Small interfering RNA targeting HIF-1α reduces hypoxia-dependent transcription and radiosensitizes hypoxic HT 1080 human fibrosarcoma cells in vitro

Background: : Hypoxia inducible factor-1 has been identified as a potential target to overcome hypoxia-induced radioresistance The aim of the present study was to investigate whether selective HIF-1 inhibition via small interfering RNA (siRNA) targeting hypoxia-inducible factor 1α (HIF-1α) affects hypoxia-induced radioresistance in HT 1080 human fibrosarcoma cells. Material and Methods: : HIF-1α expression in HT 1080 human fibrosarcoma cells in vitro was silenced using HIF-1α siRNA sequence primers. Quantitative real-time polymerase chain reaction assay was performed to quantify the mRNA expression of HIF-1α. HIF-1α protein levels were studied by Western blotting at 20% (air) or after 12 hours at 0.1% O2 (hypoxia). Cells were assayed for clonogenic survival after irradiation with 2, 5, or 10 Gy, under normoxic or hypoxic conditions in the presence of HIF-1α-targeted or control siRNA sequences. A modified oxygen enhancement ratio (OER´) was calculated as the ratio of the doses to achieve the same survival at 0.1% O2 as at ambient oxygen tensions. OER´ was obtained at cell survival levels of 50%, 37%, and 10%. Results: : HIF-1α-targeted siRNA enhanced radiation treatment efficacy under severely hypoxic conditions compared to tumor cells treated with scrambled control siRNA. OER was reduced on all survival levels after treatment with HIF-1α-targeted siRNA, suggesting that inhibition of HIF-1 activation by using HIF-1α-targeted siRNA increases radiosensitivity of hypoxic tumor cells in vitro. Conclusion: : Inhibition of HIF-1 activation by using HIF-1α-targeted siRNA clearly acts synergistically with radiotherapy and increase radiosensitivity of hypoxic cells in vitr

Autonomous Visual Detection of Defects from Battery Electrode Manufacturing

The increasing global demand for high-quality and low-cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long-term reduction of reject rates, shortening of the production ramp-up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You-Only-Look-Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Offline-online pattern recognition for enabling time series anomaly detection on older NC machine tools

Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining