Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Integration Analysis of Hair Follicle Transcriptome and Proteome Reveals the Mechanisms Regulating Wool Fiber Diameter in Angora Rabbits.

Fiber diameter is an important characteristic that determines the quality and economic value of rabbit wool. This study aimed to investigate the genetic determinants of wool fiber diameter through an integration analysis using transcriptomic and proteomic datasets from hair follicles of coarse and fine wool from Angora rabbits. Using a 4D label-free technique, we identified 423 differentially expressed proteins (DEPs) in hair follicles of coarse and fine wool in Angora rabbits. Eighteen DEPs were examined using parallel reaction monitoring, which verified the reliability of our proteomic data. Functional enrichment analysis revealed that a set of biological processes and signaling pathways related to wool growth and hair diameter were strongly enriched by DEPs with fold changes greater than two, such as keratinocyte differentiation, skin development, epidermal and epithelial cell differentiation, epidermis and epithelium development, keratinization, and estrogen signaling pathway. Association analysis and protein-protein interaction network analysis further showed that the keratin (KRT) family members, including KRT77, KRT82, KRT72, KRT32, and KRT10, as well as CASP14 and CDSN, might be key factors contributing to differences in fiber diameter. Our results identified DEPs in hair follicles of coarse and fine wool and promoted understanding of the molecular mechanisms underlying wool fiber diameter variation among Angora rabbits.

Integrated transcriptomic and proteomic analyses reveal the effects of chronic benzene exposure on the central nervous system in mice.

Benzene exposure is known to cause serious damage to the human hematopoietic system. However, recent studies have found that chronic benzene exposure may also cause neurological damage, but there were few studies in this issue. The aim of this study was to investigate the mechanism of damage to the central nervous system (CNS) by chronic benzene exposure with a multi-omics analysis. We established a chronic benzene exposure model in C57BL/6J mice by gavage of benzene-corn oil suspension, identified the differentially expressed proteins (DEPs) and differentially expressed genes (DEGs) in mice brain using 4D Label-free proteomic and RNA-seq transcriptomic. We observed that the benzene exposure mice had a significant loss of body weight, reduction in complete blood counts, abnormally high MRI signals in brain white matter, as well as extensive brain edema and neural demyelination. 162 DEPs were identified by the proteome, including 98 up-regulated and 64 down-regulated proteins. KEGG pathway analysis of DEPs showed that they were mainly involved in the neuro-related signaling pathways such as metabolic pathways, pathways of neurodegeneration, chemical carcinogenesis, Alzheimer disease, and autophagy. EPHX1, GSTM1, and LIMK1 were identified as important candidate DEGs/DEPs by integrated proteomic and transcriptomic analyses. We further performed multiple validation of the above DEGs/DEPs using fluorescence quantitative PCR (qPCR), parallel reaction monitoring (PRM), immunohistochemistry, and immunoblotting to confirm the reliability of the multi-omics study. The functions of these DEGs/DEPs were further explored and analyzed, providing a theoretical basis for the mechanism of nerve damage caused by benzene exposure.

Transcriptomic Integration Analyses Uncover Common Bisphenol A Effects Across Species and Tissues Primarily Mediated by Disruption of JUN/FOS, EGFR, ER, PPARG, and P53 Pathways.

Bisphenol A (BPA) is a common endocrine disruptor widely used in the production of electronic, sports, and medical equipment, as well as consumer products like milk bottles, dental sealants, and thermal paper. Despite its widespread use, current assessments of BPA exposure risks remain limited due to the lack of comprehensive cross-species comparative analyses. To address this gap, we conducted a study aimed at identifying genes and fundamental molecular processes consistently affected by BPA in various species and tissues, employing an effective data integration method and bioinformatic analyses. Our findings revealed that exposure to BPA led to significant changes in processes like lipid metabolism, proliferation, and apoptosis in the tissues/cells of mammals, fish, and nematodes. These processes were found to be commonly affected in adipose, liver, mammary, uterus, testes, and ovary tissues. Additionally, through an in-depth analysis of signaling pathways influenced by BPA in different species and tissues, we observed that the JUN/FOS, EGFR, ER, PPARG, and P53 pathways, along with their downstream key transcription factors and kinases, were all impacted by BPA. Our study provides compelling evidence that BPA indeed induces similar toxic effects across different species and tissues. Furthermore, our investigation sheds light on the underlying molecular mechanisms responsible for these toxic effects. By uncovering these mechanisms, we gain valuable insights into the potential health implications associated with BPA exposure, highlighting the importance of comprehensive assessments and awareness of this widespread endocrine disruptor.

Expression of interferon regulatory factor family and its prognostic value in acute myeloid leukemia.

Aim: To elucidate the clinicopathological and prognostic values of interferon regulatory factor (IRF) family genes in acute myeloid leukemia (AML). Patients & methods: Differential expression analysis and survival analysis from several reliable databases were conducted and further validated using patients with AML. Results: The expression level of IRF1/2/4/5/7/8/9 in patients with AML was upregulated, while IRF3/6 expression was downregulated. High IRF1/7/9 expression indicated a worse overall survival rate. Conclusion: Overexpression of IRF1/7/9 may be associated with poor survival in patients with AML, suggesting that the IRF family may be a promising therapeutic target.

Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease.

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.

Importance of microRNAs by mRNA-microRNA integration analysis in acute ischemic stroke patients.

OBJECTS: The roles of mRNA and microRNA (miRNA) are well known in many diseases, including ischemic stroke; thus, integration analysis using mRNA and miRNA is important to elucidate pathogenesis. However, their contribution, especially that of miRNA-targeted mRNA, to the severity of acute ischemic stroke remains unclear. Therefore, we examined mRNA and miRNA integration analysis targeted for acute ischemic stroke to clarify the pathway related to acute stroke severity. MATERIAL AND METHODS: We performed Ingenuity Pathway Analysis (IPA) using RNA extracted from the whole blood of four healthy controls, six minor acute ischemic stroke patients (MS; National Institutes of Health Stroke Scale [NIHSS] < 8), and six severe acute ischemic stroke patients (SS; NIHSS ≥ 8) on admission. mRNA and miRNA were measured using RNA sequencing and RNA expression variation; canonical pathway analysis (CPA) and upstream regulator analyses were performed. RESULTS: Acute ischemic stroke patients demonstrated different RNA expressions to healthy controls. Compared to MS patients, in the SS patients, 1222 mRNA, 96 miRNA, and 935 miRNA-targeted mRNA expressions were identified among differentially expressed RNA expressions (p<0.05, |log2 fold change| >1.1). CPA by IPA using mRNAs or miRNA-targeted mRNAs showed that macrophage-stimulating protein (MSP)-recepteur d'origine nantais (RON) signaling was mostly activated in SS patients compared to in MS patients. In addition, upstream regulator analysis in IPA showed that most mRNAs located upstream are miRNAs. CONCLUSIONS: In severe acute stroke, integration of mRNA and microRNA analysis showed activated MSP-RON signaling in macrophages, and multiple miRNAs comprehensively controlled the overall pathophysiology of stroke.

Multi-omics analysis to visualize the dynamic roles of defense genes in the response of tea plants to gray blight.

Gray blight (GB) is one of the most destructive diseases of tea plants, causing considerable damage and productivity losses; however, the dynamic roles of defense genes during pathogen infection remain largely unclear. To explore the numerous molecular interactions associated with GB stress in tea plants, we employed transcriptome, sRNAome and degradome sequencing from 1 to 13 days post-inoculation (dpi) at 3-day intervals. The transcriptomics results showed that differentially expressed genes (DEGs) related to flavonoid synthesis, such as chalcone synthase (CHS) and phenylalanine ammonia-lyase (PAL), were particularly induced at 4 dpi. Consistent with this, the contents of catechins (especially gallocatechin), which are the dominant flavonoids in tea plants, also increased in the leaves of tea plants infected with GB. Combined analysis of the sRNAome and degradome revealed that microRNAs could mediate tea plant immunity by regulating DEG expression at the post-transcriptional level. Co-expression network analysis demonstrated that miR530b-ethylene responsive factor 96 (ERF96) and miRn211-thaumatin-like protein (TLP) play crucial roles in the response to GB. Accordingly, gene-specific antisense oligonucleotide assays suggested that suppressing ERF96 decreased the levels of reactive oxygen species (ROS), whereas suppressing TLP increased the levels of ROS. Furthermore, ERF96 was induced, but TLP was suppressed, in susceptible tea cultivars. Our results collectively demonstrate that ERF96 is a negative regulator and TLP is a positive regulator in the response of tea plants to GB. Taken together, our comprehensive integrated analysis reveals a dynamic regulatory network linked to GB stress in tea plants and provides candidate genes for improvement of tea plants.

