Single muscle fiber proteomics reveals unexpected mitochondrial specialization

Mammalian skeletal muscles are composed of multinucleated cells termed slow or fast fibers according to their contractile and metabolic properties. Here, we developed a high-sensitivity workflow to characterize the proteome of single fibers. Analysis of segments of the same fiber by traditional and unbiased proteomics methods yielded the same subtype assignment. We discovered novel subtype-specific features, most prominently mitochondrial specialization of fiber types in substrate utilization. The fiber type-resolved proteomes can be applied to a variety of physiological and pathological conditions and illustrate the utility of single cell type analysis for dissecting proteomic heterogeneity

Role of dynamic Jahn-Teller distortions in Na2C60 and Na2CsC60 studied by NMR

Through 13C NMR spin lattice relaxation (T1) measurements in cubic Na2C60, we detect a gap in its electronic excitations, similar to that observed in tetragonal A4C60. This establishes that Jahn-Teller distortions (JTD) and strong electronic correlations must be considered to understand the behaviour of even electron systems, regardless of the structure. Furthermore, in metallic Na2CsC60, a similar contribution to T1 is also detected for 13C and 133Cs NMR, implying the occurence of excitations typical of JT distorted C60^{2-} (or equivalently C60^{4-}). This supports the idea that dynamic JTD can induce attractive electronic interactions in odd electron systems.Comment: 3 figure

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Single muscle fiber proteomics reveals unexpected mitochondrial specialization.

Mammalian skeletal muscles are composed of multinucleated cells termed slow or fast fibers according to their contractile and metabolic properties. Here, we developed a high-sensitivity workflow to characterize the proteome of single fibers. Analysis of segments of the same fiber by traditional and unbiased proteomics methods yielded the same subtype assignment. We discovered novel subtype-specific features, most prominently mitochondrial specialization of fiber types in substrate utilization. The fiber type-resolved proteomes can be applied to a variety of physiological and pathological conditions and illustrate the utility of single cell type analysis for dissecting proteomic heterogeneity

Backpropagated Gradient Representations for Anomaly Detection

Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal data. Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.Comment: European Conference on Computer Vision (ECCV) 202

SOFIAS – Herramienta para el análisis de ciclo de vida y la calificación ambiental de edificios

This paper describes the development process of a new software tool, called SOFIAS (Software for a Sustainable Architecture), funded by the Spanish Ministry of Economy and Competitivenes. Following CEN/TC 350 standard on environmental assessment of buildings, the tool aims at assisting building professionals on reducing the life-cycle environmental impact through the design of new buildings and the refurbishment of existing ones. In addition, SOFIAS provides a rating system based on the Life Cycle Assessment (LCA) methodology. This paper explains the innovative aspects of this software, the working methodology and the different type of results that can be obtained using SOFIAS.SOFIAS (Ref. number IPT-2011-0948-380000) project co financed by the Spanish Ministry of Economy and Competitiveness

Apolipoprotein E4 Polymorphism and Outcomes from Traumatic Brain Injury : A Living Systematic Review and Meta-Analysis

The mortality of traumatic brain injury (TBI) has been largely static despite advances in monitoring and imaging techniques. Substantial variance exists in outcome, not fully accounted for by baseline characteristics or injury severity, and genetic factors likely play a role in this variance. The aims of this systematic review were to examine the evidence for a link between the apolipoprotein E4 (APOE4) polymorphism and TBI outcomes and where possible, to quantify the effect size via meta-analysis. We searched EMBASE, MEDLINE, CINAHL, and gray literature in December 2017. We included studies of APOE genotype in relation to functional adult TBI outcomes. Methodological quality was assessed using the Quality in Prognostic Studies Risk of Bias Assessment Instrument and the prognostic studies adaptation of the Grading of Recommendations Assessment, Development and Evaluation tool. In addition, we contacted investigators and included an additional 160 patients whose data had not been made available for previous analyses, giving a total sample size of 2593 patients. Meta-analysis demonstrated higher odds of a favorable outcome following TBI in those not possessing an ApoE e4 allele compared with e4 carriers and homozygotes (odds ratio 1.39, 95% confidence interval 1.05 to 1.84; p = 0.02). The influence of APOE4 on neuropsychological functioning following TBI remained uncertain, with multiple conflicting studies. We conclude that the ApoE e4 allele confers a small risk of poor outcome following TBI, with analysis by TBI severity not possible based on the currently available published data. Further research into the long-term neuropsychological impact and risk of dementia is warranted.Peer reviewe

An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder

Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have been developed for exploiting the information contained in process data. In this article, we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computers interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.Comment: 28 pages, 13 figure

Dihydropyridine derivatives modulate heat shock responses and have a neuroprotective effect in a transgenic mouse model of Alzheimer’s disease

Heat shock proteins (Hsps) have chaperone activity and play a pivotal role in the homeostasis of proteins by preventing misfolding, by clearing aggregated and damaged proteins from cells and by maintaining proteins in an active state. Alzheimer’s disease (AD) is thought to be caused by β- amyloid peptide that triggers tau hyperphosphorylation, which is neurotoxic. Although proteostasis capacity declines with age and facilitates the manifestation of neurodegenerative diseases such as AD, the upregulation of chaperones improves prognosis. Our research goal is to identify potent Hsp co-inducers that enhance protein homeostasis for the treatment of AD, especially 1,4-dihydropyridine derivatives optimized for their ability to modulate cellular stress responses. Based on favorable toxicological data and Hsp co-inducing activity, LA1011 was selected for the in vivo analysis of its neuroprotective effect in the APPxPS1 mouse model of AD. Here, we report that 6 months of LA1011 administration effectively improved the spatial learning and memory functions in wild type mice and eliminated neurodegeneration in double mutant mice. Furthermore, Hsp co-inducer therapy preserves the number of neurons, increases dendritic spine density, and reduces tau pathology and amyloid plaque formation in transgenic AD mice. In conclusion, the Hsp co-inducer LA1011 is neuroprotective and therefore is a potential pharmaceutical candidate for the therapy of neurodegenerative diseases, particularly AD

Deep execution monitor for robot assistive tasks

We consider a novel approach to high-level robot task execution for a robot assistive task. In this work we explore the problem of learning to predict the next subtask by introducing a deep model for both sequencing goals and for visually evaluating the state of a task. We show that deep learning for monitoring robot tasks execution very well supports the interconnection between task-level planning and robot operations. These solutions can also cope with the natural non-determinism of the execution monitor. We show that a deep execution monitor leverages robot performance. We measure the improvement taking into account some robot helping tasks performed at a warehouse