The process of decline in advanced activities of daily living: a qualitative explorative study in mild cognitive impairment

Background: The notion of "minimal impairment in instrumental activities of daily living (i-ADL)" is important in the diagnosis of mild cognitive impairment (MCI), but is presently not adequately operationalized. ADL is stratified according to difficulty, complexity, and also to vulnerability to early cognitive changes in a threefold hierarchy: basic activities of daily living (b-ADL), i-ADL, and advanced activities of daily living (a-ADL). This study aims to gain a deeper understanding of the functional decline in the process of MCI. Methods: In a qualitative design, 37 consecutive patients diagnosed with amnestic (a)-MCI and their proxies were interviewed at two geriatric day hospitals. Constant comparative analysis was used for the analysis. Results: The a-ADL-concept emerged as important in the diagnosis of MCI. All participants were engaged in a wide range of activities, which could be clustered according to the International Classification of Functioning, Disability and Health (ICF). Participants reported subtle difficulties in performance. A process of functional decline was identified in which adaptation and coping mechanisms interacted with the process of reduced skills, leading to an activity disruption and an insufficiency in functioning. Conclusion: This study asserts the inclusion of an evaluation of a-ADL in the assessment of older persons. When evaluating ADL at three levels (b-ADL, i-ADL, and a-ADL), all the activities one can perform in daily living are covered

Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set

A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20- and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov–Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.45% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications

Mode-medium instability and its correction with a Gaussian reflectivity mirror

A high power CO2 laser beam is known to deteriorate after a few microseconds due to a mode-medium instability (MMI) which results from an intensity dependent heating rate related to the vibrational-to-translational decay of the upper and lower CO2 lasing levels. An iterative numerical technique is developed to model the time evolution of the beam as it is affected by the MMI. The technique is used to study the MMI in an unstable CO2 resonator with a hard-edge output mirror for different parameters like the Fresnel number and the gas density. The results show that the mode of the hard edge unstable resonator deteriorates because of the diffraction ripples in the mode. A Gaussian-reflectivity mirror was used to correct the MMI. This mirror produces a smoother intensity profile which significantly reduces the effects of the MMI. Quantitative results on peak density variation and beam quality are presented

Lineshape of the thermopower of quantum dots

Quantum dots are an important model system for thermoelectric phenomena, and may be used to enhance the thermal-to-electric energy conversion efficiency in functional materials. It is therefore important to obtain a detailed understanding of a quantum-dot's thermopower as a function of the Fermi energy. However, so far it has proven difficult to take effects of co-tunnelling into account in the interpretation of experimental data. Here we show that a single-electron tunnelling model, using knowledge of the dot's electrical conductance which in fact includes all-order co-tunneling effects, predicts the thermopower of quantum dots as a function of the relevant energy scales, in very good agreement with experiment.Comment: 10 pages, 5 figure