Mechanisms of Cynarine for treatment of non-alcoholic fatty liver disease based on the integration of network pharmacology, molecular docking and cell experiment.

BACKGROUND: Nonalcoholic Fatty Liver Disease (NAFLD) is a chronic Liver Disease prevalent all over the world. It has become more and more common in Japan, China and most western developed countries. The global prevalence rate is 25.24%, and the trend is increasing year by year. Related studies have shown that Cynarine has certain liver protection, lipid lowering and immune intervention effects. So, this study to systematically predict and analyze the mechanism of Cynarine in the treatment of non-alcoholic fatty liver disease (NAFLD) based on the integration of network pharmacology, molecular docking, and cell experiment. METHODS: We performed Heatmap and Venn diagram analyses to identify genes and targets in Cynarine treat NAFLD. The network of Cynarine-therapeutic targets and the protein-protein interaction network (PPI) was constructed. We used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to visualize associated functional pathways. The Sybyl tool was used to dock the Cynarine with key therapeutic targets molecularly. Finally, cell experiments were applied to validate the role of Cynarine in the treatment of NAFLD. RESULTS: The Cynarine could act on 48 targets of NAFLD, and the role of CASP3, TP53, MMP9, ELANE, NOTCH1 were more important. The PPI network showed that immune and inflammation-related targets played a pivotal role. The KEGG analysis found that the PI3K-Akt signaling pathway, cell cycle and MAPK signaling pathway may be the main pathways for Cynarine to prevent and treat NAFLD. Molecular docking studies confirmed that Cynarine has good binding activity with therapeutic targets. Cynarine reduced the fat deposition ability of NAFLD model cells, and effectively reduced the levels of ALT and AST released by liver cells due to excessive lipid accumulation. We also found that Cynarine inhibited the expression of AKT1 and MAPK1. CONCLUSIONS: This study revealed that Cynarine could significantly reduce the fat deposition ability of NAFLD model cells, which may be closely related to the effective regulation of AKT1 and MAPK1 expression by Cynarine.

Eight-lncRNA signature of cervical cancer were identified by integrating DNA methylation, copy number variation and transcriptome data.

BACKGROUND: Copy number variation (CNV) suggests genetic changes in malignant tumors. Abnormal expressions of long non-coding RNAs (lncRNAs) resulted from genomic and epigenetic abnormalities play a driving role in tumorigenesis of cervical cancer. However, the role of lncRNAs-related CNV in cervical cancer remained largely unclear. METHODS: The data of messenger RNAs (mRNAs), DNA methylation, and DNA copy number were collected from 292 cervical cancer specimens. The prognosis-related subtypes of cervical cancer were determined by multi-omics integration analysis, and protein-coding genes (PCGs) and lncRNAs with subtype-specific expressions were identified. The CNV pattern of the subtype-specific lncRNAs was analyzed to identify the subtype-specific lncRNAs. A prognostic risk model based on lncRNAs was established by least absolute shrinkage and selection operator (LASSO). RESULTS: Multi-omics integration analysis identified three molecular subtypes incorporating 617 differentially expressed lncRNAs and 1395 differentially expressed PCGs. The 617 lncRNAs were found to intersect with disease-related lncRNAs. Functional enrichment showed that 617 lncRNAs were mainly involved in tumor metabolism, immunity and other pathways, such as p53 and cAMP signaling pathways, which are closely related to the development of cervical cancer. Finally, according to CNV pattern consistent with differential expression analysis, we established a lncRNAs-based signature consisted of 8 lncRNAs, namely, RUSC1-AS1, LINC01990, LINC01411, LINC02099, H19, LINC00452, ADPGK-AS1, C1QTNF1-AS1. The interaction of the 8 lncRNAs showed a significantly poor prognosis of cervical cancer patients, which has also been verified in an independent dataset. CONCLUSION: Our study expanded the network of CNVs and improved the understanding on the regulatory network of lncRNAs in cervical cancer, providing novel biomarkers for the prognosis management of cervical cancer patients.

Design and Integrated Analysis of a Flexible Support Microstructure with a Honeycomb Sandwich for the Optical Window of a Hypersonic Remote Sensor.

In order to reduce the influence of the optical window on the image quality of a hypersonic visible light optical remote sensor, we propose a method of adding a double-layer semicircular honeycomb core microstructure with flexible support of a high temperature elastic alloy between a window glass and a frame to reduce the influence of complex thermal stress on the surface accuracy of the optical window. An equivalent model of a semicircular honeycomb structure was established, the elastic parameters of the semicircular honeycomb sandwich microstructure were derived by an analytical method, and a numerical verification and finite element simulation were carried out. The results show that the equivalent model is in good agreement with the detailed model. The optical-mechanical-thermal integrated simulation analysis of the optical window assembly with flexible supporting microstructure proves that the semicircular honeycomb sandwich flexible supporting structure has a positive effect on stress attenuation of the window glass and ensures the wave aberration accuracy of the transmitted optical path difference of the optical window (PV < 0.665 λ, RMS < 0.156 λ, λ = 632.8 nm). Combined with the actual optical system, the optical performance of the window assembly under the flexible support structure is verified. The simulation results show that the spatial frequency of the modulation transfer function (MTF) of the optical system after focusing is not less than 0.58 in the range of 0-63 cycle/mm and the relative decline of MTF is not more than 0.01, which meet the imaging requirements of a remote sensor. The study results show that the proposed metal-based double-layer semicircular honeycomb sandwich flexible support microstructure ensures the imaging quality of the optical window under ultra-high temperature conditions.

Diffusion functional MRI reveals global brain network functional abnormalities driven by targeted local activity in a neuropsychiatric disease mouse model.

Diffusion functional magnetic resonance imaging (DfMRI) has been proposed as an alternative functional imaging method to detect brain activity without confounding hemodynamic effects. Here, taking advantage of this DfMRI feature, we investigated abnormalities of dynamic brain function in a neuropsychiatric disease mouse model (glial glutamate transporter-knockdown mice with obsessive-compulsive disorder [OCD]-related behavior). Our DfMRI approaches consisted of three analyses: resting state brain activity, functional connectivity, and propagation of neural information. We detected hyperactivation and biased connectivity across the cortico-striatal-thalamic circuitry, which is consistent with known blood oxygen-level dependent (BOLD)-fMRI patterns in OCD patients. In addition, we performed ignition-driven mean integration (IDMI) analysis, which combined activity and connectivity analyses, to evaluate neural propagation initiated from brain activation. This analysis revealed an unbalanced distribution of neural propagation initiated from intrinsic local activation to the global network, while these were not detected by the conventional method with BOLD-fMRI. This abnormal function detected by DfMRI was associated with OCD-related behavior. Together, our comprehensive DfMRI approaches can successfully provide information on dynamic brain function in normal and diseased brains.

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis.

BACKGROUND: Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples. RESULTS: HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify "response order preserving DEGs" that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools. CONCLUSIONS: HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.

Three classical papers in respiratory physiology by Christian Bohr (1855-1911) whose work is frequently cited but seldom read.

History has been kind to Christian Bohr (1855-1911). His name is attached eponymously to three different areas of respiratory physiology. The first is the Bohr dead space, which refers to the portion of the tidal volume that does not undergo gas exchange. The second is the increase in oxygen affinity of hemoglobin caused by the addition of carbon dioxide to the blood. This is known as the Bohr effect and is a very important feature of gas exchange, both in the lung and in peripheral tissues. Both of these contributions by Bohr are familiar to most students. Bohr's third contribution refers to the calculation of the changes in the Po2 of blood as oxygen is loaded in the pulmonary capillary, the so-called Bohr integration. This contribution is less well known now, partly because of the advent of digital computing, but it was important in its day. The analysis is challenging because the very nonlinear shape of the oxygen dissociation curve means that the Po2 difference between the alveolar gas and the capillary blood, which is the driving pressure for diffusion, changes in a complicated way. All three papers are in German, and two of them are long and tedious to read. English translations are available, but few people read the papers, despite the fact that the first two articles are very frequently cited. In the present article, Bohr's contributions are reviewed, and some parts of the articles that are particularly difficult to understand are clarified.

