Coupled effects of Fenton-like systems with different concentrations of H2O2/Biochar on diethyl phthalate removal: Dominant role of environmental persistent free radicals (EPFRs).

To investigate the impact of different H2O2 concentrations on the Fenton-like systems of H2O2/biochar, this study examined the mechanism of the physical structure and environmental persistent free radicals (EPFRs) of biochar during diethyl phthalate (DEP) removal by the Fenton-like system. The peak-splitting method was utilized to differentiate EPFRs types in cotton stalk biochar produced at different temperatures. High-temperature environments promote π-electron delocalization, which facilitates phenyl π free radicals and σ-π oxygen-containing free radicals. By analyzing relationships between the removal rate K1 and removal constant Kobs of DEP with the structural properties of biochar, it was discovered that EPFRs concentrations in biochar had a significant positive correlation with K1 (r = 0.92) and Kobs (r = 0.97). Different H2O2 concentrations added to the biochar removal system resulted in varied DEP removal efficiency. Among them, CS500, CS550, and CS600 exhibited superior DEP removal efficiency when H2O2 concentration was 5 mM.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils.

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran's index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

[Construction and application of a risk index of Echinococcus infection based on the classification of echinococcosis lesions].

OBJECTIVE: To investigate the feasibility of constructing the risk index of Echinococcus infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis. METHODS: The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of Echinococcus infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of Echinococcus infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of Echinococcus infection were examined using a univariate linear regression model. RESULTS: A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of E. granulosus infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 4.12 to 708.65, all P values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of E. multilocularis infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 6.74 to 122.60, all P values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of E. granulosus infection and the detection of incident cystic echinococcosis cases during the study period (t = -0.518 to 2.265, all P values > 0.05), and strong correlations were found between the risk indices of E. multilocularis infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all r values > 0.7, t = 2.521 to 3.692, all P values < 0.05). Linear regression models were established between the risk index of E. multilocular infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant (b = 0.214 to 2.168, t = 2.458 to 3.692, F = 6.044 to 13.629, all P values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. granulosus infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. multilocular infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models (t = 16.479 to 197.304, F = 271.570 to 38 928.860, all P values < 0.05). CONCLUSIONS: The risk index of Echinococcus infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.

Bioimpedance analysis for identifying new indicators of exercise-induced muscle damage.

A noninvasive, immediate, and convenient method for assessing muscle tissue status during exercise-induced muscle damage (EIMD) has not been established. This study was designed to assess and determine parameters suitable for measuring EIMD after eccentric exercise, using multi-frequency bioimpedance analysis (BIA). Thirty-five young male participants performed dumbbell exercises with their left arm, and their BIA parameters were measured at various time points up to 168 h post exercise using a multi-frequency BIA device. At all-time points, intra and extracellular water content was greater in the left arm than in the right arm, whereas the impedance, reactance, resistance, and phase angle were lower in the left arm than in the right arm. Established EIMD indices, such as maximal isometric voluntary contraction, were measured and used in correlational analyses. Only reactance was correlated with biomarkers, indicating muscle damage (r = - 0.56 to - 0.49). Furthermore, reactance was found to correlate well with indirect indicators of EIMD, suggesting that it may be a suitable marker for evaluating EIMD. However, the relationship with the limited evaluation indices employed in this study is constrained. Future studies should investigate the correlation between reactance and direct damage indicators, such as structural damage, observed in biopsies.

Evaluation of acetic acid treatment of fresh-cut water chestnuts using gray-correlation analysis based on the variation-coefficient weight.

Introduction: The demand for fresh-cut water chestnuts, a convenient and nutritive vegetable, is increasing in market. However, the slicing of water chestnuts can cause mechanical damage to tissue, which results in quality deterioration. We aimed to select the optimal treatment through a comprehensive comparison of the preservation effect of acetic acid, which could prolong the shelf life of fresh-cut water chestnuts and improve their storage quality. Methods: A comprehensive evaluation was conducted using the gray-correlation method based on the variation-coefficient weight to observe the treatment of 0, 2 and 5% acetic acid. Their effects on color, weight loss rate, and the content of ascorbic acid, total sugar, reducing sugar, soluble protein, and free amino acid were determined. Results: The color, weight loss rate, and nutritional content of fresh-cut chestnuts varied under different processing and storage times. When stored for more than 4 days, the b* value, and the content of total sugar and soluble protein in CK were higher than those in 2% or 5% acetic acid, but the weight loss rate, and the content of ascorbic acid and free amino acid in CK were less than those in acetic acid treatments. Considering various indicators, it was difficult to determine which treatment to choose for fresh-cut water chestnut preservation. The gray-correlation analysis results indicated that when stored for 8, 12, or 16 days, the gray-correlation degree of 5% acetic acid was the highest, while that of the control was the lowest. It could be directly concluded by the gray-correlation degree that when the storage time exceeded 4 days, acetic acid could be used to improve storage quality, and 5% acetic acid had a better preservation effect than 2%. Fresh-cut water chestnuts can be stored for 4 days without the need for acetic acid treatment. Conclusion: These findings could provide information and comprehensive evaluation methods for the preservation of fresh-cut fruits and vegetables. The next step is to evaluate the preservation effect of acetic acid by measuring its effects on other indicators of fresh-cut water chestnuts (e.g., flavonoids, and microorganisms), providing ideas for the research of preservatives.

Farmland's silent threat: Comprehensive multimedia assessment of micropollutants through non-targeted screening and targeted analysis in agricultural systems.

Intricate agricultural ecosystems markedly influence the dynamics of organic micropollutants, posing substantial threats to aquatic organisms and human health. This study examined the occurrence and distribution of organic micropollutants across soils, ditch sediment, and water within highly intensified farming setups. Using a non-targeted screening method, we identified 405 micropollutants across 10 sampling sites, which mainly included pesticides, pharmaceuticals, industrial chemicals, and personal care products. This inventory comprised emerging contaminants, banned pesticides, and controlled pharmaceuticals that had eluded detection via conventional monitoring. Targeted analysis showed concentrations of 3.99-1021 ng/g in soils, 4.67-2488 ng/g in sediment, and 12.5-9373 ng/L in water, respectively, for Σ40pesticides, Σ8pharmaceuticals, and Σ3industrial chemicals, indicating notable spatial variability. Soil organic carbon content and wastewater discharge were likely responsible for their spatial distribution. Principal component analysis and correlation analysis revealed a potential transfer of micropollutants across the three media. Particularly, a heightened correlation was decerned between soil and sediment micropollutant levels, highlighting the role of sorption processes. Risk quotients surpassed the threshold of 1 for 13-23 micropollutants across the three media, indicating high environmental risks. This study highlights the importance of employing non-targeted and targeted screening in assessing and managing environmental risks associated with micropollutants.

Evolution and health risk of indicator microorganisms in landscape water replenished by reclaimed water.

As an important means to solve water shortage, reclaimed water has been widely used for landscape water supply. However, with the emergence of large-scale epidemic diseases such as SARS, avian influenza and COVID-19 in recent years, people are increasingly concerned about the public health safety of reclaimed water discharged into landscape water, especially the pathogenic microorganisms in it. In this study, the water quality and microorganisms of the Old Summer Palace, a landscape water body with reclaimed water as the only replenishment water source, were tracked through long-term dynamic monitoring. And the health risks of indicator microorganisms were analyzed using Quantitative Microbial Risk Assessment (QMRA). It was found that the concentration of indicator microorganisms Enterococcus (ENT), Escherichia coli (EC) and Fecal coliform (FC) generally showed an upward trend along the direction of water flow and increased by more than 0.6 log at the end of the flow. The concentrations of indicator microorganisms were higher in summer and autumn than those in spring. And there was a positive correlation between the concentration of indicator microorganisms and COD. Further research suggested that increased concentration of indicator microorganisms also led to increased health risks, which were more than 30% higher in other areas of the park than the water inlet area and required special attention. In addition, (water) surface operation exposure pathway had much higher health risks than other pathways and people in related occupations were advised to take precautions to reduce the risks.

