Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform.

Early diagnosis of neurodegenerative diseases has always been a major challenge that physicians and medical practitioners face. Therefore, using any method or device that helps with prognostics is of great importance. In recent years, deep neural networks have become popular in medical fields, and the reason is that these networks can help diagnose diseases quickly and precisely. In this research, two novel models based on a CNN-LSTM network are introduced. The main goal is to classify three neurodegenerative diseases, including ALS, Parkinson's disease, and Huntington's disease, from one another and from healthy control patients using the gait signals, which are transformed into spectrogram images. In the first model, the spectrogram images derived from the gait signals are fed into a CNN-LSTM network directly. This model achieved 99.42% accuracy. In the second model, the same input data was used to be classified using a CNN-LSTM network, which uses wavelet transform as a feature extractor before the LSTM unit. During the experiments with the second model, the detail sub-bands were eliminated one by one, and the classification results were compared. Comparing these two models has shown that using the wavelet transform and, in particular, the approximation sub-bands can result in a lighter and faster prognosis with nearly 103 times fewer training parameters overall. The classification result using only approximation sub-bands was 95.37%, using three sub-bands was 94.04%, and including all sub-bands was 94.53%, which is remarkable.

Multi-Agent Credit Assignment and Bankruptcy Game for Improving Resource Allocation in Smart Cities.

In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters.

DLA-H: A Deep Learning Accelerator for Histopathologic Image Classification.

It is more than a decade since machine learning and especially its leading subtype deep learning have become one of the most interesting topics in almost all areas of science and industry. In numerous contexts, at least one of the applications of deep learning is utilized or is going to be utilized. Using deep learning for image classification is now very popular and widely used in various use cases. Many types of research in medical sciences have been focused on the advantages of deep learning for image classification problems. Some recent researches show more than 90% accuracy for breast tissue classification which is a breakthrough. A huge number of computations in deep neural networks are considered a big challenge both from software and hardware point of view. From the architectural perspective, this big amount of computing operations will result in high power consumption and computation runtime. This led to the emersion of deep learning accelerators which are designed mainly for improving performance and energy efficiency. Data reuse and localization are two great opportunities for achieving energy-efficient computations with lower runtime. Data flows are mainly designed based on these important parameters. In this paper, DLA-H and BJS, a deep learning accelerator, and its data flow for histopathologic image classification are proposed. The simulation results with the MAESTRO tool showed 756 cycles for total runtime and [Formula: see text] GFLOPS roofline throughput that is an extreme performance improvement in comparison to current general-purpose deep learning accelerators and data flows.

Pulmonary nontuberculous mycobacterial disease in Florida and association with large-scale natural disasters.

BACKGROUND: Little is known about the impact of the ecosystem disruption and its contribution on the non-tuberculosis mycobacteria (NTM) diseases (cases) rate in Florida (FL), a state with a high prevalence of NTM in the United States. We aimed to evaluate the epidemiological distribution of NTM in FL and identify its association with extreme weather events. METHODS: We used OneFlorida Clinical Research Consortium dataset and extracted data on NTM cases using ICD codes 9- CM 031.0 and ICD-10 A31 during 2012-2018. The number of hurricanes during the study period which affected FL were extracted data from the National Hurricane Center (NHC) and the National Oceanic and Atmospheric Administration (NOAA). RESULTS: Prevalence of NTM gradually increased during the study period. The rate was 2012: 14.3/100,000, 2015; 20.1/100,000 and 2018; 22.6/100,00 except in 2014 where there was an 8% decrease. The incidences were 2012; 6.5/100,00, 2015; 4.9/100,000 and in 2015; 5.4/100,000. Geographical analysis demonstrated a gradual expansion of the NTM cases in Alachua, and Marion Counties throughout the study period. Notably, the 2018 heat map showed higher prevalence of NTM in the northwestern, panhandle region of FL which had been absent in the heat maps for years 2012-2018. High number of the hurricanes was associated with the higher number of the new cases of NTM infection for years 2012, 2016-2018, while the lower number of the hurricanes was associated with the lower number of the new cases of NTM infection for years 2014-2015. CONCLUSION: The current study found the prevalence rates of NTM disease in FL rose from 2012 to 2018. A higher prevalence was seen following the hurricanes.

Proposing an ensemble learning model based on neural network and fuzzy system for keratoconus diagnosis based on Pentacam measurements.

PURPOSE: The present study was done to evaluate efficiency of an ensemble learning structure for automatic keratoconus diagnosis and to categorize eyes into four different groups based on a combination of 19 parameters obtained from Pentacam measurements. METHODS: Pentacam data from 450 eyes were enrolled in the study. Eyes were separated into training, validation, and testing sets. An ensemble system was used to analyze corneal measurements and categorize the eyes into four groups. The ensemble system was trained to consider indices from both anterior and posterior corneal surfaces. Efficiency of the ensemble system was evaluated and compared in each group. RESULTS: The best accuracy was achieved by the ensemble system with both multilayer perceptron and neuro-fuzzy system classifiers alongside the Naïve Bayes combination method. The accuracy achieved in KC versus N distinction task was equal to 98.2% with 99.1% of sensitivity and 96.2% of specificity for KC detection. The global accuracy was equal to 98.2% for classification of 4 groups, with an average sensitivity of 98.5% and specificity of 99.4%. CONCLUSION: In this study, authority of an ensemble learning system to work out intricate problems was presented. Despite using fewer parameters, herein, comparable or, in some cases, better results were obtained than methods reported in the literature. The proposed method demonstrated very good accuracy in discriminating between normal eyes and different stages of keratoconus eyes. In some cases, it was not possible to directly compare our results with the literature, due to differences in definitions of KC group as well as differences in selection of items and parameters.

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method.

Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

A self-organizing deep neuro-fuzzy system approach for classification of kidney cancer subtypes using miRNA genomics data.

Kidney cancer is a dangerous disease affecting many patients all over the world. Early-stage diagnosis and correct identification of kidney cancer subtypes play an essential role in the patient's survival; therefore, its subtypes diagnosis and classification are the main challenges in kidney cancer treatment. Medical studies have proved that miRNA dysregulation can increase the risk of cancer. Thus, in this paper, we propose a new machine learning approach for significant miRNAs identification and kidney cancer subtype classification to design an automatic diagnostic tool. The proposed method contains two main steps: feature selection and classification. First, we apply the feature selection algorithm to choose the candidate miRNAs for each subtype. The feature selection algorithm utilizes the AMGM measure to select significant miRNAs with high discriminant power. Next, the candidate miRNAs are fed to a classifier to evaluate the candidate features. In the classification step, the proposed self-organizing deep neuro-fuzzy system is employed to classify kidney cancer subgroups. The new deep neuro-fuzzy system consists of a deep structure in the rule layer and novel architecture in the fuzzifier layer. The proposed self-organizing deep neuro-fuzzy system can help us to overcome the main obstacles in the field of neuro-fuzzy system applications, such as the curse of dimensionality. The goal of this paper is to illustrate that the neuro-fuzzy system can very useful in high dimensional data, such as genomics data, using the proposed deep neuro-fuzzy system. The obtained results illustrated that our proposed method has succeeded in classifying kidney cancer subtypes with high accuracy based on the selected miRNAs.

Climate change: A driver of future conflicts in the Persian Gulf Region?

Ongoing global change and its direct environmental impacts, in addition to securing economic transition to the post-oil era, could trigger complex socio-economic and political crises in oil-dependent economies of the Persian Gulf Region (PGR). To evaluate the role of climate change and related policies in degrading the environment and its socio-economic impacts in the PGR, we have used a variety of available global datasets and published data. The results show that the countries of the PGR pursue some types of socio-economic reforms to alleviate the impacts of climate change. However, it seems that these attempts are not compatible with the environment's capacity. The main problem stems from the fact that political differences between the PGR nations prevent them from managing the Persian Gulf environment as an integrated natural system and consequently they have to limit their efforts within their borders, regardless of what happens in other parts of the system. The shift to alternative revenue sources by the countries needs socioeconomic preparedness while there are environmental obstacles, political tensions and geopolitical rivalries. Unless there is a cooperative approach to mitigate the effects of climate change, accompanied by a reorientation of PGR economies, the situation is likely to worsen rather than improve. To address the challenges of climate change, integrated regional collaborations are needed. Collective action, such as more investment in regional research and development and education, is required if the PGR is to successfully transition from a commodity-based to a knowledge-based economy.

FDSR: A new fuzzy discriminative sparse representation method for medical image classification.

Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks.

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple |Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

Increasing Non-tuberculous Mycobacteria Infections in Veterans With COPD and Association With Increased Risk of Mortality.

Background: There are limited data on the epidemiology of Non-tuberculous mycobacteria (NTM) infections among patients with COPD, particularly in the veteran population. This study examined the prevalence, incidence, and mortality of pulmonary NTM infections among veterans with COPD population in the United States. Methods: We analyzed nationwide data from Veterans Affairs Hospitals from 2001 to 2015. First, we determined the incidence and prevalence rates and geographic distribution of NTM infections among veterans with COPD and then we evaluated the association between NTM infections with mortality among veterans with COPD. Pulmonary NTM and COPD diagnosis were defined based on charting claims for each condition on ≥2 occasions and ≥30 days apart. COPD diagnoses had to precede diagnosis of NTM. Cox Proportional-Hazards Regression was performed to determine the dependency of survival time of COPD patients with NTM. Results: The incidence and prevalence rates of NTM rose over the study period, with a sharp rise in incidence after 2012. The areas with the highest NTM period prevalence were Puerto Rico (370), followed by Florida (351) and District of Columbia (309) in 100,000 COPD population. Mortality registered for those patients with COPD Patients and NTM infection was 1.43 times higher compared to those that were uninfected. Conclusions: NTM rates have been increasing in veterans with COPD since 2012. NTM infection is associated with increased risk of mortality. This highlights the importance of identifying preventable risk factors associated with NTM infections in subjects with COPD.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Lung health in era of climate change and dust storms.

Dust storms are strong winds which lead to particle exposure over extensive areas. These storms influence air quality on both a local and global scale which lead to both short and long-term effects. The frequency of dust storms has been on the rise during the last decade. Forecasts suggest that their incidence will increase as a response to the effects of climate change and anthropogenic activities. Elderly people, young children, and individuals with chronic cardiopulmonary diseases are at the greatest risk for health effects of dust storms. A wide variety of infectious and non-infectious diseases have been associated with dust exposure. Influenza A virus, pulmonary coccidioidomycosis, bacterial pneumonia, and meningococcal meningitis are a few examples of dust-related infectious diseases. Among non-infectious diseases, chronic obstructive pulmonary disease, asthma, sarcoidosis and pulmonary fibrosis have been associated with dust contact. Here, we review two molecular mechanisms of dust induced lung disease for asthma and sarcoidosis. We can also then further understand the mechanisms by which dust particles disturb airway epithelial and immune cells.

Climate Change and Respiratory Infections.

The rate of global warming has accelerated over the past 50 years. Increasing surface temperature is melting glaciers and raising the sea level. More flooding, droughts, hurricanes, and heat waves are being reported. Accelerated changes in climate are already affecting human health, in part by altering the epidemiology of climate-sensitive pathogens. In particular, climate change may alter the incidence and severity of respiratory infections by affecting vectors and host immune responses. Certain respiratory infections, such as avian influenza and coccidioidomycosis, are occurring in locations previously unaffected, apparently because of global warming. Young children and older adults appear to be particularly vulnerable to rapid fluctuations in ambient temperature. For example, an increase in the incidence in childhood pneumonia in Australia has been associated with sharp temperature drops from one day to the next. Extreme weather events, such as heat waves, floods, major storms, drought, and wildfires, are also believed to change the incidence of respiratory infections. An outbreak of aspergillosis among Japanese survivors of the 2011 tsunami is one such well-documented example. Changes in temperature, precipitation, relative humidity, and air pollution influence viral activity and transmission. For example, in early 2000, an outbreak of Hantavirus respiratory disease was linked to a local increase in the rodent population, which in turn was attributed to a two- to threefold increase in rainfall before the outbreak. Climate-sensitive respiratory pathogens present challenges to respiratory health that may be far greater in the foreseeable future.