Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

A new class of glycomimetic drugs to prevent free fatty acid-induced endothelial dysfunction

Background: Carbohydrates play a major role in cell signaling in many biological processes. We have developed a set of glycomimetic drugs that mimic the structure of carbohydrates and represent a novel source of therapeutics for endothelial dysfunction, a key initiating factor in cardiovascular complications. Purpose: Our objective was to determine the protective effects of small molecule glycomimetics against free fatty acid­induced endothelial dysfunction, focusing on nitric oxide (NO) and oxidative stress pathways. Methods: Four glycomimetics were synthesized by the stepwise transformation of 2,5­dihydroxybenzoic acid to a range of 2,5­substituted benzoic acid derivatives, incorporating the key sulfate groups to mimic the interactions of heparan sulfate. Endothelial function was assessed using acetylcholine­induced, endotheliumdependent relaxation in mouse thoracic aortic rings using wire myography. Human umbilical vein endothelial cell (HUVEC) behavior was evaluated in the presence or absence of the free fatty acid, palmitate, with or without glycomimetics (1µM). DAF­2 and H2DCF­DA assays were used to determine nitric oxide (NO) and reactive oxygen species (ROS) production, respectively. Lipid peroxidation colorimetric and antioxidant enzyme activity assays were also carried out. RT­PCR and western blotting were utilized to measure Akt, eNOS, Nrf­2, NQO­1 and HO­1 expression. Results: Ex vivo endothelium­dependent relaxation was significantly improved by the glycomimetics under palmitate­induced oxidative stress. In vitro studies showed that the glycomimetics protected HUVECs against the palmitate­induced oxidative stress and enhanced NO production. We demonstrate that the protective effects of pre­incubation with glycomimetics occurred via upregulation of Akt/eNOS signaling, activation of the Nrf2/ARE pathway, and suppression of ROS­induced lipid peroxidation. Conclusion: We have developed a novel set of small molecule glycomimetics that protect against free fatty acidinduced endothelial dysfunction and thus, represent a new category of therapeutic drugs to target endothelial damage, the first line of defense against cardiovascular disease

Text classification for spoken dialogue systems

The main objective of this thesis is the application and evaluation of text classification approaches for speech-based utterance classification problems in the field of advanced spoken dialogue system (SDS) design. SDSs are speech-based human-machine interfaces that may be applied in various domains. A novel generation of SDSs should be multi-domain and user-adaptive. Designing of multi-domain user-adaptive SDSs is related to some utterance classification problems: domain detection of user utterances and user state recognition including user verbal intelligence and emotion recognition. Text classification approaches may be applied for the considered problems. Text classification consists of the following stages: feature extraction, term weighting, dimensionality reduction, and machine learning. The thesis has three aims: 1. To identify the best combinations of state-of-the-art text classification approaches for the considered utterance classification problems. 2. To improve utterance classification performance for SDSs. 3. To improve computational performance of utterance classification for SDSs. For the first aim, different term weighting methods (IDF, CW, GR, TM2, RF, TRR, and NTW), different dimensionality reduction methods („stop-word“ filtering in combination with stemming, weight-based feature selection, feature transformation based on term clustering), and different machine learning algorithms (k-NN, SVM, the Rocchio classifier) have been validated on different datasets including two corpora for domain detection of user utterances, two corpora for verbal intelligence recognition, and a corpus for text-based user emotion recognition. The best combinations of the text classification approaches were identified as follows: - For domain detection of user utterances: k-NN + TRR or SVM + IDF with feature transformation based on term clustering. - For user verbal intelligence: k-NN + CW. The CW method for verbal intelligence recognition provides a small number of useful terms that characterize only a class of higher verbal intelligence. It seems to be easier to recognize verbal intelligence in dialogues than in monologues - For text-based emotion recognition: k-NN + NTW without dimensionality reduction. Emotion recognition based on linguistic information does not demonstrate high performance in comparison with audio-based and video-based emotion recognition. The novel approaches were proposed and tested for achieving the second and the third aims of the thesis: - Collectives of different term weighting methods. This approach allows to make use of the advantages of different term weighting methods. Collectives of term weighting methods based on the majority voting procedure may significantly improve the classification performance of utterance classification with k-NN algorithm. These results may be again significantly improved by weighted voting with an optimization based on the self-adjusting genetic algorithm. - Novel feature transformation based on term belonging to classes. It significantly reduces the dimensionality: it equals to the number of classes. The novel feature transformation significantly improves the computational performance of utterance classification in terms of computational time. The novel feature transformation method is especially effective in combination with the collectives of term weighting methods. The simultaneous use of two novel approaches may significantly improve both the classification results and the computational performance. - Novel approach to neural network structure optimization. The novel approach has a simplified ANN structure representation, requires less computational resource, and has fewer parameters for tuning than the baseline approach. Additionally, the results of the novel approach to ANN structure optimization may be improved with feature selection based on the wrapper. The wrapper may be performed by the self-adjusting GA. The novel approach to ANN design significantly improves the classification results and the computational performance of utterance classification with ANN. Therefore, the novel approaches lead to improve the classification performance of utterance classification (the second aim of the thesis) and the computational performance of utterance classification (the third aim of the thesis) as well

