Tapiscia sinensis Oliv (Tapisciaceae) is an endangered species native to China famous for its androdioecious breeding system. However, there is a lack of genomic and transcriptome data on this species. In this study, the Tapiscia sinensis transcriptomes from two types of sex flower buds were sequenced. A total of 97,431,176 clean reads were assembled into 52,169 unigenes with an average length of 1116 bp. Through similarity comparison with known protein databases, 36,662 unigenes (70.27%) were annotated. A total of 10,002 (19.17%) unigenes were assigned to 124 pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. Additionally, 10,371 simple sequence repeats (SSRs) were identified in 8608 unigenes, with 16,317 pairs of primers designed for applications. 150 pairs of primers were chosen for further validation, and the 68 pairs (45.5%) were able to produce clear polymorphic bands. Six polymorphic SSR markers were used to Bayesian clustering analysis of 51 T. sinensis individuals. This is the first report to provide transcriptome information and to develop large-scale SSR molecular markers for T. sinensis. This study provides a valuable resource for conservation genetics and functional genomics research on T. sinensis for future work.

Correlated spontaneous activity in the developing retina (termed “retinal waves”) plays an instructive role in refining neural circuits of the visual system. Depolarizing (ON) and hyperpolarizing (OFF) starburst amacrine cells (SACs) initiate and propagate cholinergic retinal waves. Where cholinergic retinal waves stop, SACs are thought to be driven by glutamatergic retinal waves initiated by ON-bipolar cells. However, the properties and function of cholinergic and glutamatergic waves in ON- and OFF-SACs still remain poorly understood. In the present work, we performed whole-cell patch-clamp recordings and Ca²⁺ imaging from genetically labeled ON- and OFF-SACs in mouse flat-mount retinas. We found that both SAC subtypes exhibited spontaneous rhythmic depolarization during cholinergic and glutamatergic waves. Interestingly, ON-SACs had wave-induced action potentials (APs) in an age-dependent manner, but OFF-SACs did not. Simultaneous Ca²⁺ imaging and patch-clamp recordings demonstrated that, during a cholinergic wave, APs of an ON-SAC appeared to promote the dendritic release of acetylcholine onto neighboring ON- and OFF-SACs, which enhances their Ca²⁺ transients. These results advance the understanding of the cellular mechanisms underlying correlated spontaneous activity in the developing retina.

In order to improve the perception accuracy of the apple tree fruit recognition model and to reduce the model size, a lightweight apple target recognition method based on an improved YOLOv5s artificial intelligence algorithm was proposed, and relevant experiments were designed. The Depthwise Separable Convolution (DWConv) module has many advantages: (1) It has high computational efficiency, reducing the number of parameters and calculations in the model; (2) It makes the model lightweight and easy to deploy in hardware; (3) DWConv can be combined with other modules to enhance the multi-scale feature extraction capability of the detection network and improve the ability to capture multi-scale information; (4) It balances the detection accuracy and speed of the model; (5) DWConv can flexibly adapt to different network structures. Because of its efficient computing modes, lightweight design, and flexible structural adaptation, the DWConv module has significant advantages in multi-scale feature extraction, real-time performance improvement, and small-object detection. Therefore, this method improves the original YOLOv5s network architecture by replacing the embedded Depthwise Separable Convolution in its Backbone network, which reduces the size and parameter count of the model while ensuring detection accuracy. The experimental results show that for the test-set images, the proposed improved model has an average recognition accuracy of 92.3% for apple targets, a recognition time of 0.033 s for a single image, and a model volume of 11.1 MB. Compared with the original YOLOv5s model, the average recognition accuracy was increased by 0.8%, the recognition speed was increased by 23.3%, and the model volume was compressed by 20.7%, effectively achieving lightweight improvement of the apple detection model and improving the accuracy and speed of detection. The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. The proposed method can improve the intelligence level of orchard management and horticultural technology, reduce labor costs, assist precision agriculture technology, and promote the transformation of the horticultural industry toward sustainable development.

The composition and volume of tear fluid lining the ocular surface are governed by the rates of lacrimal gland secretion, tear film evaporation, nasolacrimal drainage, and epithelial ion and water transport. Tear fluid hyperosmolality and reduced volume are key drivers of dry eye disease (DED) pathogenesis. We constructed a mathematical model to compute the composition and volume of tear fluid and epithelial cell compartments, with transport parameters specified for the mouse eye based on published data and in vivo measurements of ocular surface potential differences. The model accounted for transcellular and paracellular transport across the epithelia under open-circuit conditions utilizing flux equations for individual transport mechanisms, with mass balance constraints on solute and water content in cytoplasm and tear fluid. Model computations revealed a rough orthogonality between the determinants of tear fluid volume and osmolality in which lacrimal secretion and nasolacrimal drainage primarily regulate tear fluid volume, whereas evaporation and transepithelial osmosis primarily regulate tear fluid osmolality. Under DED conditions established by reduced lacrimal secretion and increased evaporation, the model predicted the efficacy of currently available DED therapies, including punctal plugs, humidification goggles, lacrimal gland stimulation, and artificial tear eye drops. The model also predicted the limited efficacy of anti-absorptive and pro-secretory drugs targeting epithelial ion transporters and the high efficacy of targeting epithelial water permeability or paracellular ion permeability. The modeling herein provided quantitative predictions to prioritize drug targets for DED to drive the development of novel therapies.

Correlated spontaneous activity in the developing retina (termed “retinal waves”) plays an instructive role in refining neural circuits of the visual system. Depolarizing (ON) and hyperpolarizing (OFF) starburst amacrine cells (SACs) initiate and propagate cholinergic retinal waves. Where cholinergic retinal waves stop, SACs are thought to be driven by glutamatergic retinal waves initiated by ON-bipolar cells. However, the properties and function of cholinergic and glutamatergic waves in ON- and OFF-SACs still remain poorly understood. As expected, we found that both SAC subtypes exhibited spontaneous rhythmic depolarization during cholinergic and glutamatergic waves. Interestingly, ON-SACs had wave-induced action potentials (APs) in an age-dependent manner, but OFF-SACs did not. We further found that the number of APs in ON-SACs was correlated with the amplitude of Ca ²⁺ transients of either ON- or OFF-SACs during cholinergic retinal waves. These results advance the understanding of the cellular mechanisms underlying correlated spontaneous activity in the developing retina.