We propose an efficient open-loop channel estimator for a millimeter-wave (mm-wave) hybrid multiple-input multiple-output (MIMO) system consisting of radio-frequency (RF) beamformers with large ...antenna arrays followed by a baseband MIMO processor. A sparse signal recovery problem exploiting the sparse nature of mm-wave channels is formulated for channel estimation based on the parametric channel model with quantized angles of departures/arrivals (AoDs/AoAs), called the angle grids. The problem is solved by the orthogonal matching pursuit (OMP) algorithm employing a redundant dictionary consisting of array response vectors with finely quantized angle grids. We suggest the use of non-uniformly quantized angle grids and show that such grids reduce the coherence of the redundant dictionary. The lower and upper bounds of the sum-of-squared errors of the proposed OMP-based estimator are derived analytically: the lower bound is derived by considering the oracle estimator that assumes the knowledge of AoDs/AoAs, and the upper bound is derived based on the results of the OMP performance guarantees. The design of training vectors (or sensing matrix) is particularly important in hybrid MIMO systems, because the RF beamformer prevents the use of independent and identically distributed random training vectors, which are popular in compressed sensing. We design training vectors so that the total coherence of the equivalent sensing matrix is minimized for a given RF beamforming matrix, which is assumed to be unitary. It is observed that the estimation accuracy can be improved significantly by randomly permuting the columns of the RF beamforming matrix. The simulation results demonstrate the advantage of the proposed OMP with a redundant dictionary over the existing methods such as the least squares method and the OMP based on the virtual channel model.
Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying ...therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.
We present a novel method of targeted gene disruption that involves direct injection of recombinant Cas9 protein complexed with guide RNA into the gonad of the nematode Caenorhabditis elegans. ...Biallelic mutants were recovered among the F1 progeny, demonstrating the high efficiency of this method.
Lung cancer is one of the leading causes of cancer-related deaths worldwide and is characterized by hijacking immune system for active growth and aggressive metastasis. Neutrophils, which in their ...original form should establish immune activities to the tumor as a first line of defense, are undermined by tumor cells to promote tumor invasion in several ways. In this study, we investigate the mutual interactions between the tumor cells and the neutrophils that facilitate tumor invasion by developing a mathematical model that involves taxis-reaction-diffusion equations for the critical components in the interaction. These include the densities of tumor and neutrophils, and the concentrations of signaling molecules and structure such as neutrophil extracellular traps (NETs). We apply the mathematical model to a Boyden invasion assay used in the experiments to demonstrate that the tumor-associated neutrophils can enhance tumor cell invasion by secreting the neutrophil elastase. We show that the model can both reproduce the major experimental observation on NET-mediated cancer invasion and make several important predictions to guide future experiments with the goal of the development of new anti-tumor strategies. Moreover, using this model, we investigate the fundamental mechanism of NET-mediated invasion of cancer cells and the impact of internal and external heterogeneity on the migration patterning of tumour cells and their response to different treatment schedules.
A light-weight refractory Al0.1CrNbVMo high entropy alloy (HEA) was fabricated by high energy ball milling and spark plasma sintering (SPS). The alloy had a density of 7.96 g/cm3, which is lower than ...that of conventional Ni-base superalloys. Optimum milling time was decided by the microstructure analysis of the HEA powders. The microstructure of the bulk alloy consisted of a body-centered cubic (BCC) matrix with a minor amount of alumina inclusions. The Al0.1CrNbVMo HEA exhibited outstanding compressive mechanical properties of 2863 MPa at room temperature, and 1405 MPa at 1000 °C, respectively. The specific yield strength of 176 MPa cm3/g at 1000 °C, is much higher than that of the other refractory HEAs. The Hall-Petch coefficient of the Al0.1CrNbVMo alloy was derived to 811 MPa μm0.5.
•New refractory Al0.1CrNbVMo HEA was fabricated by the powder metallurgical process.•The alloy has lower density of 7.96 g/cm3 than conventional Ni-base superalloys.•The alloy shows the best specific yield strength at 25–1000 °C among the other HEAs.•Grain growth behavior and Hall-Petch relationship of Al0.1CrNbVMo HEA were analyzed.
Recent advances in long-read sequencing technologies have enabled accurate identification of all genetic variants in individuals or cells; this procedure is known as variant calling. However, ...benchmarking studies on variant calling using different long-read sequencing technologies are still lacking.
We used two Caenorhabditis elegans strains to measure several variant calling metrics. These two strains shared true-positive genetic variants that were introduced during strain generation. In addition, both strains contained common and distinguishable variants induced by DNA damage, possibly leading to false-positive estimation. We obtained accurate and noisy long reads from both strains using high-fidelity (HiFi) and continuous long-read (CLR) sequencing platforms, and compared the variant calling performance of the two platforms. HiFi identified a 1.65-fold higher number of true-positive variants on average, with 60% fewer false-positive variants, than CLR did. We also compared read-based and assembly-based variant calling methods in combination with subsampling of various sequencing depths and demonstrated that variant calling after genome assembly was particularly effective for detection of large insertions, even with 10 × sequencing depth of accurate long-read sequencing data.
