BeeMind AI: Development of an AI-Based System to Assess Honeybee Health, Behavior, and Nutrient Effects on Learning and Memory
Due to their pollination services, honeybees are one of the most ecologically vital animals, being singlehandedly responsible for nearly 80% of global agricultural pollination [1]. However, in recent years, they have experienced large declines in populations, and as a survey reported roughly 50% of beekeepers in the US lost their honeybee colonies [2]. These losses are experienced globally due to a combination of many factors, including but not limited to habitat loss, pesticides, climate change, and other invasive species [3, 4]. One of the biggest factors attributed to the decline of honeybee colonies is the usage of pesticides, specifically neonicotinoids [3-6]. Neonicotinoid compounds have been used globally since their introduction in the early 1990s [4]. Studies have shown that neonicotinoids can have both sublethal and lethal effects on honeybees, depending on the dosages that they are exposed to, as neonicotinoids bind to nervous system receptors of honeybees [7]. These effects can range from behavior changes to altered motor functions [7-9]. Among the reported effects, one of the more significant ones is the effect of neonicotinoids on honeybee learning and memory [10, 11]. Additionally, there is a lack of availability for methods of monitoring of honeybee hives, essentially meaning that the only methods to track honeybee health are through obtrusive physical methods of inspection. This paper aims to develop a novel AI-based honeybee health assessment system, able to monitor beehives using the following functions: continuous temperature and humidity monitoring both inside and outside the hive, as well as video and audio recording to assess honeybee health as well as population. In addition, this system can be used for honeybee-related studies such as nutrition effects and evaluation on health, learning, and memory. To do this, four types of nutrition have been studied and their effects have been analyzed by a deep learning approach.
一價銠金屬催化肉桂胺衍生物進行不對稱氫芳基化反應
Rhodium(I)-Catalyzed Asymmetric Hydroarylation of Cinnamylamine Derivatives
一價銠金屬催化反應已經被廣泛應用於有機化學合成領域中。而本研究以具保護基之肉桂胺衍生物1與四芳基硼鈉試劑2a作為起始物進行銠金屬不對稱氫芳基化催化反應,得到具有保護基的掌性2,3-雙芳基丙胺衍生物3,並探討此反應的掌性雙烯配基對於反應的影響。本研究已完成使用Ts(對甲苯磺醯基)保護基之肉桂胺衍生物1a作為起始物進行反應,並改變與銠金屬錯合的配基,發現當配基使用2,5號位為芳基取代之配基L(掌性雙環[2,2,1]雙烯配基)時,反應有較好的位置選擇性,其中最佳的是芳基取代為苯基之配基L1,其位置選擇性比例為1:0:0.09。目前將進行改變起始物1之氮上的保護基,以L1作為配基進行反應,並與1a比較,優化反應性及產率。
Revolutionizing Metabolic Health: The Therapeutic Potential of Next-Generation Probiotic Akkermansia Strains (Z62, IR119) for Metabolic Syndromes
The human gut microbiome is integral to digestion, overall health, and metabolic disorder imbalances. Recent advancements in fecal microbiota transplantation (FMT) have highlighted the therapeutic promise of restoring healthy gut microbiota in populations with high incidences of diseases. Focusing on fecal DNA samples from healthy Asian individuals, this study examines the potential of novel Akkermansia strains, specifically Akkermansia muciniphila (Z62) and Akkermansia massiliensis (IR119), as next-generation probiotics for mitigating metabolic syndrome. A key aspect of the study is the investigation of short-chain fatty acids (SCFAs), which are produced and play a crucial role in regulating metabolic processes. SCFAs such as butyrate, acetate, and propionate are essential for energy provision to colon cells and exerting anti-inflammatory effects. The methodology involves selecting two Akkermansia strains, analyzing them through 16S rRNA and WGS, evaluating their growth and survival rates under acidic and bile-salt conditions, alongside their cell adhesion capabilities. The study focuses on the production of key short-chain fatty acids (SCFAs) and tryptophan derivatives by bacteria in regulating metabolic processes, as well as their anti-inflammatory effects on colon cells. Through in vitro assays, both strains exhibited survival in acidic/bile-rich conditions, though Z62 demonstrated superior adhesion to Caco-2 cells, suggesting a higher colonization potential. Metabolomic analysis revealed both strains produce SCFAs, including propionic and acetic acids, and indole metabolites, such as indole-3-propionic acid and indole-3-acetic acid, which are known to influence lipid metabolism and insulin sensitivity. In adipocyte cell models, IR119 significantly reduced lipid accumulation, while Z62 increased lipid presence. Furthermore, IR119 reduced pro-inflammatory cytokine levels, including IL-6 and TNF-α, suggesting potential for inflammation mitigation. The future potential of IR119 as a therapeutic probiotic is extraordinary in addressing complex metabolic and inflammatory diseases, which open new avenues for managing chronic inflammatory conditions like type 2 diabetes and cardiovascular disease. Future clinical trials could refine IR119’s efficacy, positioning it as a leading probiotic in preventive and therapeutic contexts.