A screening model to explore planning decisions in automotive manufacturing systems under demand uncertainty

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 191-197).Large-scale, complex engineering systems, as for automotive manufacturing, often require significant capital investment and resources for systems configuration. Furthermore, these systems operate in environments that are constantly changing due to shifts in macroeconomic, market demand and regulations, which can significantly influence systems' performance. It is often very difficult or prohibitively expensive to change these engineering systems once they are in place. Thus, a critical question is how to design engineering systems so they can perform well under uncertainty. Conventional engineering practice often focuses on the expected value of future uncertainties, thus leaving the value of flexible designs unexplored. This research develops a new framework to design and plan large-scale and complex manufacturing systems for uncertainty. It couples a screening model to identify promising candidate solutions with an evaluation model to more extensively quantify the performance of identified solutions. The screening model adaptively explores a large decision space that is otherwise computationally intractable for conventional optimization approach. It integrates strategic and operational flexibility in a system to allow systematic consideration of multiple sources of flexibility with uncertainty. It provides a means to search the space for system's improvement by integrating the adaptive one-factor-at-a-time (OFAT) method with a Response Surface method and simulation-based linear optimization. The identified solution is then examined with Value at Risk and Gain chart and a statistics table.(cont.) Two cases are studied in this thesis. The first case is a simple hypothetical case with two products and two plants. It considers product to plant allocation, plant capacity, and overtime operation decisions that affect manufacturing flexibility. It demonstrates the value of considering demand uncertainty and overtime operational flexibility in making manufacturing planning decisions and the interactions between multiple sources of flexibility. The second case explores these manufacturing planning decisions for Body-In-White assembly systems in the automotive industry by applying the developed screening model. It shows that the screening model leads to system design with about 40% improvement in expected net present value, reduced downside risks and increased upside gains as compared to a traditional optimization approach.by Yingxia Yang.Ph.D

Image classification-based brain tumour tissue segmentation

Brain tumour tissue segmentation is essential for clinical decision making. While manual segmentation is time consuming, tedious, and subjective, it is very challenging to develop automatic segmentation methods. Deep learning with convolutional neural network (CNN) architecture has consistently outperformed previous methods on such challenging tasks. However, the local dependencies of pixel classes cannot be fully reflected in the CNN models. In contrast, hand-crafted features such as histogram-based texture features provide robust feature descriptors of local pixel dependencies. In this paper, a classification-based method for automatic brain tumour tissue segmentation is proposed using combined CNN-based and hand-crafted features. The CIFAR network is modified to extract CNN-based features, and histogram-based texture features are fused to compensate the limitation in the CIFAR network. These features together with the pixel intensities of the original MRI images are sent to a decision tree for classifying the MRI image voxels into different types of tumour tissues. The method is evaluated on the BraTS 2017 dataset. Experiments show that the proposed method produces promising segmentation results

Comparative transcriptome analysis of PBMC from HIV patients pre- and post-antiretroviral therapy

Infections of the human immunodeficiency virus (HIV) trigger host immune responses, but the virus can destroy the immune system and cause acquired immune deficiency syndrome (AIDS). Highly active antiretroviral therapy (HAART) can suppress viral replication and restore the impaired immune function. To understand HIV interactions with host immune cells during HAART, the transcriptomes of peripheral blood mononuclear cells (PBMC) from HIV patients and HIV negative volunteers before and two weeks after HAART initiation were analyzed using RNA sequencing (RNA-Seq) technology. Differentially expressed genes (DEGs) in response to HAART were firstly identified for each individual, then common features were extracted by comparing DEGs among individuals and finally HIV-related DEGs were obtained by comparing DEGs between the HIV patients and HIV negative volunteers. To demonstrate the power of this approach, minimum numbers of patients (one HIV alone; one HIV + tuberculosis, TB; one HIV + TB with immune reconstitution inflammatory syndrome during HAART) and two HIV negative volunteers were used. More than 15,000 gene transcripts were detected in each individual sample. Fourteen HAART up-regulated and eleven down-regulated DEGs were specifically identified in the HIV patients. Among them, nine up-regulated (CXCL1, S100P, AQP9, BASP1, MMP9, SOD2, LIMK2, IL1R2 and BCL2A1) and nine down-regulated DEGs (CD160, CD244, CX3CR1, IFIT1, IFI27, IFI44, IFI44L, MX1 and SIGLEC1) have already been reported as relevant to HIV infections in the literature, which demonstrates the credibility of the method. The newly identified HIV-related genes (up-regulated: ACSL1, GPR84, GPR97, ADM, LRG1; down-regulated: RASSF1, PATL2) were empirically validated using qRT-PCR. The Gene Set Enrichment Analysis (GSEA) was also used to determine pathways significantly affected by HAART. GSEA further confirmed the HAART relevance of five genes (ADM, AQP9, BASP1, IL1R2 and MMP9). The newly identified HIV-related genes, ADM (which encodes Adrenomedullin), a peptide hormone in circulation control, may contribute to HIV-associated hypertensions, providing new insights into HIV pathology and novel strategies for developing anti-HIV target. More importantly, we demonstrated that comparative transcriptome analysis is a very powerful tool to identify infection related DEGs using a very small number of samples. This approach could be easily applied to improve the understanding of pathogen-host interactions in many infections and anti-infection treatments

