The ability to learn from past experiences to inform future decision making is crucial for humans and animals alike. One question with important implications for adaptive decision making is whether we learn about the absolute values of cues we encounter (how good or bad?) or about their relative values (how much better or worse than the alternative?). Humans have been shown to use relative value learning, even when it leads to suboptimal decisions. In this study, we ask whether insects employ absolute or relative value learning. Using the larvae of the fruit fly Drosophila melanogaster, we designed associative odour–taste learning experiments to distinguish both kinds of learning and found that larvae learn about the relative rather than the absolute values of both rewards and punishments, irrespective of the number and sequence of training trials. This suggests that relative value learning is a facility shared across the animal kingdom from maggots to humans and can be accomplished even by small insect brains. Given the potential of D. melanogaster as a model organism for in-depth neurobiological analyses, our study opens up an opportunity to reveal the detailed mechanisms underlying relative value learning.

Background: Learning and memory are basic aspects of neurogenetics as most of the neurological disorders start with dementia or memory loss. Several genes associated with memory formation have been discovered. MicroRNA genes, miR-1000 and miR-375, were reported to be associated with neural integration and glucose homeostasis in some insects and vertebrates. However, the neuronal function of these genes is yet to be established in D. melanogaster. Objective: The possible role of miR-1000 and miR-375 in learning and memory formation in this fly has been explored in the present study. Methods: Both types of appetitive and aversive olfactory conditional learning were tested in the miR- 1000 and miR-375 knockout (KO) strains and compared with the wild one. Five days old third instar larvae were trained by allowing them to be associated with an odor with reward (fructose) or punishment (salt). Then, the larvae were tested to calculate their preferences to the odor they were trained with. Learning Index (LI) values and larval locomotion speed were calculated for all strains. Results: Knockout strain of miR-1000 showed significant deficiency in both appetitive and aversive memory formation whereas miR-375 KO strain showed a significantly lower response only in appetitive one. Conclusion: The results of the present study indicate an important role of miR-1000 and miR-375 genes in forming short-term memory in D. melanogaster.

Rapid emergence of covid-19 variants by continuous mutation made the world experience continuous waves of infections and as a result, a huge number of death-toll recorded so far. It is, therefore, very important to investigate the diversity and nature of the mutations in the SARS-CoV-2 genomes. In this study, the common mutations occurred in the whole genome sequences of SARS-CoV-2 variants of Bangladesh in a certain timeline were analyzed to better understand its status. Hence, a total of 78 complete genome sequences available in the NCBI database were obtained, aligned and further analyzed. Scattered Single Nucleotide Polymorphisms (SNPs) were identified throughout the genome of variants and common SNPs such as: 241:C>T in the 5′UTR of Open Reading Frame 1A (ORF1A), 3037: C>T in Non-structural Protein 3 (NSP3), 14,408: C>T in ORF6 and 23,402: A>G, 23,403: A>G in Spike Protein (S) were observed, but all of them were synonymous mutations. About 97% of the studied genomes showed a block of tri-nucleotide alteration (GGG>AAC), the most common non-synonymous mutation in the 28,881–28,883 location of the genome. This block results in two amino acid changes (203–204: RG>KR) in the SR rich motif of the nucleocapsid (N) protein of SARS-CoV-2, introducing a lysine in between serine and arginine. The N protein structure of the mutant was predicted through protein modeling. However, no observable difference was found between the mutant and the reference (Wuhan) protein. Further, the protein stability changes upon mutations were analyzed using the I-Mutant2.0 tool. The alteration of the arginine to lysine at the amino acid position 203, showed reduction of entropy, suggesting a possible impact on the overall stability of the N protein. The estimation of the non-synonymous to synonymous substitution ratio (dN/dS) were analyzed for the common mutations and the results showed that the overall mean distance among the N-protein variants were statistically significant, supporting the non-synonymous nature of the mutations. The phylogenetic analysis of the selected 78 genomes, compared with the most common genomic variants of this virus across the globe showed a distinct cluster for the analyzed Bangladeshi sequences. Further studies are warranted for conferring any plausible association of these mutations with the clinical manifestation.

The ability to learn from past experiences to inform future decision-making is crucial for humans and animals alike. One question with important implications for adaptive decision-making is whether we learn about the absolute values of cues we encounter (how good or bad?), or about their relative values (how much better or worse than the alternative?). Humans have been shown to use relative value learning, even when it leads to suboptimal decisions. In this study, we ask whether insects employ absolute or relative value learning. Using the larvae of the fruit fly Drosophila melanogaster , we designed associative odour-taste learning experiments to distinguish both kinds of learning and find that larvae learn about the relative rather than the absolute values of both rewards and punishments, irrespective of the number and sequence of training trials. This suggests that relative value learning is a facility shared across the animal kingdom from maggots to humans, and can be accomplished even by small insect brains. Given the potential of D. melanogaster as a model organism for in-depth neurobiological analyses, our study opens up an opportunity to reveal the detailed mechanisms underlying relative value learning.