Rapid advances in nanopore technologies for sequencing single long DNA and RNA molecules have led to substantial improvements in accuracy, read length and throughput. These breakthroughs have required extensive development of experimental and bioinformatics methods to fully exploit nanopore long reads for investigations of genomes, transcriptomes, epigenomes and epitranscriptomes. Nanopore sequencing is being applied in genome assembly, full-length transcript detection and base modification detection and in more specialized areas, such as rapid clinical diagnoses and outbreak surveillance. Many opportunities remain for improving data quality and analytical approaches through the development of new nanopores, base-calling methods and experimental protocols tailored to particular applications. Au and colleagues outline the field of nanopore sequencing.

Nanopore sequencing technology, bioinformatics and applications

specifically on the difficulty of recognizing learning versus language difficulties, that is, how to identify a nonnative-speaking child's need for special education services. They propose a model that administrators can employ that minimizes bias. In a similar vein, the next chapter, by Jeffery Braden and Sandra Fradd, suggests ways that administrators can anticipate difficulties and intervene before such referrals are necessary. William Tikunoff, in the eighth chapter, focuses on instructional leadership. He discusses the characteristics of an effective principal and targets specific areas, such as effective time management. The final chapter, by Beatrice Ward, addresses the greatest resource of any educational institution: the teachers. She describes the clinical approach to teacher development and how it can be implemented. Overall, this book fills a need for basic, factual information about legal requirements, program types, and effective instructional and leadership strategies with respect to the LEP population. Furthermore, it provides guidance on the complex issue of special education for LEP students, particularly the referral process. An additional chapter exploring different models for assessment and program design for these students would have provided depth and balance. Although there is necessarily some overlap between the chapters, it is reinforcing, not repetitive. In addition to being extremely useful to administrators, this book would be of value to school personnel such as psychologists, special education consultants, LEP consultants, instructors-in short, for anyone committed to the design and delivery of effective instructional programs for LEP students.

The Computational Analysis of English—A Corpus‐Based Approach

Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale. Long-read sequencing at the population scale presents specific challenges but is becoming increasingly accessible. In this Review, Sedlazeck and colleagues discuss the major platforms and analytical tools, considerations in project design and challenges in scaling long-read sequencing to populations.

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Towards population-scale long-read sequencing.

Survey of big scholarly data with respect to the different phases of the big data lifecycle.Identifies the different big data tools and technologies that can be used for development of scholarly applications.Investigates research challenges and limitations specific to big scholarly data and its applications.Provides research directions and paves way towards the development of a generic and comprehensive big scholarly data platform. Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

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A survey on scholarly data

This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.

https://dl.acm.org/doi/pdf/10.1145/3345317

Academic Plagiarism Detection: A Systematic Literature Review

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.

/pdf/integrative-genomic-analyses-identify-susceptibility-genes-5dgtf37qrg.pdf

Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization.

Proposing a candidate retrieval model for cross-lingual plagiarism detectionThe method relies on using two levels of proximity informationProposing a topic-based text segmentation methodComparing the method with other cross-lingual plagiarism detection approachesShowing improvements using text segmentation and positional language models The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection.

Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information

Producing electronic rather than paper documents has considerable benefits such as easier organizing and data management. Therefore, existence of automatic writing assistance tools such as spell and grammar checker/correctors can increase the quality of electronic texts by removing noise and correcting the erroneous sentences. Different kinds of errors in a text can be categorized into spelling, grammatical and real-word errors. In this article, we present a language-independent approach based on a statistical machine translation framework to develop a proofreading tool, which detects grammatical errors as well as context-sensitive spelling mistakes real-word errors. A hybrid model for grammar checking is suggested by combining the mentioned approach with an existing rule-based grammar checker. Experimental results on both English and Persian languages indicate that the proposed statistical method and the rule-based grammar checker are complementary in detecting and correcting syntactic errors. The results of the hybrid grammar checker, applied to some English texts, show an improvement of about 24% with respect to the recall metric with almost similar value for precision. Experiments on real-world data set show that state-of-the-art results are achieved for grammar checking and context-sensitive spell checking for Persian language. Copyright © 2012 John Wiley & Sons, Ltd.

Grammatical and context-sensitive error correction using a statistical machine translation framework

The Web offers fast and easy access to a wide range of documents in various languages, and translation and editing tools provide the means to create derivative documents fairly easily. This leads to the need to develop effective tools for detecting cross-language plagiarism. Given a suspicious document, cross-language plagiarism detection comprises two main subtasks: retrieving documents that are candidate sources for that document and analyzing those candidates one by one to determine their similarity to the suspicious document. In this paper we focus on the second subtask and introduce a novel approach for assessing cross-language similarity between texts for detecting plagiarized cases. Our proposed approach has two main steps: a vector-based retrieval framework that focuses on high recall, followed by a more precise similarity analysis based on dynamic text alignment. Experiments show that our method outperforms the methods of the best results in PAN-2012 and PAN-2014 in terms of plagdet score. We also show that aligning n-gram units, instead of aligning complete sentences, improves the accuracy of detecting plagiarism.

https://dl.acm.org/doi/pdf/10.1145/2960811.2960817

Using a Dictionary and n-gram Alignment to Improve Fine-grained Cross-Language Plagiarism Detection

With improvements in industry and information technology, large volumes of electronic texts such as newspapers, emails, weblogs, books and thesis are produced daily. Producing electrical documents has considerable benefits such as easy organizing and data management. Therefore, existence of automatic systems such as spell and grammar checker/corrector can help in reducing costs and increasing the electronic texts and it will improve the quality of electronic texts. You can input your text and the computer program will point out to you the spelling errors. It may also help with your grammar. Grammatical errors are described as wrong relation between words like subject-verb disagreement or wrong sequence of words like using plural noun where a single noun is needed. Grammar checking phase starts after spell checking is finished. This paper briefly describes the concepts and definition of grammar checkers in general followed by developing the first Persian (Farsi) grammar checker leading to an overview of the error types of Persian language. The proposed system detects and corrects about 20 frequent Persian grammar errors and tested on a sample dataset, retrieved about 70% and 83% accuracy respect to precision and recall metrics.

Nava Ehsan

Papers

Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization.

Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information

Grammatical and context-sensitive error correction using a statistical machine translation framework

Using a Dictionary and n-gram Alignment to Improve Fine-grained Cross-Language Plagiarism Detection

Towards grammar checker development for Persian language