ERP Detection Based on Smoothness Priors

<div>Abstract—Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in poststimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method outperforms the competing algorithms and improves the classification accuracy. Conclusion: The proposed SGLRT could be employed as a powerful means for different ERP detection schemes. Significance: ERP-based systems (e.g. brain-machine interfaces) mainly suffer from lack of classification accuracy, hence the proposed method is an important step toward real-life applicability of these systems.</div>

ERP Detection Based on Smoothness Priors

<div>Abstract—Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in poststimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method outperforms the competing algorithms and improves the classification accuracy. Conclusion: The proposed SGLRT could be employed as a powerful means for different ERP detection schemes. Significance: ERP-based systems (e.g. brain-machine interfaces) mainly suffer from lack of classification accuracy, hence the proposed method is an important step toward real-life applicability of these systems.</div>

Effect of probing signal on the output signals spectrum of nonlinear spin waves in a cross based on waveguides of iron-yttrium garnet film

Subject. A change in the spectrum of spin waves (SW) in a magnetic cross is investigated when two signals pass through it: a pump signal and a probe signal. Objective. Detection of specific features in formation of the spectra of the output signals of SW in the multiport structure based on a yttrium iron garnet (YIG) film in the case of excitation of two magnetostatic surface waves (MSSW) simultaneously by the input antenna, where the first, with power higher than the first-order parametric instability threshold is the pump, and the second one is a probe. Methods. The experiments were performed for a cross structure from YIG film in the form of two orthogonal waveguides with the SW wire antennas placed at the ends of the waveguides, where one of the antennas on the transversely magnetized waveguide was considered as the input. Result. It was found that by choosing the probing signal frequency, one can significantly (by 10 dB) change the relative signal levels for the satellite waves at the output antennas, which are secondary MSSWs with some new frequencies and appear in the output signals spectrum as a result of the thresholdless processes of merging of parametric spin waves generated by MSSW pumping. In this case the secondary MSSWs frequencies can differ at the output antennas located on orthogonal waveguides. Discussion. The discovered effect is associated with the nonreciprocal nature of propagation of both the pumping wave and the waves generated at parametric instability condition in the structure.

Approach to Kirana Store Product Arrangement Using Machine Learning

Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.

Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics

Submesoscale motions within the stable boundary layer were detected during the Shallow Cold Pool Experiment conducted in the Colorado plains, Colorado, U.S.A. in 2012. The submesoscale motion consisted of two air layers creating a well-defined front with a sharp temperature gradient, and further-on referred to as a thermal submesofront (TSF). The semi-stationary TSFs and their advective velocities are detected and determined by the fibre-optic distributed-sensing (FODS) technique. An objective detection algorithm utilizing FODS measurements is able to detect the TSF boundary, which enables a detailed investigation of its spatio–temporal statistics. The novel approach in data processing is to conditionally average any parameter depending on the distance between a TSF boundary and the measurement location. By doing this, a spatially-distributed feature like TSFs can be characterized by point observations and processes at the TSF boundary can be investigated. At the TSF boundary, the air layers converge, creating an updraft, strong static stability, and vigorous mixing. Further, the TSF advective velocity of TSFs is an order of magnitude lower than the mean wind speed. Despite being gentle, the topography plays an important role in TSF formation. Details on generating mechanisms and implications of TSFs on the stable boundary layer are discussed in Part 2.

Microfluidic Device for the Identification of Biological Sex by Analysis of Latent Fingermark Deposits

To date, most research regarding amino acid detection and quantification in fingermarks relies on spectrometric methods. Herein, the Sakaguchi colorimetric test was adapted to a rotationally-driven microfluidic platform and used to detect and quantify arginine in fingermarks deposited by male and female donors. A red color indicates the presence of arginine in a given sample following the reaction, and the intensity of this color is linearly proportional to the concentration. Objective detection and quantification of arginine were accomplished using image analysis software (freeware) based on this colorimetric result. The mean concentrations obtained in a blind study were 96.4 ± 5.1 µM for samples from female donors and 55.3 ± 5.3 µM for samples from males. These were not statistically different from the literature values of 94.8 µM ± 12.9 µM for females (p = 0.908) and 54.0 ± 12.6 µM for males (p = 0.914), respectively (± SEM in all cases). Conversely, the experimental means from males and female samples were statistically different from each other (p < 0.001). Objective differentiation between male and female fingermark deposits was achieved in a blind study with 93% accuracy. Additionally, the method was compatible both with samples lifted from common surfaces and with magnetically-powdered samples.

Virulence Factors of Pseudomonas aeruginosa Isolated from ICU

Background: P.aeruginosa has many virulence factors which are the main reason for infection and the emergence of antibiotic resistance leading to an increase of morbidity and mortality. Currently, multidrug resistance is the hardest problem, which made it imperative to search for alternative treatment strategies. Objective: detection of some phenotypic virulence factors of P.aeruginosa isolated from ICU patients and the possibility of any antibiotic resistance related to certain virulence factors released by P. aeruginosa. Methodology: Our study was carried out on patients admitted to ICU Department in Benha University Hospital and infected with P.aeruginosa, the isolates subjected to phenotypic detection of the virulence factors: phospholipase, alkaline protease, lipase, gelatinase, esculin hydrolysis, biofilm formation, hemolysin and DNase production using specific media for each and evaluation of the antibiotic susceptability pattern using Kirby Bauer disk diffusion assay. Results: P.aeruginosa virulence factors were recorded as follow: hemolysin (70%) followed by alkaline protease (68%), phospholipase (62%), gelatinase & biofilm formation (60%) for each, lipase & bile esculin hydrolysis (54%) for each and DNase (2%).High antibiotic resistance was detected to mostly all of the used antibiotic discs. Also, presence of invasive device, prolonged hospital stay, ICU stay and higher number of virulence factors were associated with poor outcome. Conclusions: Production of different phenotypic virulence factors in high amount reflects their important role in spread of infection and pathogenicity with increased antibiotic resistance. Therefore finding anti-virulence factors as adjuvant therapy has an important role in treatment of P. aeruginosa especially MDR isolates.

Metabolic Profiling in Mice Infected with Echinococcosis

Background: Echinococcosis is a severe zoonotic parasitic disease, which seriously affects the health of the hosts. The diagnosis of echinococcosis depends on objective detection of clinical symptoms. However, the patient is often in the late stage of the disease when the symptoms appear, limiting the early diagnosis of echinococcosis. The treatment and prognosis of the patients are seriously hampered due to long-term asymptomatic latency. Metabolomics is a new discipline developed in the late 1990s. It reflects a series of biological responses in a pathophysiological process by demonstrating the changes in metabolism under the influence of internal and external factors. When the organism is invaded by pathogens, the alteration in characteristics of metabolites in cells becomes exceedingly sensitive. Here, we used a liquid chromatography with tandem mass spectrometry (LC-MS/MS) system-based metabolomics approach to determine the molecular mechanism of cystic echinococcosis (CE) and to develop an effective method for CE diagnosis. Methods: Eight-weeks-old female Balb/c mice were divided into normal and Echinococcus granulosus infection groups. To develop the Echinococcus granulosus infection model, mice were infected with protoscolex. Six weeks later, the abdomen of mice was obviously bulged. An LC-MS/MS system-based metabolomics approach was used for the analysis of the liver and feces to reveal the metabolic profiles of mice with echinococcosis. Results: We found that the metabolism of nucleotides, alkaloids, amino acids, amides, and organic acids in mice is closely interrelated with Echinococcus granulosus infectionConclusion: The metabolomics approach used in this study provides a reference for a highly sensitive and specific diagnostic and screening method for echinococcosis.