Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Monitoring the composition of gas condensate well streams based on samples taken from multiphase flow meters

The article highlights the relevance of reliable estimation of the composition and properties of reservoir gas during the development of gas condensate fields and the complexity of the task for reservoirs containing zones of varying condensate content. The authors have developed a methodology that allows monitoring the composition of gas condensate well streams of similar reservoirs. There are successful examples of the approach applied in Achimov gas condensate reservoirs at the Urengoy oil and gas condensate field. The proposed approach is based on the use of the so-called fluid factors, which are calculated on the basis of the known component compositions of various flows of the studied hydrocarbon system. The correlation between certain "fluid factors" and the properties of reservoir gas (usually determined by more labor-consuming methods) allows one to quickly obtain important information necessary to solve various development control tasks.

Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images

Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.

Application of Artificial Intelligence (AI) in Plant Sciences Research

The current world needs to use modern technologies in plant science research due to increasing food requirements, and to cope up with global climate changes (1). Consequently, Artificial Intelligence (AI) based technologies are considered as the most recent and advanced research tools in every sector of science. Biological researches are also making progress surprisingly due to the assistance of AI affiliated tools directly or indirectly. Nowadays, plant physiology to genomic manipulation has witnessed, how it is easier and accurate than previous (2). Moreover, continuous monitoring of plants' growth, growing media, and relevant surroundings were time and labor-consuming in the past; even, the obtained results were erroneous somewhat. Data processing and analysis were based on individual performances of the participated researchers; surprisingly, the scenarios have been changed after the emergence of AI (3).

Economic assessment of application of new reforestation technologies in conditions of climate change in the forest-steppe zone of Russia

The development and application of new reforestation technologies remain relevant due to climate change. The main technology of forest sowing in Russia does not provide effective reforestation. Innovative technologies of reforestation based on seedlings with a closed root system make it possible to increase the livelihood of plants and accelerate reforestation processes. Using the method of determining costs, a comparative economic analysis of the technologies for growing oak seedlings with an open and closed root system in the forest-steppe regions of Russia was carried out. It was found that the technology of growing seedlings with a closed root system is less labor-consuming and provides a high level of income for investments. The innovative technology of growing seedlings with a closed root system in greenhouses is more competitive and its use allows to generate income from one hectare of area 50 times more than when growing seedlings in forest nurseries by increasing the number of seedling rotation. A comparative economic assessment of two oak seedling projects under forest-steppe conditions suggests the feasibility of moving to innovative reforestation technologies. In the current climate change conditions, a serious correction of approaches to reforestation in the forest-steppe zones of Russia is necessary.

The System Research and Implementation for Autorecognition of the Ship Draft via the UAV

The reading of the ship draft is an important step in the process of weighing and pricing. The traditional detection method is time-consuming and labor-consuming, and it is easy to lead to misdetection. In order to solve the above problems, this paper introduces the computer image processing technology based on deep learning, and the specific process is divided into three steps: first, the video sampling is carried out by the UAV to obtain a large number of pictures of the ship draft reading face, and the images are preprocessed; then, the deep learning target detection algorithm of improved YOLOv3 is used to process the images to predict the position of the waterline and identify the draft characters; finally, the prediction results are analyzed and processed to obtain the final reading results. The experimental results show that the ship draft reading method proposed in this paper has obvious effects. The method has a good detection effect on high-quality images, and the accuracy rate can reach 98%. The accuracy rate can also reach 73% for the images with poor quality caused by improper capture, character corrosion, bad weather, etc. This method is a kind of artificial intelligence method with safe measurement process, high measurement effect, and accuracy, providing a new idea for related research.

Induced velocities outside the ideal propeller jet

Object and purpose of research. The paper is devoted to the analysis of induced flow velocities outside the ideal propeller jet. The purpose is to widen capabilities of estimations. Materials and methods. Analysis of data on this issue has shown that complicated and quite labor-consuming methods are used for these purposes. It is attempted to devise a calculation scheme based on direct determination of induced velocities from discrete sinks simulating the ideal propeller. For this purpose an algorithm and software is developed for PC implementation. Testing of the computer program allowed us to define conditions ensuring the accuracy of calculations, it confirmed practically full agreement with the data obtained by other authors and possibility for widening the range of its application. Main results. Systematic calculations of induced velocities outside the ideal propeller jet were done allowing us to determine the flow velocity at any point of a wide area for engineering purposes. Conclusion. Longitudinal and radial component velocities induced by ideal propeller outside its jet were calculated based on directly assessed influence of the system of sinks distributed over the propeller disk on the ideal fluid flow. The algorithm for calculations was developed, the software was written and tested. Systematic calculations of induced velocities were done, which could be used for practical estimations.

The Use of Artificial Intelligence in Assessing Affective States in Livestock

In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

Leakage detection in water pipe networks using machine learning

<p><span>Leakage in the water supply system is a world problem that happens everywhere, not only in China but also in Japan, the US, and Europe. It not only results in the waste of water resources but also raises safety issues in drinking water. The traditional solution is the Minimum Night Flow method with manual leak detectors. This solution could only find leakage at night. The engineers have to search the leaking point randomly using leak detectors. It not only highly relies on domain knowledge and expertise but is also labor-consuming. The response time is quite long, might be a couple of days to several days. Here, time series analysis based on a dynamic time warping algorithm is used to detect anomalies in time series of pressure stations and flow stations, and the risk coefficient of each pipe network is determined by using a neural network combined with existing data. The water treatment plants don't even have to install new sensors if the budget is limited.</span></p>

Cre-Controlled CRISPR mutagenesis provides fast and easy conditional gene inactivation in zebrafish

Conditional gene inactivation is a powerful tool to determine gene function when constitutive mutations result in detrimental effects. The most commonly used technique to achieve conditional gene inactivation employs the Cre/loxP system and its ability to delete DNA sequences flanked by two loxP sites. However, targeting a gene with two loxP sites is time and labor consuming. Here, we show Cre-Controlled CRISPR (3C) mutagenesis to circumvent these issues. 3C relies on gRNA and Cre-dependent Cas9-GFP expression from the same transgene. Exogenous or transgenic supply of Cre results in Cas9-GFP expression and subsequent mutagenesis of the gene of interest. The recombined cells become fluorescently visible enabling their isolation and subjection to various omics techniques. Hence, 3C mutagenesis provides a valuable alternative to the production of loxP-flanked alleles. It might even enable the conditional inactivation of multiple genes simultaneously and should be applicable to other model organisms amenable to single integration transgenesis.