New trends in the linguistic study of gender, its conceptualization and representation in modern English

The article examines recent shifts in the English-based research on language and gender. It addresses the denial of gender binary structure, recognition of gender identity fluidity, which may transcend the established ideas about masculinity and femininity or constitute a complete rejection of gender. In the discursive practices of everyday life, these tendencies manifest themselves in the creation of new words and affixes, a change in the reference and combinability of the words that make up the core of the gender concept, as well as the emergence of new communicative norms and practices that legitimize individuals’ right to gender self-identification, non-heteronormative language and degenderization of communication. Research sample includes 250 text fragments from the English-language media, academic and specialized portals, lexicographic online resources, and everyday communication.

Macrocyclic Receptors for Identification and Selective Binding of Substrates of Different Nature

Molecular recognition of host/guest molecules represents the basis of many biological processes and phenomena. Enzymatic catalysis and inhibition, immunological response, reproduction of genetic information, biological regulatory functions, the effects of drugs, and ion transfer—all these processes include the stage of structure recognition during complexation. The goal of this review is to solicit and publish the latest advances in the design and sensing and binding abilities of porphyrin-based heterotopic receptors with well-defined geometries, the recognition ability of which is realized due to ionic, H-bridge, charge transfer, hydrophobic, and hydrophilic interactions. The dissection of the considered low-energy processes at the molecular scale expands our capabilities in the development of effective systems for controlled recognition, selective delivery, and prolonged release of substrates of different natures (including drugs) to their sites of functioning.

DECIMER 1.0: deep learning for chemical image recognition using transformers

The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50–100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.

Automatic Table Detection, Structure Recognition and Data Extraction from Document Images

In the recent advancement, the extensive usage of electronic devices to photograph and upload documents, the requirement for extracting the information present in the unstructured document images is becoming progressively intense. The major obstacle to the objective is, these images often contain information in tabular form and extracting the data from table images presents a series of challenges due to the various layouts and encodings of the tables. It includes the accurate detection of the table present in an image and eventually recognizing the internal structure of the table and extracting the information from it. Although some progress has been made in table detection, obtaining the table contents is still a challenge since this involves more fine-grained table structure (rows and columns) recognition. The digitization of critical information has to be carried out automatically since there are millions of documents. Based on the motivation that AI-based solutions are automating many processors, this work comprises three different stages: First, the table detection using Faster R-CNN algorithm. Second, table internal structure recognition process using morphology operation and refine operation and last the table data extraction using contours algorithm. The dataset used in this work was taken from the UNLV dataset.

Nanopore Technology for the Application of Protein Detection

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

Australian impact cratering record: Updates and recent discoveries

ABSTRACT There are currently 31 confirmed structures of impact origin in Australia. More than 49 additional structures have been proposed to have formed due to asteroid impact but await confirmation. Many discoveries have been made in Australia in the time since the last comprehensive review of the Australian impact cratering record was published in a peer-reviewed journal in 2005. These include further expanding the record of confirmed craters, and providing new insights into a variety of impact-related processes, such as shock deformation, phase transitions in accessory minerals, new impact age determinations, studies of oblique impacts, and more. This update is a review that focuses principally on summarizing discoveries made since 2005. Highlights since then include confirmation of five new Australian impact structures, identification of Earth’s oldest recognized impact structure, recognition of shock deformation in accessory minerals, discovery of the high-pressure phase reidite in Australia, determination of the links between impact craters and some ore deposits, and publication of the first generation of numerical hydrocode models for some Australian craters.