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The house market in China has been growing rapidly for the past decade and price forecasting has become a significant issue to the people and policymakers. We approach this problem by examining neu...

The composition of NdFeB-type magnets has a great impact on their performance. To enhance the remanence of the magnet, Zr and Co are alloyed into the starting materials. To develop an empirical equation that calculates the remanence based on alloying elements, the multiple linear regression model is developed in this work. It elucidates the statistical relationship between alloying elements content and magnetic remanence for Zr/Co-alloyed NdFeB magnets. The model is simple and straightforward and has a high degree of accuracy and stability, which contributes to fast low low-cost magnetic remanence estimations.

In this article, the development of a compact 10–14.5 GHz quadrature hybrid with digitally reconfigurable I/Q phase in 130 nm SiGe BiCMOS process is presented. Thanks to the switched capacitance loaded on I/Q path of the quadrature hybrid, the I/Q phase difference can be optimized and digitally reconfigured. The equivalent model is analyzed with even/odd mode theory, and the ABCD matrix is used for the circuit derivation. In order to obtain high coupling coefficient, the broadside coupled line sections are utilized, and compact hybrid size can be realized accordingly. Measured results show that the compact quadrature hybrid has optimized phase difference of 90 ± 1.0° and amplitude difference less than ±0.5 dB for 10–14.5 GHz, with an ultra-compact size of 460 µm × 151 µm, or 0.031λ0 × 0.011λ0. Meanwhile, with the seven reconfigurable phase states, the quadrature hybrid I/Q phase can be digitally reconfigured for a range of 3 degrees to compensate the I/Q phase imbalance in the quadrature system, without DC power consumption.

Purpose: Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) can significantly extend tumor response in patients with metastatic estrogen receptor–positive (ER+) breast cancer, but intrinsic and acquired resistance is common. Elucidation of the molecular features of CDK4/6i sensitivity and the efficiency of their combination with novel targeted cell death inducers may pave the way toward improving patient outcomes. Experimental Design: Ferroptosis-related characteristics were observed following treatment with the CDK4/6 inhibitor palbociclib. Transcriptomic analyses and functional assays were performed to determine the targets and regulatory mechanism of palbociclib in the ferroptosis pathway in ER+ breast cancer cell lines. A tumor xenograft model was used to study the synergistic antitumor effects of CDK4/6is in combination with ferroptosis inducers. Results: Ferroptosis, a form of regulated cell death driven by iron-dependent phospholipid peroxidation, is partly responsible for the efficacy of the CDK4/6 inhibitor palbociclib. Mechanistically, palbociclib downregulates cystine transporter SLC7A11 by inhibiting SP1 binding to the promoter region of SLC7A11. Furthermore, genetic or pharmacological inhibitors of SP1 or SLC7A11 can enhance cell sensitivity to palbociclib and synergistically inhibit ER+ breast cancer cell growth in combination with palbociclib. A syngeneic ER+ mouse mammary tumor model was used to verify that combined inhibition of SLC7A11 or SP1 and CDK4/6 resulted in marked suppression of tumor growth in vivo. Conclusions: Experimentally, ferroptosis represents some of the CDK4/6i-induced cell death response. This study illustrates the potential for targeting SLC7A11 in combination with CDK4/6 inhibitors and supports the investigation of combination therapy in ER+ breast cancer.

Machine learning approaches can serve as powerful tools in machining optimization 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 this study, we develop Gaussian process regression models to predict three cutting parameters, the cutting force ( F c ), surface roughness (Ra), and tool lifetime (T), in high speed turning processes based on the cutting speed ( v c ), feed rate (f), and depth of cut ( a p ). The models are highly stable and accurate, and are thus promising as fast, robust, and low-cost approaches for cutting parameter estimations.

Solid particle erosion caused by sand production in pipelines is a critical problem in the oil and gas industry. To control, assess, and prevent severe damage from erosion, it is important to have models to predict the erosion magnitude. As one of critical parameters, the erosion rate is of particular interest. In this study, we develop the least-squares boosting model to predict the solid particle erosion rate in elbows from material properties and the geometry of pipewall and sand particles, as well carrier fluid velocities. It applies to both single phase and multiphase flow patterns that involve varying wall materials, particle sizes, and carrier fluid velocities. The model is highly accurate and stable, and thus is promising as a fast, robust, and low-cost tool for solid particle erosion rate estimations.

The glass transition temperature, Tg, is an important thermophysical property for polymethacrylates, which can be difficult to determine experimentally. Data-driven modeling approaches provide alte...

