A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics.

Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.

MOTIVATION: With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way. METHOD: Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods. RESULTS: We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models. AVAILABILITY: Code are publicly available at https://github.com/JingxuanBao/SBFA. CONTACT: qlong@upenn.edu.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

Statistical methods for cis-Mendelian randomization with two-sample summary-level data.

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

Development of A Rural-Urban Classification System for Public Health Research that Accommodates Structural Differences Between States.

Rural environments in the United States present challenges to wellness, but there is a lack of tools to categorize rurality at the subcounty level. The most common tool, the FDA's 2010 RUCA codes, uses data that are over a decade old and cannot accommodate regional differences in rurality. The purpose of this study was to develop a census-tract classification system of rurality and demonstrate its use in describing HIV outcomes. We transformed census-tract measures (population density, natural resource workforce, walkability index, household type, and air quality) into local scales of rurality using factor analysis. We surveyed public health practitioners to determine cut-points and compared the resulting categorization to RUCA codes. We described the incidence of HIV in WA by rural category. Our classification system categorized 25% of census tracts as rural, 19% as periurban and 56% as urban. Our survey yielded cut-offs that were more conservative in categorizing areas urban than RUCA codes. The rate of HIV diagnosis was substantially higher in urban areas. Our rural-urban classification system offers an alternative to RUCA codes that is more responsive to regional differences.

A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes.

Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19.

Disentangling the heterogeneity of multiple sclerosis through identification of independent neuropathological dimensions.

Multiple sclerosis (MS) is a heterogeneous neurological disorder with regards to clinical presentation and pathophysiology. Here, we investigated the heterogeneity of MS by performing an exploratory factor analysis on quantitative and qualitative neuropathology data collected for 226 MS donors in the Netherlands Brain Bank autopsy cohort. Three promising dimensions were identified and subsequently validated with clinical, neuropathological, and genetic data. Dimension 1 ranged from a predominance of remyelinated and inactive lesions to extensive pathological changes, higher proportions of active and mixed lesions, and foamy microglia morphology. This pattern was positively correlated with more severe disease, the presence of B and T cells, and neuroaxonal damage. Scoring high on dimension 2 was associated with active lesions, reactive sites, and the presence of nodules. These donors had less severe disease, a specific pattern of cortical lesions, and MS risk variants in the human leukocyte antigen region, the latter indicating a connection between disease onset and this neuropathological dimension. Donors scoring high on dimension 3 showed increased lesional pathology with relatively more mixed and inactive lesions and ramified microglia morphology. This pattern was associated with longer disease duration, subpial cortical lesions, less involvement of the adaptive immune system, and less axonal damage. Taken together, the three dimensions may represent (1) demyelination and immune cell activity associated with pathological and clinical progression, (2) microglia (re)activity and possibly lesion initiation, and (3) loss of lesion activity and scar formation. Our findings highlight that a thorough understanding of the interplay between multiple pathological characteristics is crucial to understand the heterogeneity of MS pathology, as well as its association with genetic predictors and disease outcomes. The scores of donors on the dimensions can serve as an important starting point for further disentanglement of MS heterogeneity and translation into observations and interventions in living cohorts with MS.

Survival outcomes and treatment experience of 124 patients with primary central nervous system lymphoma.

PURPOSE: Primary central nervous system lymphoma (PCNSL) is a rare malignancy of the central nervous system with high invasiveness. There is little consensus on the treatment of PCNSL. This study retrospectively studied data from PCNSL patients in a single center to summarize treatment experience and explore prognostic factors. METHODS: Survival curves were drawn using the Kaplan-Meier method and prognostic factors were analyzed using Cox's hazards model. RESULTS: In multivariate analysis, cerebrospinal fluid lactic acid dehydrogenase (CSF LDH; p = 0.005 and p = 0.002), neutrophil to lymphocyte ratio (NLR; p = 0.014 and p = 0.038), and completion of four cycles of induction therapy (p < 0.001and p < 0.001) were significant and independent predictors of overall survival (OS) and progression-free survival (PFS), respectively. CONCLUSION: On the basis of this study, we propose that PCNSL patients should receive early induction therapy with sufficient cycles. Subsequent consolidation therapy can prevent relapses and improve survival. In patients with PCNSL, the independent prognostic factors for OS and PFS were CSF LDH level, NLR, and full cycles of induction therapy.

