6 research outputs found
The German National Registry of Primary Immunodeficiencies (2012-2017)
Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs.
Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel.
Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1–25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36% of patients. Familial cases were observed in 21% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0–88 years). Presenting symptoms comprised infections (74%) and immune dysregulation (22%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE- syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49% of all patients received immunoglobulin G (IgG) substitution (70%—subcutaneous; 29%—intravenous; 1%—unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy.
Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment
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Three Essays on Climate Risk
Climate change is forcing a shift in the characteristics of many natural hazards. Alongside other trends–such as increasing global interdependence, the rate of population and economic growth, and widening social inequalities–the risk from climate-sensitive natural hazards presents an expanding source of danger across the world. Research and practice on climate risk began by establishing standards for assessing hazards and implementing structural solutions to mitigate consequences. The field has evolved since then to include behavioral decision making and the multidimensionality in differences among people and places as determinants of exposure and vulnerability, respectively. These paradigm shifts in selecting factors for climate risk assessment happened alongside developments in modeling divergences between statistical and perceived risk as well as policy and scientific attention towards the distribution of hazards along socioeconomic and demographic lines. This dissertation, Three Essays on Climate Risk, contributes to answering pressing questions in climate risk research. The first essay, ‘Validating Social Vulnerability in Disaster Loss Models’ suggests that climate risk assessments should account for social vulnerability but practice caution since the relative contribution of social indicators varies across climate hazards. In the second essay, ‘Social and Spatial Inequalities in Climate Hazard Distributions,’ we compared multiple inequality metrics to find that exposure heterogeneously varies across metrics by choice of demographic​ and​ geographic partitioning. Researchers should therefore carefully design studies based upon theories of inequality formation and policy relevance. Preliminary results from the third essay, ‘Measuring Climate Risk Perception with Twitter Data,’ indicate that user-generated big data may soon serve as an appropriate supplement to survey data for measuring complex socio-cognitive phenomena. These essays advance climate risk measurement & modeling, unpack geographies of climate risk, and illustrate implications of improving climate risk information
IOT CONNECTIVITY WITH EDGE COMPUTING
Billions of Internet of Things (IoT) devices will be connected in the next decades. Most devices are for Massive Machine Type Communication (MMTC) applications. This requires the IoT infrastructure to be extremely efficient and scalable (like today’s Internet) to support more and more devices connected to the network over time. The cost per connection needs to be very low (like today’s Web services). The current network design with dedicated HW-based base stations (or IoT gateways) may be too costly. Furthermore, there is a vast amount of IoT radio standards, such as Narrowband-IoT (NB-IoT), LTE-M, BLE, ZigBee, Sigfox, LoRa, to give some examples, which all need to be implemented if they are supposed to be supported. The current approach requires to deploy parallel networks with dedicated base stations for different standards in one place. This further increases network costs. Cloud Radio Access Network (RAN) (c-RAN) has been proposed to centralize and cloudify baseband processing in a cloud infrastructure based on GPPs, which can potentially increase network flexibility and reduce the network Total Cost of Ownership (TCO) significantly. It can also be beneficiary for network performance by increased coordination possibilities. Nowadays, c-RAN still is on a concept level, because it is deemed difficult to implement due to complexity and reliability issues, e.g. for 4G/5G which requires sophisticated processing capabilities. The terminology of C-RAN today refers more to Centralized-RAN based on Digital Signal Processing (DSP) microcontrollers and ASICs, instead of c-RAN. However, the MMTC technologies are usually narrowband and designed with low complexity (considering cost of User Equipment (UE), power consumption, battery life time, etc.). Therefore, they are rather suitable for cloud implementation. Latency may be another issue for c-RAN. However, most of the MMTC applications are based on best-effort strategies and delay-tolerance. Therefore, c-RAN offers a promising solution to deliver the required efficiency and scalability for MMTC services. This master thesis is part of an effort to explore the possibilities, to increase the understanding and to gain hands-on experiences of IoT c-RAN implementation at the edge. It focuses on the NB-IoT downlink (DL) Physical (PHY) implementation as one example. However, IEEE 802.15.4 (PHY layer of e.g. Zigbee) has been integrated into the system within a collaboration between Ericsson and RISE SICS. This also shows, that c-RAN technology is able to unite multiple radio interfaces in one system leveraging Software (SW). In this study, we built a Software Defined Radio (SDR) testbed based on GNU Radio. The USRP B210 is the Hardware (HW) tool to test the implementation. Key components of the NB-IoT DL have been implemented. Orthogonal Frequency-Division Multiplex (OFDM) transmitter and receiver follow the NB-IoT numerology and implement algorithms for signal generation, time and frequency synchronization, as well as equalization and demodulation. The convolutional code of the Voyager missions with a coding rate R = 12 is used for performance evaluation. Different baseband modules have been tested and verified. Investigations have been carried out on the topic of latency. The measurements reveal a latency, which is higher than expected. Most likely, this is due to the large buffers underlying the GNU Radio scheduler in combination with the low speed of NB-IoT. The end-to-end system has been evaluated by field measurements (Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Packet Error Rate (PER)) conducted in an Ericsson office environment. With no Line-Of-Sight (LOS), the implemented system has a reach of >= 65 m (from the office lab on floor 4 to the other end of the corridor where GFTB ER NAP NIT Fronhaul Technologies is located) with only 0.5 % PER and a SNR of 15.9 dB. In this work, system and SW design of the testbed and implementation are presented, as well as the hands-on experiences. The testbed is ready for human interaction with a fascinating Telegram bot live demo
IOT CONNECTIVITY WITH EDGE COMPUTING
Billions of Internet of Things (IoT) devices will be connected in the next decades. Most devices are for Massive Machine Type Communication (MMTC) applications. This requires the IoT infrastructure to be extremely efficient and scalable (like today’s Internet) to support more and more devices connected to the network over time. The cost per connection needs to be very low (like today’s Web services). The current network design with dedicated HW-based base stations (or IoT gateways) may be too costly. Furthermore, there is a vast amount of IoT radio standards, such as Narrowband-IoT (NB-IoT), LTE-M, BLE, ZigBee, Sigfox, LoRa, to give some examples, which all need to be implemented if they are supposed to be supported. The current approach requires to deploy parallel networks with dedicated base stations for different standards in one place. This further increases network costs. Cloud Radio Access Network (RAN) (c-RAN) has been proposed to centralize and cloudify baseband processing in a cloud infrastructure based on GPPs, which can potentially increase network flexibility and reduce the network Total Cost of Ownership (TCO) significantly. It can also be beneficiary for network performance by increased coordination possibilities. Nowadays, c-RAN still is on a concept level, because it is deemed difficult to implement due to complexity and reliability issues, e.g. for 4G/5G which requires sophisticated processing capabilities. The terminology of C-RAN today refers more to Centralized-RAN based on Digital Signal Processing (DSP) microcontrollers and ASICs, instead of c-RAN. However, the MMTC technologies are usually narrowband and designed with low complexity (considering cost of User Equipment (UE), power consumption, battery life time, etc.). Therefore, they are rather suitable for cloud implementation. Latency may be another issue for c-RAN. However, most of the MMTC applications are based on best-effort strategies and delay-tolerance. Therefore, c-RAN offers a promising solution to deliver the required efficiency and scalability for MMTC services. This master thesis is part of an effort to explore the possibilities, to increase the understanding and to gain hands-on experiences of IoT c-RAN implementation at the edge. It focuses on the NB-IoT downlink (DL) Physical (PHY) implementation as one example. However, IEEE 802.15.4 (PHY layer of e.g. Zigbee) has been integrated into the system within a collaboration between Ericsson and RISE SICS. This also shows, that c-RAN technology is able to unite multiple radio interfaces in one system leveraging Software (SW). In this study, we built a Software Defined Radio (SDR) testbed based on GNU Radio. The USRP B210 is the Hardware (HW) tool to test the implementation. Key components of the NB-IoT DL have been implemented. Orthogonal Frequency-Division Multiplex (OFDM) transmitter and receiver follow the NB-IoT numerology and implement algorithms for signal generation, time and frequency synchronization, as well as equalization and demodulation. The convolutional code of the Voyager missions with a coding rate R = 12 is used for performance evaluation. Different baseband modules have been tested and verified. Investigations have been carried out on the topic of latency. The measurements reveal a latency, which is higher than expected. Most likely, this is due to the large buffers underlying the GNU Radio scheduler in combination with the low speed of NB-IoT. The end-to-end system has been evaluated by field measurements (Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Packet Error Rate (PER)) conducted in an Ericsson office environment. With no Line-Of-Sight (LOS), the implemented system has a reach of >= 65 m (from the office lab on floor 4 to the other end of the corridor where GFTB ER NAP NIT Fronhaul Technologies is located) with only 0.5 % PER and a SNR of 15.9 dB. In this work, system and SW design of the testbed and implementation are presented, as well as the hands-on experiences. The testbed is ready for human interaction with a fascinating Telegram bot live demo
GNU Radio
GNU Radio is a free & open-source software development toolkit that provides signal processing blocks to implement software radios. It can be used with readily-available, low-cost external RF hardware to create software-defined radios, or without hardware in a simulation-like environment. It is widely used in hobbyist, academic, and commercial environments to support both wireless communications research and real-world radio systems