Refractory and Resistant Hypertension: Antihypertensive Treatment Failure versus Treatment Resistance

Calhoun DA 대한심장학회 2016 Korean Circulation Journal Vol.46 No.5

Resistant hypertension has for many decades been defined as difficult-to-treat hypertension in order to identify patients who may benefit from special diagnostic and/or therapeutic considerations. Recently, the term “refractory hypertension” has been proposed as a novel phenotype of antihypertensive failure, that is, patients whose blood pressure cannot be controlled with maximal treatment. Early studies of this phenotype indicate that it is uncommon, affecting less than 5% of patients with resistant hypertension. Risk factors for refractory hypertension include obesity, diabetes, chronic kidney disease, and especially, being of African origin. Patients with refractory are at high cardiovascular risk based on increased rates of known heart disease, prior stroke, and prior episodes of congestive heart failure. Mechanisms of refractory hypertension need exploration, but early studies suggest a possible role of heightened sympathetic tone as evidenced by increased office and ambulatory heart rates and higher urinary excretion of norepinephrine compared to patients with controlled resistant hypertension. Important negative findings argue against refractory hypertension being fluid dependent as is typical of resistant hypertension, including aldosterone levels, dietary sodium intake, and brain natriuretic peptide levels being similar or even less than patients with resistant hypertension and the failure to control blood pressure with use of intensive diuretic therapy, including both a long-acting thiazide diuretic and a mineralocorticoid receptor antagonist. Further studies, especially longitudinal assessments, are needed to better characterize this extreme phenotype in terms of risk factors and outcomes and hopefully to identify effective treatment strategies.

Ensuring quiet operation in a dry scroll vacuum pump

John CALHOUN,James MOORE,Ronald FORNI 한국진공학회 2016 한국진공학회 학술발표회초록집 Vol.2016 No.8

FHMATM PRESENTATION OUTLINE

George Calhoun 한국통신학회 1996 한국통신학회 세미나 Vol.- No.-

FHMATM INFRASTRUCTURE HARDWARE

George Calhoun 한국통신학회 1996 한국통신학회 세미나 Vol.- No.-

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Kim, J.,Calhoun, V.D.,Shim, E.,Lee, J.H. Academic Press 2016 NeuroImage Vol.124 No.1

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L<SUB>1</SUB>-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L<SUB>1</SUB>-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L<SUB>1</SUB>-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.

Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems

Engel, Gregory S.,Calhoun, Tessa R.,Read, Elizabeth L.,Ahn, Tae-Kyu,Manč,al, Tomá,š,Cheng, Yuan-Chung,Blankenship, Robert E.,Fleming, Graham R. Nature Publishing Group 2007 Nature Vol.446 No.7137

Photosynthetic complexes are exquisitely tuned to capture solar light efficiently, and then transmit the excitation energy to reaction centres, where long term energy storage is initiated. The energy transfer mechanism is often described by semiclassical models that invoke ‘hopping’ of excited-state populations along discrete energy levels. Two-dimensional Fourier transform electronic spectroscopy has mapped these energy levels and their coupling in the Fenna–Matthews–Olson (FMO) bacteriochlorophyll complex, which is found in green sulphur bacteria and acts as an energy ‘wire’ connecting a large peripheral light-harvesting antenna, the chlorosome, to the reaction centre. The spectroscopic data clearly document the dependence of the dominant energy transport pathways on the spatial properties of the excited-state wavefunctions of the whole bacteriochlorophyll complex. But the intricate dynamics of quantum coherence, which has no classical analogue, was largely neglected in the analyses—even though electronic energy transfer involving oscillatory populations of donors and acceptors was first discussed more than 70 years ago, and electronic quantum beats arising from quantum coherence in photosynthetic complexes have been predicted and indirectly observed. Here we extend previous two-dimensional electronic spectroscopy investigations of the FMO bacteriochlorophyll complex, and obtain direct evidence for remarkably long-lived electronic quantum coherence playing an important part in energy transfer processes within this system. The quantum coherence manifests itself in characteristic, directly observable quantum beating signals among the excitons within the Chlorobium tepidum FMO complex at 77 K. This wavelike characteristic of the energy transfer within the photosynthetic complex can explain its extreme efficiency, in that it allows the complexes to sample vast areas of phase space to find the most efficient path.

Text-independent Speaker Identification Using Soft Bag-of-Words Feature Representation

Jiang, Shuangshuang,Frigui, Hichem,Calhoun, Aaron W. Korean Institute of Intelligent Systems 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.4

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

Cross-peak-specific two-dimensional electronic spectroscopy

Read, E. L.,Engel, G. S.,Calhoun, T. R.,Mancal, T.,Ahn, T. K.,Blankenship, R. E.,Fleming, G. R. National Academy of Sciences 2007 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF Vol.104 No.36

Text-independent Speaker Identification Using Soft Bag-of-Words Feature Representation

Shuangshuang Jiang,Hichem Frigui,Aaron W. Calhoun 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.4

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Jang, Hojin,Plis, Sergey M.,Calhoun, Vince D.,Lee, Jong-Hwan Elsevier 2017 NeuroImage Vol.145 No.2

<P><B>Abstract</B></P> <P>Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Deep neural network (DNN) was proposed to classify fMRI volume of sensorimotor tasks. </LI> <LI> DNN weights were optimized via non-zero value percentage in cross-validation (CV) framework. </LI> <LI> Classification performance was superior from three-layer DNN than one-/two-/four-layer DNNs. </LI> <LI> Weight/hidden representations were highly task-specific from higher than lower hidden layers. </LI> <LI> Sparsity of weights between the input and first hidden layer enhanced the performance. </LI> </UL> </P>