Oversee Risk Management as Newer and More Complex Risk Emerge in Corporate Sector

This research paper revolves around the basic concepts of risk management, corporate governance, and the strategic decisions of the board members of the firm. These three concepts are connected and correlated with each other. The good governance can lead a firm to avoid risk which can damage the firm. By reducing risk, a firm can manage its profit maximization and can maintain a healthy corporate environment. In addition, this paper also discusses the framework of the corporate governance, the problems faced by the board committee in managing risk, and the ways of reducing risk. Corporate governance can avoid the risk by practicing the suggested framework in their organization and can understand their responsibilities. Keywords: Risk management, Complex Risk, Corporate Sector. JEL Classification: G32, M10, O1

R-BERT-CNN : Drug-target interactions extraction from biomedical literature

In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging. To solve this issue, we provide a solution for Track 1, which aims to extract 10 types of interactions between drug and protein entities. We applied an Ensemble Classifier model that combines BioMed-RoBERTa, a state of art language model, with Convolutional Neural Networks (CNN) to extract these relations. Despite the class imbalances in the BioCreative VII DrugProt test corpus, our model achieves a good performance compared to the average of other submissions in the challenge, with the micro F1 score of 55.67% (and 63% on BioCreative VI ChemProt test corpus). The results show the potential of deep learning in extracting various types of DTIs.Peer reviewe

Artificial intelligence, machine learning, and drug repurposing in cancer

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.Peer reviewe

Cooperative Networks

Non

Using BERT to identify drug-target interactions from whole PubMed

Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we applied Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format.Peer reviewe

Learning Competencies for English Medium Instruction and Student Learning at Primary Level

Using English as medium of instruction at the primary level is a rapidly growing global phenomenon in the countries where English is not the first language of the learners because of the onslaught of English as global lingua franca. Public opinion is divided and controversial but not against the introduction of EMI at primary level. The researches specifically focusing the impact of EMI on the learning of the speakers of other languages at primary level are rare and most of them are based on assumptions. The same is true for the studies related to mother tongue instruction. But no research is available about the competencies of the young learners for EMI at primary or pre-primary level. The present study focused on the instincts and capabilities of children of young learners which they bring to the classroom to be exploited for learning through EMI at the primary level. Using a qualitative research design for the present study the EMI teachers working in the DJ primary English medium schools of Giligit-Baltistan province were selected for data collection through the focus group discussions and semi-structured interviews. Participants were selected using purposive sampling technique. Thematic analysis was used to analyse qualitative data. Major findings of this study highlighted a number of learning competencies children bring to an EMI classroom for effective learning at the primary level

Cartography of rhodopsin-like G protein-coupled receptors across vertebrate genomes

We conduct a cartography of rhodopsin-like non-olfactory G protein-coupled receptors in the Ensembl database. The most recent genomic data (releases 90-92, 90 vertebrate genomes) are analyzed through the online interface and receptors mapped on phylogenetic guide trees that were constructed based on a set of similar to 14.000 amino acid sequences. This snapshot of genomic data suggest vertebrate genomes to harbour 142 clades of GPCRs without human orthologues. Among those, 69 have not to our knowledge been mentioned or studied previously in the literature, of which 28 are distant from existing receptors and likely new orphans. These newly identified receptors are candidates for more focused evolutionary studies such as chromosomal mapping as well for in-depth pharmacological characterization. Interestingly, we also show that 37 of the 72 human orphan (or recently deorphanized) receptors included in this study cluster into nineteen closely related groups, which implies that there are less ligands to be identified than previously anticipated. Altogether, this work has significant implications when discussing nomenclature issues for GPCRs.Peer reviewe

Environmentally Friendly Utilization of Wheat Straw Ash in Cement-Based Composites

The open burning of biomass residue constitutes a major portion of biomass burning and leads to air pollution, smog, and health hazards. Various alternatives have been suggested for open burning of crop residue; however, each of them has few inherent drawbacks. This research suggests an alternative method to dispose wheat straw, i.e., to calcine it in a controlled environment and use the resulting ash as a replacement of cement by some percentage in cement-based composites. When wheat straw, an agricultural product, is burned, it is very rich in SiO2, which has a pozzolanic character. However, the pozzolanic character is sensitive to calcination temperature and grinding conditions. According to the authors’ best knowledge, until now, no systematic study has been devised to assess the most favorable conditions of burning and grinding for pozzolanic activity of wheat straw ash (WSA). Hence, a systematic experimental program was designed. In Phase I, calcination of WS was carried out at 500 ◦C, 600 ◦C, 700 ◦C, and 800 ◦C for 2 h. The resulting ashes were tested for color change, weight loss, XRD, XRF, Chapelle activity, Fratini, and pozzolanic activity index (PAI) tests. From test results, it was found that beyond 600 ◦C, the amorphous silica transformed into crystalline silica. The WSA calcined at 600 ◦C was found to satisfy Chapelle and Fratini tests requirements, as well as the PAI requirement of ASTM at 28 days. Therefore, WSA produced at 600 ◦C (WSA600) showed the best pozzolanic performance. In Phase II, WSA600 was ground for various intervals (15–240 min). These ground ashes were tested for SEM, Blaine fineness, Chapelle activity, Fratini, and PAI tests. From test results, it was observed that after 120 min of grinding, there was an increase of 48% in Blaine surface area, with a consequence that WSA-replaced cement cubes achieved a compressive strength almost similar to that of the control mix. Conclusively, wheat straw calcined at 600 ◦C and ground for 120 min was found to be the most effective way to use pozzolanic material in cement-based composites. The addition of WSA in cement-based composites would achieve manifold objectives, i.e., aiding in the production of environmentally friendly concrete, the use of wheat straw as fuel for electricity production, and adding economic value to wheat straw

Computational modeling and ultraviolet absorptions calculation of 5,6,7-trihydroxy-4'-methoxyflavone using DFT & TD-DFT methods

This work is a continuation of a previous investigation about the electronic structure of a flavonoid extracted from marine algae. This paper presents the results of molecular modeling and ultraviolet transitions by employing density functional DFT and TD-DFT methods. Geometry of the molecule were optimized using B3LYP functional at 6-311G (d,p) level of theory and electronic transitions were simulated using TD-DFT methods in gas phase. The major electronic transition appearing at 353.51nm corresponds to 75% contribution in HOMO to LUMO transition and can be attributed to. π-π* transition. There were 78 filled molecular orbitals were calculated by TD-DFT method and the energy gap between HOMO and LUMO orbitals were found to be 4.02 eV