We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries and the encoding representation for the classifier are all learned simultaneously. The representation is orderless and therefore is particularly useful for material and texture recognition. The Encoding Layer generalizes robust residual encoders such as VLAD and Fisher Vectors, and has the property of discarding domain specific information which makes the learned convolutional features easier to transfer. Additionally, joint training using multiple datasets of varied sizes and class labels is supported resulting in increased recognition performance. The experimental results show superior performance as compared to state-of-the-art methods using gold-standard databases such as MINC-2500, Flickr Material Database, KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. The source code for the complete system are publicly available 1 .

We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orderless texture details and local spatial information and the performance of DEP surpasses state-of-the-art methods for this task. The GTOS database (comprised of over 30,000 images of 40 classes of ground terrain in outdoor scenes) enables supervised recognition. For evaluation under realistic conditions, we use test images that are not from the existing GTOS dataset, but are instead from hand-held mobile phone videos of similar terrain. This new evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground terrain such as grass, gravel, asphalt and sand. The resultant network shows excellent performance not only for GTOS-mobile, but also for more general databases (MINC and DTD). Leveraging the discriminant features learned from this network, we build a new texture manifold called DEP-manifold. We learn a parametric distribution in feature space in a fully supervised manner, which gives the distance relationship among classes and provides a means to implicitly represent ambiguous class boundaries. The source code and database are publicly available 1 .

Thyroid cancer, the most common primary endocrine malignancy in adult, imperatively requires new therapeutic studies that could target the molecular regulatory mechanism. Even though emerging evidence showed that long noncoding RNAs (Lnc-RNAs) are involved in different biological characteristic of malignant tumor, such as cell growth and apoptosis as well as cancer progression and metastasis. Limited data are available on the function of Lnc-RNAs in thyroid cancer invasion and metastasis. Among the 5 tested lnc-RNAs , the present study demonstrates that MEG3 was significantly down-regulated in papillary thyroid carcinoma (PTC) tissues with lymph-node metastasis than in primary thyroid cancer. Moreover, the down- regulated MEG3 was associated with lymph-node metastasis. Over-expression of MEG3 could strongly inhibit the cell migration and invasion in TPC-1 and HTH83 thyroid cancer cell lines. In addition, we also showed that Rac1 was negatively regulated by lncRNA-MEG3 at the posttranscriptional level, via a specific target site within the 3΄UTR by dual luciferase reporter assay. The expression of Rac1 was inversely correlated with lncRNA-MEG3 expression in PTC tissues. Thus, this study suggests that MEG3 acts as novel suppressor of migration and invasion by targeting Rac1 gene.

Material recognition for real-world outdoor surfaces has become increasingly important for computer vision to support its operation "in the wild." Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined images of materials captured in the scene. We propose a middle-ground approach that takes advantage of both rich radiometric cues and flexible image capture. We develop a framework for differential angular imaging, where small angular variations in image capture provide an enhanced appearance representation and significant recognition improvement. We build a large-scale material database, Ground Terrain in Outdoor Scenes (GTOS), geared towards real use for autonomous agents. This publicly available database 1 consists of over 30,000 images covering 40 classes of outdoor ground terrain under varying weather and lighting conditions. We develop a novel approach for material recognition called a Differential Angular Imaging Network (DAIN) to fully leverage this large dataset. With this network architecture, we extract characteristics of materials encoded in the angular and spatial gradients of their appearance. Our results show that DAIN achieves recognition performance that surpasses single view or coarsely quantized multiview images. These results demonstrate the effectiveness of differential angular imaging as a means for flexible, in-place material recognition.

(+)-and (−)-Lesinurad were isolated as atropisomers from racemic lesinurad for the first time. No interconversion was observed between the two atropisomers under various conditions tested. The two atropisomers showed significant differences in hURAT1 highly expressed HEK293 cell-based inhibition assays, monkey pharmacokinetic studies, and in vitro human recombinant CYP2C9 stability studies. It was speculated that (+)-lesinurad might offer a better hyperuricemia/gout therapy than (−)-lesinurad or the racemate.

Due to the conversion equilibrium between solvent and H- and O-containing adsorbates, the true surface state of a catalyst under a particular electrochemical condition is often overlooked in electrocatalysis research. Herein, by using surface Pourbaix analysis, we show that many electrocatalytically active transition metal X-ides ( e.g., oxides, nitrides, carbides, and hydroxides) tend to possess the surface states different from their pristine stoichiometric forms under the pH and potential of interests due to water dissociation or generation. Herein, summarizing the density functional theory calculated surface Pourbaix diagrams of fourteen conditionally stable transition metal X-ide materials, we found that some of these surfaces tend to be covered by O-containing adsorbates at a moderate or high potential, while vacancies or H-covered surfaces may form at a low potential. These results suggest the possibility of poisoning or creation of surface sites beyond the pristine surface, implying that the surface state under reaction conditions (pH and potentials) needs to be considered before the identification and analysis of the active sites of a transition metal X-ide catalyst. In addition, we provide an explanation of the observed theory and experiment discrepancy that some transition metal X-ides are "more stable in experiment than in theory". Based on our findings, we conclude that analyzing the surface state of transition metal X-ide electrocatalysts by theoretical calculations ( e.g., surface Pourbaix diagram analysis), in-situ/ operando and post-reaction experiments are indispensable to accurately understanding the catalytic mechanisms.

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Background Esophageal squamous cell carcinoma (ESCC) is an aggressive and lethal cancer with a low 5 year survival rate. Identification of new therapeutic targets and its inhibitors remain essential for ESCC prevention and treatment. Methods TYK2 protein levels were checked by immunohistochemistry. The function of TYK2 in cell proliferation was investigated by MTT [(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] and anchorage-independent cell growth. Computer docking, pull-down assay, surface plasmon resonance, and kinase assay were used to confirm the binding and inhibition of TYK2 by cirsiliol. Cell proliferation, western blot and patient-derived xenograft tumor model were used to determine the inhibitory effects and mechanism of cirsiliol in ESCC. Results TYK2 was overexpressed and served as an oncogene in ESCC. Cirsiliol could bind with TYK2 and inhibit its activity, thereby decreasing dimer formation and nucleus localization of signal transducer and activator of transcription 3 (STAT3). Cirsiliol could inhibit ESCC growth in vitro and in vivo. Conclusions TYK2 is a potential target in ESCC, and cirsiliol could inhibit ESCC by suppression of TYK2.