Introduction: The rapid development of artificial intelligence (AI) has sparked a desire to analyse its potential applications in medicine. The aim of this article is to present the effectiveness of the ChatGPT advanced language model in the context of the pass rate of the polish National Specialty Examination (PES) in nuclear medicine. It also aims to identify its strengths and limitations through an in-depth analysis of the issues raised in the exam questions.Methods: The PES exam provided by the Centre for Medical Examinations in Łódź, consisting of 120 questions, was used for the study. The questions were asked using the openai.com platform, through which free access to the GPT-3.5 model is available. All questions were classified according to Bloom's Taxonomy to determine their complexity and difficulty, and according to two authors' subcategories. To assess the model's confidence in the validity of the answers, each questions was asked five times in independent sessions.Results: ChatGPT achieved 56%, which means it did not pass the exam. The pass rate is 60%. Of the 117 questions asked, 66 were answered correctly. In the percentage of each type and subtype of questions answered correctly, there were no statistically significant differences.Conclusion: Further testing is needed using the questions provided by Centre for Medical Examinations from the nuclear medicine specialty exam to evaluate the utility of the ChatGPT model. This opens the door for further research on upcoming improved versions of the ChatGPT.

A decade of research into MOOCs (massive online open courses) for language learning (LMOOCs) shows that they seem to have consolidated their position as a subfield of computer-assisted language learning (CALL). Since the appearance of LMOOCs in 2013, 3 key systematic reviews have been carried out,these confirm that research into student profiles is a recurring trend, with the focus on avoiding dropout rates by creating personalized learning pathways. One of the challenges for teachers and LMOOC developers is that they are not cognizant of their students or their study habits. If we could learn how students organize their study in LMOOCs, a Taxonomy could be established according to their profiles. This would enable teachers and LMOOC developers to improve their course design and so create personalized learning pathways, making the courses better suited to students’ specific learning preferences. In this study, we use techniques of learning analytics (LA) to explore the temporal patterns of LMOOC participants in order to understand the way they manage and invest their time during their online courses. As a result of this study, we propose a new Taxonomy of LMOOC participant profiles based on temporal patterns—one which would provide teachers with a tool to support them when personalizing the design and development of LMOOCs and which would, therefore, help them adapt their courses to the specific learning preferences of each profile.

The sea cucumbers of the genus Holothuria are important species in Asia. In present study in the Persian Gulf aimed to identify Holothuria species and investigate their relationships based on morphological and 16S rRNA molecular data. 30 specimens were collected from northern Persian Gulf coasts of Dayer (Bushehr Province) and Lengeh (Hormozgan Province). Based on ossicles morphology, three species were identified. DNA was isolated using CTAB method. Thereafter, 16S ribosomal RNA gene amplification was performed using universal primer and consequently PCR product was sequenced. Sequencing result was analyzed and identified in NCBI database using BLAST. 17 sequences of mitochondrial DNA of 16S rRNA gene (393bp length) were obtained after modification and alignment. The phylogenic tree of mentioned species showed a monophyletic group. The three identified species include Holothuria (Thymiosycia) arenicola, Holothuria leucospilota and Holothuria parva. Holothuria arenicola was found in high support in the sister clade of H. arenicola from China. Despite 7% genetic distance, no morphological differences were observed between H. arenicola with similar species.

Herein, a detailed molecular phylogeny analysis was developed to determine the phylogenetic position of a new freshwater histozoic myxosporean cnidarian, Henneguya markiana sp. nov. from the world’s largest tropical wetland area, Pantanal, Brazil. The new species is described using an integrative Taxonomy approach including morphology, biological traits and molecular data. Phylogenetic analysis inferred by Maximum Likehood method showed the new Henneguya species in a well-supported clade of myxosporean gill parasites of South American characids fishes. In this same clade, the new Henneguya described appeared in a sub-clade clustering with H. lacustris and H. chydadea. Nevertheless, the sequences of the new species and H. lacustris and H. chydadea have a large genetic divergence of 10.4% (148 nucleotides-nt) and 10.5% (147 nt) respectively. To the best of our knowledge, this is the first report of a cnidarian myxosporean species parasitizing a fish from Stevardiinae from South America. In the light of the differences observed from the integrative Taxonomy, we are confident that this isolate is a new species of Henneguya, increasing the knowledge of diversity of this enigmatic group of cnidarians.

The textbook analysis is a vigorous research approach for evaluating the conformity between the content and the purpose of education. Accordingly, the relatively newly-published Iranian ELT textbooks for senior high school, known as the “Vision series” were analyzed for their prominent levels of learning objectives according to Bloom’s Revised Taxonomy. Activity sections of textbooks were codified according to the Taxonomy’s coding scheme, and the inter/intra-rater reliability which were measured and approved. Results revealed that the lower-order categories of cognitive domain are more frequently represented in Vision series of 1 and 2, and chi-square statistics indicate that Vision 3 is significantly different from the other two. The inclusion of higher-order categories in Vision 3 creates hope for increasing students’ proficiency and activating students’ need to develop higher-order thinking skills which are prerequisites to critical thinking and autonomous learning. Findings also maintain that the cognitive domain and metacognitive knowledge domain were the least perceived in the three textbooks which call for inclusion of more reflective activities and supplementing higher-order cognitive activities or complementary tasks in Visions 1 and 2.

The surrounding context information of business processes is unpredictable and dynamically changing over time. Therefore, they should dynamically adapt themselves to different changes in context information such as business rules or computational changes in available resources. For example, we may add a particular delivery service for golden customers, or provide new payment services in case of unavailability of service providers, or change a service invocation based on available bandwidth. Unlike other methods which provide a context Meta model or a shallow context Taxonomy in a specific limited scope of the business domain, we focused on ontology engineering methods not only to propose a general multipurpose context ontology but also to present our proposal as an underlying ontology for other researchers to extend and customize it for their applications. In this paper, the business process adaptation knowledge is modelled in the form of concepts, relations, and axioms which comprises time, resources, performers, locations, environment, and rules to model and record whole context information of adaptation mechanism. We characterized our work in comparison with related studies to show its completeness and demonstrated it by using an online learning management system and virtual class case studies.

Background: The investigation of microbial community composition has been significantly influenced by technological innovations in the next-generation sequencing-based techniques. These techniques depend on 16S rRNA, in particular, have been frequently used to analyse the whole collection of bacteria present in a specific environment. As a response, a plethora of specific bioinformatic pipelines has been constructed for handling this data. Methods and Analysis: Meta Genome Rapid Annotation Using Subsystem Technology (MG-RAST) and Quantitative Insights into Microbial Ecology (QIIME) are two publicly accessible metagenomic analysis tools utilized in several investigations. Here, we present a comparative study of the human vs. chimpanzee dataset using QIIME and MG-RAST to assess their accuracy in Taxonomy and diversity analysis. Discussion: Here, the taxonomic assignment was more accurate using QIIME, which assigned a considerably greater number of reads at the family level. As a consequence, QIIME produced a more accurate sequence file, which enhanced the diversity analysis outcome. Finally, while installing QIIME requires informatics expertise, it provides a wide range of metrics that are relevant for downstream applications and, more importantly, it is not dependent on server delays.