N6-methyladenosine modification of RNA controls dopamine synthesis to influence labour division in ants.

The N6-methyladenosine (m6A) modification of RNA has been reported to remodel gene expression in response to environmental conditions; however, the biological role of m6A in social insects remains largely unknown. In this study, we explored the role of m6A in the division of labour by worker ants (Solenopsis invicta). We first determined the presence of m6A in RNAs from the brains of worker ants and found that m6A methylation dynamics differed between foragers and nurses. Depletion of m6A methyltransferase or chemical suppression of m6A methylation in foragers resulted in a shift to 'nurse-like' behaviours. Specifically, mRNAs of dopamine receptor 1 (Dop1) and dopamine transporter (DAT) were modified by m6A, and their expression increased dopamine levels to promote the behavioural transition from foragers to nurses. The abundance of Dop1 and DAT mRNAs and their stability were reduced by the inhibition of m6A modification caused by the silencing of Mettl3, suggesting that m6A modification in worker ants modulates dopamine synthesis, which regulates labour division. Collectively, our results provide the first example of the epitranscriptomic regulation of labour division in social insects and implicate m6A regulatory mechanism as a potential novel target for controlling red imported fire ants.

A Droplet Method for Synthesis of Multiclass Ultrathin Metal Halides.

2D metal halides have attracted increasing research attention in recent years; however, it is still challenging to synthesize them via liquid-phase methods. Here it is demonstrated that a droplet method is simple and efficient for the synthesis of multiclass 2D metal halides, including trivalent (BiI3 , SbI3 ), divalent (SnI2 , GeI2 ), and monovalent (CuI) ones. In particular, 2D SbI3 is first experimentally achieved, of which the thinnest thickness is ≈6 nm. The nucleation and growth of these metal halide nanosheets are mainly determined by the supersaturation of precursor solutions that are dynamically varying during the solution evaporation. After solution drying, the nanosheets can fall on the surface of many different substrates, which further enables the feasible fabrication of related heterostructures and devices. With SbI3 /WSe2 being a good demonstration, the photoluminescence intensity and photo responsivity of WSe2 is obviously enhanced after interfacing with SbI3 . The work opens a new pathway for 2D metal halides toward widespread investigation and applications.

Deep Learning-Based Prediction of Hematoma Expansion Using a Single Brain Computed Tomographic Slice in Patients With Spontaneous Intracerebral Hemorrhages.

OBJECTIVES: We aimed to predict hematoma expansion in intracerebral hemorrhage (ICH) patients by using the deep learning technique. METHODS: We retrospectively collected data from ICH patients treated between May 2015 and May 2019. Head computed tomography (CT) scans were performed at admission, and 6 hours, 24 hours, and 72 hours after admission. CT scans were mandatory when neurologic deficits occurred. Univariate and multivariate analyses were conducted to illustrate the association between clinical variables and hematoma expansion. Convolutional neural network (CNN) was adopted to predict hematoma expansion based on brain CT slices. In addition, 5 machine learning methods, including support vector machine, multi-layer perceptron, naive Bayes, decision tree, and random forest, were also performed to predict hematoma expansion based on clinical variables for comparisons. RESULTS: A total of 223 patients were included. It was revealed that patients' older age (odds ratio [95% confidence interval]: 1.783 [1.417-1.924]), cerebral hemorrhage and breaking into the ventricle (2.524 [1.291-1.778]), coagulopathy (2.341 [1.677-3.454]), and baseline National Institutes of Health Stroke Scale (1.545 [1.132-3.203]) and Glasgow Coma Scale scores (0.782 [0.432-0.918]) independently associated with hematoma expanding. After 4-5 epochs, the CNN framework was well trained. The average sensitivity, specificity, and accuracy of CNN prediction are 0.9197, 0.8837, and 0.9058, respectively. Compared with 5 machine learning methods based on clinical variables, CNN can also achieve better performance. CONCLUSIONS: More than 90% of hematomas with or without expansion can be precisely classified by deep learning technology within this study, which is better than other methods based on clinical variables only. Deep learning technology could favorably predict hematoma expansion from non-contrast CT scan images.