Electrocortical Activity in Older Adults Is More Influenced by Cognitive Task Complexity Than Concurrent Walking

Human cognitive-motor performance largely depends on how brain resources are allocated during simultaneous tasks. Nonetheless, little is known regarding the age-related changes in electrocortical activity when dual-task during walking presents higher complexity levels. Thus, the aim of this study was to investigate whether there are distinct changes in walking performance and electrocortical activation between young and older adults performing simple and complex upper limb response time tasks. Physically active young (23 ± 3 years, n = 21) and older adults (69 ± 5 years, n = 19) were asked to respond as fast as possible to a single stimuli or a double stimuli appearing on a touch screen during standing and walking. Response time, step frequency, step frequency variability and electroencephalographic (EEG) N200 and P300 amplitudes and latencies from frontal central and parietal brain regions were recorded. The results demonstrated that older adults were 23% slower to respond to double stimuli, whereas younger adults were only 12% slower (p 0.05). More importantly, the P300 amplitude was significantly reduced for older adults when responding to double stimuli regardless of standing or walking tasks (p < 0.05), with no changes in younger participants. Therefore, physically active older adults can attenuate potential walking deficits experienced during dual-task walking in simple cognitive tasks. However, cognitive tasks involving decision making influence electrocortical activation due to reduced cognitive resources to cope with the task demands

Searching for self-similarity in switching time and turbulent cascades in ion transport through a biochannel. A time delay asymmetry

The process of ion transport through a locust potassium channel is described by means of the Fokker-Planck equation (FPE). The deterministic and stochastic components of the process of switching between various conducting states of the channel are expressed by two coefficients, $D^{(1)}$ and $D^{(2)}$, a drift and a diffusion coefficient, respectively. The FPE leads to a Langevin equation. This analysis reveals beside the well known deterministic aspects a turbulent, cascade type of action. The (noisy-like) switching between different conducting states prevents the channel from staying in one, closed or open state. The similarity between the hydrodynamic flow in the turbulent regime and hierarchical switching between conducting states of this biochannel is discussed. A non-trivial character of $D^{(1)}$ and $D^{(2)}$ coefficients is shown, which points to different processes governing the channel's action, asymetrically depending on the history of the previously conducting states. Moreover, the Fokker-Planck and Langevin equations provide information on whether and how the statistics of the channel action change over various time scales.Comment: submitted to physica A text : 12 pages + 8 figure

Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management

Research on damage detection of road surfaces has been an active area of re-search, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure mainte-nance companies. In this paper, we make two contributions to address these issues. First, we present a large-scale road damage dataset, which includes a more balanced and representative set of damages. This dataset is composed of 18,034 road damage images captured with a smartphone, with 45,435 in-stances road surface damages. Second, we trained different types of object detection methods, both traditional (an LBP-cascaded classifier) and deep learning-based, specifically, MobileNet and RetinaNet, which are amenable for embedded and mobile and implementations with an acceptable perfor-mance for many applications. We compare the accuracy and inference time of all these models with others in the state of the art

Comparison of Genotypes and Enterotoxin Genes Between Staphylococcus aureus Isolates from Blood and Nasal Colonizers in a Korean Hospital

In this study, we investigated the genetic background of 70 Staphylococcus aureus isolates (36 methicillin-resistant S. aureus [MRSA] and 34 methicillin-susceptible S. aureus [MSSA]) obtained from blood at a Korean tertiary-care hospital, using spa typing, multilocus sequence typing, and SCCmec typing. In addition, the prevalence of enterotoxin (sea, seb, sec, sed, see, seg, seh, sei, and sek), tst, and pvl genes among the samples was assessed via polymerase chain reaction, and the results were compared with those of 95 isolates of S. aureus obtained from nasal swabs. All MRSA isolates from blood, except one, belonged to three major clones: sequence type (ST)5-MRSA-II, ST72-MRSA-II (or IVA), and ST239-MRSA-III, among which ST5-MRSA-II was the predominant clone. The prevalence of enterotoxin genes in the S. aureus isolates obtained from blood differed significantly from those from the nasal swabs for the sea, seb, sec, and seh gene. In particular, the seb and sec genes were detected exclusively in the MRSA isolates of ST5 or spa-CC002, thereby suggesting the co-adaptation of virulence genes with the genetic background and their contribution to biological fitness

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

Migraine Associated Vertigo

The interrelations of migraine and vertigo are complex, eluding a simple localization either centrally or peripherally. Spontaneous episodic vertigo, benign paroxysmal positional vertigo, and Meniere's disease all occur more frequently in patients with migraine than in those without. Family studies support a hereditary predisposition to migraine associated vertigo. In this review, we discuss definitions, epidemiology, associated syndromes, neurootological abnormalities, genetics and treatment for patients with migraine and vertigo

Overview of the Far Ultraviolet Spectroscopic Explorer Mission

The Far Ultraviolet Spectroscopic Explorer satellite observes light in the far-ultraviolet spectral region, 905 - 1187 A with high spectral resolution. The instrument consists of four coaligned prime-focus telescopes and Rowland spectrographs with microchannel plate detectors. Two of the telescope channels use Al:LiF coatings for optimum reflectivity from approximately 1000 to 1187 A and the other two use SiC coatings for optimized throughput between 905 and 1105 A. The gratings are holographically ruled to largely correct for astigmatism and to minimize scattered light. The microchannel plate detectors have KBr photocathodes and use photon counting to achieve good quantum efficiency with low background signal. The sensitivity is sufficient to examine reddened lines of sight within the Milky Way as well as active galactic nuclei and QSOs for absorption line studies of both Milky Way and extra-galactic gas clouds. This spectral region contains a number of key scientific diagnostics, including O VI, H I, D I and the strong electronic transitions of H2 and HD.Comment: To appear in FUSE special issue of the Astrophysical Journal Letters. 6 pages + 4 figure