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Eukaryotic cells maintain proteostasis through mechanisms that require cytoplasmic and mitochondrial translation. Genetic defects affecting cytoplasmic translation perturb synapse development, neurotransmission, and are causative of neurodevelopmental disorders, such as Fragile X syndrome. In contrast, there is little indication that mitochondrial proteostasis, either in the form of mitochondrial protein translation and/or degradation, is required for synapse development and function. Here we focus on two genes deleted in a recurrent copy number variation causing neurodevelopmental disorders, the 22q11.2 microdeletion syndrome. We demonstrate that SLC25A1 and MRPL40, two genes present in the microdeleted segment and whose products localize to mitochondria, interact and are necessary for mitochondrial ribosomal integrity and proteostasis. Our Drosophila studies show that mitochondrial ribosome function is necessary for synapse neurodevelopment, function, and behavior. We propose that mitochondrial proteostasis perturbations, either by genetic or environmental factors, are a pathogenic mechanism for neurodevelopmental disorders.SIGNIFICANCE STATEMENT The balance between cytoplasmic protein synthesis and degradation, or cytoplasmic proteostasis, is required for normal synapse function and neurodevelopment. Cytoplasmic and mitochondrial ribosomes are necessary for two compartmentalized, yet interdependent, forms of proteostasis. Proteostasis dependent on cytoplasmic ribosomes is a well-established target of genetic defects that cause neurodevelopmental disorders, such as autism. Here we show that the mitochondrial ribosome is a neurodevelopmentally regulated organelle whose function is required for synapse development and function. We propose that defective mitochondrial proteostasis is a mechanism with the potential to contribute to neurodevelopmental disease.

Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning‐based training data augmentation (TDA) and post‐classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post‐classification with segmentation improved the total accuracy to 86.2%. The class‐level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future. [ABSTRACT FROM AUTHOR]