The two‐spotted spider mite, Tetranychus urticae, is an economically important agricultural pest. A novel physical control method involving daily nighttime UV‐B irradiation was recently developed for ...use in strawberry greenhouses. However, the overlapping of leaves after March prevents direct irradiation to T. urticae on the lower leaf surface, decreasing control effect. Excessive UV‐B irradiation causes leaf sunscald in winter. Therefore, optimization of UV‐B irradiance and a compensatory control agent are desired. Temperature may affect the survival of organisms exposed to UV‐B, although the temperature dependence of UV‐B damage is controversial. A phytoseiid mite, Neoseiulus californicus, is a prominent predator but vulnerable to a single UV‐B irradiation. We compared dose–response and temperature dependence of UV‐B damage between T. urticae and N. californicus eggs under daily nighttime UV‐B irradiation. Unexpectedly, N. californicus showed greater resistance to UV‐B than T. urticae, and the mortality was increased and decreased at low and high temperatures, respectively. This makes possible the application of UV‐B doses that are lethal for spider mites but safe for phytoseiid mites. Overall, we concluded that combined use of phytoseiid mites with UV‐B lamps is advantageous to spider mite management in strawberry greenhouses.
Daily nighttime UV‐B irradiation is used for spider mite control in strawberry greenhouse in Japan. It has been known that UV‐B resistance is higher in spider mites (Tu) than phytoseiid mites (natural enemy; Nc) under a single irradiation. However, it reversed under daily nighttime irradiation, being advantageous to the combined use of phytoseiid mites with UV‐B in greenhouse. Our study also shows clear temperature dependence of the UV‐B damage to this prey–predator system; larger and smaller at lower and higher temperatures, respectively.
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 the synthesis of γ-aminobutyric acid (GABA)-a prominent neurotransmitter-in non-nervous tissues. An analysis of clinical samples reveals that increased GABA levels predict poor prognosis. Mechanistically, we identify a cancer-intrinsic pathway through which GABA activates the GABA
receptor to inhibit GSK-3β activity, leading to enhanced β-catenin signalling. This GABA-mediated β-catenin activation both stimulates tumour cell proliferation and suppresses CD8
T cell intratumoural infiltration, such that targeting GAD1 or GABA
R in mouse models overcomes resistance to anti-PD-1 immune checkpoint blockade therapy. Our findings uncover a signalling role for tumour-derived GABA beyond its classic function as a neurotransmitter that can be targeted pharmacologically to reverse immunosuppression.
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.
Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear ...relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is "Satisfactory". This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.
► Significant trend tendency was found for annual streamflow in nine large river basins of China. ► The non-stationary relationship exists between annual precipitation and streamflow. ► The ...non-stationary relationship is influenced by both human activities and climate changes.
In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world.
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.
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.
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.
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.
Synopsis
In response to various senescence‐inducing stimuli, normal mammalian cells rapidly upregulate the scavenger receptor CD36. Amyloid beta‐dependent CD36 signaling then triggers NF‐κB pathway activation, resulting in the production and secretion of numerous inflammatory proteins known to comprise the senescence‐associated secretory phenotype.
The multi‐ligand receptor CD36 is induced in multiple senescence contexts.
Amyloid beta activates CD36 to stimulate NF‐κB‐dependent cytokine and chemokine production.
Sustained secretory molecule production leads to the onset of a comprehensive senescent cell fate.
The scavenger receptor CD36 and its ligand amyloid beta trigger NF‐κB pathway activation and the acquisition of a senescence‐associated secretory phenotype (SASP) in response to various senescence‐inducing stimuli.
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.
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•Hypoxia-induced miR-215 is required for GIC survival and GBM progression•Biogenesis of miR-215 is enhanced post-transcriptionally by HIF-Drosha interaction•MiR-215 suppresses KDM1B expression and modulates activities of multiple pathways•MiR-215 level correlates with level of HIF1α, KDM1B, and GBM progression in patients
Hu et al. reveal an HIF-miR-215-KDM1B-mediated signaling axis in adaptation of glioma-initiating cells to hypoxia, and uncover a role of HIF in post-transcriptional regulation of miRNA biogenesis through HIF-Drosha interaction. MiR-215 level in GBM correlates with HIF1α level and tumor progression in patients.
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