Logging to "salvage" economic returns from forests affected by natural disturbances has become increasingly prevalent globally. Despite potential negative effects on biodiversity, salvage logging is often conducted, even in areas otherwise excluded from logging and reserved for nature conservation, inter alia because strategic priorities for post-disturbance management are widely lacking.A review of the existing literature revealed that most studies investigating the effects of salvage logging on biodiversity have been conducted less than 5 years following natural disturbances, and focused on non-saproxylic organisms.A meta-analysis across 24 species groups revealed that salvage logging significantly decreases numbers of species of eight taxonomic groups. Richness of dead wood dependent taxa (i.e. saproxylic organisms) decreased more strongly than richness of non-saproxylic taxa. In contrast, taxonomic groups typically associated with open habitats increased in the number of species after salvage logging.By analysing 134 original species abundance matrices, we demonstrate that salvage logging significantly alters community composition in 7 of 17 species groups, particularly affecting saproxylic assemblages. Our results suggest that salvage logging is not consistent with the management objectives of protected areas. Substantial changes, such as the retention of dead wood in naturally disturbed forests, are needed to support biodiversity. Future research should investigate the amount and spatio-temporal distribution of retained dead wood needed to maintain all components of biodiversity.

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.

SUMMARY Synaptic adhesion molecules regulate diverse aspects of synapse development and plasticity. SALM3 is a PSD-95-interacting synaptic adhesion molecule known to induce presynaptic differentiation in contacting axons, but little is known about its presynaptic receptors and in vivo functions. Here, we identify an interaction between SALM3 and LAR family receptor protein tyrosine phosphatases (LAR-RPTPs) that requires the mini-exon B splice insert in LAR-RPTPs. In addition, SALM3-dependent presynaptic differentiation requires all three types of LAR-RPTPs. SALM3 mutant (Salm3−/−) mice display markedly reduced excitatory synapse number but normal synaptic plasticity in the hippocampal CA1 region. Salm3−/− mice exhibit hypoactivity in both novel and familiar environments but perform normally in learning and memory tests administered. These results suggest that SALM3 regulates excitatory synapse development and locomotion behavior.