Effects of 4 weeks of low intensity hand grip isometric training with vascular occlusion in older adults

Introduction: By the middle of this century, the number of people of age 65 and over will more than double to 80 million (US census bureau, 1993). As more and more older people are getting added to the population, it becomes vital to understand the mechanisms and processes which can play a crucial role in their overall well being. The goal of the present study was to study the benefits of a novel method of training which can be an alternative to high intensity training and also be equally beneficial without the risks of heavy training in older adults. It was hypothesized that low intensity hand grip isometric training with vascular occlusion leads to increased peak forearm blood flow (FBF) and forearm vascular conductance (FVC) as compared to high intensity training in older adults. Methods: Older participants (60 years or more) were recruited from the Ames community (Training group, n=9; control group, n=10). They were non smokers, did not participate in any structured hand grip exercise and did not have any diagnosed cardiovascular disease, PVD or diabetes. Intervention group did low intensity hand grip isometric exercise with vascular occlusion [20% Maximum voluntary contraction (MVC), 130% resting SBP] while the control group did high intensity isometric exercise (75% MVC) for 4 weeks. Resting FBF, peak FBF, resting FVC and peak FVC were calculated before and after the training in each group. Results: No statistically significant changes were seen in either FBF or FVC in any of the groups. Also, strength and size changes did not reach significance. Conclusion: Previous literature convincingly proves that conventional resistance training leads to strength and size gains. However, there were no changes in either group in the current study. The study also had a few limitations. The 4 weeks study period might not have been long enough to evoke a convincing and significant outcome. Some gender based differences in FBF response were seen as well. Further research is warranted before any conclusions can be drawn regarding the applicability of low intensity exercise with vascular occlusion

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

We develop a computational database, web-apps and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Anderson rule, which is based on the band-alignment with respect to the vacuum potential of non-interacting monolayers.We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts.We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3) to compare the band-alignment description with the predictions from Anderson rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we develop ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://www.ctcms.nist.gov/jarvish).Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function 2D-metal contacts

Safe motion planning under uncertainty for mobile manipulators in unknown environments

For a mobile manipulator to operate and perform useful tasks in human-centered environments, it is important to work toward the realization of robust motion planners that incorporate uncertainty inherent in robot\u27s control and sensing and provide safe motion plans for reliable robot operation. Designing such planners pose a significant challenge because of computational complexity associated with mobile manipulator planning and planning under uncertainty. Current planning approaches for mobile manipulation are often conservative in nature and the uncertainty is largely ignored. In this thesis, we propose sampling-based efficient and robust mobile manipulator planners that use smart strategies to deal with computational complexity and incorporate uncertainty to generate safer plans. The first part of the research addresses the design of an efficient planner for deterministic case, where robot state is fully known, and then subsequent extension to incorporate base pose uncertainty. In the first part, we propose a Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) that plans both for the base and the arm in a judicious manner - allowing the manipulator to change its configuration autonomously when needed if the current arm configuration is in collision with the environment as the mobile manipulator moves along the planned path. We show that HAMP is probabilistically complete. We then propose an extension of HAMP (HAMP-U) to account for localization uncertainty associated with the mobile base position. The advantages of our planners are illustrated and discussed. The second part of the research deals with the computational complexity involved in planning under uncertainty. For that, we propose localization aware sampling and connection strategies that help to reduce the planning time significantly with little compromise on the quality of path. In the third part, we learnt from the shortcomings of HAMP-U and took advantage of our smart strategies developed to combat the computational complexity. We propose an efficient and robust mobile manipulator planner (HAMP-BAU) that plans judiciously and considers the base pose uncertainty and the effects of this uncertainty on manipulator motions. It uses our localization aware sampling and connection strategies to consider only those nodes and edges which contribute toward better localization. This helps to find the same quality of path in shorter time. We also extend HAMP-BAU to incorporate task space constraints (HAMP-BAU-TC). Finally, in the last part of the work, we incorporate our planners (HAMP-BAU and HAMP-BAU-TC) within an integrated and fully autonomous system for mobile pick-and-place tasks in unknown static environments. We demonstrate our system both in simulation and real experiments on SFU mobile manipulator

QSAR Rationales for the 5-HT 2A Receptor Antagonistic Activity of 2-Alkyl-4- aryl-Pyrimidine Fused Heterocycles

ABSTRACT The 5-HT 2A receptor binding affinities of the 2-alkyl-4-aryl-pyrimidine fused heterocycles have been quantitatively expressed in terms of topological and molecular features. The analysis revealed that less number of rotatable bonds (descriptor RBN), a more hydrophobic nature (descriptor MLOGP) and less polar surface area (descriptor PSA) in a molecular structure will be favorable to the binding affinity. A lower positive values of descriptors PW4 (path/walk 4 -Randic shape index) and MATS2m (Moran autocorrelation -lag 2/weighted by atomic masses) and higher value of descriptor MATS1v (Moran autocorrelation -lag 1/weighted by atomic van der Waals volumes) will augment the activity. Additionally, a lower value of descriptor BEHm1 (highest eigenvalue n. 1 of Burden matrix/weighted by atomic masses), higher value of descriptor BEHp1 (highest eigenvalue n. 1 of Burden matrix / weighted by atomic polarizabilities) and a higher value of 7 th order charge index (GGI7) will be beneficiary to the activity. The derived models and participating descriptors in them have suggested that the substituents of 2-alkyl-4-aryl-pyrimidine fused heterocycles have sufficient scope for further modification

Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks

Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98 % given the range of values within the dataset, improving significantly on the state-of-the-art. Models are tested for different defect types as well as for defect charge transition levels. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially-optimized defective structures, helping identify electronically active defects in technologically-important semiconductors

Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores

Structure–property relationships are a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore (A2B2O7) in a regime in which amorphization occurs as a consequence of defect accumulation. We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction, but when the machine learning model is restricted to only the B = Ti pyrochlores, the energetics of disordering and amorphization are critical factors. We discuss how these static quantities provide insight into an inherently kinetic property such as amorphization resistance at finite temperature. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight