Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

Cytotoxic components from the leaves of Erythrophleum fordii induce human acute leukemia cell apoptosis through caspase 3 activation and PARP cleavage.

Cassaine diterpenoids as erythrofordins A-C (1-3), pseudo-erythrosuamin (4), and erythrofordin U (5) isolated from the leaves of Vietnamese Erythrophleum fordii Oliver were tested cytotoxic activity against human leukemia cancer cells. The results showed that these metabolites exhibited dose-dependent cytotoxicity against human leukemia HL-60 and KG cells with IC50 values ranging from 15.2 ± 1.5 to 42.2 ± 3.6 µM. Treatment with erythrofordin B led to the apoptosis of HL-60 and KG cells due to the activation of caspase 3, caspase 9, and poly (ADP-ribose) polymerase (PARP). Erythrofordin B significantly increased Bak protein expression, but downregulated the anti-apoptotic protein Bcl-2, in HL-60 cells. In silico results demonstrated that erythrofordin B can bind to both the procaspase-3 allosteric site and the PARP-1 active site, with binding energies of -7.36 and -10.76 kcal/mol, respectively. These results indicated that the leaves of Vietnamese E. fordii, which contain cassaine diterpenoids, can induce the apoptosis of human leukemia cancer cells.

Metabotropic Glutamate Receptor 2 and Dopamine Receptor 2 Gene Expression Predict Sensorimotor Gating Response in the Genetically Heterogeneous NIH-HS Rat Strain.

Disruption of sensorimotor gating causes "flooding" of irrelevant sensory input and is considered a congenital trait in several neurodevelopmental disorders. Prepulse inhibition of acoustic startle response (PPI) is the operational measurement and has a high translational validity. Pharmacological studies in rodents have linked alterations in serotonin, dopamine and glutamate signalling to PPI disruption. How PPI response is associated with gene expression levels of these receptors is unknown. PPI response was assessed in 39 genetically heterogeneous National Institutes of Health-Heterogeneous Stock (NIH-HS) rats. Animals were classified as high, medium or low PPI. Expression levels of glutamate metabotropic receptor 2 (Grm2), dopamine receptor D2 (Drd2), dopamine receptor D1 (Drd1), serotonin receptor 1A (Htr1a), serotonin receptor 2A (Htr2a) and homer scaffolding protein 1 (Homer1) were investigated in prefrontal cortex (PFC) and striatum (STR). When comparing the two extreme phenotypes, only Drd2 in STR showed increased expression in the low PPI group. A multinomial model fitting all genes and all groups indicated that Grm2 in PFC, and Grm2 and Drd2 in the STR predicted PPI group. This was corroborated by a linear relationship of Grm2 with PPI in PFC, and Drd2 with PPI in STR. An interaction between levels of H3K27 trimethylation, associated with transcriptional repression, and PPI phenotype was observed for Drd2 in STR. Gene set enrichment analysis on a microarray dataset on Lewis rats confirmed enrichment of Drd2 in PFC in relation to PPI. These findings contribute to the understanding of the genetic substrate behind alterations in sensorimotor gating, relevant for its linkage to neurodevelopmental disorders.