Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data

The metabolic footprint of aging in mice

Aging is characterized by a general decline in cellular function, which ultimately will affect whole body homeostasis. Although DNA damage and oxidative stress all contribute to aging, metabolic dysfunction is a common hallmark of aging at least in invertebrates. Since a comprehensive overview of metabolic changes in otherwise healthy aging mammals is lacking, we here compared metabolic parameters of young and 2 year old mice. We systemically integrated in vivo phenotyping with gene expression, biochemical analysis, and metabolomics, thereby identifying a distinguishing metabolic footprint of aging. Among the affected pathways in both liver and muscle we found glucose and fatty acid metabolism, and redox homeostasis. These alterations translated in decreased long chain acylcarnitines and increased free fatty acid levels and a marked reduction in various amino acids in the plasma of aged mice. As such, these metabolites serve as biomarkers for aging and healthspan

Single photon lidar in mobile laser scanning:the sampling rate problem and initial solutions via spatial correlations

Abstract Single photon lidars (in solid state form) offer several benefits over pulsed lidars, such as independence of micro-mechanical moving parts or rotating joints, lower power consumption, faster acquisition rate, and reduced size. When mass produced, they will be cheaper and smaller and thus very attractive for mobile laser scanning applications. However, as these lidars operate by receiving single photons, they are very susceptible to background illumination such as sunlight. In other words, the observations contain a significant amount of noise, or to be specific, outliers. This causes trouble for measurements done in motion, as the sampling rate (i.e. the measurement frequency) should be low and high at the same time. It should be low enough so that target detection is robust, meaning that the targets can be distinguished from the single-photon avalanche diode (SPAD) triggings caused by the background photons. On the other hand, the sampling rate should be high enough to allow for measurements to be done from motion. Quick sampling reduces the probability that a sample gathered during motion would contain data from more than a single target at a specific range. Here, we study the exploitation of spatial correlations that exist between the observations as a mean to overcome this sampling rate paradox. We propose computational methods for short and long range. Our results indicate that the spatial correlations do indeed allow for faster and more robust sampling of measurements, which makes single photon lidars more attractive in (daylight) mobile laser scanning

Single photon lidar in mobile laser scanning: the sampling rate problem and initial solutions via spatial correlations

Single photon lidars (in solid state form) offer several benefits over pulsed lidars, such as independence of micro-mechanical moving parts or rotating joints, lower power consumption, faster acquisition rate, and reduced size. When mass produced, they will be cheaper and smaller and thus very attractive for mobile laser scanning applications. However, as these lidars operate by receiving single photons, they are very susceptible to background illumination such as sunlight. In other words, the observations contain a significant amount of noise, or to be specific, outliers. This causes trouble for measurements done in motion, as the sampling rate (i.e. the measurement frequency) should be low and high at the same time. It should be low enough so that target detection is robust, meaning that the targets can be distinguished from the single-photon avalanche diode (SPAD) triggings caused by the background photons. On the other hand, the sampling rate should be high enough to allow for measurements to be done from motion. Quick sampling reduces the probability that a sample gathered during motion would contain data from more than a single target at a specific range. Here, we study the exploitation of spatial correlations that exist between the observations as a mean to overcome this sampling rate paradox. We propose computational methods for short and long range. Our results indicate that the spatial correlations do indeed allow for faster and more robust sampling of measurements, which makes single photon lidars more attractive in (daylight) mobile laser scanning

The Novel μ-Opioid Receptor Antagonist, [N-Allyl-Dmt1]Endomorphin-2, Attenuates the Enhancement of GABAergic Neurotransmission by Ethanol

Aims: We investigated the effects of [N-allyl-Dmt1]endomorphin-2 (TL-319), a novel and highly potent μ-opioid receptor antagonist, on ethanol (EtOH)-induced enhancement of GABAA receptor-mediated synaptic activity in the hippocampus. Methods: Evoked and spontaneous inhibitory postsynaptic currents (eIPSCs and sIPSCs) were isolated from CA1 pyramidal cells from brain slices of male rats using whole-cell patch-clamp techniques. Results: TL-319 had no effect on the baseline amplitude of eIPSCs or the frequency of sIPSCs. However, it induced a dose-dependent suppression of an ethanol-induced increase of sIPSC frequency with full reversal at concentrations of 500 nM and higher. The non-specific competitive opioid receptor antagonist naltrexone also suppressed EtOH-induced increases in sIPSC frequency but only at a concentration of 60 μM. Conclusion: These data indicate that blockade of μ-opioid receptors by low concentrations of [N-allyl-Dmt1]endomorphin-2 can reverse ethanol-induced increases in GABAergic neurotransmission and possibly alter its anxiolytic or sedative effects. This suggests the possibility that high potency opioid antagonists may emerge as possible candidate compounds for the treatment of ethanol addiction