Search

This paper proposes a variable gain power amplifier (VG-PA) with stable linearity for n257/n258 frequency band applications. A switched-capacitor array is proposed in the cascode (CC) circuit to control the gain while maintaining linearity. A compensation inductor is introduced between the common-emitter (CE) and common-base (CB) transistors to suppress the phase variation. For the proof of concept, the VG-PA was fabricated in a 0.13-μm SiGe BiCMOS technology with a core size of 0.59 × 0.46 mm. Measurements show that at 24/26/28/29.5 GHz, over 5.7/5.5/7.0/7.5-dB gain tuning range, the output 1-dB compression point (OP1dB) is better than 14 dBm with OP1dB less than 1.2 dB, the third-order output intercept point (OIP3) is better than 22 dBm with OIP3 less than 3.4 dB, and the phase variation (φ) is from 3 to 9.5∘. To the author’s best knowledge, this work has the lowest linearity variation among Si-based variable gain amplifiers at millimeter-wave frequency band.

In this letter, a novel planar gate SiC MOSFET with embedded auto-adjust normally-on JFET (ADJ-MOSFET) is proposed and characterized to improve short circuit capability. The normally-on auto-adjust JFET (AD-JFET) is embedded in P-well region as the conduction path of electrons from N-type source to the channel region of SiC MOSFET. Meanwhile, the P-type source of device splits into two parts, one of which serves as the gate of AD-JFET. Under high DC bus voltage (short circuit) conditions, the effective channel width of AD-JFET will be reduced by the increased depletion charge. Therefore, the potential barrier of AD-JFET channel will increase rapidly, making it difficult for electrons transporting. The potential barrier of AD-JFET will be adjusted by that of the JFET region in drift region automatically, resulting in a reduction of the saturation (peak short circuit) current caused by the higher potential barrier of SiC MOSFET. Compared with conventional planar gate SiC MOSFET, ADJ-MOSFET not only reduces 33% peak short circuit current, but also increases short circuit withstanding time from 8 μs to 14 μs under 800 V DC bus voltage. Furthermore, an auto-adjust JFET barrier model is proposed for SiC ADJ-MOSFET.

In this article, short-circuit capability prediction and failure mode of 1200-V-class SiC MOSFET s with a double and asymmetric trench structure are proposed under single-pulse short-circuit stress. A short-circuit prediction model is established to evaluate short-circuit withstand time and corresponding critical energy of devices under various dc bus voltages. This model can provide quick predictive guidance even if there are few test results, and the predicted values are consistent with practical values. Furthermore, two failure modes are investigated in a short-circuit test. For asymmetric trench SiC MOSFET s, failure modes are gate damage at lower dc bus voltages and thermal runaway at higher dc bus voltages; whereas failure mode for double trench SiC MOSFET s is thermal runaway at all dc bus voltages. In addition, the internal thermal-electro stress of the device is analyzed until it fails during short-circuit condition, and proves that failure mode depends on the dc bus voltage and peak short-circuit current of the device. Finally, the top view of failed devices confirms the two failure modes of trench SiC MOSFET s by the postdecapsulation.

Objective Incisors tubed prosthesis with bilateral anterior elevation (BAE) relation had been reported to stimulate the proliferative response in the mandibular condylar cartilage of mice, thus the prosthetic occlusion elevation had been proposed to treat cartilage degeneration. Currently, we aimed to detect the long-term effect of BAE on temporomandibular joints (TMJs). Materials and methods Twelve 6-week-old female mice were assigned to age-matched control and BAE groups (n = 6). Micro-CT images and the macro- and micro-morphology of the mandibular condyles were analyzed at 29 weeks. Results Compared with the age-matched controls, in BAE group, there were loss of subchondral cortical bone, and heavy loss of the subchondral trabecular bone at the superior sites of the TMJ condyles, but hyperostosis at the inferior sites as revealed by micro-CT images and histological slices. In BAE group, cartilage thickness and matrix area were increased with up-regulated expression of type II , type X collagen and Ki67, but the expression of cleaved caspase-3 was down-regulated (all, P Conclusion In addition to cartilage thickening, long-term BAE induces loss of the subchondral cortical bone and heavy loss of the underneath subchondral trabecular bone, but hyperostosis further underneath. Using BAE as a treatment remains double-edged.

Displays are basic building blocks of modern electronics1,2. Integrating displays into textiles offers exciting opportunities for smart electronic textiles—the ultimate goal of wearable technology, poised to change the way in which we interact with electronic devices3–6. Display textiles serve to bridge human–machine interactions7–9, offering, for instance, a real-time communication tool for individuals with voice or speech difficulties. Electronic textiles capable of communicating10, sensing11,12 and supplying electricity13,14 have been reported previously. However, textiles with functional, large-area displays have not yet been achieved, because it is challenging to obtain small illuminating units that are both durable and easy to assemble over a wide area. Here we report a 6-metre-long, 25-centimetre-wide display textile containing 5 × 105 electroluminescent units spaced approximately 800 micrometres apart. Weaving conductive weft and luminescent warp fibres forms micrometre-scale electroluminescent units at the weft–warp contact points. The brightness between electroluminescent units deviates by less than 8 per cent and remains stable even when the textile is bent, stretched or pressed. Our display textile is flexible and breathable and withstands repeated machine-washing, making it suitable for practical applications. We show that an integrated textile system consisting of display, keyboard and power supply can serve as a communication tool, demonstrating the system’s potential within the ‘internet of things’ in various areas, including healthcare. Our approach unifies the fabrication and function of electronic devices with textiles, and we expect that woven-fibre materials will shape the next generation of electronics. A large electronic display textile that is flexible, breathable and withstands repeated machine-washing is integrated with a keyboard and power supply to create a wearable, durable communication tool.

Machine learning approaches can help facilitate the optimization of machining processes. Model performance, including accuracy, stability, and robustness, are major criteria to choose among different methods. Besides, the applicability, ease of implementations, and cost-effectiveness should be considered for industrial applications. In the current study, we present the Gaussian process regression model to predict the material removal rate during electrical discharge diamond surface grinding of Inconel-718. The model uses descriptors that include the wheel speed, current, pulse-on-time, and duty factor. The model is simple and manifests high accuracy and stability, which contributes to fast material removal rate estimations. By combining the optimization results from the Taguchi method and GPR approach, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time.

A 13.5 kV 4H-SiC PiN rectifier with a considerable active area of 0.1 cm2 is fabricated in this paper. Charge-field-modulated junction termination extension (CFM-JTE) has been proposed for satisfying the requirement of ultra-high reverse voltage, which enlarges the JTE dose tolerance window, making it approximately 2.8 times that of the conventional two-zone JTE. Besides, the CFM-JTE can be implemented through the conventional two-zone JTE process. The measured forward current is up to 100 A @ VF = 5.2 V in the absence of carrier lifetime enhancement technology. The CFM-JTE structure accomplishes 96% of the theoretical breakdown voltage of the parallel plane junction with a relatively small terminal area of 400 μm, which contributes to achieving the Baliga’s figure of merit of 58.8 GW/cm2.

Breast cancer is one of the most common malignancies that seriously threaten women's health. In the process of the malignant transformation of breast cancer, metabolic reprogramming and immune evasion represent the two main fascinating characteristics of cancer and facilitate cancer cell proliferation. Breast cancer cells generate energy through increased glucose metabolism. Lipid metabolism contributes to biological signal pathways and forms cell membranes except energy generation. Amino acids act as basic protein units and metabolic regulators in supporting cell growth. For tumor-associated immunity, poor immunogenicity and heightened immunosuppression cause breast cancer cells to evade the host's immune system. For the past few years, the complex mechanisms of metabolic reprogramming and immune evasion are deeply investigated, and the genes involved in these processes are used as clinical therapeutic targets for breast cancer. Here, we review the recent findings related to abnormal metabolism and immune characteristics, regulatory mechanisms, their links, and relevant therapeutic strategies.

Titanium dioxide (TiO2) photocatalysts in the form of thin films are of great interest due to their tunable optical band gaps, Eg’s, which are promising candidates for applications of visible-light photocatalytic activities. Previous studies have shown that processing conditions, dopant types and concentrations, and different combinations of the two have great impacts on structural, microscopic, and optical properties of TiO2 thin films. The lattice parameters and surface area are strongly correlated with Eg values, which are conventionally simulated and studied through first-principle models, but these models require significant computational resources, particularly in complex situations involving codoping and various surface areas. In this study, we develop the Gaussian process regression model for predictions of anatase TiO2 photocatalysts’ energy band gaps based on the lattice parameters and surface area. We explore 60 doped-TiO2 anatase photocatalysts with Eg’s between 2.280 and 3.250 eV. Our model demonstrates a high correlation coefficient of 99.99% between predicted Eg’s and their experimental values and high prediction accuracy as reflected through the prediction root-mean-square error and mean absolute error being 0.0012 and 0.0010% of the average experimental Eg, respectively. This modeling method is simple and straightforward and does not require a lot of parameters, which are advantages for applications and computations.

Electronic textiles may revolutionize many fields, such as communication, health care and artificial intelligence. To date, unfortunately, computing with them is not yet possible. Memristors are compatible with the interwoven structure and manufacturing process in textiles because of its two-terminal crossbar configuration. However, it remains a challenge to realize textile memristors owing to the difficulties in designing advanced memristive materials and achieving high-quality active layers on fiber electrodes. Herein we report a robust textile memristor based on an electrophoretic-deposited active layer of deoxyribonucleic acid (DNA) on fiber electrodes. The unique architecture and orientation of DNA molecules with the incorporation of Ag nanoparticles offer the best-in-class performances, e.g., both ultra-low operation voltage of 0.3 V and power consumption of 100 pW and high switching speed of 20 ns. Fundamental logic calculations such as implication and NAND are demonstrated as functions of textile chips, and it has been thus integrated with power-supplying and light emitting modules to demonstrate an all-fabric information processing system.

This research aims to explore the relationships between gender, educational attainment, and job quality, including work autonomy, work intensity, and job satisfaction across Germany, Sweden, and the UK. The European Working Conditions Survey 2015 was used to achieve this research objective. Descriptive statistics and multivariate regression analysis were used to determine how educational level plays an important role in creating gender differences in job quality across three countries. The findings show that receiving postsecondary education can improve work autonomy for both German and Swedish women. However, postsecondary education has different impacts on gender gaps in job quality in these countries. While postsecondary education lowers the gender gap in work autonomy and intensity in Sweden, postsecondary education increases the gender gap in work autonomy and intensity in Germany. Postsecondary education does not significantly decrease gender differences in job satisfaction in Germany or Sweden or any of our job quality measures in the UK. These findings challenge the commonly held belief that higher education has a positive effect on job quality. In fact, gender norms and national institutional factors may also play important roles in this relationship.

The heterojunction photodetector between n‐SrTiO3 (n‐STO) and p‐CuS‐ZnS (p‐CZS) is prepared by a simple chemical bath deposition. The p‐CZS/n‐STO photodetector exhibits excellent self‐powered characteristics under 390‐nm light illumination, including high photosensitivity (on/off ratio of 300), fast response speed (0.7/94.6 ms), and good wavelength selectivity (410‐380 nm). More importantly, the self‐powered n‐STO photodetectors can be regulated by varying the composition of CZS film (p‐CZS, p‐CuS, and n‐ZnS). All devices exhibit a large open‐circuit voltage and show different self‐powered behaviors because of the different built‐in electric fields. The open‐circuit voltages of the CZS/STO, CuS/STO, and ZnS/STO devices are measured to be 0.56, 0.30, and 0.35 V, respectively. This work provides a simple and effective way to fabricate tunable self‐powered photodetectors by varying the composition of the nanocomposite film.

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu–Zn–S thin films prepare...

Colloidal semiconductor nanocrystals are important building blocks for low-cost, solution-processed electronic devices with tunable functionalities. Considerable progress is made in improving charge transport through nanocrystal films by exchanging long insulating ligands with shorter passivating ligands. To take full advantage of this strategy, it is equally important to fabricate close-packed structures that reduce the average interparticle spacing. Yet it remains a challenge to retain long-range, close-packed order after ligand exchange. Here, a novel one-step in situ ligand-exchange method is demonstrated that enables rapid (5 min) ligand exchange of nanocrystal films, which are more than 50 layers thick. Using this simple and efficient method, it is shown that the face-centered cubic ordering of 500 nm thick PbSe nanocrystal films is retained after ligand exchange from oleic acid to benzoic acid. Moreover, it is demonstrated that PbSe nanocrystal photodetectors with a well-ordered structure have superior optoelectronic properties compared to disordered films; ordered films have a 16× higher responsivity of ≈0.25 A W-1 at 1 V and a 2× faster response time. As far as it is known, this is the first report to realize a rapid one-step ligand exchange through a thick superlattice film with retention of long-range order.

Risk of metastasis is increased by the presence of chromosome 3 monosomy in uveal melanoma (UM). This study aimed to identify more accurate biomarker for risk of metastasis in UM. A total of 80 patients with UM from TCGA were assigned to two groups based on the metastatic status, and bioinformatic analyses were performed to search for critical genes for risk of metastasis. SLC25A38, located on chromosome 3, was the dominant downregulated gene in metastatic UM patients. Low expression of SLC25A38 was an independent predictive and prognostic factor in UM. The predictive potential of SLC25A38 expression was superior to that of pervious reported biomarkers in both TCGA cohort and GSE22138 cohort. Subsequently, its role in promoting metastasis was explored in vitro and in vivo. Knock-out of SLC25A38 could enhance the migration ability of UM cells, and promote distant metastasis in mice models. Through the inhibition of CBP/HIF-mediated pathway followed by the suppression of pro-angiogenic factors, SLC25A38 was situated upstream of metastasis-related pathways, especially angiogenesis. Low expression of SLC25A38 promotes angiogenesis and metastasis, and identifies increased metastatic risk and worse survival in UM patients. This finding may further improve the accuracy of prognostic prediction for UM.

Objective. To investigate the expression of transient receptor potential (TRP) superfamily genes, especially TRPC7-AS1 in hepatitis B virus- (HBV-) related hepatocellular carcinoma (HCC). Methods. Three cancer samples of HBV-related HCC at phase IV and matched paracancerous liver tissues were included in the study. Total RNA was extracted, and differential expression of RNA was screened by high-throughput transcriptome sequencing. The expression of TRPC7-AS1 was detected by quantitative real-time PCR. The N6-adenosyl methylation RNA in MHCC97H, HepG2, and HL-7702 was enriched by coimmunoprecipitation with m6A antibody, and the relative level of N6-adenosyl methylation RNA in TRPC7-AS1 was detected. Results. The expression of TRP family genes in cancer tissues was higher than that in paracancerous liver tissues, including TRPC7-AS1, TRPC4AP, PKD1P6, and PKD1P1. Moreover, the expression level of TRPC7-AS1 in MHCC97H and HepG2 was also significantly higher than that in L02, a normal liver cell. The methylation level of N6-adenosine of TRPC7-AS1 was lower in HepG2 cells than that in L02 cells. Conclusion. TRP superfamily genes, especially TRPC7-AS1, were highly expressed in HBV-related HCC. TRPC7-AS1 could be a potential therapeutic target or diagnostic marker for HCC.

Sybil attacks are increasingly rampant in online social networks (OSNs); thus, Sybil detection is one of the key issues in OSN security research. Sybils in OSNs are often used by attackers for public opinion intervention, topic flow filling, and dissemination of false and malicious messages. Therefore, if the credibility of the Sybil can be analyzed, then the harm of Sybil attacks can be prevented to a certain extent. Based on the analysis of existing Sybil detection research, this paper proposes an end-to-end Sybil detection model based on the Bidirectional Encoder Representations from Transformers (BERT) model that analyzes tweet text content. Considering the problems of the existing datasets, we built a dataset for text content analysis of tweets based on the hot political topic of the 2020 US presidential election. Accordingly, this study used a distilled version of BERT, DistilBERT, as the sentence embedding model, and the double self-normalizing long short-term memory (Double-SN-LSTM) recurrent neural network model as the classification detection model. The final experimental effect was greatly improved compared with the existing analysis methods, and it had a better detection effect for the more concealed Sybils.

Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. However, reconstructing a 3D caricature from a 2D caricature image remains a challenging task, mostly due to the lack of data. We propose to fill this gap by introducing 3DCaricShop, the first large-scale 3D caricature dataset that contains 2000 high-quality diversified 3D caricatures manually crafted by professional artists. 3DCaricShop also provides rich annotations including a paired 2D caricature image, camera parameters and 3D facial landmarks. To demonstrate the advantage of 3DCaricShop, we present a novel baseline approach for single-view 3D caricature reconstruction. To ensure a faithful reconstruction with plausible face deformations, we propose to connect the good ends of the detailrich implicit functions and the parametric mesh representations. In particular, we first register a template mesh to the output of the implicit generator and iteratively project the registration result onto a pre-trained PCA space to resolve artifacts and self-intersections. To deal with the large deformation during non-rigid registration, we propose a novel view-collaborative graph convolution network (VCGCN) to extract key points from the implicit mesh for accurate alignment. Our method is able to generate highfidelity 3D caricature in a pre-defined mesh topology that is animation-ready. Extensive experiments have been conducted on 3DCaricShop to verify the significance of the database and the effectiveness of the proposed method., Comment: CVPR 2021. Project page:https://qiuyuda.github.io/3DCaricShop/

A novel SiC MOSFET with embedded unipolar diode (UD-MOSFET) is proposed and investigated by TCAD simulation in this paper. The embedded unipolar diode (UD) exhibits 3 times lower turn-on voltage $(V_{\mathrm{on}})$ than the parasitic body diode, resulting in a 2 times smaller reverse recovery charge $(Q_{\mathrm{rr}})$ and successful elimination of the bipolar degradation issue. Meanwhile, the gate to drain charge $(Q_{\mathrm{gd}})$ is reduced by 1.8 times, contributing to a superior switching characteristics with significantly reduced switching loss. The overall improved performance makes UD-MOSFET an excellent choice for high frequency applications.

Metal halide perovskites have attracted great attention in the past decade due to unique and tunable optical and electrical properties, which are promising candidates for various applications such as solar cells, light-emitting diodes, and laser cooling devices. For cubic perovskites, the lattice constant, a, representing the size of the unit cell, has a significant impact on the structural stability, bandgap structure, and thus materials performance. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship among ionic radii, electronegativities, and lattice constants for cubic perovskite $$A_{2}XY_{6}$$ compounds. A total of 79 samples with lattice constants ranging from 8.109 to 11.790 $$\mathring{\rm A}$$ are examined. The model has a high degree of accuracy and stability, contributing to fast, robust, and low-cost estimations of lattice constants.

Mechanoluminescence has attracted increasing attentions because it can convert the kinetic energy during human daily motions into light to be used in sensors and displays. However, its practical applications are still hindered by the weak brightness and limited color while under large forces. Herein, we developed novel piezoluminescent devices (PLDs) which could effectively emit visible light under low pressing forces through the stress-concentration and enhancing deformation on the basis of carefully-designed array structures. The emitting colors were also tunable by using bilayer luminescent film under different pressures. This work not only provides a new strategy to effectively harvest mechanical energy into light, but also presents a scalable, low-cost and color-tunable PLD which shows great potentials in various applications such as luminescent floors, shoes and stress-activated displays.

Information security has aroused wide concern in this new era of information; thus, the development of security methods for storing and expressing confidential information is of great importance. Herein, we provided a novel information storage and visual expression device based on mechanoluminescent materials and poly(dimethylsiloxane) using a simple fabrication method. The device could record the region where the stress was applied and store the information and finally express it via light emission under mechanical stimuli. Furthermore, the luminescence properties of the resulting device could be tuned by adjusting the composition of the mechanoluminescent layer or the initially applied stress. More importantly, the as-fabricated device exhibited a fast and sensitive response to the stimuli along with excellent repeatability as well as long-term stability, making it reliable for promising applications in confidential information storage, anti-counterfeiting and secure communication.

A self-powering feature is particularly appealing for wearable electronic devices when facing the challenges of energy and environmental crises. Photodetectors (PDs), as promising candidates for health and environment monitoring, are urgently desired to meet the requirements of being wearable and powerless. Unlike conventional photovoltaic-type PDs, we have come up with a novel approach to make a self-powered wearable PD, which involves connecting PDs with thermoelectric generators (TEGs) in series on the surface of a three-dimensional helical polymer substrate. The wearable system is light-weight, flexible and breathable. More importantly, compared with photovoltaic-type self-powered PDs, wearable PDs powered by TEGs will work as a system whose output signal can be greatly amplified by the temperature difference between the human body and the environment in winter when UV intensity is relatively low.

It has always been a challenge to prepare self-powered ultraviolet photodetectors (UV PDs) utilizing a green, facile and low-cost method. Here, we provide such an approach for fabricating self-powered photodetectors based on Te nanowire/TiO2 nanoparticle (NP) and Te nanowire/ZnO nanoparticle (NP) heterojunctions. It is noteworthy that such devices were prepared via simply brushing non-toxic solution-processed Te nanowires and commercial TiO2 and ZnO nanoparticles onto paper. The as-obtained UV PDs can work under zero bias due to photovoltaic effects from the Te/TiO2 and Te/ZnO heterojunctions. This study not only presents high-performance UV PDs, but also provides a general and effective method for developing environmentally-friendly and energy-efficient optoelectronic devices for practical applications. The concept of employing daily-use brushes and recyclable paper provides a facile way to fabricate green low-cost PDs.

The photovoltaic effect occurs at the interface between two different types of semiconductors that are joined together to create a p–n junction, and it has been explored as a basic principle to harvest solar energy to generate electricity. The active materials are typically protected from water for stable performances, and the photovoltaic effect is observed in photovoltaic materials with appropriate energy bands and interfaces. Here we have discovered another new and general photo-to-electricity phenomenon in semiconducting materials in water. The photo-to-electricity conversion could be realized by using the identical type of semiconductor as the active material. Aligned carbon nanotube sheets were demonstrated to output high open-circuit voltages up to 0.48 V. Due to the intrinsic flexibility of the designed carbon nanotube sheets, the output voltage could be well maintained after deforming for 8000 cycles.

Efficient solid-state refrigeration techniques have drawn increasing attention due to their potential for improving energy efficiency of refrigeration temperature control systems without using harmful gas as in conventional gas compression techniques. Research on magnetocaloric lanthanum manganites with a large maximum magnetic entropy change near room temperature shows promising results for further developments of magnetic refrigeration devices. By incorporating chemical substitutions, oxygen content modifications, and various synthesis methods, these manganites experience lattice distortions from perovskite cubic structures to pseudocubic, orthorhombic, and rhombohedral structures. Further changes in lattice parameters can also be achieved by the introduction of strain due to lattice mismatches. Empirical results and previous models through thermodynamics and first principles show that changes in lattice parameters correlate with those in MMCE, but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed as a machine learning tool to find statistical correlations between the MMCE and lattice parameters among lanthanum manganites. More than 100 lattices, cubic, pseudocubic, orthorhombic, and rhombohedral, with the MMCE ranging from 0.65 to $$8.00\,\hbox {J}\,\hbox {kg}^{-1}\,\hbox {K}^{-1}$$ under a field change of 5 T are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of the magnetocaloric effect. Furthermore, the machine learning algorithm predicts close MMCE results on epitaxial films with strained lattices against experimental results, which can provide guidance on thin film structure design and help understandings of magnetic phase transformations and magnetocaloric effects.

Cryptotanshinone (CTS) has emerged as an anti-inflammatory agent in osteoarthritis (OA). However, the molecular mechanism underlying its potent therapeutic effect on OA remains largely unknown. MicroRNAs (miRNAs) act as crucial regulators in maintaining cartilage homeostasis. To investigate whether CTS protects against developing OA through regulation of miRNAs, we examined the potential CTS-mediated miRNA molecules using microarray analysis. We found that CTS significantly promoted miR-106a-5p expression in chondrocytes. Using the OA mouse model created by anterior cruciate ligament transection, we revealed that intra-articular injection of miR-106a-5p agomir attenuated OA. In addition, miR-106a-5p inhibited GLI-similar 3 (GLIS3) production by directly targeting the 3′ untranslated region. CTS promoted miR-106a-5p expression through recruitment of a member of the paired box (PAX) family of transcription factors, PAX5, to the miR-106a-5p promoter. Inhibition of PAX5 mimicked the effect of miR-106a-5p and abolished the CTS ability to regulate miR-106a-5p expression. In OA patients, miR-106-5p is downregulated which is accompanied by downregulation of PAX5 and upregulation of GLIS3. Collectively, these data highlight that the PAX5/miR-106a-5p/GLIS3 axis acts as a novel pleiotropic regulator in CTS-mediated OA cartilage protection, suggesting that miR-106a-5p and PAX5 activation and GLIS3 inhibition might be useful and attractive for therapeutic strategies to treat OA patients., Graphical Abstract

CD30 has been considered a unique diagnostic and therapeutic target for CD30-positive lymphomas and some lung diseases. Additionally, CD30 has shown high expression in clinical lung cancer samples. In this study, (89)Zr-radiolabeled brentuximab vedotin (BV) was developed for in vivo tracking of BV and imaging CD30 expression in lung cancer models via conjugation with desferrioxamine (Df). CD30 expression in three lung cancer cell lines (H460, H358, and A549) was quantified by Western blot. Flow cytometry and saturation binding assays were used to evaluate the binding capabilities of the tracer in vitro. After longitudinal positron emission tomography (PET) imaging and quantitative analysis were performed, ex vivo biodistribution and histological studies were used to verify PET results. Finally, dosimetric extrapolation of murine data to humans was performed. At the cellular level, CD30 was found to be expressed on H460 and A549 cells with the highest and lowest levels of expression, respectively. Both Df-BV and (89)Zr-Df-BV displayed high binding affinity to H460 cells. PET images and their quantification verified that BV accumulated in H460 tumor models (9.93 ± 2.70% ID/g at 24 h after injection; n = 4) at the highest level, followed by H358 and A549 tumors (8.05 ± 2.43 and 5.00 ± 1.56% ID/g; n = 4). The nonspecific (89)Zr-labeled IgG showed a low tumor uptake of 5.2 ± 1.0% ID/g for H460 models. Ex vivo biodistribution and fluorescence immunohistochemistry also corroborated these findings. Dosimetric results displayed safe dose estimations. Therefore, (89)Zr-Df- BV provides a potential agent for evaluating CD30 expression noninvasively in lung cancer, and also for imaging of brentuximab vedotin for better understanding of its pharmacokinetics.

In this work, a back-to-back symmetric Schottky type ultraviolet (UV) photodetector based on ternary alloy BeZnO was reported. High quality ternary alloy BeZnO was grown on a c-sapphire substrate via rf-plasma assisted molecular beam epitaxy (rf-MBE). The bandgap of BeZnO film was confirmed to be ∼3.54 eV and two-mode phonon vibration behavior was found in the resonant Raman spectrum. According to the modified random element isodisplacement (MREI) model, the two-mode behavior is due to the smaller effective mass of the substituting element Be in comparison with that of ZnO. Subsequently, we fabricated a symmetric Schottky type photodetector through using the high work function metal Au as the electrodes. Due to the incorporation of Be, the dark current of the device under 10 V is as low as ∼1 pA. The device showed a noticeable UV response with a response peak at 325 nm and a cut-off edge at 365 nm. At 5 V, the peak responsivity was as high as 10 mA W−1, which is the best result in BeZnO based photodetectors. Compared with ZnO-based photoconductive type photodetectors, the Schottky type device has a much faster response speed with a rise time of 1.48 ms and a decay time of 4 ms. Our findings provide a promising approach to realize a high performance UV photodetector based on novel ternary alloy BeZnO.

The chalcogenide glass, GexSe1−x, has been widely used in advanced electronics and photonics. The onset temperature of glass transition, Tg, plays an important role in manufacturing processes and practical applications. To investigate the Tg, experimental methods to observe the glass transition step are commonly used, but the correlation between influencing factors and the Tg remains vague. In this study, we develop the multivariate linear regression (MLR) model to present the relationship between physical attributes and GexSe1−x Tg. This simple model is highly accurate and contributes to rapid calculations of Tg. Compared with previous models that are more complicated in model construction and applications, the MLR model shows better performance and the direct statistical correlation between descriptors and the target variable.

Extensible, stable and accurate indoor positioning technology is the main goal of future large-scale perception services. With the widespread deployment of wireless hotspots, the demand for location-based services is also increasing. Location fingerprint technology is one of the main localization algorithms in this field, because it does not need expensive hardware facilities and can be located through existing resources and software. With the increase of the number of wireless access points in fingerprints or the number of fingerprints in database, the complexity of location algorithm will increase, and it may be difficult to achieve location fingerprint for large-scale multi-building and multi-floor. Therefore, we introduce the relevant classification technology of integrated learning, and use the XGBoost positioning algorithm to design the classification of indoor positioning to improve the positioning accuracy and reduce the computational complexity. Finally, according to the simulation results, the performance of the algorithm is analyzed, and the localization effects of the algorithm and other localization algorithms are compared and analyzed.

It is a tough challenge to find a remote sensing image fusion method which can acquire spatial and spectral information as much as possible from panchromatic (PAN) image and multispectral (MS) image. Sparse representation (SR) can realize remote sensing image fusion better than other popular methods, which is a powerful tool for dealing with the signals of high dimensionality. In addition, to gain better fusion results without color distortion, this paper propose a remote sensing image fusion algorithm with SR and color matching in stead of the intensity hue saturation (IHS) color model and Brovey transform. The experimental results show that proposed method can make fused image with both better spatial details and spectral information compared with three well-known methods.

For ultra-high voltage SiC devices of 10kV and above, the length of conventional FLR even reaches up to millimeters, which impedes the miniaturization and development in ultra-high voltage applications. In this paper, a novel termination structure of the Etching Uniform Field Limiting Ring (EU-FLR) for 10kV SiC power device is proposed and analyzed based on the theory of charge field modulation. The blocking capability achieves at 14.2kV and the EU-FLR exhibits a reduction of more than 30% in size compared with the conventional FLRs. The voltage efficiency factor η 1 of EU-FLR is 90% and the area efficiency factor η 2 is 17.8V/μm, respectively. The influence of the pivotal structural parameters of EU-FLRs on termination protection efficiency has been analyzed and researched by TCAD Silvaco.

