Time-varying radome slope estimation for passive homing anti-ship missiles

This paper addresses a time-varying radome slope (RS) estimation problem for passive homing anti-ship missiles. Apart from conventional approaches, the non-linear characteristics of the radome aberration error is taken into account for modeling the RS dynamics. In addition, it is shown that the acceleration dither is necessary for ensuring the observability of the RS estimation with passive seeker measurements. Based on this observation, a linear RS measurement equation is set up by analyzing the seeker response to the high-frequency acceleration dither. Thus, the RS estimation problem can be easily resolved by designing a time-varying Kalman filter. Since the proposed approach adopts a simple linear filter structure, it is suitable for an in-flight real-time RS estimation. Through the computer simulation for a typical ASM-target engagement scenario, the usefulness of the suggested scheme is demonstrated

Specific heat study of Ga1-xMnxAs

Specific heat measurements were used to study the magnetic phase transition in Ga1-xMnxAs. Two different types of Ga1-xMnxAs samples have been investigated. The sample with a Mn concentration of 1.6% shows insulating behavior, and the sample with a Mn concentration of 2.6% is metallic. The temperature dependence of the specific heat for both samples reveals a pronounced lambda-shaped peak near the Curie temperature, which indicates a second-order phase transition in these samples. The critical behavior of the specific heat for Ga1-xMnxAs samples is consistent with the mean-field behavior with Gaussian fluctuations of the magnetization in the close vicinity of TC.Comment: 12 pages, 5 figure

Building the process-drug–side effect network to discover the relationship between biological Processes and side effects

<p>Abstract</p> <p>Background</p> <p>Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched.</p> <p>Methods</p> <p>We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone.</p> <p>Results</p> <p>The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner.</p> <p>Conclusions</p> <p>We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.</p

Discovering context-specific relationships from biological literature by using multi-level context terms

<p>Abstract</p> <p>Background</p> <p>The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.</p> <p>Methods</p> <p>We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation. Subsequently, we evaluate our hypothesis with the datasets of the "Alzheimer's disease" related 77,711 PubMed abstracts. As golden standards, PharmGKB and CTD databases are used. Evaluation is conducted in 2 ways: first, comparing precision of the proposed method and the previous method and second, analysing top 10 ranked results to examine whether highly ranked interactions are truly meaningful or not.</p> <p>Results</p> <p>The results indicate that context-based relation inference achieved better precision than the previous ABC model approach. The literature analysis also shows that interactions inferred by the context-based approach are more meaningful than interactions by the previous ABC model.</p> <p>Conclusions</p> <p>We propose a novel interaction inference technique that incorporates context term vectors into the ABC model to discover meaningful hidden relationships. By utilizing multi-level context terms, our model shows better performance than the previous ABC model.</p

Next-generation sequencing analysis of hepatitis C virus resistance–associated substitutions in direct-acting antiviral failure in South Korea

Background/Aims We used next-generation sequencing (NGS) to analyze resistance-associated substitutions (RASs) and retreatment outcomes in patients with chronic hepatitis C virus (HCV) infection who failed direct-acting antiviral agent (DAA) treatment in South Korea. Methods Using prospectively collected data from the Korean HCV cohort study, we recruited 36 patients who failed DAA treatment in 10 centers between 2007 and 2020; 29 blood samples were available from 24 patients. RASs were analyzed using NGS. Results RASs were analyzed for 13 patients with genotype 1b, 10 with genotype 2, and one with genotype 3a. The unsuccessful DAA regimens were daclatasvir+asunaprevir (n=11), sofosbuvir+ribavirin (n=9), ledipasvir/sofosbuvir (n=3), and glecaprevir/pibrentasvir (n=1). In the patients with genotype 1b, NS3, NS5A, and NS5B RASs were detected in eight, seven, and seven of 10 patients at baseline and in four, six, and two of six patients after DAA failure, respectively. Among the 10 patients with genotype 2, the only baseline RAS was NS3 Y56F, which was detected in one patient. NS5A F28C was detected after DAA failure in a patient with genotype 2 infection who was erroneously treated with daclatasvir+asunaprevir. After retreatment, 16 patients had a 100% sustained virological response rate. Conclusions NS3 and NS5A RASs were commonly present at baseline, and there was an increasing trend of NS5A RASs after failed DAA treatment in genotype 1b. However, RASs were rarely present in patients with genotype 2 who were treated with sofosbuvir+ribavirin. Despite baseline or treatment-emergent RASs, retreatment with pan-genotypic DAA was highly successful in Korea, so we encourage active retreatment after unsuccessful DAA treatment

