Canonical Correlation Research Articles

Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response.

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.

A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features

This research presents an innovative technique for semantic segmentation of Hyperspectral Image (HSI) while focusing on its dimensionality reduction. A unique technique is applied to three distinct HSI landcover datasets, Indian Pines, Pavia University, and Salinas Valley, acquired from diverse sensors. HSIs are inherently multi-view structures, causing redundancy and computation overload due to their high dimensionality. The technique utilizes Canonical Correlation Analysis (CCA) variants, Pairwise CCA (PCCA) and Multiple Set CCA (MCCA), to extract features from multiple views of the input image simultaneously. The performance of PCCA and MCCA is compared with the traditional Principal Component Analysis (PCA) on all datasets. The superior performance of CCA variants, particularly MCCA, is demonstrated in achieving higher Overall Accuracy (OA) for semantic segmentation compared to PCA. The research extends the analysis by integrating machine learning classifiers for per pixel prediction, demonstrating the effectiveness of the proposed techniques i.e., PCCA-SVM and MCCA-SVM.

Associations between regional blood-brain barrier permeability, aging, and Alzheimer's disease biomarkers in cognitively normal older adults.

Increased blood-brain barrier permeability (BBBp) has been hypothesized as a feature of aging that may lead to the development of Alzheimer's disease (AD). We sought to identify the brain regions most vulnerable to greater BBBp during aging and examine their regional relationship with neuroimaging biomarkers of AD. We studied 31 cognitively normal older adults (OA) and 10 young adults (YA) from the Berkeley Aging Cohort Study (BACS). Both OA and YA received dynamic contrast-enhanced MRI (DCE-MRI) to quantify Ktrans values, as a measure of BBBp, in 37 brain regions across the cortex. The OA also received Pittsburgh compound B (PiB)-PET to create distribution volume ratios (DVR) images and flortaucipir (FTP)- PET to create partial volume corrected standardized uptake volume ratios (SUVR) images. Repeated measures ANOVA assessed the brain regions where OA showed greater BBBp than YA. In OA, Ktrans values were compared based on sex, Aβ positivity status, and APOE4 carrier status within a composite region across the areas susceptible to aging. We used linear models and sparse canonical correlation analysis (SCCA) to examine the relationship between Ktrans and AD biomarkers. OA showed greater BBBp than YA predominately in the temporal lobe, with some involvement of parietal, occipital and frontal lobes. Within an averaged ROI of affected regions, there was no difference in Ktrans values based on sex or Aβ positivity, but OA who were APOE4 carriers had significantly higher Ktrans values. There was no direct relationship between averaged Ktrans and global Aβ pathology, but there was a trend for an Ab status by tau interaction on Ktrans in this region. SCCA showed increased Ktrans was associated with increased PiB DVR, mainly in temporal and parietal brain regions. There was not a significant relationship between Ktrans and FTP SUVR. Our findings indicate that the BBB shows regional vulnerability during normal aging that overlaps considerably with the pattern of AD pathology. Greater BBBp in brain regions affected in aging is related to APOE genotype and may also be related to the pathological accumulation of Aβ.

Visually exploring canonical correlation patterns of high-dimensional industrial control datasets based on multi-sensor fusion

Visually exploring canonical correlation patterns of high-dimensional industrial control datasets based on multi-sensor fusion

Evaluation of the relationships between oxidative stress, inflammation, hormonal status and sperm parameters in rats: Canonical correlation analysis

Many endocrine or non-endocrine factors are involved in sperm production. Although reproductive hormones are very important for the initiation and maintenance of spermatogenesis, other factors, such as inflammation and oxidative stress, affect spermatogenesis. The aim of this study is to evaluate the relationships between sperm parameters and hormones, oxidative stress, and inflammation status. We conducted this study on 40 rats. Sperm parameters (motility, abnormal sperm rate, and dead sperm rate), oxidative stress (malondialdehyde, glutathione, glutathione peroxidase, and catalase), inflammation (NF-κβ, TNF-α, IL-1β, IL-6, and IL-10), and hormone parameters (follicle-stimulating hormone, luteinizing hormone, testosterone, melatonin, and corticosterone) were determined. Relationships between mentioned parameters were investigated by canonical correlation analysis. Canonical correlation coefficients for these data sets (sperm-oxidative stress, sperm-inflammation, and sperm-hormone parameters) were found to be strongly significant (rc= 0.875, p<0.001; rc= 0.868, p<0.001; rc= 0.886, p<0.001, respectively). The rate of explanation of oxidative stress, inflammation parameters and hormones by sperm parameters was 61.80 %, 56.10 % and 63.90 %, respectively. Canonical correlation analysis results have revealed that dead sperm rate is mostly related to nuclear factor-kappa beta (NF-κβ), catalase, and corticosterone. CCA, which has taken into account the multiple relationships, has revealed that multidimensional evaluation of data sets can provide important and innovative information to researchers for the assessment of relationships between sperm, oxidative stress, inflammation, and hormone parameters.

