Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

Public Health Public Health

Mathematical models in nursing research.

In Journal of public health research

This paper discusses the use of advanced mathematical tools in nursing research, such as mathematical models used in medicine for description and prediction of experimental tumor growth. They are rarely used in nursing research, but fortunately in the last decade, their use is increased, mainly due to artificial intelligence and Big Data, with great benefits for further nursing development. Therefore, a strong interaction between nurses and mathematicians is needed to improve nursing research, and consequently, the nurses' performance in daily work.

Rea Teresa, Guillari Assunta, Sergi Consolato, Serra Nicola

2020-Oct-14

Big Data, Nurse, artificial neural networks, mathematical models, nursing research, nursing tools, questionnaires

Radiology Radiology

Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis.

In Cardiovascular diagnosis and therapy

In the last decades, significant advances have been made in the preventive approaches to cardiovascular disease. Even so, coronary artery disease remains one of the main causes of morbidity and mortality worldwide. Invasive imaging modalities, such as intravascular ultrasound or optical coherence tomography, have played a key role in the comprehension of the pathological processes underlying myocardial infarction and cerebrovascular disease. These imaging techniques have contributed greatly to the identification and phenotyping of the culprit lesion, the so-called vulnerable plaque. Coronary computed tomographic angiography (CCTA) has emerged in more recent years as the non-invasive modality of choice in the study of coronary atherosclerosis, showing in many studies a diagnostic yield comparable to invasive approaches. Moreover, being able to describe extra-luminal characteristics of the affected vessel, CCTA has greatly contributed towards shifting the attention of researchers from the mere quantification of luminal stenosis to the identification of adverse plaque features, which appear to have a stronger prognostic value. However, the identification of some of the hallmarks of vulnerable plaques is qualitative in nature and, therefore, subject to some degree of inter-reader variability. Moreover, CCTA is still unable to identify some fine markers of plaque vulnerability which can be detected by invasive techniques, such as neovascularization and plaque erosion, among others. Nonetheless, radiological images can be viewed as vast 3-D datasets which, via the use of recent technology, allow for the extraction of numerous quantitative features that may be used to accurately phenotype a given lesion. Radiomics is the process of extrapolating innumerable parameters from a given region of interest, with the goal of establishing correlations between quantitative variables and clinical data. These datasets can then be manipulated to create predictive models via the use of automated algorithms in a process called machine learning. As a result of these approaches, radiological images may offer information regarding the characterization of a plaque which can go much beyond the boundaries of what can be qualitatively asserted by the human eye, contributing to expanding the knowledge of the disease and ultimately assist clinical decisions. Thus far, radiomics has found its more consistent area of application in the field of oncology; to present date, the amount of clinical data regarding coronary artery disease is still relatively small, partly due to the technical difficulties associated with the implementation of such techniques to the study of a small and geometrically complex lesion such as the coronary plaque. The present review, after a summary of the imaging modalities most commonly used nowadays in the study of coronary plaques, will provide a perspective on the application of radiomic analysis to coronary artery disease.

Murgia Alessandro, Balestrieri Antonella, Crivelli Paola, Suri Jasjit S, Conti Maurizio, Cademartiri Filippo, Saba Luca

2020-Dec

Imaging modalities, coronary atherosclerosis, coronary plaques, radiomics

General General

DEEP MOUSE: AN END-TO-END AUTO-CONTEXT REFINEMENT FRAMEWORK FOR BRAIN VENTRICLE & BODY SEGMENTATION IN EMBRYONIC MICE ULTRASOUND VOLUMES.

In Proceedings. IEEE International Symposium on Biomedical Imaging

The segmentation of the brain ventricle (BV) and body in embryonic mice high-frequency ultrasound (HFU) volumes can provide useful information for biological researchers. However, manual segmentation of the BV and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.

Xu Tongda, Qiu Ziming, Das William, Wang Chuiyu, Langerman Jack, Nair Nitin, Aristizábal Orlando, Mamou Jonathan, Turnbull Daniel H, Ketterling Jeffrey A, Wang Yao

2020-Apr

Image segmentation, high-frequency ultrasound, mouse embryo, volumetric deep learning

General General

A t-SNE Based Classification Approach to Compositional Microbiome Data.

In Frontiers in genetics ; h5-index 62.0

As a data-driven dimensionality reduction and visualization tool, t-distributed stochastic neighborhood embedding (t-SNE) has been successfully applied to a variety of fields. In recent years, it has also received increasing attention for classification and regression analysis. This study presented a t-SNE based classification approach for compositional microbiome data, which enabled us to build classifiers and classify new samples in the reduced dimensional space produced by t-SNE. The Aitchison distance was employed to modify the conditional probabilities in t-SNE to account for the compositionality of microbiome data. To classify a new sample, its low-dimensional features were obtained as the weighted mean vector of its nearest neighbors in the training set. Using the low-dimensional features as input, three commonly used machine learning algorithms, logistic regression (LR), support vector machine (SVM), and decision tree (DT) were considered for classification tasks in this study. The proposed approach was applied to two disease-associated microbiome datasets, achieving better classification performance compared with the classifiers built in the original high-dimensional space. The analytic results also showed that t-SNE with Aitchison distance led to improvement of classification accuracy in both datasets. In conclusion, we have developed a t-SNE based classification approach that is suitable for compositional microbiome data and may also serve as a baseline for more complex classification models.

Xu Xueli, Xie Zhongming, Yang Zhenyu, Li Dongfang, Xu Ximing

2020

Aitchison distance, classification, dimension reduction, microbiome data, t-SNE

General General

Attention-Based Recurrent Neural Network for Plant Disease Classification.

In Frontiers in plant science

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

Lee Sue Han, Goëau Hervé, Bonnet Pierre, Joly Alexis

2020

automated visual crops analysis, crops monitoring, deep learning, pests analysis, plant disease classification, precision agriculture technologies, recurrent neural network, smart farming

General General

Surface electromyography signal denoising via EEMD and improved wavelet thresholds.

In Mathematical biosciences and engineering : MBE

The acquisition of good surface electromyography (sEMG) is an important prerequisite for correct and timely control of prosthetic limb movements. sEMG is nonlinear, nonstationary, and vulnerable against noise and a new sEMG denoising method using ensemble empirical mode decomposition (EEMD) and wavelet threshold is hence proposed to remove the random noise from the sEMG signal. With this method, the noised sEMG signal is first decomposed into several intrinsic mode functions (IMFs) by EEMD. The first IMF is mostly noise, coupled with a small useful component which is extracted using a wavelet transform based method by defining a peak-to-sum ratio and a noise-independent extracting threshold function. Other IMFs are processed using an improved wavelet threshold denoising method, where a noise variance estimation algorithm and an improved wavelet threshold function are combined. Key to the threshold denoising method, a threshold function is used to retain the required wavelet coefficients. Our denoising algorithm is tested for different sEMG signals produced by different muscles and motions. Experimental results show that the proposed new method performs better than other methods including the conventional wavelet threshold method and the EMD method, which guaranteed its usability in prosthetic limb control.

Sun Ziyang, Xi Xugang, Yuan Changmin, Yang Yong, Hua Xian

2020-Oct-16

** denoising; wavelet transform , electromyography , empirical mode decomposition , ensemble empirical mode decomposition , prosthetic control **