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The role of lncRNAs in the pathogenesis of cancer, including colorectal cancer (CRC), has repeatedly been demonstrated. However, very few lncRNAs have been well annotated functionally. Our study identified a novel lncRNA upregulated in CRC, NONHSAT136151, which was correlated with clinical progression. In functional assays, NONHSAT136151 significantly enhanced CRC cell proliferation, migration and invasion. Mechanistically, NONHSAT136151 interacted with RNA‐binding protein (RBP) QKI (Quaking) to interfere with QKI binding to target mRNAs and regulate their expression. As well, FOXP3 may be causally related to the dysregulation of NONHSAT136151 in CRC cells through its transcriptional activity. In conclusion, our findings identified a novel lncRNA regulated by FOXP3 participates in CRC progression through interacting with QKI, indicating a novel lncRNA‐RBP interaction mechanism is involved in CRC pathogenesis. [ABSTRACT FROM AUTHOR]

Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications. [ABSTRACT FROM AUTHOR]

Phase transition behaviors of poly(N-isopropylacrylamide) nanogels with different compositions induced by (−)-epigallocatechin-3-gallate (EGCG) and ethyl gallate (EG) has been investigated systematically. Monodisperse poly(N-isopropylacrylamide-co-N-hydroxymethyl acrylamide) (P(NIPAM-co-NMAM)) and poly(N-isopropylacrylamide-co-2-hydroxyethyl methacrylate) (P(NIPAM-co-HEMA)) nanogels with different feeding monomer ratios were prepared by emulsion polymerization. P(NIPAM-co-NMAM) nanogels exhibit rapid isothermal phase transition behavior in EGCG solutions with low concentration (10−3 mol/L) in less than 10 minutes. The thermosensitive phase transition behaviors of nanogels are affected not only by the copolymerized monomers but also by the concentrations of EGCG and EG in aqueous solutions. Nanogels remain in a shrunken state and do not exhibit thermosensitive phase transition behaviors in EGCG solutions (≥5 mmol/L), whereas they display thermo-responsive phase transition behaviors in EG solutions. The volume phase transition temperature (VPTT) shifts to lower temperatures with increasing EG concentration. The diameters of P(NIPAM-co-NMAM) nanogels decrease with increasing EG concentration at temperatures between 29 and 33 °C. In contrast, the diameters of P(NIPAM-co-HEMA) nanogels increase with increasing EGCG concentration at temperatures between 37 and 45 °C. The results demonstrate the potential of nanogels for simple detection of EG and EGCG concentrations in aqueous solutions over a wide temperature range, and EGCG can serve as a signal for the burst-release of drugs from the P(NIPAM-co-NMAM)-based carriers at physiological temperature. [ABSTRACT FROM AUTHOR]

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution. [ABSTRACT FROM AUTHOR]

The three-dimensional parametric models of RV reducer are constructed by using SolidWorks software,and then the model is imported into ADAMS software,a batch of dynamic models are established and simulated by macro program,and the exporting data of rotation speed and rotation angle is imported into Matlab.After data processing,the transmission error and the fluctuation of rotation speed that is the variance of the rotation speed are calculated. The simulation result shows that under the condition of the variation of error value is the same,decreasing the radius of gear pin and increasing the value of modification of positive equidistance,the value of fluctuation of rotation speed will increase greatly,increasing the value of the modification of negative moved distance and modification of negative equidistance, it will decrease less. For transmission error,decreasing the radius of gear pin and increasing the value of the value of modification of positive equidistance,it will increase greatly,increasing the value of the eccentricity of pin wheel has tiny influence on it.

