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In the U.S. and Canada, Fusarium graminearum (Fg) is the predominant etiological agent of Fusarium head blight (FHB), an economically devastating fungal disease of wheat and other small grains. Besides yield losses, FHB leads to grain contamination with trichothecene mycotoxins that are harmful to plant, human, and livestock health. Three genetic North American populations of Fg, differing in their predominant trichothecene chemotype (i.e., NA1/15ADON, NA2/3ADON, and NA3/NX-2), have been identified. To improve our understanding of the newly discovered population NA3 and how population-level diversity influences FHB outcomes, we inoculated heads of the moderately resistant wheat cultivar Alsen with 15 representative strains from each population and evaluated disease progression, mycotoxin accumulation, and mycotoxin production per unit Fg biomass. Additionally, we evaluated population-specific differences in induced host defense responses. The NA3 population was significantly less aggressive than the NA1 and NA2 populations but posed a similar mycotoxigenic potential. Multi-omics analyses revealed patterns in mycotoxin production per unit Fg biomass, expression of Fg aggressiveness-associated genes, and host defense responses that did not always correlate with the NA3-specific severity difference. Our comparative disease assay of NA3/NX-2 and admixed NA1/NX-2 strains indicated that the reduced NA3 aggressiveness is not due solely to the NX-2 chemotype. Notably, the NA1 and NA2 populations did not show a significant advantage over NA3 in perithecia production, a fitness-related trait. Together, our data highlight that the disease outcomes were not due to mycotoxin production or host defense alone, indicating that other virulence factors and/or host defense mechanisms are likely involved.

Sleep disturbance and cognitive impairment now represent two of the most common and debilitating conditions facing seropositive (HIV+) individuals who are otherwise well controlled with antiretroviral therapy. Sleep-assessment-based biomarkers represent an important step towards improving understanding the unique mechanistic features that may link sleep disruption and cognition in HIV+ individuals and can ultimately advance early detection and treatment opportunities in this cohort. In this study, a risk score was computed via a generalized linear model (GLM), which optimally combines polysomnography (PSG) features extracted from EEG, EMG, and EOG signals, to distinguish 18 HIV+ Black male individuals with and without cognitive impairment. The optimal set of features was identified via the least absolute shrinkage and selection operator (LASSO) approach, and the risk separation between the two groups, i.e., cognitively normal and cognitive impaired, was significant (and has a P-value < .001). Interestingly, the optimal set of features were all EEG derived and sleep stage-specific. These preliminary findings suggest that sleep-based EEG markers may be used as a diagnostic and prognostic for cognition in HIV+ patients.

Use of pressure sensor arrays as an unobtrusive way of monitoring physiological characteristics of human beings is a growing field of research. In such pressure sensor arrays, monitoring respiratory signal during movement is a challenge that researchers and engineers are currently faced with. This paper presents an improved method to reliably find breathing rate during movement using unobtrusive bed-based pressure-sensor array. We use spectral flatness ratio and signal variance to select the most powerful sensors for which breathing dominates the signals. We also apply movement detection prior to breathing rate estimation based on a recently developed movement detection algorithm in order to minimize movement effects. The proposed method was applied to nocturnal data collected from a male subject and a female subject, and performance is analyzed. Our results show that this scheme can lead to a higher reliability of estimate even when more than 50% of data are corrupted with movement.

According to the American Academy of Sleep Medicine (AASM) 2007 scoring manual, the sum of dual respiratory inductive plethysmography signals, RIP-sum, can be used as an alternative signal instead of airflow signal to detect sleep apnea events. In this study, a new method is proposed to detect apnea-hypopnea events using the RIP-sum signal. An event-based metric is used to evaluate the results of the proposed method. The results showed that the RIP-sum signal could be a reliable alternative signal to detect sleep apnea-hypopnea events using the proposed method.

Use of automated and unobtrusive sensors for physiological monitoring has become popular nowadays, since no devices need to be worn by individuals and it does not require any user interaction. However, when bodily movements occur, movement artifacts are introduced which can interfere with the breathing signal. This paper proposes a method to automatically identify movement onset and offset times when using an unobtrusive bed-based pressure-sensor array. This work makes use of a previously developed method for movement detection based on control levels. The novel contribution of this paper is employing an adaptive window length to calculate a moving average and a moving variance, by measuring the distance between two consecutive peaks in the signal which relates to consecutive movements. We also impose a threshold based on the weight and height of an individual to flag true movements and discard false ones. The proposed method is applicable for different postures and breath patterns of the bed occupant. Our experimental results show that the proposed scheme can lead to an average movement detection offset as low as 1.32 second, with no false-positive events and low false-negatives, and it provides significant improvements compared to a previous method.

One of the non-invasive ways to measure respiratory effort is in-bed pressure sensor arrays. Based on the area of the bed and the sensor array covered by a patient's body, some sensors may not include significant respiratory effort components or may have low signal to noise ratios. When combining signals from the different sensors, this can produce a low quality output signal. Signal combiners can overcome this problem. This paper describes two different methods of signal combining to achieve a good estimation of the respiratory rate and the respiratory signal itself. To assess the performance, a participant was asked to lay on the bed in supine position while having normal breathing. Our results indicate that both methods can perform very satisfactorily when compared to a gold standard signal, and that they can outperform some previously published methods.

Person authentication based on only the name, password or person identification number is not secured enough. In recent years researchers have focused on human physiological and behavioral parameters, because these parameters are more unique and human-specific than traditional ones. This approach of person authentication is usually called biometric authentication. Signature is the most commonly used behavioral biometric which is investigated in two ways of online and offline by researchers. In online procedure, the temporal indices of signature such as signing velocity, and acceleration are involved to increase the accuracy relative to offline methods and to recognize counterfeit signatures. In this work, a unimodal authentication system based on the sounds of the signing of thirty persons is proposed. Signals are analyzed in online approach. Four different types of features based on cepstrum analysis and parametric models are extracted and classified with three different distance based classifiers. For the evaluation of the proposed authentication system, 10×10 fold cross-validation method is used and the results are reported in terms of False Accept Rate (FAR) and False Reject Rate (FRR) metrics.