Anti-NSE polyclonal antibody (DPAB1979RH)


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A synthetic peptide from the middle region of the human MMP-1.


Alternative Names
MMP1; matrix metallopeptidase 1 (interstitial collagenase); CLG; CLGN; interstitial collagenase; fibroblast collagenase
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Product Background

Androgen Receptor Signaling Pathway; Basigin interactions; Bladder cancer; Cell surface interactions at the vascular wall; Diabetes pathways; Endothelins; Glucocorticoid receptor regulatory network; Hemostasis; Matrix Metalloproteinases; PPAR signaling pathway; Pathways in cancer; Regulation of Insulin-like Growth Factor (IGF) Activity by Insulin-like Growth Factor Binding Proteins (IGFBPs); Rheumatoid arthritis; Syndecan-1-mediated signaling events; Validated transcriptional targets of AP1 fami


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We offer labeled antibodies using our catalogue antibody products and a broad range of intensely fluorescent dyes and labels including HRP, biotin, ALP, Alexa Fluor® dyes, DyLight® Fluor dyes, R-phycoerythrin (R-PE), at scales from less than 100 μg up to 1 g of IgG antibody. Learn More

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Trend analysis of evapotranspiration over Iran based onNEX-GDDPhigh-resolution dataset


Authors: Ghalami, Vahid; Saghafian, Bahram; Raziei, Tayeb

Estimation of evapotranspiration, representing a highly sensitive variable to global warming, is often required in water resources and agricultural studies. This study explores the possible effects of climate change on evapotranspiration (ETo) across Iran. The ET(o)was computed using the Penman-Monteith Temperature (PMT) method that requires only minimum and maximum temperature (T(min)and T-max), provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) with a 0.25 degrees x 0.25 degrees spatial resolution. Accuracy of the NEX-GDDP T(min)and T(max)were evaluated against ground truth observations at 41 synoptic weather stations distributed across the country using a set of statistical measures, including the coefficient of determination (R-2) and Nash-Sutcliffe Efficiency (NSE). Similarly, the PMT estimated evapotranspiration based on the NEX-GDDP T(min)and T-max(ETo-GDDP) were statistically evaluated against the ET(o)computed with observed variables at selected stations (ETo-obs). Then, at each grid cell, the 45-years ET(o-GDDP)time series was partitioned into three 15-years subperiods such that the differences between three subperiods were computed and inter-compared. Furthermore, annual, seasonal, and monthly ET(o)time series trends of each cell were evaluated using the Mann-Kendall trend test (MK) while the corresponding change rates were estimated using Theil-Sen's slope estimator (TSSE). The results demonstrated a good agreement between ET(o-GDDP)and ET(o-obs)with R(2)greater than 0.8 and NSE greater than 0.7 in 90.2% and 65.8% of the stations, respectively. The maps of differences between the three subperiods showed negligible changes in ET(o)between 1975-1990 and 1950-1975 subperiods. The spatial patterns of annual and seasonal MK statistics showed a significant increasing trend in most parts of Iran. On seasonal scale, the highest and lowest ET(o)changes were observed in spring (0.00035-0.0045) and in autumn (0.0005-0.0015), respectively.

Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model


Authors: Malik, Anurag; Rai, Priya; Heddam, Salim; Kisi, Ozgur; Sharafati, Ahmad; Salih, Sinan Q.; Al-Ansari, Nadhir; Yaseen, Zaher Mundher

Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EP(m)estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), Willmott index (WI), and relative error (RE)) and graphical interpretation (time variation plot, scatter plot, relative error plot, and Taylor diagram) were performed for the modeling evaluation. The results of appraisal showed that the CANFIS-1 model with six input variables provided better NRMSE (0.1364, 0.0904, 0.0947, and 0.0898), NSE (0.9439, 0.9736, 0.9703, and 0.9799), PCC (0.9790, 0.9872, 0.9877, and 0.9922), and WI (0.9860, 0.9934, 0.9927, and 0.9949) values for Pantnagar station, and NRMSE (0.1543, 0.1719, 0.2067, and 0.1356), NSE (0.9150, 0.8962, 0.8382, and 0.9453), PCC (0.9643, 0.9649, 0.9473, and 0.9762), and WI (0.9794, 0.9761, 0.9632, and 0.9853) values for Nagina stations in all applied modeling scenarios for estimating the monthly EPm. This study also confirmed the supremacy of the proposed integrated GT-CANFIS model under four different scenarios in estimating monthly EPm. The results of the current application demonstrated a reliable modeling methodology for water resource management and sustainability.

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