An online dynamic security assessment integrated scheme for power systems based on sparse multinomial naive bayes and canonical correlation forest

With the expansion of power systems and renewable energies, the security of power systems is confronted with severe situations. In this research, an online integrated DSA scheme based on sparse multinomial naive Bayes (SMNB) and canonical correlation forest (CCF) is proposed. The SMNB-based feature selection process selects the key features according to the correlations between the system operating variables and the DSA classification labels. Then, the DSA model is obtained by training CCFs with the key features. Finally, the results of DSA can be provided by the model on the basis of the real-time data of phasor measurement units (PMUs) in the wide area measurement system (WAMS). The online DSA integrated scheme is tested on the IEEE 39-bus system and a practical 1648-bus system provided by PSS/E software. The test results show that the proposed scheme is suitable for online application with providing satisfactory assessment accuracy. Furthermore, topology change, data noise and missing data are also taken into account for the robustness tests of the integrated scheme.

Domestic Competition, Trade Openness and Entrepreneurial Culture: Canonical Correlation Analysis

Abstract The paper analyses canonical correlations between domestic competition, trade openness and entrepreneurial culture. The research covered 141 countries ranked by World Competitiveness Index in 2019. Canonical correlation analysis is applied to find relationship between two canonical variables. The first canonical variable includes sub-indexes from Domestic competition and Trade openness pillars. The second variable contains sub-indexes from Entrepreneurial culture pillar. The results of the analysis showed there is a strong, positive, statistically significant canonical correlation between these canonical variables with a Pearson coefficient of 0.86. The linear regression analysis is also applied. The regression analysis shows that the variable Distortive effects of taxes and subsidies on competition is the most important for all dependent variables. The extent of market dominance, Competition in services and Border clearance efficiency are important, but not as much as taxes and subsidies. It confirms that regulation of domestic competition and trade openness are supreme for entrepreneurial culture.

Removal of Ocular and Muscular Artifacts From Multi-Channel EEG Using Improved Spatial-Frequency Filtering.

Over recent decades, electroencephalogram (EEG) has become an essential tool in the field of clinical analysis and neurological disease research. However, EEG recordings are notably vulnerable to artifacts during acquisition, especially in clinical settings, which can significantly impede the accurate interpretation of neuronal activity. Blind source separation is currently the most popular method for EEG denoising, but most of the sources it separates often contain both artifacts and brain activity, which may lead to substantial information loss if handled improperly. In this paper, we introduce a dual-threshold denoising method combining spatial filtering with frequency-domain filtering to automatically eliminate electrooculogram (EOG) and electromyogram (EMG) artifacts from multi-channel EEG. The proposed method employs a fusion of second-order blind identification (SOBI) and canonical correlation analysis (CCA) to enhance source separation quality, followed by adaptive threshold to localize the artifact sources, and strict fixed threshold to remove strong artifact sources. Stationary wavelet transform (SWT) is utilized to decompose the weak artifact sources, with subsequent adjustment of wavelet coefficients in respective frequency bands tailored to the distinct characteristics of each artifact. The results of synthetic and real datasets show that our proposed method maximally retains the time-domain and frequency-domain information in the EEG during denoising. Compared with existing techniques, the proposed method achieves better denoising performance, which establishes a reliable foundation for subsequent clinical analyses.