Определение темы для маршрутизации вызовов на естественном языке на основе коллективов методов взвешивания термов

Natural language call routing is an important data analysis problem which can be applied in different do- mains including airspace industry. This paper presents the investigation of collectives of term weighting methods for natural language call routing based on text classification. The main idea is that collectives of different term weighting methods can provide classification effectiveness improvement with the same classification algorithm. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. After that different combinations of term weighting methods were formed as collectives. Two approaches for the handling of the collectives were considered: the meta-classifier based on the rule induction and the majority vote procedure. The nu- merical experiments have shown that the best result is provided with the vote of all seven different term weighting methods. This combination provides a significant increasing of classification effectiveness in comparison with the most effective term weighting methodsМаршрутизация вызовов на естественном языке – актуальная задача анализа данных, которая может найти применение в различных областях, включая аэрокосмическую индустрию. В ста- тье представлено исследование коллективов методов взвешивания термов для машрутизации вызовов на естественном языке на основе классификации текста. Основная идея предлагаемого подхода заключается в том, что коллективы методов взвешивания термов могу обеспечить по- вышение эффективности классификации при использовании одного и того же алгоритма класси- фикации. Семь различных методов взвешивания термов были протестированы и сравнены между собой с использованием метода ближайших соседей в качестве алгоритма классификации. После этого были сформированы различные комбинации методов взвешивания термов для дальнейшего использования в коллективных решающих правилах. Рассмотрено два подхода для формирования коллективных решающих правил: мета-классификатор на основе индукции правил и голосование простым большинством. Численные исследования показали, что наилучший результат дости- гается при включении всех семи рассматриваемых методов взвешивания термов в коллективное решающее правило на основе голосования простым большинством. Такая комбинация обеспечи- вает статистически значимое улучшение эффективности классификации в сравнении с лучшим по эффективности отедльным методом взвешивания термо

Mathematical Modeling of Induction Heating of Waveguide Path Assemblies during Induction Soldering

The waveguides used in spacecraft antenna feeders are often assembled using external couplers or flanges subject to further welding or soldering. Making permanent joints by means of induction heating has proven to be the best solution in this context. However, several physical phenomena observed in the heating zone complicate any effort to control the process of making a permanent joint by induction heating; these phenomena include flux evaporation and changes in the emissivity of the material. These processes make it difficult to measure the temperature of the heating zone by means of contactless temperature sensors. Meanwhile, contact sensors are not an option due to the high requirements regarding surface quality. Besides, such sensors take a large amount of time and human involvement to install. Thus, it is a relevant undertaking to develop mathematical models for each waveguide assembly component as well as for the entire waveguide assembly. The proposed mathematical models have been tested by experiments in kind, which have shown a great degree of consistency between model-derived estimates and experimental data. The paper also shows how to use the proposed models to test and calibrate the process of making an aluminum-alloy rectangular tube flange waveguide by induction soldering. The Russian software, SimInTech, was used in this research as the modeling environment. The approach proposed herein can significantly lower the labor and material costs of calibrating and testing the process of the induction soldering of waveguides, whether the goal is to adjust the existing process or to implement a new configuration that uses different dimensions or materials

Определение темы для маршрутизации вызовов на естественном языке на основе коллективов методов взвешивания термов

Natural language call routing is an important data analysis problem which can be applied in different do- mains including airspace industry. This paper presents the investigation of collectives of term weighting methods for natural language call routing based on text classification. The main idea is that collectives of different term weighting methods can provide classification effectiveness improvement with the same classification algorithm. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. After that different combinations of term weighting methods were formed as collectives. Two approaches for the handling of the collectives were considered: the meta-classifier based on the rule induction and the majority vote procedure. The nu- merical experiments have shown that the best result is provided with the vote of all seven different term weighting methods. This combination provides a significant increasing of classification effectiveness in comparison with the most effective term weighting methodsМаршрутизация вызовов на естественном языке – актуальная задача анализа данных, которая может найти применение в различных областях, включая аэрокосмическую индустрию. В ста- тье представлено исследование коллективов методов взвешивания термов для машрутизации вызовов на естественном языке на основе классификации текста. Основная идея предлагаемого подхода заключается в том, что коллективы методов взвешивания термов могу обеспечить по- вышение эффективности классификации при использовании одного и того же алгоритма класси- фикации. Семь различных методов взвешивания термов были протестированы и сравнены между собой с использованием метода ближайших соседей в качестве алгоритма классификации. После этого были сформированы различные комбинации методов взвешивания термов для дальнейшего использования в коллективных решающих правилах. Рассмотрено два подхода для формирования коллективных решающих правил: мета-классификатор на основе индукции правил и голосование простым большинством. Численные исследования показали, что наилучший результат дости- гается при включении всех семи рассматриваемых методов взвешивания термов в коллективное решающее правило на основе голосования простым большинством. Такая комбинация обеспечи- вает статистически значимое улучшение эффективности классификации в сравнении с лучшим по эффективности отедльным методом взвешивания термо