Beyond Landscape’s Visible Realm:Recorded sound, nature and wellbeing

This article draws on an AHRC/EPSRC funded project called ‘A Sense of Place: Exploring nature and wellbeing through the non-visual senses’. The project used sound and smell technologies, as well as material textures and touch, to ask: what does ‘wellbeing’ mean for people in relation to the non-visual aspects of nature, and how might technology play a role in promoting it (if at all)? This article takes recorded sound as a case study. It argues that recorded soundscapes should be understood on their own terms rather than as ‘less than’ or a simulation of natural environments. They have specific value in creating space for imagination, particularly when delivered with care and as part of the co-creation of sensory experience. Overall, the article argues that the value of emerging immersive technologies is not to simulate nature better. An ‘immersive experience’ is richest when it allows for – and reveals – the nuances and complexities of individual responses to natural environments

Top-of-atmosphere albedo bias from neglecting three-dimensional radiative transfer through clouds

Clouds cover on average nearly 70% of Earth’s surface and are important for the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and 3D structure. Earth system models are unable to perform 3D radiative transfer calculations and thus partially neglect the effect of cloud morphology on albedo. We show how the resulting radiative flux bias depends on cloud morphology and solar zenith angle. Using large-eddy simulations to produce 3D cloud fields, a Monte Carlo code for 3D radiative transfer, and observations of cloud climatology, we estimate the effect of this flux bias on global climate. The flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is largest (and negative) for large zenith angles. Globally, the radiative flux bias is estimated to be 1.6 W m⁻² and locally can be on the order of 5 W m⁻²

Design and applicability of DNA arrays and DNA barcodes in biodiversity monitoring

<p>Abstract</p> <p>Background</p> <p>The rapid and accurate identification of species is a critical component of large-scale biodiversity monitoring programs. DNA arrays (micro and macro) and DNA barcodes are two molecular approaches that have recently garnered much attention. Here, we compare these two platforms for identification of an important group, the mammals.</p> <p>Results</p> <p>Our analyses, based on the two commonly used mitochondrial genes cytochrome <it>c </it>oxidase I (the standard DNA barcode for animal species) and cytochrome b (a common species-level marker), suggest that both arrays and barcodes are capable of discriminating mammalian species with high accuracy. We used three different datasets of mammalian species, comprising different sampling strategies. For DNA arrays we designed three probes for each species to address intraspecific variation. As for DNA barcoding, our analyses show that both cytochrome <it>c </it>oxidase I and cytochrome b genes, and even smaller fragments of them (mini-barcodes) can successfully discriminate species in a wide variety of specimens.</p> <p>Conclusion</p> <p>This study showed that DNA arrays and DNA barcodes are valuable molecular methods for biodiversity monitoring programs. Both approaches were capable of discriminating among mammalian species in our test assemblages. However, because designing DNA arrays require advance knowledge of target sequences, the use of this approach could be limited in large scale monitoring programs where unknown haplotypes might be encountered. DNA barcodes, by contrast, are sequencing-based and therefore could provide more flexibility in large-scale studies.</p

Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces

Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems. For instance, increased greenhouse gas concentrations are predicted to lead to drastic decreases in low cloud cover, referred to as a climatological tipping point. In this paper, we learn the evolution of such non-stationary dynamical systems using a novel recurrent neural operator (RNO), which learns mappings between function spaces. After training RNO on only the pre-tipping dynamics, we employ it to detect future tipping points using an uncertainty-based approach. In particular, we propose a conformal prediction framework to forecast tipping points by monitoring deviations from physics constraints (such as conserved quantities and partial differential equations), enabling forecasting of these abrupt changes along with a rigorous measure of uncertainty. We illustrate our proposed methodology on non-stationary ordinary and partial differential equations, such as the Lorenz-63 and Kuramoto-Sivashinsky equations. We also apply our methods to forecast a climate tipping point in stratocumulus cloud cover. In our experiments, we demonstrate that even partial or approximate physics constraints can be used to accurately forecast future tipping points.Comment: 29 pages, 15 figure

Executable network of SARS-CoV-2-host interaction predicts drug combination treatments

The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic

PySDM v1 : particle-based cloud modeling package for warm-rain microphysics and aqueous chemistry

PySDM is an open-source Python package for simulating the dynamics of particles undergoing condensational and collisional growth, interacting with a fluid flow and subject to chemical composition changes. It is intended to serve as a building block for process-level as well as computational-fluid-dynamics simulation systems involving representation of a continuous phase (air) and a dispersed phase (aerosol), with PySDM being responsible for representation of the dispersed phase. The PySDM package core is a Pythonic high-performance implementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm for representing collisional growth, hence the name. PySDM has two alternative parallel number-crunching backends available: multi-threaded CPU backend based on Numba and GPU-resident backend built on top of ThrustRTC. The usage examples are built on top of four simple atmospheric cloud modelling frameworks: box, adiabatic parcel, single-column and 2D prescribed flow kinematic models. In addition, the package ships with tutorial code depicting how PySDM can be used from Julia and Matlab

Interrupted Time-Series Analysis of Regulations to Reduce Paracetamol (Acetaminophen) Poisoning

BACKGROUND: Paracetamol (acetaminophen) poisoning is the leading cause of acute liver failure in Great Britain and the United States. Successful interventions to reduced harm from paracetamol poisoning are needed. To achieve this, the government of the United Kingdom introduced legislation in 1998 limiting the pack size of paracetamol sold in shops. Several studies have reported recent decreases in fatal poisonings involving paracetamol. We use interrupted time-series analysis to evaluate whether the recent fall in the number of paracetamol deaths is different to trends in fatal poisoning involving aspirin, paracetamol compounds, antidepressants, or nondrug poisoning suicide. METHODS AND FINDINGS: We calculated directly age-standardised mortality rates for paracetamol poisoning in England and Wales from 1993 to 2004. We used an ordinary least-squares regression model divided into pre- and postintervention segments at 1999. The model included a term for autocorrelation within the time series. We tested for changes in the level and slope between the pre- and postintervention segments. To assess whether observed changes in the time series were unique to paracetamol, we compared against poisoning deaths involving compound paracetamol (not covered by the regulations), aspirin, antidepressants, and nonpoisoning suicide deaths. We did this comparison by calculating a ratio of each comparison series with paracetamol and applying a segmented regression model to the ratios. No change in the ratio level or slope indicated no difference compared to the control series. There were about 2,200 deaths involving paracetamol. The age-standardised mortality rate rose from 8.1 per million in 1993 to 8.8 per million in 1997, subsequently falling to about 5.3 per million in 2004. After the regulations were introduced, deaths dropped by 2.69 per million (p = 0.003). Trends in the age-standardised mortality rate for paracetamol compounds, aspirin, and antidepressants were broadly similar to paracetamol, increasing until 1997 and then declining. Nondrug poisoning suicide also declined during the study period, but was highest in 1993. The segmented regression models showed that the age-standardised mortality rate for compound paracetamol dropped less after the regulations (p = 0.012) but declined more rapidly afterward (p = 0.031). However, age-standardised rates for aspirin and antidepressants fell in a similar way to paracetamol after the regulations. Nondrug poisoning suicide declined at a similar rate to paracetamol after the regulations were introduced. CONCLUSIONS: Introduction of regulations to limit availability of paracetamol coincided with a decrease in paracetamol-poisoning mortality. However, fatal poisoning involving aspirin, antidepressants, and to a lesser degree, paracetamol compounds, also showed similar trends. This raises the question whether the decline in paracetamol deaths was due to the regulations or was part of a wider trend in decreasing drug-poisoning mortality. We found little evidence to support the hypothesis that the 1998 regulations limiting pack size resulted in a greater reduction in poisoning deaths involving paracetamol than occurred for other drugs or nondrug poisoning suicide