By directly comparing the two long-read sequencing technologies, we demonstrated that variant calling after genome assembly with 10 × or more depth of accurate long-read sequencing data allowed reliable detection of true-positive variants. Considering the high cost of HiFi sequencing, we herein propose appropriate methodologies for performing cost-effective and high-quality variant calling: 10 × assembly-based variant calling. The results of the present study may facilitate the development of methods for identifying all genetic variants at the population level.
•Chloroplasts have three membranes, outer/inner envelopes and thylakoid membranes.•AKR2 coordinates cytosolic sorting and insertion of proteins into outer envelope.•Two pathways operate for ...biogenesis of inner envelope proteins.•The cpSRP pathway plays a key role in biogenesis of LHCPs into thylakoid membranes.•Besides LHCPs, most thylakoid membrane proteins are inserted spontaneously.
Among the many organelles in eukaryotic cells, chloroplasts have the most complex structure, with multiple suborganellar membranes, making protein targeting to chloroplasts, particularly to various suborganellar membranes, highly challenging. Multiple mechanisms function in the biogenesis of chloroplast membrane proteins. Nuclear-encoded nascent proteins can be targeted to the outer envelope membrane directly from the cytosol after translation, but their targeting to the inner envelope and thylakoid membranes requires multiple steps, including cytosolic sorting, translocation across the envelope membranes, sorting in the stroma, and insertion into their target membranes. In this review, we discuss the current knowledge about the sorting mechanisms of proteins to the two envelope membranes and the thylakoid membrane, along with perspectives for future research.
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•We characterized optical properties of single triangular gold nanoplate under DF microscopy.•The optical properties are mainly dominated by the dipole resonance in single Au ...nanoplate with aspect ratio of 5.•We observed the polarization-dependent, periodic DF defocused images and intensities of single Au nanoplates.
We synthesized triangular gold nanoplates (AuNPs) through a one-pot seedless growth method and characterized their optical properties under dark-field (DF) microscopy at the single particle level. We experimentally demonstrated that the dipole resonance is not completely separated from the quadrupole resonance for single AuNPs with an aspect ratio of ∼5. We further used a defocused orientation and position imaging technique to visualize the spatial scattering field distributions from dipole and quadrupole modes. Their optical properties were mainly dominated by the dipole resonance, which resulted in the polarization-dependent, periodic DF defocused images and intensities of single AuNPs.
Wind power is one of the most potential energies and the major available renewable energy sources. Precisely predicting wind power production is essential for the management and the integration of ...wind power in a smart grid. The goal of this study is to predict wind power production with sufficient accuracy based on various factors using ensemble learning-based methods that consider the time-dependent nature of the wind power measurements. Essentially, the ensemble learning methods combine multiple learners to obtain an enhanced prediction performance in comparison to conventional standalone learners. In addition, they reduce the overall prediction error and have the capacity to merge various models. At first, this paper investigates the prediction capability of the well-known ensemble approaches Boosted Trees, Random Forest, and Generalized Random Forest for wind power prediction. We compared the prediction performance of these ensemble models to two frequently used prediction methods: Gaussian process regression, and Support Vector Regression. Experimental measurements recorded every ten minutes from actual wind turbines located in France and Turkey are used to test the prediction efficiency of the studied models. Experimental results have shown that the ensemble methods can predict wind power production with high accuracy compared to the standalone models. Furthermore, the findings clearly reveal that the lagged variables contribute significantly to the ensemble models, and permits constructing more parsimonious models.
All cancers harbor molecular alterations in their genomes. The transcriptional consequences of these somatic mutations have not yet been comprehensively explored in lung cancer. Here we present the ...first large scale RNA sequencing study of lung adenocarcinoma, demonstrating its power to identify somatic point mutations as well as transcriptional variants such as gene fusions, alternative splicing events, and expression outliers. Our results reveal the genetic basis of 200 lung adenocarcinomas in Koreans including deep characterization of 87 surgical specimens by transcriptome sequencing. We identified driver somatic mutations in cancer genes including EGFR, KRAS, NRAS, BRAF, PIK3CA, MET, and CTNNB1. Candidates for novel driver mutations were also identified in genes newly implicated in lung adenocarcinoma such as LMTK2, ARID1A, NOTCH2, and SMARCA4. We found 45 fusion genes, eight of which were chimeric tyrosine kinases involving ALK, RET, ROS1, FGFR2, AXL, and PDGFRA. Among 17 recurrent alternative splicing events, we identified exon 14 skipping in the proto-oncogene MET as highly likely to be a cancer driver. The number of somatic mutations and expression outliers varied markedly between individual cancers and was strongly correlated with smoking history of patients. We identified genomic blocks within which gene expression levels were consistently increased or decreased that could be explained by copy number alterations in samples. We also found an association between lymph node metastasis and somatic mutations in TP53. These findings broaden our understanding of lung adenocarcinoma and may also lead to new diagnostic and therapeutic approaches.
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