This paper presents a novel and efficient multiple-step-modulated JTE (MSM-JTE) termination technique for ultrahigh voltage (>10 kV) silicon carbide (SiC) devices, to extend the ultrahigh voltage JTE dose window and increase the breakdown voltage. MSM-JTE takes advantage of ring assisted JTE, etched JTE and space modulated JTE, to relief local electric field concentration and form a gradual decrease of effective charges overall. This is similar to lateral variation doping (VLD) technique which is widely used in silicon. A practical fabrication processes is also described. Compared with conventional TZ-JTE, MSM-JTE requires only one extra etching process and is insensitive to doping dose and energy of ion implantation. The MSM-JTE is applied to 15 kV PiN rectifier and simulated by Silvaco TCAD. The simulation result shows MSM-JTE could reach a nearly ideal maximum efficiency of 99 % and keep an efficiency of 95 % in a doping interval of 7×1012 cm−2. Tolerance to etching depth uncertainties is also high enough for process reliability and repeatability.

Strategic design of experiments combined with machine learning is used to optimize the synthesis of p-type transparent conductors.

In industry 4.0, the most popular way to identify and track objects is to add tags. Because the cost of smart tag is still high, most companies still use cheap barcodes or QR codes. In order to solve the real-time positioning problem of upgrading traditional tags into smart tags, this paper proposes a QR tag location method based on Binarized Normed Gradients (BING) and AdaBoost-SVM. BING algorithm is the fastest general object detection algorithm at present, but its disadvantage is that the recall rate decreases sharply with the increase of Intersection-over-Union (IoU) threshold. In the proposed algorithm, Adaboost-SVM method is introduced to make up for the shortcomings of BING algorithm. More specifically, before training and prediction process of Adaboost-SVM, Contrast Limited Adaptive Histogram Equalization (CLAHE) mechanism is used for image enhancement, and thus can greatly shorten the training time and improve the precision of prediction. As a result, the precision of low-quality image prediction is significantly improved. Compared with the existing methods based on Neural Network (NN), the proposed algorithm does not depend on the parallel acceleration hardware such as GPU, hence, it can significantly reduce the hardware cost, and expand QR code automatic positioning algorithm utilization in the field of lower hardware requirements.

Background: Drug-eluting balloon (DEB) and drug-eluting stent (DES) have been proposed for the treatment of infrapopliteal artery disease. We performed a systematic review and meta-analysis of the current available studies investigating outcomes of DEB and DES in the treatment of infrapopliteal artery disease. Methods: Multiple databases were systematically searched to identify studies investigating the outcomes of DEB and DES in the treatment of patients with infrapopliteal artery disease. The quality of studies was assessed by Cochrane Collaboration method. The demographic data, risk factors, outcomes, and antiplatelet strategy were extracted. Results: Nine studies were identified with 707 and 606 patients in DEB/DES and standard percutaneous balloon angioplasty (PTA)/bare metal stenting (BMS) group, respectively. The risk of target lesion revascularization (TLR; odds ratio [OR] = 0.38, 95% confidence interval [CI]: 0.23-0.63, P < .01), restenosis rate (OR = 0.30, 95% CI: 0.18-0.50, P < .01), and amputation rate (OR = 0.49, 95% CI: 0.29-0.83, P < .01) significantly decreased in the DES group. The overall survival (OR = 0.86, 95% CI: 0.56-1.32, P = .50) was similar in DES and standard PTA/BMS group; TLR (OR = 0.59, 95% CI: 0.32-1.09, P = .09), restenosis rate (OR = 0.49, 95% CI: 0.11-2.14, P = .35), amputation rate (OR = 1.32, 95% CI: 0.51-3.40, P = .57), and overall survival (OR = 1.40, 95% CI: 0.72-2.71, P = .32) were similar in DEB and standard PTA group. Conclusion: The present meta-analysis suggests that compared with standard PTA/BMS, DES may decrease the risk of clinically driven TLR, restenosis rate, and amputation rate without any impact on mortality. However, DEB has no obvious advantage in the treatment of infrapopliteal disease. Due to the limitations of our study, more randomized controlled trials, especially those for DEB, are necessary.

Huatan Tongluo Fang (HTTLF) is a traditional herbal formula that can resolve phlegm and dredge collaterals. HTTLF has also been used to treat rheumatoid arthritis (RA); however, the mechanism underlying the therapeutic effects of HTTLF on RA has not been clearly elucidated at the molecular level. In the present study, an integrated model of system pharmacology containing chemical space analysis, potential active compound prediction and compound-target-disease network was constructed to investigate the molecular mechanisms of HTTLF. The compounds from HTTLF dispersed well in the chemical space. Most of the compounds from HTTLF had similar chemical spaces to drug/drug-like compounds associated with RA, according to the MDL Drug Data Report. A total of 127 potentially active compounds and 17 targets of RA were identified. Among them, 50 compounds interacted with ≥2 targets, while 77 compounds interacted with only one target. In addition, 17 targets were associated with 82 diseases that belonged to 26 categories. These results indicate that HTTLF has diverse chemical spaces and polypharmacology with regards to the treatment of RA. In addition, HTTLF demonstrated therapeutic potential against diverse diseases other than RA, including osteoarthritis, atherosclerosis and brain cancer. This study provides a novel platform for understanding how HTTLF treats RA; this is beneficial for explaining the diverse functions of HTTLF with regards to RA, and may help develop novel compounds with desirable therapeutic targets to treat RA.

Lymphoma is a heterogeneous disease with varying clinical manifestations and outcomes. Many subtypes of lymphoma, such as Burkitt′s lymphoma and diffuse large B cell lymphoma, are highly aggressive with dismal prognosis even after conventional chemotherapy and radiotherapy. As such, exploring specific biomarkers for lymphoma is of high clinical significance. Herein, a potential marker, CD38, is investigated for differentiating lymphoma. A CD38‐targeting monoclonal antibody (mAb, daratumumab) is then radiolabeled with Zr‐89 and Lu‐177 for theranostic applications. As the diagnostic component, the Zr‐89‐labeled mAb is highly specific in delineating CD38‐positive lymphoma via positron emission tomography (PET) imaging, while the Lu‐177‐labeled mAb serves well as the therapeutic component to suppress tumor growth after a one‐time administration. These results strongly suggest that CD38 is a lymphoma‐specific marker and prove that 89Zr/177Lu‐labeled daratumumab facilitates immunoPET imaging and radioimmunotherapy of lymphoma in preclinical models. Further clinical evaluation and translation of this CD38‐targeted theranostics may be of significant help in lymphoma patient stratification and management.

Metal halide perovskites have attracted great attention in the past decade due to unique and tunable optical and electrical properties, which are promising candidates for various applications such as solar cells, light emitting diodes, and laser cooling devices. The lattice constant, a, as the only variable parameter among the six parameters in the crystal structure, has a significant impact on the structural stability, bandgap structure, and thus materials performance. In this work, the Gaussian process regression (GPR) model is developed to elucidate the statistical relationship between ionic radii and lattice constants for cubic perovskite A 2 X Y 6 compounds. A total of 79 samples with lattice constants ranging from 8.109 A to 11.790 A are explored. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants.

Tailoring the optical bandgap, E g , of ZnO nanostructured thin films is of great interest to meet increasing demands for diverse practical applications, such as optoelectronics, spintronics, and photonics. Processing parameters, dopant selection, and various combinations of the two have significant effects on the crystal structure and microstructure of fabricated films, and thus optical performance. Empirical results and previous models through the first principle show that changes in lattice parameters and the grain size correlate with those in E g . But correlations are complicated and the models rarely apply to complex situations such as co-doped films. In this work, the Gaussian process regression (GPR) model is developed to elucidate the statistical relationship among the lattice parameters, grain size, and energy bandgap for doped-ZnO films. A total of 65 ZnO films with E g ranging from 2.100 eV to 3.760 eV are explored. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of E g .

The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity of their alloying elements. Nearly 140 half-Heusler samples, containing alloying elements of Cr, Mn, Fe, Co, Ni, Rh, Ti, V, Al, Ga, In, Si, Ge, Sn, P, As, and Sb, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants of half-Heusler compounds.

Background Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability. Results Here, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Initially, we extract a wide variety of 913 sequence and structure features with a sliding window of 11. Then, we apply the random forest algorithm to sort the features in descending order of importance and obtain the optimal subset of features using incremental feature selection. Based on the selected feature set, we use a light gradient boosting machine to build the prediction model for DNA-binding residues. Our PDRLGB method shows better overall predictive accuracy and relatively less training time than other widely used machine learning (ML) methods such as random forest (RF), Adaboost and support vector machine (SVM). We further compare PDRLGB with various existing approaches on the independent test datasets and show improvement in results over the existing state-of-the-art approaches. Conclusions PDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.

Light-actuating devices that can produce selective motions at small scales are highly desired for on-demand manipulation. For conventional photothermal motors that mostly encounter the homogenous light-induced heat diffusion at the liquid/air interface, it is challenging to effectively control the actuating direction and enhance the actuating speed. To this end, here, we explore aligned thermally conducting one-dimensional nanomaterials to make light-driving motors where the light-induced heat can be transmitted to the water surface along the length direction of the aligned one-dimensional nanomaterials to generate a localized surface tension gradient for high spatial resolution propulsion. When multiwalled carbon nanotubes were studied as a demonstration, the aligned active layer generated sufficient propulsion to drive a centimeter-sized motor that was 10 000 times higher in mass of the actuating layer on water. In addition, the actuating direction had been accurately controlled by varying the illuminated region of the active aligned nanotube layer. The resulting light-driving motors can move as fast as 4.19 cm/s (or 5.2 body length per second), which exceeded the previous motors based on the light activation.

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06 percent measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and many more.

Background Detection of microRNA (miRNA) aberrations in the peripheral plasma is a new approach for hepatocellular carcinoma (HCC) screening. The aim of this study was to characterise miR-148a in the peripheral plasma as a non-invasive biomarker for diagnosis of HCC. Methods Plasma-based miR148a analysis was performed on 346 plasma samples, including 155 HCCs, 96 liver cirrhosis and 95 healthy controls by using quantitative Real-Time PCR (qRT-PCR). Subsequently, plasma-based miR148a levels were validated in 97 pairs of HCC followed-up after removal of the primary tumour. Finally, Receiver operating characteristics (ROC) curves were generated to confirm predicting the value of plasma-based miR148a in HCC. Results Plasma-based miR-148a expression was significantly lower in 155HCCs compared to 96 liver cirrhosis (p Conclusions Plasma-based miR-148a can be applied as a potential non-invasive biomarker for HCC screening, especially for HCC with negative or less expressing AFP, which can make up AFP deficiency in predicting HCC occurrence. Notes This project was funded by Application Project of Clinical Features in Capital City (Z141107002514507).