Fundamental limits in structured principal component analysis and how to reach them.

How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is rotation invariant, we show that standard spectral PCA is optimal. However, for more general priors, both PCA and the existing approximate message-passing algorithm (AMP) fall short of achieving the information-theoretic limits, which we compute using the replica method from statistical physics. We thus propose an AMP, inspired by the theory of adaptive Thouless-Anderson-Palmer equations, which is empirically observed to saturate the conjectured theoretical limit. This AMP comes with a rigorous state evolution analysis tracking its performance. Although we focus on specific noise distributions, our methodology can be generalized to a wide class of trace matrix ensembles at the cost of more involved expressions. Finally, despite the seemingly strong assumption of rotation-invariant noise, our theory empirically predicts algorithmic performance on real data, pointing at strong universality properties.

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.

A cogent technique to circumvent the use of (some) ratio measures in physiology.

Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value - if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. KEY POINTS: The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known. A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values. A sample drawn from a population of these PCA-derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios. The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition.

Distribution and functional perspective analysis of epiphytic and endophytic bacterial communities associated with marine seaweeds, Alexandria shores, Egypt.

There is an enormous diversity of life forms present in the extremely intricate marine environment. The growth and development of seaweeds in this particular environment are controlled by the bacteria that settle on their surfaces and generate a diverse range of inorganic and organic chemicals. The purpose of this work was to identify epiphytic and endophytic bacterial populations associated with ten common marine macroalgae from various areas along the Mediterranean Sea coast in Alexandria. This was done to target their distribution and possible functional aspects. Examine the effects of the algal habitat on the counting and phenotypic characterization of bacteria, which involves grouping bacteria based on characteristics such as shape, colour, mucoid nature, type of Gram stain, and their ability to generate spores. Furthermore, studying the physiological traits of the isolates under exploration provides insight into the optimum environmental circumstances for bacteria associated with the formation of algae. The majority of the bacterial isolates exhibited a wide range of enzyme activities, with cellulase, alginase, and caseinase being the most prevalent, according to the data. Nevertheless, 26% of the isolates displayed amylolytic activity, while certain isolates from Miami, Eastern Harbor, and Montaza lacked catalase activity. Geographical variations with the addition of algal extract may impact on the enumeration of the bacterial population, and this might have a relationship with host phylogeny. The most significant observation was that endophytic bacteria associated with green algae increased in all sites, while those associated with red algae increased in Abu Qir and Miami sites and decreased in Eastern Harbor. At the species level, the addition of algal extract led to a ninefold increase in the estimated number of epiphytic bacteria for Cladophora pellucida in Montaza. Notably, after adding algal extract, the number of presented endophytic bacteria associated with Codium sp. increased in Abu Qir while decreasing with the same species in Montaza. In addition to having the most different varieties of algae, Abu Qir has the most different bacterial isolates.

New evidence for the sensorimotor mismatch theory of weight perception and the size-weight illusion.

The size-weight illusion is a phenomenon where a smaller object is perceived heavier than an equally weighted larger object. The sensorimotor mismatch theory proposed that this illusion occurs because of a mismatch between efferent motor commands and afferent sensory feedback received when lifting large and small objects (i.e., the application of too little and too much lifting force, respectively). This explanation has been undermined by studies demonstrating a separation between the perceived weight of objects and the lifting forces that are applied on them. However, this research suffers from inconsistencies in the choice of lifting force measures reported. Therefore, we examined the contribution of sensorimotor mismatch in the perception of weight in the size-weight illusion and in non-size-weight illusion stimuli and evaluated the use of a lifting force aggregate measure comprising the four most common lifting force measures used in previous research. In doing so, the sensorimotor mismatch theory was mostly supported. In a size-weight illusion experiment, the lifting forces correlated with weight perception and, contrary to some earlier research, did not adapt over time. In a non-size-weight illusion experiment, switches between lifting light and heavy objects resulted in perceiving the weight of these objects differently compared to no switch trials, which mirrored differences in the manner participants applied forces on the objects. Additionally, we reveal that our force aggregate measure can allow for a more sensitive and objective examination of the effects of lifting forces on objects.

Association of dietary patterns with sarcopenia in adults aged 50 years and older.

PURPOSE: Although numerous studies have investigated the impact of dietary factors on the prevention of decreased muscle mass and function, limited research has examined the relationship between dietary patterns and sarcopenia. This study aimed to assess the associations between dietary patterns, and sarcopenia, muscle strength, and mass in adults following a Mediterranean diet residing in southern Italian cities. METHODS: This cross-sectional study utilized data from an existing database, comprising 528 individuals aged 50 years or older who underwent health-screening tests at the Clinical Nutrition Unit of the "R.Dulbecco" University Hospital. Strength was assessed through handgrip strength, and appendicular skeletal muscle mass was estimated using bioelectrical impedance analysis. Dietary intake information was collected through a food frequency questionnaire linked to the MetaDieta 3.0.1 nutrient composition database. Principal Component Analysis, a statistical technique identifying underlying relationships among different nutrients, was employed to determine dietary patterns. Multinomial logistic regression analysis was conducted to estimate the odds ratio for sarcopenia or low handgrip strength in relation to the lowest tertile of dietary pattern adherence compared to the highest adherence. RESULTS: The participants had a mean age of 61 ± 8 years. Four dietary patterns were identified, with only the Western and Mediterranean patterns showing correlations with handgrip strength and appendicular skeletal muscle mass. However, only the Mediterranean pattern exhibited a correlation with sarcopenia (r = - 0.17, p = 0.02). The highest tertile of adherence to the Mediterranean dietary pattern demonstrated significantly higher handgrip strength compared to the lowest tertile (III Tertile: 28.3 ± 0.5 kg vs I Tertile: 26.3 ± 0.5 kg; p = 0.01). Furthermore, even after adjustment, the highest tertile of adherence to the Mediterranean pattern showed a significantly lower prevalence of sarcopenia than the lowest adherence tertile (4% vs 16%, p = 0.04). The lowest adherence to the Mediterranean dietary pattern was associated with increased odds of having low muscle strength (OR = 2.38; p = 0.03; 95%CI = 1.05-5.37) and sarcopenia (OR = 9.69; p = 0.0295; %CI = 1.41-66.29). CONCLUSION: A high adherence to the Mediterranean dietary pattern, characterized by increased consumption of legumes, cereals, fruits, vegetables, and limited amounts of meat, fish, and eggs, is positively associated with handgrip strength and appendicular skeletal muscle mass. The highest adherence to this dietary model is associated with the lowest odds of low muscle strength and sarcopenia. Despite the changes brought about by urbanization in southern Italy compared to the past, our findings continue to affirm the superior benefits of the Mediterranean diet in postponing the onset of frailty among older adults when compared to other dietary patterns that are rich in animal foods.

Graphene Oxide, Carbon Nanotubes, and Polyelectrolytes-Based Impedanciometric E-Tongue for Estrogen Detection in Complex Matrices.

Currently, it is necessary to maintain the quality of aquifers and water bodies, which means the need for sensors that detect molecules as emerging pollutants (EPs) at low concentrations in aqueous complex solutions. In this work, an electronic tongue (e-tongue) prototype was developed to detect 17ß-estradiol in tap water. To achieve such a prototype, an array of sensors was prepared. Each sensor consists of a solid support with interdigitated electrodes without or with thin films prepared with graphene oxide, nanotubes, and other polyelectrolytes molecules adsorbed on them. To collect data from each sensor, impedance spectroscopy was used to analyze the electrical characteristics of samples of estrogen solutions with different concentrations. To analyze the collected data from the sensors, principal components analysis (PCA) method was used to create a three-dimensional plane using the calculated principal components, namely PC1 and PC2, and the estrogen concentration values. Then, damped least squares (DLS) was used to find the optimal values for the hyperplane calibration, as the sensitivity of this e-tongue was not represented by a straight line but by a surface. For the collected data, from nanotubes and graphene oxide sensors, a calibration curve for concentration given by the 10PC1×0.492-PC2×0.14-14.5 surface was achieved. This e-tongue presented a detection limit of 10-16 M of 17ß-estradiol in tap water.

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data.

