Predictive design analytics for optimal system design

“Predictive Design Analytics” proposed by this dissertation is a new paradigm to enable design engineers to extract important patterns from large-scale data characterized by four dimensions (volume, variety, velocity and veracity), and combine the extracted knowledge and its trend with complex systems optimization for various design decision making problems such as economical life cycle design, product family design and sustainable design. The goal of this research is the development of predictive design analytics methods for optimal systems design: Demand Trend Mining, Continuous Preference Trend Mining, Predictive Data-Driven Product Family Design, and Predictive Usage Mining for Life Cycle Assessment. To the best of the author’s knowledge, this is one of the first attempts to provide a systematic framework of predictive analytics for design, which comprises data preprocessing, data representation, predictive analytics algorithms, mathematical formulation of design problems, and design decision making. Demand trend mining (DTM) is developed to link pre-life (design and manufacturing) and end-of-life (remanufacturing and recycling) stages of a product for the improvement of initial product design. In order to capture hidden and upcoming trends of product demand, the algorithm combines three different models: decision tree for large-scale data, discrete choice analysis for demand modeling, and automatic time series forecasting for trend analysis. DTM dynamically reveals design attribute patterns that affect demands. A new design framework, Predictive Life Cycle Design (PLCD), is formulated, which connects DTM and optimal product design. The DTM algorithm interacts with the optimization-based model to maximize the total profit of a product through its life. For illustration, the developed model is applied to an example of smart-phone design, assuming that used phones are taken back for remanufacturing after one year. The result shows that the PLCD framework with the DTM algorithm identifies a more profitable product design over a product’s life cycle when compared to traditional design approaches that focus on the pre-life stage only. Continuous Preference Trend Mining (CPTM) is developed to generate multiple profit cycles of product design while addressing some fundamental challenges in previous studies. The CPTM algorithm captures a hidden trend of customer purchase patterns from accumulated transactional data. Unlike traditional, static data mining algorithms, the CPTM does not assume stationarity, but dynamically extracts valuable knowledge from customers over time. By generating trend embedded future data, the CPTM algorithm not only shows higher prediction accuracy in comparison with well-known static models, but also provides essential properties that could not be achieved with previously proposed models: utilizing historical data selectively, avoiding an over-fitting problem, identifying performance information of a constructed model, and allowing a numeric prediction. Furthermore, the formulation of the initial design problem is proposed, which can reveal an opportunity for multiple profit cycles. This mathematical formulation enables design engineers to optimize product design over multiple life cycles while reflecting customer preferences and technological obsolescence using the CPTM algorithm. For illustration, the developed framework is applied to an example of tablet PC design in the leasing market, and the result shows that the determination of optimal design is achieved over multiple life cycles. Predictive, data-driven product family design (PDPFD) is proposed as one of the predictive design analytics methods to address the challenge of determining optimal product family architectures with large-scale customer preference data. The proposed model expands clustering based data-driven approaches to incorporate a market-driven approach. The market-driven approach provides a profit model in the near future to determine the optimal position and number of product architectures among product architecture candidates generated by the k-means clustering algorithm. Unlike discrete choice analysis models which were used in previous market-driven approaches, a market value prediction method is proposed as a dynamic model which can capture and reflect the trend of customer preferences. Prediction intervals provide market uncertainties of the dynamic profit model for product family architecture design. A universal electric motors design example is used to demonstrate the implementation of the proposed framework with large-scale data. The comparative study shows that the PDPFD algorithm can generate more profit than pure clustering based data-driven models, which shows the necessity of combining data-driven and market-driven approaches. Predictive usage mining for life cycle assessment (PUMLCA) is developed to provide the usage modeling in life cycle assessment (LCA) which has been rarely discussed despite the magnitude of environmental impact from the usage stage. The PUMLCA algorithm can serve as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on the change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, formulate a predictive LCA for existing and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations

TIMP-2 Fusion Protein with Human Serum Albumin Potentiates Anti-Angiogenesis-Mediated Inhibition of Tumor Growth by Suppressing MMP-2 Expression

TIMP-2 protein has been intensively studied as a promising anticancer candidate agent, but the in vivo mechanism underlying its anticancer effect has not been clearly elucidated by previous works. In this study, we investigated the mechanism underlying the anti-tumor effects of a TIMP-2 fusion protein conjugated with human serum albumin (HSA/TIMP-2). Systemic administration of HSA/TIMP-2 effectively inhibited tumor growth at a minimum effective dose of 60 mg/kg. The suppressive effect of HSA/TIMP-2 was accompanied by a marked reduction of in vivo vascularization. The anti-angiogenic activity of HSA/TIMP-2 was directly confirmed by CAM assays. In HSA/TIMP-2-treated tumor tissues, MMP-2 expression was profoundly decreased without a change in MT1-MMP expression of PECAM-1-positive cells. MMP-2 mRNA was also decreased by HSA/TIMP-2 treatment of human umbilical vein endothelial cells. Zymographic analysis showed that HSA/TIMP-2 substantially decreased extracellular pro-MMP-2 activity (94–99% reduction) and moderately decreased active MMP-2 activity (10–24% reduction), suggesting MT1-MMP-independent MMP-2 modulation. Furthermore, HSA/TIMP-2 had no effect on in vitro active MMP-2 activity and in vivo MMP-2 activity. These studies show that HSA/TIMP-2 potentiates anti-angiogenic activity by modulating MMP-2 expression, but not MMP-2 activity, to subsequently suppress tumor growth, suggesting an important role for MMP-2 expression rather than MMP-2 activity in anti-angiogenesis

Methylsulfonylmethane Suppresses Breast Cancer Growth by Down-Regulating STAT3 and STAT5b Pathways

Breast cancer is the most aggressive form of all cancers, with high incidence and mortality rates. The purpose of the present study was to investigate the molecular mechanism by which methylsulfonylmethane (MSM) inhibits breast cancer growth in mice xenografts. MSM is an organic sulfur-containing natural compound without any toxicity. In this study, we demonstrated that MSM substantially decreased the viability of human breast cancer cells in a dose-dependent manner. MSM also suppressed the phosphorylation of STAT3, STAT5b, expression of IGF-1R, HIF-1α, VEGF, BrK, and p-IGF-1R and inhibited triple-negative receptor expression in receptor-positive cell lines. Moreover, MSM decreased the DNA-binding activities of STAT5b and STAT3, to the target gene promoters in MDA-MB 231 or co-transfected COS-7 cells. We confirmed that MSM significantly decreased the relative luciferase activities indicating crosstalk between STAT5b/IGF-1R, STAT5b/HSP90α, and STAT3/VEGF. To confirm these findings in vivo, xenografts were established in Balb/c athymic nude mice with MDA-MB 231 cells and MSM was administered for 30 days. Concurring to our in vitro analysis, these xenografts showed decreased expression of STAT3, STAT5b, IGF-1R and VEGF. Through in vitro and in vivo analysis, we confirmed that MSM can effectively regulate multiple targets including STAT3/VEGF and STAT5b/IGF-1R. These are the major molecules involved in tumor development, progression, and metastasis. Thus, we strongly recommend the use of MSM as a trial drug for treating all types of breast cancers including triple-negative cancers

SPARC: a matricellular regulator of tumorigenesis

Although many clinical studies have found a correlation of SPARC expression with malignant progression and patient survival, the mechanisms for SPARC function in tumorigenesis and metastasis remain elusive. The activity of SPARC is context- and cell-type-dependent, which is highlighted by the fact that SPARC has shown seemingly contradictory effects on tumor progression in both clinical correlative studies and in animal models. The capacity of SPARC to dictate tumorigenic phenotype has been attributed to its effects on the bioavailability and signaling of integrins and growth factors/chemokines. These molecular pathways contribute to many physiological events affecting malignant progression, including extracellular matrix remodeling, angiogenesis, immune modulation and metastasis. Given that SPARC is credited with such varied activities, this review presents a comprehensive account of the divergent effects of SPARC in human cancers and mouse models, as well as a description of the potential mechanisms by which SPARC mediates these effects. We aim to provide insight into how a matricellular protein such as SPARC might generate paradoxical, yet relevant, tumor outcomes in order to unify an apparently incongruent collection of scientific literature

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Health-related effects and improving extractability of cereal arabinoxylans

Arabinoxylans (AXs) are major dietary fibers. They are composed of backbone chains of -(1–4)- linked xylose residues to which -l-arabinose are linked in the second and/or third carbon positions. Recently, AXs have attracted a great deal of attention because of their biological activities such as their immunomodulatory potential. Extraction of AXs has some difficulties; therefore, various methods have beenusedto increase the extractability ofAXs withvaryingdegrees of success, suchas alkaline, enzymatic, mechanical extraction. However, some of these treatments have been reported to be either expensive, such as enzymatic treatments, or produce hazardous wastes and are non-environmentally friendly, such as alkaline treatments. On the other hand, mechanical assisted extraction, especially extrusion cooking, is an innovative pre-treatment that has been used to increase the solubility of AXs. The aim of the current review article is to point out the health-related effects and to discuss the current research on the extraction methods of AXs

Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues