Pathogenic Fusarium oxysporum f. sp. cepae growing inside onion bulbs emits volatile organic compounds that correlate with the extent of infection

Diseases develop during the storage of onions. To minimize losses, new methods are needed to identify diseased bulbs early in storage. Volatile organic compounds (VOCs), the respiration rate, weight loss, and the dry matter content were investigated for 1-7 weeks post inoculation of bulbs with water (control) and two strains (Fox006 or Fox260) of Fusarium oxysporum f. sp. cepae. Photos, multispectral image analysis, and real-time polymerase chain reaction (PCR) showed no infection in the control onions, weak pathogenic infection in Fox006-onions, and strong pathogenic infection in Fox260-onions at week 7 post inoculation. Infected bulbs exhibited increased respiration rate, increased VOC emission rate, and increased weight loss. The control and Fox006-onions did not respond to inoculation and had similar reaction pattern. Forty-three different VOCs were measured, of which 17 compounds had sulfur in their chemical structure. 1-Propanethiol, methyl propyl sulfide, and styrene were emitted in high concentrations and were positively correlated with the extent of infection (r = 0.82 - 0.89). Therefore, these compounds were the most promising volatile markers of Fusarium basal rot infection. For the first time, we show that the extent of fungal infection determined by real-time PCR in onion bulbs is related with VOC emission.Peer reviewe

Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach

Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.

An intelligent system for hearing disorder detection using auditory evoked potential (AEP) signals

Approximately 466 million people have different kinds of hearing impairment in the world, and 34 million are children. Brain-computer interfaces (BCIs) are systems that link the human brain to external technology, which is the best way to address these concerns. Auditory evoked potentials (AEPs) are a type of EEG signal which has been commonly employed to detect early hearing disorder

Investigation of Time-Domain and Frequency-Domain Based Features to Classify the EEG Auditory Evoked Potentials (AEPs) Responses

The auditory evoked potentials (AEPs) are a kind of electroencephalographic (EEG) signal that is produced by an acoustic stimulus from the region of the brain. The people who are unable to maintain the verbal communication and behavioral response through the sound stimulation, EEG based brain-computer interface (BCI) technology could be an effective alternative to rehabilitate their hearing ability. In this paper, the AEP responses of three distinct English words namely bed, please and sad have been recognized. The EEG features in terms of Fast Fourier Transform (FFT), power spectral density (PSD), spectral centroids, standard deviation, Log energy entropy, mean, skewness, kurtosis has been selected as a feature extraction method. Support Vector Machine (SVM), Linear discriminant analysis (LDA) and K-Nearest Neighbors (K-NN) have been employed to classify the extracted features. Among all these features, power spectral density with SVM classification has achieved the best accuracy. Different performance measures were evaluated to identify the best set of features as well as model. The best classification accuracy was demonstrated by the developed SVM model was observed as 82.86% which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses

EEG mechanism interaction to evaluate vehicle’s driver microsleep

Microsleep or more commonly known as momentary uncontrollable fall asleep in a very short period of time usually occurs between one second to fifteen seconds. In Malaysia, one of the factors that contribute to accidents is due to the microsleep factor when the driver is driving without them being aware. This factor also often occurs when driving in a tired state and traveling too long distance. Weather factors can also contribute to microsleep. Therefore, in this research, a system has been developed to detect frequency waves from the brain based on signals from electroencephalogram (EEG) electrodes to prevent drivers from experiencing microsleep and getting involved in accidents. To conduct this research, five subjects of different ages and gender were selected to collect their brainwave data using the NeuroSky Mindwave Mobile Headset device and the EegID Record application in two different situations, namely by driving the simulation in a challenging condition for 30 minutes and the second situation is by driving the simulation in a relaxed condition for 30 minutes. In addition, the use of MATLAB in this research is to pre-process the wave signal to remove unwanted noise interference. Then, a bandpass filter is used to classify and separate the signal into Theta, Alpha, and Beta waves. These three waves will be analyzed and studied based on the age and gender differences of the subjects. After the spectrum of the wave is drawn to trigger the alarm system and the steering vibration motor if microsleep is detected for some period of one to 3 seconds

A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology

Resistance and co-resistance of metallo-beta-lactamase genes in diarrheal and urinary tract pathogens in Bangladesh

Carbapenem antibiotics are the drug of choice for treating multidrug-resistant bacterial infections. Metallo-beta-lactamases (MBLs) are carbapenemase capable of hydrolyzing nearly all therapeutically available beta-lactam antibiotics. Consequently, a need to assess the frequency and phenotypic resistance phenomena of two MBL genes in diarrheal and urinary tract infections (UTIs). Samples were collected through a cross-sectional study, with MBLs genes detected via PCR. Two hundred twenty eight diarrheal bacteria were isolated from 240 samples. The most predominant pathogens were Escherichia coli (32%) and Klebsiella spp. (7%). Phenotypic resistance to amoxicillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 50.4%, 65.6%, 66.8%, 80.5%, 54.4%, 41.6%, 25.7%, 41.2%, 37.2%, and 42.9%, respectively. Total 142 UTI pathogens were obtained from 150 urine samples, with Klebsiella spp. (39%) and Escherichia coli (24%) are the major pathogens. Phenotypic resistance to amoxycillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 93.7%, 75.0%, 91.5%, 93.7%, 88.0%, 72.5%, 13.6%, 44.4%, 71.1%, and 43%, respectively. Twenty four diarrheal isolates carried either blaNDM-1 or blaVIM genes; the overall MBL gene prevalence was 10.5%. Thirty six UTI pathogens carried either blaNDM-1 or blaVIM genes (25.4%). Seven isolates carried both blaNDM-1 and blaVIM genes. MBL genes exhibited a strong association with phenotypic carbapenem and other beta-lactam antibiotic resistance. Resistance to carbapenems requires active surveillance and stewardship