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In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%. [ABSTRACT FROM AUTHOR]

Biologically inspired pneumatic ring-shaped soft grippers have been extensively studied in the field of soft robotics. However, the effect of the number of air chambers on the grasping performance (grasping range and load capacity) of ring-shaped soft grippers has not been studied. In this article, we propose three ring-shaped soft grippers with the same area of inner walls of air chambers and different numbers of air chambers (two-chamber, three-chamber, and four-chamber) for analyzing and comparing their grasping performance. Finite element method (FEM) models and experimental measurements are conducted to compare the deformation of the inner walls of the three ring-shaped soft grippers, the results indicate that the grasping range of the three-chamber ring-shaped soft gripper is larger than that of the two-chamber ring-shaped soft gripper and the four-chamber ring-shaped soft gripper. Then we choose the three-chamber ring-shaped soft gripper to study the relationship between contact force and air pressure by FEM models and experimental measurements. Several groups of experiments are constructed to compare the load capacity of the three ring-shaped soft grippers, the results indicate that the load capacity of the three-chamber ring-shaped soft gripper is higher than that of the two-chamber ring-shaped soft gripper and the four-chamber ring-shaped soft gripper. The above results reveal that the grasping performance of the three-chamber ring-shaped soft gripper is better than that of other two ring-shaped soft grippers. Furthermore, the application experiments indicate that the three ring-shaped soft grippers can grasp various objects with different weights, material properties, and shapes. This study provides a new idea for investigating ring-shaped soft grippers. [ABSTRACT FROM AUTHOR]

Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (DCP) to the bright channel prior (BCP) as an image prior for solving the BID problem. Specifically, the above two channel priors obtained from RGB images are used to construct an innovative sparse channel prior at first, and then the learned prior is incorporated into the BID tasks. The proposed sparse channel prior enhances the sparsity of the DCP. At the same time, it also shows the inverse relationship between the DCP and BCP. We employ the auxiliary variable technique to integrate the proposed sparse prior information into the iterative restoration procedure. Extensive experiments on real and synthetic blurry sets show that the proposed algorithm is efficient and competitive compared with the state-of-the-art methods and that the proposed sparse channel prior for blind deblurring is effective. [ABSTRACT FROM AUTHOR]

Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a "UUV-task" matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance. [ABSTRACT FROM AUTHOR]

A comprehensive test system for proton exchange membrane fuel cells (PEMFCs) is designed and developed for monitoring and controlling the inlet and outlet parameters and safety issues of fuel cells. The data acquisition and output instruction rely on the connection between PLC (programmable logic controller) and OPC (object linking and embedding for process control). Based on Siemens S7-200 series PLC and PID (proportion integration differentiation) technology, the margin of error in relative humidity of inlet air is controlled at less than 0.7%. Furthermore, a hydrogen recycling system and an alarm module are introduced, considering the hydrogen or nitrogen solenoid valve power failure, cooling fan power failure, temperature anomaly, and hydrogen leakage. This developed test system is evaluated by the experimental investigation of PEMFC performance. The results show that the test system has very good test and control performances. At a cell temperature of 40 °C, enhanced performance in the polarization tests is depicted at a high humidification temperature of 60 °C. [ABSTRACT FROM AUTHOR]

Deep neural networks have been applied for speech enhancements efficiently. However, for large variations of speech patterns and noisy environments, an individual neural network with a fixed number of hidden layers causes strong interference, which can lead to a slow learning process, poor generalisation in an unknown signal-to-noise ratio in new inputs, and some residual noise in the enhanced output. In this paper, we present a new approach for the hearing impaired based on combining two stages: (1) a set of bandpass filters that split up the signal into eight separate bands each performing a frequency analysis of the speech signal; (2) multiple deep denoising autoencoder networks, with each working for a small specific enhancement task and learning to handle a subset of the whole training set. To evaluate the performance of the approach, the hearing-aid speech perception index, the hearing aid sound quality index, and the perceptual evaluation of speech quality were used. Improvements in speech quality and intelligibility were evaluated using seven subjects of sensorineural hearing loss audiogram. We compared the performance of the proposed approach with individual denoising autoencoder networks with three and five hidden layers. The experimental results showed that the proposed approach yielded higher quality and was more intelligible compared with three and five layers. [ABSTRACT FROM AUTHOR]

Astrocytes have many important functions in the brain, but their roles in psychiatric disorders and their responses to psychotropic medications are still being elucidated. Here, we used gene enrichment analysis to assess the relationships between different astrocyte subtypes, psychiatric diseases, and psychotropic medications (antipsychotics, antidepressants and mood stabilizers). We also carried out qPCR analyses and "look-up" studies to assess the chronic effects of these drugs on astrocyte marker gene expression. Our bioinformatic analysis identified gene enrichment of different astrocyte subtypes in psychiatric disorders. The highest level of enrichment was found in schizophrenia, supporting a role for astrocytes in this disorder. We also found differential enrichment of astrocyte subtypes associated with specific biological processes, highlighting the complex responses of astrocytes under pathological conditions. Enrichment of protein phosphorylation in astrocytes and disease was confirmed by biochemical analysis. Analysis of LINCS chemical perturbagen gene signatures also found that kinase inhibitors were highly discordant with astrocyte-SCZ associated gene signatures. However, we found that common gene enrichment of different psychotropic medications and astrocyte subtypes was limited. These results were confirmed by "look-up" studies and qPCR analysis, which also reported little effect of psychotropic medications on common astrocyte marker gene expression, suggesting that astrocytes are not a primary target of these medications. Conversely, antipsychotic medication does affect astrocyte gene marker expression in postmortem schizophrenia brain tissue, supporting specific astrocyte responses in different pathological conditions. Overall, this study provides a unique view of astrocyte subtypes and the effect of medications on astrocytes in disease, which will contribute to our understanding of their role in psychiatric disorders and offers insights into targeting astrocytes therapeutically. [ABSTRACT FROM AUTHOR]

Dielectric elastomer actuators (DEAs) are an emerging type of soft actuation technology. As a fundamental unit of a DEA, the characteristics of compliant electrodes play a crucial role in the actuation performances of DEAs. Generally, the compliant electrodes can be categorized into uncured and cured types, of which the cured one commonly involves mixing conductive particles into an elastomeric matrix before curing, thus demonstrating a better long-term performance. Along with the increasing proportion of conductive particles, the electrical conductivity increases at the cost of a stiffer electrode and lower elongation at break ratio. For different DEA applications, it can be more desirable to minimize the electrode stiffness or to maximize its conductivity. In examination of the papers published in recent years, few works have characterized the effects of elastomeric electrodes on the outputs of DEAs, or of their optimizations under different application scenarios. In this work, we propose an experimental framework to characterize the performances of elastomeric electrodes with different formulas based on the two key parameters of stiffness and conductivity. An optimizing method is developed and verified by two different application cases (e.g., quasi-static and dynamic). The findings and the methods developed in this work can offer potential approaches for developing high-performance DEAs. [ABSTRACT FROM AUTHOR]

Surface urban heat islands (SUHIs) have been investigated in many regions around the world, but little attention has been given with regard to SUHIs in South America. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data was used to investigate the diurnal, seasonal, and interannual variations in the SUHI intensity (SUHII, the urban LST minus the rural LST) in 44 South American cities in different climate zones and types of rural land. To examine the effects of factors that may influence the SUHII, correlations between the SUHII and the enhanced vegetation index (EVI), urban area, population, altitude, and anthropogenic heat emissions were performed. The results showed that the SUHI effect was obvious in South America. The mean daytime SUHII was higher than the mean night-time SUHII in all areas except for the arid climate zone. In the daytime, the summer displayed a stronger SUHII in the warm temperate climate zone than the other seasons. The night-time SUHII showed less obvious seasonal variations. In addition, the surrounding land cover influenced the SUHII. During the day, the SUHII was therefore stronger in rural areas that were covered by forests than in other types of rural land. Interannually, most cities showed an insignificant temporal trend in the SUHII from 2003 to 2016. The daytime SUHII was significantly and negatively correlated with the ∆EVI (the urban EVI minus the rural EVI) across the 44 cities, but a poor relationship was observed at night. In addition, anthropogenic heat emissions were positively correlated with the night-time SUHII. Urban area, population, and altitude were weakly correlated with the SUHII, which suggested that these factors may not have a significant impact on the spatial variations in the SUHII in South America. [ABSTRACT FROM AUTHOR]