Expression levels and clinical significance of serum miR-19a/CCL20 in patients with acute cerebral infarction.

Acute cerebral infarction (ACI) is a lethal disease whose early diagnosis is critical for treatment. microRNA (miR)-19a targets CC chemokine ligand 20 (CCL20) in myocardial infarction. We investigated the expression patterns of serum miR-19a and CCL20 of ACI patients and assessed their clinical values. Serum samples of 50 healthy subjects and110 ACI patients were collected. Serum levels of miR-19a, CCL20 mRNA, and biochemical indexes were assessed. miR-19a downstream target gene and the binding relationship between miR-19a and CCL20 were predicted and verified. miR-19a and CCL20 mRNA were subjected to correlation and diagnostic efficiency analysis. miR-19a was poorly expressed in the serum of ACI patients, especially in patients with unstable plaque and large infarction. tumor necrosis factor-α, low-density lipoprotein, and platelet/lymphocyte ratio negatively correlated with serum miR-19a level and positively correlated with CCL20. Dual-luciferase assay revealed that miR-19a could negatively regulate CCL20 expression. CCL20 was highly expressed in the serum of ACI patients. The area under receiver-operating characteristic curve of miR-19a combined with CCL20 was 0.9741 (98.00% specificity, 90.91% sensitivity), higher than their single diagnosis. Collectively, miR-19a had high diagnostic value for ACI and could target to restrain CCL20. The combination of miR-19a and CCL20 improved diagnostic value for ACI.

Impact of emotional states on tinnitus sound therapy efficacy based on ECG signals and emotion recognition model.

BACKGROUND: Diagnosis and severity assessment of tinnitus are mostly based on the patient's descriptions and subjective questionnaires, which lacks objective means of diagnosis and assessment bases, the accuracy of which fluctuates with the clarity of the patient's description. This complicates the timely modification of treatment strategies or therapeutic music to improve treatment efficacy. NEW METHOD: We employed a novel random convolutional kernel-based method for electrocardiogram (ECG) signal analysis to identify patients' emotional states during Music Tinnitus Sound Therapy (Music-TST) sessions. Then analyzed correlations between emotional changes in different treatment phase and Tinnitus Handicap Inventory (THI) score differences to determine the impact of emotions on tinnitus treatment efficacy. RESULTS: This study revealed a significant correlation between patients' emotion changes during Music-TST and the therapy's effectiveness. Changes in arousal and dominance dimension, were strongly linked to THI variations. These findings highlight the substantial impact of emotional responses on sound therapy's efficacy, offering a new perspective for understanding and optimizing tinnitus treatment. COMPARISON WITH EXISTING METHODS: Compared to existing methods, we proposed an objective indicator to assess the progress of sound therapy, the indicator could also be used to provide feedback to optimize sound therapy music. CONCLUSIONS: This study revealed the critical role of emotion changes in tinnitus sound therapy. By integrating objective ECG-based emotion analysis with traditional subjective scale like THI, we present an innovative approach to assess and potentially optimize therapy effectiveness. This finding could lead to more personalized and effective treatment strategies for tinnitus sound therapy.

Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer.

PURPOSE: To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. METHODS: This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. RESULTS: Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05). CONCLUSION: IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

Characterization of flavour components and identification of lipid flavour precursors in different cuts of pork by phospholipidomics.

The lipids and volatile compounds in pork from different parts, including the loin, belly, shoulder and hind leg were analyzed by triple quadrupole tandem time-of-flight mass spectrometer (Q-TOF/MS) and gas chromatography-olfactometry-mass spectrometry (GC-O-MS), respectively. Partial least squares regression (PLSR) and Pearson correlation analysis were utilized to establish the relationship between the lipids and volatile compounds. A total of 8 main flavour substances, 38 main phospholipids, and 32 main fatty acids were identified. The results showed that the key flavour compounds were mainly derived from unsaturated fatty acids and phospholipids containing unsaturated fatty acids, including oleic acid (C18:2n6c), α-Linolenic acid (C18:3n3), arachidonic acid (C20:4n6), PE O (18:1/20:4), PE O (18:2/20:4), and PE O (18:2/18:2), etc. Understanding the relationship between flavour compounds and lipids of pork will be helpful to control the quality of pork.

Effect of ZIC2 on immune infiltration and ceRNA axis regulation in lung adenocarcinoma via bioinformatics and experimental studies.

OBJECTIVE: This study aimed to conlude the effect and mechanism of ZIC2 on immune infiltration in lung adenocarcinoma (LUAD). METHODS: Expression of ZIC2 in several kinds of normal tissues of TCGA data was analyzed and its correlation with the baseline characteristic of LUAD patients were analyzed. The immune infiltration analysis of LUAD patients was performed by CIBERSORT algorithm. The correlation analysis between ZIC2 and immune cell composition was performed. Additionally, the potential upstream regulatory mechanisms of ZIC2 were predicted to identify the possible miRNAs and lncRNAs that regulated ZIC2 in LUAD. In vitro and in vivo experiments were also conducted to confirm the potential effect of ZIC2 on cell proliferation and invasion ability of LUAD cells. RESULTS: ZIC2 expression was decreased in various normal tissues, but increased in multiple tumors, including LUAD, and correlated with the prognosis of LUAD patients. Enrichement by GO and KEGG suggested the possible association of ZIC2 with cell cycle and p53 signal pathway. ZIC2 expression was significantly correlated with T cells CD4 memory resting, Macrophages M1, and plasma cells, indicating that dysregulated ZIC2 expression in LUAD may directly influence immune infiltration. ZIC2 might be regulated by several different lncRNA-mediated ceRNA mechanisms. In vitro experiments validated the promotive effect of ZIC2 on cell viability and invasion ability of LUAD cells. In vivo experiments validated ZIC2 can accelerate tumor growth in nude mouse. CONCLUSION: ZIC2 regulated by different lncRNA-mediated ceRNA mechanisms may play a critical regulatory role in LUAD through mediating the composition of immune cells in tumor microenvironment.

A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations.

Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective process control leads to highly unstable plasmid yields. In this study, a multi-parameter correlation analysis was first performed to discover a dynamic metabolic balance among the oxygen uptake rate, temperature, and plasmid yield, whilst revealing the heating rate and timing as the most important optimization factor for balanced cell growth and plasmid production. Then, based on the acquired on-line parameters as well as outputs of kinetic models constructed for describing process dynamics of biomass concentration, plasmid yield, and substrate concentration, a machine learning (ML) model with Random Forest (RF) as the best machine learning algorithm was established to predict the optimal heating strategy. Finally, the highest plasmid yield and specific productivity of 1167.74 mg L-1 and 8.87 mg L-1/OD600 were achieved with the optimal heating strategy predicted by the RF model in the 50 L bioreactor, respectively, which was 71% and 21% higher than those obtained in the control cultures where a traditional one-step temperature upshift strategy was applied. In addition, this study transformed empirical fermentation process optimization into a more efficient and rational self-optimization method. The methodology employed in this study is equally applicable to predict the regulation of process dynamics for other products, thereby facilitating the potential for furthering the intelligent automation of fermentation processes.

The importance of starch chain-length distribution for in vitro digestion of ungelatinized and retrograded foxtail millet starch.