In this paper we propose the method for detecting potential anomalous cosmic ray particle tracks in big data image dataset acquired by Complementary Metal-Oxide-Semiconductors (CMOS). Those sensors are part of scientific infrastructure of Cosmic Ray Extremely Distributed Observatory (CREDO). The use of Incremental PCA (Principal Components Analysis) allowed approximation of loadings which might be updated at runtime. Incremental PCA with Sequential Karhunen-Loeve Transform results with almost identical embedding as basic PCA. Depending on image preprocessing method the weighted distance between coordinate frame and its approximation was at the level from 0.01 to 0.02 radian for batches with size of 10,000 images. This significantly reduces the necessary calculations in terms of memory complexity so that our method can be used for big data. The use of intuitive parameters of the potential anomalies detection algorithm based on object density in embedding space makes our method intuitive to use. The sets of anomalies returned by our proposed algorithm do not contain any typical morphologies of particle tracks shapes. Thus, one can conclude that our proposed method effectively filter-off typical (in terms of analysis of variance) shapes of particle tracks by searching for those that can be treated as significantly different from the others in the dataset. We also proposed method that can be used to find similar objects, which gives it the potential, for example, to be used in minimal distance-based classification and CREDO image database querying. The proposed algorithm was tested on more than half a million (570,000+) images that contains various morphologies of cosmic particle tracks. To our knowledge, this is the first study of this kind based on data collected using a distributed network of CMOS sensors embedded in the cell phones of participants collaborating within the citizen science paradigm.

Development and psychometric testing of a scale to measure effective rural emergency transfer (RET).

BACKGROUND: The process of transferring patients from small rural primary care facilities to referral facilities impacts the quality of care and effectiveness of the referral healthcare system. The study aimed to develop and evaluate the psychometric properties of a scale measuring requirements for effective rural emergency transfer. METHODS: An exploratory sequential design was utilized to develop a scale designed to measure requirements for effective emergency transport. Phase one included a qualitative, interview study with 26 nursing transport providers. These transcripts were coded, and items developed for the proposed scale. Phase two included a content validity review by these 16 transport providers of the domains and items developed. Phase three included development and evaluation of psychometric properties of a scale designed to measure requirements for effective emergency transport. This scale was then tested initially with 84 items and later reduced to a final set of 58 items after completion by 302 transport nurses. The final scale demonstrated three factors (technology & tools; knowledge & skills; and organization). Each factor and the total score reported excellent scale reliability. RESULTS: The initial item pool consisted of 84 items, generated, and synthesized from an extensive literature review and the qualitative descriptive study exploring nurses' experiences in rural emergency patient transportation. A two-round modified Delphi method with experts generated a scale consisting of 58 items. A cross-sectional study design was used with 302 nurses in rural clinics and health in four rural health districts. A categorical principal components analysis identified three components explaining 63.35% of the total variance. The three factors, technology, tools, personal knowledge and skills, and organization, accounted for 27.32%, 18.15 and 17.88% of the total variance, respectively. The reliability of the three factors, as determined by the Categorical Principal Component Analysis (CATPCA)'s default calculation of the Cronbach Alpha, was 0.960, 0.946, and 0.956, respectively. The RET Cronbach alpha was 0.980. CONCLUSIONS: The study offers a three-factor scale to measure the effectiveness of emergency patient transport in rural facilities to better understand and improve care during emergency patient transport.

Determining the relationship between oral health and physical function in elderly people.

BACKGROUND: Poor oral conditions in the elderly may have numerous effects on general health, including physical fitness and performance. OBJECTIVES: This study aimed to determine the relationship between oral health and physical function in elderly people. METHODS: Physical function and oral health parameters were compared using parametric comparison tests and Pearson correlation analyses. In addition, principal components analysis, hierarchical clustering and multidimensional scaling analysis clustered the patients' physical and oral health scores. The relationship between the groups was also determined using decision tree analysis. RESULTS: A total of 112 elderly patients participated in the study. Grip strength (GS) was higher in patients with high chewing ability, and Timed Up and Go (TUG) scores were lower in the high oral health group (p < .05). GS was correlated with Decay, Missing, and Filled Teeth Index (DMFT) and the number of remaining and functional teeth (p < .05). According to principal component analysis, it was seen that there were three components (oral, functional and quality of life (QoL) parameters), and the features that were related to each other were gathered together. TUG and GS showed the highest relative importance among physical function criteria in the classification based on chewing ability. They were GS and physical activity for oral health-related QoL. CONCLUSION: In the elderly, higher physical function parameters, especially GS may be an indicator of a better oral health and oral health-related QoL. Preventive physical rehabilitation practices, in addition to oral treatments, may be effective in improving oral health in the elderly.

3D human ear modelling with parameterization technique and variation analysis.

Anthropometry is vital to provide design references when seeking proper product fit. Nowadays, 3D anthropometry is widely used to provide more size and shape details for improving product designs. However, 3D ear anthropometry is still at an explorative stage, considering the complex ear morphology and other technical obstacles. The proposed research method in this study is applicable to analyse the 3D point cloud of the entire external ear. With the cross-parameterisation technique, the dataset was used to explore the morphological characteristics of the ear. Ear dimensions were automatically extracted and further analysed to explore the gender and symmetry differences using two-way ANOVA. The 3D ear models were investigated through Principal Component Analysis (PCA). The most significant variation was found in the helix and concha region, and the overall ear size is the second important factor determining ear variance. The statistical models were generated as 3D design references for ear-related products.Practitioner summary: This study revealed the morphological variations of the entire 3D external ear with a parameterised 3D ear dataset. Based on the PCA findings, a set of statistical models were generated as design references for product evaluation digitally or physically.

Association between dietary patterns and glycemic control in type II diabetes mellitus patients.

OBJECTIVE: To assess the association between dietary patterns and glycemic control among patients with type II diabetes mellitus (T2DM). DESIGN: A cross-sectional study. SITE: The 2015-2018 National Health and Nutrition Examination Survey (NHANES). PARTICIPANTS: A total of 1646 T2DM patients were included, of whom 854 were hyperglycemia. METHODS: Main dietary patterns were identified using the sparse principal components analysis (SPCA). Logistic regression analysis was applied to investigate the association between each dietary pattern and the risk of hyperglycemia with odds ratios (OR) and 95% confidence intervals (CI). SPCA analysis yielded five significant principal components (PC), which represented five main dietary patterns. RESULTS: PC1, characterized by a high intake of sweets, red meat and processed meat, was associated with higher odds of hyperglycemia in patients who underwent hyperglycemic drug or insulin treatments (OR: 1.71, 95% CI: 1.10-2.64). PC5, characterized by high in red meat, while low in coffee, sweets, and high-fat dairy consumption. The relationship between the PC5 and hyperglycemia was marginal significance (OR: 0.63, 95% CI: 0.38-1.02). PC2 was characterized by a high consumption of green vegetables, other vegetables, and whole grains, and low intake of potatoes and processed meat. In patients with the hyperglycemic drug and insulin free, higher PC2 levels were related to lower odds of hyperglycemia (OR: 0.45, 95% CI: 0.21-0.96). CONCLUSIONS: High intake of sweets, red meat, and processed meat might be detrimental to glycemic control in patients with drug-treated T2DM. High in red meat, while low in coffee, sweets, and high-fat dairy consumption may be beneficial to glycemic control. In addition, high consumption of green vegetables, other vegetables, and whole grains, and low intake of potatoes and processed meat may be good for glycemic control in patients without drug-treated T2DM.

Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis.

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling.

INTRODUCTION: This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation. METHOD: A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS). RESULTS: The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure. CONCLUSIONS: The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Relationship between dietary patterns and physical performance in the very old population: a cross-sectional study from the Kawasaki Aging and Wellbeing Project.

OBJECTIVES: As the world's population is ageing, improving the physical performance (PP) of the older population is becoming important. Although diets are fundamental to maintaining and improving PP, few studies have addressed the role of these factors in adults aged ≥ 85 years, and none have been conducted in Asia. This study aimed to determine the dietary patterns (DP) and examine their relationship with PP in this population. DESIGN: This cross-sectional study (Kawasaki Aging and Wellbeing Project) estimated food consumption using a brief-type self-administered diet history questionnaire. The results were adjusted for energy after aggregating into thirty-three groups, excluding possible over- or underestimation. Principal component analysis was used to identify DP, and outcomes included hand grip strength (HGS), timed up-and-go test, and usual walking speed. SETTING: This study was set throughout several hospitals in Kawasaki city. PARTICIPANTS: In total, 1026 community-dwelling older adults (85-89 years) were enrolled. RESULTS: Data of 1000 participants (median age: 86·9 years, men: 49·9 %) were included in the analysis. Three major DP (DP1: various foods, DP2: red meats and coffee, DP3: bread and processed meats) were identified. The results of multiple regression analysis showed that the trend of DP2 was negatively associated with HGS (B, 95 % CI -0·35, -0·64, -0·06). CONCLUSIONS: This study suggests a negative association between HGS and DP characterised by red meats and coffee in older adults aged ≥ 85 years in Japan.

