WisecondorFF: Improved Fetal Aneuploidy Detection from Shallow WGS through Fragment Length Analysis

In prenatal diagnostics, NIPT screening utilizing read coverage-based profiles obtained from shallow WGS data is routinely used to detect fetal CNVs. From this same data, fragment size distributions of fetal and maternal DNA fragments can be derived, which are known to be different, and often used to infer fetal fractions. We argue that the fragment size has the potential to aid in the detection of CNVs. By integrating, in parallel, fragment size and read coverage in a within-sample normalization approach, it is possible to construct a reference set encompassing both data types. This reference then allows the detection of CNVs within queried samples, utilizing both data sources. We present a new methodology, WisecondorFF, which improves sensitivity, while maintaining specificity, relative to existing approaches. WisecondorFF increases robustness of detected CNVs, and can reliably detect even at lower fetal fractions (<2%).

Technical Efficiency of Universities in Telangana State through Data Envelopment Analysis (DEA) Approach

In this present research paper we analyze the universities data by CCR, BCC models through Data Envelopment Analysis (DEA) approach for the State of Telangana. To know the Performance of student’s university wise in state of Telangana. University wise Performances is presented along with technical efficiency, Pure Technical Efficiency, Scale Efficiency, CRS, VRS, Reference set and Peers. Measuring the Technical Efficiency (TE) and Pure Technical Efficiency (PTE) of the universities by CCR, BCC Model through DEA approach.

Atlas of RNA editing events affecting protein expression in aged and Alzheimer’s disease human brain tissue

RNA editing is a feature of RNA maturation resulting in the formation of transcripts whose sequence differs from the genome template. Brain RNA editing may be altered in Alzheimer’s disease (AD). Here, we analyzed data from 1,865 brain samples covering 9 brain regions from 1,074 unrelated subjects on a transcriptome-wide scale to identify inter-regional differences in RNA editing. We expand the list of known brain editing events by identifying 58,761 previously unreported events. We note that only a small proportion of these editing events are found at the protein level in our proteome-wide validation effort. We also identified the occurrence of editing events associated with AD dementia, neuropathological measures and longitudinal cognitive decline in: SYT11, MCUR1, SOD2, ORAI2, HSDL2, PFKP, and GPRC5B. Thus, we present an extended reference set of brain RNA editing events, identify a subset that are found to be expressed at the protein level, and extend the narrative of transcriptomic perturbation in AD to RNA editing.

A novel strategy to uncover specific GO terms/phosphorylation pathways in phosphoproteomic data in Arabidopsis thaliana

Background Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to an experimental query from a list of phosphoproteins. Comparison of the frequency distribution of GO (Gene Ontology) terms in a given phosphoproteome set with that observed in the genome reference set (GenRS) is the most widely used tool to infer biological significance. Yet, this comparison assumes that GO term distribution between the phosphoproteome and the genome are identical. However, this hypothesis has not been tested due to the lack of a comprehensive phosphoproteome database. Results In this study, we test this hypothesis by constructing three phosphoproteome databases in Arabidopsis thaliana: one based in experimental data (ExpRS), another based in in silico phosphorylation protein prediction (PredRS) and a third that is the union of both (UnRS). Our results show that the three phosphoproteome reference sets show default enrichment of several GO terms compared to GenRS, indicating that GO term distribution in the phosphoproteomes does not match that of the genome. Moreover, these differences overshadow the identification of GO terms that are specifically enriched in a particular condition. To overcome this limitation, we present an additional comparison of the sample of interest with UnRS to uncover GO terms specifically enriched in a particular phosphoproteome experiment. Using this strategy, we found that mRNA splicing and cytoplasmic microtubule compounds are important processes specifically enriched in the phosphoproteome of dark-grown Arabidopsis seedlings. Conclusions This study provides a novel strategy to uncover GO specific terms in phosphoproteome data of Arabidopsis that could be applied to any other organism. We also highlight the importance of specific phosphorylation pathways that take place during dark-grown Arabidopsis development.

Annotation of Spatially Resolved Single-cell Data with STELLAR

Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.

Why-questions and focus in Italian

In this study we argue that the appropriateness of an answer to a why-question, potentially bearing on multiple contrast classes, is mainly influenced by the focalized argument, which identifies the relevant reference set. The focalization structure, however, interacts in a non-trivial way with the thematic structure and its accessibility, suggesting a general (independent) prominence of the direct object (DO) over the indirect one (IO). In correlation to that, we also observed that DO appears more resistant to extraction compared to IO, while it seems felicitous in a post-IO focalized low position (light NP-shifting). These contrasts are obtained by running five distinct experiments in Italian targeting various dislocation configurations: Four forced choice tasks manipulating leftward dislocation (i) clefting versus (ii) fronting versus (iii) clitic left dislocation and (iv) postverbal reordering (canonical DO IO order vs. IO DO) in ditransitive predicates. Then (v) an acceptability judgment study was administered to assess the difficulty in figuring out a licensing context coherent with the argument ordering, provided in the why-question. To minimize the interacting factors, all sentences included null subjects and a context was provided for each experimental item in the forced choice tasks, introducing the relevant contrast classes.

Model of Translation of International Legal Discourse: to the Problem of Applying the Transformation Model and the Model of the Reference Set of Features

The article focuses on analyzes of the translation of international legal discourse applying the transformational model of translation analysis and the model involving tertium comparacionis - the language-mediator as a standard for comparing the original and target texts. The aim of the article is to highlight the peculiarities of the application of the transformation model and the model of the reference set of features for translation analysis of international legal discourse. The article used a complex research methodology involving methods of translation analysis, componential analysis, structural transformational analysis, as well as the elements of comparative method and text-interpretive analysis. The study reached the following conclusions. First, the transformation of nuclear structures of the English original text into surface text structures of the Ukrainian translation, carried out in accordance with the three-stage model of transformational analysis, shows the need to reproduce in Ukrainian translation conditional relations implied in the form of explanatory or attributive meanings. It is found that none of the obtained transformational structures completely coincides with the official translation into Ukrainian. In addition, the Ukrainian translation omits the modal verb of obligation shall, which is present in the English original text, so that the fragment of the translation loses the connotations of imperative modality, which does not correspond to the style of the translated text. The application of the translation analysis model related to the identification of the reference set of features for the original and target texts showed the presence of both common and different features from the reference set. The difference in the text of the translation is revealed by such allomorphic features as (a) "object of action in the singular", in contrast to the original fragment with the object of action in the plural, (b) change from modality of possibility to epistemic modality with verb in the present tense, as well as (c) implicated condition relations in the target Ukrainian text, in contrast to the explicit expression of the condition of the text of the original.

A Novel Strategy to Uncover Specific Go Terms/phosphorylation Pathways in Phosphoproteomic Data in Arabidopsis Thaliana

Background. Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to an experimental query from a list of phosphoproteins. Comparison of the frequency distribution of GO (Gene Ontology) terms in a given phosphoproteome set with that observed in the genome reference set (GenRS) is the most widely used tool to infer biological significance. Yet, this comparison assumes that GO term distribution between the phosphoproteome and the genome are identical. However, this hypothesis has not been tested due to the lack of a comprehensive phosphoproteome database.Results. In this study, we test this hypothesis by constructing three phosphoproteome databases in Arabidopsis thaliana: one based in experimental data (ExpRS), another based in in silico phosphorylation protein prediction (PredRS) and a third that is the union of both (UnRS). Our results show that the three phosphoproteome reference sets show default enrichment of several GO terms compared to GenRS, indicating that GO term distribution in the phosphoproteomes does not match that of the genome. Moreover, these differences overshadow the identification of GO terms that are specifically enriched in a particular condition. To overcome this limitation, we present an additional comparison of the sample of interest with UnRS to uncover GO terms specifically enriched in a particular phosphoproteome experiment. Using this strategy, we found that mRNA splicing and cytoplasmic microtubule compounds are important processes specifically enriched in the phosphoproteome of dark-grown Arabidopsis seedlings. Conclusions. This study provides a novel strategy to uncover GO specific terms in phosphoproteome data of Arabidopsis that could be applied to any other organism. We also highlight the importance of specific phosphorylation pathways that take place during dark-grown Arabidopsis development.

Multi-reference Computational Method for De-novo Design, Optimization, and Repositioning of Pharmaceutical Compounds Illustrated by Identifying Multi-target SARS-CoV-2 Ligands

In this work a novel computational multi-reference poly-conformational algorithm is presented for design, optimization, and repositioning of pharmaceutical compounds. The algorithm searches for candidates by comparing similarities between conformers of the same compound and identifies target compounds whose conformers are simultaneously “close” to the conformers for each of the compounds in a reference set. The reference compounds can have very different MoAs, which directly and simultaneously shapes the properties of the target candidate compounds. The algorithm functionality has been validated in silico by scoring ChEMBL drugs against FDA-approved reference compounds which either had the highest predicted binding affinity to our chosen SARS-COV-2 targets or confirmed to be inhibiting such targets in-vivo. All our top scoring ChEMBL compounds also turned out to be either high-affinity ligands to the chosen targets (as confirmed separately in other studies) or showing significant efficacy in-vivo against those selected targets.In addition to method validation in silico search for new compounds within two virtual libraries from the Enamine database is presented. The library’s virtual compounds have been compared to the same set of reference drugs that we used for validation: Olaparib, Tadalafil, Ergotamine and Remdesivir. The large reference set of four potential SARS-CoV-2 compounds have been selected, since no drug has been identified to be 100% effective against the virus so far, possibly because each candidate drug was targeting only one particular MoA. The goal here was to introduce methodology for identifying potential candidate(s) that cover multiple MoA-s presented within a set of reference compounds.