In many regions, even if waste vegetable oil is used as raw material, the profits of biodiesel (fatty acid methyl ester, FAME) enterprises still remain restricted, and they are facing environmental problems caused by the by‐product glycerol. Hence, value‐added utilization of FAME and glycerol is a promising alternative to improve the competitiveness of biodiesel enterprises in the economy and environmental protection. In this paper, FAME and glycerol were utilized to prepare epoxidized acyl glycerides by transesterification and epoxidation. In the transesterification, acyl glycerides were prepared by reacting FAME and glycerol catalyzed by K2CO3. The contents of monoacyl glyceride, diacyl glyceride, and triacyl glyceride in the acyl glycerides were about 50%, 40%, and 10%, and the optimal conditions were a temperature of 200°C and the molar ratio of FAME to glycerol of 1:1. In the epoxidation, selectivity of epoxides was significantly affected by monoacyl glyceride content, and a treatment method was developed to control the monoacyl glyceride content <5%. In addition, the plasticity of epoxidized acyl glycerides to partially substitute dioctyl phthalate in polyvinyl chloride was studied, the overall mechanical properties, thermal stability, and extraction resistance were improved. In this work, FAME and glycerol were utilized to prepare epoxidized acyl glycerides with higher value‐added, which provide a potential solution for improving the market competitiveness of biodiesel enterprises. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is one of the most concerning diseases in forestry and poses a considerable threat to forests. Since the deep learning approach can interpret the raw images acquired by UAVs, it provides an effective means for forest health detection. However, the fact that only PWD can be detected but not the degree of infection can be evaluated hinders forest management, so it is necessary to establish an effective method to accurately detect PWD and extract regions infected by PWD. Therefore, a Mask R-CNN-based PWD detection and extraction algorithm is proposed in this paper. Firstly, the extraction of image features is improved by using the advanced ConvNeXt network. Then, it is proposed to change the original multi-scale structure to PA-FPN and normalize it by using GN and WS methods, which effectively enhances the data exchange between the bottom and top layers under low Batch-size training. Finally, a branch is added to the Mask module to improve the ability to extract objects using fusion. In addition, a PWD region extraction module is proposed in this paper for evaluating the damage caused by PWD. The experimental results show that the improved method proposed in this paper can achieve 91.9% recognition precision, 90.2% mapping precision, and 89.3% recognition rate of the affected regions on the PWD dataset. It can effectively identify the distribution of diseased pine trees and calculate the damage proportion in a relatively accurate way to facilitate the management of forests. [ABSTRACT FROM AUTHOR]

A set of non-conforming quadratic basis functions is introduced to formulate the mass and stiffness matrices that enable a superior frequency accuracy for isogeometric free vibration analysis of thin beams and plates. The non-conforming basis functions are expressed as a simple combination of the original basis functions and their second-order derivatives with an adjustable parameter. By construction, these quadratic non-conforming basis functions only affect the mass matrices and do not alter the stiffness matrices. The adjustable parameter arising from the non-conforming basis functions are determined through optimizing the frequency accuracy. In the case of thin beams, the proposed non-conforming isogeometric formulation leads to an increase of frequency accuracy order or superconvergence. For thin plates, the frequency error of the proposed method is guaranteed to be no larger than that of the standard isogeometric approach. Numerical results for thin beams and plates consistently verify that the proposed formulation with non-conforming basis functions is quite robust and produces very favorable frequency accuracy for both uniform and non-uniform meshes. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) threatens public health all over the world. It is well-accepted that the immune cells in peripheral blood are widely involved in the pathological process of COVID-19. However, hematopoietic stem and progenitor cells (HSPCs), as the main source of peripheral immune cells, have not been well studied during COVID-19 infection. We comprehensively revealed the transcriptome changes of peripheral blood HSPCs after COVID-19 infection and vaccination by single-cell RNA-seq. Compared with healthy individuals, the proportion of HSPCs in COVID-19 patients significantly increased. The increase in the proportion of HSPCs might be partly attributed to the enhancement of the HSPCs proliferation upon COVID-19 infection. However, the stemness damage of HSPCs is reflected by the decrease of differentiation signal, which can be used as a potential specific indicator of the severity and duration of COVID-19 infection. Type I interferon (IFN-I) and translation signals in HSPCs were mostly activated and inhibited after COVID-19 infection, respectively. In addition, the response of COVID-19 vaccination to the body is mild, while the secondary vaccination strengthens the immune response of primary vaccination. In conclusion, our study provides new insights into understanding the immune mechanism of COVID-19 infection. [ABSTRACT FROM AUTHOR]

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model's accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models. [ABSTRACT FROM AUTHOR]

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average R 2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner. [ABSTRACT FROM AUTHOR]

Oily sludge, as a critical hazardous waste, requires appropriate treatment for resource recovery and harmfulness reduction. Here, fast microwave-assisted pyrolysis (MAP) of oily sludge was conducted for oil removal and fuel production. The results indicated the priority of the fast MAP compared with the MAP under premixing mode, with the oil content in solid residues after pyrolysis reaching below 0.2%. The effects of pyrolysis temperature and time on product distribution and compositions were examined. In addition, pyrolysis kinetics can be well described using the Kissinger-Akahira-Sunose (KAS) and the Flynn-Wall-Ozawa (FWO) methods, with the activation energy being 169.7–319.1 kJ/mol in the feedstock conversional fraction range of 0.2–0.7. Subsequently, the pyrolysis residues were further treated by thermal plasma vitrification to immobilize the existing heavy metals. The amorphous phase and the glassy matrix were formed in the molten slags, resulting in bonding and, hence, immobilization of heavy metals. Operating parameters, including working current and melting time, were optimized to reduce the leaching concentrations of heavy metals, as well as to decrease their volatilization during vitrification. [ABSTRACT FROM AUTHOR]

Power MOSFETs are found to be very vulnerable to single-event burnout (SEB) in space irradiation environments, and the military components generally require that devices could operate reliably as the temperature varies from 218 K to 423 K (−55 °C to 150 °C); thus, the temperature dependence of single-event burnout (SEB) in power MOSFETs should be investigated. Our simulation results showed that the Si power MOSFETs are more tolerant to SEB at a higher temperature at the lower LET (10 MeV∙cm2/mg) due to the decrease of the impact ionization rate, which is in good agreement with the previous research. However, the state of the parasitic BJT plays a primary role in the SEB failure mechanism when the LET value is greater than 40 MeV∙cm2/mg, which exhibits a completely different temperature dependence from that of 10 MeV∙cm2/mg. Results indicate that with the temperature increasing, the lower difficulty to turn on the parasitic BJT and the increasing current gain all make it easier to build up the regenerative feedback process responsible for SEB failure. As a result, the SEB susceptibility of power MOSFETs increases as ambient temperature increases when the LET value is greater than 40 MeV∙cm2/mg. [ABSTRACT FROM AUTHOR]

Based on Doppler radar observation and reanalysis data, the statistical characteristics of mesovortices (MVs) during the first rainy season (April–June) in Guangdong, South China, from 2017 to 2019 are studied, including their spatiotemporal distributions, structural features and favorable environmental conditions. The results show that the MVs usually exhibit short lifetimes; about 70% last for less than 30 min. The intensity and horizontal scale of the MVs are proportional to their lifetime. Long-lived MVs have larger horizontal scales and stronger intensities than short-lived ones. The MVs are mainly observed over the Pearl River Delta region, followed by western Guangdong Province, but relatively fewer in both eastern and northern Guangdong Province. The uneven spatial distribution of the MVs is closely related to the differences in the topographical features and the environment conditions over South China. MVs are prone to form over flat regions. The Pearl River Delta and eastern Guangdong regions are relatively flat compared to the more mountainous western and northern Guangdong regions. Moreover, the monsoonal south-westerlies, water vapor flux, atmospheric instability and vertical wind shear over southwest Guangdong are significantly larger than those in other regions and are favorable for the formation of MVs. The occurrence frequencies of MVs in central and southern parts of Guangdong display similar diurnal variations, reaching the peak during the late afternoon and early evening while dropping to the minimum overnight. However, the situation is opposite in northern Guangdong, with the peak overnight and the minimum during the late afternoon and early evening. The regional differences in diurnal variation are likely related to the moving direction of mesoscale convective systems (MCSs) in Guangdong. [ABSTRACT FROM AUTHOR]

The deformation and fracture characterization of an Mg−2Sn−1Ca alloy were studied through uniaxial isothermal compression tests. The flow stress curves, the efficiency of power dissipation, the instability parameter and the fracture behavior of an Mg−2Sn−1Ca alloy under the condition of various hot working parameters were investigated according to the experimental data. Processing maps were established by superimposing the instability map over the power dissipation map. It was found that flow stress reduces with increases in the deformation temperature and decreases in the strain rate. The processing of Mg−2Sn−1Ca alloys should avoid the instability region in which the conditions are high strain under high temperature and low strain under low temperature. At 473 K or a high strain rate, unidirectional cracks and fish scale cracks can be produced, and cracks can be avoided under the optimum processing area of 623–723 K/0.001–0.1 s−1. [ABSTRACT FROM AUTHOR]

The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future. [ABSTRACT FROM AUTHOR]

To achieve a wide range and high accuracy detection of the vacuum level, for example, in an encapsulated vacuum microcavity, a composite-type MEMS Pirani gauge has been designed and fabricated. The Pirani gauge consists of two gauges of different sizes connected in series, with one gauge having a larger heat-sensitive area and a larger air gap for extending the lower measurable limit of pressure (i.e., the high vacuum end) and the other gauge having a smaller heat-sensitive area and a smaller air gap for extending the upper measurable limit. The high-resistivity titanium metal was chosen as the thermistor; SiNx was chosen as the dielectric layer, considering the factors relevant to simulation and manufacturing. By simulation using COMSOL Multiphysics and NI Multisim, a range of measurement of 2 × 10−2 to 2 × 105 Pa and a sensitivity of 52.4 mV/lgPa were obtained in an N2 environment. The performance of the fabricated Pirani gauge was evaluated by using an in-house made vacuum test system. In the test, the actual points of measurement range from 6.6 × 10−2 to 1.12 × 105 Pa, and the highest sensitivity is up to 457.6 mV/lgPa. The experimental results are better in the range of measurement, sensitivity, and accuracy than the simulation results. The Pirani gauge proposed in this study is simple in structure, easy to manufacture, and suitable for integration with other MEMS devices in a microcavity to monitor the vacuum level therein. [ABSTRACT FROM AUTHOR]

We investigate r -process nucleosynthesis and kilonova emission resulting from binary neutron star (BNS) mergers based on a three-dimensional (3D) general-relativistic magnetohydrodynamic (GRMHD) simulation of a hypermassive neutron star (HMNS) remnant. The simulation includes a microphysical finite-temperature equation of state (EOS) and neutrino emission and absorption effects via a leakage scheme. We track the thermodynamic properties of the ejecta using Lagrangian tracer particles and determine its composition using the nuclear reaction network SkyNet. We investigate the impact of neutrinos on the nucleosynthetic yields by varying the neutrino luminosities during post-processing. The ejecta show a broad distribution with respect to their electron fraction Ye , peaking between ∼0.25–0.4 depending on the neutrino luminosity employed. We find that the resulting r -process abundance patterns differ from solar, with no significant production of material beyond the second r -process peak when using luminosities recorded by the tracer particles. We also map the HMNS outflows to the radiation hydrodynamics code SNEC and predict the evolution of the bolometric luminosity as well as broadband light curves of the kilonova. The bolometric light curve peaks on the timescale of a day and the brightest emission is seen in the infrared bands. This is the first direct calculation of the r -process yields and kilonova signal expected from HMNS winds based on 3D GRMHD simulations. For longer-lived remnants, these winds may be the dominant ejecta component producing the kilonova emission. [ABSTRACT FROM AUTHOR]

Copy number variations have been frequently associated with developmental delay, intellectual disability and autism spectrum disorders (1). MECP2 duplication syndrome is one of the most common genomic rearrangements in males (2) and is characterized by autism, intellectual disability, motor dysfunction, anxiety, epilepsy, recurrent respiratory tract infections and early death (3-5). The broad range of deficits caused by methyl-CpG-binding protein 2 (MeCP2) overexpression poses a daunting challenge to traditional biochemical-pathway-based therapeutic approaches. Accordingly, we sought strategies that directly target MeCP2 and are amenable to translation into clinical therapy. The first question that we addressed was whether the neurological dysfunction is reversible after symptoms set in. Reversal of phenotypes in adult symptomatic mice has been demonstrated in some models of monogenic loss-of-function neurological disorders (6-8), including loss of MeCP2 in Rett syndrome (9), indicating that, at least in some cases, the neuroanatomy may remain sufficiently intact so that correction of the molecular dysfunction underlying these disorders can restore healthy physiology. Given the absence of neurodegeneration in MECP2 duplication syndrome, we propose that restoration of normal MeCP2 levels in MECP2 duplication adult mice would rescue their phenotype. By generating and characterizing a conditional Mecp2-overexpressing mouse model, here we show that correction of MeCP2 levels largely reverses the behavioural, molecular and electrophysiological deficits. We also reduced MeCP2 using an antisense oligonucleotide strategy, which has greater translational potential. Antisense oligonucleotides are small, modified nucleic acids that can selectively hybridize with messenger RNA transcribed from a target gene and silence it (10,11), and have been successfully used to correct deficits in different mouse models (12-18). We find that antisense oligonucleotide treatment induces a broad phenotypic rescue in adult symptomatic transgenic MECP2 duplication mice (MECP2-TG) (19,20), and corrected MECP2 levels in lymphoblastoid cells from MECP2 duplication patients in a dose-dependent manner., To determine whether MECP2 duplication syndrome is reversible, we generated a conditional MECP2 overexpression mouse model that carries two functional alleles with species-matched endogenous control elements: a human wild-type MECP2 [...]

Deep brain stimulation (DBS) has improved the prospects for many individuals with diseases affecting motor control, and recently it has shown promise for improving cognitive function as well. Several studies [...]

Introduction: Due to its progressive nature, early evaluation and timely prediction of legal blindness are important in patients with neovascular age-related macular degeneration (nAMD). We examined the association between early-stage variation in central retinal thickness (CRT) and long-term visual outcomes in patients with nAMD. Methods: We included 103 nAMD patients who were administered anti-vascular endothelial growth factor (anti-VEGF). Participants were considered qualified if they were (1) 50 years and older, (2) treatment-naïve, (3) received standard anti-VEGF treatment and had complete baseline information. We further excluded patients with less than 1-year follow-up data and those who experienced best corrected visual acuity ≤35. Early-stage variability in CRT was measured as the first-year coefficient of variability (CV) of CRT. Patients were then classified into the high-variability and low-variability groups according to the X-tile. A product-limit plot was used to demonstrate the differences and tested with the log-rank test. The association between first-year variability and visual outcomes was quantified using Cox regression models. Time-to-event primary endpoint was the overall visual preservation (OVP) rate, defined as the time from the first injection to legal blindness, i.e., best corrected visual acuity ≤35 Early Treatment Diabetic Retinopathy Study (ETDRS) letters. Results: A threshold of 20% of first-year CV in CRT was used to categorize 76 qualified patients into high variability (35, 46.1%) and low variability (41, 53.9%). The 5- and 10-year OVPs for patients with high versus low variability were 76% versus 48% and 59% versus 22%, respectively. High early-stage CRT variability showed a significantly higher risk of legal blindness. Even after adjusting for the demographic and clinical features, the variability remained significant (HR = 2.39, 95% CI: 1.20–4.78). Conclusion: First-year variability of CRT was predictive of long-term visual outcomes in patients with nAMD, and 20% of the variability could be used as a clinically convenient threshold to qualitatively classify patients into high- and low-variability groups. The current study is important for identifying high-risk populations and for long-term disease management. [ABSTRACT FROM AUTHOR]

