OrysPSSP: a comparative platform for small secreted proteins from rice and other plants.

Plants have large diverse families of small secreted proteins (SSPs) that play critical roles in the processes of development, differentiation, defense, flowering, stress response, symbiosis, etc. Oryza sativa is one of the major crops worldwide and an excellent model for monocotyledonous plants. However, there had not been any effort to systematically analyze rice SSPs. Here, we constructed a comparative platform, OrysPSSP (http://www.genoportal.org/PSSP/index.do), involving >100 000 SSPs from rice and 25 plant species. OrysPSSP is composed of a core SSP database and a dynamic web interface that integrates a variety of user tools and resources. The current release (v0530) of core SSP database contains a total of 101 048 predicted SSPs, which were generated through a rigid computation/curation pipeline. The web interface consists of eight different modules, providing users with rich resources/functions, e.g. browsing SSP by chromosome, searching and filtering SSP, validating SSP with omics data, comparing SSP among multiple species and querying core SSP database with BLAST. Some cases of application are discussed to demonstrate the utility of OrysPSSP. OrysPSSP serves as a comprehensive resource to explore SSP on the genome scale and across the phylogeny of plant species.

Deciphering Bitcoin Blockchain Data by Cohort Analysis.

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.

Analysis of the Effect of Urban Residents' Sports Consumption on GDP Growth Based on Deep Learning.

Nowadays, emerging industries are emerging, and the sports industry has become a remarkable new economic growth point. Vigorously tapping the potential of residents' sports consumption has important theoretical and practical significance for promoting the development of the sports industry, improving people's living standards, and stimulating economic growth. In this paper, a deep learning model is constructed, and the random forest and random network models in the deep learning network are used to analyze the pulling effect of urban residents' sports consumption on economic growth. Since the consumption level of urban residents is much higher than that of rural residents, urban residents are in a dominant position in sports consumption, so this paper takes urban residents' sports consumption as the core to explore the pulling effect of urban residents' sports consumption on economic growth. The research theme of this paper is the pulling effect of urban residents' sports consumption on economic growth, so this paper sets the explanatory variable as the added value of GDP, expressed by GDP. Sports consumption has the characteristics of inevitability, gradualness, and diversity. With the continuous change of people's living standards, sports consumption also presents several stages of relative consumption pattern changes. Research shows that sports consumption has a positive role in promoting economic growth and the transformation and upgrading of economic development mode. Every one percentage point change in sports consumption leads to an economic growth of 0.186 percentage points, and with the increase of the lag period, urban residents' sports consumption will gradually increase the driving effect of economic growth. This effect can be analyzed at the micro- and macrolevels and enhanced by a causal cumulative cycle mechanism.

Effects of Gibberellin Pre-Treatment on Seed Germination and Seedling Physiology Characteristics in Industrial Hemp under Drought Stress Condition.

The present study aimed to explore the effects of exogenous gibberellins (GAs) on seed germination and subsequent seedling growth of hemp (Cannabis sativa L.) under drought stress. Seeds of two industrial hemp cultivars i.e., 'Yunma 1', (YM) and 'Bamahuoma', (BM) were treated with different concentrations of GA3 solution (0, 200, 400, 600, 800 mg/L) at 20 °C for 8 h. The effect of pre-treatment was assessed on germination characteristics and physiological indexes on subsequent exposure to drought stress using 20% (m/v) polyethylene glycol (PEG) for 7 days. The results revealed that seed germination in hemp was sensitive to drought stress, as the germination indexes (germination rate and germination potential) decreased significantly, and seedling growth (hypocotyl length and radicle length) was impeded under 20% PEG-6000 condition. GA3 pre-treatment affected germination rate, germination potential, hypocotyl length and radicle length. With increasing GA3 concentration, these indexes first increased and then decreased. For seedling physiology characteristics in hemp, GA3-pretreatment remarkedly increased the osmotic regulating substances (soluble sugar and soluble protein contents) and the activities of antioxidant enzymes (SOD, superoxide dismutase and POD, peroxidase), while sharply decreased the lipid peroxidation (malondialdehyde, MDA) in seedlings grown under PEG-6000 induced drought stress. These results suggested that seeds pre-treated with GA3 could enhance the drought tolerance of hempseeds, and the optimal effect of GA3 for seed pre-treatment of YM and BM could be obtained when the concentration of GA3 solution reached 400 mg/L and 600 mg/L, respectively.

A hybrid intelligent diagnosis approach for quick screening of Alzheimer's disease based on multiple neuropsychological rating scales.

Neuropsychological testing is an effective means for the screening of Alzheimer's disease. Multiple neuropsychological rating scales should be used together to get subjects' comprehensive cognitive state due to the limitation of a single scale, but it is difficult to operate in primary clinical settings because of the inadequacy of time and qualified clinicians. Aiming at identifying AD's stages more accurately and conveniently in screening, we proposed a computer-aided diagnosis approach based on critical items extracted from multiple neuropsychological scales. The proposed hybrid intelligent approach combines the strengths of rough sets, genetic algorithm, and Bayesian network. There are two stages: one is attributes reduction technique based on rough sets and genetic algorithm, which can find out the most discriminative items for AD diagnosis in scales; the other is uncertain reasoning technique based on Bayesian network, which can forecast the probability of suffering from AD. The experimental data set consists of 500 cases collected by a top hospital in China and each case is determined by the expert panel. The results showed that the proposed approach could not only reduce items drastically with the same classification precision, but also perform better on identifying different stages of AD comparing with other existing scales.

Mobilizing clinical decision support to facilitate knowledge translation: a case study in China.

BACKGROUND: A wide gulf remains between knowledge and clinical practice. Clinical decision support has been demonstrated to be an effective knowledge tool that healthcare organizations can employ to deliver the "right knowledge to the right people in the right form at the right time". How to adopt various clinical decision support (CDS) systems efficiently to facilitate evidence-based practice is one challenge faced by knowledge translation research. METHOD: A computer-aided knowledge translation method that mobilizes evidence-based decision supports is proposed. The foundation of the method is a knowledge representation model that is able to cover, coordinate and synergize various types of medical knowledge to achieve centralized and effective knowledge management. Next, web-based knowledge-authoring and natural language processing based knowledge acquisition tools are designed to accelerate the transformation of the latest clinical evidence into computerized knowledge content. Finally, a batch of fundamental services, such as data acquisition and inference engine, are designed to actuate the acquired knowledge content. These services can be used as building blocks for various evidence-based decision support applications. RESULTS: Based on the above method, a computer-aided knowledge translation platform was constructed as a CDS infrastructure. Based on this platform, typical CDS applications were developed. A case study of drug use check demonstrates that the CDS intervention delivered by the platform has produced observable behavior changes (89.7% of alerted medical orders were revised by physicians). DISCUSSION: Computer-aided knowledge translation via a CDS infrastructure can be effective in facilitating knowledge translation in clinical settings.

CcbP, a calcium-binding protein from Anabaena sp. PCC 7120, provides evidence that calcium ions regulate heterocyst differentiation.

Although it is known that calcium is a very important messenger involved in many eukaryotic cellular processes, much less is known about calcium's role in bacteria. CcbP, a Ca(2+)-binding protein, was isolated from the heterocystous cyanobacterium Anabaena sp. PCC 7120, and the ccbP gene was cloned and inactivated. In the absence of combined nitrogen, inactivation of ccbP resulted in multiple contiguous heterocysts, whereas overexpression of ccbP suppressed heterocyst formation. Calmodulin, which is not present in Anabaena species, could also suppress heterocyst formation in both Anabaena sp. PCC 7120 and Anabaena variabilis. HetR induction upon nitrogen step-down was slow in the strain overexpressing ccbP. The Ca(2+) reporter protein obelin was used to show that mature heterocysts had a high intracellular free Ca(2+)concentration {[Ca(2+)](i)}, and immunoblotting showed that CcbP was absent from heterocysts. A regular pattern of cells with higher [Ca(2+)](i) was established during heterocyst differentiation before the appearance of proheterocysts. A rapid increase of [Ca(2+)](i) could be detected 4 h after the removal of combined nitrogen, and this increase was suppressed by excessive CcbP. These results suggest that Ca(2+) ions play very important roles in hetR induction and heterocyst differentiation.