In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

In the realm of artificial intelligence, knowledge graphs have become a fascinating area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1,500 generic medicines. It offers a groundbreaking approach to storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, as well as queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as diverse range of healthcare professionals as well as AI applications.

Lily–belong to the genus Lilium is one of the top cut flowers worldwide. Production and propagation of bulblets in vitro is an important approach for high-volume production, but not proved satisfactory. Hence, the aim of this study was to describe and compare the performances of morphological characteristics of the lily bulb in vivo produced by in vitro and conventional culture method and compare the production timelines in vitro vs conventional culture method. In results, it seems clear that in vitro re-culture of lily bulblet was able to be sustained and maintained its growth and so, ontogenic development after their performance in soil. Our results demonstrate that in the conventional pathway, the course of bulblet growth to bulb and ontogenic development took 3–4 growing seasons to reach the adult flowering phase. On the other hand, along with the re-culture in vitro, the course of bulb growth and ontogenic development took 1–2 growing seasons to reach the adult flowering phase because of the increasing initial bulb size and advancement of ontogenic development. Other than bulblet production through bulb scale explant, this study represents the first report on the method of in vitro bulb production of lily through re-culture by in vitro pathway with a comparison of the timelines and ontogenic development obtained from in vitro versus conventional pathway.

Transferring in vitro-cultured Eustoma seedlings to an ex vitro condition (acclimation) is a big challenge that may expose the seedlings to biotic and abiotic stresses, and affect the internal and external structure of the plants. In addition, in vitro-cultured seedlings of Eustoma are difficult to handle and phenotype and physiological traits such as survival and rosette rate may have altered in the acclimation stage. Therefore, the present study aims to examine the effects of blue, red, and white LED light on the growth and development ex vitro of in vitro-cultured seedlings of Eustoma. The results showed that blue LEDs resulted in greater plant height, internode length, and leaf number, increased upper and lower fresh biomass, and higher chlorophyll content compared with treatment by the other LED lights. Higher stomatal density on the abaxial leaf surface was also observed in the blue LED-treated plants, which also showed a higher survival rate and lower rosette rate. In contrast, the white LED-treated plants had the highest leaf width and internode diameter. Acclimation of the Eustoma plants ex vitro suggests that a combination of blue and white LEDs may be advantageous for better growth and development for large-scale production in a controlled environment.

The COVID-19 outbreak has focused attention on the use of social distancing as the primary defence against community infection, yet forcing social animals to maintain that physical distance has presented significant challenges for health authorities and law enforcement. Anecdotal media reports suggest widespread dissatisfaction with social distancing as a policy, yet there is little prior work aimed at measuring community acceptance of social distancing. In this paper, we propose a new approach to measuring attitudes towards social distancing by using social media and sentiment analysis. Over a 4 month period, we found 82.5% of tweets were in favour of social distancing. The results indicate a widespread acceptance of social distancing in the community. We conclude by examining options for estimating the optimal (minimal) social distance required at scale, and the implications for securing widespread community support and acceptance of public health messaging.

Staphylococcus aureus is a major food-borne pathogen. The ability of S. aureus to produce biofilm is a significant virulence factor triggering its persistence in hostile environments. In this study, we screened a total of 420 different food samples and human hand swabs to detect S. aureus and to determine their biofilm formation ability. Samples analyzed were meat, milk, egg, fish, fast foods, and hand swabs. S. aureus were detected by culturing, staining, biochemical, and PCR. Biofilm formation ability was determined by Congo Red Agar (CRA) plate and Crystal Violet Microtiter Plate (CVMP) tests. The icaA, icaB, icaC, icaD, and bap genes involved in the synthesis of bio-film-forming intracellular adhesion compounds were detected by PCR. About 23.81% (100/420; 95% CI: 14.17-29.98%) samples harbored S. aureus as revealed by detection of the nuc gene. CRA plate revealed 20% of S. aureus isolates as strong biofilm producers, while 69% and 11% as in-termediate and non-biofilm producers, respectively. By the CVMP staining method, 20%, 77%, and 3% of the isolates were found strong, intermediate, and non-biofilm producers. Furthermore, 21% of S. aureus isolates carried at least one biofilm-forming gene, where icaA, icaB, icaC, icaD, and bap genes were detected in 15%, 20%, 7%, 20%, and 10% of the S. aureus isolates, respectively. Bivariate analysis showed high significant correlations (p<0.001) between any of the two adhesion genes of S. aureus isolates. To the best of our knowledge, this is the first study in Bangladesh describing the detection of biofilm-forming S. aureus from foods and hand swabs with molecular-based evidence. Our findings suggest that food samples should be deemed a potential reservoir of biofilm-forming S. aureus that indicates a potential public health significance.