Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.

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Vanadate (sodium orthovanadate), an inhibitor of phosphotyrosine phosphatases (PTPs), mimics many of the metabolic actions of insulin in vitro and in vivo. The potential of vanadate to stimulate glucose transport independent of the early steps in insulin signaling prompted us to test its effectiveness in an in vitro model of insulin resistance. In primary rat adipocytes cultured for 18 h in the presence of high glucose (15 mM) and insulin (10 ؊7 M), sensitivity to insulin-stimulated glucose transport was decreased. In contrast, there was a paradoxical enhanced sensitivity to vanadate of the insulinresistant cells (EC 50 for control, 325 ؎ 7.5 M; EC 50 for insulin-resistant, 171 ؎ 32 M; p < 0.002). Enhanced sensitivity was also present for vanadate stimulation of insulin receptor kinase activity and autophosphorylation and Akt/protein kinase B Ser-473 phosphorylation consistent with more effective PTP inhibition in the resistant cells. Investigation of this phenomenon revealed that 1) depletion of GSH with buthionine sulfoximine reproduced the enhanced sensitivity to vanadate while preincubation of resistant cells with N-acetylcysteine (NAC) prevented it, 2) intracellular GSH was decreased in resistant cells and normalized by NAC, 3) exposure to high glucose and insulin induced an increase in reactive oxygen species, which was prevented by NAC, 4) EPR (electron paramagnetic resonance) spectroscopy showed a decreased amount of vanadyl (؉4) in resistant and buthionine sulfoximine-treated cells, which correlated with decreased GSH and increased vanadate sensitivity, while total vanadium uptake was not altered, and 5) inhibition of recombinant PTP1B in vitro was more sensitive to vanadate (؉5) than vanadyl (؉4). In conclusion, the parodoxical increased sensitivity to vanadate in hyperglycemia-induced insulin resistant adipocytes is due to oxidative stress and decreased reduction of vanadate (؉5) to vanadyl (؉4). Thus, sensitivity of PTP inhibition and glucose transport to vanadate is regulated by cellular redox state.

Our previous studies have characterized mesenchyme-derived proteins to identify biologically active proteins and novel markers for stromal cell paracrine action relative to stromal-epithelial interactions. Previous reports have characterized properties of a growth inhibitory activity (to bladder and prostatic epithelial cells), secreted by U4F fetal rat urogenital sinus mesenchymal cells, not cross-reactive with antibodies to known cytokines, and provisionally termed UGIF. The present study reports the characterization, purification, and biological properties of a 20-21-kDa protein responsible for UGIF activity. The 20-21-kDa protein (termed ps20) was purified to near homogeneity, the amino-terminal sequence was determined, and biological properties were characterized in vitro. Amino-terminal sequence analysis indicated no direct matches or regions of homology with known proteins. Purified ps20 induced a linear and saturable inhibition of [ 3 H]thymidine incorporation in PC-3 prostatic carcinoma cells (half-maximal activity at 2.6 nM), inhibited cell proliferation (increased population doubling time from 19.8 to 25.8 h), and induced a 210% stimulation in the synthesis of secreted proteins. These data suggest that ps20 may be a candidate paracrine effector protein and may play a role in stromal-epithelial cell interactions in the prostate gland.

The major risk factors for hepatocellular carcinomas (HCC) in high incidence areas include infection with hepatitis B and C viruses (HBV, HCV) and exposure to aflatoxin. Genetic alterations in 24 liver resection specimens from Shanghai and Qidong were studied. Hepatitis B virus was integrated in all patient samples, and a null phenotype for the GSTM1 enzyme was present in 63% of patients. Alteration of p53 was present in 95% (23/24) of cases: mutations of the p53 gene in 12 HCC, p53 overexpression in 13 and loss of heterozygosity (LOH) of chromosome 17p in 17. All seven HCCs with a p53 mutation from Qidong and three of five from Shanghai had the aflatoxin-associated point mutation with a G to T transversion at codon 249, position 3. No HCC had microsatellite instability. LOH of chromosome 4q, 1p, 16q and 13q was present in 50%, 46%, 42% and 38%, respectively, and 4q was preferentially lost in HCCs containing a p53 mutation: LOH of 4q was present in 75% (9/12) of HCC with, but only 25% (3/12) of HCC without, a p53 gene mutation ( P = 0.01). These data indicate a possible interaction between p53 gene mutation and 4q loss in the pathogenesis of HCC. © 1999 Cancer Research Campaign

In the Changjiang Estuary, interactions between the sea and the river result in the development of a turbidity maximum zone (TMZ). Riverine sediments are an important source for TMZ formation. Since the 1960s, sediment discharge from the river basin to the estuary has decreased due to dam construction, water and soil conservation, and water diversion projects. Thirty-two Landsat images of the estuary, covering the period from 1979 to 2008, were collected to identify the TMZ response to sediment decline. A threshold value of suspended sediment concentration (SSC) of 0.7 kg/m 3 , corresponding to a spectrum reflectance of 5% of Landsat MSS band 7 and 7% of Landsat TM/ETM band 4, was used to identify the Changjiang Estuary TMZ. The TMZ area was then extracted from each image to investigate its temporal and spatial variations during the past 30 years. The images were grouped into five time series; the average TMZ area of each series was estimated. The results show that the TMZ area declined 23% from series (a) to series (e), responding to a 77% reduction in riverine sediment discharge. In addition, the TMZ had strong seasonal and tidal variations; it was generally larger during flood seasons than during dry seasons and during spring tides compared to neap tides. The spring / neap tidal cycle played a more important role in TMZ change than did the seasonal cycle. Due to the continued reduction of sediment discharge to the estuary resulting from dams already constructed and to those that will be constructed upstream in the Changjiang River, it is predicted that the TMZ area will continue decreasing and that the re-suspension of local sediments will play a more important role in the formation of the TMZ.

Aims: This study aims to evaluate the diagnostic capabilities of neuropathy symptom and change (NSC) score, neuropathy impairment score (NIS) and Michigan neuropathy screening instrument (MNSI) in diagnosing diabetic peripheral neuropathy (DPN). Methods: A total of 131 patients with type II diabetes received NSC, NIS and MNSI scoring systems. Electromyography/nerve conduction velocity (EMG/NCV) test was taken as gold standard. Correlations between EMG/NCV test and the 3 scorings, and their sensitivity, specificity, positive and negative predictive values, accuracy and kappa (κ) value were analyzed. Results: The prevalence of DPN was 43.5% according to EMG/NCV findings. EMG/NCV test was significantly positive correlated with all the 3 scorings, highest with NIS scoring (r = 0.653, p < 0.001). Compared with EMG/NCV test, NSC score was most sensitive (85.96%) but least specific (77.03%); NIS score had lower sensitivity (59.65%) but best specificity (98.65%) and accuracy (81.68%). Both had high concordance with EMG/NCV test (κ = 0.61). Sensitivity, specificity and accuracy of MNSI were highest (70.18, 98.65 and 80.15%) at the cutoff values of >1.0, >2.5 and >1.5, respectively (κ = 0.58). Conclusions: Both NSC and NIS were accurate and reliable diagnostic methods for DPN. The combined application of NSC and NIS was recommended in DPN diagnosis.

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.