In order to achieve high quality polishing of a M300 mold steel curved surface, an elastic abrasive is introduced in this paper and its polishing parameters are optimized so that the mirror roughness can be achieved. Based on the Preston equation and Hertz Contact Theory, the theoretical material removal rate (MRR) equation for surface polishing of elastic abrasives is obtained. The effects of process parameters on MRR are analyzed and the polishing parameters to be optimized are as follows: particle size (S), rotational speed (Wt), cutting depth (Ap) and feed speed (Vf). The Taguchi method is applied to design the orthogonal experiment with four factors and three levels. The influence degree of various factors on the roughness of the polished surface and the combination of parameters to be optimized were obtained by the signal-to-noise ratio method. The particle swarm optimization algorithm optimized with the back propagation (BP) neural network algorithm (PSO-BP) is used to optimize the polishing parameters. The results show that the rotational speed has the greatest influence on the roughness, the influence degree of abrasive particle size is greater than that of feed speed, and cutting depth has the least influence. The optimum parameters are as follows: particle size (S) = #1200, rotational speed (Wt) = 4500 rpm, cutting depth (Ap) = 0.25 mm and feed speed (Vf) = 0.8 mm/min. The roughness of the surface polishing with optimum parameters is reduced to 0.021 μm. [ABSTRACT FROM AUTHOR]

Maize is one of the main food crops and is widely planted in China; however, it is difficult to get timely and precise information on yields. Because of the benefits of remote sensing technology, satellite-based models (e.g., eddy covariance light use efficiency, EC-LUE) have a lot of potential for monitoring crop productivity. In this study, the gross primary productivity (GPP) of maize in the NCP was estimated using the EC-LUE model, and the GPP was subsequently transformed into yield using the harvest index. Specifically accounting for the spatiotemporal variation in the harvest index, the statistical yield and estimated GPP from the previous year were used to generate region-specific harvest indexes at the county scale. The model's performance was assessed using statistical yield data. The results demonstrate that the increase in the total GPP in the summer maize-growing season in the NCP is directly related to the increase in the planting area, and the harvest index has significant heterogeneity in space, and the fluctuation in time is small, and the estimated yield can simulate 64% and 55%, respectively, of the variability in the yield at the county and city scales. The model also accurately captures the inter-annual changes in yield (the average absolute percentage errors are less than 20% for almost all years), but model performance varies by region. It performs better in continuous areas of maize-growing. The results from this study demonstrate that the EC-LUE model can be applied to estimate the yield from a variety of crops (other than winter wheat) and that it can be used in conjunction with a region-specific harvest index to track the production of large-scale crops. [ABSTRACT FROM AUTHOR]

Cities are the main emission sources of the CO2 produced by energy use around the globe and have a great impact on the variation of climate. Although the implications of urbanization and socioeconomic elements for carbon emission have been extensively explored, previous studies have mostly focused on developed cities, and there is a lack of research into naturally related elements due to the limited data. At present, remote sensing data provide favorable conditions for the study of large-scale and long-time series. Also, the spillover mechanism of urbanization effects on the discharge of carbon has not been fully studied. Therefore, it is necessary to distinguish the types of influence that various urbanization factors have on emissions of CO2. Firstly, this study quantifies the urban CO2 emissions in China by utilizing nighttime lighting images. Then, the spatio-temporal variations and spatial dependence modes of CO2 emissions are explored for 284 cities in China from 2000–2018. Finally, the study further ascertains that multi-dimensional urbanization, socio-economic and climate variables affect the discharge of carbon using spatial regression models. The results indicate that CO2 emissions have a remarkable positive spatial autocorrelation. Urbanization significantly increases CO2 emissions, of which the land urbanization contribution towards CO2 emissions is the most important in terms of spillover effects. Specifically, the data on urbanization's direct effects reveal that CO2 emissions will increase 0.066%when the urbanization level of a city rises 1%, while the spillover effect indicates that an 0.492% emissions increase is associated with a 1% rise of bordering cities' average urbanization level. As for the socio-economic factors, population density suppresses CO2 emissions, while technological levels boost CO2 emissions. The natural control factors effect a remarkable impact on CO2 emissions by adjusting energy consumption. This study can provide evidence for regional joint prevention in urban energy conservation, emission reduction, and climate change mitigation. [ABSTRACT FROM AUTHOR]

The tuberous root of Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) is a traditional Chinese medicine with a wide range of clinical applications. However, the scarcity of its wild resources, its low yield, and the variable quality that results from its artificial cultivation leads to expensive market prices that are not conducive to the further industrial development of T. hemsleyanum. In this study, transcriptomic and non-targeted metabolomic analyses were integrated to explore the underlying molecular mechanisms and metabolite biosynthesis that occur during its root development. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that differentially expressed genes (DEGs) were predominantly enriched for processes associated with flavonoid and phenylpropanoid biosynthesis, starch and sucrose metabolism, and plant hormone signal transduction. Genes related to lignin were downregulated in tuberous roots (TRs), resulting in a decrease in lignification and the downregulation of metabolites related to flavonoids and phenylpropanoid biosynthesis. In addition, the expression levels of starch- and sucrose-related genes were upregulated in TRs. The root development of SYQ is also related to IAA, GA, ABA, and JA signaling pathways. Collectively, this study lays the foundation for analyzing the root development and quality-modulating mechanisms employed by T. hemsleyanum; this will be beneficial in conducting molecular-assisted breeding and controlling its secondary metabolite production. [ABSTRACT FROM AUTHOR]

Nucleolar and spindle-associated protein 1 (NUSAP1) is a potential molecular marker and intervention target for glioblastoma (GBM). In this study, we aim to investigate upstream regulatory lncRNAs and miRNAs of NUSAP1 through both experimental and bioinformatic methods. We screened upstream lncRNAs and miRNAs of NUSAP1 through multiple databases based on ceRNA theory. Then, in vitro and in vivo experiments were performed to elucidate the relevant biological significance and regulatory mechanism among them. Finally, the potential downstream mechanism was discussed. LINC01393 and miR-128-3p were screened as upstream regulatory molecules of NUSAP1 by TCGA and ENCORI databases. The negative correlations among them were confirmed in clinical specimens. Biochemical studies revealed that overexpression or knockdown of LINC01393 respectively enhanced or inhibited malignant phenotype of GBM cells. MiR-128-3p inhibitor reversed LINC01393 knockdown-mediated impacts on GBM cells. Then, dual-luciferase reporter assay and RNA immunoprecipitation assay were conducted to validate LINC01393/miR-128-3p/NUSAP1 interactions. In vivo, LINC01393-knockdown decreased tumor growth and improved mice survival, while restoration of NUSAP1 partially reversed these effects. Additionally, enrichment analysis and western blot revealed that the roles of LINC01393 and NUSAP1 in GBM progression were associated with NF-κB activation. Our findings showed that LINC01393 sponged miR-128-3p to upregulate NUSAP1, thereby promoting GBM development and progression via activating NF-κB pathway. This work deepens understanding of GBM mechanisms and provides potential novel therapeutic targets for GBM. [ABSTRACT FROM AUTHOR]

Water molecules play a very important role in the hydration and dehydration process of hydrates, which may lead to distinct physical and chemical properties, affecting their availability in practical applications. However, miniaturized, integrated sensors capable of the rapid, sensitive sensing of water molecules in the hydrate are still lacking, limiting their proliferation. Here, we realize the high-sensitivity sensing of water molecules in copper sulfate pentahydrate (CuSO4·5H2O), based on an on-chip terahertz whispering gallery mode resonator (THz-WGMR) fabricated on silicon material via CMOS-compatible technologies. An integrated THz-WGMR with a high-Q factor of 3305 and a resonance frequency of 410.497 GHz was proposed and fabricated. Then, the sensor was employed to distinguish the CuSO4·xH2O (x = 5, 3, 1). The static characterization from the CuSO4·5H2O to the copper sulfate trihydrate (CuSO4·3H2O) experienced blueshifts of 0.55 GHz/μmol, whereas the dehydration process of CuSO4·3H2O to copper sulfate monohydrate (CuSO4·H2O) exhibited blueshifts of 0.21 GHz/μmol. Finally, the dynamic dehydration processes of CuSO4·5H2O to CuSO4·3H2O at different temperatures were monitored. We believe that our proposed THz-WGMR sensors with highly sensitive substance identification capabilities can provide a versatile and integrated platform for studying the transformation between substances, contributing to hydrated/crystal water-assisted biochemical applications. [ABSTRACT FROM AUTHOR]

For over a century, a complex relationship between schizophrenia diagnosis and development of many cancers has been observed. Findings from epidemiological studies are mixed, with reports of increased, reduced, or no difference in cancer incidence in schizophrenia patients. However, as risk factors for cancer, including elevated smoking rates and substance abuse, are commonly associated with this patient population, it is surprising that cancer incidence is not higher. Various factors may account for the proposed reduction in cancer incidence rates including pathophysiological changes associated with disease. Perturbations of the adenosine system are hypothesized to contribute to the neurobiology of schizophrenia. Conversely, hyperfunction of the adenosine system is found in the tumor microenvironment in cancer and targeting the adenosine system therapeutically is a promising area of research in this disease. We outline the current biochemical and pharmacological evidence for hypofunction of the adenosine system in schizophrenia, and the role of increased adenosine metabolism in the tumor microenvironment. In the context of the relatively limited literature on this patient population, we discuss whether hypofunction of this system in schizophrenia, may counteract the immunosuppressive role of adenosine in the tumor microenvironment. We also highlight the importance of studies examining the adenosine system in this subset of patients for the potential insight they may offer into these complex disorders. [ABSTRACT FROM AUTHOR]

Nogo-B has been reported to play a critical role in angiogenesis and the repair of damaged blood vessels; however, its role in the tumor microenvironment remains unclear. Here, we observed the differential expression of Nogo-B in endothelial cells from hepatocellular carcinoma (HCC) and glioma samples. Downregulation of Nogo-B expression correlated with the malignant phenotype of cancer and a poor prognosis for patients. In subsequent studies, endothelial Nogo-B inhibition robustly promoted the growth of HCC or glioma xenografts in nude mice. Intriguingly, endothelial Nogo-B silencing dramatically suppressed endothelial cell expansion and tumor angiogenesis, but potently enhanced the proliferation of neighboring HCC and glioma cells. Based on the results of the ELISA assay, Nogo-B silencing reduced TGF-β production in endothelial cells, which attenuated the phosphorylation and nuclear translocation of Smad in neighboring cancer cells. The endothelial Nogo-B silencing-mediated increase in cancer cell proliferation was abolished by either a TGF-β neutralizing antibody or TGF-β receptor inhibitor, indicating the essential role for TGF-β in endothelial Nogo-B-mediated suppression of cancer growth. These findings not only broaden our understanding of the crosstalk between cancer cells and endothelial cells but also provide a novel prognostic biomarker and a therapeutic target for cancer treatments. [ABSTRACT FROM AUTHOR]

Cinnamon procyanidin oligomers (CPOs) are water-soluble components extracted from cinnamon. This study aims to explore the neuroprotection of B-type CPO (CPO-B) against 1-methyl-4-phenylpyridinium (MPP+)-mediated cytotoxicity and the molecular mechanisms underlying its protection. The results demonstrated that CPO-B showed protection by increasing cell viability, attenuating an intracellular level of reactive oxygen species, downregulating cleaved caspase-3 expression, and upregulating the Bcl-2/Bax ratio. Moreover, CPO-B completely blocked the dephosphorylation of extracellular, signal-regulated kinase 1 and 2 (Erk1/2) caused by MPP+. Treatment with an Erk1/2 inhibitor, SCH772984, significantly abolished the neuroprotection of CPO-B against MPP+. Taken together, we demonstrate that CPO-B from cinnamon bark provided protection against MPP+ in cultured SH-SY5Y cells, and the potential mechanisms may be attributed to its ability to modulate the dysregulation between pro-apoptotic and anti-apoptotic proteins through the Erk1/2 signaling pathway. Our findings suggest that the addition of cinnamon to food or supplements might benefit patients with PD. [ABSTRACT FROM AUTHOR]

Simple Summary: Exosomal miRNAs are associated with colorectal cancer liver metastasis (CRLM)-related biological behavior and prognosis. However, an exosomal miRNA signature predicting postoperative survival and the value of adjuvant chemotherapy for CRLM remains elusive. Using miRNA sequencing and the LASSO model, we constructed an miRNA signature comprising four exosomes. The signature showed a good predictive performance for patient outcome and the advantage of adjuvant chemotherapy after hepatectomy in two institutions' training and validation cohorts. In addition, we found that the four miRNAs could target signaling molecules playing crucial roles in colorectal cancer metastasis, vesicle-related processing, and T cell activation. Furthermore, the exosomal miRNA score also increased with the decreasing Immunoscore. We believe that our signature can predict the prognosis and guide adjuvant chemotherapy decisions after liver metastasectomy in CRLM patients, further improving the predictive performance of the current CRLM predictive model system. Background: The clinical risk score (CRS) for prediction and treatment decision in colorectal liver metastasis (CRLM) is important, but imprecise. Exosomal miRNAs play critical roles in CRLM-related biological behavior. However, an exosomal miRNA score system for predicting posthepatectomy survival and the adjuvant chemotherapy benefit of CRLM remains elusive. Methods: miRNA sequencing was used to identify differentially expressed miRNAs, and the LASSO model was used to select miRNAs to construct the intent model. The predictive performance of the model was evaluated by the area under the ROC curve (AUC) in the training, internal validation, and external validation cohorts. Results: Sixteen differentially expressed exosomal miRNAs were identified, and four miRNAs were selected for model construction. Our model performed well in predicting prognosis with five-year AUCs of 0.70 (95% CI: 0.59–0.81), 0.70 (0.61–0.81), and 0.72 (057–0.86) in the training, internal, and external validation cohorts, respectively. miRNA classifier high-risk patients had better survival benefit from adjuvant chemotherapy regardless of CRS. All four miRNAs target signaling molecules play crucial roles in colorectal cancer metastasis, vesicle-related processing, and T cell activation. It also negatively correlated with the liver metastasis Immunoscore. Conclusion: We developed a circulating exosomal miRNA signature that can predict the prognosis and guide adjuvant chemotherapy decisions after hepatectomy in CRLM. [ABSTRACT FROM AUTHOR]

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms. [ABSTRACT FROM AUTHOR]

Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification. [ABSTRACT FROM AUTHOR]

ATP-binding cassette (ABC) transporters, such as P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), often reduce drug efficacy and are the major cause of drug resistance. Astragaloside IV (ASIV), one of the bioactive saponins isolated from Astragalus membranaceus, has been demonstrated to alleviate the progression of experimental autoimmune encephalomyelitis (EAE) in mice, an animal model for multiple sclerosis (MS). In the present study, we found for the first time that ASIV induced the upregulation of P-gp and BCRP in the central nervous system (CNS) microvascular endothelial cells of EAE mice. Further study disclosed that tariquidar, a P-gp inhibitor, could facilitate the penetration of ASIV into CNS. On bEnd.3 cells, a mouse brain microvascular endothelial cell line, tariquidar benefited the net uptake and transport of ASIV. Additional molecular docking experiment suggested that ASIV might be a potential substrate of P-gp. In EAE mice, tariquidar was demonstrated to enhance the efficacy of ASIV, as shown by attenuated clinical symptom and reduced incidence rate as well as mitigated inflammatory infiltration and decreased demyelination in the CNS. Collectively, our findings implicate that P-gp inhibitor can promote the therapeutic efficacy of ASIV on EAE mice, which may boost its clinical usage together with ASIV in the therapy of MS. [ABSTRACT FROM AUTHOR]