COVID and nutrition: A machine learning perspective

A self-report questionnaire survey was conducted online to collect big data from over 16000 Iranian families (who were the residents of 1000 urban and rural areas of Iran). The resulting data storage contained over 1 M records of data and over 1G records of automatically inferred information. Based on this data storage, a series of machine learning experiments was conducted to investigate the relationship between nutrition and the risk of contracting COVID-19. With highly accurate scores, the findings strongly suggest that foods and water sources containing certain natural bioactive and phytochemical agents may help to reduce the risk of apparent COVID-19 infection.

Some natural hypomethylating agents in food, water and environment are against distribution and risks of COVID-19 pandemic: Results of a big-data research

OBJECTIVE

This study analyzes the effects of lifestyle, nutrition, and diets on the status and risks of apparent (symptomatic) COVID-19 infection in Iranian families.

MATERIALS AND METHODS

A relatively extensive questionnaire survey was conducted on more than 20,000 Iranian families (residing in more than 1000 different urban and rural areas in the Islamic Republic of Iran) to collect the big data of COVID-19 and develop a lifestyle dataset. The collected big data included the records of lifestyle effects (e.g. nutrition, water consumption resources, physical exercise, smoking, age, gender, health and disease factors, etc.) on the status of COVID-19 infection in families (i.e. residents of homes). Therefore, an online self-reported questionnaire was used in this retrospective observational study to analyze the effects of lifestyle factors on the COVID-19 risks. The data collection process spanned from May 10, 2020 to March 19, 2021 by selecting 132 samples from more than 40 different social network communities.

RESULTS

The research results revealed that food and water sources, which contain some natural hypomethylating agents, mitigated the risks of apparent (symptomatic) COVID-19 infection. Furthermore, the computations on billions of permutations of nutrition conditions and dietary regime items, based on the data collected from people's diets and infection status, showed that there were many dietary conditions alleviating the risks of apparent (symptomatic) COVID-19 infection by 90%. However, some other diets tripled the infection risk.

CONCLUSION

Some natural hypomethylating agents in food, water, and environmental resources are against the spread and risks of COVID-19.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

A Temporal Neural Trace of Wavelet Coefficients in Human Object Vision: An MEG Study

Wavelet transform has been widely used in image and signal processing applications such as denoising and compression. In this study, we explore the relation of the wavelet representation of stimuli with MEG signals acquired from a human object recognition experiment. To investigate the signature of wavelet descriptors in the visual system, we apply five levels of multi-resolution wavelet decomposition to the stimuli presented to participants during MEG recording and extract the approximation and detail sub-bands (horizontal, vertical, diagonal) coefficients in each level of decomposition. Apart from, employing multivariate pattern analysis (MVPA), a linear support vector classifier (SVM) is trained and tested over the time on MEG pattern vectors to decode neural information. Then, we calculate the representational dissimilarity matrix (RDM) on each time point of the MEG data and also on wavelet descriptors using classifier accuracy and one minus Pearson correlation coefficient, respectively. Given the time-courses calculated from performing the Pearson correlation between the wavelet descriptors RDMs and MEG decoding accuracy in each time point, our result shows that the peak latency of the wavelet approximation time courses occurs later for higher level coefficients. Furthermore, studying the neural trace of detail sub-bands indicates that the overall number of statistically significant time points for the horizontal and vertical detail coefficients is noticeably higher than diagonal detail coefficients, confirming the evidence of the oblique effect that the horizontal and vertical lines are more decodable in the human brain.

Calculation of the contrast of the calcification in digital mammography system: Gate validation

Purpose

Validation of the Gate tool in digital mammography image simulation from the viewpoint of image quality (contrast of calcifications).

Materials and Methods

The polymethyl methacrylate (PMMA) phantom containing aluminum foils in different thicknesses is used for measuring the contrast of calcifications in a real system. In this research, the phantom and mammography system have been simulated by the Gate tool with the maximum possible details. The contrast of the aluminum foil in simulations and practical method has been compared with each other and the standard errors in the mean (SEM) for various voltages of X-ray tube, aluminum foil, and PMMA thicknesses have been reported.

Results

Based on the obtained results, by increasing the X-ray tube voltage from 20 to 39 kVp, the image contrast has been decreased in both simulation and practical methods. The minimum and maximum average SEM of the contrast of the aluminum foils among various voltages between two simulations and practical methods for different PMMA thicknesses of 2, 4, and 6 cm have been reported as 0.0105 and 0.0117, 0.0049 and 0.0154, and 0.0037 and 0.0072, respectively.

Discussion

According to the SEM rate reported in this research for calculating the contrast of the aluminum foils in the mammography system based on simulation and practical methods, the capability of the Gate tool for simulating digital mammography system and the images created in it from the viewpoint of image contrast can be confirmed.

Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system

Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.

Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU

Digital Breast Tomosynthesis (DBT) is a technology that creates three dimensional (3D) images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study ultra-fast image reconstruction technique for Tomosynthesis Mammography systems using Graphics Processing Unit (GPU). At first, projections of Tomosynthesis mammography have been simulated. In order to produce Tomosynthesis projections, it has been designed a 3D breast phantom from empirical data. It is based on MRI data in its natural form. Then, projections have been created from 3D breast phantom. The image reconstruction algorithm based on FBP was programmed with C++ language in two methods using central processing unit (CPU) card and the Graphics Processing Unit (GPU). It calculated the time of image reconstruction in two kinds of programming (using CPU and GPU).

Segmentation of Crohn, Lymphangiectasia, Xanthoma, Lymphoid hyperplasia and Stenosis diseases in WCE

Wireless capsule endoscopy (WCE) is a great breakthrough for Gastrointestinal (GI) Tract diagnoses which can view the entire gastrointestinal tract, especially the small bowel, without invasiveness and sedation. However, a tough problem associated with this new device is that too many images to be inspected by naked eyes is difficult for physicians, Thus it is essential to find an automatic and intelligent diagnosis method to help physicians. In this paper, a new segmentation algorithm for detection of Lymphangiectasia, Xanthoma, Crohn, and Stenosis in WCE images is proposed. This new approach mainly uses the HSV color space, sigmoid function and canny edge detector. We compare our method with a fuzzy c-mean clustering. We show that sensitivities of the sigmoid function for Lymphangiectasia, Lymphoid hyperplasia, severe Crohn's disease, Xanthoma and ulcerated Stenosis are respectively 89.32%, 91.27%, 95.45%, 87.01%, 97% and sensitivities of the fuzzy c-means clustering with same order are 83.91%, 86.7%, 96.38%, 90.4%, 93.83%. Totally, the sigmoid function is more specific and sensitive, with same accuracy.

Femtosecond pulse propagation in air: variational analysis

We use a variational method to study the phenomenon of intense femtosecond pulse propagation in air. This method allows us to obtain a semianalytical solution to the problem in which a wide range of initial conditions can be studied. In addition, it provides a simple physical interpertation, where the problem is reduced to an analogous problem of a particle moving in a potential well. Different types of possible solutions are considered, with focus upon the main physical interpretations. The results recapture at least qualitatively some of the major experimental observations, and previous numerical simulations.