Identification and functional analysis of NOL7 nuclear and nucleolar localization signals

<p>Abstract</p> <p>Background</p> <p>NOL7 is a candidate tumor suppressor that localizes to a chromosomal region 6p23. This locus is frequently lost in a number of malignancies, and consistent loss of NOL7 through loss of heterozygosity and decreased mRNA and protein expression has been observed in tumors and cell lines. Reintroduction of NOL7 into cells resulted in significant suppression of <it>in vivo </it>tumor growth and modulation of the angiogenic phenotype. Further, NOL7 was observed to localize to the nucleus and nucleolus of cells. However, the mechanisms regulating its subcellular localization have not been elucidated.</p> <p>Results</p> <p>An <it>in vitro </it>import assay demonstrated that NOL7 requires cytosolic machinery for active nuclear transport. Using sequence homology and prediction algorithms, four putative nuclear localization signals (NLSs) were identified. NOL7 deletion constructs and cytoplasmic pyruvate kinase (PK) fusion proteins confirmed the functionality of three of these NLSs. Site-directed mutagenesis of PK fusions and full-length NOL7 defined the minimal functional regions within each NLS. Further characterization revealed that NLS2 and NLS3 were critical for both the rate and efficiency of nuclear targeting. In addition, four basic clusters within NLS2 and NLS3 were independently capable of nucleolar targeting. The nucleolar occupancy of NOL7 revealed a complex balance of rapid nucleoplasmic shuttling but low nucleolar mobility, suggesting NOL7 may play functional roles in both compartments. In support, targeting to the nucleolar compartment was dependent on the presence of RNA, as depletion of total RNA or rRNA resulted in a nucleoplasmic shift of NOL7.</p> <p>Conclusions</p> <p>These results identify the minimal sequences required for the active targeting of NOL7 to the nucleus and nucleolus. Further, this work characterizes the relative contribution of each sequence to NOL7 nuclear and nucleolar dynamics, the subnuclear constituents that participate in this targeting, and suggests a functional role for NOL7 in both compartments. Taken together, these results identify the requisite protein domains for NOL7 localization, the kinetics that drive this targeting, and suggest NOL7 may function in both the nucleus and nucleolus.</p

Effects of Shading on Carbohydrates of Syzygium samarangense

Wax apple (Syzygium samarangense) is an important tropical fruit tree cultivated in Southeast Asian. It produces red pear-like shape fruits. The fruit flesh is considered high in antioxidants, phenolics, and flavonoids that have a potential to contribute to the human healthy diet, and was proved to have anti-inflammatory and antimicrobial characteristics. To allow year-round marketing of high quality wax apple fruit, growers always perform shading to inhibit new flushes so as to repress vegetative growth and promote reproductive growth. To investigate the effect of shading on carbohydrates, wax apple trees were shaded with sun shade nets under field conditions. The effects of shading on shoot growth were studied and leaf carbohydrate levels of the trees were determined. The results showed that shading inhibit the the growth of the terminal shoots and promoted bud dormancy. Shading also reduced total soluble sugar, sucrose, glucose, fructose, and starch levels of leaves. The results suggested that shading reduced carbohydrate accumulation and repressed vegetative growth

Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?

Molecular conformation generation (MCG) is a fundamental and important problem in drug discovery. Many traditional methods have been developed to solve the MCG problem, such as systematic searching, model-building, random searching, distance geometry, molecular dynamics, Monte Carlo methods, etc. However, they have some limitations depending on the molecular structures. Recently, there are plenty of deep learning based MCG methods, which claim they largely outperform the traditional methods. However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks. In particular, our design algorithm is simply the clustering of the RDKIT-generated conformations. We hope our findings can help the community to revise the deep learning methods for MCG. The code of the proposed algorithm could be found at https://gist.github.com/ZhouGengmo/5b565f51adafcd911c0bc115b2ef027c

Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition

Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulates ASR for each source language into many small ASR tasks and meta-learns a model initialization on all tasks from different source languages to access fast adaptation on unseen target languages. However, for different source languages, the quantity and difficulty vary greatly because of their different data scales and diverse phonological systems, which leads to task-quantity and task-difficulty imbalance issues and thus a failure of multilingual meta-learning ASR (MML-ASR). In this work, we solve this problem by developing a novel adversarial meta sampling (AMS) approach to improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language. Specifically, for each source language, if the query loss is large, it means that its tasks are not well sampled to train ASR model in terms of its quantity and difficulty and thus should be sampled more frequently for extra learning. Inspired by this fact, we feed the historical task query loss of all source language domain into a network to learn a task sampling policy for adversarially increasing the current query loss of MML-ASR. Thus, the learnt task sampling policy can master the learning situation of each language and thus predicts good task sampling probability for each language for more effective learning. Finally, experiment results on two multilingual datasets show significant performance improvement when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS to other low-resource speech tasks and transfer learning ASR approaches.Comment: accepted in AAAI202

The Organic Amendment Improve the Yield and Quality of Vegetable

Using biotechnology, we can change agricultural wastes into high‐quality organic fertilizers, which leads us in the direction of the development in modern agriculture and act as substitute to the chemical fertilizers. In this chapter, five types of technologies of organic amendment are elaborated. Each method can be selected based on the specific circumstance. The effects of the technology in the production are introduced and the principles of the technologies are explained in a simple manner

SAGE is far more sensitive than EST for detecting low-abundance transcripts

BACKGROUND: Isolation of low-abundance transcripts expressed in a genome remains a serious challenge in transcriptome studies. The sensitivity of the methods used for analysis has a direct impact on the efficiency of the detection. We compared the EST method and the SAGE method to determine which one is more sensitive and to what extent the sensitivity is great for the detection of low-abundance transcripts. RESULTS: Using the same low-abundance transcripts detected by both methods as the targeted sequences, we observed that the SAGE method is 26 times more sensitive than the EST method for the detection of low-abundance transcripts. CONCLUSIONS: The SAGE method is more efficient than the EST method in detecting the low-abundance transcripts

Investigation of the Effect of Dimple Bionic Nonsmooth Surface on Tire Antihydroplaning

Inspired by the idea that bionic nonsmooth surfaces (BNSS) reduce fluid adhesion and resistance, the effect of dimple bionic nonsmooth structure arranged in tire circumferential grooves surface on antihydroplaning performance was investigated by using Computational Fluid Dynamics (CFD). The physical model of the object (model of dimple bionic nonsmooth surface distribution, hydroplaning model) and SST k-ω turbulence model are established for numerical analysis of tire hydroplaning. By virtue of the orthogonal table L16(45), the parameters of dimple bionic nonsmooth structure design compared to the smooth structure were analyzed, and the priority level of the experimental factors as well as the best combination within the scope of the experiment was obtained. The simulation results show that dimple bionic nonsmooth structure can reduce water flow resistance by disturbing the eddy movement in boundary layers. Then, optimal type of dimple bionic nonsmooth structure is arranged on the bottom of tire circumferential grooves for hydroplaning performance analysis. The results show that the dimple bionic nonsmooth structure effectively decreases the tread hydrodynamic pressure when driving on water film and increases the tire hydroplaning velocity, thus improving tire antihydroplaning performance

Optogenetic Control of Voltage-Gated Calcium Channels

Voltage-gated Ca(2+) (CaV ) channels mediate Ca(2+) entry into excitable cells to regulate a myriad of cellular events following membrane depolarization. We report the engineering of RGK GTPases, a class of genetically encoded CaV channel modulators, to enable photo-tunable modulation of CaV channel activity in excitable mammalian cells. This optogenetic tool (designated optoRGK) tailored for CaV channels could find broad applications in interrogating a wide range of CaV -mediated physiological processes