Mutational Analysis of Highly Conserved Residues in the Phage PhiC31 Integrase Reveals Key Amino Acids Necessary for the DNA Recombination

Background: Amino acid sequence alignment of phage phiC31 integrase with the serine recombinases family revealed highly conserved regions outside the catalytic domain. Until now, no system mutational or biochemical studies have been carried out to assess the roles of these conserved residues in the recombinaton of phiC31 integrase. Methodology/Principal Findings: To determine the functional roles of these conserved residues, a series of conserved residues were targeted by site-directed mutagenesis. Out of the 17 mutants, 11 mutants showed impaired or no recombination ability, as analyzed by recombination assay both in vivo and in vitro. Results of DNA binding activity assays showed that mutants (R18A, I141A, L143A,E153A, I432A and V571A) exhibited a great decrease in DNA binding affinity, and mutants (G182A/F183A, C374A, C376A/G377A, Y393A and V566A) had completely lost their ability to bind to the specific target DNA attB as compared with wild-type protein. Further analysis of mutants (R18A, I141A, L143A and E153A) synapse and cleavage showed that these mutants were blocked in recombination at the stage of strand cleavage. Conclusions/Significance: This data reveals that some of the highly conserved residues both in the N-terminus and C-terminus region of phiC31 integrase, play vital roles in the substrate binding and cleavage. The cysteine-rich motif and th

MODIS-Derived Spatiotemporal Changes of Major Lake Surface Areas in Arid Xinjiang, China, 2000–2014

Inland water bodies, which are critical freshwater resources for arid and semi-arid areas, are very sensitive to climate change and human disturbance. In this paper, we derived a time series of major lake surface areas across Xinjiang Uygur Autonomous Region (XUAR), China, based on an eight-day MODIS time series in 500 m resolution from 2000 to 2014. A classification approach based on water index and dynamic threshold selection was first developed to accommodate varied spectral features of water pixels at different temporal steps. The overall classification accuracy for a MODIS-derived water body is 97% compared to a water body derived using Landsat imagery. Then, monthly composites of water bodies were derived for the months of April, July, and September to identify seasonal patterns and inter-annual dynamics of 10 major lakes (\u3e100 km2) in XUAR. Our results indicate that the changing trends of surface area of major lakes varied across the region. The surface areas of the Ebinur and Bosten Lakes showed a significant shrinking trend. The Ulungur-Jili Lake remained relatively stable during the entire period. For mountain lakes, the Barkol Lake showed a decreasing trend in April and July, but the Sayram Lake showed a significant expanding trend in September. The four plateau lakes exhibited significant expanding trends in all three seasons except for Arkatag Lake in July. The shrinking of major lakes reflects severe anthropogenic impacts due to agricultural and industrial needs, in addition to the impact of climate change. The pattern of lake changes across the XUAR can provide insight into the impact of climate change and human activities on regional water resources in this arid and semi-arid region

Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies

SphK1/S1P Mediates PDGF-Induced Pulmonary Arterial Smooth Muscle Cell Proliferation via miR-21/BMPRII/Id1 Signaling Pathway

Background/Aims: The underlying molecular mechanisms involved in sphingosine kinase 1 (SphK1)/sphingosine 1-phosphate (S1P) mediation of platelet-derived growth factor (PDGF)-induced pulmonary arterial smooth muscle cell (PASMC) proliferation are still unclear, and the present study aims to address this issue. Methods: Small interfering RNA (siRNA) and microRNA inhibitor transfection was performed to block the expression of SphK1, bone morphogenetic protein receptor II (BMPRII) and microRNA-21 (miR-21). Gene expression levels of SphK1, BMPRII and inhibitor of DNA binding 1 (Id1) were detected by immunoblotting, miR-21 expression level was examined with qRT-PCR, and S1P production was measured by ELISA. Additionally, PASMC proliferation was determined by BrdU incorporation assay. Results: Our results indicated that PDGF increased the expression of SphK1 protein and S1P production, up-regulated miR-21 expression, reduced BMPRII and Id1 expression, and promoted PASMCs proliferation. Pre-silencing of SphK1 with siRNA reversed PDGF-induced S1P production, miR-21 up-regulation, BMPRII and Id1 down-regulation, as well as PASMC proliferation. Pre-inhibition of miR-21 also blocked BMPRII and Id1 down-regulation as well as PASMC proliferation caused by PDGF. Knockdown of BMPRII down-regulated Id1 expression in PASMCs. We further found that inhibition of PI3K/Akt and ERK signaling pathways, particularly ERK cascade, suppressed PDGF-induced above changes. Conclusion: Our study indicates that SphK1/S1P pathway plays an important role in PDGF-induced PASMC proliferation via miR-21/BMPRII/Id1 axis and targeting against SphK1/S1P axis might be a novel strategy in the prevention and treatment of pulmonary arterial hypertension (PAH)

Establishment and validation of a 3-month prediction model for poor functional outcomes in patients with acute cardiogenic cerebral embolism related to non-valvular atrial fibrillation

ObjectivesCardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations.MethodsWe included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables.ResultsThe 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model’s Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854–0.902), 0.918 (95% CI, 0.857–0.979), and 0.839 (95% CI, 0.744–0.934), respectively.ConclusionThe N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation

Experimental Study of the Planting Uniformity of Sugarcane Single-Bud Billet Planters

Planting uniformity is a key evaluation index for planters. This paper investigated the effect of rotational speed, the angle of the rake bar chain, and the number of billets on the planting uniformity of a seed-metering device in the laboratory. The experimental results showed that the optimal planting uniformity can be achieved under a rake bar chain angle of 117°, a number of billets of 500, and a rotational speed of the rake bar chain of 70 rpm. Under this condition, the quality index Zq was 97.22% and the multiple index Zm was 0%, while the miss index Ze was 2.78%. Based on the above parameters, a single-bud planter was improved with three rake bar chains per seed box. Field experiments with different operation parameters (rotational speed, forward speed) were conducted. Results indicated that when the rotational speed was 40 rpm and the forward speed was 2.26 km/h, the planting uniformity was the best and the quality index Zq was 93.38%. The research results provide a basis for the application of single-bud billet planters in the field

Highly Sensitive Ethanol Sensing Using NiO Hollow Spheres Synthesized via Hydrothermal Method

Excessive ethanol gas is a huge safety hazard, and people will experience extreme discomfort after inhalation, so efficient ethanol sensors are of great importance. This article reports on ethanol gas sensors that use NiO hollow spheres assembled from nanoparticles, nanoneedles, and nanosheets prepared by the hydrothermal method. All of the samples were characterized for performance evaluation. The sensors based on the NiO hollow spheres showed a good response to ethanol, and the hollow spheres assembled from nanosheets (NiO-S) obtained the best ethanol gas-sensing performance. NiO-S provided a larger response value (38.4) at 350 °C to 200 ppm ethanol, and it had good stability and reproducibility. The nanosheet structure and the fluffy surface of NiO-S obtained the largest specific surface area (55.20 m2/g), and this structure was beneficial for the sensor to adsorb more gas molecules in an ethanol atmosphere. In addition, the excellent sensing performance could ascribe to the larger Ni3+/Ni2+ of NiO-S, which achieved better electronic properties. Furthermore, in terms of commercial production, the template-free preparation of NiO-S eliminated one step, saving time and cost. Therefore, the sensors based on NiO-S will serve as candidates for ethanol sensing