Compound Kushen Injection suppresses human breast cancer stem-like cells by down-regulating the canonical Wnt/β-catenin pathway

<p>Abstract</p> <p>Background</p> <p>Cancer stem cells (CSCs) play an important role in cancer initiation, relapse and metastasis. To date, no specific medicine has been found to target CSCs as they are resistant to most conventional therapies and proliferate indefinitely. Compound Kushen Injection (CKI) has been widely used for cancer patients with remarkable therapeutic effects in Chinese clinical settings for many years. This study focused on whether CKI could inhibit MCF-7 SP cells in vitro and in vivo.</p> <p>Methods</p> <p>The analysis of CKI on SP population and the main genes of Wnt signaling pathway were studied first. Then we studied the tumorigenicity of SP cells and the effects of CKI on SP cells in vivo. The mice inoculated with 10,000 SP cells were randomly divided into three groups (6 in each group) and treated with CKI, cisplatin and saline (as a control) respectively for 7 weeks. The tumor formation rates of each group were compared. The main genes and proteins of the Wnt signaling pathway were analyzed by RT-PCR and western blot.</p> <p>Results</p> <p>CKI suppressed the size of SP population (approximately 90%), and down-regulated the main genes of Wnt signaling pathway. We also determined that MCF-7 SP cells were more tumorigenic than non-SP and unsorted cells. The Wnt signaling pathway was up-regulated in tumors derived from SP cells compared with that in tumors from non-SP cells. The tumor formation rate of the CKI Group was 33% (2/6, <it>P </it>< 0.05), and that of Cisplatin Group was 50%(3/6, <it>P </it>< 0.05), whereas that of the Control Group was 100% (6/6).The RT-PCR and western blot results indicated that CKI suppressed tumor growth by down-regulating the Wnt/β-catenin pathway, while cisplatin activated the Wnt/β-catenin pathway and might spare SP cells.</p> <p>Conclusions</p> <p>It suggested that CKI may serve as a novel drug targeting cancer stem-like cells, though further studies are recommended.</p

Significant Upregulation of Alzheimer's β-Amyloid Levels in a Living System Induced by Extracellular Elastin Polypeptides

Alzheimer's disease (AD) is a neurodegenerative disorder and the primary cause of age-related dementia. The etiology of AD is complex and has not been completely elucidated. Herein, we report that treatment with elastin-like polypeptides (ELPs), a component of the brain extracellular matrix (ECM), significantly increased the levels of AD-related amyloid-β peptides (Aβ) both in vitro and in vivo. Regarding the molecular mechanism(s), the upregulation of Aβ levels was related to increased proteolytic processing of the amyloid precursor protein. Furthermore, nesting tests demonstrated that the ELP-treated animals showed significant neurobehavioral deficits with cognitive impairment. These results suggest that the elastin is associated with AD-related pathological and behavioral changes. This finding presents a new aspect for Alzheimer's amyloidosis event and provides a great promise in developing ELP-based model systems to better understand the pathogenesis of AD. © 201

Combined features in region of interest for brain tumor segmentation

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively

PREPARED FOR The Texas Clean Energy Coalition PREPARED BY

and George Mitchell Foundation. All results and any errors are the responsibility of the authors and do not represent the opinion of the project’s sponsors, The Brattle Group, Inc. or its clients. Acknowledgement: We would like to thank the following individuals for their assistance. Comverge: Colin Meeha

Zinc finger and interferon-stimulated genes play a vital role in TB-IRIS following HAART in AIDS

Aim: Co-infection in HIV-1 patients with Mycobacterium tuberculosis poses considerable risk of developing the immune reconstitution inflammatory syndrome (IRIS), especially upon the initiation of antiretroviral therapy (ART). Methodology &amp; results: For transcriptomic analysis, peripheral blood mononuclear cells’ whole gene expression was used from three patient groups: HIV+ (H), HIV-TB+ (HT), HIV-TB+ with IRIS (HTI). Pathway enrichment and functional analysis was performed before and after highly active ART. Genes in the interferon-stimulating and ZNF families maintained tight functional interaction and tilted the balance in favor of TB-IRIS. Discussion &amp; conclusion: The functional impairment of interaction between ZNF genes and interferon-stimulated genes, along with higher expression of S100A8/S100A9 genes possibly forms the genomic basis of TB-IRIS in a subset of HIV patients while on highly active ART