In order to obtain high-quality through-silicon via (TSV) arrays for high voltage applications, we optimized the fabrication processes of the Si holes, evaluated the dielectric layers, carried out hole filling by Cu plating, and detected the final structure and electric properties of the TSVs. The Si through-hole array was fabricated in an 8-inch Si substrate as follows: First, a blind Si hole array was formed by the Si deep reactive etching (DRIE) technique using the Bosch process, but with the largest width of the top scallops reduced to 540 nm and the largest notch elimidiameternated by backside grinding, which also opens the bottom ends of the Si blind holes and forms 500-μm-deep Si through holes. Then, the sidewalls of the Si holes were further smoothed by a combination of thermal oxidation and wet etching of the thermal oxide. The insulating capability of the dielectric layers was evaluated prior to metal filling by using a test kit. The metal filling of the through holes was carried out by bottom-up Cu electroplating and followed by annealing at 300 °C for 1 h to release the electroplating stress and to prevent possible large metal thermal expansion in subsequent high-temperature processes. The TSV arrays with different hole diameters and spacing were detected: no visible defects or structure peeling was found by scanning electron microscopy (SEM) observations, and no detectable interdiffusion between Cu and the dielectric layers was detected by energy dispersive X-ray (EDX) analyses. Electric tests indicated that the leakage currents between two adjacent TSVs were as low as 6.80 × 10−10 A when a DC voltage was ramped up from 0 to 350 V, and 2.86 × 10−9 A after a DC voltage was kept at 100 V for 200 s. [ABSTRACT FROM AUTHOR]

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of environmental variables were not considered in conventional monitoring data repairing methods (such as the single time series model and spatial interpolation model), a spatial model for repairing monitoring data combining the variable importance for projection (VIP) method and cokriging was put forward in this paper. In order to improve the accuracy of the model, the influence of different combinations of covariates on it was discussed, and the VIPj value greater than 0.8 was proposed as the threshold of covariates. The engineering verification shows that the VIP-cokriging spatial model had the advantages of high precision and strong applicability compared with the inverse distance weighting (IDW) model, the ordinary kriging model, and the universal kriging model, and the overall error can be reduced by more than 60%, which could better realize the expansion of the monitoring effect variable to the whole area of the dam space. The engineering application of the PBG dam showed that the model scientifically correlated the existing monitoring points with the spatial location of the dam, and reasonably repaired the measured values of the stopping and abnormal measured points, effectively ensuring that the spatial regular of the monitoring data could truly reflect the actual safety and operational status of the dam. [ABSTRACT FROM AUTHOR]

Copper serves as a co-factor for a host of metalloenzymes, particularly cytochrome c oxidase (COX). Although it is known that impaired COX function can lead to the excessive accumulation of reactive oxygen species (ROS), the mechanisms underlying how copper depletion leads to cell damage are poorly understood. Here, we have investigated the role of copper depletion during ferroptosis. The bathocuproinedisulfonic (BCS) treatment depolarized the mitochondrial membrane potential, increased the total cellular ROS levels, stimulated oxidative stress, and reduced the glutathione levels. Moreover, the depletion of copper limited the protein expression of glutathione peroxidase 4 (GPX4), which is the only enzyme that is known to prevent lipid peroxidation. Furthermore, we found that copper depletion decreased the sensitivity of the dermal papilla cells (DPCs) to erastin (an inducer of ferroptosis), and the ferroptosis inhibitor ferrostatin-1 (Fer-1) partially prevented BCS-mediated cell death. Overall, these findings establish a direct link between copper and ferroptosis; BCS-mediated copper depletion strongly enhances ferroptosis via mitochondrial perturbation and a reduction in antioxidative mechanisms. [ABSTRACT FROM AUTHOR]