Preliminary results on noncollocated torque control of space robot actuators

In the Space Station era, more operations will be performed robotically in space in the areas of servicing, assembly, and experiment tending among others. These robots may have various sets of requirements for accuracy, speed, and force generation, but there will be design constraints such as size, mass, and power dissipation limits. For actuation, a leading motor candidate is a dc brushless type, and there are numerous potential drive trains each with its own advantages and disadvantages. This experiment uses a harmonic drive and addresses some inherent limitations, namely its backdriveability and low frequency structural resonances. These effects are controlled and diminished by instrumenting the actuator system with a torque transducer on the output shaft. This noncollocated loop is closed to ensure that the commanded torque is accurately delivered to the manipulator link. The actuator system is modelled and its essential parameters identified. The nonlinear model for simulations will include inertias, gearing, stiction, flexibility, and the effects of output load variations. A linear model is extracted and used for designing the noncollocated torque and position feedback loops. These loops are simulated with the structural frequency encountered in the testbed system. Simulation results are given for various commands in position. The use of torque feedback is demonstrated to yield superior performance in settling time and positioning accuracy. An experimental setup being finished consists of a bench mounted motor and harmonic drive actuator system. A torque transducer and two position encoders, each with sufficient resolution and bandwidth, will provide sensory information. Parameters of the physical system are being identified and matched to analytical predictions. Initial feedback control laws will be incorporated in the bench test equipment and various experiments run to validate the designs. The status of these experiments is given

Genomic biomarkers of prenatal intrauterine inflammation in umbilical cord tissue predict later life neurological outcomes

Preterm birth is a major risk factor for neurodevelopmental delays and disorders. This study aimed to identify genomic biomarkers of intrauterine inflammation in umbilical cord tissue in preterm neonates that predict cognitive impairment at 10 years of age

Placental CpG methylation of infants born extremely preterm predicts cognitive impairment later in life

Abstract: Background The placenta is the central regulator of maternal and fetal interactions. Perturbations of placental structure and function have been associated with adverse neurodevelopmental outcomes later in life. Placental CpG methylation represents an epigenetic modification with the potential to impact placental function, fetal development and child health later in life. Study design Genome-wide placental CpG methylation levels were compared between spontaneous versus indicated deliveries from extremely preterm births (EPTBs) (n = 84). The association between the identified differentially methylated CpG sites and neurocognitive outcome at ten years of age was then evaluated. Results Spontaneous EPTB was associated with differential CpG methylation levels in 250 CpG sites (217 unique genes) with the majority displaying hypermethylation. The identified genes are known to play a role in neurodevelopment and are enriched for basic helix-loop-helix transcription factor binding sites. The placental CpG methylation levels for 17 of these sites predicted cognitive function at ten years of age. Conclusion A hypermethylation signature is present in DNA from placentas in infants with spontaneous EPTB. CpG methylation levels of critical neurodevelopment genes in the placenta predicted..

The Ursinus Weekly, November 18, 1971

Name contest for new student faculty lounge • U.C. announces soloists for annual Messiah performance • President presents Ronnie Hollyman • Helicopter comes to U.C.; Nightlights installed • Buddhi\u27s Follies at Valley Forge • Education office offers new plan • Editorial: Rumors • Focus: Eileen Shrager • Students of many backgrounds participate in Sunday seminar • The science of setback • Century II: A bird\u27s eye view • Letter to the editor • Faculty portrait: Mr. Taras Zakydalsky • Pre-med meeting • The Administration answers • Bears close season with 4-4 record • Ursinus hoopla • Snellbelles undefeated • Ursinus wrestlers are bears • Bears drop 4: 1 game to gohttps://digitalcommons.ursinus.edu/weekly/1113/thumbnail.jp

Caffeine and Progression of Parkinson Disease: A Deleterious Interaction With Creatine.

OBJECTIVE: Increased caffeine intake is associated with a lower risk of Parkinson disease (PD) and is neuroprotective in mouse models of PD. However, in a previous study, an exploratory analysis suggested that, in patients taking creatine, caffeine intake was associated with a faster rate of progression. In the current study, we investigated the association of caffeine with the rate of progression of PD and the interaction of this association with creatine intake. METHODS: Data were analyzed from a large phase 3 placebo-controlled clinical study of creatine as a potentially disease-modifying agent in PD. Subjects were recruited for this study from 45 movement disorders centers across the United States and Canada. A total of 1741 subjects with PD participated in the primary clinical study, and caffeine intake data were available for 1549 of these subjects. The association of caffeine intake with rate of progression of PD as measured by the change in the total Unified Parkinson Disease Rating Scale score and the interaction of this association with creatine intake were assessed. RESULTS: Caffeine intake was not associated with the rate of progression of PD in the main analysis, but higher caffeine intake was associated with significantly faster progression among subjects taking creatine. CONCLUSIONS: This is the largest and longest study conducted to date that addresses the association of caffeine with the rate of progression of PD. These data indicate a potentially deleterious interaction between caffeine and creatine with respect to the rate of progression of PD

Signal-dependent splicing of tissue factor pre-mRNA modulates the thrombogenecity of human platelets

Tissue factor (TF) is an essential cofactor for the activation of blood coagulation in vivo. We now report that quiescent human platelets express TF pre-mRNA and, in response to activation, splice this intronic-rich message into mature mRNA. Splicing of TF pre-mRNA is associated with increased TF protein expression, procoagulant activity, and accelerated formation of clots. Pre-mRNA splicing is controlled by Cdc2-like kinase (Clk)1, and interruption of Clk1 signaling prevents TF from accumulating in activated platelets. Elevated intravascular TF has been reported in a variety of prothrombotic diseases, but there is debate as to whether anucleate platelets—the key cellular effector of thrombosis—express TF. Our studies demonstrate that human platelets use Clk1-dependent splicing pathways to generate TF protein in response to cellular activation. We propose that platelet-derived TF contributes to the propagation and stabilization of a thrombus

Application of pharmacogenomics and bioinformatics to exemplify the utility of human <i>ex vivo</i> organoculture models in the field of precision medicine

Here we describe a collaboration between industry, the National Health Service (NHS) and academia that sought to demonstrate how early understanding of both pharmacology and genomics can improve strategies for the development of precision medicines. Diseased tissue ethically acquired from patients suffering from chronic obstructive pulmonary disease (COPD), was used to investigate inter-patient variability in drug efficacy using ex vivo organocultures of fresh lung tissue as the test system. The reduction in inflammatory cytokines in the presence of various test drugs was used as the measure of drug efficacy and the individual patient responses were then matched against genotype and microRNA profiles in an attempt to identify unique predictors of drug responsiveness. Our findings suggest that genetic variation in CYP2E1 and SMAD3 genes may partly explain the observed variation in drug response

Robust probabilistic superposition and comparison of protein structures

<p>Abstract</p> <p>Background</p> <p>Protein structure comparison is a central issue in structural bioinformatics. The standard dissimilarity measure for protein structures is the root mean square deviation (RMSD) of representative atom positions such as α-carbons. To evaluate the RMSD the structures under comparison must be superimposed optimally so as to minimize the RMSD. How to evaluate optimal fits becomes a matter of debate, if the structures contain regions which differ largely - a situation encountered in NMR ensembles and proteins undergoing large-scale conformational transitions.</p> <p>Results</p> <p>We present a probabilistic method for robust superposition and comparison of protein structures. Our method aims to identify the largest structurally invariant core. To do so, we model non-rigid displacements in protein structures with outlier-tolerant probability distributions. These distributions exhibit heavier tails than the Gaussian distribution underlying standard RMSD minimization and thus accommodate highly divergent structural regions. The drawback is that under a heavy-tailed model analytical expressions for the optimal superposition no longer exist. To circumvent this problem we work with a scale mixture representation, which implies a weighted RMSD. We develop two iterative procedures, an Expectation Maximization algorithm and a Gibbs sampler, to estimate the local weights, the optimal superposition, and the parameters of the heavy-tailed distribution. Applications demonstrate that heavy-tailed models capture differences between structures undergoing substantial conformational changes and can be used to assess the precision of NMR structures. By comparing Bayes factors we can automatically choose the most adequate model. Therefore our method is parameter-free.</p> <p>Conclusions</p> <p>Heavy-tailed distributions are well-suited to describe large-scale conformational differences in protein structures. A scale mixture representation facilitates the fitting of these distributions and enables outlier-tolerant superposition.</p

Androgen Receptor Functional Analyses by High Throughput Imaging: Determination of Ligand, Cell Cycle, and Mutation-Specific Effects

Understanding how androgen receptor (AR) function is modulated by exposure to steroids, growth factors or small molecules can have important mechanistic implications for AR-related disease therapies (e.g., prostate cancer, androgen insensitivity syndrome, AIS), and in the analysis of environmental endocrine disruptors.We report the development of a high throughput (HT) image-based assay that quantifies AR subcellular and subnuclear distribution, and transcriptional reporter gene activity on a cell-by-cell basis. Furthermore, simultaneous analysis of DNA content allowed determination of cell cycle position and permitted the analysis of cell cycle dependent changes in AR function in unsynchronized cell populations. Assay quality for EC50 coefficients of variation were 5–24%, with Z' values reaching 0.91. This was achieved by the selective analysis of cells expressing physiological levels of AR, important because minor over-expression resulted in elevated nuclear speckling and decreased transcriptional reporter gene activity. A small screen of AR-binding ligands, including known agonists, antagonists, and endocrine disruptors, demonstrated that nuclear translocation and nuclear “speckling” were linked with transcriptional output, and specific ligands were noted to differentially affect measurements for wild type versus mutant AR, suggesting differing mechanisms of action. HT imaging of patient-derived AIS mutations demonstrated a proof-of-principle personalized medicine approach to rapidly identify ligands capable of restoring multiple AR functions.HT imaging-based multiplex screening will provide a rapid, systems-level analysis of compounds/RNAi that may differentially affect wild type AR or clinically relevant AR mutations