Computational network analysis of host genetic risk variants of severe COVID-19.

BACKGROUND: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

Domain-specific associations between psychopathology and neurocognitive functioning.

BACKGROUND: Neurocognitive dysfunction is a transdiagnostic finding in psychopathology, but relationships among cognitive domains and general and specific psychopathology dimensions remain unclear. This study aimed to examine associations between cognition and psychopathology dimensions in a large youth cohort. METHOD: The sample (N = 9350; age 8-21 years) was drawn from the Philadelphia Neurodevelopmental Cohort. Data from structured clinical interviews were modeled using bifactor confirmatory factor analysis (CFA), resulting in an overall psychopathology ('p') factor score and six orthogonal psychopathology dimensions: dysphoria/distress, obsessive-compulsive, behavioral/externalizing, attention-deficit/hyperactivity, phobias, and psychosis. Neurocognitive data were aggregated using correlated-traits CFA into five factors: executive functioning, memory, complex cognition, social cognition, and sensorimotor speed. We examined relationships among specific and general psychopathology dimensions and neurocognitive factors. RESULTS: The final model showed both overall and specific associations between cognitive functioning and psychopathology, with acceptable fit (CFI = 0.91; TLI = 0.90; RMSEA = 0.024; SRMR = 0.054). Overall psychopathology and most psychopathology dimensions were negatively associated with neurocognitive functioning (phobias [p < 0.0005], behavioral/externalizing [p < 0.0005], attention-deficit/hyperactivity [p < 0.0005], psychosis [p < 0.0005 to p < 0.05]), except for dysphoria/distress and obsessive-compulsive symptoms, which were positively associated with complex cognition (p < 0.05 and p < 0.01, respectively). CONCLUSION: By modeling a broad range of cognitive and psychopathology domains in a large, diverse sample of youth, we found aspects of neurocognitive functioning shared across clinical phenotypes, as well as domain-specific patterns. Findings support transdiagnostic examination of cognitive performance to parse variability in the link between neurocognitive functioning and clinical phenotypes.

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors.

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

High-dimensional covariate-augmented overdispersed poisson factor model.

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.

Determinants of pharmacists' confidence to participate in antimicrobial stewardship: A confirmatory factor analysis.

AIM: Pharmacists are essential members of hospital antimicrobial stewardship (AMS) teams. A lack of self-perceived confidence can limit pharmacists' involvement and contributions. Pharmacists working in AMS have reported a lack of confidence. There is currently a lack of validated measures to assess pharmacists' self-perceived confidence when working in AMS and contributors to this confidence. This study aimed to identify variables contributing to pharmacist self-perceived confidence and validate an AMS hospital pharmacist survey tool using confirmatory factor analysis (CFA). METHODS: Responses from a survey of Australian and French hospital pharmacists were used to undertake CFA and path analysis on factors related to pharmacists' self-perceived confidence. It was hypothesized that pharmacists' self-perceived confidence would be impacted by time working in AMS, perceived importance of AMS programmes, perceived barriers to participating in AMS and current participation. RESULTS: CFA demonstrated a good model fit between the factors. Items included in the model loaded well to their respective factors with acceptable reliability. Path analysis demonstrated that time working in AMS had a significant impact on pharmacists' self-perceived confidence, while perceived barriers had a negatively significant relationship. Pharmacy participation in AMS and perceived importance of AMS programmes had a non-significant impact. CONCLUSION: Findings demonstrated that the survey tool showed good validity and identified factors that can impact pharmacists' self-perceived confidence when working in hospital AMS programmes. Having a validated survey tool can identify factors that can reduce pharmacists' self-perceived confidence. Strategies can then be developed to address these factors and subsequently improve pharmacists' self-perceived confidence.

Factor structure of the Harmonized Cognitive Assessment Protocol neuropsychological battery in the Health and Retirement Study.

OBJECTIVE: The Harmonized Cognitive Assessment Protocol (HCAP) describes an assessment battery and a family of population-representative studies measuring neuropsychological performance. We describe the factorial structure of the HCAP battery in the US Health and Retirement Study (HRS). METHOD: The HCAP battery was compiled from existing measures by a cross-disciplinary and international panel of researchers. The HCAP battery was used in the 2016 wave of the HRS. We used factor analysis methods to assess and refine a theoretically driven single and multiple domain factor structure for tests included in the HCAP battery among 3,347 participants with evaluable performance data. RESULTS: For the eight domains of cognitive functioning identified (orientation, memory [immediate, delayed, and recognition], set shifting, attention/speed, language/fluency, and visuospatial), all single factor models fit reasonably well, although four of these domains had either 2 or 3 indicators where fit must be perfect and is not informative. Multidimensional models suggested the eight-domain model was overly complex. A five-domain model (orientation, memory delayed and recognition, executive functioning, language/fluency, visuospatial) was identified as a reasonable model for summarizing performance in this sample (standardized root mean square residual = 0.05, root mean square error of approximation = 0.05, confirmatory fit index = 0.94). CONCLUSIONS: The HCAP battery conforms adequately to a multidimensional structure of neuropsychological performance. The derived measurement models can be used to operationalize notions of neurocognitive impairment, and as a starting point for prioritizing pre-statistical harmonization and evaluating configural invariance in cross-national research.

Perception of cognitive change by individuals with Parkinson's disease or essential tremor seeking deep brain stimulation: Utility of the cognitive change index.

OBJECTIVE: The Cognitive Change Index (CCI-20) is a validated questionnaire that assesses subjective cognitive complaints (SCCs) across memory, language, and executive domains. We aimed to: (a) examine the internal consistency and construct validity of the CCI-20 in patients with movement disorders and (b) learn how the CCI-20 corresponds to objective neuropsychological and mood performance in individuals with Parkinson's disease (PD) or essential tremor (ET) seeking deep brain stimulation (DBS). METHODS: 216 participants (N = 149 PD; N = 67 ET) underwent neuropsychological evaluation and received the CCI-20. The proposed domains of the CCI-20 were examined via confirmatory (CFA) and exploratory (EFA) factor analyses. Hierarchical regressions were used to assess the relationship among subjective cognitive complaints, neuropsychological performance and mood symptoms. RESULTS: PD and ET groups were similar across neuropsychological, mood, and CCI-20 scores and were combined into one group who was well educated (m = 15.01 ± 2.92), in their mid-60's (m = 67.72 ± 9.33), predominantly male (63%), and non-Hispanic White (93.6%). Previously proposed 3-domain CCI-20 model failed to achieve adequate fit. Subsequent EFA revealed two CCI-20 factors: memory and non-memory (p < 0.001; CFI = 0.924). Regressions indicated apathy and depressive symptoms were associated with greater memory and total cognitive complaints, while poor executive function and anxiety were associated with more non-memory complaints. CONCLUSION: Two distinct dimensions were identified in the CCI-20: memory and non-memory complaints. Non-memory complaints were indicative of worse executive function, consistent with PD and ET cognitive profiles. Mood significantly contributed to all CCI-20 dimensions. Future studies should explore the utility of SCCs in predicting cognitive decline in these populations.

A comparison of Swedish IBS patients and general practitioners regarding viewpoints on IBS: a Q-methodology study.

OBJECTIVES: Irritable bowel syndrome (IBS) is a common functional gastrointestinal condition. A respectful patient-doctor relationship with good communication is crucial for optimal treatment. Q-methodology is a combination of qualitative and quantitative methods used to study subjectivity. The aim of this study was to compare viewpoints on IBS between patients with IBS and general practitioners (GPs). METHODS: We conducted a Q-methodology study by including 30 patients and 30 GPs. All participants were asked to complete Q- sorting of 66 statements on IBS using an online software program. Data were processed using factor analysis. In addition, 3 patients and 3 GPs were interviewed. RESULTS: Three factors were extracted from both groups: Patient Factor 1 'Question the diagnosis of IBS', Patient Factor 2 'Lifestyle changes for a physical disorder', Patient Factor 3 'Importance of a diagnosis', GP Factor 1 'Unknown causes of great suffering', GP Factor 2 'Lifestyle changes are important, stress makes IBS worse', GP Factor 3 'Recognized the way IBS affects patients'. There was a strong and statistically significant correlation between patient Factor 1 and GP Factor 1, with a Pearson's r of 0.81 (p < 0.001). Correlations between other factors varied. CONCLUSIONS: There was consensus between patients and GPs that IBS is a physical and not a psychiatric disorder of unknown etiology. They also seemed to agree that IBS has a great negative impact on patients' lives and that lifestyle changes are beneficial. There were conflicting opinions regarding gender, cultural factors and the use of antidepressants.

Identification, spatial distribution, and associated factors of urban protected areas in China.

The increasing proximity between protected areas (PAs) and urban areas, which can lead to urban protected areas (UPAs), is now commonplace. Use of Euclidean distance to measure the distance between PAs and cities has not correctly portrayed the spatial relationship between PAs and cities. We devised an isochronous circle model to accurately measure the distance between 2706 national PAs in 5 categories and 2844 cities in China based on human accessibility to identify urban human activity-influenced protected areas (UHAIPAs) and to quantitatively analyze their distribution patterns and relationships with China's economy, population distribution patterns, and urban development indicators. Most of the PAs in China were established near cities. Of 2746 PAs in China, 18.35% (n = 504) became UPAs, and 58.27% (n = 1600), 16.72% (n = 459), and 3.31% (n = 91) of PAs were within 0-30, 30-60, and 60-90 min, respectively. Both UPAs and UHAIPAs in China in general exhibited obvious spatial aggregation characteristics (e.g., wetland parks and scenic areas), and there was a significant spatial dependence effect among characteristics. The degree of spatial distribution and aggregation of UPAs was correlated with 16 indicators across urban economic development, urban natural substrate, and urban policy support factors. Based on the results of our study, we call for various governments and scholars to focus on areas where wetland parks and PAs overlap with urban boundaries. It is important to emphasize the potential link between the development of agriculture, forestry, livestock and fisheries industries, and UPAs. Overall, we believe that examining the accessibility of PAs can more accurately measure the distance between PAs and cities, and more realistically reflect the possible impacts of urban human activities on PAs, which is helpful for strengthening the conservation and management of PAs.

Identificación, distribución espacial y factores asociados de las áreas urbanas protegidas en China Resumen La creciente proximidad entre las áreas protegidas (AP) y las áreas urbanas, que puede derivar en áreas urbanas protegidas (AUP), es muy común hoy en día. La distancia euclidiana no ha representado correctamente la distancia entre las AP y las ciudades. Por lo anterior, diseñamos un modelo de círculo isócrono para medir con certeza la distancia entre 2706 AP nacionales en cinco categorías y 2844 ciudades de China con base en la accesibilidad humana para identificar las áreas urbanas protegidas con influencia de actividad humana (AUPIAH). También lo usamos para analizar cuantitativamente los patrones de distribución y su relación con la economía china, los patrones de distribución poblacional y los indicadores de desarrollo urbano. La mayoría de las AP en China están establecidas cerca de las ciudades. De las 2746 AP, el 18.35% (n = 504) se convirtieron en AUP y el 58.27% (n = 1600), 16.72% (n = 459) y 3.31% (n = 91) se encontraban de 0­30 minutos, 30­60 y de 60­90 minutos de una ciudad, respectivamente. Tanto las AUP y las AUPIAH exhibieron características obvias de agregación espacial (p. ej.: parques de humedales y áreas escénicas) y hubo un efecto significativo de dependencia espacial entre dichas características. El nivel de distribución y agregación espacial de las AUP tuvo correlación con 16 indicadores de desarrollo económico urbano, sustrato natural urbano y factores de apoyo para las políticas urbanas. Con base en los resultados de nuestro estudio, hacemos un llamado para que diferentes gobiernos y académicos se enfoquen en las áreas en donde se traslapan las AP y los parques de humedales con los límites urbanos. Es importante resaltar la conexión potencial entre el desarrollo de la agricultura, silvicultura, ganadería y pesquería y las AUP. En general, consideramos que analizar la accesibilidad de las AP puede medir de forma más certera la distancia entre las AP y las ciudades, además de que refleja de forma más realista el posible impacto de las actividades humanas sobre las AP, lo cual es útil para fortalecer la conservación y gestión de las AP.

Inflammatory bowel disease patients' perspectives of non-medical needs.

BACKGROUND: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). METHODS: IBD patients were recruited from different centers. Considering inclusion and exclusion criteria, 521 participants were filled a predefined questionnaire. This questionnaire was prepared through literature review of the recent well-known guidelines on the needs of IBD patients, which was further approved by the experts of IBD area in three rounds of Delphi consensus. It includes 56 items in four sections of informational needs (25), supportive needs (15), sources of information (7), and methods of obtaining information (9). RESULTS: In particular, EFA was used to apply data reduction and structure detection. Given that this study tries to identify patterns, structures as well as inter-relationships and classification of the variables, EFA was utilized to simplify presentation of the variables in a way that large amounts of observations transform into fewer ones. Accordingly, the EFA identified five factors out of 25 items in the information needs section, three factors out of 15 items in the supportive needs section, two factors out of 7 items in the information sources section, and two factors out of 9 items in the information presentation methods. Through the CFA, all 4 models were supported by Root Mean Squared Error of Approximation (RMSEA); Incremental Fit Index (IFI); Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); and SRMR. These values were within acceptable ranges, indicating that the twelve factors achieved from EFA were validated. CONCLUSIONS: This study introduced a reliable 12-factor model as an efficient tool to comprehensively identify preferences of IBD patients in informational and supportive needs along with sources and methods of obtaining information. An in-depth understanding of the needs of IBD patients facilitates informing and supporting health service provision. It also assists patients in a fundamental way to improve adaptation and increase the quality of life. We suggest that health care providers consider the use of this tool in clinical settings in order to precisely assess its efficacy.

Qualitative and Quantitative Analysis of Three-Dimensional Fluorescence Spectra by Improved Parallel Factor Analysis with Internal Standard Sample Embedding.

The strategy of parallel factor analysis, combined with the internal standard method, has been increasingly applied to the qualitative and quantitative analysis of three-dimensional fluorescence spectra of unknown mixed fluorophores. Nevertheless, the disparity in the number of fluorophores included in the internal standard sample set and the number included in test samples may impact the qualitative and quantitative outcomes of parallel factor analysis. In this work, we systematically established the framework of the parallel factor analysis with internal standard sample embedding (ISSE-PARAFAC) strategy. We applied this framework to six datasets representing two scenarios and a real dataset and conducted a detailed discussion on the effects of the disparity between the number of fluorophores in the internal standard sample set and the number in the test set on both qualitative and quantitative results. Additionally, we introduced an enhancement to PARAFAC by aggregating fluorophores with similar emission wavelengths, corresponding to the peaks of emission loadings (spectra) obtained from PARAFAC, as a single fluorophore. This aggregation aimed to mitigate the strong correlation between similar fluorophores. The results imply that the presence of irrelevant fluorophores in the internal standard sample set, whether increased or decreased, does not significantly affect the qualitative and quantitative analysis of target fluorophores in the test set. Moreover, we demonstrated that the improved parallel factor analysis with internal standard sample embedding not only fully decomposes the uncorrelated mixed fluorophores for qualitative analysis but also allows the established linear concentration model for fluorescent components to predict the corresponding fluorophore concentration of test samples, enabling quantitative analysis at the ppm level (mg/L).

The low dimensionality of post-stroke cognitive deficits: it's the lesion anatomy!

For years, dissociation studies on neurological single-case patients with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value. Still, associational computational methods for dimensionality reduction-such as principal component analysis or factor analysis-became popular for the identification of fundamental cognitive functions and to understand human cognitive brain architecture from post-stroke neuropsychological profiles. In the present in silico study with lesion imaging of 300 stroke patients, we investigated the dimensionality of artificial simulated neuropsychological profiles that exclusively contained independent fundamental cognitive functions without any underlying low-dimensional cognitive architecture. Still, the anatomy of stroke lesions alone was sufficient to create a dependence between variables that allowed a low-dimensional description of the data with principal component analysis. All criteria that we used to estimate the dimensionality of data, including the Kaiser criterion, were strongly affected by lesion anatomy, while the Joliffe criterion provided the least affected estimates. The dimensionality of profiles was reduced by 62-70% for the Kaiser criterion, up to the degree that is commonly found in neuropsychological studies on actual cognitive measures. The interpretability of such low-dimensional factors as deficits of fundamental cognitive functions and their provided insights into human cognitive architecture thus seem to be severely limited, and the heavy focus of current cognitive neuroscience on group studies and associations calls for improvements. We suggest that qualitative criteria and dissociation patterns could be used to refine estimates for the dimensionality of the cognitive architecture behind post-stroke deficits. Further, given the strong impact of lesion anatomy on the associational structure of data, we see the need for further optimization of interpretation strategies of computational factors in post-stroke lesion studies of cognitive deficits.