Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.Comment: 23 pages, 6 figures, 1 tabl

Diversification of importin-α isoforms in cellular trafficking and disease states.

The human genome encodes seven isoforms of importin α which are grouped into three subfamilies known as α1, α2 and α3. All isoforms share a fundamentally conserved architecture that consists of an N-terminal, autoinhibitory, importin-β-binding (IBB) domain and a C-terminal Arm (Armadillo)-core that associates with nuclear localization signal (NLS) cargoes. Despite striking similarity in amino acid sequence and 3D structure, importin-α isoforms display remarkable substrate specificity in vivo. In the present review, we look at key differences among importin-α isoforms and provide a comprehensive inventory of known viral and cellular cargoes that have been shown to associate preferentially with specific isoforms. We illustrate how the diversification of the adaptor importin α into seven isoforms expands the dynamic range and regulatory control of nucleocytoplasmic transport, offering unexpected opportunities for pharmacological intervention. The emerging view of importin α is that of a key signalling molecule, with isoforms that confer preferential nuclear entry and spatiotemporal specificity on viral and cellular cargoes directly linked to human diseases

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Computational Studies on the Selective Polymerization of Lactide Catalyzed by Bifunctional Yttrium NHC Catalyst

A theoretical investigation of the ring-opening polymerization (ROP) mechanism of rac-lactide (LA) with an yttrium complex featuring a N-heterocyclic carbine (NHC) tethered moiety is reported. It was found that the carbonyl of lactide is attacked by N(SiMe3)2 group rather than NHC species at the chain initiation step. The polymerization selectivity was further investigated via two consecutive insertions of lactide monomer molecules. The insertion of the second monomer in different assembly modes indicated that the steric interactions between the last enchained monomer unit and the incoming monomer together with the repulsion between the incoming monomer and the ligand framework are the primary factors determining the stereoselectivity. The interaction energy between the monomer and the metal center could also play an important role in the stereocontrol

Losing the Arms Race: Greater Wax Moths Sense but Ignore Bee Alarm Pheromones

The greater wax moth, Galleria mellonella L., is one of main pests of honeybees. The larvae burrow into the wax, damaging the bee comb and degenerating bee products, but also causes severe effects like driving the whole colony to abscond. In the present study, we used electroantennograms, a Y maze, and an oviposition site choice bioassay to test whether the greater wax moth can eavesdrop on bee alarm pheromones (isopentyl acetate, benzyl acetate, octyl acetate, and 2-heptanone), to target the bee colony, or if the bee alarm pheromones would affect their preference of an oviposition site. The results revealed that the greater wax moth showed a strong electroantennogram response to these four compounds of bee alarm pheromones even in a low concentration (100 ng/μL), while they showed the highest response to octyl acetate compared to the other three main bee alarm components (isopentyl acetate, benzyl acetate, and 2-heptanone). However, the greater wax moth behavioral results showed no significant preference or avoidance to these four bee alarm pheromones. These results indicate that bees are currently losing the arms race since the greater wax moth can sense bee alarm pheromones, however, these alarm pheromones are ignored by the greater wax moth