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.

Due to significant implications for resource and food sectors that directly influence social well-being, commodity price comovements represent an important issue in agricultural economics. In this study, we approach this issue by concentrating on daily prices of the corn futures market and 496 cash markets from 16 states in the United States for the period of July 2006 – February 2011 through correlation based hierarchical analysis and synchronization analysis, which allow for determining interactions and interdependence among these prices, heterogeneities in price synchronization, and their changing patterns over time. As the first study of the issue focusing on prices of the futures and hundreds of spatially dispersed cash markets for a commodity of indubitable economic significance, empirical findings show that the degree of comovements is generally higher after March 2008 but no persistent increase is observed. Different groups of cash markets are identified, each of which has its members exhibit relatively stable price synchronization over time that is generally at a higher level than the synchronization among the futures and all of the 496 cash markets. The futures is not found to show stable price synchronization with any cash market. Certain cash markets have potential of serving as cash price leaders. Results here benefit resource and food policy analysis and design for economic welfare. The empirical framework has potential of being adapted to network analysis of prices of different commodities.

Almost all Langmuir-Blodgett (LB) films were prepared with the classical Langmuir film balance, developed more than a century ago. To date, the success of the classical Langmuir film balance and the LB transfer technique is primarily restricted to the study of self-assembled monolayers at the air-water surface. It is challenging to study self-assembled monolayers at the oil-water interface, since the Langmuir film balance requires stacked oil and water layers. We hypothesize that a newly developed experimental method, called constrained drop surfactometry (CDS), is capable of preparing and characterizing LB films from the oil-water interface.We have developed a novel droplet-based LB transfer technique capable of preparing LB films from the oil-water interface. In conjunction with atomic force microscopy, we have demonstrated the capacity of the CDS in studying a natural pulmonary surfactant film self-assembled at the perfluorocarbon-water interface, and have compared to the LB films prepared from the air-water surface using the classical Langmuir film balance.Our findings have demonstrated a novel paradigm for studying self-assembled monolayers and for preparing LB films from the oil-water interface. The CDS holds great promise for expanding the applicability of the traditional LB transfer technique from the air-water surface to the oil-water interface.

As major elements of the basement membrane, laminins play a significant role in angiogenesis, migration, and adhesion of various cells.

Objective The superficial zone cells in temporomandibular joint (TMJ) cartilage are proliferative. The purpose of the present work was to delineate the relation of calcium-sensing receptor (CaSR) and parathyroid hormone-related peptide (PTHrP) nuclear localization sequence, and their role in the proliferation behaviors of the superficial zone cells. Methods A gain- and loss-of-function strategy were used in an in vitro fluid flow shear stress (FFSS) model and an in vivo bilateral elevation bite (BAE) model, which showed TMJ cartilage thickening. CaSR and PTHrP nuclear localization sequence (PTHrP87 − 139), were modulated through treating the isolated superficial zone cells with activator/SiRNA and via deleting CaSR or PTHrP gene in mice with the promoter gene of proteoglycan 4 (Prg4-CreERT2) in the tamoxifen-inducible pattern with or without additional injection of cinacalcet, the CaSR agonist, or PTHrP87 − 139 peptide. Results FFSS stimulated CaSR and PTHrP expression, and accelerated proliferation of the Prg4-expressing superficial zone cells, in which process CaSR acted as an up-streamer of PTHrP. Prg4-specific knockout of CaSR or PTHrP reduced the cartilage thickness, suppressed the proliferation and early differentiation of the superficial zone cells, and inhibited cartilage thickening and matrix production promoted by BAE. Injections of CaSR agonist Cinacalcet could not improve the phenotype caused by PTHrP mutation. Injections of PTHrP87 − 139 peptide rescued the TMJ cartilage from knockout of CaSR gene. Conclusions CaSR modulates proliferation of the superficial zone cells in TMJ cartilage through activation of PTHrP nuclear localization sequence. Our data support the therapeutic target of CaSR in promoting PTHrP production in superficial zone cartilage.

With an increasing prevalence of electronic cigarette (e-cigarette) use, especially among youth, there is an urgent need to better understand the biological risks and pathophysiology of health conditions related to e-cigarettes. A majority of e-cigarette aerosols are in the submicron size and would deposit in the alveolar region of the lung, where they must first interact with the endogenous pulmonary surfactant. To date, little is known whether e-cigarette aerosols have an adverse impact on the pulmonary surfactant. We have systematically studied the effect of individual e-cigarette ingredients on an animal-derived clinical surfactant preparation, bovine lipid extract surfactant, using a combination of biophysical and analytical techniques, including in vitro biophysical simulations using constrained drop surfactometry, molecular imaging with atomic force microscopy, chemical assays using carbon nuclear magnetic resonance and circular dichroism, and in silico molecular dynamics simulations. All data collectively suggest that flavorings used in e-cigarettes, especially menthol, play a predominant role in inhibiting the biophysical function of the surfactant. The mechanism of biophysical inhibition appears to involve menthol interactions with both phospholipids and hydrophobic proteins of the natural surfactant. These results provide novel insights into the understanding of the health impact of e-cigarettes and may contribute to better regulation of e-cigarette products.

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.

Empirical equations, thermodynamics frameworks, and neural network modeling have been developed to predict steel martensite start temperature, [Formula: see text], but they might not tend to generalize well when composition includes a wide range of alloying elements. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship between alloying elements and [Formula: see text] temperature for steels. A total of 1119 steels with [Formula: see text] ranging from 153 K to 938 K are examined. The model has a high degree of accuracy and stability, contributing to fast low-cost [Formula: see text] temperature estimations.

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.

The longitudinal Varestraint test is the most widely used technique to quantify the weld solidification cracking susceptibility, which provides the total crack length, TCL, as an indicator. But experimental work requires a significant amount of time and resource. Data-driven approaches can serve as alternatives to estimate the TCL through influencing factors, including metallurgical factors, welding conditions, and test parameters. We develop the least-squares boosting model to predict the TCL, a solidification cracking susceptibility indicator, based on the alloy composition, welding parameters, and applied strain. The model manifests high accuracy and stability, and applies to a wide range of stainless steels. It serves as a fast, robust, and low-cost tool for the assessment of solidification cracking susceptibility.

For tetragonal zircon-type structures, the stoichiometry, ionic radii, and electronegativities of alloying elements affect lattice constants, a and c. Lattice distortions have significant impacts on the stability, electronic structure, ionic conductivity, and thus materials performance. Here, we develop the Gaussian process regression (GPR) model to elucidate statistical relationships among ionic radii, electronegativities, and lattice constants for zircon-group minerals MXO4 compounds with 100 samples. The modeling approach shows a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants, which can be employed in further computational studies on materials behavior.

Glass transition temperature, Tg, is an important thermophysical property of polyacrylamides, which can be difficult to determine experimentally and resource-intensive to calculate. Data-driven modeling approaches provide alternative methods to predict Tg in a rapid and robust way. We develop the Gaussian process regression model to predict the glass transition temperature of polyacrylamides based on quantum chemical descriptors. The modeling approach shows a high degree of stability and accuracy, which contributes to fast and low-cost glass transition temperature estimations.

The lattice constants of spinel compounds AM2X4 are correlated with the constituent elemental properties using support vector regression (SVR) optimized with Bayesian optimization.

Recently, Fe-based superconductors have shown promising properties of high critical temperature and high upper critical fields, which are prerequisites for applications in high-field magnets. Critical temperature, T c, is an important characteristic correlated with crystallographic and electronic structures. By doping with foreign ions in the crystal structure, T c can be modified, which however requires significant manpower and resources for materials synthesis and characterizations. In this study, we develop the Gaussian process regression model to predict T c of doped Fe-based superconductors based on structural and topological parameters, including the lattice constants, volume, and bonding parameter topological index H 31. The model is stable and accurate, contributing to fast T c estimations.

The carbon fibre reinforced plastic (CFPR) has been widely used in aircraft structural applications due to the superior modulus, specific tensile strength, and fatigue strength. The inhomogeneous and anisotropic nature of these composites poses great challenges on the machining process. Particularly, the delamination is one of major defects associated with drilling, which has a significant impact on CFRP’s structure integrity and application. Machine learning approaches can help facilitate the optimization of machining processes. In this study, we develop the Gaussian process regression (GPR) model to predict delaminations in carbon fibre reinforced plastic composites during drilling from machining parameters. The model is simple and highly accurate and stable that contributes to fast delamination 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.

Electrolyte additives for lithium-ion battery (LIB), commonly categorized into anode additives, cathode additives, redox shuttle additives, and fire retardants, can improve properties of electrolyt...

