Longitudinal PTSD network structure: Measuring PTSD symptom networks over a five year period

Background: Network modeling has been applied in a range of trauma exposed samples, yet results are limited by an over reliance on cross-sectional data. The current analyses used posttraumatic stress disorder (PTSD) symptom data collected over a five-year period to estimate a more robust between-subject network and an associated symptom change network. Methods: A PTSD symptom network is measured in a sample of military veterans across four time points (Ns = 1254, 1231, 1106, 925). The repeated measures permits isolating between-subject associations by limiting effects of within-subject variability. The result is a highly reliable PTSD symptom network. A symptom slope network depicting covariation of symptom change over time is also estimated. Results: Negative trauma-related emotions had particularly strong associations with the network. Trauma-related amnesia, sleep disturbance, and self-destructive behavior had weaker overall associations with other PTSD symptoms. Conclusions: PTSD’s network structure appears stable over time. There is no single “most important” node or node cluster. The relevance of self-destructive behavior, sleep disturbance, and trauma-related amnesia to the PTSD construct may deserve additional consideration.

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

Loop-free tensor networks for high-energy physics

This brief review introduces the reader to tensor network methods, a powerful theoretical and numerical paradigm spawning from condensed matter physics and quantum information science and increasingly exploited in different fields of research, from artificial intelligence to quantum chemistry. Here, we specialize our presentation on the application of loop-free tensor network methods to the study of high-energy physics problems and, in particular, to the study of lattice gauge theories where tensor networks can be applied in regimes where Monte Carlo methods are hindered by the sign problem. This article is part of the theme issue ‘Quantum technologies in particle physics’.

An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).

Taxonomic and genetic assessment of captive White-Handed Gibbons (Hylobates lar) in Peninsular Malaysia with implications towards conservation translocation and reintroduction programmes

Conservation translocation and reintroduction for the purpose of repopulating and reinforcing extirpated or depleted populations has been recognised as an important conservation tool, particularly for gibbon conservation in the immediate future. Feasibility assessments involving multiple factors, including taxonomic and genetic assessment of rescued and captive gibbons, are imperative prior to translocation and reintroduction programmes. In this study, we attempt to determine the subspecies and origin of captive Hylobates lar, White-handed gibbons, from Peninsular Malaysia to assist in future translocation and reintroduction programmes. A total of 12 captive and rescued H. lar samples were analysed using the control region segment of mitochondrial DNA. Sequence analyses and phylogenetic trees constructed using neighbour-joining, maximum likelihood, Bayesian inference, and network methods congruently differentiate all 12 captive individuals used in this study from other H. lar subspecies suggesting that these individuals belong to the H. lar lar subspecies. In addition, two populations of H. l. lar were observed: (1) a southern population consisting of all 12 individuals from Peninsular Malaysia, and (2) a possible northern population represented by three individuals (from previous studies), which might have originated from the region between the Isthmus of Kra, Surat Thani-Krabi depression, and Kangar-Pattani. Our findings suggest that the complete control region segment can be used to determine the subspecies and origin of captive H. lar.

A Survey of Recent Indoor Localization Scenarios and Methodologies

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

Improvement of methods of hydrological forecasting using geoinformation technologies

This article discusses the possibility of improving hydrological forecasting methods based on a neural network. The hydrological series, its importance and forecasting features are considered. For hydrological forecasting using the MapInfoProfessional geoinformation system, an electronic map has been developed containing information about the rivers of Russia, as well as gauging stations on the Ob River. The electronic map is the basis for creating a module for short-term hydrological forecasting based on an artificial neural network. The features of a neural network, methods of its training and implementation are considered. The developed artificial neural network is a layer of neurons with a linear activation function and a delay line at the input. To predict the levels of hydrological series, real water levels at gauging stations of the Ob River in the Novosibirsk region will be used. The developed module and its capabilities have been tested. The study was carried out on the basis of models of hydrological series, as well as on the basis of levels of real hydrological series. Based on the study, dependence of the root-mean-square error on the number of previous values of series was revealed. The study also shows that it is possible to use a neural network for the current one-step forecasting of levels of hydrological series in conditions of insufficient information about the runoff region and its characteristics.