Revisiting protein aggregation as pathogenic in sporadic Parkinson and Alzheimer diseases.

The gold standard for a definitive diagnosis of Parkinson disease (PD) is the pathologic finding of aggregated α-synuclein into Lewy bodies and for Alzheimer disease (AD) aggregated amyloid into plaques and hyperphosphorylated tau into tangles. Implicit in this clinicopathologic-based nosology is the assumption that pathologic protein aggregation at autopsy reflects pathogenesis at disease onset. While these aggregates may in exceptional cases be on a causal pathway in humans (e.g., aggregated α-synuclein in SNCA gene multiplication or aggregated β-amyloid in APP mutations), their near universality at postmortem in sporadic PD and AD suggests they may alternatively represent common outcomes from upstream mechanisms or compensatory responses to cellular stress in order to delay cell death. These 3 conceptual frameworks of protein aggregation (pathogenic, epiphenomenon, protective) are difficult to resolve because of the inability to probe brain tissue in real time. Whereas animal models, in which neither PD nor AD occur in natural states, consistently support a pathogenic role of protein aggregation, indirect evidence from human studies does not. We hypothesize that (1) current biomarkers of protein aggregates may be relevant to common pathology but not to subgroup pathogenesis and (2) disease-modifying treatments targeting oligomers or fibrils might be futile or deleterious because these proteins are epiphenomena or protective in the human brain under molecular stress. Future precision medicine efforts for molecular targeting of neurodegenerative diseases may require analyses not anchored on current clinicopathologic criteria but instead on biological signals generated from large deeply phenotyped aging populations or from smaller but well-defined genetic-molecular cohorts

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Spatiotemporal correlations of handset-based service usages

We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the spatiotemporal trajectories of the users we detect several meaningful places or contexts for each one of them and show how the context affects the service usage patterns. We find that temporal patterns of service usages are bound to the typical weekly cycles of humans, yet they show maximal activities at different times. We first discuss their temporal correlations and then investigate the time-ordering behavior of communication services like calls being followed by the non-communication services like applications. We also find that the behavioral overlap network based on the clustering of temporal patterns is comparable to the communication network of users. Our approach provides a useful framework for handset-based data analysis and helps us to understand the complexities of information and communications technology enabled human behavior.Comment: 11 pages, 15 figure

Sparsest factor analysis for clustering variables: a matrix decomposition approach

We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA

The condition-dependent transcriptional landscape of Burkholderia pseudomallei

This is the final version of the article. Available from the publisher via the DOI in this record.Burkholderia pseudomallei (Bp), the causative agent of the often-deadly infectious disease melioidosis, contains one of the largest prokaryotic genomes sequenced to date, at 7.2 Mb with two large circular chromosomes (1 and 2). To comprehensively delineate the Bp transcriptome, we integrated whole-genome tiling array expression data of Bp exposed to >80 diverse physical, chemical, and biological conditions. Our results provide direct experimental support for the strand-specific expression of 5,467 Sanger protein-coding genes, 1,041 operons, and 766 non-coding RNAs. A large proportion of these transcripts displayed condition-dependent expression, consistent with them playing functional roles. The two Bp chromosomes exhibited dramatically different transcriptional landscapes--Chr 1 genes were highly and constitutively expressed, while Chr 2 genes exhibited mosaic expression where distinct subsets were expressed in a strongly condition-dependent manner. We identified dozens of cis-regulatory motifs associated with specific condition-dependent expression programs, and used the condition compendium to elucidate key biological processes associated with two complex pathogen phenotypes--quorum sensing and in vivo infection. Our results demonstrate the utility of a Bp condition-compendium as a community resource for biological discovery. Moreover, the observation that significant portions of the Bp virulence machinery can be activated by specific in vitro cues provides insights into Bp's capacity as an "accidental pathogen", where genetic pathways used by the bacterium to survive in environmental niches may have also facilitated its ability to colonize human hosts.This work was funded by a core grant provided by the Agency for Science, Technology and Research to the Genome Institute of Singapore, and funding from the Defence Medical and Environmental Research Institute, Singapore. This work was supported in part through NIAID contract HHSN266200400035C to BWS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Histone Deacetylases Regulate Gonadotropin-Releasing Hormone I Gene Expression via Modulating Otx2-Driven Transcriptional Activity

BACKGROUND: Precise coordination of the hypothalamic-pituitary-gonadal axis orchestrates the normal reproductive function. As a central regulator, the appropriate synthesis and secretion of gonadotropin-releasing hormone I (GnRH-I) from the hypothalamus is essential for the coordination. Recently, emerging evidence indicates that histone deacetylases (HDACs) play an important role in maintaining normal reproductive function. In this study, we identify the potential effects of HDACs on Gnrh1 gene transcription. METHODOLOGY/PRINCIPAL FINDINGS: Inhibition of HDACs activities by trichostatin A (TSA) and valproic acid (VPA) promptly and dramatically repressed transcription of Gnrh1 gene in the mouse immortalized mature GnRH neuronal cells GT1-7. The suppression was connected with a specific region of Gnrh1 gene promoter, which contains two consensus Otx2 binding sites. Otx2 has been known to activate the basal and also enhancer-driven transcription of Gnrh1 gene. The transcriptional activity of Otx2 is negatively modulated by Grg4, a member of the Groucho-related-gene (Grg) family. In the present study, the expression of Otx2 was downregulated by TSA and VPA in GT1-7 cells, accompanied with the opposite changes of Grg4 expression. Chromatin immunoprecipitation and electrophoretic mobility shift assays demonstrated that the DNA-binding activity of Otx2 to Gnrh1 gene was suppressed by TSA and VPA. Overexpression of Otx2 partly abolished the TSA- and VPA-induced downregulation of Gnrh1 gene expression. CONCLUSIONS/SIGNIFICANCE: Our data indicate that HDAC inhibitors downregulate Gnrh1 gene expression via repressing Otx2-driven transcriptional activity. This study should provide an insight for our understanding on the effects of HDACs in the reproductive system and suggests that HDACs could be potential novel targets for the therapy of GnRH-related diseases

An Effective Amperometric Biosensor Based on Gold Nanoelectrode Arrays

A sensitive amperometric biosensor based on gold nanoelectrode array (NEA) was investigated. The gold nanoelectrode array was fabricated by template-assisted electrodeposition on general electrodes, which shows an ordered well-defined 3D structure of nanowires. The sensitivity of the gold NEA to hydrogen peroxide is 37 times higher than that of the conventional electrode. The linear range of the platinum NEA toward H2O2is from 1 × 10−6to 1 × 10−2 M, covering four orders of magnitudes with detection limit of 1 × 10−7 M and a single noise ratio (S/N) of four. The enzyme electrode exhibits an excellent response performance to glucose with linear range from 1 × 10−5to 1 × 10−2 M and a fast response time within 8 s. The Michaelis–Menten constantkm and the maximum current densityimaxof the enzyme electrode were 4.97 mM and 84.60 μA cm−2, respectively. This special nanoelectrode may find potential application in other biosensors based on amperometric signals

Accurate classification of RNA structures using topological fingerprints

While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC \u3e 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint