Emotion regulation (ER) refers to the “implementation of a conscious or non-conscious goal to start, stop or otherwise modulate the trajectory of an emotion” (Etkin et al., 2015). Whereas multiple brain areas have been found to be involved in ER, relatively little is known about whether and how ER is associated with the global functioning of brain networks. Recent advances in brain connectivity research using graph-theory based analysis have shown that the brain can be organized into complex networks composed of functionally or structurally connected brain areas. Global efficiency is one graphic metric indicating the efficiency of information exchange among brain areas and is utilized to measure global functioning of brain networks. The present study examined the relationship between trait measures of ER (expressive suppression (ES) and cognitive reappraisal (CR)) and global efficiency in resting-state functional brain networks (the whole brain network and ten predefined networks) using structural equation modeling (SEM). The results showed that ES was reliably associated with efficiency in the fronto-parietal network and default-mode network. The finding advances the understanding of neural substrates of ER, revealing the relationship between ES and efficient organization of brain networks.

In this paper, we propose the differentiable maskmatching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying on the Mask R-CNN backbone, we extract mask proposals per frame and formulate the matching between object templates and proposals at one time step as a linear assignment problem where the cost matrix is predicted by a CNN. We propose a differentiable matching layer by unrolling a projected gradient descent algorithm in which the projection exploits the Dykstra's algorithm. We prove that under mild conditions, the matching is guaranteed to converge to the optimum. In practice, it performs similarly to the Hungarian algorithm during inference. Meanwhile, we can back-propagate through it to learn the cost matrix. After matching, a refinement head is leveraged to improve the quality of the matched mask. Our DMM-Net achieves competitive results on the largest video object segmentation dataset YouTube-VOS. On DAVIS 2017, DMM-Net achieves the best performance without online learning on the first frames. Without any fine-tuning, DMM-Net performs comparably to state-of-the-art methods on SegTrack v2 dataset. At last, our matching layer is very simple to implement; we attach the PyTorch code (< 50 lines) in the supplementary material. Our code is released at https://github.com/ZENGXH/DMM_Net.

Esteya vermicola, as the first recorded endoparasitic fungus of pinewood nematodes, exhibits great potential as a biological agent against nematodes. However, only two strains of this species have been described so far. In this study, we identified a novel endoparasitic fungal strain, CNU 120806, isolated from infected nematodes in forest soil samples during a survey of nematophagous fungi in Korea. This strain showed similar morphological characteristics and infection mode with the two previously described strains of E. vermicola. All strains are characterized by the ability to produce two types of conidiogenous cells and conidia, and to parasitize nematodes with lunate adhesive conidia. Moreover, the CNU 120806 strain showed 100% identity with E. vermicola CBS 115803 when their partial sequences of 28S rRNA gene were compared. Molecular phylogenetic analysis further identified CNU 120806 as a strain of E. vermicola, by clustering CNU 120806 and E. vermicola CBS 115803 into a single subclade. Culture medium influenced the proportion of dimorphic CNU 120806 conidia, and further changed the adhesive and mortality rates of nematodes. The CNU 120806 strain exhibits high infection activity against nematodes on nutrient-rich PDA medium. Almost all tested nematodes were killed within 8 approximately 10 days after inoculation. This study provides justification for further research of E. vermicola, and the application and formulation of this fungus as a bio-control agent against nematodes.

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Rehmannia glutinosa is a common bulk medicinal material that has been widely used in China due to its active ingredients. Acteoside, one of the ingredients, has antioxidant, antinephritic, anti-inflammatory, hepatoprotective, immunomodulatory, and neuroprotective effects, is usually selected as a quality-control component for R. glutinosa herb in the Chinese Pharmacopeia. The acteoside biosynthesis pathway in R. glutinosa has not yet been clearly established. Herein, we describe the establishment of a genetic transformation system for R. glutinosa mediated by Agrobacterium rhizogenes. We screened the optimal elicitors that markedly increased acteoside accumulation in R. glutinosa hairy roots. We found that acteoside accumulation dramatically increased with the addition of salicylic acid (SA); the optimal SA dose was 25 μmol/L for hairy roots. RNA-seq was applied to analyze the transcriptomic changes in hairy roots treated with SA for 24 h in comparison with an untreated control. A total of 3,716, 4,018, and 2,715 differentially expressed transcripts (DETs) were identified in 0 h-vs.-12 h, 0 h-vs.-24 h, and 12 h-vs.-24 h libraries, respectively. KEGG pathway-based analysis revealed that 127 DETs were enriched in “phenylpropanoid biosynthesis.” Of 219 putative unigenes involved in acteoside biosynthesis, 54 were found to be up-regulated at at least one of the time points after SA treatment. Selected candidate genes were analyzed by quantitative real-time PCR (qRT-PCR) in hairy roots with SA, methyl jasmonate (MeJA), AgNO3 (Ag+), and putrescine (Put) treatment. All genes investigated were up-regulated by SA treatment, and most candidate genes were weakly increased by MeJA to some degree. Furthermore, transcription abundance of eight candidate genes in tuberous roots of the high-acteoside-content (HA) cultivar QH were higher than those of the low-acteoside-content (LA) cultivar Wen 85-5. These results will pave the way for understanding the molecular basis of acteoside biosynthesis in R. glutinosa, and can serve as a basis for future validation studies.