Impact of Mild SARS-CoV-2 Infection on Hematological Parameters and Suitability of Apheresis Platelets Donation in Blood Donors

To investigate the effects of mild SARS-CoV-2 infection on hematological parameters of adult blood donors and the suitability of apheresis platelet donation, the changes of the hematological parameters in blood donors with mild infection of the SARS-CoV-2 Omicron variant strain were evaluated. Seventy-two blood donors with mild COVID-19 symptoms who donated consecutive apheresis platelets for 3 times from December 2022 to January 2023, 42 cases among which were included in the infection-positive group, and 30 cases in the suspected infection group. Forty-two donors un-vaccinated against SARS-CoV-2, un-infected, and donated three consecutive apheresis platelets from October to November 2022 were included in the control group. The changes of blood routine testing in the positive group and the suspected infection group were retrospectively compared before (Time1) and after (Time2 and Time3) the onset of symptoms, three consecutive times (Time1, Time2, Time3) in the control group by repeated measures analysis of variance. The Bayesian discriminant method was used to establish a discriminant equation to determine whether the recent infection of SARS-CoV-2 occurred or not. Simple effect of the number times of tests in the positive and suspected infection groups was significant（ Finfection-positive group=6.98, P < 0.001, partial η2=0.79, Fsuspected infection group=4.31, P < 0.001, partial η2=0.70）. The positive group and the suspected infection group had lower RBC, HCT, and HGB, and higher PLT and PCT at Time2 compared to Time1 and Time3(P < 0.05). The positive group and the suspected infection group showes RDW-CV and RDW-SD at Time3 higher than Time1 and Time2 (P < 0.001). The simple effect of the number times of tests in the control group was not significant ( F=0.96, P =0.55, partial η2=0.34). The difference of the whole blood count parameters in the control group for three times was not statistically significant (P >0.05). We established a discriminant equation to determine whether the recent infection of SARS-CoV-2 occurred or not. The equation had an eigenvalue of 0.22, a canonical correlation of 0.43 (χ2=27.81, P < 0.001), and an analysis accuracy of 72.9%. The hematological indicators of RBC, HCT, HGB, PLT, PCT, RDW-CV and RDW-SD in blood donors who had infected with mild COVID-19 showed dynamic changes. The discriminant equation for whether they are infected recently with COVID-19 has a high accuracy rate.

PENGARUH KREDIT BERMASALAH TERHADAP LIKUIDITAS DAN PROFITABILITAS PADA KOPERASI ARTA MAKMUR DI REJOSO NGANJUK

This research aims to examine the effect of non-performing loans consisting of substandard loans, doubtful loans and bad loans on liquidity and profitability. The sampling technique uses the quota sample method. Data was obtained from secondary data from the monthly report of the Arta Makmur Rejoso Nganjuk Cooperative with canonical correlation with the SPSS version 25 program. The canonical weight shows the highest variable results, namely bad credit 1.08872 and profitability 0.88710. The canonical loadings show the highest variable results, namely bad credit 0.95348; liquidity 0.94779 and profitability 0.99907. Canonical cross-loading shows the highest variable results, namely substandard credit 0.65801; bad debt 4.21658 and profitability 4.09189. The results of the hypothesis research show that the variable that has no influence is doubtful credit. From the results of canonical weights, canonical loadings and canonical cross-loading, it can be concluded that there is no significant influence between substandard credit and liquidity. There is no significant influence between doubtful credit and liquidity. There is a significant influence between bad credit and liquidity. There is a significant influence between substandard credit on profitability. There is no significant influence between doubtful credit on profitability. There is a significant influence between bad credit and profitability. There is a significant influence between non-performing loans on liquidity and profitability.

Green technology innovations, natural gas and resource extraction strategies in BRICS: Modeling impacts on CO2 emission intensity

The article extends beyond conventional models and indicators by initially attempting to capture the complex effects of green technological innovations and changes in natural resource extraction and natural gas production on a novel environmental quality indicator (CO, intensity, CO2ei) for the BRICS countries from 1990 to 2020. The empirical analysis for the candidate panel was conducted using robust analytical techniques, including the fully modified OLS (FMOLS), dynamic OLS (DOLS), canonical correlation regression (CCR), and seemingly unrelated regression (SUR). The results corroborated the role of green technological innovations as an effective deterrent against increasing levels of CO2ei. Still, the outcomes suggested that changes in natural gas production and natural resource extraction had escalated CO2ei in the candidate panel. The country-wise analysis reflected significant variance among the candidate panel countries. For instance. the model revealed that natural gas production abated CO2ei in China and India only, while its environmental effects remained adverse in the remaining countries. Per estimations, the unrequited extraction of natural resources facilitated the reduction of CO2ei in Brazil and Russia, but it also raised CO2ei in the rest of the BRICS countries. In addition, the causality estimation highlighted the following causal flows: i) the feedback effect from natural gas production to CO2ei; ii) one-way causality from CO2ei to green technological innovations and from natural resources extraction to CO2ei. The study offers novel and actionable policy recommendations for policymakers and theorists to steer sustainable and environmentally conscious initiatives in the BRICS countries.

Chronic endometritis and the endometrial microbiota: implications for reproductive success in patients with recurrent implantation failure

BackgroundChronic endometritis (CE) is associated with poor reproductive outcomes, yet the role of endometrial microbiota in patients with recurrent implantation failure (RIF) and CE remains unclear. This study aims to characterize endometrial microbiota in RIF patients with CE and assess its implications for reproductive outcomes.MethodsIn this prospective study, we enrolled RIF patients both with and without CE. Endometrial and cervical samples were collected for 16 S rRNA gene sequencing. Microbiota composition was compared between groups using diversity indices, phylum, and genus-level analysis. Canonical correlation analysis (CCA) and Spearman’s correlation coefficients were used to assess relationships between CE, reproductive outcomes, and microbiota. Predictive functional profiling was performed to evaluate metabolic pathways associated with CE.ResultsEndometrial microbiota in CE patients exhibited greater diversity and evenness compared to non-CE patients. Principal coordinates analysis (PCoA) revealed distinct clustering between CE and non-CE groups. Linear discriminant analysis (LDA) identified Proteobacteria, Aminicenantales, and Chloroflexaceae as characteristic of CE, while Lactobacillus, Acinetobacter, Herbaspirillum, Ralstonia, Shewanela, and Micrococcaceae were associated with non-CE. CCA demonstrated associations between CE, adverse reproductive outcomes, and specific bacterial taxa. Microbial metabolic pathways significantly differed between CE and non-CE groups, with enrichment in pathways related to cofactors, vitamins, secondary metabolites, and the immune system in CE patients.ConclusionRIF patients with CE exhibit distinct endometrial microbiota compositions associated with adverse reproductive outcomes. The increased microbial diversity and altered metabolic pathways in CE suggest a potential correlation with reproductive outcomes, although further studies are necessary to elucidate the causal relationship between microbiota alterations and fertility. Modulating the endometrial microbiome may represent a novel therapeutic strategy to improve IVF outcomes in patients with CE.

Do fungicides induce biostimulation in soybean cultivation? A multivariate approach

Some fungicides have demonstrated not only protective effects but also biostimulatory responses in crop physiology. Thus, the objective was to assess soybean responses to the application of fungicide groups on morphophysiological dynamics at three reproductive stages. The trial was conducted under field conditions in Gurupi, TO – Brazil. The experimental design was randomized blocks, comprising seven chemical groups of fungicides and one control, with three repeated measurements over time and four replications. Fungicides were applied at the beginning of R1, R3, and R5 stages of the crop. Five days after each application, composite samples of leaf area, specific leaf area, shoots dry mass, leaf percentage, relative growth rate, average grain weight, and leaf contents of N, P, and K were evaluated. The results were analyzed through canonical correlation. Leaf area and specific leaf area were the most sensitive variables to treatments, while thousand grain weight did not respond to treatments. The chemical groups TRI (Azoxystrobin+Tebuconazole+Mancozeb),ATI(Pyraclostrobin+Epoxiconazole+Fluxapyroxad), OXI (Fluxapyroxad+Oxychloride), and SPOT (Dimoxystrobin+Boscalid) were the major contributors to discriminant function formation. These were strongly associated with physiological responses at the three reproductive stages.

Distinct personality profiles associated with disease risk and diagnostic status in eating disorders

BackgroundPersonality traits have been associated with eating disorders (EDs) and comorbidities. However, it is unclear which personality profiles are premorbid risk rather than diagnostic markers. MethodsWe explored associations between personality and ED-related mental health symptoms using canonical correlation analyses. We investigated personality risk profiles in a longitudinal sample, associating personality at age 14 with onset of mental health symptoms at ages 16 or 19. Diagnostic markers were identified in a sample of young adults with anorexia nervosa (AN, n = 58) or bulimia nervosa (BN, n = 63) and healthy controls (n = 47). ResultsTwo significant premorbid risk profiles were identified, successively explaining 7.93 % and 5.60 % of shared variance (Rc2). The first combined neuroticism (canonical loading, rs = 0.68), openness (rs = 0.32), impulsivity (rs = 0.29), and conscientiousness (rs = 0.27), with future onset of anxiety symptoms (rs = 0.87) and dieting (rs = 0.58). The other, combined lower agreeableness (rs = −0.60) and lower anxiety sensitivity (rs = −0.47), with future deliberate self-harm (rs = 0.76) and purging (rs = 0.55). Personality profiles associated with “core psychopathology” in both AN (Rc2 = 80.56 %) and BN diagnoses (Rc2 = 64.38 %) comprised hopelessness (rs = 0.95, 0.87) and neuroticism (rs = 0.93, 0.94). For BN, this profile also included impulsivity (rs = 0.60). Additionally, extraversion (rs = 0.41) was associated with lower depressive risk in BN. LimitationsThe samples were not ethnically diverse. The clinical cohort included only females. There was non-random attrition in the longitudinal sample. ConclusionsThe results suggest neuroticism and impulsivity as risk and diagnostic markers for EDs, with neuroticism and hopelessness as shared diagnostic markers. They may inform the design of more personalised prevention and intervention strategies.

Hematological Profile of Bader Fish (Barbonymus altus) in the Brantas River, Blitar Region in Measurement of River Water Quality

Brantas River is habitat to a fish species known as the bader fish (Barbonymus altus). Research aims to evaluate the hematological profile of the bader fish as a means to assess the quality of the Brantas River in the Blitar region. Determining the waste flow from community activities that influences blood quality of bader fish is another goal of this research. To achieve this, various water quality indices were measured in the river and hematological parameters measured in the fish. Data collection was conducted once every week for three weeks during 17 January to 17 March 2022. The measurements aimed to observe any changes in the fish's hematological profile over this period. Two data analysis techniques were employed for the research: Pollution Index (IP) and Canonical Correlation Analysis (CCA). IP measurements reveal that all three stations share a common assessment, classifying them as having light pollution. When analyzing correlation measurements through CCA, it was found that erythrocytes exhibited a robust positive correlation with a pH value of 0.549, while conversely, DO (Dissolved Oxygen) showed a strong negative correlation with leukocytes with value -0.717. In summary, the water conditions in the Brantas region remain conducive for the habitat of bader fish.

CANONICAL CORRELATION ANALYSIS OF ECONOMIC GROWTH AND UNEMPLOYMENT RATE

The paper discusses the relationship between economic growth and the unemployment rate in Indonesia in each province in 2021. Both variables are considered as the dependent variable and there are 5 independent variables used in this research such as human index development, wage minimum region, poor citizens percentage, investment, and farmer rate value in each province. The method used to analyze is canonical correlation analysis, which is one of the dependent methods that are used for multivariate analysis. This method was used to determine which variable had the most significant relationship between dependent and independent variables, the data was taken from the Center of Statistics Bureau Indonesia in 2021. The result shows that among independent variables the human index development had the strongest relation it had 79%, while the correlation between the dependent and independent variable the unemployment rate gives the strongest influence it is 68%.

Shades of green: Unveiling the impact of municipal green bonds on the environment