Hierarchical structural component modeling of microRNA-mRNA integration analysis.

BACKGROUND: Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification. RESULTS: It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. CONCLUSION: As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.

Integrated microRNA and mRNA signatures in peripheral blood lymphocytes of familial epithelial ovarian cancer.

PURPOSE: Characterization of the genetic landscapes of familial ovarian cancer through integrated analysis of microRNA and mRNA by partial least squares (PLS) and Monte Carlo technique based on genome-wide association studies (GWAS). METHODS: The miRNA and mRNA transcriptional data in familial ovarian cancer were characterized from the Gene Expression Omnibus (GEO) database. The miRNA and mRNA expression profiles in peripheral blood lymphocytes (PBLs) of 74 familial ovarian cancer patients and 47 control subjects were analyzed with the integration of partial least squares (PLS) and Monte Carlo techniques. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed. RESULTS: Total of 16 miRNA-mRNA pairs were identified with the target gene prediction results of miRNAs and mRNAs. An innovated miRNA-mRNA integrated network was constructed in which 6 downregulated miRNAs and 1 upregulated miRNAs were included. KEGG and GO pathway enrichment analysis revealed over-representation of dysregulated miRNAs in various biological processes especially in cancer pathology. Hsa-miR-34b played a pivotal role in this network and interacted with other miRNAs. Hsa-miR-136 and hsa-miR-335 were associated with p53 and Erk1/2 pathways and tumor suppressors, such as PTEN. CONCLUSIONS: The results from this research provide insights on miRNA-mRNA networks and offer new tools for studying transcriptional variants in familial ovarian cancer.

Deregulated microRNA and mRNA expression profiles in the peripheral blood of patients with Marfan syndrome.

BACKGROUND: MicroRNAs (miRNAs) are small RNAs regulating gene expression post-transcriptionally. While acquired changes of miRNA and mRNA profiles in cancer have been extensively studied, little is known about expression changes of circulating miRNAs and messenger RNAs (mRNA) in monogenic constitutional anomalies affecting several organ systems, like Marfan syndrome (MFS). We performed integrated miRNA and mRNA expression profiling in blood samples of Marfan patients in order to investigate deregulated miRNA and mRNA networks in these patients which could serve as potential diagnostic and prognostic tools for MFS therapy. METHODS: MiRNA and mRNA expression profiles were determined in blood samples from MFS patients (n = 7) and from healthy volunteer controls (n = 7) by microarray analysis. Enrichment analyses of altered mRNA expression were identified using bioinformatic tools. RESULTS: A total of 28 miRNAs and 32 mRNAs were found to be significantly altered in MFS patients compared to controls (> 2.0-fold change, adjusted P < 0.05). The expression of 11 miRNA and 6 mRNA candidates was validated by RT-qPCR in an independent cohort of 26 MFS patients and 26 matched HV controls. Significant inverse correlations were evident between 8 miRNAs and 5 mRNAs involved in vascular pathology, inflammation and telomerase regulation. Significant positive correlations were present for 7 miRNAs with age, for 2 miRNAs with the MFS aortic root status (Z-score) and for 7 miRNAs with left ventricular end-diastolic diameter in MFS patients. In addition, miR-331-3p was significantly up-regulated in MFS patients without mitral valve prolapse (MVP) as compared with patients with MVP. CONCLUSIONS: Our data show deregulated gene and miRNA expression profiles in the peripheral blood of MFS patients, demonstrating several candidates for prognostic biomarkers for cardiovascular manifestations in MFS as well as targets for novel therapeutic approaches. A deregulation of miRNA expression seems to play an important role in MFS, highlighting the plethora of effects on post-transcriptional regulation of miRNAs and mRNAs initiated by constitutional mutations in single genes. Trial registration Nr: EA2/131/10 . Registered 28 December, 2010.

Magnetic micro-particle conditioning-pressurized vertical electro-osmotic dewatering (MPEOD) of activated sludge: Role and behavior of moisture and organics.

In this study, a magnetic micro-particle conditioning-pressurized vertical electro-osmotic dewatering (MPEOD) process with magnetic micro-particle conditioning-drainage under gravity-mechanical compression-electrical compression (MMPC-DG-MC-EC) stages was established to study the distribution and migration of water, extracellular polymeric substances (EPS), and other organic matter in the activated sludge (AS) matrix at each stage. Results showed that the MPEOD process could attain 53.52% water content (WC) in dewatered AS with bound water (BW) and free water (FW) reduction rates of 82.97% and 99.67%, respectively. The coagulation and time-delayed magnetic field effects of magnetic micro-particles (MMPs) along the MMPC-DG-MC stages initiated the transformation of partial BW to FW in AS. EC had a coupling driving effect of electro-osmosis and pressure on BW, and the changes in pH and temperature at EC stage induced the aggregation of AS flocs and the release of partial BW. Additionally, MMPs dosing further improved the dewatering performance of AS by acting as skeleton builders to provide water passages. Meanwhile, MMPs could disintegrate sludge cells and EPS fractions, thereby reducing tryptophan-like protein and byproduct-like material concentrations in LB-EPS as well as protein/polysaccharide ratio in AS matrix, which could improve AS filterability. At EC stage, the former four Ex/Em regions of fluorescence regional integration analysis for EPS were obviously reduced, especially the protein-like substances in LB- and TB-EPS, which contributed to improvement of AS dewaterability.

Integration of small RNAs, degradome and transcriptome sequencing in hyperaccumulator Sedum alfredii uncovers a complex regulatory network and provides insights into cadmium phytoremediation.

The hyperaccumulating ecotype of Sedum alfredii Hance is a cadmium (Cd)/zinc/lead co-hyperaccumulating species of Crassulaceae. It is a promising phytoremediation candidate accumulating substantial heavy metal ions without obvious signs of poisoning. However, few studies have focused on the regulatory roles of miRNAs and their targets in the hyperaccumulating ecotype of S. alfredii. Here, we combined analyses of the transcriptomics, sRNAs and the degradome to generate a comprehensive resource focused on identifying key regulatory miRNA-target circuits under Cd stress. A total of 87 721 unigenes and 356 miRNAs were identified by deep sequencing, and 79 miRNAs were differentially expressed under Cd stress. Furthermore, 754 target genes of 194 miRNAs were validated by degradome sequencing. A gene ontology (GO) enrichment analysis of differential miRNA targets revealed that auxin, redox-related secondary metabolism and metal transport pathways responded to Cd stress. An integrated analysis uncovered 39 pairs of miRNA targets that displayed negatively correlated expression profiles. Ten miRNA-target pairs also exhibited negative correlations according to a real-time quantitative PCR analysis. Moreover, a coexpression regulatory network was constructed based on profiles of differentially expressed genes. Two hub genes, ARF4 (auxin response factor 4) and AAP3 (amino acid permease 3), which might play central roles in the regulation of Cd-responsive genes, were uncovered. These results suggest that comprehensive analyses of the transcriptomics, sRNAs and the degradome provided a useful platform for investigating Cd hyperaccumulation in S. alfredii, and may provide new insights into the genetic engineering of phytoremediation.

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps.

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is an open problem, which has motivated many researches. The most important challenge in EEG-fMRI integration is the unknown relationship between these two modalities. In this paper, we extract the same features (spatial map of neural activity) from both modality. Therefore, the proposed integration method does not need any assumption about the relationship of EEG and fMRI. We present a source localization method from scalp EEG signal using jointly fMRI analysis results as prior spatial information and source separation for providing temporal courses of sources of interest. The performance of the proposed method is evaluated quantitatively along with multiple sparse priors method and sparse Bayesian learning with the fMRI results as prior information. Localization bias and source distribution index are used to measure the performance of different localization approaches with or without a variety of fMRI-EEG mismatches on simulated realistic data. The method is also applied to experimental data of face perception of 16 subjects. Simulation results show that the proposed method is significantly stable against the noise with low localization bias. Although the existence of an extra region in the fMRI data enlarges localization bias, the proposed method outperforms the other methods. Conversely, a missed region in the fMRI data does not affect the localization bias of the common sources in the EEG-fMRI data. Results on experimental data are congruent with previous studies and produce clusters in the fusiform and occipital face areas (FFA and OFA, respectively). Moreover, it shows high stability in source localization against variations in different subjects.