Cool tadpoles from Arctic environments waste fewer nutrients - high gross growth efficiencies lead to low consumer-mediated nutrient recycling in the North.

Endothermic organisms can adapt to short growing seasons, low temperatures and nutrient limitation by developing high growth rates and high gross growth efficiencies (GGEs). Animals with high GGEs are better at assimilating limiting nutrients and thus should recycle (or lose) fewer nutrients. Longer guts in relation to body mass may facilitate higher GGE under resource limitation. Within the context of ecological stoichiometry theory, this study combines ecology with evolution by relating latitudinal life-history adaptations in GGE, mediated by gut length, to its ecosystem consequences, such as consumer-mediated nutrient recycling. In common garden experiments, we raised Rana temporaria tadpoles from two regions (Arctic/Boreal) under two temperature regimes (18/23 °C) crossed with two food quality treatments (high/low-nitrogen content). We measured tadpole GGEs, total nutrient loss (excretion + egestion) rates and gut length during ontogeny. In order to maintain their elemental balance, tadpoles fed low-nitrogen (N) food had lower N excretion rates and higher total phosphorous (P) loss rates than tadpoles fed high-quality food. In accordance with expectations, Arctic tadpoles had higher GGEs and lower N loss rates than their low-latitude conspecifics, especially when fed low-N food, but only in ambient temperature treatments. Arctic tadpoles also had relatively longer guts than Boreal tadpoles during early development. That temperature and food quality interacted with tadpole region of origin in affecting tadpole GGEs, nutrient loss rates and relative gut length, suggests evolved adaptation to temperature and resource differences. With future climate change, mean annual temperatures will increase. Additionally, species and genotypes will migrate north. This will change the functioning of Boreal and Arctic ecosystems by affecting consumer-mediated nutrient recycling and thus affect nutrient dynamics in general. Our study shows that evolved latitudinal adaption can change key ecosystem functions.

DHM-Net: Deep Hypergraph Modeling for Robust Feature Matching.

We present a novel deep hypergraph modeling architecture (called DHM-Net) for feature matching in this paper. Our network focuses on learning reliable correspondences between two sets of initial feature points by establishing a dynamic hypergraph structure that models group-wise relationships and assigns weights to each node. Compared to existing feature matching methods that only consider pair-wise relationships via a simple graph, our dynamic hypergraph is capable of modeling nonlinear higher-order group-wise relationships among correspondences in an interaction capturing and attention representation learning fashion. Specifically, we propose a novel Deep Hypergraph Modeling block, which initializes an overall hypergraph by utilizing neighbor information, and then adopts node-to-hyperedge and hyperedge-to-node strategies to propagate interaction information among correspondences while assigning weights based on hypergraph attention. In addition, we propose a Differentiation Correspondence-Aware Attention mechanism to optimize the hypergraph for promoting representation learning. The proposed mechanism is able to effectively locate the exact position of the object of importance via the correspondence aware encoding and simple feature gating mechanism to distinguish candidates of inliers. In short, we learn such a dynamic hypergraph format that embeds deep group-wise interactions to explicitly infer categories of correspondences. To demonstrate the effectiveness of DHM-Net, we perform extensive experiments on both real-world outdoor and indoor datasets. Particularly, experimental results show that DHM-Net surpasses the state-of-the-art method by a sizable margin. Our approach obtains an 11.65% improvement under error threshold of 5° for relative pose estimation task on YFCC100M dataset. Code will be released at https://github.com/CSX777/DHM-Net.

Hot tadpoles from cold environments need more nutrients--life history and stoichiometry reflects latitudinal adaptation.

1. High-latitude species (and populations within species) are adapted to short and cold summers. They often have high growth and development rates to fully use the short growing season and mature before the onset of winter. 2. Within the context of ecological stoichiometry theory, this study combines ecology with evolution by relating latitudinal life-history adaptations to their molecular consequences in body nutrient composition in Rana temporaria tadpoles. 3. Temperature and food quality were manipulated during the development of tadpoles from Arctic and Boreal origins. We determined tadpole growth rate, development rate, body size and nutrient content, to test whether (i) Arctic tadpoles could realize higher growth rates and development rates with the help of higher-quality food even when food quantity was unchanged, (ii) Arctic and Boreal tadpoles differed in their stoichiometric (and life history) response to temperature changes, (iii) higher growth rates lead to higher tadpole P content (growth rate hypothesis) and (iv) allometric scaling affects tadpole nutrient allocation. 4. We found that especially Arctic tadpoles grew and developed faster with the help of higher-quality food and that tadpoles differed in their stoichiometric (and life history) response to temperature changes depending on region of origin (probably due to different temperature optima). There was no evidence that higher growth rates mediated the positive effect of temperature on tadpole P content. On the contrary, the covariate growth rate was negatively connected with tadpole P content (refuting the growth rate hypothesis). Lastly, tadpole P content was not related to body size, but tadpole C content was higher in larger tadpoles, probably due to increased fat storage. 5. We conclude that temperature had a strong effect on tadpole life history, nutrient demand and stoichiometry and that this effect depended on the evolved life history.

Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond.

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.

Surface Texturing-Plasma Nitriding Duplex Treatment for Improving Tribological Performance of AISI 316 Stainless Steel.

Surface texturing-plasma nitriding duplex treatment was conducted on AISI 316 stainless steel to improve its tribological performance. Tribological behaviors of ground 316 substrates, plasma-nitrided 316 (PN-316), surface-textured 316 (ST-316), and duplex-treated 316 (DT-316) in air and under grease lubrication were investigated using a pin-on-disc rotary tribometer against counterparts of high carbon chromium bearing steel GCr15 and silicon nitride Si3N4 balls. The variations in friction coefficient, mass loss, and worn trace morphology of the tested samples were systemically investigated and analyzed. The results showed that a textured surface was formed on 316 after electrochemical processing in a 15 wt % NaCl solution. Grooves and dimples were found on the textured surface. As plasma nitriding was conducted on a 316 substrate and ST-316, continuous and uniform nitriding layers were successfully fabricated on the surfaces of the 316 substrate and ST-316. Both of the obtained nitriding layers presented thickness values of more than 30 µm. The nitriding layers were composed of iron nitrides and chromium nitride. The 316 substrate and ST-316 received improved surface hardness after plasma nitriding. When the tribological tests were carried out under dry sliding and grease lubrication conditions, the tested samples showed different tribological behaviors. As expected, the DT-316 samples revealed the most promising tribological properties, reflected by the lowest mass loss and worn morphologies. The DT-316 received the slightest damage, and its excellent tribological performance was attributed to the following aspects: firstly, the nitriding layer had high surface hardness; secondly, the surface texture was able to capture wear debris, store up grease, and then provide continuous lubrication.