3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System

Millimeter-wave (mmW) radar is widely applied to advanced autopilot assistance systems. However, its small antenna aperture causes a low imaging resolution. In this paper, a new distributed mmW radar system is designed to solve this problem. It forms a large sparse virtual planar array to enlarge the aperture, using multiple-input and multiple-output (MIMO) processing. However, in this system, traditional imaging methods cannot apply to the sparse array. Therefore, we also propose a 3D super-resolution imaging method specifically for this system in this paper. The proposed method consists of three steps: (1) using range FFT to get range imaging, (2) using 2D adaptive diagonal loading iterative adaptive approach (ADL-IAA) to acquire 2D super-resolution imaging, which can satisfy this sparsity under single-measurement, (3) using constant false alarm (CFAR) processing to gain final 3D super-resolution imaging. The simulation results show the proposed method can significantly improve imaging resolution under the sparse array and single-measurement

Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong’an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong’an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable

Ginsenoside Rg1 Attenuates Oligomeric Aβ1-42-Induced Mitochondrial Dysfunction

Mitochondrial dysfunction is one of the major pathological changes seen in Alzheimer's disease (AD). Amyloid beta-peptide (Aβ), a neurotoxic peptide, accumulates in the brain of AD subjects and mediates mitochondrial and neuronal stress. Therefore, protecting mitochondrion from Aβ-induced toxicity holds potential benefits for halting and treating and perhaps preventing AD. Here, we report that administration of ginsenoside Rg1, a known neuroprotective drug, to primary cultured cortical neurons, rescues Aβ-mediated mitochondrial dysfunction as shown by increases in mitochondrial membrane potential, ATP levels, activity of cytochrome c oxidase (a key enzyme associated with mitochondrial respiratory function), and decreases in cytochrome c release. The protective effects of Rg1 on mitochondrial dysfunction correlate to neuronal injury in the presence of Aβ. This finding suggests that ginsenoside Rg1 may attenuate Aβ-induced neuronal death through the suppression of intracellular mitochondrial oxidative stress and may rescue neurons in AD

Isolation, purification and PEG-mediated transient expression of mesophyll protoplasts in Camellia oleifera

Background: Camellia oleifera (C. oleifera) is a woody edible oil crop of great economic importance. Because of the lack of modern biotechnology research, C. oleifera faces huge challenges in both breeding and basic research. The protoplast and transient transformation system plays an important role in biological breeding, plant regeneration and somatic cell fusion. The objective of this present study was to develop a highly efficient protocol for isolating and purifying mesophyll protoplasts and transient transformation of C. oleifera. Several critical factors for mesophyll protoplast isolation from C. oleifera, including starting material (leaf age), pretreatment, enzymatic treatment (type of enzyme, concentration and digestion time), osmotic pressure and purification were optimized. Then the factors affecting the transient transformation rate of mesophyll protoplasts such as PEG molecular weights, PEG4000 concentration, plasmid concentration and incubation time were explored.Results: The in vitro grown seedlings of C. oleifera 'Huashuo' were treated in the dark for 24 h, then the 1st to 2nd true leaves were picked and vacuumed at - 0.07 MPa for 20 min. The maximum yield (3.5 x 10(7)/g.W) and viability (90.9%) of protoplast were reached when the 1st to 2nd true leaves were digested in the enzymatic solution containing1.5% (w/v) Cellulase R-10, 0.5% (w/v) Macerozyme R-10 and 0.25% (w/v) Snailase and 0.4 M mannitol for 10 h. Moreover, the protoplast isolation method was also applicable to the other two cultivars, the protoplast yield for 'TXP14' and 'DP47' was 1.1 x 10(7)/g.FW and 2.6 x 10(7)/g. FW, the protoplast viability for 'TXP14' and 'DP47' was 90.0% and 88.2%. The purification effect was the best when using W buffer as a cleaning agent by centrifugal precipitation. The maximum transfection efficiency (70.6%) was obtained with the incubation of the protoplasts with 15 mu g plasmid and 40% PEG4000 for 20 min.Conclusion: In summary, a simple and efficient system for isolation and transient transformation of C. oleifera mesophyll protoplast is proposed, which is of great significance in various aspects of C. oleifera research, including the study of somatic cell fusion, genome editing, protein function, signal transduction, transcriptional regulation and multi-omics analyses

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs

Towards precise classification of cancers based on robust gene functional expression profiles

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level