Identification of potential new biomarkers of oxygenated polycyclic aromatic hydrocarbon (OPAH) exposures in zebrafish.

Oxygenated polycyclic aromatic hydrocarbons (OPAHs) are a class of environmentally prevalent compounds with little known information on their effect in vivo. In a recent study conducted using zebrafish as a model, 38 OPAHs were screened for toxicity. This investigation focuses on two of these OPAHs, 7,12-B[a]AQ and BEZO. Both 7,12-B[a]AQ and BEZO show AHR2-dependent toxicity, however only 7,12-B[a]AQ consistently and significantly induces expression of cyp1a protein. Cyp1a is a diverse xenobiotic metabolizing protein that is used as a biomarker for many AHR-dependent toxicity studies in vertebrates. As 7,12-B[a]AQ and BEZO differentially express cyp1a, this study focused on four genes, ctsl.1, wfikkn1, ponzr3, and ponzr4, identified by RNA-sequencing as potential alternative biomarkers for AHR-dependent OPAH toxicity. A time point analysis of gene expression of ctsl.1 and wfikkn1 concluded that the stage of development is important in selecting alternative biomarkers. Preliminary localization of all four gene mRNA transcripts using in situ hybridization suggests that expression is sensitive to both stage of development and specific OPAH exposure. Investigating the candidacy of potential alternative biomarkers for AHR-dependent OPAH toxicity would help determine the pathway of toxicity of OPAHs in any environmental sample

Impacts of the Tropical Pacific/Indian Oceans on the Seasonal Cycle of the West African Monsoon

The current consensus is that drought has developed in the Sahel during the second half of the twentieth century as a result of remote effects of oceanic anomalies amplified by local land–atmosphere interactions. This paper focuses on the impacts of oceanic anomalies upon West African climate and specifically aims to identify those from SST anomalies in the Pacific/Indian Oceans during spring and summer seasons, when they were significant. Idealized sensitivity experiments are performed with four atmospheric general circulation models (AGCMs). The prescribed SST patterns used in the AGCMs are based on the leading mode of covariability between SST anomalies over the Pacific/Indian Oceans and summer rainfall over West Africa. The results show that such oceanic anomalies in the Pacific/Indian Ocean lead to a northward shift of an anomalous dry belt from the Gulf of Guinea to the Sahel as the season advances. In the Sahel, the magnitude of rainfall anomalies is comparable to that obtained by other authors using SST anomalies confined to the proximity of the Atlantic Ocean. The mechanism connecting the Pacific/Indian SST anomalies with West African rainfall has a strong seasonal cycle. In spring (May and June), anomalous subsidence develops over both the Maritime Continent and the equatorial Atlantic in response to the enhanced equatorial heating. Precipitation increases over continental West Africa in association with stronger zonal convergence of moisture. In addition, precipitation decreases over the Gulf of Guinea. During the monsoon peak (July and August), the SST anomalies move westward over the equatorial Pacific and the two regions where subsidence occurred earlier in the seasons merge over West Africa. The monsoon weakens and rainfall decreases over the Sahel, especially in August.Peer reviewe

The ChatGPT artificial intelligence chatbot: How well does it answer accounting assessment questions?

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research