Fission yeast 26S proteasome mutants are multi-drug resistant due to stabilization of the pap1 transcription factor

Here we report the result of a genetic screen for mutants resistant to the microtubule poison methyl benzimidazol-2-yl carbamate (MBC) that were also temperature sensitive for growth. In total the isolated mutants were distributed in ten complementation groups. Cloning experiments revealed that most of the mutants were in essential genes encoding various 26S proteasome subunits. We found that the proteasome mutants are multi-drug resistant due to stabilization of the stress-activated transcription factor Pap1. We show that the ubiquitylation and ultimately the degradation of Pap1 depend on the Rhp6/Ubc2 E2 ubiquitin conjugating enzyme and the Ubr1 E3 ubiquitin-protein ligase. Accordingly, mutants lacking Rhp6 or Ubr1 display drug-resistant phenotypes

The structure of Organizational Virtual Social Networks

Organizational virtual social networks (OVSN) reshape social structures due to their ability to strengthen social ties, to change power relations and to enable new forms of cooperation. Research in Information and Communication Technologies (ICT) has led to various approaches that analyze the impact of OVSN on organizations in terms of structure and behavior. Our study aims to analyze important features related to the structure of OVSN. It also aims to strengthen a network approach to analyze organizational phenomena such as working groups and connected individuals, as well as the impact of online networks in organizations. This study was based on the lines of approach described by Oinas-Kukkonen et al. (2010) and on the research carried out by Bobsin & Hoppen (2012) to understand the process of structuring OVSN. Our main results are an OVSN structure consisting of actors and roles, interactions, operating elements and articulating goals. We also analyzed some structural elements of networks which may contribute to the development of a network based approach to study organizational phenomena

The reliability of side to side measurements of upper extremity activity levels in healthy subjects

<p>Abstract</p> <p>Background</p> <p>In both clinical and occupational settings, ambulatory sensors are becoming common for assessing all day measurements of arm motion. In order for the motion of a healthy, contralateral side to be used as a control for the involved side, the inherent side to side differences in arm usage must be minimal. The goal of the present study was to determine the reliability of side to side measurements of upper extremity activity levels in healthy subjects.</p> <p>Methods</p> <p>Thirty two subjects with no upper extremity pathologies were studied. Each subject wore a triaxial accelerometer on both arms for three and a half hours. Motion was assessed using parameters previously reported in the literature. Side to side differences were compared with the intraclass correlation coefficient, standard error of the mean, minimal detectable change scores and a projected sample size analysis.</p> <p>Results</p> <p>The variables were ranked based on their percentage of minimal detectable change scores and sample sizes needed for paired t-tests. The order of these rankings was found to be identical and the top ranked parameters were activity counts per hour (MDC% = 9.5, n = 5), jerk time (MDC% = 15.8, n = 8) and percent time above 30 degrees (MDC% = 34.7, n = 9).</p> <p>Conclusions</p> <p>In general, the mean activity levels during daily activities were very similar between dominant and non-dominant arms. Specifically, activity counts per hour, jerk time, and percent time above 30 degrees were found to be the variables most likely to reveal significant difference or changes in both individuals and groups of subjects. The use of ambulatory measurements of upper extremity activity has very broad uses for occupational assessments, musculoskeletal injuries of the shoulder, elbow, wrist and hand as well as neurological pathologies.</p

Behaviour of motor unit action potential rate, estimated from surface EMG, as a measure of muscle activation level

BACKGROUND: Surface electromyography (EMG) parameters such as root-mean-square value (RMS) are commonly used to assess the muscle activation level that is imposed by the central nervous system (CNS). However, RMS is influenced not only by motor control aspects, but also by peripheral properties of the muscle and recording setup. To assess motor control separately, the number of motor unit action potentials (MUAPs) per second, or MUAP Rate (MR) is a potentially useful measure. MR is the sum of the firing rates of the contributing MUs and as such reflects the two parameters that the CNS uses for motor control: number of MUs and firing rate. MR can be estimated from multi-channel surface EMG recordings. The objective of this study was to explore the behaviour of estimated MR (eMR) in relation to number of active MUs and firing rate. Furthermore, the influence of parameters related to peripheral muscle properties and recording setup (number of fibers per MU, fiber diameter, thickness of the subcutaneous layer, signal-to-noise-ratio) on eMR was compared with their influence on RMS. METHODS: Physiological parameters were varied in a simulation model that generated multi-channel EMG signals. The behaviour of eMR in simulated conditions was compared with its behaviour in experimental conditions. Experimental data was obtained from the upper trapezius muscle during a shoulder elevation task (20–100 N). RESULTS: The simulations showed strong, monotonously increasing relations between eMR and number of active MUs and firing rate (r(2 )> 0.95). Because of unrecognized superimpositions of MUAPs, eMR was substantially lower than the actual MUAP Rate (aMR). The percentage of detected MUAPs decreased with aMR, but the relation between eMR and aMR was rather stable in all simulated conditions. In contrast to RMS, eMR was not affected by number of fibers per MU, fiber diameter and thickness of the subcutaneous layer. Experimental data showed a strong relation between eMR and force (individual second order polynomial regression: 0.96 < r(2 )< 0.99). CONCLUSION: Although the actual number of MUAPs in the signal cannot be accurately extracted with the present method, the stability of the relation between eMR and aMR and its independence of muscle properties make eMR a suitable parameter to assess the input from the CNS to the muscle at low contraction levels non-invasively

Pooling job physical exposure data from multiple independent studies in a consortium study of carpal tunnel syndrome

Pooling data from different epidemiological studies of musculoskeletal disorders (MSDs) is necessary to improve statistical power and to more precisely quantify exposure–response relationships for MSDs. The pooling process is difficult and time-consuming, and small methodological differences could lead to different exposure–response relationships. A subcommittee of a six-study research consortium studying carpal tunnel syndrome: (i) visited each study site, (ii) documented methods used to collect physical exposure data and (iii) determined compatibility of exposure variables across studies. Certain measures of force, frequency of exertion and duty cycle were collected by all studies and were largely compatible. A portion of studies had detailed data to investigate simultaneous combinations of force, frequency and duration of exertions. Limited compatibility was found for hand/wrist posture. Only two studies could calculate compatible Strain Index scores, but Threshold Limit Value for Hand Activity Level could be determined for all studies. Challenges of pooling data, resources required and recommendations for future researchers are discussed

Cross-sectional associations between occupational factors and musculoskeletal pain in women teachers, nurses and sonographers

A. Technical measurements of the physical workload. (DOCX 18 kb

The effect of work pace on workload, motor variability and fatigue during simulated light assembly work

This study investigated the effect of work pace on workload, motor variability and fatigue during light assembly work. Upper extremity kinematics and electromyography (EMG) were obtained on a cycle-to-cycle basis for eight participants during two conditions, corresponding to "normal" and "high" work pace according to a predetermined time system for engineering. Indicators of fatigue, pain sensitivity and performance were recorded before, during and after the task. The level and variability of muscle activity did not differ according to work pace, and manifestations of muscle fatigue or changed pain sensitivity were not observed. In the high work pace, however, participants moved more efficiently, they showed more variability in wrist speed and acceleration, but they also made more errors. These results suggest that an increased work pace, within the range addressed here, will not have any substantial adverse effects on acute motor performance and fatigue in light, cyclic assembly work. © 2011 Taylor & Francis

Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases