Identifying the criterion spontaneously minimized during the take-off phase of a sub-maximal long jump through optimal synthesis

Optimal synthesis of human movement or the prediction of the kinematics of a new movement require not only that the multi-body system be modeled but also that a performance criterion is specified. For sub-maximal movements the selection of a suitable performance criterion, able to generate realistic dynamic behavior is difficult. A two-dimensional simulation model of the take-off phase of a sub-maximal long jump was developed to study the effect of criterion choice on the realism of simulated movements. A parametric optimization technique was employed to obtain solutions to the constrained equations of motion. Seven different criteria were evaluated, by comparing simulated movements with an actual performance, to identify the criterion which most closely approximated that spontaneously minimized by the athlete. Synthesis of the take-off phase of a sub-maximal long jump was found to be sensitive to the chosen criterion, with a criterion based on minimizing joint intersegmental forces found to perform well.     

A code tagging approach to software product line development

Software product line engineering seeks to systematise reuse when developing families of similar software systems so as to minimise development time, cost and defects. To realise variability at the code level, product line methods classically advocate usage of inheritance, components, frameworks, aspects or generative techniques. However, these might require unaffordable paradigm shifts for developers if the software was not thought at the outset as a product line. Furthermore, these techniques can be conflicting with a company’s coding practices or external regulations. These concerns were the motivation for the industry–university collaboration described in this paper in which we developed a minimally intrusive coding technique based on tags. The approach was complemented with traceability from code to feature diagrams which were exploited for automated configuration. It is supported by a toolchain and is now in use in the partner company for the development of flight-grade satellite communication software libraries.     

Learning local linear Jacobians for flexible and adaptive robot arm control

Successful planning and control of robots strongly depends on the quality of kinematic models, which define mappings between configuration space (e.g. joint angles) and task space (e.g. Cartesian coordinates of the end effector). Often these models are predefined, in which case, for example, unforeseen bodily changes may result in unpredictable behavior. We are interested in a learning approach that can adapt to such changes—be they due to motor or sensory failures, or also due to the flexible extension of the robot body by, for example, the usage of tools. We focus on learning locally linear forward velocity kinematics models by means of the neuro-evolution approach XCSF. The algorithm learns self-supervised, executing movements autonomously by means of goal-babbling. It preserves actuator redundancies, which can be exploited during movement execution to fulfill current task constraints. For detailed evaluation purposes, we study the performance of XCSF when learning to control an anthropomorphic seven degrees of freedom arm in simulation. We show that XCSF can learn large forward velocity kinematic mappings autonomously and rather independently of the task space representation provided. The resulting mapping is highly suitable to resolve redundancies on the fly during inverse, goal-directed control.     

A Distributed Workflow Management System with Case Study of Real-life Scientific Applications on Grids

Next-generation scientific applications feature complex workflows comprised of many computing modules with intricate inter-module dependencies. Supporting such scientific workflows in wide-area networks especially Grids and optimizing their performance are crucial to the success of collaborative scientific discovery. We develop a Scientific Workflow Automation and Management Platform (SWAMP), which enables scientists to conveniently assemble, execute, monitor, control, and steer computing workflows in distributed environments via a unified web-based user interface. The SWAMP architecture is built entirely on a seamless composition of web services: the functionalities of its own are provided and its interactions with other tools or systems are enabled through web services for easy access over standard Internet protocols while being independent of different platforms and programming languages. SWAMP also incorporates a class of efficient workflow mapping schemes to achieve optimal end-to-end performance based on rigorous performance modeling and algorithm design. The performance superiority of SWAMP over existing workflow mapping schemes is justified by extensive simulations, and the system efficacy is illustrated by large-scale experiments on real-life scientific workflows for climate modeling through effective system implementation, deployment, and testing on the Open Science Grid.     

Structure-Activity Relationships of Benzamides and Isoindolines Designed as SARS-CoV Protease Inhibitors Effective against SARS-CoV-2

Inhibition of coronavirus (CoV)-encoded papain-like cysteine proteases (PL^pro) represents an attractive strategy to treat infections by these important human pathogens. Herein we report on structure-activity relationships (SAR) of the noncovalent active-site directed inhibitor (R)-5-amino-2-methyl-N-(1-(naphthalen-1-yl)ethyl) benzamide ( 2 b ), which is known to bind into the S3 and S4 pockets of the SARS-CoV PL^pro. Moreover, we report the discovery of isoindolines as a new class of potent PL^pro inhibitors. The studies also provide a deeper understanding of the binding modes of this inhibitor class. Importantly, the inhibitors were also confirmed to inhibit SARS-CoV-2 replication in cell culture suggesting that, due to the high structural similarities of the target proteases, inhibitors identified against SARS-CoV PL^pro are valuable starting points for the development of new pan-coronaviral inhibitors.     

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.     

Investigation of the Anti-Methicillin-Resistant Staphylococcus aureus Activity of (+)-Tanikolide- and (+)-Malyngolide-Based Analogues Prepared by Asymmetric Synthesis

Herein, we report antibacterial and antifungal evaluation of a series of previously prepared (+)-tanikolide analogues. One analogue, (4S,6S)-4-methyltanikolide, displayed promising anti-methicillin-resistant Staphylococcus aureus activity with a MIC of 12.5 µg/mL. Based on the antimicrobial properties of the structurally related (−)-malyngolide, two further analogues (4S,6S)-4-methylmalyngolide and (4R,6S)-4-methylmalyngolide bearing a shortened n-nonyl alkyl side chain were prepared in the present study using a ZrCl₄-catalysed deprotection/cyclisation as the key step in their asymmetric synthesis. When these were tested for activity against anti-methicillin-resistant Staphylococcus aureus, the MIC increased to 50 µg/mL.     

CYP154C5 Regioselectivity in Steroid Hydroxylation Explored by Substrate Modifications and Protein Engineering**

CYP154C5 from Nocardia farcinica is a P450 monooxygenase able to hydroxylate a range of steroids with high regio- and stereoselectivity at the 16α-position. Using protein engineering and substrate modifications based on the crystal structure of CYP154C5, an altered regioselectivity of the enzyme in steroid hydroxylation had been achieved. Thus, conversion of progesterone by mutant CYP154C5 F92A resulted in formation of the corresponding 21-hydroxylated product 11-deoxycorticosterone in addition to 16α-hydroxylation. Using MD simulation, this altered regioselectivity appeared to result from an alternative binding mode of the steroid in the active site of mutant F92A. MD simulation further suggested that the entrance of water to the active site caused higher uncoupling in this mutant. Moreover, exclusive 15α-hydroxylation was observed for wild-type CYP154C5 in the conversion of 5α-androstan-3-one, lacking an oxy-functional group at C17. Overall, our data give valuable insight into the structure–function relationship of this cytochrome P450 monooxygenase for steroid hydroxylation.     

Quantification of secondary structure prediction improvement using multiple alignments

The use of multiple sequence alignments for secondary structurepredictions is analysed. Seven different protein families, containingonly sequences of known structure, were considered to providea range of alignment and prediction conditions. Using alignmentsobtained by spatial superposition of main chain atoms in knowntertiary protein structures allowed a mean of 8% in secondarystructure prediction accuracy, when compared to those obtainedfrom the individual sequences. Substitution of these alignmentsby those determined directly from an automated sequence alignmentalgorithm showed variations in the prediction accuracy whichcorrelated with the quality of the multiple alignments and distanceof the primary sequence. Secondary structure predictions canbe reliably improved using alignments from an automatic alignmentprocedure with a mean increase of 6.87percnt;, giving an overallprediction accuracy of 68.5%, if there is a minimum of 25% sequenceidentity between all sequences in a family.     

Some aspects of the role of coal thermoplasticity and coke structure in coal gasification. 2. The effects of pressure and heating rate on dilatometric parameters and optical anisotropy development

An examination of the influence of pressure and heating rate on the thermoplastic properties of a weakly coking coal and of coal with various pitch-like additives was carried out using a high pressure dilatometer. Pressure up to 4 MPa markedly increased the swelling properties and increased the plastic range by decreasing the softening temperature, but the effects of pressure were strongly influenced by rate of heating with high heating rates enhancing the effects. Additions of tar or pitch also enhanced swelling at low pressure. The solid carbonization residues from the dilatometer were examined by polarized light microscopy to determine the content and composition of optically anisotropic species. The anisotropic content was increased by increases in pressure and heating rate and pitch additives enhanced the anisotropic content, especially at low pressure, without affecting the composition of the anisotropy. No direct correlations exist between the dilatometric parameters and the optical anisotropy but their dependence on the conditions emphasizes that when considering high pressure gasification of coal, it is necessary to obtain data under appropriate conditions.