A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting

Design and economic investigation of shell and tube heat exchangers using Improved Intelligent Tuned Harmony Search algorithm

This study explores the thermal design of shell and tube heat exchangers by using Improved Intelligent Tuned Harmony Search (I-ITHS) algorithm. Intelligent Tuned Harmony Search (ITHS) is an upgraded version of harmony search algorithm which has an advantage of deciding intensification and diversification processes by applying proper pitch adjusting strategy. In this study, we aim to improve the search capacity of ITHS algorithm by utilizing chaotic sequences instead of uniformly distributed random numbers and applying alternative search strategies inspired by Artificial Bee Colony algorithm and Opposition Based Learning on promising areas (best solutions). Design variables including baffle spacing, shell diameter, tube outer diameter and number of tube passes are used to minimize total cost of heat exchanger that incorporates capital investment and the sum of discounted annual energy expenditures related to pumping and heat exchanger area. Results show that I-ITHS can be utilized in optimizing shell and tube heat exchangers. © 2014 Production and hosting by Elsevier B.V

GRHL2-controlled gene expression networks in luminal breast cancer

Grainyhead like 2 (GRHL2) is an essential transcription factor for development and function of epithelial tissues. It has dual roles in cancer by supporting tumor growth while suppressing epithelial to mesenchymal transitions (EMT). GRHL2 cooperates with androgen and estrogen receptors (ER) to regulate gene expression. We explore genome wide GRHL2 binding sites conserved in three ER⍺/GRHL2 positive luminal breast cancer cell lines by ChIP-Seq. Interaction with the ER⍺/FOXA1/GATA3 complex is observed, however, only for a minor fraction of conserved GRHL2 peaks. We determine genome wide transcriptional dynamics in response to loss of GRHL2 by nascent RNA Bru-seq using an MCF7 conditional knockout model. Integration of ChIP- and Bru-seq pinpoints candidate direct GRHL2 target genes in luminal breast cancer. Multiple connections between GRHL2 and proliferation are uncovered, including transcriptional activation of ETS and E2F transcription factors. Among EMT-related genes, direct regulation of CLDN4 is corroborated but several targets identified in other cells (including CDH1 and ZEB1) are ruled out by both ChIP- and Bru-seq as being directly controlled by GRHL2 in luminal breast cancer cells. Gene clusters correlating positively (including known GRHL2 targets such as ErbB3, CLDN4/7) or negatively (including TGFB1 and TGFBR2) with GRHL2 in the MCF7 knockout model, display similar correlation with GRHL2 in ER positive as well as ER negative breast cancer patients. Altogether, this study uncovers gene sets regulated directly or indirectly by GRHL2 in luminal breast cancer, identifies novel GRHL2-regulated genes, and points to distinct GRHL2 regulation of EMT in luminal breast cancer cells. Video Abstract.Toxicolog