PU-MFA: Point Cloud Up-Sampling via Multi-Scale Features Attention

Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with the high cost of obtaining high-quality point clouds. Therefore, with the recent remarkable development of deep learning, point cloud up-sampling research, which uses deep learning to generate high-quality point clouds from low-quality point clouds, is one of the fields attracting considerable attention. This paper proposes a new point cloud up-sampling method called Point cloud Up-sampling via Multi-scale Features Attention (PU-MFA). Inspired by prior studies that reported good performance at generating high-quality dense point set using the multi-scale features or attention mechanisms, PU-MFA merges the two through a U-Net structure. In addition, PU-MFA adaptively uses multi-scale features to refine the global features effectively. The PU-MFA was compared with other state-of-the-art methods in various evaluation metrics through various experiments using the PU-GAN dataset, which is a synthetic point cloud dataset, and the KITTI dataset, which is the real-scanned point cloud dataset. In various experimental results, PU-MFA showed superior performance of generating high-quality dense point set in quantitative and qualitative evaluation compared to other state-of-the-art methods, proving the effectiveness of the proposed method. The attention map of PU-MFA was also visualized to show the effect of multi-scale features

Low-Power Graphene/ZnO Schottky UV Photodiodes with Enhanced Lateral Schottky Barrier Homogeneity

The low-power, high-performance graphene/ZnO Schottky photodiodes were demonstrated through the direct sputter-growth of ZnO onto the thermally-cleaned graphene/SiO2/Si substrate at room temperature. Prior to the growth of ZnO, a thermal treatment of the graphene surface was performed at 280 &#176;C for 10 min in a vacuum to desorb chemical residues that may serve as trap sites at the interface between graphene and ZnO. The device clearly showed a rectifying behavior with the Schottky barrier of &#8776;0.61 eV and an ideality factor of 1.16. Under UV illumination, the device exhibited the excellent photoresponse characteristics in both forward and reverse bias regions. When illuminating UV light with the optical power density of 0.62 mW/cm2, the device revealed a high on/off current ratio of &gt;103 even at a low bias voltage of 0.1 V. For the transient characteristics upon switching of UV light pulses, the device represented a fast and stable photoresponse (i.e., rise time: 0.16 s, decay time: 0.19 s). From the temperature-dependent current&#8722;voltage characteristics, such an outstanding photoresponse characteristic was found to arise from the enhanced Schottky barrier homogeneity via the thermal treatment of the graphene surface. The results suggest that the ZnO/graphene Schottky diode holds promise for the application in high-performance low-power UV photodetectors

Excellent Oxygen Evolution Reaction of Activated Carbon-Anchored NiO Nanotablets Prepared by Green Routes

A sustainable and efficient electrocatalyst for the oxygen evolution reaction (OER) is vital to realize green and clean hydrogen production technology. Herein, we synthesized the nanocomposites of activated carbon-anchored nickel oxide (AC-NiO) via fully green routes, and characterized their excellent OER performances. The AC-NiO nanocomposites were prepared by the facile sonication method using sonochemically prepared NiO nanoparticles and biomass-derived AC nanosponges. The nanocomposites exhibited an aggregated structure of the AC-NiO nanotablets with an average size of 40 nm. When using the nanotablets as an OER catalyst in 1 M KOH, the sample displayed superb electrocatalytic performances, i.e., a substantially low value of overpotential (320 mV at 10 mA/cm2), a significantly small Tafel slope (49 mV/dec), and a good OER stability (4% decrease of overpotential after 10 h). These outstanding OER characteristics are considered as attributing to the synergetic effects from both the ample surface area of the electrochemically active NiO nanoparticles and the high electrical conductivity of the AC nanosponges. The results pronounce that the fully ecofriendly synthesized AC-NiO nanotablets can play a splendid role as high-performance electrocatalysts for future green energy technology