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Nature-based solutions could offset coastal squeeze of tidal wetlands from sea-level rise on the U.S. Pacific coast

Abstract In this study, we explored the opportunities for tidal wetland landward migration in response to sea-level rise on the Pacific Coast of the United States. By employing a systematic spatial approach, we quantified the available space for wetland migration with sea-level rise across 61 estuarine drainage areas. Although many of the existing tidal wetlands are small patches, our analyses show that 63% of the estuaries lacked the landward migration space needed to replace current tidal wetland extent, thereby threatening a wide range of protected species and ecosystem services. Developed lands and steep topography represent common barriers to migration along the Pacific coast, especially in central and southern California. The available wetland migration space consists primarily of agriculture, pasture, and freshwater wetlands, with most of the area available for migration occurring in just a few watersheds. In most watersheds tidal wetland migration would only occur with human intervention or facilitation. The greatest amount of area available for wetland migration was in the San Francisco Bay-Delta and Columbia River estuaries, together accounting for 58% of all available migration space on the Pacific Coast. Nature-based solutions to reduce tidal wetland loss from sea-level rise can include restoration in suitable areas, removal of barriers to tidal wetland migration, and elevation building approaches. Tidal wetland restoration opportunities could increase area by 59%, underscoring it as a plausible approach to prevent tidal wetland loss in those estuaries and a viable Nature-based solution. 54% of estuaries building elevations of existing tidal wetlands may be the most feasible approach needed. Our analyses illustrate the importance of management efforts that use Nature-based approaches to prevent tidal wetland ecosystem and species loss over the coming decades from sea-level rise.

The global scientific community must keep studying LGBT+ health

Difficulty aware programming knowledge tracing via large language models

Action needed to mitigate effects of slashing USAID

Divergent dimerization mechanisms and conserved DNA-binding function in PFam12 proteins of Borrelia burgdorferi

How to get rid of toxic ‘forever chemical’ pollution

Investigating the mediating role of classroom achievement emotions in the relationship between school physical environment and physical literacy of adolescents

Six roadblocks to net zero — and how to get around them

Proof of concept study on differentiating metastatic brain samples by their originating organ using multimodal autofluorescence spectroscopy

Style over substance? What birds’ mating behaviours reveal about sexual selection

Development of simulated human milk ultrafiltrate (SHMUF) for analysis of native particles in human milk

Deep dive: How I use robots to survey coral reefs

Prognostic value of circulating HPV cell-free DNA in cervical cancer using liquid biopsy

Application of HEMA-AAm copolymer to achieve faster optical tissue transparency for 2D/3D fluorescence imaging

Crosslinking a nanocomposite by zirconium sulfate to synthesize a high-durable new-generation polymer gel

Peptides developed against receptor binding sites of the E glycoprotein neutralize tick-borne encephalitis virus

Publisher Correction: Sleep and stress in athletes with disabilities around the 2021 Tokyo Paralympic games during the pandemic

Intranasal oxytocin modulates brain activity during emotional processing in children with treatment resistant conduct problems

Abstract One of the most highly replicated neural correlates of Conduct Problems (CP) is amygdala hypoactivity to another person’s fear. We recently reported that this correlate was only observed in boys with persistent CP (i.e. antisocial behaviour that persisted following a gold-standard psychological intervention), suggesting that amygdala hypoactivity to fear could be an important neural signature for treatment-resistant CP, and a putative target for future treatments. Potential treatment candidates include the oxytocin system, as this has been reported to modulate amygdala activity and social behaviour across species. Further, in adults with antisocial personality disorder, intranasal oxytocin improved facial emotion recognition for fearful and happy faces. However, to-date, no-one has studied whether intranasal oxytocin can normalise neural processing differences in children with CP. Twenty boys (mean age 9.85±1.26 years) with persistent CP underwent functional magnetic resonance imaging in a within-subject randomised control design to investigate whether, compared to placebo, a single-dose of intranasal oxytocin could ‘shift’ abnormal neural processing to fear. Oxytocin failed to reduce amygdala hypoactivity to fearful faces, but increased activation in the posterior cingulate cortex / precuneus to happy faces. These findings tentatively suggest that intranasal oxytocin may promote a more neurotypical profile in treatment-resistant CP children, therefore, supporting the merit of investigating oxytocin in further larger clinical studies in this population.

No hyponatremia despite continuous plasma sodium decline in female runners during a seven stage ultramarathon

Adaptive feature interaction enhancement network for text classification

Abstract Text classification aims to establish text distinctions, which face difficulty in capturing global text semantics and local details. To address this issue, we propose an Adaptive Feature Interactive Enhancement Network (AFIENet). Specifically, AFIENet uses two branches to model the text globally and locally. The adaptive segmentation module in the local network can dynamically split the text and capture key phrases, while the global network grasps the overall central semantics. After obtaining the results from the two branches, an interaction gate is designed to evaluate the confidence of the global features and selectively fuse them with the local features effectively. Finally, the interactively enhanced features are re-input into the classifier to improve text classification performance. Experiment results show that our proposed method can effectively enhance the performance of backbone networks such as TextCNN, RNN, and Transformer with fewer parameters. AFIENet achieved an average accuracy of 3.82% and an F1-score of 3.88% improvement across the three datasets when using Transformer as the backbone network. The comparable results to MacBERT that obtained with static word vectors also reflect the applicability of the proposed method.