On the Electronic Database of the Armenian Word-Formation

The formal description of the languages has become more and more practical; however, it should be noted that the full formal description of the Armenian language has not yet been done. However, the fact that certain attempts have been made is undeniable. The Electronic database of the Armenian word-formation (https://formlang.am/) is the first stage of the complex project in the full formal description of the Armenian language. In the current article, we want to present the advantages of the electronic database of the Armenian word-formation. The electronic database containing the word-formation analysis of thousands of words can search for words and morphemes within them. It means that searching for any root or affix appears all the simple, compound, derived and derived-compound words made up of them. It enables us to reveal the regular structures and the variative forms, deflections, and irregularities with frequency data with their automatic analysis and the possibility of derivation. The database gives a great opportunity to study the Armenian word-formation on synchronic and diachronic points, to discover the basic patterns of the formation of new words, by the thousands of examples to find out the principles and ways of word-formation in the Armenian language, to have the full list of the distinguished simple, compound, derived and derived-compound Armenian words. The current database has not only practical great value to involve the Armenian language in the domain of the modern informational technologies as the communicative mean, but also significant theoretical value to present the accurate description of the vocabulary structure. Thus, it will give an excellent perspective for solving the problems of theoretical linguistics and the practical -applied tasks. It can be significant for the further development of Armenology.

Cat owners’ perceptions towards feline microchipping

Compulsory feline microchipping has become a legal requirement in 2021 for domestic cats (Felis catus) in the UK, following the introduction of compulsory microchipping for dogs (Canis lupus familiaris) in 2016. The concept of compulsory feline microchipping attracts a combination of perceptions from the public, both positive and negative. An online survey was designed to obtain cat owners’ perception toward feline microchipping, evaluating attitudes and knowledge, and offering an opportunity for participants to provide insights into their reasoning for, or against, microchipping. Findings suggested that demographics are key predictors for influencing cat owners’ perception toward feline microchipping. In particular, men are less likely to formulate opinions regarding feline welfare and microchipping, and concern for feline welfare and empathy toward cats increases with age across both men and women. When asked to provide more details about their decision to microchip, or not microchip, the survey responses revealed 66% agent-centred reasoning compared to 24% welfare, suggesting that regardless of a person’s decision, reasoning was respectively agent-centred. This suggests that potential human benefits may influence cat owners’ perception toward feline microchipping. 75% of participants support compulsory microchipping. Of those who would not support the legislation, feline welfare concerns, and a negative outlook surrounding the current database and scanning processes that support microchipping, was revealed. A focus on addressing negative perceptions toward feline microchipping could highlight approaches to change cat owners’ perceptions toward the technique positively. The results herein are useful for feline welfare organisations to promote understanding about feline microchipping.

A list of English–Turkish cognates and false-cognates

This article presents a list of English–Turkish cognates and false cognates which was compiled from a corpus of over 80,000 words in dictionary entries. The list contains 2411 English words that are either cognates or false cognates in Turkish. It was revealed that there are at least 1287 cognates, excluding all proper nouns of people, places, and things; and 1124 false cognates, 96 of which share at least one sense of meaning in each language, and thus are partial false cognates. The total number of English–Turkish cognates and false cognates suggests that cognate status between the two languages is around 3%. For cognates, the rate is 1.6%, and for false cognates the rate is 1.2%. The current database of English–Turkish cognates and false cognates can be used to prepare reading texts that contain words from the list presented here, and to investigate how they affect reading comprehension, guessing from context, and language learning or processing of a language issues. It can be also used as a resource for researchers investigating the bilinguals of English and Turkish, and learners who study Turkish and/or English as a second or foreign language. The list provides a useful basis for further research into the lexical, linguistic, and psychological issues.

Peekbank: Exploring children's word recognition through an open, large-scale repository for developmental eye-tracking data

The ability to rapidly recognize words and link them to referents in context is central to children's early language development. This ability, often called word recognition in the developmental literature, is typically studied in the looking-while-listening paradigm, which measures infants' fixation on a target object (vs. a distractor) after hearing a target label. We present a large-scale, open database of infant and toddler eye-tracking data from looking-while-listening tasks. The goal of this effort is to address theoretical and methodological challenges in measuring vocabulary development. We present two analyses of the current database (N=1,320): (1) capturing age-related changes in infants' word recognition while generalizing across item-level variability and (2) assessing how a central methodological decision -- selecting the time window of analysis -- impacts the reliability of measurement. Future efforts will expand the scope of the current database to advance our understanding of participant-level and item-level variation in children's vocabulary development.

Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell™

Background Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets. Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections. Main body In this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells. We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in < 60 s. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic. Conclusion In this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.

COPLA, a taxonomic classifier of plasmids

The Plasmid Taxonomic Unit (PTU) concept is an initial step for a natural classification of plasmids. Here we present COPLA, a software for plasmid assignation to existing PTUs. To assess its performance, we used a sample of 1,000 plasmids missing from its current database. Overall, 41% of samples could be assigned an existing PTU (63% within the most abundant order, Enterobacterales), while 4% of samples could help to define new PTUs once COPLA database was updated.

Genenames.org: the HGNC and VGNC resources in 2021

The HUGO Gene Nomenclature Committee (HGNC) based at EMBL’s European Bioinformatics Institute (EMBL-EBI) assigns unique symbols and names to human genes. There are over 42,000 approved gene symbols in our current database of which over 19 000 are for protein-coding genes. While we still update placeholder and problematic symbols, we are working towards stabilizing symbols where possible; over 2000 symbols for disease associated genes are now marked as stable in our symbol reports. All of our data is available at the HGNC website https://www.genenames.org. The Vertebrate Gene Nomenclature Committee (VGNC) was established to assign standardized nomenclature in line with human for vertebrate species lacking their own nomenclature committee. In addition to the previous VGNC core species of chimpanzee, cow, horse and dog, we now name genes in cat, macaque and pig. Gene groups have been added to VGNC and currently include two complex families: olfactory receptors (ORs) and cytochrome P450s (CYPs). In collaboration with specialists we have also named CYPs in species beyond our core set. All VGNC data is available at https://vertebrate.genenames.org/. This article provides an overview of our online data and resources, focusing on updates over the last two years.

Coordination Polymer Frameworks for Next Generation Optoelectronic Devices

Metal–organic frameworks (MOFs), which belong to a sub-class of coordination polymers, have been significantly studied in the fields of gas storage and separation over the last two decades. There are 80,000 synthetically known MOFs in the current database with known crystal structures and some physical properties. However, recently, numerous functional MOFs have been exploited to use in the optoelectronic field owing to some unique properties of MOFs with enhanced luminescence, electrical, and chemical stability. This book chapter provides a comprehensive summary of MOFs chemistry, isoreticular synthesis, and properties of isoreticular MOFs, synthesis advancements to tailor optical and electrical properties. The chapter mainly discusses the research advancement made towards investigating optoelectronic properties of IRMOFs. We also discuss the future prospective of MOFs for electronic devices with a proposed roadmap suggested by us. We believe that the MOFs-device roadmap should be one meaningful way to reach MOFs milestones for optoelectronic devices, particularly providing the potential roadmap to MOF-based field-effect transistors, photovoltaics, thermoelectric devices, and solid-state electrolytes and lithium ion battery components. It may enable MOFs to be performed in their best, as well as allowing the necessary integration with other materials to fabricate fully functional devices in the next few decades.

Rapid Single Cell Evaluation of Human Disease and Disorder Targets Using REVEAL: SingleCell™

Single-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypothesis by querying extensive single-cell datasets.Large single-cell datasets like human cell atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics and vaccine targets; as the world struggles to cope with the on-going spread of COVID-19 infections.In this manuscript we describe, REVEAL: SingleCell which enables storage, retrieval and rapid query of single-cell datasets inclusive of millions of cells. The analytical database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypothesis. One such example includes identifying the most prevalent cell type annotation tag on returned cells.We used REVEAL: SingleCell to evaluate expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in <60 seconds. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic.