This paper describes the implement of Web Services functionality on Android platform.And design a program for inquiries to phone numbers attribution,to show the way to remote calls Web Service function.
<p>Supplementary Figure S1-S9. Figure S1. Specificity of antibody directed against the L2Δ13 splice form of LOXL2 in histological analysis. Figure S2. LOXL2 and L2Δ13 promote ESCC cell migration and invasion. Figure S3. Histological analysis of tumors and popliteal lymph nodes derived from the xenografts. Figure S4. LOXL2 and L2Δ13 interact with actin-binding proteins ezrin, fascin, HSPB1 and TMOD3. Figure S5. Co-localizations of L2Δ13 with four cytoskeletal binding proteins in ESCC cells. Figure S6. Kaplan-Meier curves and log-rank tests of fascin, HSPB1 and TMOD3 for overall survival and disease-free survival in ESCC patients. Figure S7. Molecular models of LOXL2/L2Δ13 and its actin-binding interacting partners predict poor prognosis in ESCC patients. Figure S8. LOXL2 is an oncogenic modulator in the activation of ezrin via phosphorylating ezrin at T567. Figure S9. LOXL2 modulates PKC-stimulated ezrin-T567 phosphorylation in ESCC cells.</p>
<div>Abstract<p>Lysyl oxidase-like 2 (LOXL2), a copper-dependent enzyme of the lysyl oxidase family and its nonsecreted, catalytically dead spliced isoform L2Δ13, enhance cell migration and invasion, stimulate filopodia formation, modulate the expression of cytoskeletal genes, and promote tumor development and metastasis <i>in vivo</i>. We previously showed that LOXL2 reorganizes the actin cytoskeleton in esophageal squamous cell carcinoma (ESCC) cells, however, the underlying molecular mechanisms were not identified. Here, using interactome analysis, we identified ezrin (EZR), fascin (FSCN1), heat shock protein beta-1 (HSPB1), and tropomodulin-3 (TMOD3) as actin-binding proteins that associate with cytoplasmic LOXL2, as well as with its L2Δ13 variant. High levels of LOXL2 and L2Δ13 and their cytoskeletal partners correlated with poor clinical outcome in patients with ESCC. To better understand the significance of these interactions, we focused on the interaction of LOXL2 with ezrin. Phosphorylation of ezrin at T567 was greatly reduced following depletion of LOXL2 and was enhanced following LOXL2/L2Δ13 reexpression. Furthermore, LOXL2 depletion inhibited the ability of ezrin to promote tumor progression. These results suggest that LOXL2-induced ezrin phosphorylation, which also requires PKCα, is critical for LOXL2-induced cytoskeletal reorganization that subsequently promotes tumor cell invasion and metastasis in ESCC. In summary, we have characterized a novel molecular mechanism that mediates, in part, the protumorigenic activity of LOXL2. These findings may enable the future development of therapeutic agents targeting cytoplasmic LOXL2.</p>Significance:<p>LOXL2 and its spliced isoform L2Δ13 promote cytoskeletal reorganization and invasion of esophageal cancer cells by interacting with cytoplasmic actin-binding proteins such as ezrin.</p></div>
Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion-matching framework. Specifically, we first present a gesture VQ-VAE module to learn a codebook to summarize meaningful gesture units. With each code representing a unique gesture, random jittering problems are alleviated effectively. We then use Levenshtein distance to align diverse gestures with different speech. Levenshtein distance based on audio quantization as a similarity metric of corresponding speech of gestures helps match more appropriate gestures with speech, and solves the alignment problem of speech and gestures well. Moreover, we introduce phase to guide the optimal gesture matching based on the semantics of context or rhythm of audio. Phase guides when text-based or speech-based gestures should be performed to make the generated gestures more natural. Extensive experiments show that our method outperforms recent approaches on speech-driven gesture generation. Our code, database, pre-trained models, and demos are available at https://github.com/YoungSeng/QPGesture.
Ordinal regression with anchored reference samples (ORARS) has been proposed for predicting the subjective Mean Opinion Score (MOS) of input stimuli automatically. The ORARS addresses the MOS prediction problem by pairing a test sample with each of the pre-scored anchored reference samples. A trained binary classifier is then used to predict which sample, test or anchor, is better statistically. Posteriors of the binary preference decision are then used to predict the MOS of the test sample. In this paper, rigorous framework, analysis, and experiments to demonstrate that ORARS are advantageous over simple regressions are presented. The contributions of this work are: 1) Show that traditional regression can be reformulated into multiple preference tests to yield a better performance, which is confirmed with simulations experimentally; 2) Generalize ORARS to other regression problems and verify its effectiveness; 3) Provide some prerequisite conditions which can insure proper application of ORARS.
In a computer-aided pronunciation training (CAPT) system, corrective feedback is desired to provide contrastive comparisons between user's and canonical pronunciations. This paper presents a hierarchical perturbation model to generate emphasis for English by modifying acoustic features of neutral speech to highlight such important speech segments. Synthesis of emphasis needs to be realized hierarchically at word, syllable and phone layers. A two-pass decision tree is constructed to cluster acoustic variations between emphatic and neutral speeches. The questions for decision tree construction are designed according to the above layers. The questions related to word and syllable layers are used to construct the main tree and then the questions related to phone layer are used to expand the leaves of main tree (deriving a set of subtrees). Support vector machines (SVMs) are used to predict acoustic variations for all the leaves of main tree (at word and syllable layers) and sub-trees (at phone layer). Experiments indicate that the proposed hierarchical perturbation model can generate emphatic speech with high quality for both naturalness and emphasis.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(144 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(216 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(288 450 300)'/%3e%3c/svg%3e)