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Due to the development of acaricide resistance in Tetranychusurticae, a rapid and accurate monitoring method for acaricide resistance gene frequency is required for resistance management. In this study, we developed a new diagnostic gene frequency prediction method, using quantitative real-time PCR with allele-specific primer sets, for mutation of I1017F in chitin synthase I, G126S in mitochondrial cytochrome b, and H110R in the PSST subunit of mitochondrial electron transport complex I, which are involved in etoxazole, bifenazate, and pyridaben resistance in T. urticae, respectively. Resistance allele frequencies computed using the 2[−ΔΔCq] method, in mixtures of resistance and wild-type (susceptible) alleles, were strongly correlated with the actual frequencies of the resistance alleles in DNA samples for all three acaricides (regression slopes of 0.945–0.996; R² > 0.99). This result strongly indicates that resistance allele frequency in a population can be accurately predicted using a diagnostic quantitative real-time PCR method with a resistance allele-specific primer set. Finally, we applied this method to nine field populations and successfully determined resistance allele frequencies for the three acaricides. Overall, this diagnostic prediction method may contribute to the development of acaricide resistance management strategies, via monitoring resistance allele frequencies for a wide range of acaricides.

Cellular senescence (CS) is a state of stable cell cycle arrest characterized by the production and secretion of inflammatory molecules. Early studies described oncogene-induced senescence (OIS) as a barrier to tumorigenesis, such that the therapeutic induction of CS might represent a rational anti-cancer strategy. Indeed, the validity of this approach has been borne out by the development and approval of the cyclin-dependent kinase (CDK) inhibitor palbociclib for the treatment of breast cancer. Apart from tumors, senescent cells have also been shown to accumulate during natural mammalian aging, where they produce detrimental effects on the physiology of surrounding tissues. Thus, pharmacological senescent cell depletion has been proposed as an approach to delay age-related functional decline; this has been formally demonstrated in animal models. In this review article, we describe the current mechanistic understanding of cellular senescence at the molecular level and how it informs the development of new therapeutic strategies to combat cancer and aging.

Many cancers have an unusual dependence on glutamine. However, most previous studies have focused on the contribution of glutamine to metabolic building blocks and the energy supply. Here, we report that cancer cells with aberrant expression of glutamate decarboxylase 1 (GAD1) rewire glutamine metabolism for synthesis of γ-aminobutyric acid (GABA), a prominent neurotransmitter, in non-nervous tissues. Clinical sample analysis reveals increased GABA levels predicting poor prognosis. Mechanistically, we identify a cancer-intrinsic pathway by which GABA activates the GABA(B) receptor to inhibit GSK-3β activity, leading to enhanced β-catenin signaling. This GABA-mediated β-catenin activation both stimulates tumor cell proliferation and suppresses CD8(+) T cell intratumoral infiltration, such that targeting GAD1 or GABA(B)R in mouse models overcomes resistance to anti-PD-1 immune checkpoint blockade therapy. Our findings uncover an unexpected signaling role for tumor-derived GABA beyond its classic function as a neurotransmitter that can be exploited pharmacologically to reverse immunosuppression.

Reliable and accurate streamflow prediction plays a critical role in watershed water resources planning and management. We developed a new hybrid SWAT-WSVR model based on 12 hydrological sites in the Illinois River watershed (IRW), U.S., that integrated the Soil and Water Assessment Tool (SWAT) model with a Support Vector Regression (SVR) calibration method coupled with discrete wavelet transforms (DWT) to better support modeling watersheds with limited data availability. Wavelet components of the simulated streamflow from the SWAT-Calibration Uncertainty Procedure (SWAT-CUP) and precipitation time series were used as inputs to SVR to build a hybrid SWAT-WSVR. We examined the performance and potential of the SWAT-WSVR model and compared it with observations, SWAT-CUP, and SWAT-SVR using statistical metrics, Taylor diagrams, and hydrography. The results showed that the average of RMSE-observation’s standard deviation ratio (RSR), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root mean square error (RMSE) from SWAT-WSVR is 0.02, 1.00, −0.15, and 0.27 m3 s−1 in calibration and 0.14, 0.98, −1.88, and 2.91 m3 s−1 in validation on 12 sites, respectively. Compared with the other two models, the proposed SWAT-WSVR model possessed lower discrepancy and higher accuracy. The rank of the overall performance of the three SWAT-based models during the whole study period was SWAT-WSVR > SWAT-SVR > SWAT-CUP. The developed SWAT-WSVR model supplies an additional calibration approach that can improve the accuracy of the SWAT streamflow simulation of watersheds with limited data.

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

In secret image sharing, the image is divided into several stego images, which are managed by corresponding participants. The secret image can be recovered only when the number of authorized participants is no less than the threshold. Thus, it is widely used to protect essential images, such as engineering drawings and product design drawings. In the traditional secret image sharing scheme, the threshold is fixed and unique. However, in practice, the security policy and the adversarial structure may change; therefore, the threshold must be adjusted dynamically. In this paper, we propose a novel secret image sharing scheme with a changeable threshold capability. Our scheme eliminates the limit of the changeable threshold and reduces the computation required. Also, our scheme is the first threshold changeable secret image sharing scheme that can recover an undistorted cover image. The theoretical analysis shows that our scheme is safe even if the threshold is changed. The experiments demonstrated that the stego image generated by our algorithm has better quality than other changeable-threshold, secret image sharing algorithms.

In the domestic production of recycled plastics, it is difficult to separate olefin polymers such as polyethylene and polypropylene. Therefore, the prerequisite for identifying the cargo-solid waste properties of recycled plastics is to determine the polymer composition. In this paper, Fourier transform infrared spectroscopy was used to detect polypropylene content in polyethylene and polypropylene blends. Under 2%-8% polypropylene content, an exponential relationship was shown in the ratio of characteristic peak area. This method features fast detection speed and accurate judgment. It can quickly determine whether the polypropylene content in the polyethylene and polypropylene blends is greater than 5% in the tariff inspection. Also, it can determine plastic solid waste attributes and identify whether polyethylene is mixed with a small amount of polypropylene content.

The analysis of the composition of recycled plastics is the prerequisite for identifying the properties of recycled plastics goods-solid waste. This paper systematically studies the composition of crystalline polymers by differential scanning calorimetry, and studies the common blends of polyethylene and polypropylene. The composition of polyamide 6 and polyamide 66 blends, polyethylene and polyamide blends and other common recycled plastics. The standard enthalpy value method and the melting enthalpy area normalization method are compared. The conclusion is that the melting enthalpy area normalization method is more beneficial Determine the composition of recycled plastics. For polyethylene/polypropylene blends, the repeatability of polyethylene and polypropylene should not exceed 5%; for polyamide 6/polyamide 66 blends, the repeatability of polyamide should not exceed 5%. More than 3%; For blends of polyethylene and polyamide, the repeatability of polyethylene and polyamide should not exceed 6%.

Watershed-scale nonpoint source (NPS) pollution models have become important tools to understand, evaluate, and predict the negative impacts of NPS pollution on water quality. Today, there are many NPS models available for users. However, different types of models possess different form and structure as well as complexity of computation. It is difficult for users to select an appropriate model for a specific application without a clear understanding of the limitations or strengths for each model or tool. This review evaluates 14 more commonly used watershed-scale NPS pollution models to explain how and when the application of these different models are appropriate for a given effort. The models that are assessed have a wide range of capacities that include simple models used as rapid screening tools (e.g., Long-Term Hydrologic Impact Assessment (L-THIA) and Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT/OpenNSPECT)), medium-complexity models that require detail data input and limited calibration (e.g., Generalized Watershed Loading Function (GWLF), Loading Simulation Program C (LSPC), Source Loading and Management Model (SLAMM), and Watershed Analysis Risk Management Frame (WARMF)), complex models that provide sophisticated simulation for NPS pollution processes with intensive data and rigorous calibration (e.g., Agricultural Nonpoint Source pollution model (AGNPS/AnnAGNPS), Soil and Water Assessment Tool (SWAT), Stormwater Management Model (SWMM), and Hydrologic Simulation Program Fortran (HSPF)), and modeling systems that integrate various sub-models and tools, and contain the highest complexity to solve all phases of hydrologic, hydraulic, and chemical dynamic processes (e.g., Automated Geospatial Watershed Assessment Tool (AGWA), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) and Watershed Modeling System (WMS)). This assessment includes model intended use, components or capabilities, suitable land-use type, input parameter type, spatial and temporal scale, simulated pollutants, strengths and limitations, and software availability. Understanding the strengths and weaknesses of each watershed-scale NPS model will lead to better model selection for suitability and help to avoid misinterpretation or misapplication in practice. The article further explains the crucial criteria for model selection, including spatial and temporal considerations, calibration and validation, uncertainty analysis, and future research direction of NPS pollution models. The goal of this work is to provide accurate and concise insight for watershed managers and planners to select the best-suited model to reduce the harm of NPS pollution to watershed ecosystems.

With the rapid development of Computer Graphics, the computer-generated images (CG) are almost as realistic as real photographs(PG) and it is difficult to distinguish between CG and PG accurately with the naked eye. Image is an important carrier for people to get information on a daily basis. However the spread of CG produced for malicious purposes may disrupt social order and even undermine social stability. Therefore, the accurate detection of CG and PG is of great significance. In this paper, we (1) introduce 11 approaches that apply deep learning to the implementations of CG detection, and divide them into 4 categories based on the network structure; (2) give an introduction to the available datasets; (3) design a series of experiments to test the detection performance of each approach,then analyze the experimental results; The experimental results show that most approaches can differentiate CG from PG, while the detection accuracy and efficiency of each model are different. Nevertheless none of these methods is valid when the images tampered by noise. Above all (4) summarize the problems and challenges in this field, and look forward to the trends in future research.

Cellular senescence is a fundamental cell fate playing a significant role throughout the natural aging process. However, the molecular determinants distinguishing senescence from other cell-cycle arrest states such as quiescence and post-mitotic state, and the specified mechanisms underlying cell-fate decisions towards senescence versus cell death in response to cellular stress stimuli remain less understood. Employing multi-omics approaches, we revealed that switching off the specific mitochondrial processing machinery involving the peptidase IMMP2L serves as the foundation of the senescence program, which was also observed during the mammalian aging process. Mechanistically, we demonstrate that IMMP2L processes and thus activates at least two substrates, mitochondrial metabolic enzyme glycerol-3-phosphate dehydrogenase (GPD2) and cell death regulator apoptosis-inducing factor (AIF). For cells destined to senesce, concerted shutdown of the IMMP2L-GPD2 and IMMP2L-AIF signaling axes collaboratively drives the senescent process by reprogramming mitochondria-associated redox status, phospholipid metabolism and signaling network, and simultaneously blocking cell death under oxidative stress conditions.

Correct identifying analog circuit incipient faults is useful to the circuit's health monitoring, and yet it is very hard. In this paper, an analog circuit incipient fault diagnosis method using deep belief network (DBN) based features extraction is presented. In the diagnosis scheme, time responses of analog circuits are measured, and then features are extracted by using the DBN method. Meanwhile, the learning rates of DBN are produced by using quantum-behaved particle swarm optimization (QPSO) algorithm, which is beneficial to optimizing the structure parameters of DBN. Afterward, a support vector machine (SVM) based incipient fault diagnosis model is constructed on basis of the extracted features to classify incipient faulty components, where the regularization parameter and width factor of SVM are yielded by using the QPSO algorithm. Sallen-Key bandpass filter and four-op-amp biquad high pass filter incipient fault diagnosis simulations are conducted to demonstrate the proposed diagnosis method, and comparisons verify that the proposed diagnosis method can produce higher diagnosis accuracy than other typical analog circuit fault diagnosis methods.

Currently, users’ location information is collected to provide better services or research. Using a central server to collect, store and share location information has inevitable defects in terms of security and efficiency. Using the point-to-point sharing method will result in high computation overhead and communication overhead for users, and the data are hard to be verified. To resolve these issues, we propose a new blockchain-based multi-level location secure sharing scheme. In our proposed scheme, the location data are set hierarchically and shared with the requester according to the user’s policy, while the received data can be verified. For this, we design a smart contract to implement attribute-based access control and establish an incentive and punishment mechanism. We evaluate the computation overhead and the experimental results show that the computation overhead of our proposed scheme is much lower than that of the existing scheme. Finally, we analyze the performances of our proposed scheme and demonstrate that our proposed scheme is, overall, better than existing schemes.

Lithium-ion batteries are crucial to many types of electric equipments. Hence, lithium-ion battery capacity prognostic is significantly important, and it is yet very hard for the measured battery data that are regularly polluted by miscellaneous noises. In this paper, a battery capacity prognostic approach using the empirical mode decomposition (EMD) denoising method and multiple kernel relevance vector machine (MKRVM) approach is presented. The EMD denoising method is employed to process the measured capacity data to produce noise-free capacity data. The battery capacity prediction model using MKRVM is constructed based on the denoised capacity data. The MKRVM's kernel keeps diversity by using multiple heterogeneous kernel learning method. Meanwhile, sparse weights of basic kernel functions are yielded by using particle swarm optimization (PSO) algorithm. The measured battery capacity data are used to demonstrate the effect of EMD denoising method, and battery capacity prediction experiments reveal that the proposed MKRVM approach can predict the battery's future capacity precisely.

Intrinsic resistance to anti-HER2 therapy in breast cancer remains an obstacle in the clinic, limiting its efficacy. However, the biological basis for intrinsic resistance is poorly understood. Here we performed a CRISPR/Cas9-mediated loss-of-function genetic profiling and identified TALDO1, which encodes the rate-limiting transaldolase (TA) enzyme in the non-oxidative pentose phosphate pathway, as essential for cellular survival following pharmacological HER2 blockade. Suppression of TA increases cell susceptibility to HER2 inhibition in two intrinsically resistant breast cancer cell lines with HER2 amplification. Mechanistically, TA depletion combined with HER2 inhibition significantly reduces cellular NADPH levels, resulting in excessive ROS production and deficient lipid and nucleotide synthesis. Importantly, higher TA expression correlates with poor response to HER2 inhibition in a breast cancer patient cohort. Together, these results pinpoint TA as a novel metabolic enzyme possessing synthetic lethality with HER2 inhibition that can potentially be exploited as a biomarker or target for combination therapy., Resistance to anti-HER2 therapy in breast cancer remains a major obstacle in the clinic. Here the authors performed a CRISPR-selective vulnerability screen to identify transaldoloase as a target that is synthetically lethal with HER2 inhibition in breast cancer cells.

Cellular senescence is a unique cell fate characterized by stable proliferative arrest and the extensive production and secretion of various inflammatory proteins, a phenomenon known as the senescence‐associated secretory phenotype (SASP). The molecular mechanisms responsible for generating a SASP in response to senescent stimuli remain largely obscure. Here, using unbiased gene expression profiling, we discover that the scavenger receptor CD36 is rapidly upregulated in multiple cell types in response to replicative, oncogenic, and chemical senescent stimuli. Moreover, ectopic CD36 expression in dividing mammalian cells is sufficient to initiate the production of a large subset of the known SASP components via activation of canonical Src–p38–NF‐κB signaling, resulting in the onset of a full senescent state. The secretome is further shown to be ligand‐dependent, as amyloid‐beta (Aβ) is sufficient to drive CD36‐dependent NF‐κB and SASP activation. Finally, loss‐of‐function experiments revealed a strict requirement for CD36 in secretory molecule production during conventional senescence reprogramming. Taken together, these results uncover the Aβ–CD36–NF‐κB signaling axis as an important regulator of the senescent cell fate via induction of the SASP.

In secret image sharing schemes, the threshold may have to be adjusted in case of changes in the security policy and the adversary structure before recovering the secret image. For example, if participants leave the group, their stego images are useless to them and may not be kept safely. As a result, these images can be easily stolen and utilized by intruders, which reduces the security of the scheme. To solve this problem, we propose a novel threshold changeable secret image sharing scheme withNpotential changeable thresholdst1,t2,…,tN. By preparing advance shares for thresholdst1,t2,…,tNand using the two-variable one-way function to generate the identification value, we can change the threshold when necessary. The experiments show that the quality of the stego images in our scheme is satisfactory and the secret image can be recovered without distortion. Moreover, the security analysis shows that we can change the threshold safely.

Targeted inhibitors of the human epidermal growth factor receptor 2 (HER2), such as trastuzumab and lapatinib, are among the first examples of molecularly targeted cancer therapy and have proven largely effective for the treatment of HER2-positive breast cancers. However, approximately half of those patients either do not respond to these therapies or develop secondary resistance. Although a few signaling pathways have been implicated, a comprehensive understanding of mechanisms underlying HER2 inhibitor drug resistance is still lacking. To address this critical question, we undertook a concerted approach using patient expression data sets, HER2-positive cell lines, and tumor samples biopsied both before and after trastuzumab treatment. Together, these methods revealed that high expression and activation of a specific subset of receptor tyrosine kinases (RTKs) was strongly associated with poor clinical prognosis and the development of resistance. Mechanistically, these RTKs are capable of maintaining downstream signal transduction to promote tumor growth via the suppression of cellular senescence. Consequently, these findings provide the rationale for the design of therapeutic strategies for overcoming drug resistance in breast cancer via combinational inhibition of the limited number of targets from this specific subset of RTKs.

After any distribution of secret sharing shadows in a threshold changeable secret sharing scheme, the threshold may need to be adjusted to deal with changes in the security policy and adversary structure. For example, when employees leave the organization, it is not realistic to expect departing employees to ensure the security of their secret shadows. Therefore, in 2012, Zhang et al. proposed ( t -> t', n) and ({t(1), t(2), ..., t(N)}, n) threshold changeable secret sharing schemes. However, their schemes suffer from a number of limitations such as strict limit on the threshold values, large storage space requirement for secret shadows, and significant computation for constructing and recovering polynomials. To address these limitations, we propose two improved dealer-free threshold changeable secret sharing schemes. In our schemes, we construct polynomials to update secret shadows, and use two-variable one-way function to resist collusion attacks and secure the information stored by the combiner. We then demonstrate our schemes can adjust the threshold safely. Refereed/Peer-reviewed

Eco-economic regionalization (EER) is to divide an area into geographical zones. Each zone is linked to the others based on the eco-economic similarity and disparity. In this study, models of EER of the Chinese Loess Plateau were constructed. Data on 22 selected indicators were gathered for the 282 administrative counties. Then they were analyzed through an approach of PCA integrated with CA and GIS. In the generated model, the plateau was assigned to 4 belts and 18 eco-economic zones. The relationship among the subdivisions was able to represent the real situation. It demonstrates the division scheme is very comprehensive, concise and applicable. The work has established a novel methodological framework for EER of large-scale area.

Currently, the OGC GML (Geography Markup Language) specification has been the de facto standard for GIS data sharing and exchanging and spatial interoperation. Adopting nested association expression approach of XML data, GML data documents can store both spatial information and semantic relationship information of geographical elements. To improve the efficiency of path query on GML two type information, the paper describes a holistic index method for GML data, which we call EKR+ (optimized R+-tree base on Extend Region Code and K-means extent partition of GML feature elements). The experiment results show that the efficiency of semantic-spatial query can be improved greatly by utilizing EKR+.

The hypoxic tumor microenvironment serves as a niche for maintaining the glioma-initiating cells (GICs) that are critical for glioblastoma (GBM) occurrence and recurrence. Here, we report that hypoxia-induced miR-215 is vital for reprograming GICs to fit the hypoxic microenvironment via suppressing the expression of an epigenetic regulator KDM1B and modulating activities of multiple pathways. Interestingly, biogenesis of miR-215 and several miRNAs is accelerated post-transcriptionally by hypoxia-inducible factors (HIFs) through HIF-Drosha interaction. Moreover, miR-215 expression correlates inversely with KDM1B while correlating positively with HIF1α and GBM progression in patients. These findings reveal a direct role of HIF in regulating miRNA biogenesis and consequently activating the miR-215-KDM1B-mediated signaling required for GIC adaptation to hypoxia.

Secret image sharing schemes have been extensively studied by far. However, there are just a few schemes that can restore both the secret image and the cover image losslessly. These schemes have one or more defects in the following aspects: (1) high computation cost; (2) overflow issue existing when modulus operation is used to restore the cover image and the secret image; (3) part of the cover image being severely modified and the stego images having worse visual quality. In this paper, we combine the methods of least significant bits construction (LSBC) and dynamic embedding with one-dimensional cellular automata to propose a new lossless scheme which solves the above issues and can resist differential attack and support parallel computing. Experimental results also show that this scheme has the merit of big embedding capacity.

Cellular senescence is a cell fate characterized by an irreversible cell cycle arrest, but the molecular mechanism underlying this senescence hallmark remains poorly understood. Through an unbiased search for novel senescence regulators in airway basal cells, we discovered that the epigenetic regulator ubiquitin-like with PHD and ring finger domain-containing protein 1 (UHRF1) is critical for regulating cell cycle progression. Upon injury, basal cells in the mouse airway rapidly induce the expression of UHRF1 in order to stimulate stem cell proliferation and tissue repair. Targeted depletion of Uhrf1 specifically in airway basal cells causes a profound defect in cell cycle progression. Consistently, cultured primary human basal cells lacking UHRF1 do not exhibit cell death or differentiation phenotypes but undergo a spontaneous program of senescence. Mechanistically, UHRF1 loss induces G1 cell cycle arrest by abrogating DNA replication factory formation as evidenced by loss of proliferating cell nuclear antigen (PCNA) puncta and an inability to enter the first cell cycle. This proliferation defect is partially mediated by the p15 pathway. Overall, our study provides the first evidence of an indispensable role of UHRF1 in somatic stem cells proliferation during the process of airway regeneration.

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.