Reduning Injection (RDNI) is a traditional Chinese medicine formula indicated for the treatment of inflammatory diseases. However, the molecular mechanism of RDNI is unclear. The information of RDNI ingredients was collected from previous studies. Targets of them were obtained by data mining and molecular docking. The information of targets and related pathways was collected in UniProt and KEGG. Networks were constructed and analyzed by Cytoscape to identify key compounds, targets, and pathways. Data mining and molecular docking identified 11 compounds, 84 targets, and 201 pathways that are related to the anti-inflammatory activity of RDNI. Network analysis identified two key compounds (caffeic acid and ferulic acid), five key targets (Bcl-2, eNOS, PTGS2, PPARA, and MMPs), and four key pathways (estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and calcium signaling pathway) which would play critical roles in the treatment of inflammatory diseases by RDNI. The cross-talks among pathways provided a deeper understanding of anti-inflammatory effect of RDNI. RDNI is capable of regulating multiple biological processes and treating inflammation at a systems level. Network pharmacology is a practical approach to explore the therapeutic mechanism of TCM for complex disease.

Purpose This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021. Design/methodology/approach Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices. Findings The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities. Originality/value This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.

Extensive research has been conducted to find new superconducting materials that exhibit high critical temperature T $$_{c}$$ , in order to fulfill the needs of practical applications with liquid-helium-free refrigeration or even at room temperature. Iron-based superconductors show high T $$_{c}$$ and high upper critical field. The research, however, requires significant manpower for materials synthesis and characterization, and costly equipment and facilities. Computational approaches have contributed greatly to investigate the properties of solid-state matter in many fields, which can be integrated to machine learning and big-data analysis. In this work, the Gaussian process regression model is developed to predict Fe-based superconductor critical temperature based on lattice parameters. This modeling approach demonstrates a high degree of accuracy and stability that lead to the statistical relationship between the lattice parameters and T $$_{c}$$ . The results disclosed by this work can also lead to a better understanding of the origin of superconductivity in these materials.

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.

Ni-based single crystal superalloys exhibit superb mechanical strength, particularly, creep resistance at elevated temperature. The unique microstructure, which is consisted of $$\gamma$$ and $$\gamma ^{\prime }$$ phases, is a major factor that determines the mechanical behavior of these alloys. The lattice misfit between the two phases is of particular interest in understanding and predicting the deformation mechanism. The measurement of the lattice misfit by advanced analytical instruments is costly and difficult. In current study, we develop the Gaussian process regression model to predict lattice misfits for Ni-based single crystal superalloys based on chemical composition, temperature, and two morphological indicators. The model is highly stable and accurate and promising as a fast, robust, and low-cost tool for lattice misfit estimations.

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.

Shape memory alloys (SMAs) are a class of metallic compounds that can return to their original forms, shapes, or sizes, when subjected to environmental stimuli, such as temperature and magnetic fields. Due to their unique shape memory effects and pseudoelasticity, SMAs, particularly NiTi-based ones, are of great interest in structures and composites, electronics, automobiles, biomedicine, and robotics. To tailor phase transformation temperature for practical applications, chemical substitutions have been extensively investigated and utilized. However, with multiple elements substituting for Ni, the correlation between the composition and transformation temperature is not elucidated but only the general trend is revealed with limited doping situations. In this study, we develop the Gaussian process regression model to find statistical correlations between NiTi-based SMAs’ transformation temperature ( $${T_{p}}$$ ) upon heating and nine pertinent physical parameters of alloying elements. More than 50 samples, with Ni partially substituted by one to three elements, are explored for this purpose. The modeling approach shows a high degree of stability and accuracy that contributes to low-cost $${T_{p}}$$ estimations.

The central magnetic flux density is usually simulated via finite element methods that require a significant amount of inputs and computation resources. We develop the Gaussian process regression (...

The thermal stability of lubricant additives is a fundamental parameter in practical applications, which is determined by the molecular structure. The ability to predict thermal properties, particularly lubricant additives’ decomposition onset temperature, is of ultimate importance. We develop the Gaussian process regression model to present the relationship between molecular descriptors and onset temperature of decomposition of lubricant additives. This model is highly stable and accurate, which is promising as a fast, robust, and low-cost tool for estimating various types of lubricant additives’ decomposition onset temperature.

A novel planar gate SiC MOSFET embedding low barrier diode (LBD-MOSFET) with improved third quadrant and switching performance is proposed and characterized in this letter. The LBD-MOSFET not only exhibits about 3 times lower diode turn on voltage than the body diode, but also successfully eliminates bipolar degradation phenomena. A low potential barrier for electrons transporting from JFET region to N+ source region is formed in LBD-MOSFET owing to the existence of the depletion charge in LBD base region. Meanwhile, the gate-to-drain charge ( ${Q}_{\text {gd}}){}$ and gate-to-drain capacitance ( ${C}_{\text {gd}}){}$ of LBD-MOSFET are significantly reduced by about $21\times $ and $15\times $ in comparison with the conventional MOSFET (C-MOSFET), due to the reduction of the overlapping area of the gate and drift region. Therefore, the obtained high frequency figures of merit (HF-FOM $1= {R}_{\text {on,sp}}\times {Q}_{\text {gd}}$ and HF-FOM $2= {R}_{\text {on,sp}}\times {C}_{\text {gd}}$ ) for the LBD-MOSFET are improved by about 13 times and 9 times compared with C-MOSFET. Furthermore, a compact potential barrier analytical model based on Poisson’s Law is developed to understand the origin of low potential barrier diode in SiC LBD-MOSFET. The overall enhanced performances suggest SiC LBD-MOSFET is an excellent choice for high frequency power electronic applications.

Background Renal cell cancer (RCC) is one of the most lethal malignancies of the kidney in adults. mTOR (mammalian target of rapamycin) signaling pathway plays a pivotal role in RCC tumorigenesis and progression and inhibitors targeting the mTOR pathway have been widely used in advanced RCC treatment. Therefore, it is of great significance to explore the potential regulators of the mTOR pathway as RCC therapeutic targets. Materials and methods Bioinformatics analysis was used to screen out the most significant differentially expressed genes in the RCC dataset of The Cancer Genome Atlas (TCGA). Real-time PCR and Western-blot analysis were utilized to examine the expression of inositol-1,4,5-trisphosphate-3-kinase-A (ITPKA) in four RCC cell lines and one human embryonic kidney cell line. Cell counting Kit-8 and colony formation assay were performed to estimate the effect of ITPKA on the proliferation ability of RCC cells. Wound healing and Transwell assays were used to test the effect of ITPKA on RCC cell migration and invasion. Xenograft formation assay was performed in nude mice to investigate the effect of ITPKA in vivo. mTORC1 pathway inhibitor was added to explore the mechanisms by which ITPKA regulates RCC cell growth and progression. Results Based on bioinformatics analysis, ITPKA is screened out as one of the most significant differentially expressed genes in RCC. ITPKA is upregulated and positively correlated with RCC malignancy and poorer prognosis. ITPKA promotes RCC growth, migration and invasion in cultured cells, and accelerates tumor growth in nude mice. Mechanistically, ITPKA stimulates the mTORC1 signaling pathway which is a requirement for ITPKA modulation of RCC cell proliferation, migration and invasion. Conclusion Our data demonstrate a critical regulatory role of the ITPKA in RCC and suggest that ITPKA/mTORC1 axis may be a promising target for diagnosis and treatment of RCC.

The increase in critical temperature of high-temperature superconductors fulfills needs of practical applications with liquid-helium-free refrigeration and a delay in magnet failure. But the research requires significant manpower for materials synthesis, characterization, and quench detection, as well as costly equipment and facilities. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship between process parameters and superconducting transition temperature for BiPbSrCaCuOF superconductors. The modeling approach has a high degree of accuracy and stability, contributing to fast low-cost estimations of superconducting transition temperature.

CRISPR (clustered, regularly interspaced, short palindromic repeats)/CRISPR-associated protein 9 (Cas9) system has emerged as a powerful genome-editing tool to correct genetic disorders. However, successful intracellular delivery of CRISPR/Cas9, especially in the form of ribonucleoprotein (RNP), remains elusive for clinical translation. Herein, we describe a supramolecular polymer that can mediate efficient controlled delivery of Cas9 RNP in vitro and in vivo. This supramolecular polymer system is prepared by complexing disulfide-bridged biguanidyl adamantine (Ad-SS-GD) with β-cyclodextrin-conjugated low-molecular-weight polyethyleneimime (CP) through supramolecular assembly to generate CP/Ad-SS-GD. Due to multiple, strong hydrogen bonding and salt bridge effects, CP/Ad-SS-GD well interact with Cas9 RNP to form stable nanocomplex CP/Ad-SS-GD/RNP, which can be readily released in the reductive intracellular milieu as a result of the cleavage of disulfide bonds. The supramolecular polymer ensures the efficient intracellular delivery and the release of Cas9 RNP into 293T cells and colorectal cancer (CRC) cells, thus displaying high genome-editing activity in vitro. Importantly, we also found that hyaluronic acid (HA)-decorated CP/Ad-SS-GD/RNP nanocomplexes targeting mutant KRAS effectively inhibit tumor growth as well as metastasis in the tumor-bearing mouse models. Collectively, our findings provide a promising therapeutic strategy against mutant KRAS for the treatment of CRC-activated RAS pathways, offering a new therapeutic genome-editing modality for the colorectal cancer treatment.

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.

On the basis of growing environment, maize can largely be classified into temperate and tropical groups, leaving extensive genetic variation and evolutionary signatures in the maize genome. To identify candidate genes governing flowering time and photoperiod sensitivity, selective signature analysis and SNP- and haplotype-based GWAS were performed using 39,350 high-quality SNP markers in temperate and tropical maize groups consisting of 410 inbred lines phenotyped in three representative experiments in different latitudes. Selective signature analysis revealed 106 selective-sweep regions containing 423 candidate genes involved mainly in biological regulation and biosynthesis pathways. Among these genes, 25 overlapped with known genes governing flowering time and photoperiod sensitivity and 37 were also detected by GWAS for days to tassel, anthesis-silk interval, and photoperiod sensitivity measured by days to silking. Only two of the candidate genes governing flowering time overlapped selective signals. Most haplotype alleles within significant haplotype loci showed the same direction of effect on flowering time and photoperiod sensitivity. The inbred lines carrying GATT at HapL499 (haplotype locus 499) on chromosome 1 had relatively short flowering times. Lines carrying CA at HapL4054 on chromosome 10, TA at HapL4055 on chromosome 10, and GTTGT at HapL978 on chromosome 2 were less sensitive to photoperiod than lines carrying other haplotype alleles. Haplotype loci associated with flowering time and photoperiod sensitivity explained respectively 17.5%–18.6% and 11.2%–15.5% of phenotypic variation. Candidate genes and favorable haplotypes identified in this study may support the more efficient utilization of maize germplasm groups. Keywords: Selective signature analysis, GWAS, Maize, Flowering time, Photoperiod sensitivity

Sine oculis homeobox 1 (SIX1), a key transcription factor for regulating aerobic glycolysis, participates in the occurrence of various cancer types. However, the role of SIX1 in melanoma and the upstream regulating mechanisms of SIX1 remain to be further investigated. MicroRNAs (miRNAs) have emerged as key regulators in tumorigenesis and progression. Here, we show that miR-489-3p suppresses SIX1 expression by directly targeting its 3′ untranslated region (3′ UTR) in melanoma cells. miR-489-3p suppressed melanoma cell proliferation, migration, and invasion through inhibition of SIX1. Mechanistically, by targeting SIX1, miR-489-3p dampens glycolysis, with decreased glucose uptake, lactate production, ATP generation, and extracellular acidification rate (ECAR), as well as an increased oxygen consumption rate (OCR). Importantly, glycolysis regulated by the miR-489-3p/SIX1 axis is critical for its regulation of melanoma growth and metastasis both in vitro and in vivo. In melanoma patients, miR-489-3p expression is negatively correlated with SIX1 expression. In addition, patients who had increased glucose uptake in tumors and with metastasis assessed by positron emission tomography (PET) scans showed decreased miR-489-3p expression and increased expression of SIX1. Collectively, our study demonstrates the importance of the miR-489-3p/SIX1 axis in melanoma, which can be a potential and a promising therapeutic target in melanoma., Graphical Abstract

We examine the forecasting problem in a data set of daily corn cash prices from seven states: Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We assess thirty individual time series models and ten combined forecasts based on six trimming strategies across three out‐of‐time evaluation periods, seven horizons, and two systems (bi‐ and multivariate). Using the unrestricted least squares without an intercept to estimate combination weights of individual models without trimming arrives at the lowest root mean squared errors across all evaluation dimensions. Incorporating local cash prices in a model could benefit accuracy, especially for relatively longer out‐of‐time evaluation periods and forecast horizons. Our results suggest model recalibration frequency no lower than one month. Discussions of empirical findings at a more granular level also are presented, including comparisons of individual time series models and those of combined forecasts based on different trimming strategies. The forecasting framework shown here is not difficult to implement and has potential of generalizing to other commodities. This article thus contributes to fulfilling different forecasting users' information needs for decision making under various circumstances.