An Extended Jahn-Teller Hamiltonian for Large-Amplitude Motion: Application To Vibrational Conical Intersections in CH3SH and CH3OH

An extended Jahn-Teller Hamiltonian is presented for the case where the (slow) nuclear motion extends far from the symmetry point and may be described approximately as motion on a sphere. Rather than the traditional power series expansion in the displacement from the C3v symmetry point, an expansion in the spherical harmonics is employed. Application is made to the vibrational Jahn-Teller effect in CH3XH, with X = S, O, where the equilibrium CXH angles are 83° and 72°, respectively. In addition to the symmetry-required conical intersection (CI) at the C3v symmetry point, ab initio calculations reveal sets of six symmetry-allowed vibrational CIs in each molecule. The CIs for each molecule are arranged differently in the large-amplitude space, and that difference is reflected in the infrared spectra. The CIs in CH3SH are found in both eclipsed and staggered geometries, whereas those for CH3OH are found only in the eclipsed geometry near the torsional saddle point. This difference between the two molecules is reflected in the respective high-resolution spectra in the CH stretch fundamental region

Comparing methods for assessment of facial dynamics in patients with major neurocognitive disorders

International audienceAssessing facial dynamics in patients with major neurocogni-tive disorders and specifically with Alzheimers disease (AD) has shown to be highly challenging. Classically such assessment is performed by clinical staff, evaluating verbal and non-verbal language of AD-patients, since they have lost a substantial amount of their cognitive capacity, and hence communication ability. In addition, patients need to communicate important messages, such as discomfort or pain. Automated methods would support the current healthcare system by allowing for telemedicine, i.e., lesser costly and logistically inconvenient examination. In this work we compare methods for assessing facial dynamics such as talking, singing, neutral and smiling in AD-patients, captured during music mnemotherapy sessions. Specifically, we compare 3D Con-vNets, Very Deep Neural Network based Two-Stream ConvNets, as well as Improved Dense Trajectories. We have adapted these methods from prominent action recognition methods and our promising results suggest that the methods generalize well to the context of facial dynamics. The Two-Stream ConvNets in combination with ResNet-152 obtains the best performance on our dataset, capturing well even minor facial dynamics and has thus sparked high interest in the medical community

Complete Phenotypic Recovery of an Alzheimer's Disease Model by a Quinone-Tryptophan Hybrid Aggregation Inhibitor

The rational design of amyloid oligomer inhibitors is yet an unmet drug development need. Previous studies have identified the role of tryptophan in amyloid recognition, association and inhibition. Furthermore, tryptophan was ranked as the residue with highest amyloidogenic propensity. Other studies have demonstrated that quinones, specifically anthraquinones, can serve as aggregation inhibitors probably due to the dipole interaction of the quinonic ring with aromatic recognition sites within the amyloidogenic proteins. Here, using in vitro, in vivo and in silico tools we describe the synthesis and functional characterization of a rationally designed inhibitor of the Alzheimer's disease-associated β-amyloid. This compound, 1,4-naphthoquinon-2-yl-L-tryptophan (NQTrp), combines the recognition capacities of both quinone and tryptophan moieties and completely inhibited Aβ oligomerization and fibrillization, as well as the cytotoxic effect of Aβ oligomers towards cultured neuronal cell line. Furthermore, when fed to transgenic Alzheimer's disease Drosophila model it prolonged their life span and completely abolished their defective locomotion. Analysis of the brains of these flies showed a significant reduction in oligomeric species of Aβ while immuno-staining of the 3rd instar larval brains showed a significant reduction in Aβ accumulation. Computational studies, as well as NMR and CD spectroscopy provide mechanistic insight into the activity of the compound which is most likely mediated by clamping of the aromatic recognition interface in the central segment of Aβ. Our results demonstrate that interfering with the aromatic core of amyloidogenic peptides is a promising approach for inhibiting various pathogenic species associated with amyloidogenic diseases. The compound NQTrp can serve as a lead for developing a new class of disease modifying drugs for Alzheimer's disease

Internet of things controlled home objects for the elderly

Abstract The number of elderly people suffering from physical or cognitive difficulty is increasing continuously. Elderly people prefer to live in their familiar environment where they can easily perform different activities of their daily life which is also good for their mental and physical well-being. Internet of Things is a mechanism through which any objects can be monitored, controlled, and manipulated. In order to develop efficient application for the elderly living at home independently, the researcher should be aware of the home objects as well as of the living environment. This study uses systematic literature review to determine applications developed to assist elderly people inside their home. A total of 25 primary studies are identified. With the analysis of those studies, important and relevant objects in the daily life of the elderly are identified. Using the results from the review, a new scenario of home environment is visualized. The visualization is expected to provide caret akers with a better view of the living condition of the elderly and position and state of the home objects. This new home scenario is expected to offer a secure and easy living environment for the elderly, where Internet of Things can be used to control all the frequently used home objects by the elderly

Automatic Identification of Behavior Patterns in Mild Cognitive Impairments and Alzheimer's Disease Based on Activities of Daily Living

The growing number of older adults worldwide places high pressure on identifying dementia at its earliest stages so that early management and intervention strategies could be planned. In this study, we proposed a machine learning based method for automatic identification of behavioral patterns of people with mild cognitive impairments and Alzheimer's disease through the analysis of data related to their activities of daily living collected in two smart homes environments. Our method employs first a feature selection technique to extract relevant features for classification and reduce the dimensionality of the data. Then, the output of the feature selection is fed into a random forest classifier for classification. We recruited three groups of participants in our study: healthy older adults, older adults with mild cognitive impairments and older adults with Alzheimer's disease. We conducted extensive experiments to validate our proposed method. We experimentally showed that our method outperforms state-of-the-art machine learning algorithms

Human Fall Detection by Using an Innovative Floor Acoustic Sensor

Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for elderly people is due to the consequences of a fall. In this paper, we propose a human fall classification system based on an innovative floor acoustic sensor able to capture the acoustic waves transmitted through the floor. The algorithm employed is able to discriminate human falls from non falls and it is based on Mel-Frequency Cepstral Coefficients and a two class Support Vector Machine. The dataset employed for performance evaluation is composed by falls of a human mimicking doll, everyday objects and everyday noises. The obtained results show that the proposed solution is suitable for human fall detection in realistic scenarios, allowing to guarantee a 0% miss probability at very low false positive rates

Synthesis and Pharmacokinetic Evaluation of Siderophore Biosynthesis Inhibitors for Mycobacterium tuberculosis

MbtA catalyzes the first committed biosynthetic step of the mycobactins, which are important virulence factors associated with iron acquisition in Mycobacterium tuberculosis. MbtA is a validated therapeutic target for antitubercular drug development. 5′-<i>O</i>-[<i>N</i>-(Salicyl)­sulfamoyl]­adenosine (<b>1</b>) is a bisubstrate inhibitor of MbtA and exhibits exceptionally potent biochemical and antitubercular activity. However, <b>1</b> suffers from suboptimal drug disposition properties resulting in a short half-life (<i>t</i>_1/2), low exposure (AUC), and low bioavailability (<i>F</i>). Four strategies were pursued to address these liabilities including the synthesis of prodrugs, increasing the p<i>K</i>_a of the acyl-sulfonyl moiety, modulation of the lipophilicity, and strategic introduction of fluorine into <b>1</b>. Complete pharmacokinetic (PK) analysis of all compounds was performed. The most successful modifications involved fluorination of the nucleoside that provided substantial improvements in <i>t</i>_1/2 and AUC. Increasing the p<i>K</i>_a of the acyl-sulfonyl linker yielded incremental enhancements, while modulation of the lipophilicity and prodrug approaches led to substantially poorer PK parameters