6-Oxo-5-[(trifluoro­meth­yl)sulfon­yl]-1,2,4a,5,6,11b-hexa­hydro-1,3-dioxolo[4,5-j]phenanthridin-2-yl benzoate

In the title compound, C22H16F3NO7S, the two benzene rings are almost perpendicular, the dihedral angle between their mean planes being 87.1 (1)°. The terminal O atom of the benzoate moiety is disordered over two positions with site occupancies of 0.244 (15) and 0.756 (15). The crystal structure is stablized by two types of weak inter­molecular C—H⋯O hydrogen bonds

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that (1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1–98.3% and 90.2–98.5%, respectively. (2) 3DC achieved similar accuracy to supervised methods without the need for samples. (3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics

Qian Yuan - Chinesische Gartenkunst in Bochum

DOI: http://dx.doi.org/10.13154/RUB.58.61 Professor Zhang, Architekt des Chinesischen Gartens Qian Yuan im Botanischen Garten der Ruhr-Universität Bochum, lässt uns teilhaben am Entwurfsprozess; Zeichnungen, Skizzen, Kommentare und Details werden gemäß chinesischer Bildsprache spannungsvoll in die Folge der exquisiten Gartenfotos komponiert, so dass man am Schluss dieses außergewöhnlichen Buches den Eindruck hat, nicht nur durch den Garten gewandelt zu sein, sondern auch auf charmante Weise professionell in die chinesische Gartenkunst und –philosophie eingeführt worden zu sein!Weitere InformationenDas neue Buch Qian Yuan - Chinesische Gartenkunst in Bochum von Prof. Zhang Zhenshan ist eine bilinguale, deutsch-chinesische Publikation, 176 Seiten Innenteil, mit ca. 200 Farbbildern und Zeichnungen. Vor kurzem ist das Buch bei der Deutsch-Chinesischen Verlagsanstalt in Düsseldorf erschienen und kann dort portofrei bestellt werden. ISBN: 978-3-943343-08-3 . Preis: € 28,80. Mehr Informationen über das Buch finden Sie unter http://www.dcva.deDas vorliegende Dokument stellt einen Auszug der deutschen Texte mit 33 ausgewählten Bildern/ Zeichnungen dar. Die Bilder wurden komprimiert, um die Dateigröße zu reduzieren und zum schnelleren Laden.Für weitere Fragen und Anregungen steht Ihnen Herr Chen ([email protected]) gerne per E-Mail zur Verfügung

In vivo genome editing improves muscle function in a mouse model of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a devastating disease affecting about 1 out of 5000 male births and caused by mutations in the dystrophin gene. Genome editing has the potential to restore expression of a modified dystrophin gene from the native locus to modulate disease progression. In this study, adeno-associated virus was used to deliver the CRISPR/Cas9 system to the mdx mouse model of DMD to remove the mutated exon 23 from the dystrophin gene. This includes local and systemic delivery to adult mice and systemic delivery to neonatal mice. Exon 23 deletion by CRISPR/Cas9 resulted in expression of the modified dystrophin gene, partial recovery of functional dystrophin protein in skeletal myofibers and cardiac muscle, improvement of muscle biochemistry, and significant enhancement of muscle force. This work establishes CRISPR/Cas9-based genome editing as a potential therapy to treat DMD

Classical risk factors of cardiovascular disease among Chinese male steel workers: a prospective cohort study for 20 years

<p>Abstract</p> <p>Background</p> <p>Cardiovascular disease (CVD) constitutes a major public health problem in China and worldwide. We aimed to examine classical risk factors and their magnitudes for CVD in a Chinese cohort with over 20 years follow-up.</p> <p>Methods</p> <p>A cohort of 5092 male steelworkers recruited from 1974 to 1980 in Beijing of China was followed up for an average of 20.84 years. Cox proportional-hazards regression model were used to evaluate the risk of developing a first CVD event in the study participants who were free of CVD at the baseline.</p> <p>Results</p> <p>The multivariable-adjusted hazard ratio (HR) associated with every 20 mmHg rise in systolic blood pressure (SBP) was 1.63 in this Chinese male population, which was higher than in Caucasians. Compared to non-smokers, men who smoked not less than one-pack-a-day had a HR of 2.43 (95% confidence interval [CI], 1.75-3.38). The HR (95% CI) for every 20 mg/dl increase in total serum cholesterol (TC) and for every point rise in body mass index (BMI) was 1.13 (1.04-1.23) and 1.06 (1.02-1.09), respectively.</p> <p>Conclusions</p> <p>Our study documents that hypertension, smoking, overweight and hypercholesterolemia are major conventional risk factors of CVD in Chinese male adults. Continued strengthening programs for prevention and intervention on these risk factors are needed to reduce the incidence of CVD in China.</p

Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification

Group intelligence algorithms have been widely used in support vector machine (SVM) parameter optimization due to their obvious characteristics of strong parallel processing ability, fast optimization, and global optimization. However, few studies have made optimization performance comparisons of different group intelligence algorithms on SVMs, especially in terms of their application to hyperspectral remote sensing classification. In this paper, we compare the optimization performance of three different group intelligence algorithms that were run on a SVM in terms of five aspects by using three hyperspectral images (one each of the Indian Pines, University of Pavia, and Salinas): the stability to parameter settings, convergence rate, feature selection ability, sample size, and classification accuracy. Particle swarm optimization (PSO), genetic algorithms (GAs), and artificial bee colony (ABC) algorithms are the three group intelligence algorithms. Our results showed the influence of these three optimization algorithms on the C-parameter optimization of the SVM was less than their influence on the &sigma;-parameter. The convergence rate, the number of selected features, and the accuracy of the three group intelligence algorithms were statistically significant different at the p = 0.01 level. The GA algorithm could compress more than 70% of the original data and it was the least affected by sample size. GA-SVM had the highest average overall accuracy (91.77%), followed by ABC-SVM (88.73%), and PSO-SVM (86.65%). Especially, in complex scenes (e.g., the Indian Pines image), GA-SVM showed the highest classification accuracy (87.34%, which was 8.23% higher than ABC-SVM and 16.42% higher than PSO-SVM) and the best stability (the standard deviation of its classification accuracy was 0.82%, which was 5.54% lower than ABC-SVM, and 21.63% lower than PSO-SVM). Therefore, when compared with the ABC and PSO algorithms, the GA had more advantages in terms of feature band selection, small sample size classification, and classification accuracy

A Cuboid Model for Assessing Surface Soil Moisture

This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (&Delta;LST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2/1/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0&ndash;0.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters