1-Dibenzylamino-1-de­oxy-4,5-O-isopropyl­idene-β-d-fructopyran­ose

The title compound C23H29NO5, synthesized by the Amadori rearrangement of α-d-glucose with dibenzyl­amine and the ketalization, is shown to be a β-anomer. The fructopyran­ose ring adopts a chair conformation. The two benzene rings form a dihedral angle of 68.9 (1)°. In the crystal, non–classical inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into a three–dimensional network

Along-strike segmentation of the South China Sea margin imposed by inherited pre-rift basement structures

Multibeam bathymetric, seismic and borehole data are used to investigate a large-scale strike-slip structure, the Baiyun-Liwan Fault Zone, in the northern South China Sea. This fault zone comprises NW- to NE-striking faults and negative flower structures that were generated by oblique extensional displacement. Notably, the interpreted data reveals that the Baiyun-Liwan Fault Zone was active during the Cenozoic, recording intense magmatism, and accommodating significant intraplate deformation during progressive continental rifting and ocean spreading. It bounds two distinct crustal segments and played a significant role in segmenting the northern margin of the South China Sea. The geometry of faults and strata within the Baiyun-Liwan Fault Zone also controlled local sediment routing and depocentre evolution during the Cenozoic. As basement and syn-rift structures change markedly across the Baiyun-Liwan Fault Zone, we propose this structure to be inherited from a lithospheric-scale fault zone separating the Mesozoic arc from forearc-related terrains. We therefore stress the importance of pre-existing structures in the development of rifted margins, with the example provided by the Baiyun-Liwan Fault Zone having profound implications for palaeogeographic reconstructions in the South China Sea. At present, the Baiyun-Liwan Fault Zone is incised by the Pearl River Canyon and eroded by recurrent submarine landslides, forming a major area of sediment bypass towards the abyssal plain

Neural Network Compression via Low Frequency Preference

Network pruning has been widely used in model compression techniques, and offers a promising prospect for deploying models on devices with limited resources. Nevertheless, existing pruning methods merely consider the importance of feature maps and filters in the spatial domain. In this paper, we re-consider the model characteristics and propose a novel filter pruning method that corresponds to the human visual system, termed Low Frequency Preference (LFP), in the frequency domain. It is essentially an indicator that determines the importance of a filter based on the relative low-frequency components across channels, which can be intuitively understood as a measurement of the “low-frequency components”. When the feature map of a filter has more low-frequency components than the other feature maps, it is considered more crucial and should be preserved during the pruning process. We conduct the proposed LFP on three different scales of datasets through several models and achieve superior performances. The experimental results obtained on the CIFAR datasets and ImageNet dataset demonstrate that our method significantly reduces the model size and FLOPs. The results on the UC Merced dataset show that our approach is also significant for remote sensing image classification

BACA: Superpixel Segmentation with Boundary Awareness and Content Adaptation

Superpixels could aggregate pixels with similar properties, thus reducing the number of image primitives for subsequent advanced computer vision tasks. Nevertheless, existing algorithms are not effective enough to tackle computing redundancy and inaccurate segmentation. To this end, an optimized superpixel generation framework termed Boundary Awareness and Content Adaptation (BACA) is presented. Firstly, an adaptive seed sampling method based on content complexity is proposed in the initialization stage. Different from the conventional uniform mesh initialization, it takes content differentiation into consideration to incipiently eliminate the redundancy of seed distribution. In addition to the efficient initialization strategy, this work also leverages contour prior information to strengthen the boundary adherence from whole to part. During the similarity calculation of inspecting the unlabeled pixels in the non-iterative clustering framework, a multi-feature associated measurement is put forward to ameliorate the misclassification of boundary pixels. Experimental results indicate that the two optimizations could generate a synergistic effect. The integrated BACA achieves an outstanding under-segmentation error (3.34%) on the BSD dataset over the state-of-the-art performances with a minimum number of superpixels (345). Furthermore, it is not limited to image segmentation and can be facilitated by remote sensing imaging analysis

BACA: Superpixel Segmentation with Boundary Awareness and Content Adaptation

Superpixels could aggregate pixels with similar properties, thus reducing the number of image primitives for subsequent advanced computer vision tasks. Nevertheless, existing algorithms are not effective enough to tackle computing redundancy and inaccurate segmentation. To this end, an optimized superpixel generation framework termed Boundary Awareness and Content Adaptation (BACA) is presented. Firstly, an adaptive seed sampling method based on content complexity is proposed in the initialization stage. Different from the conventional uniform mesh initialization, it takes content differentiation into consideration to incipiently eliminate the redundancy of seed distribution. In addition to the efficient initialization strategy, this work also leverages contour prior information to strengthen the boundary adherence from whole to part. During the similarity calculation of inspecting the unlabeled pixels in the non-iterative clustering framework, a multi-feature associated measurement is put forward to ameliorate the misclassification of boundary pixels. Experimental results indicate that the two optimizations could generate a synergistic effect. The integrated BACA achieves an outstanding under-segmentation error (3.34%) on the BSD dataset over the state-of-the-art performances with a minimum number of superpixels (345). Furthermore, it is not limited to image segmentation and can be facilitated by remote sensing imaging analysis

Identification of Tea Plant Purple Acid Phosphatase Genes and Their Expression Responses to Excess Iron

Purple acid phosphatase (PAP) encoding genes are a multigene family. PAPs require iron (Fe) to exert their functions that are involved in diverse biological roles including Fe homeostasis. However, the possible roles of PAPs in response to excess Fe remain unknown. In this study, we attempted to understand the regulation of PAPs by excess Fe in tea plant (Camellia sinensis). A genome-wide investigation of PAP encoding genes identified 19 CsPAP members based on the conserved motifs. The phylogenetic analysis showed that PAPs could be clustered into four groups, of which group II contained two specific cysteine-containing motifs “GGECGV„ and “YERTC„. To explore the expression patterns of CsPAP genes in response to excessive Fe supply, RNA-sequencing (RNA-seq) analyses were performed to compare their transcript abundances between tea plants that are grown under normal and high iron conditions, respectively. 17 members were shown to be transcribed in both roots and leaves. When supplied with a high amount of iron, the expression levels of four genes were significantly changed. Of which, CsPAP15a, CsPAP23 and CsPAP27c were shown as downregulated, while the highly expressed CsPAP10a was upregulated. Moreover, CsPAP23 was found to be alternatively spliced, suggesting its post-transcriptional regulation. The present work implicates that some CsPAP genes could be associated with the responses of tea plants to the iron regime, which may offer a new direction towards a further understanding of iron homeostasis and provide the potential approaches for crop improvement in terms of iron biofortification

2,3:4,5-Di-O-isopropylidenefructos-1-yl p-toluenesulfonate

The title compound, C19H26O8S, has been synthesized from 2,3:4,5-di-O-isopropylidene-β-d-fructopyranose. The absolute configuration of the fused ring is confirmed by anomalous dispersion effects in the diffraction measurement. The six-membered β-fructopyranose ring has a twist-boat conformation with the two five-membered rings trans to each other. In the crystal, intermolecular non-classical C—H...O hydrogen bonds link the molecules into a three-dimensional network

SCN: A Novel Shape Classification Algorithm Based on Convolutional Neural Network

Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources

MR-Guided Microwave Ablation in T1 Renal Cell Carcinoma: Initial Results in Clinical Routine

Objective. Percutaneous tumor ablation is usually performed using computed tomography (CT) or ultrasound (US) guidance, although reliable visualization of the target tumor could be challenging. Magnetic resonance- (MR-) guided ablation provides more reliable visualization of the target tumors and allows multiplanar imaging of the treatment process, making it the modality of choice, in particular if lesions are small. Methods. From March 2016 to January 2018, 32 patients scheduled for percutaneous treatment of T1 RCC underwent MR-guided MWA. Complications were classified according to the Clavien grade. Kaplan–Meier survival estimates were calculated to evaluate progression-free survival (PFS). Results. Technical success was achieved in all lesions. The mean energy and procedure duration were 61.6±8.7 kJ and 118.2±26.7 min, respectively. The glomerular filtration rate (GFR) dropped rapidly after 1 month of treatment and slowly recovered within three months (P<0.05). Postoperative pain and fever were the most common adverse events after treatment. Perirenal hematoma, thermal injury of the psoas muscle, and abdominal distension were common complications after MWA, and the incidence rates were 9.4% (3/32), 6.3% (2/32), and 6.3% (2/32), respectively. According to the Clavien grade classification, serious complications include hydrothorax, bowel injury, and renal failure, all of which have a probability of 3.1%. Of note, the three serious complications occurred in one patient. The 1-, 2-, and 3-year PFS rates were 96.9%, 93.8%, and 83.9%, respectively. The mean PFS rates were 33.972 months (95% CI: 33.045, 35.900). Conclusion. Microwave ablation is feasible under MR guidance and provides effective treatment of RCC in one session