Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It.

Honeybees (Apis mellifera L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.

Genes and elements involved in the regulation of the nervous system and growth affect the development of spinal deformity in Cyprinus carpio.

Spinal deformity is a serious economic and animal welfare problem in intensive fish farming systems, which will be a significant unsolved problem for the fish sector. The aim of this study was to determine the relative expression of genes (Akt1 substrate 1, Calreticulin, Collagen type I alpha 2 chain, Corticotropin-releasing hormone, Chromodomain-Helicase DNA-binding, Growth hormone, Insulin like growth factor 1, Myostatin, Sine oculis-related homeobox 3, Toll-like receptor 2) in different tissues associated with spinal deformity and to determine the macroelement (calcium, magnesium, phosphorus, potassium, sodium, sulfur) and microelement (barium, copper, iron, manganese, strontium, zinc) content of spine in healthy and deformed common carps (Cyprinus carpio) in Hungary. The mRNA levels of the genes were measured in 7 different tissues (abdominal fat, blood, brain, dorsal muscle, genitals, heart, liver) by qRT-PCR. Correlations between gene expression and element content were analyzed by using linear regression and Spearman rank correlation. In a total of 15 cases, we found a statistically significant connection between gene expression in a tissue and the macro- or microelement content of the spine. In these contexts, the genes Akt1 substrate 1 (3), Collagen type I alpha 2 chain (2), Corticotropin-releasing hormone (4), Insulin-like growth factor 1 (4), and Myostatin (2), the tissue's blood (3), brain (6), heart (5), and liver (1), the macroelements sodium (4), magnesium (4), phosphorus (1) and sulfur (2) as well as the microelement iron (4) were involved. We also found statistically significant mRNA level differences between healthy and deformed common carps in tissues that were not directly affected by the deformation. Based on our results, genes regulating the nervous system and growth, elements, and tissues are the most associated components in the phenomenon of spinal deformity. With our study, we wish to give direction to and momentum for the exploration of these complex processes.

Association study between relative expression levels of eight genes and growth rate in Hungarian common carp (Cyprinus carpio).

One of the most important issues in improving the competitiveness of the fish production sector is to improve the growth rate of fish. The genetic background to this trait is at present poorly understood. In this study, we compared the relative gene expression levels of the Akt1s1, FGF, GH, IGF1, MSTN, TLR2, TLR4 and TLR5 genes in blood in groups of common carps (Cyprinus carpio), which belonged to different growth types and phenotypes. Fish were divided into groups based on growth rate (normal group: n = 6; slow group: n = 6) and phenotype (scaled group: n = 6; mirror group: n = 6). In the first 18 weeks, we measured significant differences (p < 0.05) between groups in terms of body weight and body length. Over the next 18 weeks, the fish in the slow group showed more intense development. In the same period, the slow group was characterized by lower expression levels for most genes, whereas GH and IGF1 mRNA levels were higher compared to the normal group. We found that phenotype was not a determining factor in differences of relative expression levels of the genes studied.