Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

Image genetics reveal the connection between microscopic genetics and macroscopic imaging and then detect diseases’ biomarkers. In this research, we make full use of the prior knowledge that has significant reference value for exploring the correlation of brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization (JCB-SNMF). The algorithm simultaneously projects structural Magnetic Resonance Imaging (sMRI), Single Nucleotide Polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information of each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROI), SNP, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a protein-protein interaction network (PPI), we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also find some significant SNP-ROI and Gene-ROI pairs. Among them, two SNPs of rs4472239 and rs11918049 and three genes of KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.

An Observational Study on Cephalometric Characteristics and Patterns Associated with the Prader–Willi Syndrome: A Structural Equation Modelling and Network Approach

Examining specific patterns of major cranio-facial alterations through cephalometric measurements in order to improve the Prader–Willi (PWS) syndrome diagnostic poses a major challenge of identifying interlinkages between numerous credentials. These interactions can be captured through probabilistic models of conditional independence between heterogeneous variables. Our research included 18 subjects (aged 4 to 28 years) genetically diagnosed with Prader–Willi syndrome and a healthy control group (matched age and sex). A morphometric and cephalometric analysis was performed upon all the subjects in order to obtain the needed specific data. We have, therefore, firstly deployed several integrated Gaussian graphical models (GGMs) to capture the positive and negative partial correlations and the intensity of the connections between numerous credentials configured to determine specific cranio-facial characteristics of patients with PWS compared to others without this genetic disorder (case-control analysis). Afterwards, we applied structural equation modelling (SEM) with latent class analysis to assess the impact of these coordinates on the prevalence of the Prader–Willi diagnostic. We found that there are latent interactions of features affected by external variables, and the interlinkages are strapping particularly between cranial base (with an important role in craniofacial disharmonies) and facial heights, as important characteristic patterns in determining the Prader–Willi diagnostic, while the overall patterns are significantly different in PWS and the control group. These results impact the field by providing an enhanced comprehensive perspective on cephalometric characteristics and specific patterns associated with Prader–Willi syndrome that can be used as benchmarks in determining the diagnostic of this rare genetic disorder. Furthermore, the two innovative exploratory research tools applied in this paper are very useful to the craniofacial field to infer the connections/dependencies between variables (particularly biological variables and genes) on cephalometric characteristics and specific patterns associated with Prader–Willi syndrome.

Effects of air quality on the health of Mediterranean forests

<p>The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.</p><p>However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled. </p><p>The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.</p><p>That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns). </p><p>In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out. </p><p>The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables. </p>

AN AFRICAN BASKETRY OF HETEROGENEOUS VARIABLES DAHOMEYDAHALO-DANDI

— In terms of scientific systems approach to the knowledge of human origins, human organizations, human histories, human kingdoms, human languages, human populations and above all the human genes, unquestionable scientific evidence with human dignity flabbergasted the European strong world of slavemasters and colonialist-policy-rulers. This deduces that the early Europeans knew nothing scientific about the mankind beforehand unleashing their one-up-man-ship over Africa and the Africans except that they were the white skinned flocks and so, not the kith and kin of the Africans in black skin living in what they called the ‘Dark Continent’! Of course, in later times, the same masters and rulers committed to not repeating their colonialist racial geo-political injustices. The whites were domineering and weaponized to the hilt on their own mentality, for their own interests and by their own logic opposing the geopolitically distant African blacks inhabiting the natural resources enriched frontiers. Those ‘twists and twitches’ in time-line led to the black’s slavery and white’s slave-trade with meddling Christian Adventist Missionaries, colonialists, religious conversionists, Anglican Universities’ Missions , inter-sexual-births, the associative asomi , the dissociative asomi and the non-asomi divisions within African natives in concomitance . And yet, an indelible African Awe Era (AWE) prevailed in the African Continent. How is it made possible by the Africans? No people or no language or no kingdom exists without some kind of moral, social and political framework and when this is not written-down, it may be strongest as the unwritten-down. A number of early European-written-down accounts in respect of the European-called ‘Black Africans’ boomeranged when the European scientific researchers discovered that the genetic roots of mankind exploded in Africa proclaiming African commonality superimposed. The first woman of all human beings is an African Mother indeed as confir

Mortality rate trend of meningitis by region of Brazil, between the year 2010 to 2019

Background: Meningitis is an inflammatory process in the membranes that line the central nervous system (CNS). Objectives: To analyze the behavior of meningitis mortality rate by region of Brazil, between 2010 and 2019. Desing and setting: Analytical, observational and retrospective study. Deaths number from meningitis was recorded through the Mortality Information System (SIM) stratified by Brazilian region, and population data from IBGE. Methods: Mortality rate (MR) per 1 million inhabitants was calculated. MR trend in each region was provided by segmented linear regression. MR annual percentage changes (APCs) were adjusted with a 95% confidence interval (CI). Results: In this period, there were 10007 deaths from meningitis in Brazil. MR by region varied from 3.77 (North Region) to 6.03 (Southeast Region), while in Brazil it was 4.93. A decreasing trend is noticeable in Brazil MR (APC= -3.2; 95%CI= -3.9; -2.4). Central-West region had the largest drop (APC= - 4.1; 95%CI= - 7.9; -0.2), followed by Southeast. In contrast, South and North regions had stationary trend and Northeast had a double behavior: decreasing from 2010 to 2015 (APC= -7.7; 95%CI= -11.7; -3.5) and stationary from 2015 to 2019. Conclusions: Despite the country decreasing trend, these results are heterogeneous. Variables such as vaccination coverage, population adherence and socioeconomic status can influence meningitis MR trends. Thus, it is understood the need for programs aimed at disease treatment and prevention to reduce regional inequalities and meningitis mortality.

Eruptive Cherry Angiomas and Skin Melanoma: Further Insights into an Intriguing Association

<b><i>Background:</i></b> Some risk factors for malignant melanoma (MM) are recognized. <b><i>Objective:</i></b> To compare the strength of association between MM and eruptive cherry angiomas (CAs) with that of other well-known associations. <b><i>Methods:</i></b> This cross-sectional study included all subjects referred to the Outpatient Dermatology-Oncology and Dermoscopy Units of the Universities of Ferrara and Bologna, Italy, over a 5-month period and submitted to total body skin examination. We recorded: age, sex, cutaneous and non-cutaneous malignancies, presence of CAs, arbitrarily considered as “eruptive” when &#x3e;10, &#x3e;40 common melanocytic naevi or &#x3e;2 clinically atypical naevi. The strength of association between the possible risk factors and MM was calculated by odds ratio in both the whole population and age quartiles. Variables associated with MM were included in multiple logistic regression analysis. <b><i>Results:</i></b> 1,190 subjects were included; 615 had malignant skin tumours, 462 MM, 85 extracutaneous tumours. Five hundred and eighty-seven subjects had eruptive CAs, 485 subjects &#x3e;40 melanocytic naevi and 368 more than 2 atypical melanocytic naevi. Eruptive CAs, especially in subjects younger than 70, and &#x3e;2 atypical melanocytic naevi, mostly in subjects older than 50, were significantly associated with MM. The strength of these 2 associations was similar. The presence of &#x3e;40 melanocytic naevi was not associated with MM. <b><i>Conclusions:</i></b> These findings confirmed an association between MM and eruptive CAs, which was as strong as the one between MM and &#x3e;2 atypical melanocytic naevi. CAs seem an intriguing model of interaction between heterogeneous variables, like immunocompetence, stimuli inducing endothelial cell proliferation, and oncogenesis, which deserves further investigation.

Factors affecting the purchase of private label food products

PurposeThis research paper aims to understand which factors influence the purchase of private label food products, by measuring the importance of 14 variables for purchasing frequency.Design/methodology/approachData were collected through questionnaires to a sample of consumers. They have been analysed according to the extra tree classifier methodology, which allows providing a more reliable classification – compared to previous studies – of factors affecting consumers' choices of private label products.FindingsResults show that consumers' choices related to private label food products are influenced by groups of heterogeneous variables related to their perception on products, satisfaction of post-consumption, store's role and trust built over time by retailers.Research limitations/implicationsData have been collected through an online survey, which could generate the bias of self-selection; the sampling method is non-probabilistic.Practical implicationsThe study provides useful indications on the role of private labels in retailer management policies and on marketing competences and skills that are necessary for managing retailers' assortments.Originality/valueThe existing literature lacks clarity on the factors that influence the frequency of purchasing private label food products. By considering a higher number of variables than previous studies, it has been possible to classify and measure the importance of each variable included in the analysis framework adopted, also in case of correlation between variables.

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

Harmonising data from different sources to conduct research using linked survey and routine datasets

IntroductionHarmonization of different data sources from various electronic health records across systems enhances the potential scope and granularity of data available to health data research, providing more opportunities for research by improving the generalizability and effective sample size of a range of outcome metrics. Objectives and ApproachThis study describes data harmonisation for a UK longitudinal birth cohort, the Millennium Cohort Study (MCS) which was linked to routine inpatient and emergency department, and, where available, general practice and child health records for 1838 Welsh and 1431 Scottish consenting MCS participants. Datasets requiring harmonisation were: from Wales, Patient Episode Dataset for Wales (PEDW) and Emergency Department Data Set (EDDS) data and from Scotland, Scottish Medical Record 01 (SMR01) and Accident and Emergency dataset (A&E2). Heterogeneous variables were created by transforming variable names, concepts, codes to improve scope for analysis. ResultsA harmonized dataset of 2166 participants and 5747 hospital admissions were derived of cohort members who had at least 1 hospital inpatient or A&E event before their 14th birthday. Harmonisation included: dealing with date granularity by generating random dates of birth; standardising periods of data collection; identifying inconsistencies and then mapping and bridging differences in definitions of periods of care and levels of diagnostic and operational coding across countries and datasets. Conclusion/ImplicationsHeterogeneous variables from different data sources were pooled and converted into standardised data for research, extending existing harmonisation work, including curation of a population based anonymously linkable longitudinal cohort. [AA1] These methods are reproducible and can be utilised by other researchers and projects applying to use these routine data sources.