Longitudinal Reading Measures and Genome Imputation in the National Child Development Study: Prospects for Future Reading Research.

Reading difficulties are prevalent worldwide, including in economically developed countries, and are associated with low academic achievement and unemployment. Longitudinal studies have identified several early childhood predictors of reading ability, but studies frequently lack genotype data that would enable testing of predictors with heritable influences. The National Child Development Study (NCDS) is a UK birth cohort study containing direct reading skill variables at every data collection wave from age 7 years through to adulthood with a subsample (final n = 6431) for whom modern genotype data are available. It is one of the longest running UK cohort studies for which genotyped data are currently available and is a rich dataset with excellent potential for future phenotypic and gene-by-environment interaction studies in reading. Here, we carry out imputation of the genotype data to the Haplotype Reference Panel, an updated reference panel that offers greater imputation quality. Guiding phenotype choice, we report a principal components analysis of nine reading variables, yielding a composite measure of reading ability in the genotyped sample. We include recommendations for use of composite scores and the most reliable variables for use during childhood when conducting longitudinal, genetically sensitive analyses of reading ability.

New bands in the sleep stages of spider monkeys (Ateles geoffroyi): Electroencephalographic correlations and spatial distribution.

The study of electroencephalographic (EEG) signals in nonhuman primates has led to important discoveries in neurophysiology and sleep behavior. Several studies have analyzed digital EEG data from primate species with prehensile tails, like the spider monkey, and principal component analysis has led to the identification of new EEG bands and their spatial distribution during sleep and wakefulness in these monkeys. However, the spatial location of the EEG correlations of these new bands during the sleep-wake cycle in the spider monkey has not yet been explored. Thus, the objective of this study was to determine the spatial distribution of EEG correlations in the new bands during wakefulness, rapid eye movement (REM) sleep, and non-REM sleep in this species. EEG signals were obtained from the scalp of six monkeys housed in experimental conditions in a laboratory setting. Regarding the 1-21 Hz band, a significant correlation between left frontal and central regions was recorded during non-REM 2 sleep. In the REM sleep, a significant correlation between these cortical areas was seen in two bands: 1-3 and 3-13 Hz. This reflects a modification of the degree of coupling between the cortical areas studied, associated with the distinct stages of sleep. The intrahemispheric EEG correlation found between left perceptual and motor regions during sleep in the spider monkey could indicate activation of a neural circuit for the processing of environmental information that plays a critical role in monitoring the danger of nocturnal predation.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis.

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

Association between multiple vitamins and bone mineral density: a cross-sectional and population-based study in the NHANES from 2005 to 2006.

BACKGROUND: Bone mineral density (BMD) alterations in response to multivitamin exposure were rarely studied. Our study assessed the association of coexposure to six types of vitamins (i.e., vitamins B12, B9, C, D, A and E) with BMD measurements in adults in the US. METHODS: Data were collected from participants aged ≥ 20 years (n = 2757) in the U.S. National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2006. Multiple linear regression, restricted cubic splines, principal component analysis (PCA) and weighted quantile sum (WQS) regression were performed for statistical analysis. RESULTS: The circulating levels of vitamins B12 and C were positively associated with BMDs, and an inverted L-shaped exposure relationship was observed between serum vitamin C and BMDs. PCA identified two principal components: one for 'water-soluble vitamins', including vitamins B12, B9 and C, and one for 'fat-soluble vitamins', including vitamins A, D and E. The former was positively associated with total femur (ß = 0.009, 95%CI: 0.004, 0.015) and femoral neck (ß = 0.007, 95%CI: 0.002, 0.013) BMDs, and the latter was negatively associated with BMDs with non-statistical significance. The WQS index constructed for the six vitamins was significantly related to total femur (ß = 0.010, 95%CI: 0.001, 0.018) and femoral neck (ß = 0.008, 95%CI: 0.001, 0.015) BMDs, and vitamins B12 and C weighted the most. The WQS index was inversely related to BMDs with non-statistical significance, and vitamins E and A weighted the most. CONCLUSION: Our findings suggested a positive association between water-soluble vitamin coexposure and BMD, and the association was mainly driven by vitamins B12 and C. Negative association between fat-soluble vitamin coexposure and BMD was indicated, mainly driven by vitamins E and A. An inverted L-shaped exposure relationship was found between vitamin C and BMD.