Strategies to enhance cottonseed oil contents and reshape fatty acid profile employing different breeding and genetic engineering approaches

Cottonseed oil is the valuable byproduct extracted after seed cotton processing for lint. It confers a huge contribution to total vegetable oil production and ranked the 2nd to meet global edible oil requirements. Over centuries, breeders mainly focused to improve lint production and fiber quality. Now attention has been given to improve the cottonseed oil percentage, its functional and nutritional properties. However, these efforts are less than other major oilseed crops which left cottonseed oil market behind in term of consumer demand and kept cott on seed oil industry at vuln erable positi on. Con siderable progress has been made to alter the relative percentage of fatty acid composition still intensified efforts have been required to meet the global oilseed demand. The objective of this review is to explore the cotton germplasm variation for seed oil carrying potential, its utilization in suitable breeding programs, seed oil biosynthetic pathways, major genes, and QTLs linked to quantity and quality enhancement of oil and deployment of modern genomic tools, viz., gene silencing and transgenic development to ameliorate its nutritional properties.

3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.