The Source Region of the Yellow River Basin (SRYRB), China
To improve daily runoff prediction accuracy in data-scarce areas, this study focuses on incorporating multiple grid-based data ...(precipitation, EVI, soil moisture (SM)) to drive the CNN-LSTM hybrid model. The spatial features of precipitation and underlying surface of the basin can be extracted by CNN, while the temporal features of the input data series can be captured by the LSTM. The hybrid model is compared with the single models (CNN, LSTM), and hybrid model performances under different driven data are also investigated.
Driven by the in-situ precipitation, grid-based precipitation (GPM) and SM data, the CNN-LSTM hybrid model achieved the best prediction result with NSE of 0.834, outperforming the single LSTM model (NSE=0.510) and the CNN model (NSE=0.612). It indicates that the hybrid model captures the spatiotemporal change features of precipitation and underlying surface of the basin. When using only GPM and SM data as input, the hybrid model achieved comparable result with NSE of 0.827. It implies that GPM could serve as a good alternative of in-situ precipitation and SM could provide additional value to improve prediction. This study highlights the value of using multiple grid-based data to drive the hybrid model, which provides new insights into runoff prediction in data-scarce regions.
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•Propose a novel CNN-LSTM hybrid model for daily runoff prediction.•The model performed best driven by rain gauge data, grid-based GPM and SM.•The model achieved comparable result driven only by grid-based GPM and SM.•GPM serves as good alternative to rain gauge data for runoff prediction.•Reanalysis-based soil moisture data enhanced prediction.
MicroRNA (miRNA) is a promising new type of biomarkers but at a low fM level and hard to be analyzed. Herein proposed is an innovated surface plasmon resonance imaging (SPRi) method merged with a ...novel in-plane and vertical signal amplification strategy, that is, orthogonal signal amplification to enable a direct determination of sub-fM miRNA-15a (a multiple tumor diagnostic biomarker). The core idea is to add more mass on a target sample spot first along the surficial direction, then upward from the surface. In detection of miRNA, this was realized by coupling a miRNA-initiated surficial cyclic DNA–DNA hybridization reaction with a DNA-initiated upward cyclic polymerization reaction. A perfect SPRi sensing chip with isolated gold islands bordered by hydrophobic CYTOP was fabricated and used to obtain high-quality chip with low fabrication difficulty. As a result, SPRi contrast largely increases, able to reach a limit of detection and limit of quantification down to 0.56 and 5fM for miRNA-15a, about 107-fold improvement of sensitivity compared with a common SPRi detection. The method could quantify standard miRNA-15a spiked in human serum with an ideal recovery ranging from 98.6% to 104.9% and was validated to be applicable to the direct determination of miRNA-15a in healthy and cancer human serums. The orderly and controllable in situ sensitizing strategy is powerful and readily extendable to detection of other miRNAs.
•A climate-adaptive transfer learning framework for soil moisture estimation is proposed.•The framework mainly uses ERA5-Land data, ISMN data, and global Köppen climate classification data.•The ...framework is designed for data-scarce region and performed well on the Qinghai-Tibet Plateau.•The framework can contribute to historical soil moisture data reconstruction.•A long-term (1960–2019) soil moisture dataset with accuracy improvement is produced.
Soil moisture (SM) plays essential roles in revealing complex interaction mechanisms among air–soil-water-plant processes. In the Qinghai-Tibet Plateau (QTP), the in-situ SM data is sparse and limited, satellite-based SM data has short period, while reanalysis SM data has advantages on long-term and high spatiotemporal resolution but has relatively high error. In this study, to improve soil moisture estimation in the QTP, we aim to propose a Climate-Adaptive Transfer Learning (CATL) framework for data scarce region based on reanalysis data (ERA5-LAND dataset) and the in-situ data (International Soil Moisture Network (ISMN) data). Specifically, regarding the QTP as the target region, selecting the areas with similar climate types with QTP as the source region, we train the CNN-LSTM fusion model in the source region and then transfer it to the target region via fine-tuning strategy. Results indicate that the produced soil moisture data based on CATL framework achieves CC of 0.755 and ubRMSE of 0.042, which has better quality than SMAPL3 during 2015–2019. Additionally, the CATL framework also produced the historical SM data reconstruction during 1960–2010, with CC increased by 11.3 % and ubRMSE reduced by 1.5 % compared with the original ERA5-Land reanalysis data. Furthermore, compared to the direct fine-tuning strategy (without climate adaptive), the CATL framework showed an increase of CC with 2.6 %, and decreases in RMSE, MAE, and ubRMSE of 5.3 %, 4.2 %, and 7.5 %, respectively. Finally, an improved soil moisture dataset (daily, 0.05°) ranging from 1960 to 2019 is produced for the QTP. This study provides a new tool for soil moisture estimation improvement in data-scarce region which will also benefit basin hydrology and water resources management.
Surface plasmon resonance imaging (SPRi) has more and more applications in the fields of biology and life sciences due to its unique features of label-free and high-throughput detection and ...workability in physiological conditions, however it is suffering from insufficient sensitivity, especially in detecting the small molecules or low level of substances. Various methods have since been developed to improve its sensitivity, but it lacks of summary except for their working principles, setups and applications. This review is thus designed to summary the sensitivity-oriented research progresses, covering signal enhancement and/or amplification, and noise and background suppression, with chemistry-enhancing strategies as focus and non-chemistry as supplement. A brief prospect in near future is also given.
•The advances on improving the detection sensitivity of SPRi were summarized after analysis of 89 papers since 2005.•The focus is on non-instrumental measures, especially on chemistry-based signal amplification.•Instrumental measures are also summarized based on our opinion.
An ultra-sensitive sensor was fabricated to measure dopamine through quenching and restoring FITC fluorescence by the competitive binding of dopamine and
N
-acetylneuraminic acid with ...mercaptophenylboronic acid anchored on the gold nanoparticles. Assessed by quantifying dopamine in human urine samples, it reached a limit of detection of as low as 50 pM dopamine over a linear range from 10
−10
M to 10
−7
M. It also features low technical barriers and operation cost, and the strategy proposed could be extendable to other analytes and not restricted to the gold nanoparticles.
An ultra-sensitive sensor was fabricated to measure dopamine through quenching and restoring FITC fluorescence by the competitive binding of dopamine and
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-acetylneuraminic acid with mercaptophenylboronic acid anchored on the gold nanoparticles.
In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual ...system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity.
Polypropylene microplastics (PP-MPs) particles, the most common microplastics in the aquatic environment, can cause serious harm to the aquatic environment when they enter natural water bodies with ...urban stormwater runoff. It has been proved that bioretention cells can effectively retain PP-MPs. However, the influence of the migration and accumulation of PP-MPs in the filler on the pollutant removal effect of the bioretention cell is still unclear. Four bioretention devices were constructed, and different concentrations of PP-MPs (particle size range 30–100 μm) were input in the form of disordered dry-wet alternating rainfall, to explore the migration pattern of PP-MPs and its influence on the purification effect of bioretention cells. The results showed that PP-MPs were effectively retained in the surface layer (0–5 cm) of the bioretention filler (>99 %). Increasing concentrations of PP-MPs were detrimental to their vertical transport in the filler. PP-MPs improved the removal of nitrate nitrogen (73.82–94.22 %) and total nitrogen (24.75–53.14 %) from the bioretention cells and substantially attenuated the negative effects of the dry period on the pollutant removal effect. Compared with the low concentration (250 mg/L), the total nitrogen removal efficiency of high concentration PP-MPs (500–1000 mg/L) decreased by 12.90–28.39 %, and the concentration had no obvious effect on the removal of total phosphorus. In summary, the bioretention cell demonstrated good retention capacity of PP-MPs. And the long-term accumulation of PP-MPs promoted the nitrogen removal effect of the bioretention cells and had little effect on the phosphorus removal effect.
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•The high concentration of PP-MPs is unfavorable to their migration.•The accumulation of PP-MPs is unfavorable to the removal of ammonia nitrogen.•The accumulation of PP-MPs favors the removal of nitrate-nitrogen.•The presence of PP-MPs can alleviate the negative impact of drought.•The accumulation of PP-MPs increases the DOM content in the effluent.
Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with ...more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.
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•Evolution map of the memristor is extended by the negative differential resistance•Second-order associate memory circuit is full hardware implementation•Ion accumulation, migration, and formation of conduction paths are major reason•All evolution processes are verified by the FeOx-based memristor
Hardware architecture; Memory structure.
G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. ...GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.
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