Green bonds allocate proceeds towards environmentally beneficial projects and sustainable development goals, distinguishing themselves from traditional bonds primarily in the use of proceeds determination. However, investors often find it challenging to assess the carbon reduction potential of these bonds because of the lack of standardised environmental impact reporting. In response to this, our research constructs a unique set of indicators derived from financial and environmental datasets, using multivariate analysis techniques that can accommodate the detection of both linear and non-linear relationships. A novel method combining kernel Principal Component Analysis (kPCA) and Canonical Correlation Analysis (CCA) is applied to detect spatial–temporal cross-correlation in multivariate datasets. This approach handles variable comparability issues and the differential treatment of categorical and numerical variables. A significant finding of this study emerges when this methodology is applied to financial attributes obtained from green bonds issued by municipal agencies (muni bonds), pollution data and environmental (climate) data from nine California counties.The results of the detailed analysis indicate that there is measurable evidence to indicate relationships between green bond issuance and their use of proceeds for pollution reduction efforts. In particular, the results show a clear and interpretable correlation directly linked to the amount of green bond issuance and the effect this is having on pollution reduction, underscoring the tangible impact these financial instruments have on pollution reduction efforts in California.Conversely, when it comes to detecting spatial–temporal relationships between the use of proceeds from green bond issuance and positive climate change effects, this is inconclusive from the current studies’ analysis. It was found that there were weaker cross-correlation relationships observed between climate and green bond financial data set attributes which is perhaps indicative of the fact that climate change effects take a much longer time frame to occur. As such the findings of the analysis in this regard may not indicate that positive climate change effects are not occurring from green bond initiatives, but rather that the ability to measure detectable improvements to climate with regard to the issuance of green bonds is currently limited and will take longer for such effects to manifest in a statistically detectable fashion from the given data. This is particularly likely to be the case given green bonds are only in their infancy as a financial market, having had the earliest issuance only occurring in the last 15-20 years and only substantial growth in the market over the last 10 years. Therefore, this aspect of the research investigating longer-term climate effects with regard to green bond issuance will take longer to develop.

DCCA: detecting differential covariation patterns between two types of high-throughput omics data.

The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes. We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions. The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children.

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

Prediction of Nili-Ravi buffalo bull fertility through Fourier harmonic analysis of sperm

Fourier harmonic analysis (FHA) is a robust method for identification of minute changes in sperm nuclear shape that are indicative of reduced fertility. The current study was designed to develop a fertility prediction model for Nili-Ravi buffalo bulls through FHA of sperm. In experiment I, FHA technique was standardized, average sperm nuclear perimeter was measured and sperm nuclear shape plot of buffalo bull was constructed. Sperm of buffalo bulls (n = 10) were stained with YOYO-1 and Hoechst-33342 to differentiate live and dead, and digital images were captured using phase contrast and fluorescent microscopy. The images were analyzed by ImageJ software and 100 sperm/bull were evaluated. The results are described as mean ± SEM values of mean harmonic amplitude (mharm), skewness harmonic amplitude (skharm), kurtosis harmonic amplitude (kurharm) and variance harmonic amplitude (varharm) at Fourier frequencies 0–5 along with the cartesian and polar coordinate plots of buffalo bull sperm. In experiment II, a fertility prediction model was developed based on FHA of buffalo bull sperm. Semen samples of low (n = 6), medium (n = 3) and high (n = 8) fertility bulls were investigated for FHA of sperm and harmonic amplitudes (HA) were generated. Firstly, to determine if live and dead sperm population have unique nuclear shape distribution; the mean, skewness, kurtosis and variance HA 0–5 of 1700 live and 1294 dead spermatozoa of 17 bulls were evaluated. T-test signified a difference in the mharm0 (2.363 ± 0.01 vs. 2.439 ± 0.02), skharm0 (−0.0002 ± 0.07 vs. −0.266 ± 0.09), kurharm0 (−0.156 ± 0.07 vs. 0.260 ± 0.18), kurharm2 (0.142 ± 0.11 vs. 1.031 ± 0.32) and varharm4 (0.109 ± 0.00 vs. 0.082 ± 0.00) of live vs. dead sperm population (p < 0.05). Therefore, 100 live sperm/bull were further evaluated for mean, skewness, kurtosis and variance HA 0–5 values among high (n = 6) and low-fertility (n = 6) groups. Results of T-test showed higher values of mharm2 (0.739 ± 0.01 vs. 0.686 ± 0.00), mharm4 (0.105 ± 0.001 vs. 0.007 ± 0.001), and skharm0 (0.214 ± 0.109 vs. −0.244 ± 0.097) in high vs. low-fertility group (p < 0.05). In next step, five significantly different combinations of discriminant measures between high and low-fertility groups were obtained by discriminant analysis. In conclusion, mharm4, skharm0 and varharm2 correctly identified 91.7 % of bulls into their respective fertility groups, and upon cross validation the value of the canonical correlation was 0.928.