Extended Preintegration for Relative State Estimation of Leader-Follower Platform

Relative state estimation using exteroceptive sensors suffers from limitations of the field of view (FOV) and false detection, that the proprioceptive sensor (IMU) data are usually engaged to compensate. Recently ego-motion constraint obtained by Inertial measurement unit (IMU) preintegration has been extensively used in simultaneous localization and mapping (SLAM) to alleviate the computation burden. This paper introduces an extended preintegration incorporating the IMU preintegration of two platforms to formulate the motion constraint of relative state. One merit of this analytic constraint is that it can be seamlessly integrated into the unified graph optimization framework to implement the relative state estimation in a high-performance real-time tracking thread, another point is a full smoother design with this precise constraint to optimize the 3D coordinate and refine the state for the refinement thread. We compare extensively in simulations the proposed algorithms with two existing approaches to confirm our outperformance. In the real virtual reality (VR) application design with the proposed estimator, we properly realize the visual tracking of the six degrees of freedom (6DoF) controller suitable for almost all scenarios, including the challenging environment with missing features, light mutation, dynamic scenes, etc. The demo video is at https://www.youtube.com/watch?v=0idb9Ls2iAM. For the benefit of the community, we make the source code public

A Frequent Pattern Mining Algorithm Based on Concept Lattice

The concept lattice is an effective tool for data analysis and rule extraction, it is often well to mine frequent patterns by making use of concept lattice. In this paper, a frequent itemset mining algorithm FPCL based on concept lattice which builds lattice in batches, the algorithm builds lattice down layer by layer through the layer concept nodes and temporary nodes based on hierarchical concept lattice; and seeks up the parent-child relationship upward concept nodes layer by layer, which can be generated the Hasse diagram with the inter-layer connection. In addition, in the process of the generation of each lattice node, we do the dynamic pruning for the concept lattice based on the minimum support degree and relevant properties, and delete a large number of non-frequent, repeat and containing nodes, such that redundant lattice nodes do not generate, thus the space and time complexities of the algorithm are greatly enhanced. The experimental results show that the algorithm has a good performance

PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact reduction (MAR) task. The supervised dual-domain methods perform well on synthesized data, while unsupervised methods with unpaired data are more generalized on clinical data. However, most existing methods intend to restore the corrupted sinogram within metal trace, which essentially remove beam hardening artifacts but ignore other components of metal artifacts, such as scatter, non-linear partial volume effect and noise. In this paper, we mathematically derive a physical property of metal artifacts which is verified via Monte Carlo (MC) simulation and propose a novel physics based non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component, and an image restoration network (IR-Net) is proposed to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the integrity of anatomical structures in the image domain, and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. Extensive experiments on simulation and clinical data demonstrate that our method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure

Molecular Programming of Biodegradable Nanoworms via Ionically Induced Morphology Switch toward Asymmetric Therapeutic Carriers

Engineering biodegradable nanostructures with precise morphological characteristics is a key objective in nanomedicine. In particular, asymmetric (i.e., nonspherical) nanoparticles are desirable due to the advantageous effects of shape in a biomedical context. Using molecular engineering, it is possible to program unique morphological features into the self-assembly of block copolymers (BCPs). However, the criteria of biocompatibility and scalability limit progress due to the prevalence of nondegradable components and the use of toxic solvents during fabrication. To address this shortfall, a robust strategy for the fabrication of morphologically asymmetric nanoworms, comprising biodegradable BCPs, has been developed. Modular BCPs comprising poly (ethylene glycol)-block-poly(caprolactone-gradient-trimethylene carbonate) (PEG−PCLgTMC), with a terminal chain of quaternary ammonium-TMC (PTMC-Q), undergo self-assembly via direct hydration into well-defined nanostructures. By controlling the solution ionic strength during hydration, particle morphology switches from spherical micelles to nanoworms (of varying aspect ratio). This ionically-induced switch is driven by modulation of chain packing with salts screening interchain repulsions, leading to micelle elongation. Nanoworms can be loaded with cytotoxic cargo (e.g., doxorubicin) at high efficiency, preferentially interact with cancer cells, and increase tumor penetration. This work showcases the ability to program assembly of BCPs and the potential of asymmetric nanosystems in anticancer drug delivery

Proteomic analysis and biochemical correlates of mitochondrial dysfunction following low-intensity primary blast exposure

Service members during military actions or combat training are frequently exposed to primary blasts by weaponry. Most studies have investigated moderate or severe brain injuries from blasts generating overpressures over 100-kPa, while understanding the pathophysiology of low-intensity blast (LIB)-induced mild traumatic brain injury (mTBI) leading to neurological deficits remains elusive. Our recent studies, using an open-field LIB-induced mTBI mouse model with an peak overpressure at 46.6-kPa, demonstrated behavioral impairments and brain nanoscale damages, notably mitochondrial and axonal ultrastructural changes. In this study, we used tandem mass tagged (TMT) quantitative proteomics and bioinformatics analysis to seek insights into the molecular mechanisms underlying ultrastructural pathology. Changes in global- and phospho-proteomes were determined at 3 and 24 hours, 7 and 30 days post injury (DPI), and to investigate the biochemical and molecular correlates of mitochondrial dysfunction. Results showed striking dynamic changes in a total of 2216 global and 459 phosphorylated proteins at vary time points after blast. Disruption of key canonical pathways included evidence of mitochondrial dysfunction, oxidative stress, axonal/cytoskeletal/synaptic dysregulation, and neurodegeneration. Bioinformatic analysis identified blast induced trends in networks related to cellular growth/development/movement/assembly and cell-to-cell signaling interactions. With observations of proteomic changes, we found LIB-induced oxidative stress associated with mitochondrial dysfunction mainly at 7 and 30 DPI. These dysfunctions included impaired fission-fusion dynamics, diminished mitophagy, decreased oxidative phosphorylation, and compensated respiration-relevant enzyme activities. Insights on the early pahtogenesis of primary LIB-induced brain damage provide a template for further characterization of its chronic effects, identification of potential biomarkers and targets for intervention.Hailong song (1), Mei Chen (6), Chen Chen (2), Jiankun Cui (1,7), Catherine Johnson (3), Jianlin Cheng (2), Xiaowan Wang (4), Russell H. Swerdlow (4), Ralph DePalma (5), Weiming Xia (6), Zezong Gu (1,7) ; 1. Department of Pathology & Anatomical Sciences, University of Missouri School of Medicine; 2. Department of Computer Sciences, University of Missouri; 3. Department of Mining and Nuclear Engineering, Missouri University of Science and Technology; 4. Department of Neurology, University of Kansas Medical Center; 5. Office of Research and Development, Department of Veterans Affairs; 6. Bedford VA Medical Center; 7. Truman VA Hospital Research Servic

Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies

Protein content (PC), an important trait in soybean (Glycine max) breeding, is controlled by multiple genes with relatively small effects. To identify the quantitative trait nucleotides (QTNs) controlling PC, we conducted a multi-locus genome-wide association study (GWAS) for PC in 144 four-way recombinant inbred lines (FW-RILs). All the FW-RILs were phenotyped for PC in 20 environments, including four locations over 4 years with different experimental treatments. Meanwhile, all the FW-RILs were genotyped using SoySNP660k BeadChip, producing genotype data for 109,676 non-redundant single-nucleotide polymorphisms. A total of 129 significant QTNs were identified by five multi-locus GWAS methods. Based on the 22 common QTNs detected by multiple GWAS methods or in multiple environments, pathway analysis identified 8 potential candidate genes that are likely to be involved in protein synthesis and metabolism in soybean seeds. Using superior allele information for 22 common QTNs in 22 elite and 7 inferior lines, we found higher superior allele percentages in the elite lines and lower percentages in the inferior lines. These findings will contribute to the discovery of the polygenic networks controlling PC in soybean, increase our understanding of the genetic foundation and regulation of PC, and be useful for molecular breeding of high-protein soybean varieties

Transposon-mediated BAC transgenesis in human ES cells

Transgenesis is a cornerstone of molecular biology. The ability to integrate a specifically engineered piece of DNA into the genome of a living system is fundamental to our efforts to understand life and exploit its implications for medicine, nanotechnology and bioprospecting. However, transgenesis has been hampered by position effects and multi-copy integration problems, which are mainly due to the use of small, plasmid-based transgenes. Large transgenes based on native genomic regions cloned into bacterial artificial chromosomes (BACs) circumvent these problems but are prone to fragmentation. Herein, we report that contrary to widely held notions, large BAC-sized constructs do not prohibit transposition. We also report the first reliable method for BAC transgenesis in human embryonic stem cells (hESCs). The PiggyBac or Sleeping Beauty transposon inverted repeats were integrated into BAC vectors by recombineering, followed by co-lipofection with the corresponding transposase in hESCs to generate robust fluorescent protein reporter lines for OCT4, NANOG, GATA4 and PAX6. BAC transposition delivers several advantages, including increased frequencies of single-copy, full-length integration, which will be useful in all transgenic systems but especially in difficult venues like hESCs

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