Observing change in pelagic animals as sampling methods shift: the case of Antarctic krill

Understanding and managing the response of marine ecosystems to human pressures including climate change requires reliable large-scale and multi-decadal information on the state of key populations. These populations include the pelagic animals that support ecosystem services including carbon export and fisheries. The use of research vessels to collect information using scientific nets and acoustics is being replaced with technologies such as autonomous moorings, gliders, and meta-genetics. Paradoxically, these newer methods sample pelagic populations at ever-smaller spatial scales, and ecological change might go undetected in the time needed to build up large-scale, long time series. These global-scale issues are epitomised by Antarctic krill (Euphausia superba), which is concentrated in rapidly warming areas, exports substantial quantities of carbon and supports an expanding fishery, but opinion is divided on how resilient their stocks are to climatic change. Based on a workshop of 137 krill experts we identify the challenges of observing climate change impacts with shifting sampling methods and suggest three tractable solutions. These are to: improve overlap and calibration of new with traditional methods; improve communication to harmonise, link and scale up the capacity of new but localised sampling programs; and expand opportunities from other research platforms and data sources, including the fishing industry. Contrasting evidence for both change and stability in krill stocks illustrates how the risks of false negative and false positive diagnoses of change are related to the temporal and spatial scale of sampling. Given the uncertainty about how krill are responding to rapid warming we recommend a shift towards a fishery management approach that prioritises monitoring of stock status and can adapt to variability and change

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Assigning Optical Absorption Transitions with Light-Induced Crystal Structures: Case Study of a Single-Crystal Nanooptomechanical Transducer

UV/vis absorption spectroscopy affords indirect structural information about the photochemistry and photophysics of molecules by inferring types of electronic transitions from spectral features. Direct structural information would become available, though, if light-induced crystal structures could be mapped against changes in optical absorption spectra as a photochemical process evolves. We present a series of light-induced crystal structures that track real-time changes in solid-state optical absorption spectra of a crystalline nanooptomechanical transducer, while the transduction process unfolds within its crystal lattice at 100 K. Results afford a combined structural and spectral mapping of its solid-state optical absorption, from which the operational mechanism of nanooptomechanical transduction is revealed. Metal-to-ligand and metal-centered charge-transfer bands are assigned to optical absorption peaks directly from their 3-D light-induced crystal structures. This approach could be used to characterize many solid-state optoelectronic materials.STFC, Royal Academy of Engineerin