Technical studies for operations with real-time communications in robotic missions

Robotic telepresence operations between earth and space are of high research value for science as they enable operators on ground to perform physical tasks in space without the need of human presence. Real-Time telepresence with haptic-feedback and stereoscopic imaging, however, poses new requirements to physical parameters of the communication channel like loss, delay and jitter as well as to the protocols spoken between the participants. To meet the new requirements, past robotic missions like ROKVISS chose to use specialized and dedicated communication channels while bypassing the established ground station network infrastructure. However, performing robotic and standard TM/TC operations in parallel was impossible because the Space Link could only be locked by either of the communication chains. For future missions, we present a setup that multiplexes robotic science data and standard TM/TC into one physical channel. Real-time requirements are met because the setup makes use of several FPGAs that forward UDP packets in synchronization with a common master clock. We present test results and test measurements of this technology and compare the proposed setup to a software based solution. Furthermore we present general approaches, tools and techniques for real-time related tasks. Finally we discuss the use of Space Link and Space Link Extension protocols in the communication chain and their impact on the real-time requirements. Operational aspects of the new setup and protocols are discussed as well

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.BMBF, 01IS14013A, BBDC - Berliner Kompetenzzentrum für Big DataBMBF, 01IS18056A, TraMeExCo - Transparenter Begleiter für medizinische AnwendungDFG, EXC 2046, MATH+: Berlin Mathematics Research Cente

Reducing global CO2 emissions with the technologies we have

The energy intensities of the various industrial sectors differ considerably across countries. This suggests a potential for emissions reductions through improved accessibility to efficient technologies. This paper estimates an upper-bound CO2 emission mitigation potential that could theoretically be achieved by improved access to efficient technologies in industrial sectors. We develop a linear optimization framework that facilitates the exchange of sectoral production technologies based on the World Input-Output Database (WIOD), assuming perfect substitutability of technologies and homogeneity within economic sectors, while ignoring barriers to technological adoption and price driven adjustments. We consider the full global supply chain network and multiple upstream production inputs in addition to energy demand. In contrast to existing literature our framework allows to consider supply chain effects of technology replacements. We use our model to calculate emission reduction potentials for varying levels of access to technology. If best practice technologies were made available globally, CO2 emissions could theoretically be reduced by more than 10 gigatons (Gt). In fact, even second-tier production technologies would create significant global reduction potentials. We decompose sectoral emission reductions to identify contributions by changes in energy intensity, supply chain effects and changes in carbon intensities. Excluding the latter, we find that considering supply chain effects increases total mitigation potentials by 14%. The largest CO2 emission reduction potentials are found for a small set of developing countries.DFG, SFB 1026, Sustainable Manufacturing - Globale Wertschöpfung nachhaltig gestalte

Trends in age- and sex-specific lung cancer mortality in Europe and Northern America: Analysis of vital registration data from the WHO Mortality Database between 2000 and 2017

Background: In the context of new targeted therapies and immunotherapy as well as screening modalities for lung cancer patients, detailed mortality trends in Europe and Northern America are unknown. Methods: Time-trend analysis using vital registration data of Northern America and Europe from the WHO Mortality Database (years 2000/2017). To assess improvements in lung cancer mortality, we performed a population-averaged Poisson autoregressive analysis. The average annual percent change (AAPC) was used as a summary measure of overall and country-specific trends in mortality. Second, we studied time trends of lung cancer incidence and smoking prevalence rates. Findings: In the total population of 872·5 million people between 2015 and 2017, the average annual age-standardised mortality from lung cancer was 54·6 deaths per 100 000, with substantial differences across countries. Lung cancer was reported as the primary cause of death in 5·4 cases per 100 deaths. The age-standardised mortality rate decreased constantly (AAPC –1·5%) between 2000 and 2017. While mortality in men dropped annually by an average of −2·3%, mortality in women decreased by an average of −0·3%. This slight decline was driven exclusively by the USA. In contrast, 21 out of 31 countries registered a significant increase in female lung cancer mortality between 2000 and 2017, with Spain (AAPC 4·1%) and France (AAPC 3·6%) leading the list. Interpretation: Despite overall decreases in lung cancer mortality trends, female mortality remained unchanged or increased significantly in all countries except the USA. National mortality outcomes reflect variabilities in tobacco control, screening, therapeutic advances, and access to health care

Diamond-on-chip infrared absorption magnetic field camera

Integrated and fiber-packaged magnetic field sensors with a sensitivity sufficient to sense electric pulses propagating along nerves in life science applications and with a spatial resolution fine enough to resolve their propagation directions will trigger a tremendous step ahead not only in medical diagnostics, but in understanding neural processes. Nitrogen-vacancy centers in diamond represent the leading platform for such sensing tasks under ambient conditions. Current research on uniting a good sensitivity and a high spatial resolution is facilitated by scanning or imaging techniques. However, these techniques employ moving parts or bulky microscope setups. Despite being far developed, both approaches cannot be integrated and fiber-packaged to build a robust, adjustment-free hand-held device. In this work, we introduce novel concepts for spatially resolved magnetic field sensing and 2-D gradiometry with an integrated magnetic field camera. The camera is based on infrared absorption optically detected magnetic resonance (IRA-ODMR) mediated by perpendicularly intersecting infrared and pump laser beams forming a pixel matrix. We demonstrate our 3-by-3 pixel sensor's capability to reconstruct the position of an electromagnet in space. Furthermore, we identify routes to enhance the magnetic field camera's sensitivity and spatial resolution as required for complex sensing applications.Comment: Main part: 14 pages, 5 figure

Focused ion beam-scanning electron microscopy links pathological myelin outfoldings to axonal changes in mice lacking Plp1 or Mag

Healthy myelin sheaths consist of multiple compacted membrane layers closely encasing the underlying axon. The ultrastructure of CNS myelin requires specialized structural myelin proteins, including the transmembrane-tetraspan proteolipid protein (PLP) and the Ig-CAM myelin-associated glycoprotein (MAG). To better understand their functional relevance, we asked to what extent the axon/myelin-units display similar morphological changes if PLP or MAG are lacking. We thus used focused ion beam-scanning electron microscopy (FIB-SEM) to re-investigate axon/myelin-units side-by-side in Plp- and Mag-null mutant mice. By three-dimensional reconstruction and morphometric analyses, pathological myelin outfoldings extend up to 10 μm longitudinally along myelinated axons in both models. More than half of all assessed outfoldings emerge from internodal myelin. Unexpectedly, three-dimensional reconstructions demonstrated that both models displayed complex axonal pathology underneath the myelin outfoldings, including axonal sprouting. Axonal anastomosing was additionally observed in Plp-null mutant mice. Importantly, normal-appearing axon/myelin-units displayed significantly increased axonal diameters in both models according to quantitative assessment of electron micrographs. These results imply that healthy CNS myelin sheaths facilitate normal axonal diameters and shape, a function that is impaired when structural myelin proteins PLP or MAG are lacking

Myelin dysfunction drives amyloid-β deposition in models of Alzheimer's disease

The incidence of Alzheimer's disease (AD), the leading cause of dementia, increases rapidly with age, but why age constitutes the main risk factor is still poorly understood. Brain ageing affects oligodendrocytes and the structural integrity of myelin sheaths(1), the latter of which is associated with secondary neuroinflammation(2,3). As oligodendrocytes support axonal energy metabolism and neuronal health(4-7), we hypothesized that loss of myelin integrity could be an upstream risk factor for neuronal amyloid-beta (A beta) deposition, the central neuropathological hallmark of AD. Here we identify genetic pathways of myelin dysfunction and demyelinating injuries as potent drivers of amyloid deposition in mouse models of AD. Mechanistically, myelin dysfunction causes the accumulation of the A beta-producing machinery within axonal swellings and increases the cleavage of cortical amyloid precursor protein. Suprisingly, AD mice with dysfunctional myelin lack plaque-corralling microglia despite an overall increase in their numbers. Bulk and single-cell transcriptomics of AD mouse models with myelin defects show that there is a concomitant induction of highly similar but distinct disease-associated microglia signatures specific to myelin damage and amyloid plaques, respectively. Despite successful induction, amyloid disease-associated microglia (DAM) that usually clear amyloid plaques are apparently distracted to nearby myelin damage. Our data suggest a working model whereby age-dependent structural defects of myelin promote A beta plaque formation directly and indirectly and are therefore an upstream AD risk factor. Improving oligodendrocyte health and myelin integrity could be a promising target to delay development and slow progression of AD