Tailoring smart hydrogels through manipulation of heterogeneous subdomains.

The mechanical interactions among integrated cellular structures in soft tissues dictate the mechanical behaviors and morphogenetic deformations observed in living organisms. However, replicating these multifaceted attributes in synthetic soft materials remains a challenge. In this work, we develop a smart hydrogel system featuring engineered stiff cellular patterns that induce strain-driven heterogeneous subdomains within the hydrogel film. These subdomains arise from the distinct mechanical responses of the pattern and film domains under applied mechanical forces. Unlike previous studies that incorporate reinforced inclusions into soft matrices to tailor material properties, our method manipulates the localization, integration, and interaction of these subdomain building blocks within the soft film. This enables extensive tuning of both local and global behaviors. Notably, we introduce a subdomain-interface mechanism that allows for the concurrent customization and decoupling of mechanical properties and shape transformations within a single material system-an achievement rarely accomplished with current synthetic soft materials. Additionally, our use of in-situ imaging characterizations, including full-field strain mapping via digital imaging correlation and reciprocal-space patterns through fast Fourier transform analysis of real-space pattern domains, provides rapid real-time monitoring tools to uncover the underlying principles governing tailored multiscale heterogeneities and intricate behaviors

Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China\u27s three largest freshwater lakes

Inherent optical properties (IOPs) play an important role in underwater light field, and are difficult to estimate accurately using satellite data in optically complex waters. To study water quality in appropriate temporal and spatial scales, it is necessary to develop methods to obtain IOPs form space-based observation with quantified uncertainties. Field-measured IOP data (N = 405) were collected from 17 surveys between 2011 and 2017 in the three major largest freshwater lakes of China (Lake Chaohu, Lake Taihu, and Lake Hongze) in the lower reaches of the Yangtze River and Huai River (LYHR). Here we provide a case-study on how to use in-situ observation of IOPs to devise an improved algorithm for retrieval of IOPs. We then apply this algorithm to observation with Sentinel-3A OLCI (Ocean and Land Colour Instrument, corrected with our improved AC scheme), and use in-situ data to show that the algorithm performs better than the standard OLCI IOP product. We use the satellite derived products to study the spatial and seasonal distributions of IOPs and concentrations of optically active constituents in these three lakes, including chlorophyll-a (Chla) and suspended particulate matter (SPM), using all cloud-free OLCI images (115 scenes) over the lakes in the LYHR basin in 2017. Our study provides a strategy for using local and remote observations to obtain important water quality parameters necessary to manage resources such as reservoirs, lakes and coastal waters

Determination of the downwelling diffuse attenuation coefficient of lakewater with the sentinel-3A OLCI

The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400-1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (Kd(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available Kd(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R2) = 0.81, root-mean-square error (RMSE) = 0.99m-1and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R2= 0.83, RMSE = 1.06 m-1and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI Kd(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of Kd(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The Kd(490) values for Lake Taihu exhibit notable spatial and temporal variations. Kd(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Evaluation of the Influence of Aquatic Plants and Lake Bottom on the Remote-Sensing Reflectance of Optically Shallow Waters

Aquatic plants and lake bottoms in optically shallow waters (OSWs) wield great influence on reflectance spectra, resulting in the inapplicability of most existing bio-optical models for water colour remote sensing in lakes. Based on radiative transfer theory and measured spectra from a campaign for Lake Taihu in October 2008, absorption and backscattering coefficients were used to simulate the remote-sensing reflectance, which are considered to be reliable if matched to their measured counterparts. Several cases of measured spectra at different depths, Secchi disk depth transparency, and aquatic plant height and coverage were analyzed thoroughly for spectral properties. The contribution of aquatic plants was evaluated and compared with the measured and simulated remote-sensing reflectance values. This is helpful for removing the influence of aquatic plants and lake bottoms from the spectra and for constructing an improved chlorophyll a retrieval model for OSWs, such as that for Lake Taihu, China

Spatial-temporal evolution and driving factors of green high-quality agriculture development in China

The fundamental means of addressing the challenges concerning China’s agricultural resources and environment is to achieve green and high-quality development within the agricultural sector. In this study, we measured the level of green high-quality agricultural development (GHQAD) in China from 2003 to 2020, and used Theil index, Moran’s I and Geographic detector to reveal the evolution trend and driving factors of GHQAD in China. The results show that the development level of GHQAD in China is constantly improving while the spatial difference is decreasing, and the primary contributor to this overall variation is the intra-regional variation. The spatial distribution of GHQAD in China was positively correlated, with high concentration in eastern and central regions, and low concentration in western regions. Notably, topographic relief degree and urbanization level are the key driving factors contributing to the spatial differences in GHQAD across China. The insights gained from this study will be particularly valuable for the government decision-making processes, thereby elevating GHQAD development in China and ultimately achieving coordinated development within the agricultural sector

Optical approaches to examining the dynamics of dissolved organic carbon in optically complex inland waters

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An improved nomogram including elastography for the prediction of non-sentinel lymph node metastasis in breast cancer patients with 1 or 2 sentinel lymph node metastases

BackgroundThe rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs.MethodsA total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram.ResultsThe multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA.ConclusionsWe conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND