Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.

Integrative learning of multiple datasets has the potential to mitigate the challenge of small n and large p that is often encountered in analysis of big biomedical data such as genomics data. Detection of weak yet important signals can be enhanced by jointly selecting features for all datasets. However, the set of important features may not always be the same across all datasets. Although some existing integrative learning methods allow heterogeneous sparsity structure where a subset of datasets can have zero coefficients for some selected features, they tend to yield reduced efficiency, reinstating the problem of losing weak important signals. We propose a new integrative learning approach which can not only aggregate important signals well in homogeneous sparsity structure, but also substantially alleviate the problem of losing weak important signals in heterogeneous sparsity structure. Our approach exploits a priori known graphical structure of features and encourages joint selection of features that are connected in the graph. Integrating such prior information over multiple datasets enhances the power, while also accounting for the heterogeneity across datasets. Theoretical properties of the proposed method are investigated. We also demonstrate the limitations of existing approaches and the superiority of our method using a simulation study and analysis of gene expression data from ADNI.

Up to a 15-Year Survival Rate and Marginal Bone Resorption of 1780 Implants with or without Microthreads: A Multi Center Retrospective Study.

The effect of microthreads at the implant neck on the amount of marginal bone resorption is controversial. This multicenter retrospective study compared the implant survival rate and amount of marginal bone resorption between two platform-switching internal connection implant systems with or without microthreads. Patient-related (age and sex), surgery-related (implant installation site, type, diameter, and length), and prosthesis-related (prosthesis type) data were collected from patient charts from the implant placement surgery to the final recall visit. A total of 1780 implants, including 1379 with microthreads and 401 without microthreads, were placed in 804 patients. For implants with and without microthreads, the longest follow-up period after prosthesis delivery was 15 and 6 years, respectively. Twenty implants failed during the 15-year follow-up period (98.8% survival rate) due to failed osseointegration, peri-implantitis, implant fractures, and non-functioning implants. The mean marginal bone loss was < 0.1 mm for both implant systems at the 1-year follow-up and 0.18 mm and 0.09 mm at the 4-year follow-up for implants with and without microthreads, respectively, without statistical significance. Microthreads did not significantly affect the amount of marginal bone loss or the implant survival rate for implants with an internal connection with a platform-switching design.

Accounting for technical noise in Bayesian graphical models of single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNAseq) data contain a high level of noise, especially in the form of zero-inflation, that is, the presence of an excessively large number of zeros. This is largely due to dropout events and amplification biases that occur in the preparation stage of single-cell experiments. Recent scRNAseq experiments have been augmented with unique molecular identifiers (UMI) and External RNA Control Consortium (ERCC) molecules which can be used to account for zero-inflation. However, most of the current methods on graphical models are developed under the assumption of the multivariate Gaussian distribution or its variants, and thus they are not able to adequately account for an excessively large number of zeros in scRNAseq data. In this article, we propose a single-cell latent graphical model (scLGM)-a Bayesian hierarchical model for estimating the conditional dependency network among genes using scRNAseq data. Taking advantage of UMI and ERCC data, scLGM explicitly models the two sources of zero-inflation. Our simulation study and real data analysis demonstrate that the proposed approach outperforms several existing methods.

GRIA: Graphical Regularization for Integrative Analysis.

Integrative analysis jointly analyzes multiple data sets to overcome curse of dimensionality. It can detect important but weak signals by jointly selecting features for all data sets, but unfortunately the sets of important features are not always the same for all data sets. Variations which allows heterogeneous sparsity structure-a subset of data sets can have a zero coefficient for a selected feature-have been proposed, but it compromises the effect of integrative analysis recalling the problem of losing weak important signals. We propose a new integrative analysis approach which not only aggregates weak important signals well in homogeneity setting but also substantially alleviates the problem of losing weak important signals in heterogeneity setting. Our approach exploits a priori known graphical structure of features by forcing joint selection of adjacent features, and integrating such information over multiple data sets can increase the power while taking into account the heterogeneity across data sets. We confirm the problem of existing approaches and demonstrate the superiority of our method through a simulation study and an application to gene expression data from ADNI.

Proteomic and metabolomic profiling reveals the involvement of apoptosis in meat quality characteristics of ovine M. longissimus from different callipyge genotypes.

Proteome and metabolome changes in muscles from callipyge mutation (+/C) and non-callipyge phenotype (+/+, C/+, and C/C) lambs were profiled to provide insight into the biochemical changes affecting meat quality attributes. M. longissimus thoracis from lambs with all four possible callipyge genotype (n = 4, C/+, C/C, +/C, and +/+) were collected after 3d aging and analyzed using mass-spectrometry based platforms. Among identified proteomes, cytochrome c (pro-apoptotic protein) was detected with significantly lower abundances in +/C. Anti-apoptotic HSP70, BAG3, and PARK7 were over-abundant in +/C, which could result in delayed apoptosis and possibly attributed to tougher meat in callipyge lambs. Eight glycolysis enzymes were overabundant in +/C lambs, whereas 3 enzymes involved in TCA cycle were overabundant in non-callipyge ones (C/C and/or C/+). Twenty-five metabolites were affected by genotypes (P < .05), including metabolic co-factors, polyphenols, and AA/short peptides. Our omics results provided insightful information for revealing the differences in biochemical attributes caused by callipyge mutation.

Knowledge-Guided Biclustering via Sparse Variational EM Algorithm.

A biclustering in the analysis of a gene expression data matrix, for example, is defined as a set of biclusters where each bicluster is a group of genes and a group of samples for which the genes are differentially expressed. Although many data mining approaches for biclustering exist in the literature, only few are able to incorporate prior knowledge to the analysis, which can lead to great improvements in terms of accuracy and interpretability, and all are limited in handling discrete data types. We propose a generalized biclustering approach that can be used for integrative analysis of multi-omics data with different data types. Our method is capable of utilizing biological information that can be represented by graph such as functional genomics and functional proteomics and accommodating a combination of continuous and discrete data types. The proposed method builds on a generalized Bayesian factor analysis framework and a variational EM approach is used to obtain parameter estimates, where the latent quantities in the loglikelihood are iteratively imputed by their conditional expectations. The biclusters are retrieved via the sparse estimates of the factor loadings and the conditional expectation of the latent factors. In order to obtain the sparse conditional expectation of the latent factors, a novel sparse variational EM algorithm is used. We demonstrate the superiority of our method over several existing biclustering methods in extensive simulation experiements and in integrative analysis of multi-omics data.

The new designer drug buphedrone produces rewarding properties via dopamine D1 receptor activation.

Substituted cathinones are synthetic analogs of the active components of natural products and are widely abused worldwide. However, the rewarding properties of these agents have not yet been evaluated. In this study, we investigated the abuse potential of buphedrone [2-(methylamino)-1-phenylbutan-1-one, α-methylamino-butyrophenone] and its effects on the mesolimbic dopaminergic system in mice using conditioned place preference (CPP) analysis, a self-administration test, a locomotor activity test, a behavioral sensitization test and Western blot analysis. Treatment with buphedrone supported CPP and self-administration, enhanced locomotor activity and produced behavioral sensitization when mice were challenged with methamphetamine. SCH23390, a D1 dopamine antagonist, prevented buphedrone-induced CPP, whereas raclopride, a D2 dopamine antagonist, had no effect. SCH23390 also blocked locomotor activity increase by buphedrone, while raclopride partially attenuated locomotor activation. Western blot analysis revealed that repeated buphedrone treatment increased D1 dopamine receptor expression in the dorsal striatum and nucleus accumbens in mice. Collectively, these findings suggest the abuse potential of buphedrone and demonstrate the involvement of the dopaminergic system in the establishment of its rewarding properties.

Metabolomics Profiling to Determine the Effect of Postmortem Aging on Color and Lipid Oxidative Stabilities of Different Bovine Muscles.

The objective of this study was to identify metabolites that could be associated with oxidative stability of aged bovine muscles. Three muscles (longissimus lumbrum (LL), semimembranosus (SM), and psoas major (PM)) from seven beef carcasses at 1 day postmortem were divided into three sections and assigned to three aging periods (9, 16, and 23 days). Although an increase in discoloration was found in all muscles with aging, LL was the most color/lipid oxidative stable, followed by SM and PM (P < 0.05). Lower myoglobin and nonheme iron contents were observed in LL compared to those in SM and PM (P < 0.05). The HPLC-ESI-MS-based metabolomics analysis identified metabolites that were significantly responsive to aging and/or muscle type, such as acyl carnitines, free amino acids, nucleotides, nucleosides, and glucuronides. The results from the current study suggest that color and oxidative stability could be associated with aging but are also muscle-specific. Further studies determining the exact role of the identified metabolites in the color and oxidative stability of beef muscles are warranted.

The new stimulant designer compound pentedrone exhibits rewarding properties and affects dopaminergic activity.

Cathinone derivatives are new recreational drugs known to produce psychostimulant effects. However, unlike other psychostimulants, the addictive potential of cathinone derivatives has not been widely studied. Here, we investigated the effects of pentedrone, a type of cathinone derivative, on the dopaminergic system using reverse transcription polymerase chain reaction and Western blot. We also evaluated the addictive potential of pentedrone using conditioned place preference and self-administration. We found that pentedrone increased the mRNA expression of dopamine 1 receptor, dopamine 2 receptor and dopamine transporter, as well as induced phosphorylation of cAMP response element-binding protein in PC-12 cells. Additionally, pentedrone at 3 and 10 mg/kg significantly increased conditioned place preference in mice, while pentedrone at 0.3 mg/kg/infusion significantly increased self-administration in rats. Finally, we found that acute administration of pentedrone enhanced locomotor activity in a dose-dependent manner. Collectively, these data suggest that the addictive properties of pentedrone may be due to its effects on the dopaminergic system.

SESN2/sestrin2 suppresses sepsis by inducing mitophagy and inhibiting NLRP3 activation in macrophages.

Proper regulation of mitophagy for mitochondrial homeostasis is important in various inflammatory diseases. However, the precise mechanisms by which mitophagy is activated to regulate inflammatory responses remain largely unknown. The NLRP3 (NLR family, pyrin domain containing 3) inflammasome serves as a platform that triggers the activation of CASP1 (caspase 1) and secretion of proinflammatory cytokines. Here, we demonstrate that SESN2 (sestrin 2), known as stress-inducible protein, suppresses prolonged NLRP3 inflammasome activation by clearance of damaged mitochondria through inducing mitophagy in macrophages. SESN2 plays a dual role in inducing mitophagy in response to inflammasome activation. First, SESN2 induces "mitochondrial priming" by marking mitochondria for recognition by the autophagic machinery. For mitochondrial preparing, SESN2 facilitates the perinuclear-clustering of mitochondria by mediating aggregation of SQSTM1 (sequestosome 1) and its binding to lysine 63 (Lys63)-linked ubiquitins on the mitochondrial surface. Second, SESN2 activates the specific autophagic machinery for degradation of primed mitochondria via an increase of ULK1 (unc-51 like kinase 1) protein levels. Moreover, increased SESN2 expression by extended LPS (lipopolysaccharide) stimulation is mediated by NOS2 (nitric oxide synthase 2, inducible)-mediated NO (nitric oxide) in macrophages. Thus, Sesn2-deficient mice displayed defective mitophagy, which resulted in hyperactivation of inflammasomes and increased mortality in 2 different sepsis models. Our findings define a unique regulatory mechanism of mitophagy activation for immunological homeostasis that protects the host from sepsis.

Eucommia ulmoides Oliv. Bark. protects against hydrogen peroxide-induced neuronal cell death in SH-SY5Y cells.

ETHNOPHARMACOLOGICAL RELEVANCE: Eucommia ulmoides Oliv. Bark. (EUE), has commonly been used to fortify the muscles and lungs, lower blood pressure, prevent miscarriage, improve the tone of liver and kidneys, and promote longevity the traditional tonic medicines of Korea, China, and Japan. AIM OF THE STUDY: In this study, we investigated that the neuroprotective activities and possible mechanisms of EUE aqueous extract in hydrogen peroxide (H(2)O(2))-induced neuronal cell death in human SH-SY5Y neuroblastoma cells. MATERIAL AND METHOD: We examined the effects of EUE against H(2)O(2)-induced cytotoxicity, DNA condensation, the production of reactive oxygen species (ROS), loss of mitochondria membrane potential (MMP), the proteolysis of cleaved poly-ADP-ribose polymerase (PARP), and the expression of Bcl-2, Bcl-xL, cleaved caspase-3, and release of cytochrome c. Moreover, we attempted to determine whether EUE suppressed the phosphorylation of c-Jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase 1/2 (ERK 1/2), and phosphoinositide 3-kinase (PI3K)/Akt. RESULTS: Pretreatment with EUE increased cell viability and inhibited cytotoxicity and DNA condensation. EUE also attenuated the increase in ROS production and MMP reduction. Western blot data revealed that EUE inhibited H(2)O(2)-induced up- or down-regulation of cleaved PARP, cleaved caspase-3, Bcl-2, and Bcl-xL. The EUE inhibited release of cytochrome c from mitochondria to the cytosol, and significantly attenuated H(2)O(2)-induced phosphorylation of JNK, p38 MAPK, ERK 1/2, and PI3K/Akt. CONCLUSION: The potent neuroprotective capacity of EUE, shown in these experiments, may potentially be applied in the prevention or treatment of neurodegenerative diseases such as Alzheimer's disease (AD).