The digestibility of ungelatinized, short-term retrograded and long-term retrograded starch from foxtail millet was investigated and correlated with starch chain length distributions (CLDs). Some variations in starch CLDs of different varieties were obtained. Huangjingu and Zhonggu 9 had higher average chain lengths of debranched starch and lower average chain length ratios of amylopectin and amylose than Dajinmiao and Jigu 168. Compared to ungelatinized starch, retrogradation significantly increased the estimated glycemic index (eGI), whereas significantly decreased the resistant starch (RS). In contrast, long-term retrograded starches have lower eGI (93.33-97.37) and higher RS (8.04-14.55%) than short-term retrograded starch. PCA and correlation analysis showed that amylopectin with higher amounts of long chains and longer long chains contributed to reduced digestibility in ungelatinized starch. Both amylose and amylopectin CLDs were important for the digestibility of retrograded starch. This study helps a better understanding of the interaction of starch CLDs and digestibility during retrogradation.

Dataset on extreme thunderstorm ground enhancements registered on Aragats in 2023.

To advance high-energy atmospheric physics, studying atmospheric electric fields (AEF) and cosmic ray fluxes as an interconnected system is crucial. At Mt. Argats, simultaneous measurements of particle fluxes, electric fields, weather conditions, and lightning locations have significantly enhanced the validation of models that describe the charge structures of thunderclouds and the mechanics of internal electron accelerators. In 2023, observations of the five largest thunderstorm ground enhancements (TGEs) revealed electric fields exceeding 2.0 kV/cm at elevations just tens of meters above ground-potentially hazardous to rockets and aircraft during launch and charging operations. Utilizing simple yet effective monitoring equipment developed at Aragats, we can mitigate the risks posed by these high-intensity fields. The Mendeley dataset, comprising various measured parameters during thunderstorm activities, enables researchers to perform advanced correlation analysis and uncover complex relationships between these atmospheric phenomena. This study underscores the critical importance of integrated atmospheric studies for ensuring the safety of high-altitude operations and advancing atmospheric science.

A Cross Talking between the Gut Microbiota and Metabolites of Participants in a Confined Environment.

Certain workplaces, like deep-sea voyages, subject workers to chronic psychological stress and circadian rhythm disorders due to confined environments and frequent shifts. In this study, participants lived in a strictly controlled confined environment, and we analyzed the effects of a confined environment on gut microbiota and metabolites. The results showed that living in confined environments can significantly alter both the gut microbiota and the gut metabolome, particularly affecting lipid metabolism pathways like glycerophospholipid metabolism. There was a significant reduction in the abundance of Faecalibacterium and Bacteroides, while Blautia, Bifidobacterium, and Collinsella showed significant increases. An association analysis revealed a strong correlation between changes in the gut microbiota and the metabolome. Four upregulated lipid metabolites may serve as biomarkers for damage induced by confined environments, and certain gut microbiota alterations, such as those involving Faecalibacterium and Bacteroides, could be potential psychobiotics or therapeutic targets for enhancing mental health in a confined environment.

Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals.

Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.

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

MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.

Abnormal resting-state hyperconnectivity in schizophrenia: A whole-head near-infrared spectroscopy study.

Near-infrared spectroscopy (NIRS) is a noninvasive functional neuroimaging modality that can detect changes in blood oxygenation levels by tracking cortical neural activity. We recorded the resting-state brain activity of 24 individuals with schizophrenia and 90 healthy controls for 8 min using a whole-head NIRS arrangement and then used partial correlation analysis to estimate the resting-state functional connectivity (RSFC) between 17 cortical regions. We found that the RSFC between the bilateral orbitofrontal cortices (OFCs) and between the right temporal and parietal lobes was significantly higher in patients with schizophrenia than in healthy controls. The RSFC between the bilateral OFCs was positively correlated with negative symptom severity, whereas the RSFC between the right temporal and parietal lobes was positively correlated with the chlorpromazine equivalent for antipsychotics prescribed to patients with schizophrenia. This finding was consistent with that for the RSFC calculated using the anterior 52-channel signals. Our results suggest that NIRS-based RSFC measurements have potential clinical applications.