Investigating core genetic-and-epigenetic cell cycle networks for stemness and carcinogenic mechanisms,and cancer drug design using big database mining and genome-wide next-generation sequencing data

Recent studies have demonstrated that cell cycle plays a central role in development and carcinogenesis. Thus, the use of big databases and genome-wide high-throughput data to unravel the genetic and epigenetic mechanisms underlying cell cycle progression in stem cells and cancer cells is a matter of considerable interest.

Real genetic-and-epigenetic cell cycle networks (GECNs) of embryonic stem cells (ESCs) and HeLa cancer cells were constructed by applying system modeling, system identification, and big database mining to genome-wide next-generation sequencing data. Real GECNs were then reduced to core GECNs of HeLa cells and ESCs by applying principal genome-wide network projection. In this study, we investigated potential carcinogenic and stemness mechanisms for systems cancer drug design by identifying common core and specific GECNs between HeLa cells and ESCs. Integrating drug database information with the specific GECNs of HeLa cells could lead to identification of multiple drugs for cervical cancer treatment with minimal side-effects on the genes in the common core. We found that dysregulation of miR-29C, miR-34A, miR-98, and miR-215; and methylation of ANKRD1, ARID5B, CDCA2, PIF1, STAMBPL1, TROAP, ZNF165, and HIST1H2AJ in HeLa cells could result in cell proliferation and anti-apoptosis through NFκB, TGF-β, and PI3K pathways. We also identified 3 drugs, methotrexate, quercetin, and mimosine, which repressed the activated cell cycle genes, ARID5B, STK17B, and CCL2, in HeLa cells with minimal side-effects.     

Parametric analysis of the biomechanical response of head subjected to the primary blast loading – a data mining approach

Traumatic brain injury due to primary blast loading has become a signature injury in recent military conflicts and terrorist activities. Extensive experimental and computational investigations have been conducted to study the interrelationships between intracranial pressure response and intrinsic or ‘input’ parameters such as the head geometry and loading conditions. However, these relationships are very complicated and are usually implicit and ‘hidden’ in a large amount of simulation/test data. In this study, a data mining method is proposed to explore such underlying information from the numerical simulation results. The heads of different species are described as a highly simplified two-part (skull and brain) finite element model with varying geometric parameters. The parameters considered include peak incident pressure, skull thickness, brain radius and snout length. Their interrelationship and coupling effect are discovered by developing a decision tree based on the large simulation data-set. The results show that the proposed data-driven method is superior to the conventional linear regression method and is comparable to the nonlinear regression method. Considering its capability of exploring implicit information and the relatively simple relationships between response and input variables, the data mining method is considered to be a good tool for an in-depth understanding of the mechanisms of blast-induced brain injury. As a general method, this approach can also be applied to other nonlinear complex biomechanical systems.     

Emplacement and resistance: Social and political complexities in development‐induced displacement in Papua New Guinea

This paper argues that attending to social and political factors in development‐induced displacement is critical even for projects that involve resettlement of small populations. Taking the resettlement of two villages by the Lihir Gold Mine in Papua New Guinea as a unique case study, I analyse why one village was relatively successfully resettled, while the other has been very complicated, leading to hostility and conflict. In these cases the initial focus of all concerned in reaching a resettlement agreement was on the adequacy of compensation and housing, which mimicked the focus in the literature during the 1990s on impoverishment and compensation. From the late 1990s there have been calls for greater attention to social and political aspects of displacement and resettlement. Early attention to these factors in the Lihir case, particularly the key concepts of emplacement and disemplacement, would have highlighted flaws in the resettlement agreement and would have made it easier to avert the conflict and resistance that arose.     

基于专利信息分析的中国3D生物打印技术发展与产业化研究*

目的:基于专利信息对我国3D生物打印技术的发展态势进行分析。方法:本文基于incopat和TDA两大专利分析平台对中国3D生物打印的专利发展态势从专利统计分析与专利计量分析两个维度进行了跨库组合分析,总结了我国3D生物打印技术的专利前沿动态特征。结果:研究发现,中国3D生物打印技术从2013年起进入专利激增态势,中国作为潜在技术市场的国际竞争日趋激烈,本文还从专利申请人、技术领域分布、专利文本关键词聚类、专利价值、专利合作等方面进行了深度挖掘分析。结论:最后,结合对中国3D生物打印专利申请人的专利产业化案例深度分析与专利特征总结,为中国3D生物打印技术发展与产业化提供参考建议。     

Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.     

Concentration of arsenic in groundwater,vegetables, human hair and nails in mining site in the Northern Thai Nguyen province,Vietnam: human exposure and risks assessment

This study was aimed to examine the risk of chronic arsenic (As) exposure for the residents living in Nui Phao, Thai Nguyen in the northern Vietnam. Groundwater, vegetables, human hair, and nail samples were collected from volunteers living in Nui Phao. The results revealed that 75% of the groundwater samples had As exceeding the World Health Organization (WHO) drinking water guideline of 10 µg L^?1. The result of As concentration for most of the vegetable samples was greater than the WHO/FAO safe (0.1?mg kg^?1). The result of hair and nail samples in this study showed that 3.5 and 20% of the samples had As concentration exceeding the level of As toxicity in hair and nails, respectively. The result of health risks indicated that the potential health risk of As contamination is greater for groundwater than vegetables. The total hazard quotient (HQ) value through vegetables ingestion and drinking water exceeded 1.0 suggesting potential health risk for local residents. The calculation of potential carcinogenic risk through both consumption of vegetables and drinking water was low cancer risk in adults. Other food sources and the exposure pathways are needed to exactly assess health risks in this area.     

Determination of the Blood,Hormone and Obesity Value Ranges that Indicate the Breast Cancer,Using Data Mining Based Expert System

Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.     

关联分析在作物种质资源分子评价中的应用

发掘优异基因资源是作物种质资源分子评价的重要部分,对作物育种尤其是分子育种具有非常重要的实践意义。基于连锁不平衡(LD)的关联分析是基因发掘也是等位基因发掘的有效途径。本文系统介绍了关联分析的基本理论、策略、特点及应用现状,并探讨了其在作物种质资源新(等位)基因发掘中的发展趋势及展望。可以预见,与传统QTL作图及功能基因组学相整合的关联分析必将大大加快我国作物种质资源的研究进程,实现我国种质资源优势向基因资源优势的转变。