Optimized digital filtering techniques for radiation detection with HPGe detectors

This paper describes state-of-the-art digital filtering techniques that are part of GEANA, an automatic data analysis software used for the GERDA experiment. The discussed filters include a novel, nonlinear correction method for ballistic deficits, which is combined with one of three shaping filters: a pseudo-Gaussian, a modified trapezoidal, or a modified cusp filter. The performance of the filters is demonstrated with a 762 g Broad Energy Germanium (BEGe) detector, produced by Canberra, that measures {\gamma}-ray lines from radioactive sources in an energy range between 59.5 and 2614.5 keV. At 1332.5 keV, together with the ballistic deficit correction method, all filters produce a comparable energy resolution of ~1.61 keV FWHM. This value is superior to those measured by the manufacturer and those found in publications with detectors of a similar design and mass. At 59.5 keV, the modified cusp filter without a ballistic deficit correction produced the best result, with an energy resolution of 0.46 keV. It is observed that the loss in resolution by using a constant shaping time over the entire energy range is small when using the ballistic deficit correction method

Modeling Infection with Multi-agent Dynamics

Developing the ability to comprehensively study infections in small populations enables us to improve epidemic models and better advise individuals about potential risks to their health. We currently have a limited understanding of how infections spread within a small population because it has been difficult to closely track an infection within a complete community. The paper presents data closely tracking the spread of an infection centered on a student dormitory, collected by leveraging the residents' use of cellular phones. The data are based on daily symptom surveys taken over a period of four months and proximity tracking through cellular phones. We demonstrate that using a Bayesian, discrete-time multi-agent model of infection to model real-world symptom reports and proximity tracking records gives us important insights about infec-tions in small populations

Crowdbreaks: Tracking Health Trends Using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labeling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labeling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work describes the technical aspects of the platform, thereby covering the functionalities at its current state and exploring its future use cases and extensions

The Twitter of Babel: Mapping World Languages through Microblogging Platforms

Large scale analysis and statistics of socio-technical systems that just a few short years ago would have required the use of consistent economic and human resources can nowadays be conveniently performed by mining the enormous amount of digital data produced by human activities. Although a characterization of several aspects of our societies is emerging from the data revolution, a number of questions concerning the reliability and the biases inherent to the big data “proxies” of social life are still open. Here, we survey worldwide linguistic indicators and trends through the analysis of a large-scale dataset of microblogging posts. We show that available data allow for the study of language geography at scales ranging from country-level aggregation to specific city neighborhoods. The high resolution and coverage of the data allows us to investigate different indicators such as the linguistic homogeneity of different countries, the touristic seasonal patterns within countries and the geographical distribution of different languages in multilingual regions. This work highlights the potential of geolocalized studies of open data sources to improve current analysis and develop indicators for major social phenomena in specific communities

In vitro culturing of porcine tracheal mucosa as an ideal model for investigating the influence of drugs on human respiratory mucosa

It has been previously shown that fresh mucosa from different mammals could serve as raw material for in vitro culturing with the differentiation of cilia, which are the most important morphological structures for the function of the mucociliary system. Increasing legal restrictions on the removal of human tissue and changing surgical techniques have led to a lack of fresh human mucosa for culturing. Most of the animals that have been used as donors up to now are genetically not very close to human beings and must all be sacrificed for such studies. We, therefore, established a modified system of culturing mucosa cells from the trachea of pigs, which is available as a regular by-product after slaughtering. With respect to the possibility of developing “beating” cilia, it could be shown that the speed of cell proliferation until adhesion to the coated culture dishes, the formation of conjunctions of cell clusters and the proliferation of cilia were comparable for porcine and human mucosa. Moreover, it could be demonstrated that the porcine cilia beat frequency of 7.57 ± 1.39 Hz was comparable to the human mucosa cells beat frequency of 7.3 ± 1.4 Hz and that this beat frequency was absolutely constant over the investigation time of 360 min. In order to prove whether the reaction to different drugs is comparable between the porcine and human cilia, we initially tested benzalkonium chloride, which is known to be toxic for human cells, followed by naphazoline, which we found in previous studies on human mucosa to be non-toxic. The results clearly showed that the functional and morphological reactions of the porcine ciliated cells to these substances were similar to the reaction we found in the in vitro cultured human mucosa

Mobile Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection

The addition of contextual sensors to mobile radiation sensors provides valuable information about radiological source encounters that can assist in adjudication of alarms. This study explores how computer-vision based object detection and tracking analyses can be used to augment radiological data from a mobile detector system. We study how contextual information (streaming video and LiDAR) can be used to associate dynamic pedestrians or vehicles with radiological alarms to enhance both situational awareness and detection sensitivity. Possible source encounters were staged in a mock urban environment where participants included pedestrians and vehicles moving in the vicinity of an intersection. Data was collected with a vehicle equipped with 6 NaI(Tl) 2 inch times 4 inch times 16 inch detectors in a hexagonal arrangement and multiple cameras, LiDARs, and an IMU. Physics-based models that describe the expected count rates from tracked objects are used to correlate vehicle and/or pedestrian trajectories to measured count-rate data through the use of Poisson maximum likelihood estimation and to discern between source-carrying and non-source-carrying objects. In this work, we demonstrate the capabilities of our source-object attribution approach as applied to a mobile detection system in the presence of moving sources to improve both detection sensitivity and situational awareness in a mock urban environment

Sleep Hygiene and Problem Behaviors in Snoring and Non- Snoring School-Age Children

Objectives—The effects of sleep-disordered breathing, sleep restriction, dyssomnias, and parasomnias on daytime behavior in children have been previously assessed. However, the potential relationship(s) between sleep hygiene and children’s daytime behavior remain to be explored. The primary goal of this study was to investigate the relationship between sleep hygiene and problematic behaviors in non-snoring and habitually snoring children. Methods—Parents of 100 5- to 8-year-old children who were reported to snore “frequently” to “almost always,” and of 71 age-, gender-, and ethnicity-matched children who were reported to never snore participated in this study. As part of a larger, ongoing study, children underwent nocturnal polysomnography and parents were asked to complete the Children’s Sleep Hygiene Scale (CSHS) and the Conners’ Parent Rating Scales-Revised (CPRS-R:L). Results—In the snoring group, strong negative correlations (r = −.39, p Conclusions—Parental reports of behavioral patterns in snoring children indicate that poorer sleep hygiene is more likely to be associated with behavior problems, including hyperactivity, impulsivity, and oppositional behavior. In contrast, no significant relationships between slee