changes were mostly observed among countries with NHP. These changes in search trends, in addition to being significant, were reiterated over time and occurred especially in countries belonging to the Beveridge Model (Portugal, Spain, and Sweden) and with NHPs in place. Greater awareness of diabetes mellitus among the population and continuous improvements in NHP can improve the patients' quality of life, thus impacting in disease management and healthcare expenditure.

Background: While there seems to be a consensus that a decrease in gut microbiome diversity is related to a decline in health status, the associations between respiratory microbiome diversity and chronic lung disease remain a matter of debate. We provide a systematic review and meta-analysis of studies examining lung microbiota alpha-diversity in patients with asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF) or bronchiectasis (NCFB), in which a control group based on disease status or healthy subjects is provided for comparison.Results: We reviewed 351 articles on title and abstract, of which 27 met our inclusion criteria for systematic review. Data from 24 of these studies were used in the meta-analysis. We observed a trend that CF patients have a less diverse respiratory microbiota than healthy individuals. However, substantial heterogeneity was present and detailed using random-effects models, which limits the comparison between studies.Conclusions: Knowledge on respiratory microbiota is under construction, and for the moment, it seems that alpha-diversity measurements are not enough documented to fully understand the link between microbiota and health, excepted in CF context which represents the most studied chronic respiratory disease with consistent published data to link alpha-diversity and lung function. Whether differences in respiratory microbiota profiles have an impact on chronic respiratory disease symptoms and/or evolution deserves further exploration. [ABSTRACT FROM AUTHOR]

Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectly measured data in practice. We focus on unsupervised detection and the continuous and categorical (mixed) variable case. We show that detecting anomalies in mixed data is enhanced through first embedding the data then assessing an anomaly scoring scheme. We propose a kurtosis‐weighted Factor Analysis of Mixed Data for anomaly detection to obtain a continuous embedding for anomaly scoring. We illustrate that anomalies are highly separable in the first and last few ordered dimensions of this space, and test various anomaly scoring experiments within this subspace. Results are illustrated for both simulated and real datasets, and the proposed approach is highly accurate for mixed data throughout these diverse scenarios. [ABSTRACT FROM AUTHOR]

Background: The aim of the study was to investigate whether MwoA and MwA are different manifestations of a single disease, distinct clinical entities, or located at two poles of a spectrum. Methods: In this cross-sectional study, 5438 patients from 10 hospitals in China were included: 4651 were diagnosed with migraine without aura (MwoA) and 787 with migraine with aura (MwA). We used a validated standardized electronic survey to collect multidimensional data on headache characteristics and evaluated the similarities and differences between migraine subtypes. To distinguish migraine subtypes, we employed correlational analysis, factor analysis of mixed data (FAMD), and decision tree analysis. Results: Compared to MwA, MwoA had more severe headaches, predominantly affected females, were more easily produced by external factors, and were more likely to have accompanying symptoms and premonitory neck stiffness. Patients with MwA are heterogeneous, according to correlation analysis; FAMD divided the subjects into three clear clusters. The majority of the differences between MwoA and MwA were likewise seen when typical aura with migraine headache (AWM) and typical aura with non-migraine headache (AWNM) were compared. Furthermore, decision trees analysis revealed that the chaotic MwA data reduced the decision tree's accuracy in distinguishing MwoA from MwA, which was significantly increased by splitting MwA into AWM and AWNM. Conclusions: The clinical phenomics of headache phenotype varies gradually from MwoA to AWM and AWNM, and AWM is a mid-state between MwoA and AWNM. We tend to regard migraine as a spectrum disorder, and speculate that different migraine subtypes have different "predominant regions" that generate attacks. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought great challenges to healthcare resources around the world. Patients with COVID-19 exhibit a broad spectrum of clinical characteristics. In this study, the Factor Analysis of Mixed Data (FAMD)-based cluster analysis was applied to demographic information, laboratory indicators at the time of admission, and symptoms presented before admission. Three COVID-19 clusters with distinct clinical features were identified by FAMD-based cluster analysis. The FAMD-based cluster analysis results indicated that the symptoms of COVID-19 were roughly consistent with the laboratory findings of COVID-19 patients. Furthermore, symptoms for mild patients were atypical. Different hospital stay durations and survival differences among the three clusters were also found, and the more severe the clinical characteristics were, the worse the prognosis. Our aims were to describe COVID-19 clusters with different clinical characteristics, and a classifier model according to the results of FAMD-based cluster analysis was constructed to help provide better individualized treatments for numerous COVID-19 patients in the future.

Chronic obstructive pulmonary disease (COPD) is a complex, multidimensional and heterogeneous disease. The main purpose of the present study was to identify clinical phenotypes through cluster analysis in adults suffering from COPD. A retrospective study was conducted on 178 COPD patients in stable state recruited from ambulatory care at University hospital of Liege. All patients were above 40 years, had a smoking history of more than 20 pack years, post bronchodilator FEV1/FVC <70% and denied any history of asthma before 40 years. In this study, the patients were described by a total of 84 mixed sets of variables with some missing values. Hierarchical clustering on principal components (HCPC) was applied on multiple imputation. In the final step, patients were classified into homogeneous distinct groups by consensus clustering. Three different clusters, which shared similar smoking history were found. Cluster 1 included men with moderate airway obstruction (n = 67) while cluster 2 comprised men who were exacerbation-prone, with severe airflow limitation and intense granulocytic airway and neutrophilic systemic inflammation (n = 56). Cluster 3 essentially included women with moderate airway obstruction (n = 55). All clusters had a low rate of bacterial colonization (5%), a low median FeNO value (<20 ppb) and a very low sensitization rate toward common aeroallergens (0-5%). CAT score did not differ between clusters. Including markers of systemic airway inflammation and atopy and applying a comprehensive cluster analysis we provide here evidence for 3 clusters markedly shaped by sex, airway obstruction and neutrophilic inflammation but not by symptoms and T2 biomarkers. [ABSTRACT FROM AUTHOR]

Vaccination against SARS-CoV-2 with BNT162b2 mRNA vaccine plays a critical role in COVID-19 prevention. Although BNT162b2 is highly effective against COVID-19, a time-dependent decrease in neutralizing antibodies (NAbs) is observed. The aim of this study was to identify the individual features that may predict NAbs levels after vaccination. Machine learning techniques were applied to data from 302 subjects. Principal component analysis (PCA), factor analysis of mixed data (FAMD), k-means clustering, and random forest were used. PCA and FAMD showed that younger subjects had higher levels of neutralizing antibodies than older subjects. The effect of age is strongest near the vaccination date and appears to decrease with time. Obesity was associated with lower antibody response. Gender had no effect on NAbs at nine months, but there was a modest association at earlier time points. Participants with autoimmune disease had lower inhibitory levels than participants without autoimmune disease. K-Means clustering showed the natural grouping of subjects into five categories in which the characteristics of some individuals predominated. Random forest allowed the characteristics to be ordered by importance. Older age, higher body mass index, and the presence of autoimmune diseases had negative effects on the development of NAbs against SARS-CoV-2, nine months after full vaccination.

Paraneoplastic neurologic syndromes (PNSs) are a heterogeneous group of disorders caused by the remote effects of cancer with immune-mediated pathogenesis. Anti-Ma2 antibody was defined as one of the well-characterized onconeural antibodies that could help establish a definite PNS diagnosis. We aimed to report and explore patients with anti-Ma2 antibody-associated paraneoplastic neurologic syndrome (Ma2-PNS) who frequently exhibit sensorimotor neuropathy (SMN) using a new method of factor analysis of mixed data (FAMD). Clinical data from a case series of eight patients with definite diagnoses were retrospectively reviewed. FAMD conducted further analyses with a comprehensive visualization in R software. Our cohort, with a predominance of females (5/8), presented more frequently with SMN (4/8), followed by limbic encephalitis (LE) (3/8). Two patients with LE were found to have a testicular germ-cell tumor and a thymoma, respectively. In addition, a patient who developed chronic SMN was diagnosed with multiple myeloma (MM) involving multiple organs. FAMD exhibited the overall features into a two-dimensional coordinate and located each individual into their corresponding position with high relevance. It provided a clue for determining their potential relationships and predictors. Our findings indicated that Ma2-PNS could frequently involve the peripheral nervous system, MM might be one of its associated cancers with a presentation of chronic SMN, and FAMD might be a clinically valuable tool.

Slopes in and around Thomas Lee Inlet (Devon Island, Nunavut, Canada) are eroded by means of gullying, yet the driving factors, such as the nature of the substrate and availability of different sources for water, influencing gully morphology remain poorly understood. Here we investigate the factors that contribute to gully formation using a combination of satellite mapping, field observations and statistical analysis of morphological data. In total, 161 gullies were mapped within the 126 km2 study area. Factors linked to gullies, such as the nature of its substrate and the presence of glaciers, were integrated into a spatial geodatabase. A Factor Analysis of Mixed Data performed on the geodatabase was used to discriminate which factors may influence gully geometry. Our results show that the type of geological formation has a strong impact on gully slope. In addition, supplemental sources of water are often found near alcoves of the steep, longer and mature gullies, and levees often form in their aprons. Immature debris flow‐like gullies were dryer and found on short and variable (from steep to gentle) slopes. This detailed study of the rocky and arid Thomas Lee Inlet plateaus and slopes provides the first insight into gullied slopes as a hydrological component connecting upland units to downslope in this area. [ABSTRACT FROM AUTHOR]

Sediment acts as both important sink and source for contaminants in aquatic systems. In the present study, the multivariable cokriging method and multiple statistical techniques, including group analysis (GA), factor analysis of mixed data (FAMD), correlation matrices (CMs), and enrichment factor (EF) were used to identify spatial distribution and sources of potentially toxic elements (PTEs) Cr, Cu, Cd, Pb, and Zn in surficial sediments of Dongting Lake in China. The results showed significant spatial heterogeneity in these PTEs in Dongting Lake. The average concentrations of Cr, Cu, Cd, Pb, and Zn were 131.5 ± 45.4, 49.7 ± 17.4, 2.2 ± 1.8, 59.2 ± 18.6, and 152.9 ± 48.0 mg/kg, respectively. The potential ecological risks posed by the trace elements (Cr, Cu, Cd, Pb, and Zn) were evaluated based on the multivariable cokriging predictions, and the results indicated that the ecological risks were high in Southern and Western Dongting Lakes. Multiple analyses showed that Cr, Cu and Zn primarily originated from industrial and domestic sewage wastewater, while Cd and Pb were primarily from agricultural and industrial sources. [ABSTRACT FROM AUTHOR]

Purpose: The present study was aimed at investigating whether imatinib pharmacogenetics is related to its pharmacodynamics in patients affected by chronic myeloid leukemia.Methods: Through a procedure based on a sequence of classical statistics methods, we investigated the possible relationships between treatment efficacy/tolerability and combinations of time-independent variables as gender and genetic covariates in the form of single nucleotide polymorphisms (SNPs) or combinations thereof. Moreover, since the drug tolerability has a strong incidence on the discontinuation of the therapy, we investigated whether the time of manifestation of the most frequent toxic effects can be related to time-independent patients' characteristics or not.Results: We found that a combination of two polymorphisms, namely hOCT1 c.480C>G (rs683369) and ABCB1 c.3435C>T (rs1045642), seems to play the role of predictor for imatinib in both efficacy and toxicity. Furthermore, the time of manifestation of edema toxicity is found to be associated to a combination of gender and ABCB1 c.3435C>T, whereas the time of manifestation of cramp toxicity appears related to gender.Conclusions: The novelty of this study is dual: the achievement of results that potentially have a significant clinical interest and the demonstration that the adoption of composed covariates may represent a unique tool to study different aspects of the treatment with imatinib. [ABSTRACT FROM AUTHOR]

Purpose: Unpredictable drug efficacy and safety of combined antiepileptic therapy is a major challenge during pharmacotherapy decisions in everyday clinical practice. The aim of this study was to describe the pharmacokinetics of valproic acid (VA), lamotrigine (LTG), and levetiracetam (LEV) in a pediatric population using nonlinear mixed-effect modeling, while machine learning (ML) algorithms were applied to identify any relationships among the plasma levels of the three medications and patients' characteristics, as well as to develop a predictive model for epileptic seizures., Methods: The study included 71 pediatric patients of both genders, aged 2-18 years, on combined antiepileptic therapy. Population pharmacokinetic (PopPK) models were developed separately for VA, LTG, and LEV. Based on the estimated pharmacokinetic parameters and the patients' characteristics, three ML approaches were applied (principal component analysis, factor analysis of mixed data, and random forest). PopPK models and ML models were developed, allowing for greater insight into the treatment of children on antiepileptic treatment., Results: Results from the PopPK model showed that the kinetics of LEV, LTG, and VA were best described by a one compartment model with first-order absorption and elimination kinetics. Reliance on random forest model is a compelling vision that shows high prediction ability for all cases. The main factor that can affect antiepileptic activity is antiepileptic drug levels, followed by body weight, while gender is irrelevant. According to our study, children's age is positively associated with LTG levels, negatively with LEV and without the influence of VA., Conclusion: The application of PopPK and ML models may be useful to improve epilepsy management in vulnerable pediatric population during the period of growth and development., Competing Interests: The authors declare that there is no conflict of interest., (© The Author(s), 2023.)

• Key message : We show that joint multivariate analyses of leaf morphological characters and molecular-genetic markers improve the taxonomic assignment in hybridizing European white oaks. However, model-based approaches using genetic data alone represent straightforward alternatives to laborious, detailed morphological assessments. • Context : In European white oaks, species delimitation is debated because of large overlap of morphological characteristics likely due to hybridization. • Aims : We tested whether joint multivariate analyses of leaf morphology and molecular markers improve the identification of three oak species ( Quercus petraea, Quercus pubescens, Quercus robur) compared to approaches using morphological or genetic variables only. • Methods : We assessed 13 leaf morphological characters and applied eight nuclear microsatellite markers in almost 1400 trees of 71 oak populations across Switzerland. We performed two multivariate approaches with three variable sets (morphology, genetics, combined) and assessed their performance in separating the taxa. We also compared the taxon assignment to a model-based clustering approach ( Structure) based on genetic data alone. • Results : A joint use of morphological and genetic variables led to an improved taxon assignment. Whereas Q. robur could clearly be separated from the two other taxa, there was a certain overlap between Q. petraea and Q. pubescens. The Structure clustering led to the same taxon assignment in 85 % of the individuals. • Conclusion : It is important to consider both morphological and genetic properties in morphologically similar and hybridizing species. However, it might be more efficient to concentrate only on genetic markers than on time-consuming morphological assessments. [ABSTRACT FROM AUTHOR]

Vaccination against SARS-CoV-2 with BNT162b2 mRNA vaccine plays a critical role in COVID-19 prevention. Although BNT162b2 is highly effective against COVID-19, a time-dependent decrease in neutralizing antibodies (NAbs) is observed. The aim of this study was to identify the individual features that may predict NAbs levels after vaccination. Machine learning techniques were applied to data from 302 subjects. Principal component analysis (PCA), factor analysis of mixed data (FAMD), k-means clustering, and random forest were used. PCA and FAMD showed that younger subjects had higher levels of neutralizing antibodies than older subjects. The effect of age is strongest near the vaccination date and appears to decrease with time. Obesity was associated with lower antibody response. Gender had no effect on NAbs at nine months, but there was a modest association at earlier time points. Participants with autoimmune disease had lower inhibitory levels than participants without autoimmune disease. K-Means clustering showed the natural grouping of subjects into five categories in which the characteristics of some individuals predominated. Random forest allowed the characteristics to be ordered by importance. Older age, higher body mass index, and the presence of autoimmune diseases had negative effects on the development of NAbs against SARS-CoV-2, nine months after full vaccination. [ABSTRACT FROM AUTHOR]

Paraneoplastic neurologic syndromes (PNSs) are a heterogeneous group of disorders caused by the remote effects of cancer with immune-mediated pathogenesis. Anti-Ma2 antibody was defined as one of the well-characterized onconeural antibodies that could help establish a definite PNS diagnosis. We aimed to report and explore patients with anti-Ma2 antibody-associated paraneoplastic neurologic syndrome (Ma2-PNS) who frequently exhibit sensorimotor neuropathy (SMN) using a new method of factor analysis of mixed data (FAMD). Clinical data from a case series of eight patients with definite diagnoses were retrospectively reviewed. FAMD conducted further analyses with a comprehensive visualization in R software. Our cohort, with a predominance of females (5/8), presented more frequently with SMN (4/8), followed by limbic encephalitis (LE) (3/8). Two patients with LE were found to have a testicular germ-cell tumor and a thymoma, respectively. In addition, a patient who developed chronic SMN was diagnosed with multiple myeloma (MM) involving multiple organs. FAMD exhibited the overall features into a two-dimensional coordinate and located each individual into their corresponding position with high relevance. It provided a clue for determining their potential relationships and predictors. Our findings indicated that Ma2-PNS could frequently involve the peripheral nervous system, MM might be one of its associated cancers with a presentation of chronic SMN, and FAMD might be a clinically valuable tool. [ABSTRACT FROM AUTHOR]

We investigated how people's recreational activities, values, and land use preferences are related to the protection level, biodiversity and cultural heritage values of nature-based tourism areas in northern Finland. We assessed peoples' opinions using a public participation geographic information system (PPGIS) and analyzed the data together with spatial biodiversity and cultural heritage data from the same area. Associations between the PPGIS place markings with the protection level and biodiversity values were quite low, and for the cultural heritage sites they were altogether missing. Negative preferences were often marked in areas with high numbers of sites rated as pleasant and they overlapped with each other, indicating conflicting preferences. Since most activities are not noticeably related to the protection level or biodiversity values of a site they can be planned so as to protect the biodiversity of the area. • We merged PPGIS data with spatial biodiversity and cultural heritage data in a nature-based tourism area. • Associations between PPGIS markings with protection level and biodiversity were low and for cultural heritage missing. • Negative preferences towards tourism, protection and forestry often overlapped, indicating conflicting preferences. • Since most activities were not related to protection level or biodiversity they can be placed to protect biodiversity. • Differences between opinions among respondents can be used to manage the area to fulfil the needs of various users. [ABSTRACT FROM AUTHOR]