BackgroundCircular RNAs(circRNAs) have been reported as a diverse class of endogenous RNA that regulate gene expression in eukaryotes. Recent evidence suggested that many circular RNAs can act as oncogenes or tumor suppressors through sponging microRNAs. However, the function of circular RNAs in gastric cancer remains largely unknown.Materials and methodsThe circRNA levels in gastric carcinoma tissues and plasmas were detected by real-time quantitative reverse transcription-polymerase chain reaction. The correlation between the expression of circRNA and clinic pathological features was analyzed. Rate of inhibiting of proliferation was measured using a CCK-8 cell proliferation assay. Clone formation ability was assessed with a clone formation inhibition test. We used the bioinformatics software to predict circRNA-miRNA and miRNA-mRNA interactions. Relative gene expression was assessed using quantitative real-time polymerase chain reaction and relative protein expression levels were determined with western blotting. CircRNA and miRNA interaction was confirmed by dual-luciferase reporter assays.ResultsWe characterized that one circRNA named circ-SFMBT2 showed an increased expression level in gastric cancer tissues compared to adjacent non-cancerous tissues and was associated with higher tumor stages of gastric cancer. Silencing of circ-SFMBT2 inhibited the proliferation of gastric cancer cells significantly. Importantly, we demonstrated that circ-SFMBT2 could act as a sponge of miR-182-5p to regulate the expression of CREB1 mRNA, named as cAMP response element binding protein 1, and further promote the proliferation of gastric cancer cells.ConclusionOur study reveals that circ-SFMBT2 participates in progression of gastric cancer by competitively sharing miR-182-5p with CREB1, providing a novel target to improve the treatment of gastric cancer. mutation-analysis-of-beta-thalassemia-in-east-western-indian-populatio-peer-reviewed-article-TACG for an example.

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Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.

Silhouettes are robust image features that provide considerable evidence about the three-dimensional (3D) shape of a human body. The information they provide is, however, incomplete and prior knowledge has to be integrated to reconstruction algorithms in order to obtain realistic body models. This paper presents a method that integrates both geometric and statistical priors to reconstruct the shape of a subject assuming a standardized posture from a frontal and a lateral silhouette. The method is comprised of three successive steps. First, a non-linear function that connects the silhouette appearances and the body shapes is learnt and used to create a first approximation. Then, the body shape is deformed globally along the principal directions of the population (obtained by performing principal component analysis over 359 subjects) to follow the contours of the silhouettes. Finally, the body shape is deformed locally to ensure it fits the input silhouettes as well as possible. Experimental results showed a mean absolute 3D error of 8 mm with ideal silhouettes extraction. Furthermore, experiments on body measurements (circumferences or distances between two points on the body) resulted in a mean error of 11 mm. Keywords Human models • Statistical prior • Shape-from-silhouettes • Three-dimensional reconstruction 1I n t r o d u c t i o n Three-dimensional (3D) human shape models are instrumental to a large number of applications. Special effects, video games, ergonomic design and biomedical engineering are

We consider the problem of computing accurate point-to-point correspondences among a set of human bodies in varying postures using a landmark-free approach. The approach learns the locations of the anthropometric landmarks present in a database of human models in strongly varying postures and uses this knowledge to automatically predict the locations of these anthropometric landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a rigged template model and the newly available scan.

We present a data-driven approach to build a human body model from a single photograph by performing Principal Component Analysis (PCA) on a database of body segments. We segment a collection of human bodies to compile the required database prior to performing the analysis. Our approach then builds a single PCA for each body segment -head, left and right arms, torso and left and right legs -yielding six PCAs in total. This strategy improves on the flexibility of conventional data-driven approaches in 3D modeling and allows our approach to take variations in ethnicity, age and body posture into account. We demonstrate our approach in practice by constructing models of a Caucasian male, an Asian male and a toddler from corresponding photographs and a Caucasian adult oriented database. We also discuss rapid consistent parameterization based on Radial Basis Functions (RBF) and non-optimization based learning systems to reduce execution time.

Analysis on a dataset of 3D scanned surfaces have presented problems because of incompleteness on the surfaces and because of variances in shape, size and pose. In this paper, a high-resolution generic model is aligned to data in the Civilian American and European Surface Anthropometry Resources (CAESAR) database in order to obtain a consistent parameterization. A Radial Basis Function (RBF) network is built for rough deformation by using landmark information from the generic model, anatomical landmarks provided by CAESAR dataset and virtual landmarks created automatically for geometric deformation. Fine mapping then successfully applies a weighted sum of errors on both surface data and the smoothness of deformation. Compared with previous methods, our approach makes robust alignment in a higher efficiency. This consistent parameterization also makes it possible for Principal Components Analysis (PCA) on the whole body as well as human body segments. Our analysis on segmented bodies displays a richer variation than that of the whole body. This analysis indicates that a wider application of human body reconstruction with segments is possible in computer animation.