Cooperative Sensor Anomaly Detection Using Global Information

Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves.     

重组大肠杆菌利用醋酸及乳酸为碳源合成PHB

以廉价的工业副产物醋酸及乳酸为碳源,对产PHB重组大肠杆菌进行培养,考察醋酸及乳酸的添加对重组大肠杆菌生长及PHB产量的影响。将来自Cupriavidusnecator的PHB合成操纵子phaCAB基因簇克隆至pBAD载体,得到产PHB菌株BL21＿pBAD＿phaCAB,以阿拉伯糖为诱导剂,在大肠杆菌中进行重组表达。分别使用LB及M9培养基,对重组菌株BL21＿pBAD＿phaCAB进行培养,研究其生长速度及PHB产量,探索产PHB重组大肠杆菌最适培养基。以添加0.04 g/L乳酸、1.2 g/L乳酸、0.02 g/L醋酸、0.6 g/L醋酸、0.04 g/L乳酸+0.02g/L醋酸、1.2g/L乳酸+0.4g/L醋酸的M9培养基(均含2 g/L葡萄糖)为实验组,以M9培养基(含2g/L葡萄糖)为对照组,考察醋酸及乳酸的添加对重组大肠杆菌生长及PHB产量的影响。分别取第6,12,24和36小时的培养液,分析其葡萄糖、醋酸及乳酸含量的变化。结果表明,低氮型M9培养基更适合产PHB重组大肠杆菌在低糖培养环境中生长。在葡萄糖消耗殆尽后,大肠杆菌能够以醋酸及乳酸为碳源进行代谢,因此在培养基中...     

Common variants near MC4R are associated with fat mass, weight and risk of obesity

To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.     

Senescence surveillance of pre-malignant hepatocytes limits liver cancer development

Upon the aberrant activation of oncogenes, normal cells can enter the cellular senescence program, a state of stable cell-cycle arrest, which represents an important barrier against tumour development in vivo. Senescent cells communicate with their environment by secreting various cytokines and growth factors, and it was reported that this 'secretory phenotype' can have pro- as well as anti-tumorigenic effects. Here we show that oncogene-induced senescence occurs in otherwise normal murine hepatocytes in vivo. Pre-malignant senescent hepatocytes secrete chemo- and cytokines and are subject to immune-mediated clearance (designated as 'senescence surveillance'), which depends on an intact CD4(+) T-cell-mediated adaptive immune response. Impaired immune surveillance of pre-malignant senescent hepatocytes results in the development of murine hepatocellular carcinomas (HCCs), thus showing that senescence surveillance is important for tumour suppression in vivo. In accordance with these observations, ras-specific Th1 lymphocytes could be detected in mice, in which oncogene-induced senescence had been triggered by hepatic expression of Nras(G12V). We also found that CD4(+) T cells require monocytes/macrophages to execute the clearance of senescent hepatocytes. Our study indicates that senescence surveillance represents an important extrinsic component of the senescence anti-tumour barrier, and illustrates how the cellular senescence program is involved in tumour immune surveillance by mounting specific immune responses against antigens expressed in pre-malignant senescent cells.     

硬脂酸改性处理TC4钛合金微弧氧化膜层耐蚀性的研究

在Ti₆Al₄V合金微弧氧化膜层上采用硬脂酸改性处理实现了陶瓷膜层的疏水化转变，显著提高了钛合金的耐蚀性能。利用接触角测试仪、扫描电子显微镜（SEM）、能谱仪（EDS）和傅里叶变换红外光谱仪（FT-IR）对膜层的润湿性、稳定性、微观结构和化学组成进行了分析，并通过动电位极化曲线和电化学阻抗分析了其腐蚀行为。结果表明，改性处理得到的疏水微弧氧化膜层试样能有效修复微弧氧化膜层缺陷，提高钛合金的耐蚀性能，说明硬脂酸改性处理与微弧氧化技术相结合有助于拓宽微弧氧化技术在钛合金上的应用。