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Hedegaard Fink posted an update 6 months, 3 weeks ago
The Research Domain Criteria (RDoC) and the Hierarchical Taxonomy of Psychopathology (HiTOP) represent major dimensional frameworks proposing two alternative approaches to accelerate progress in the way psychopathology is studied, classified, and treated. RDoC is a research framework rooted in neuroscience aiming to further the understanding of transdiagnostic biobehavioral systems underlying psychopathology and ultimately inform future classifications. HiTOP is a dimensional classification system, derived from the observed covariation among symptoms of psychopathology and maladaptive traits, which seeks to provide more informative research and treatment targets (i.e., dimensional constructs and clinical assessments) than traditional diagnostic categories. This article argues that the complementary strengths of RDoC and HiTOP can be leveraged in order to achieve their respective goals. RDoC’s biobehavioral framework may help elucidate the underpinnings of the clinical dimensions included in HiTOP, whereas HiTOP may provide psychometrically robust clinical targets for RDoC-informed research. We present a comprehensive mapping between dimensions included in RDoC (constructs and subconstructs) and HiTOP (spectra and subfactors) based on narrative review of the empirical literature. The resulting RDoC-HiTOP interface sheds light on the biobehavioral correlates of clinical dimensions and provides a broad set of dimensional clinical targets for etiological and neuroscientific research. We conclude with future directions and practical recommendations for using this interface to advance clinical neuroscience and psychiatric nosology. Ultimately, we envision that this RDoC-HiTOP interface has the potential to inform the development of a unified, dimensional, and biobehaviorally-grounded psychiatric nosology.Pharmaceuticals represent a group of emerging contaminants. The short-term effect (3 and 7 days) of warfarin (1 and 10 mg L-1), dexamethasone (0.392 and 3.92 mg L-1) and imidazole (0.013 and 0.13 mg L-1) exposure was evaluated on mussels (Mytilus galloprovincialis). Total antioxidant status, glutathione reductase, glutathione peroxidase (GPx) and superoxide dismutase enzyme activities, and the expression of genes involved in the xenobiotic response (ATP binding cassette subfamily B member 1 (abcb1) and several nuclear receptor family J (nr1j) isoforms), were evaluated. All nr1j isoforms are suggested to be the xenobiotic receptor orthologs of the NR1I family. All drugs increased GPx activity and altered the expression of particular nr1j isoforms. Dexamethasone exposure also decreased abcb1 expression. These findings raised some concerns regarding the release of these pharmaceuticals into the aquatic environment. Thus, further studies might be needed to perform an accurate environmental risk assessment of these 3 poorly studied drugs.Algal turfs are an abundant and highly productive component of coral reef ecosystems. However, our understanding of the drivers that shape algal turf productivity across studies and among reefs is limited. Based on published studies we considered how different factors may shape turf productivity and turnover rates. Of the factors considered, depth was the primary driver of turf productivity rates, while turnover was predominantly related to turf biomass. https://www.selleckchem.com/ We also highlight shortcomings in the available data collected on turf productivity to-date; most data were collected prior to global coral bleaching events, within a limited geographic range, and were largely from experimental substrata. Despite the fact turfs are a widespread benthic covering on most coral reefs, and one of the major sources of benthic productivity, our understanding of their productivity is constrained by both a paucity of data and methodological limitations. We offer a potential way forward to address these challenges.Fundus diseases classification is vital for the health of human beings. However, most of existing methods detect diseases by means of single angle fundus images, which lead to the lack of pathological information. To address this limitation, this paper proposes a novel deep learning method to complete different fundus diseases classification tasks using ultra-wide field scanning laser ophthalmoscopy (SLO) images, which have an ultra-wide field view of 180-200˚. The proposed deep model consists of multi-branch network, atrous spatial pyramid pooling module (ASPP), cross-attention and depth-wise attention module. Specifically, the multi-branch network employs the ResNet-34 model as the backbone to extract feature information, where the ResNet-34 model with two-branch is followed by the ASPP module to extract multi-scale spatial contextual features by setting different dilated rates. The depth-wise attention module can provide the global attention map from the multi-branch network, which enables the network to focus on the salient targets of interest. The cross-attention module adopts the cross-fusion mode to fuse the channel and spatial attention maps from the ResNet-34 model with two-branch, which can enhance the representation ability of the disease-specific features. The extensive experiments on our collected SLO images and two publicly available datasets demonstrate that the proposed method can outperform the state-of-the-art methods and achieve quite promising classification performance of the fundus diseases.The focus of this study was to compare the effectiveness of MALDI-TOF MS and partial 16S rRNA gene sequencing for the identification of bacteria isolated from VP lamb meat stored chilled at 5 °C for 21 days, at the same time gaining insights into bacterial changes over time. The identity of bacterial isolates on non-selective and selective agars was determined by both methods and results compared. Results showed that total bacterial numbers increased over the 21 days (as expected) with Staphylococcus and Pseudomonas (day 0) being replaced by Carnobacterium, Brochothrix and members of the Enterobacteriaceae family by day 21. A high level of agreement (86-100%) for bacterial isolates’ identity at genus level was observed between MALDI-TOF MS and partial 16S rRNA gene-based sequencing for isolates where identification was possible. With its cheaper cost and faster turnaround time, once optimized, MALDI-TOF MS could become a useful alternative to 16S rRNA gene-sequencing for the rapid identification of red meat bacterial isolates.
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