Comprehensive view on genetic features, therapeutic modalities and prognostic models in adult T-cell lymphoblastic lymphoma

Adult T-cell lymphoblastic lymphoma(T-LBL)is a rare and aggressive subtype of non-Hodgkin’s lymphoma that differs from pediatric T-LBL and has a worse prognosis.Due to its rarity,little is known about the genetic and molecular characteristics,optimal treatment modalities,and prognostic factors of adult T-LBL.Therefore,we summarized the existing studies to comprehensively discuss the above issues in this review.Genetic mutations of NOTCH1/FBXW7,PTEN,RAS,and KMT2D,together with abnormal activation of signaling pathways,such as the JAK-STAT signaling pathway were described.We also discussed the therapeutic modalities.Once diagnosed,adult T-LBL patients should receive intensive or pediatric acute lymphoblastic leukemia regimen and central nervous system prophylaxis as soon as possible,and cranial radiation-free protocols are appropriate.Mediastinal radiotherapy improves clinical outcomes,but adverse events are of concern.Hematopoietic stem cell transplantation may be considered for adult T-LBL patients with high-risk factors or those with relapsed/refractory disease.Besides,several novel prognostic models have been constructed,such as the 5-miRNAs-based classifier,11-gene-based classifier,and 4-CpG-based classifier,which have presented significant prognostic value in adult T-LBL.

Apple leaf disease identification using genetic algorithm and correlation based feature selection method

Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.