Snydersavage0615

Studying individual mammalian oocytes has been extremely valuable for the understanding of the molecular composition of oocytes including RNA storage. Here, a detailed protocol for isolation of oocytes, extraction of total RNA from single oocytes followed by full-length cDNA amplification, and library preparation is presented. The procedure permits the production of cost-effective and high-quality sequencing libraries. This protocol can be adapted for transcriptome analysis of oocytes from other species and be used to generate high-quality data from single embryos. For complete details on the use and execution of this protocol, please refer to Biase and Kimble (2018).NLR family CARD domain containing protein 4 (NLRC4) inflammasome activation and the associated pyroptosis are critical for protection against infection by bacterial pathogens. This protocol presents a detailed procedure to activate and measure NLRC4 inflammasome activation and pyroptosis upon Salmonella Typhimurium infection. The techniques can be adapted to monitoring the activation of other types of inflammasomes and pathogenic stimuli. For comprehensive details on the use and execution of this protocol, please refer to Dong et al. (2021).Recycling of waste CO2 to bulk chemicals has a tremendous potential for the decarbonization of the chemical industry. Quantitative analysis of the prospects of this technology is hindered by the lack of flexible techno-economic assessment (TEA) models that enable evaluation of the processing costs under different deployment scenarios. In this protocol, we explain how to convert literature data into metrics useful for evaluation of the emerging electrolysis technologies, derive TEA models, and illustrate their use with a CO2-to-ethylene example. For complete details on the use and execution of this protocol, please refer to Barecka et al. (2021a).The COVID-19 pandemic creates a significant impact on everyone's life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (https//www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.In almost all contemporary power systems, the battery is an elementary component, and it is routinely used in a variety of critical applications such as drones, avionics, and cell phones. Due to their superior characteristics compared to the concurrent technologies, Li-ion batteries are widely utilized. iJMJD6 mw Since batteries are costly, their usage is closely monitored by battery management systems (BMSs). It ensures that batteries survive and serve longer. Modern BMSs' are complex and sophisticated and can deal with hundreds of cells in a battery pack. It results in an increased processing resources requirement and can cause an overhead power consumption. The aim of this work is to improve current BMSs by redesigning their associative processing chain. It focuses on improving data collection, processing and prediction processes for Li-ion battery cell capacities. To prevent the processing of a large amount of unnecessary data, the classical sensing approach that is fix-rate is avoided and replaced by event-driven sensing (EDS) mechanism to digitize battery cell parameters such as voltages, currents, and temperatures in a way that allows for real-time data compressing. A new approach is proposed for event-driven feature extraction. The robust machine-learning algorithms are employed for processing the extracted features and to predict the capacity of considered battery cell. Results show a considerable compression gain with a correlation coefficient of 0.999 and the relative absolute error (RAE) and root relative squared error (RRSE) of 1.88% and 2.08%, respectively.The novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen-Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78..6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.The diversity forest algorithm is an alternative candidate node split sampling scheme that makes innovative complex split procedures in random forests possible. While conventional univariable, binary splitting suffices for obtaining strong predictive performance, new complex split procedures can help tackling practically important issues. For example, interactions between features can be exploited effectively by bivariable splitting. With diversity forests, each split is selected from a candidate split set that is sampled in the following way for l = 1 , ⋯ , nsplits (1) sample one split problem; (2) sample a single or few splits from the split problem sampled in (1) and add this or these splits to the candidate split set. The split problems are specifically structured collections of splits that depend on the respective split procedure considered. This sampling scheme makes innovative complex split procedures computationally tangible while avoiding overfitting. Important general properties of the diversity forest algorithm are evaluated empirically using univariable, binary splitting. Based on 220 data sets with binary outcomes, diversity forests are compared with conventional random forests and random forests using extremely randomized trees. It is seen that the split sampling scheme of diversity forests does not impair the predictive performance of random forests and that the performance is quite robust with regard to the specified nsplits value. The recently developed interaction forests are the first diversity forest method that uses a complex split procedure. Interaction forests allow modeling and detecting interactions between features effectively. Further potential complex split procedures are discussed as an outlook.

The online version contains supplementary material available at 10.1007/s42979-021-00920-1.

The online version contains supplementary material available at 10.1007/s42979-021-00920-1.Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography's influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.In this paper I argue in favour of the adoption of an interdisciplinary approach based on computational methods for the development of language policies. As a consequence of large-scale phenomena such as globalization, economic and political integration and the progress in information and communication technologies, social systems have become increasingly interconnected. Language-related systems are no exception. Besides, language matters are never just language matters. Their causes and consequences are to be found in many seemingly unrelated fields. Therefore, we can no longer overlook the numerous variables involved in the unfolding of linguistic and sociolinguistic phenomena if we wish to develop effective language policy measures. A genuinely interdisciplinary approach is key to address language matters (as well as many other public policy matters). In this regard, the tools of complexity theory, such as computational methods based on computer simulations, have proved useful in other fields of public policy.The growing maturity of the "science of happiness" raises the prospect of enabling government policy to be more accountable to the measurable subjective experience of the population. In its ideal form, the application of this science promises to inform decision makers about the likely distribution of life satisfaction resulting from any prospective policy, allowing for the selection of more optimal policy. Such "budgeting for wellbeing" invites three natural objections, beyond normative quibbles with the subjective objective (1) non-incremental changes are unlikely in large bureaucracies, so a new accounting system for devising and costing government policies and budgets is too radical, (2) governments do not have an authoritative set of credible cost/benefit coefficients to use in analysis, and (3) long-run objectives, risks, and environmental considerations cannot be feasibly captured in quantitative projections of human subjective wellbeing. Three institutions are needed to address these challenges. I describe (a) an evolving collection of largely objective indicators for monitoring progress, with life satisfaction providing quantitative structure and overarching visibility to the system, (b) a publicly curated, evidence-based Database of Happiness Coefficients, and (c) independent public agencies that decide on a growing list of material constraints on the economy.