Nature-based Solutions (NbS) – What are They and What are the Barriers and Enablers to Their Use?

This rapid review examines literature around Nature-based Solutions (NbS), what are NbS, the pros and cons of NbS, design and implementation issues (including governance, indigenous knowledge), finance and the enabling environment. The breadth of NbS and the evidence base means that this rapid review only provides a snapshot of the information available, and therefore does not consider all types of NbS, nor all sectors that they have been used in. Considering this limited scope, this report highlights many issues, some of which are that Covid-19 has highlighted the importance of NbS, Pros of NbS include the low cost compared to infrastructure alternatives; the flexibility in addressing multiple climate challenges; potential co-benefits such as better water quality, improved health, cultural benefits, biodiversity conservation. The literature also notes the cons of NbS including slow adaptation or co-benefits, very context specific making effectiveness difficult to measure and many of the benefits are non-monetary and hard to measure. The literature consulted suggest a number of knowledge gaps in the evidence base for NbS effectiveness including lack of: robust and impartial assessments of current NbS experiences; site specific knowledge of field deployment of NbS; timescales over which benefits are seen and experienced; cost-effectiveness of interventions compared to or in conjunction with alternative solutions; and integrated assessments considering broader social and ecological outcomes

DRSIR: A Deep Reinforcement Learning Approach for Routing in Software-Defined Networking

Traditional routing protocols employ limited information to make routing decisions which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of the applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in SoftwareDefined Networking (SDN). However, RL-based solutions usually suffer an increase in the learning process when dealing with large action and state spaces. This paper introduces a different routing approach called Deep Reinforcement Learning and SoftwareDefined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on the Dijkstra’s algorithm and RSIR, in relation to stretching (stretch), packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and viable solution for routing in SDN.

DRSIR: A Deep Reinforcement Learning Approach for Routing in Software-Defined Networking

Traditional routing protocols employ limited information to make routing decisions which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of the applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in SoftwareDefined Networking (SDN). However, RL-based solutions usually suffer an increase in the learning process when dealing with large action and state spaces. This paper introduces a different routing approach called Deep Reinforcement Learning and SoftwareDefined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on the Dijkstra’s algorithm and RSIR, in relation to stretching (stretch), packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and viable solution for routing in SDN.

Hidden genetic variation in plasticity increases the potential to adapt to novel environments

Adaptive plasticity increases population persistence, but can slow adaptation to changing environments by hiding the effects of different alleles on fitness. However, if plastic responses are no longer adaptive in novel environments, then differences among alleles can emerge and increase genetic variation in fitness that allows rapid adaptation. We tested this hypothesis by transplanting cuttings and seeds of a Sicilian daisy within and outside its native range, and quantifying variation in morphology, physiology, gene expression and fitness. We show that genetic variance in plasticity increases the potential for rapid adaptation to novel environments. Genetic variation in fitness was low across native environments where plasticity effectively tracked familiar environments. In the novel environment however, genetic variation in fitness increased threefold, and correlated with genetic variation in plasticity. Furthermore, genetic variation that can increase fitness in the novel environment had the lowest fitness at the native site, suggesting that adaptation to novel environments relies on genetic variation in plasticity that is selected against in native environments.

Decades-old model of slow adaptation in sensory hair cells is not supported in mammals

Hair cells detect sound and motion through a mechano-electric transduction (MET) process mediated by tip links connecting shorter stereocilia to adjacent taller stereocilia. Adaptation is a key feature of MET that regulates a cell’s dynamic range and frequency selectivity. A decades-old hypothesis proposes that slow adaptation requires myosin motors to modulate the tip-link position on taller stereocilia. This “motor model” depended on data suggesting that the receptor current decay had a time course similar to that of hair-bundle creep (a continued movement in the direction of a step-like force stimulus). Using cochlear and vestibular hair cells of mice, rats, and gerbils, we assessed how modulating adaptation affected hair-bundle creep. Our results are consistent with slow adaptation requiring myosin motors. However, the hair-bundle creep and slow adaptation were uncorrelated, challenging a critical piece of evidence upholding the motor model. Considering these data, we propose a revised model of hair cell adaptation.

Fast and slow ships from China: The speed of Dutch East Indiamen reconsidered

This article questions the commonly held assumption that the ships of the Dutch East India Company VOC were slower than those of other East India companies. Recently, Solar and De Zwart showed that Dutch ships were slower on outward voyages to a number of Asian destinations during the periods 1770–1775 and 1783–1792. They cited as plausible explanations differences in ship design resulting from constraints imposed by the Dutch shallow inland waterways and the slow adaptation of copper sheathing in the late eighteenth century. Research by the author of this article leads to a critical assessment of these explanations. Moreover, additional new research into homebound voyages from China undertaken by ships of four East India companies, for the periods 1730–1740, 1750–1755, 1770–1775 and 1783–1792, leads to the outcome that – concerning speed – Dutch ships could compete very well with those of the English, Swedish and Danish companies.

Continuous, multidimensional coding of 3D complex tactile stimuli by primary sensory neurons of the vibrissal system

To reveal the full representational capabilities of sensory neurons, it is essential to observe their responses to complex stimuli. In the rodent vibrissal system, mechanical information at the whisker base drives responses of primary sensory neurons in the trigeminal ganglion (Vg). Studies of how Vg neurons encode stimulus properties are typically limited to 2D analyses and restricted stimulus sets. Here we record from Vg neurons during 3D stimulation while quantifying the complete 3D whisker shape and mechanics. Results show that individual Vg neurons simultaneously represent multiple mechanical features of the stimulus, do not preferentially encode principal components of the stimuli, and represent continuous and tiled variations of all available mechanical information. As a population, the neurons span a continuum of rapid and slow adaptation properties; a binary distinction between these adaptation classes is oversimplified. These results contrast with proposed codes in which Vg neurons segregate into functional classes.

Time course of chromatic adaptation under dynamic lighting

Chromatic adaptation is an extensively studied concept. However, less is known about the time course of chromatic adaptation under gradually-changing lighting. Two experiments were carried out to quantify the time course of chromatic adaptation under dynamic lighting. In the first experiment, a step change in lighting chromaticity was used. The time course of adaptation was well described by the Rinner and Gegenfurtner slow adaptation exponential model [Vision Research, 40(14), 2000], and the adaptation state after saturation differed between observers. In the second experiment, chromatic adaptation was measured in response to two different speeds of lighting chromaticity transitions. An adjusted exponential model was able to fit the observed time course of adaptation for both lighting transition speeds.

During environmental change, cooperation can promote rescue or lead to evolutionary suicide

The adaptation of populations to changing conditions may be affected by interactions between individuals. For example, cooperative interactions may allow populations to maintain high densities, and thus keep track of moving environmental optima. At the same time, changes in population density alter the marginal benefits of cooperative investments, creating a feedback loop between population dynamics and the evolution of cooperation. Here we model how the evolution of cooperation is affected by, and in turn affects, adaptation to a changing environment. We hypothesize that changes in the environment lower population size and thus promote the evolution of cooperation, and that this in turn helps the population keep up with the moving optimum. However, we find that the evolution of cooperation can have qualitatively different effects, depending on which fitness component is reduced by the costs of cooperation. If the costs decrease fecundity, cooperation indeed speeds adaptation by increasing population density; if, in contrast, the costs decrease viability, cooperation may in fact slow adaptation by lowering the effective population size, leading to evolutionary suicide. Thus, we show that cooperation can either promote or—counter-intuitively—hinder adaptation to a changing environment.

S. cerevisiae Strain Lacking Mitochondrial IF3 Shows Increased Levels of Tma19p during Adaptation to Respiratory Growth

After billions of years of evolution, mitochondrion retains its own genome, which gets expressed in mitochondrial matrix. Mitochondrial translation machinery rather differs from modern bacterial and eukaryotic cytosolic systems. Any disturbance in mitochondrial translation drastically impairs mitochondrial function. In budding yeast Saccharomyces cerevisiae, deletion of the gene coding for mitochondrial translation initiation factor 3 - AIM23, leads to an imbalance in mitochondrial protein synthesis and significantly delays growth after shifting from fermentable to non-fermentable carbon sources. Molecular mechanism underlying this adaptation to respiratory growth was unknown. Here, we demonstrate that slow adaptation from glycolysis to respiration in the absence of Aim23p is accompanied by a gradual increase of cytochrome c oxidase activity and by increased levels of Tma19p protein, which protects mitochondria from oxidative stress.