Cwbr Author Interview: Appomattox: Victory, Defeat, And Freedom At The End Of The Civil War

Interview with Elizabeth Varon, the Langbourne M. Williams Professor of American History at the University of Virginia Interviewed by Michael Frawley Civil War Book Review (CWBR): Today the Civil War Book Review is proud to speak with Elizabeth Varon, the Langbourne M. Williams Professor of American History at the University of Virginia, and discuss her recent book Appomattox: Victory, Defeat, and Freedom at the End of the Civil War. Thank you for joining us today. Elizabeth Varon (EV): It is a delight to do so, thank you

Evaluacion de los analisis de suelos de las zonas productoras de yuca en Colombia

The results are given of physicochemical analyses of soils and estimated cassava yields for five production zones in Colombia. There was little correlation between yield and the factors studied. Yields tended to increase as P increased; and there was a positive response to K in 2 zones and Ca/Mg in 2 others. However, there were contradictory results with K and pH in three of the zones. Other factors affecting yield should be studied to find the reasons for this discrepancy. (CIAT

Quantification of Linear and Nonlinear Cardiorespiratory Interactions under Autonomic Nervous System Blockade

This paper proposes a methodology to extract both linear and nonlinear respiratory influences from the heart rate variability (HRV), by decomposing the HRV into a respiratory and a residual component. This methodology is based on least-squares support vector machines (LS-SVM) formulated for nonlinear function estimation. From this decomposition, a better estimation of the respiratory sinus arrhythmia (RSA) and the sympathovagal balance (SB) can be achieved. These estimates are first analyzed during autonomic blockade and an orthostatic maneuver, and then compared against the classical HRV and a model that considers only linear interactions. Results are evaluated using surrogate data analysis and they indicate that the classical HRV and the linear model underestimate the cardiorespiratory interactions. Moreover, the linear and nonlinear interactions appear to be mediated by different control mechanisms. These findings will allow to better assess the ANS and to improve the understanding of the interactions within the cardiorespiratory system

Effect of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions during Autonomic Nervous System Blockade

The Heart Rate Variability (HRV) is a noninvasive tool to evaluate the activity of the autonomic nervous system. To study the HRV, different mathematical representations can be used. The selection of a representation might have an effect on the evaluation of the mechanisms that modulate the Heart Rate (HR). One of these mechanisms is the Respiratory Sinus Arrhythmia (RSA), i.e. an increased HR during inhalation and a decreased HR during exhalation. Different methods exist to quantify the RSA. A common approach is to calculate the power in the High Frequency (HF, 0.15 - 0.4 Hz) band of the spectrum of the HRV representation. More recently proposed methods use the respiratory signals to estimate the strength of the RSA.This paper studies the effect of the HRV representations on the quantification of the RSA. To this end, an experiment is used in which the sympathetic and parasympathetic branches of the autonomic nervous system are selectively blocked. Three different HRV representations are considered. Afterwards, the strength of the RSA is estimated using three approaches, namely the spectral content in the HF band of the HRV representations, orthogonal subspace projections and a time-frequency representation.The results suggest that the selection of an HRV representation does not have a significant impact on the RSA estimates in a healthy population

A Study to Assess the Relationship between Attention Deficit Hyperactivity Disorder and Obstructive Sleep Apnea in Adults

The association between obstructive sleep apnea (OSA) and attention deficit hyperactivity disorder (ADHD) is well-established in children. However, there is a paucity of literature regarding this association in adults. The aim of this study was to determine if ADHD is more common in adult patients with OSA. All patients referred to a sleep center for sleep evaluation were administered the Adult ADHD Self-Report Scale and diagnostic polysomnogram. The ADHD screen is considered positive if 4 of 6 questions in part A of the screening questionnaire were answered abnormally. The study population consisted of 194 participants, predominantly male (62%), Caucasian (54%), and Hispanic (44%). OSA was identified in 160 (83%) of participants, with 116 (60%) having moderate to severe OSA. The ADHD screen was positive in 37 (19%) of participants. There was no significant association between the severity of OSA and presence of ADHD symptoms. Patients with OSA who screened positive for ADHD had higher Epworth Sleepiness Scale scores than those that did not. These data suggest that ADHD is more prevalent in patients with OSA, but do not demonstrate a relationship between OSA severity and ADHD symptoms. Interestingly, sleepiness is more prominent in patients with ADHD

Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery

Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emissions from oil and gas production. However, current methods lack performance calibration with ground-truth testing. This study developed a multi-band–multi-pass–multi-comparison-date methane retrieval algorithm that enhances Sentinel-2 sensitivity to methane plumes. The method was calibrated using data from a large-scale controlled-release test in Ehrenberg, Arizona, in fall 2021, with three algorithm parameters tuned based on the true emission rates. Tuned parameters are the pixel-level concentration upper-bound threshold during extreme value removal, the number of comparison dates, and the pixel-level methane concentration percentage threshold when determining the spatial extent of a plume. We found that a low value of the upper-bound threshold during extreme value removal can result in false negatives. A high number of comparison dates helps enhance the algorithm sensitivity to the plumes in the target date, but values in excess of 12 d are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quantification accuracy, but it may harm the yes/no detection accuracy. We found that there is a trade-off between quantification accuracy and detection accuracy. In a scenario with the highest quantification accuracy, we achieved the lowest quantification error and had zero false-positive detections; however, the algorithm missed three true plumes, which reduced the yes/no detection accuracy. In contrast, all of the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We illustrated a two-step method that updates the emission rate estimates in an interim step, which improves quantification accuracy while keeping high yes/no detection accuracy. We also validated the algorithm's ability to detect true positives and true negatives in two application studies.</p

Heart Rate Variability Analysis Assessment for Asthma Control Stratification

Autonomic nervous system (ANS) has been suggested to play a major role in the pathogenesis of asthma. This hypothesis has motivated large research, revealing a reduced modulation of the heart rate in subjects with uncontrolled asthma, when compared to asthmatics with controlled symptomatology. In this work, we assessed ANS activity through heart rate variability analysis in a group of asthmatics classified attending to the control of their symptoms. This information was later used for training a logistic regression classifier aiming at differentiating between the levels of control in asthmatic patients. The accuracy of the classifier improved when including ANS information (71.77%, versus 64.73% when only clinical parameters were considered), suggesting that ANS assessment could contribute to better non-invasive asthma monitoring

Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients

The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA