Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM

Localization and mapping with heterogeneous multi-sensor fusion have been prevalent in recent years. To adequately fuse multi-modal sensor measurements received at different time instants and different frequencies, we estimate the continuous-time trajectory by fixed-lag smoothing within a factor-graph optimization framework. With the continuous-time formulation, we can query poses at any time instants corresponding to the sensor measurements. To bound the computation complexity of the continuous-time fixed-lag smoother, we maintain temporal and keyframe sliding windows with constant size, and probabilistically marginalize out control points of the trajectory and other states, which allows preserving prior information for future sliding-window optimization. Based on continuous-time fixed-lag smoothing, we design tightly-coupled multi-modal SLAM algorithms with a variety of sensor combinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems, in which online timeoffset calibration is also naturally supported. More importantly, benefiting from the marginalization and our derived analytical Jacobians for optimization, the proposed continuous-time SLAM systems can achieve real-time performance regardless of the high complexity of continuous-time formulation. The proposed multi-modal SLAM systems have been widely evaluated on three public datasets and self-collect datasets. The results demonstrate that the proposed continuous-time SLAM systems can achieve high-accuracy pose estimations and outperform existing state-of-the-art methods. To benefit the research community, we will open source our code at ~\url{https://github.com/APRIL-ZJU/clic}

Fabrication of a Metal Micro Mold by Using Pulse Micro Electroforming

Microfluidic devices have been widely used for biomedical and biochemical applications. Due to its unique characteristics, polymethyl methacrylate (PMMA) show great potential in fabricating microfluidic devices. Hot embossing technology has established itself as a popular method of preparing polymer microfluidic devices in both academia and industry. However, the fabrication of the mold used in hot embossing is time-consuming in general and often impractical for economically efficient prototyping. This paper proposes a modified technology for preparing metal micro molds by using pulse micro electroforming directly on metallic substrate, which could save time and reduce costs. In this method, an additive was used to avoid surface defect on deposited nickel. A chemical etching process was performed on the metallic substrate before the electroforming process in order to improve the bonding strength between the deposited structure and substrate. Finally, with the aim of obtaining a metal micro mold with high surface quality (low surface roughness), an orthogonal experiment was designed and conducted to optimize the electroforming parameters. Additionally, metal micro molds with different structures were well prepared by using the optimized parameters

Programming cell entry of molecules via reversible synthetic DNA circuits on cell membrane

Cellular uptake of biomolecules is crucial for regulating cell function. However, powerful and biocompatible tools for dynamically manipulating the cell entry of single-stranded DNAs (ssDNAs) remain elusive. Herein, we constructed synthetic DNA circuits on the cell membrane to program the cell entry of ssDNAs, using toehold-mediated DNA strand displacement reactions. We found that the dimerization and trimerization of cholesterol-ssDNAs enhanced membrane-anchoring and cellular uptake of ssDNAs. Moreover, we demonstrated that de-dimerization and de-trimerization of cholesterol-ssDNAs could be accomplished by inputting recovery ssDNAs into the synthetic DNA circuits, which could simultaneously decrease the cellular uptake of ssDNAs. We speculate that operating the synthetic DNA circuits on the cell membrane will be a powerful strategy for regulating the cellular uptake of exogenous materials, which has important implications for bioimaging, drug delivery, and gene therapy

The Value of Cardiopulmonary Exercise Testing in Predicting the Severity of Coronary Artery Disease

Background: There have been a limited number of quantitative studies on the relationship between coronary artery disease (CAD) and cardiorespiratory fitness (CRF), as measured by cardiopulmonary exercise testing (CPET). Thus, we aimed to investigate the association between CRF and the severity of coronary artery disease from the most comprehensive perspective possible, and to affirm the predictive value of CPET in the severity assessment of CAD. Methods: Our study included 280 patients with coronary angiography, who had undergone CPET in Tongji Hospital. The patients’ CRF was measured through their peak oxygen uptake (VO2@peak), their oxygen uptake at the anaerobic threshold (VO2@AT) and through other parameters of CPET on a bicycle ergometer. The severity of the coronary artery disease was assessed in the following three layers: functionally significant lesions (quantitative flow ratio [QFR] ≤ 0.8), the number of stenotic coronary arteries (SCA, stenosis ≥ 50%) and the Gensini score. The correlation analyses were carried out between the CRF and the severity of the coronary artery disease. A ROC curve was plotted, and the AUC was calculated to distinguish the severe CAD and the non-severe CAD patients, as measured by the QFR, the number of SCA, and the Gensini score. Results: The VO2@AT and VO2@peak were inversely associated with the QFR. The VO2@AT, VO2@peak and VO2/kg@peak were associated with the number of SCA. Meanwhile, the VO2@AT, VO2/kg@AT, VO2@peak and VO2/kg@peak were associated with the Gensini score. An ROC analysis proved that a combination of traditional clinical risk factors and the VO2@peak/VO2prediction is valuable in predicting CAD severity. Conclusions: Our study demonstrated a strong and inverse association between CRF and the severity of CAD. A combination of traditional clinical risk factors and CRF is valuable in predicting CAD severity

DNA biomolecular-electronic encoder and decoder devices constructed by multiplex biosensors

We fabricated and tested encoders and decoders based on a multiplex, DNA-based electrochemical biosensor that uses electronic (electrochemical) signals as its readout. These devices use two or more sequence-specific DNA probes, with each being modified with a distinct redox reporter. These probes, when interrogated together, serve as encoders and decoders, converting patterns that are encoded and decoded by the presence or absence of specific DNA sequences into specific electronic outputs. We demonstrated these multifunctional, bio-electrochemical devices, for example, 4-to-2 and 8-to-3 encoders and 1-to-2 and 2-to-3 decoders. Accordingly, these devices bridge the division between DNA-based devices and silicon-based electronics. NPG Asia Materials (2012) 4, e1; doi:10.1038/am.2012.1; published online 18 January 2012 Keywords: biosensors; decoder; DNA-based; encoder; multiplex INTRODUCTION In recent decades, serious attempts have been made to implement computational approaches, models and paradigms that utilize the information transfer and processing abilities of naturally occurring and modified biomolecules. 1-11 A decade ago, for example, Adleman performed a now-classic experiment in which computations were conducted using DNA molecules, demonstrating that biology can provide new computing substrates

Pathway-based Analysis of the Hidden Genetic Heterogeneities in Cancers

Many cancers apparently showing similar phenotypes are actually distinct at the molecular level, leading to very different responses to the same treatment. It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers. Nevertheless, it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers. Therefore, we aimed to test this possibility in the present study. First, we used a NCI60 dataset to validate the ability of pathways to correctly partition samples. Next, we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma (DLBCL). Finally, the clinical significance of the identified subtypes was verified using survival analysis. For the NCI60 dataset, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Subsequently, for a DLBCL dataset, we identified three hidden subtypes that showed very different 10-year overall survival rates (90%, 46% and 20%) and were highly significantly (P = 0.008) correlated with the clinical survival rate. This study demonstrated that the pathway-based approach is promising for unveiling genetic heterogeneities in complex human diseases

Selection of Appropriate Spatial Resolution for the Meteorological Data for Regional Winter Wheat Potential Productivity Simulation in China Based on WheatGrow Model

The crop model based on physiology and ecology has been widely applied to the simulation of regional potential productivity. By determining the appropriate spatial resolution of meteorological data required for model simulation for different regions, we can reduce the difficulty of acquiring model input data, thereby improving the regional computing efficiency of the model and increasing the model applications. In this study, we investigated the appropriate spatial resolution of meteorological data needed for the regional potential productivity simulation of the WheatGrow model by scale effect index and verify the feasibility of using the landform to obtain the appropriate spatial resolution of meteorological data required by the potential productivity simulation for the winter wheat region of China. The research results indicated that the spatial variation of landforms in the winter wheat region of China is significantly correlated to the spatial variation of multi-year meteorological data. Based on the scale effect index, we can obtain a spatial distribution of appropriate spatial resolution for the meteorological data required for the regional potential productivity simulation of the WheatGrow model for the winter wheat region of China. Moreover, although we can use the spatial heterogeneity of landforms to guide the selection of appropriate spatial resolution for the meteorological data, in the regions where the spatial heterogeneity of the landform is relatively weak or relatively strong over a small range, the method of using a single heterogeneity index derived from semi-variogram cannot well reflect the scale effect of simulation results and needs further improvement

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

<div><p>Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.</p></div