A STUDY OF SELECTED COLLABORATIONS: PREPARING AND COACHING PIANO CHAMBER MUSIC FOR PERFORMANCE

Every devoted musician strives for excellence in performance. In chamber music rehearsals, it is crucially important that this same devoted musician should do more than simply get the music ready for successful performance. The work of rehearsal involves striving to fulfill the intentions of the composer, an effort which will hopefully satisfy both the musicians and the audience. It is equally important for musicians to learn more about shared leadership and collaborative teamwork, which lie at the heart of the chamber music genre as well as at the heart of the rehearsal itself. This entire process requires proper guidance towards the relevant information and knowledge and I feel that the type of learning and development acquired through a successful chamber music experience will benefit music students and encourage them to take ownership of their musical growth and long-term learning. However, a major roadblock to acquiring this knowledge is the lack of written pedagogical material. Therefore, in this dissertation, topics pertaining to music preparation and the rehearsal process involved in the three programs of selected piano chamber music as well as related coaching ideas will be discussed. Hopefully, the performances along with this document will contribute to the information available on how one learns to organize and prepare for piano chamber music performances in a more systematic and group-oriented way. The recital programs were presented on September 30th and December 8th, 2020, and March 30th, 2021. Recordings of these three recitals can be found in the Digital Repository at the University of Maryland (DRUM)

Do we really need temporal convolutions in action segmentation?

Action classification has made great progress, but segmenting and recognizing actions from long untrimmed videos remains a challenging problem. Most state-of-the-art methods focus on designing temporal convolution-based models, but the inflexibility of temporal convolutions and the difficulties in modeling long-term temporal dependencies restrict the potential of these models. Transformer-based models with adaptable and sequence modeling capabilities have recently been used in various tasks. However, the lack of inductive bias and the inefficiency of handling long video sequences limit the application of Transformer in action segmentation. In this paper, we design a pure Transformer-based model without temporal convolutions by incorporating temporal sampling, called Temporal U-Transformer (TUT). The U-Transformer architecture reduces complexity while introducing an inductive bias that adjacent frames are more likely to belong to the same class, but the introduction of coarse resolutions results in the misclassification of boundaries. We observe that the similarity distribution between a boundary frame and its neighboring frames depends on whether the boundary frame is the start or end of an action segment. Therefore, we further propose a boundary-aware loss based on the distribution of similarity scores between frames from attention modules to enhance the ability to recognize boundaries. Extensive experiments show the effectiveness of our model

Generation and Characterization of Novel Human IRAS Monoclonal Antibodies

Imidazoline receptors were first proposed by Bousquet et al., when they studied antihypertensive effect of clonidine. A strong candidate for I1R, known as imidazoline receptor antisera-selected protein (IRAS), has been cloned from human hippocampus. We reported that IRAS mediated agmatine-induced inhibition of opioid dependence in morphine-dependent cells. To elucidate the functional and structure properties of I1R, we developed the newly monoclonal antibody against the N-terminal hIRAS region including the PX domain (10–120aa) through immunization of BALB/c mice with the NusA-IRAS fusion protein containing an IRAS N-terminal (10–120aa). Stable hybridoma cell lines were established and monoclonal antibodies specifically recognized full-length IRAS proteins in their native state by immunoblotting and immunoprecipitation. Monoclonal antibodies stained in a predominantly punctate cytoplasmic pattern when applied to IRAS-transfected HEK293 cells by indirect immunofluorescence assays and demonstrated excellent reactivity in flow immunocytometry. These monoclonal antibodies will provide powerful reagents for the further investigation of hIRAS protein functions

High throughput 16SrRNA gene sequencing reveals the correlation between Propionibacterium acnes and sarcoidosis

Sequences of the unique OTUs. (XLS 3616 kb

Therapeutic Efficacy of Fuzheng-Huayu Tablet Based Traditional Chinese Medicine Syndrome Differentiation on Hepatitis-B-Caused Cirrhosis: A Multicenter Double-Blind Randomized Controlled Trail

Aim. To evaluate and predict the therapeutic efficacy of Fuzheng-Huayu tablet (FZHY) based traditional Chinese Medicine (TCM) syndrome differentiation or TCM symptoms on chronic hepatitis B caused cirrhosis (HBC). Methods. The trial was designed according to CONSORT statement. It was a multi-center, double-blind, randomized, placebo-controlled trail. Several clinical parameters, Child-Pugh classification and TCM symptoms were detected and evaluated. The FZHY efficacy was predicted by an established Bayes forecasting method following the Bayes classification model. Results. The levels of HA and TCM syndrome score in FZHY group were significantly decreased (P<0.05) compared to placebo group, respectively. The efficacy of FZHY on TCM syndrome score in HBC patients with some TCM syndromes was better. In TCM syndrome score evaluation, there were 53 effective and 22 invalid in FZHY group. TCM symptoms predicted FZHY efficacy on HBC were close to Child-Pugh score prediction. Conclusion. FZHY decreases the levels of HA and TCM syndrome scores, improves the life quality of HBC patients. Moreover, there were different therapeutic efficacies among different TCM syndromes, indicating that accurate TCM syndrome differentiation might guide the better TCM treatment. Furthermore, the FZHY efficacy was able to predict by Bayes forecasting method through the alteration of TCM symptoms

A highly efficient rice green tissue protoplast system for transient gene expression and studying light/chloroplast-related processes

<p>Abstract</p> <p>Background</p> <p>Plant protoplasts, a proven physiological and versatile cell system, are widely used in high-throughput analysis and functional characterization of genes. Green protoplasts have been successfully used in investigations of plant signal transduction pathways related to hormones, metabolites and environmental challenges. In rice, protoplasts are commonly prepared from suspension cultured cells or etiolated seedlings, but only a few studies have explored the use of protoplasts from rice green tissue.</p> <p>Results</p> <p>Here, we report a simplified method for isolating protoplasts from normally cultivated young rice green tissue without the need for unnecessary chemicals and a vacuum device. Transfections of the generated protoplasts with plasmids of a wide range of sizes (4.5-13 kb) and co-transfections with multiple plasmids achieved impressively high efficiencies and allowed evaluations by 1) protein immunoblotting analysis, 2) subcellular localization assays, and 3) protein-protein interaction analysis by bimolecular fluorescence complementation (BiFC) and firefly luciferase complementation (FLC). Importantly, the rice green tissue protoplasts were photosynthetically active and sensitive to the retrograde plastid signaling inducer norflurazon (NF). Transient expression of the GFP-tagged light-related transcription factor OsGLK1 markedly upregulated transcript levels of the endogeneous photosynthetic genes <it>OsLhcb1</it>, <it>OsLhcp</it>, <it>GADPH </it>and <it>RbcS</it>, which were reduced to some extent by NF treatment in the rice green tissue protoplasts.</p> <p>Conclusions</p> <p>We show here a simplified and highly efficient transient gene expression system using photosynthetically active rice green tissue protoplasts and its broad applications in protein immunoblot, localization and protein-protein interaction assays. These rice green tissue protoplasts will be particularly useful in studies of light/chloroplast-related processes.</p

CD24-p53 axis suppresses diethylnitrosamine-induced hepatocellular carcinogenesis by sustaining intrahepatic macrophages

It is generally assumed that inflammation following diethylnitrosamine (DEN) treatment promotes development of hepatocellular carcinoma (HCC) through the activity of intrahepatic macrophages. However, the tumor-promoting function of macrophages in the model has not been confirmed by either macrophage depletion or selective gene depletion in macrophages. Here we show that targeted mutation of Cd24 dramatically increased HCC burden while reducing intrahepatic macrophages and DEN-induced hepatocyte apoptosis. Depletion of macrophages also increased HCC burden and reduced hepatocyte apoptosis, thus establishing macrophages as an innate effector recognizing DEN-induced damaged hepatocytes. Mechanistically, Cd24 deficiency increased the levels of p53 in macrophages, resulting in their depletion in Cd24 -/- mice following DEN treatment. These data demonstrate that the Cd24-p53 axis maintains intrahepatic macrophages, which can remove hepatocytes with DNA damage. Our data establish a critical role for macrophages in suppressing HCC development and call for an appraisal of the current dogma that intrahepatic macrophages promote HCC development

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further