Optimization Strategies for Interactive Classification of Interstitial Lung Disease Textures

For computerized analysis of textures in interstitial lung disease, manual annotations of lung tissue are necessary. Since making these annotations is labor intensive, we previously proposed an interactive annotation framework. In this framework, observers iteratively trained a classifier to distinguish the different texture types by correcting its classification errors. In this work, we investigated three ways to extend this approach, in order to decrease the amount of user interaction required to annotate all lung tissue in a computed tomography scan. First, we conducted automatic classification experiments to test how data from previously annotated scans can be used for classification of the scan under consideration. We compared the performance of a classifier trained on data from one observer, a classifier trained on data from multiple observers, a classifier trained on consensus training data, and an ensemble of classifiers, each trained on data from different sources. Experiments were conducted without and with texture selection (ts). In the former case, training data from all eight textures was used. In the latter, only training data from the texture types present in the scan were used, and the observer would have to indicate textures contained in the scan to be analyzed. Second, we simulated interactive annotation to test the effects of (1) asking observers to perform ts before the start of annotation, (2) the use of a classifier trained on data from previously annotated scans at the start of annotation, when the interactive classifier is untrained, and (3) allowing observers to choose which interactive or automatic classification results they wanted to correct. Finally, various strategies for selecting the classification results that were presented to the observer were considered. Classification accuracies for all possible interactive annotation scenarios were compared. Using the best-performing protocol, in which observers select the textures that should be distinguished in the scan and in which they can choose which classification results to use for correction, a median accuracy of 88% was reached. The results obtained using this protocol were significantly better than results obtained with other interactive or automatic classification protocols

Significant effects in bread-making quality associated with the gene cluster Glu-D3/Gli-D1 from the bread wheat cultivar Prointa Guazú

Seed storage proteins (gliadins and glutenins) play a key role in the determination of dough and bread-making quality in bread wheat. This is due to the interaction between high and low molecular weight glutenins subunits and gliadins, via complex inter- and intramolecular bondings. In contrast to high molecular weight glutenins, low molecular weight glutenins and gliadins analysis is difficult due to the large number of expressed subunits and coding genes. For these reasons the role of individual proteins/subunits in the determination of wheat quality is less clear. In this work we studied the effect of gene clusters Glu-A3/Gli-A1 and Glu-D3/Gli-D1 in bread-making quality parameters using 20 F4-6 families from the cross Prointa Guazú × Prointa Oasis, both cultivars carrying identical high molecular weight glutenins subunits composition and presence of 1BL/1RS wheat-rye translocation, but differing in Glu-A3/Glu-D3 low molecular weight glutenins subunits and Gli-A1/Gli-D1 gliadins patterns. ANCOVA analysis showed a significant contribution of the Glu-D3/Gli-D1 gene cluster provided by Prointa Guazú to gluten strength explained by mixograph parameters MDS and PW, and Zeleny Test. Markers tagging Prointa Guazú Glu-D3/Gli-D1 alleles are available for strong gluten selection in breeding programs

Effects of microRNA156 on Flowering Time and Plant Architecture in Medicago sativa

MiR156 regulates plant biomass production through regulation of members of Squamosa-Promoter Binding Protein-Like (SPL) genes. In this study, I investigated function of miR156 in Medicago sativa (alfalfa). Alfalfa plants overexpressing alfalfa miR156 and Lotus japonicus miR156 were generated, and the miR156 cleavage targets were validated. In silico analysis showed that some alfalfa sequence reads (~ 60 bp) are similar to miR156 precursors but the hairpin secondary structure could not be produced from these sequences. Of the five predicted target SPLs genes, three (SPL6, SPL12 and SPL13) contain miR156 cleavage sites and their expression was downregulated in transgenic alfalfa overexpressing miR156. These transgenic alfalfa genotypes had reduced internode length, enhanced shoot branching, and elevated biomass. Although alfalfa miR156 had little effect on nodulation and flowering time, L. japonicus miR156 reduced nodulation and delayed flowering time (up to 50 days). Our observations imply that miR156 could be employed in improving alfalfa biomass

Automated assessment of transthoracic echocardiogram image quality using deep neural networks

Background Standard views in two-dimensional echocardiography are well established but the quality of acquired images are highly dependent on operator skills and are assessed subjectively. This study is aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators. Consequently, image quality assessment can thus be automated to enhance clinical measurements, interpretation, and real-time optimization. Methods We have developed deep neural networks for the automated assessment of echocardiographic frame which were randomly sampled from 11,262 adult patients. The private echocardiography dataset consists of 33,784 frames, previously acquired between 2010 and 2020. Unlike non-medical images where full-reference metrics can be applied for image quality, echocardiogram's data is highly heterogeneous and requires blind-reference (IQA) metrics. Therefore, deep learning approaches were used to extract the spatiotemporal features and the image's quality indicators were evaluated against the mean absolute error. Our quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility, clarity, depth-gain and foreshortedness, respectively. Results The model performance accuracy yielded 94.4%, 96.8%, 96.2%, 97.4% for anatomical visibility, clarity, depth-gain and foreshortedness, respectively. The mean model error of 0.375±0.0052 with computational speed of 2.52 ms per frame (real-time performance) was achieved. Conclusion The novel approach offers new insight to objective assessment of transthoracic echocardiogram image quality and clinical quantification in A4C and PLAX views. Also lays stronger foundations for operator's guidance system which can leverage the learning curve for the acquisition of optimum quality images during transthoracic exam

A Survey From Distributed Machine Learning to Distributed Deep Learning

Artificial intelligence has achieved significant success in handling complex tasks in recent years. This success is due to advances in machine learning algorithms and hardware acceleration. In order to obtain more accurate results and solve more complex problems, algorithms must be trained with more data. This huge amount of data could be time-consuming to process and require a great deal of computation. This solution could be achieved by distributing the data and algorithm across several machines, which is known as distributed machine learning. There has been considerable effort put into distributed machine learning algorithms, and different methods have been proposed so far. In this article, we present a comprehensive summary of the current state-of-the-art in the field through the review of these algorithms. We divide this algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of studies worked on this algorithms. As a result, most of the articles we discussed here belong to this category. Based on our investigation of algorithms, we highlight limitations that should be addressed in future research

Improving RNA-Seq Precision with MapAl

With currently available RNA-Seq pipelines, expression estimates for most genes are very noisy. We here introduce MapAl, a tool for RNA-Seq expression profiling that builds on the established programs Bowtie and Cufflinks. In the post-processing of RNA-Seq reads, it incorporates gene models already at the stage of read alignment, increasing the number of reliably measured known transcripts consistently by 50%. Adding genes identified de novo then allows a reliable assessment of double the total number of transcripts compared to other available pipelines. This substantial improvement is of general relevance: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising