Design and Implementation of a Modular Human-Robot Interaction Framework

With the increasing longevity that accompanies advances in medical technology comes a host of other age-related disabilities. Among these are neuro-degenerative diseases such as Alzheimer\u27s disease, Parkinson\u27s disease, and stroke, which significantly reduce the motor and cognitive ability of affected individuals. As these diseases become more prevalent, there is a need for further research and innovation in the field of motor rehabilitation therapy to accommodate these individuals in a cost-effective manner. In recent years, the implementation of social agents has been proposed to alleviate the burden on in-home human caregivers. Socially assistive robotics (SAR) is a new subfield of research derived from human-robot interaction that aims to provide hands-off interventions for patients with an emphasis on social rather than physical interaction. As these SAR systems are very new within the medical field, there is no standardized approach to developing such systems for different populations and therapeutic outcomes. The primary aim of this project is to provide a standardized method for developing such systems by introducing a modular human-robot interaction software framework upon which future implementations can be built. The framework is modular in nature, allowing for a variety of hardware and software additions and modifications, and is designed to provide a task-oriented training structure with augmented feedback given to the user in a closed-loop format. The framework utilizes the ROS (Robot Operating System) middleware suite which supports multiple hardware interfaces and runs primarily on Linux operating systems. These design requirements are validated through testing and analysis of two unique implementations of the framework: a keyboard input reaction task and a reaching-to-grasp task. These implementations serve as example use cases for the framework and provide a template for future designs. This framework will provide a means to streamline the development of future SAR systems for research and rehabilitation therapy

Functional Derivative of the Zero Point Energy Functional from the Strong Interaction Limit of Density Functional Theory

We derive an explicit expression for the functional derivative of the subleading term in the strong interaction limit expansion of the generalized Levy--Lieb functional for the special case of two electrons in one dimension. The expression is derived from the zero point energy (ZPE) functional, which is valid if the quantum state reduces to strongly correlated electrons in the strong coupling limit. The explicit expression is confirmed numerically and respects the relevant sum-rule. We also show that the ZPE potential is able to generate a bond mid-point peak for homo-nuclear dissociation and is properly of purely kinetic origin. Unfortunately, the ZPE diverges for Coulomb systems, whereas the exact peaks should be finite.Comment: 12 pages, 7 figure

Fermionic statistics in the strongly correlated limit of Density Functional Theory

Exact pieces of information on the adiabatic connection integrand $W_{\lambda}[\rho]$, which allows to evaluate the exchange-correlation energy of Kohn-Sham density functional theory, can be extracted from the leading terms in the strong coupling limit ($\lambda\to\infty$, where $\lambda$ is the strength of the electron-electron interaction). In this work, we first compare the theoretical prediction for the two leading terms in the strong coupling limit with data obtained via numerical implementation of the exact Levy functional in the simple case of two electrons confined in one dimension, confirming the asymptotic exactness of these two terms. We then carry out a first study on the incorporation of the fermionic statistics at large coupling $\lambda$, both numerical and theoretical, confirming that spin effects enter at orders $\sim e^{-\sqrt{\lambda}}$

Brassica and Sinapis Seeds in Medieval Archaeological Sites:An Example of Multiproxy Analysis for Their Identification and Ethnobotanical Interpretation

The genus Brassica includes some of the most important vegetable and oil crops worldwide. Many Brassica seeds (which can show diagnostic characters useful for species identification) were recovered from two archaeological sites in northern Italy, dated from between the Middle Ages and the Renaissance. We tested the combined use of archaeobotanical keys, ancient DNA barcoding, and references to ancient herbarium specimens to address the issue of diagnostic uncertainty. An unequivocal conventional diagnosis was possible for much of the material recovered, with the samples dominated by five Brassica species and Sinapis. The analysis using ancient DNA was restricted to the seeds with a Brassica-type structure and deployed a variant of multiplexed tandem PCR. The quality of diagnosis strongly depended on the molecular locus used. Nevertheless, many seeds were diagnosed down to species level, in concordance with their morphological identification, using one primer set from the core barcode site (matK). The number of specimens found in the Renaissance herbaria was not high; Brassica nigra, which is of great ethnobotanical importance, was the most common taxon. Thus, the combined use of independent means of species identification is particularly important when studying the early use of closely related crops, such as Brassicaceae

Protein abundance profiling of the Escherichia coli cytosol

<p>Abstract</p> <p>Background</p> <p>Knowledge about the abundance of molecular components is an important prerequisite for building quantitative predictive models of cellular behavior. Proteins are central components of these models, since they carry out most of the fundamental processes in the cell. Thus far, protein concentrations have been difficult to measure on a large scale, but proteomic technologies have now advanced to a stage where this information becomes readily accessible.</p> <p>Results</p> <p>Here, we describe an experimental scheme to maximize the coverage of proteins identified by mass spectrometry of a complex biological sample. Using a combination of LC-MS/MS approaches with protein and peptide fractionation steps we identified 1103 proteins from the cytosolic fraction of the <it>Escherichia coli </it>strain MC4100. A measure of abundance is presented for each of the identified proteins, based on the recently developed emPAI approach which takes into account the number of sequenced peptides per protein. The values of abundance are within a broad range and accurately reflect independently measured copy numbers per cell.</p> <p>As expected, the most abundant proteins were those involved in protein synthesis, most notably ribosomal proteins. Proteins involved in energy metabolism as well as those with binding function were also found in high copy number while proteins annotated with the terms metabolism, transcription, transport, and cellular organization were rare. The barrel-sandwich fold was found to be the structural fold with the highest abundance. Highly abundant proteins are predicted to be less prone to aggregation based on their length, pI values, and occurrence patterns of hydrophobic stretches. We also find that abundant proteins tend to be predominantly essential. Additionally we observe a significant correlation between protein and mRNA abundance in <it>E. coli </it>cells.</p> <p>Conclusion</p> <p>Abundance measurements for more than 1000 <it>E. coli </it>proteins presented in this work represent the most complete study of protein abundance in a bacterial cell so far. We show significant associations between the abundance of a protein and its properties and functions in the cell. In this way, we provide both data and novel insights into the role of protein concentration in this model organism.</p

Protein abundance profiling of the Escherichia coli cytosol

<p>Abstract</p> <p>Background</p> <p>Knowledge about the abundance of molecular components is an important prerequisite for building quantitative predictive models of cellular behavior. Proteins are central components of these models, since they carry out most of the fundamental processes in the cell. Thus far, protein concentrations have been difficult to measure on a large scale, but proteomic technologies have now advanced to a stage where this information becomes readily accessible.</p> <p>Results</p> <p>Here, we describe an experimental scheme to maximize the coverage of proteins identified by mass spectrometry of a complex biological sample. Using a combination of LC-MS/MS approaches with protein and peptide fractionation steps we identified 1103 proteins from the cytosolic fraction of the <it>Escherichia coli </it>strain MC4100. A measure of abundance is presented for each of the identified proteins, based on the recently developed emPAI approach which takes into account the number of sequenced peptides per protein. The values of abundance are within a broad range and accurately reflect independently measured copy numbers per cell.</p> <p>As expected, the most abundant proteins were those involved in protein synthesis, most notably ribosomal proteins. Proteins involved in energy metabolism as well as those with binding function were also found in high copy number while proteins annotated with the terms metabolism, transcription, transport, and cellular organization were rare. The barrel-sandwich fold was found to be the structural fold with the highest abundance. Highly abundant proteins are predicted to be less prone to aggregation based on their length, pI values, and occurrence patterns of hydrophobic stretches. We also find that abundant proteins tend to be predominantly essential. Additionally we observe a significant correlation between protein and mRNA abundance in <it>E. coli </it>cells.</p> <p>Conclusion</p> <p>Abundance measurements for more than 1000 <it>E. coli </it>proteins presented in this work represent the most complete study of protein abundance in a bacterial cell so far. We show significant associations between the abundance of a protein and its properties and functions in the cell. In this way, we provide both data and novel insights into the role of protein concentration in this model organism.</p

Large coupling-strength expansion of the M{\o}ller-Plesset adiabatic connection: From paradigmatic cases to variational expressions for the leading terms

We study in detail the first three leading terms of the large coupling-strength limit of the adiabatic connection that has as weak-interaction expansion the M{\o}ller-Plesset perturbation theory. We first focus on the H atom, both in the spin-polarized and the spin-unpolarized case, reporting numerical and analytical results. In particular, we derive an asymptotic equation that turns out to have simple analytical solutions for certain channels. The asymptotic H atom solution for the spin-unpolarized case is then shown to be variationally optimal for the many-electron spin-restricted closed-shell case, providing expressions for the large coupling-strength density functionals up to the third leading order. We also analyze the H2 molecule and the uniform electron gas

Enhanced river runoff and permafrost thaw affect Arctic shelf processes

Enhanced river runoff and coastal erosion are causing greater amounts of terrestrial material supply to Arctic shelf waters. Increasing freshwater export of carbon and nutrient loads from land (terr-OM) together with compositional shifts - due to changing hydrologic flow paths and permafrost thaw, can modify shelf water chemistry and biogeochemical processes. Here, we examine how shifts in land-ocean terr-OM supply may alter shelf primary productivity, respiration and ultimately net regional CO2 air–sea fluxes. Unique insights into terr-OM dynamics and composition during transit through riverine, deltaic and shelf waters were collected through multiple field campaigns on the Lena River and Laptev Sea shelf region. Harnessing these field data, we examine the effects of contemporary and future terr-OM supply to shelf waters using newly developed 1-D and 3-D regional biogeochemical models specifically capable of parameterising terr-OM, composition and degradation. In agreement with prior studies, we find that land-derived nutrients could strengthen coastal production sustaining up to ~50% of primary productivity under current terr-OM conditions. However, we also found that additional terr-OM supply caused increased light limitation in coastal waters, offsetting nutrient fertilization effects and stimulating zooplankton grazing. Model experiments indicate that future increases in terr-OM of between 25-50% and/ or shifts to more biologically reactive coastal OM -such as to be expected with permafrost thaw, will reduce net CO2 uptake and lead to positive CO2 feedback from Arctic shelf waters. Our results question the capacity of the coastal Arctic Ocean to serve as a net sink for atmospheric CO2 with future increasing land-ocean connectivity and terr-OM supply