A method for modal loss factor estimation based on Gauss-Newton iteration

A new approach based on Gauss-Newton iteration is proposed to estimate modal damping. Noise resistance of the proposed method and half-power bandwidth method are analyzed and compared by plenty of simulations with different signal-to-noise ratios (SNR). The proposed method is more accurate and stable than half-power bandwidth method in all SNRs, especially when the noise level is high. If SNR ≤ 30 dB, the proposed method should be used for damping estimation instead of half-power bandwidth method. A damping estimation experiment is carried out with both methods, and the results indicate and verify that there is smaller variability for the proposed method

I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections

Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current methods based on images or point clouds have achieved satisfactory performance, localizing the images on a large-scale point cloud map remains a fairly unexplored problem. This cross-modal matching task is challenging due to the difficulty in extracting consistent descriptors from images and point clouds. In this paper, we propose the I2P-Rec method to solve the problem by transforming the cross-modal data into the same modality. Specifically, we leverage on the recent success of depth estimation networks to recover point clouds from images. We then project the point clouds into Bird's Eye View (BEV) images. Using the BEV image as an intermediate representation, we extract global features with a Convolutional Neural Network followed by a NetVLAD layer to perform matching. The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1\% over 80\% and 90\%, when localizing monocular and stereo images on point cloud maps, respectively. We further evaluate I2P-Rec on a 1 km trajectory dataset collected by an autonomous logistics car and show that I2P-Rec can generalize well to previously unseen environments.Comment: Accepted by IROS 202

Lithium-storage properties of gallic acid-reduced graphene oxide and silicon-graphene composites

Graphene oxide (GO) was de-oxygenated using gallic acid under mild conditions to prepare reduced graphene oxide (RGO). The resultant RGO showed a lithium-ion storage capacity of 1280\ua0mA\ua0h\ua0g at a current density of 200\ua0mA\ua0g after 350 cycles when used as an anode for lithium ion batteries. The RGO was further used to stabilize silicon (Si) nanoparticles to prepare silicon-graphene composite electrode materials. Experimental results showed that a composite electrode prepared with a mass ratio of Si:GO\ua0=\ua01:2 exhibited the best lithium ion storage performance

Pharmacokinetics, distribution, metabolism, and excretion of body-protective compound 157, a potential drug for treating various wounds, in rats and dogs

Body-protective compound (BPC) 157 demonstrates protective effects against damage to various organs and tissues. For future clinical applications, we had previously established a solid-phase synthesis process for BPC157, verified its biological activity in different wound models, and completed preclinical safety evaluations. This study aimed to investigate the pharmacokinetics, excretion, metabolism, and distribution profiles of BPC157. After a single intravenous (IV) administration, single intramuscular (IM) administrations at three doses in successive increments along with repeated IM administrations, the elimination half-life (t1/2) of prototype BPC157 was less than 30 min, and BPC157 showed linear pharmacokinetic characteristics in rats and beagle dogs at all doses. The mean absolute bioavailability of BPC157 following IM injection was approximately 14%–19% in rats and 45%–51% in beagle dogs. Using [3H]-labeled BPC157 and radioactivity examination, we proved that the main excretory pathways of BPC157 involved urine and bile. [3H]BPC157 was rapidly metabolized into a variety of small peptide fragments in vivo, thus forming single amino acids that entered normal amino acid metabolism and excretion pathways. In conclusion, this study provides the first analysis of the pharmacokinetics of BPC157, which will be helpful for its translation in the clinic

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

Revealing Nighttime Construction-related Activities from a Distributed Air Quality Sensor Network

82 pagesCoarse particulate matter (PMc) refers to aerosol particles between 2.5 and 10 μm in diameter. Exposure to ambient PMc has been associated with adverse health effects such as cardiovascular diseases and respiratory mortality. In this study, we analyzed the spatial and temporal patterns of PMc levels in a 165-node PM monitoring network in Xi’an, China. We employed a technique called network analysis, focusing on peer-to-peer comparison within the network. The network analysis revealed that the highest PMc concentrations in the city occurred during late night and early morning. Through further analysis using satellite-based aerial imagery and data mining of internet resources, we confirmed with high confidence that the construction-related emission sources, both at the construction sites and from traffic transporting construction materials and debris, are a key contributor. It could be found that both local policies and construction practices incentivized construction contractors to implement earthwork at nighttime, leading to distinct peak PMc concentrations from late night to early morning, which often triggered both noise and air pollution complaints from residents. Our work demonstrated the potential of utilizing air quality monitoring networks for construction-related environmental monitoring and enforcement. Based on our findings, we also recommend that policymakers re-assess construction-related policies by considering the trade-offs among efficiency, safety, air quality, and noise

Unsupervised learning for robust working memory.

Working memory is a core component of critical cognitive functions such as planning and decision-making. Persistent activity that lasts long after the stimulus offset has been considered a neural substrate for working memory. Attractor dynamics based on network interactions can successfully reproduce such persistent activity. However, it requires a fine-tuning of network connectivity, in particular, to form continuous attractors which were suggested for encoding continuous signals in working memory. Here, we investigate whether a specific form of synaptic plasticity rules can mitigate such tuning problems in two representative working memory models, namely, rate-coded and location-coded persistent activity. We consider two prominent types of plasticity rules, differential plasticity correcting the rapid activity changes and homeostatic plasticity regularizing the long-term average of activity, both of which have been proposed to fine-tune the weights in an unsupervised manner. Consistent with the findings of previous works, differential plasticity alone was enough to recover a graded-level persistent activity after perturbations in the connectivity. For the location-coded memory, differential plasticity could also recover persistent activity. However, its pattern can be irregular for different stimulus locations under slow learning speed or large perturbation in the connectivity. On the other hand, homeostatic plasticity shows a robust recovery of smooth spatial patterns under particular types of synaptic perturbations, such as perturbations in incoming synapses onto the entire or local populations. However, homeostatic plasticity was not effective against perturbations in outgoing synapses from local populations. Instead, combining it with differential plasticity recovers location-coded persistent activity for a broader range of perturbations, suggesting compensation between two plasticity rules