Explore the vast range of titles available.

Atomic Force Microscopy (AFM) analysis of single cells, especially nonadherent, is inherently slow and analysis-heavy. To address the inherent difficulty of measuring individual cells, and to scale up toward a large number of cells, we take a two-fold approach; first, we introduce an easy-to-fabricate reusable poly(dimethylsiloxane)-based array that consists of micron-sized traps for single-cell trapping, second, we apply a deep-learning method directly on the extracted curves to facilitate and automate the analysis. Our approach is validated using suspended cells, and by applying a small compression with a tipless cantilever AFM probe, we investigate the effect of various cytoskeletal drugs on their deformability. We then apply deep learning models to extract the elasticity of the cell directly from the raw data (with a Coefficient of Determination of 0.47) as well as for binary (with an Area Under the Curve score of 0.91) and multi-class classification (with accuracy scores exceeding 0.9 for each drug). Overall, the versatility to fabricate the microwells in conjunction with the automated analysis and classification streamline the analysis process and demonstrate their ability to generalize to other tasks, such as drug detection.

The aqueous system is being polluted by the untreated direct discharge of industrial oily wastewater into the ecosystem. Due to its low cost, energy economy, and sustainability, the advanced membrane filtration method is regarded as one of the best methods for treating oily wastewater. Its exceptional atomic thickness and superior amphiphilic properties of graphene oxide (GO) nanosheet make it one of the finest 2D constituents for creating membranes with high permeability. Nevertheless, the interlayer d-spacing of multi-stacked GO membranes is crucial since it is responsible for the permeability/selectivity trade-off. To efficiently separate oil-in-water emulsion using an in-situ polymerization technique, we developed an aquaporin-like 3D hierarchical multi-functionalized nanoporous graphene (NPG) membrane with tripartite nanochannels. The extraordinary, prepared membrane displayed both ultra-water-permeability of 2490 L m −2 h −1 .bar along with superior selectivity. Consequently, the permeance of the aquaporin-like 3D hierarchical multi-functionalized NPG membrane achieves a higher flux than the GO membrane, while the oil rejection reaches ~97%.

Baffin Bay, located at the Arctic Ocean’s ‘doorstep’, is a heterogeneous environment where a warm and salty eastern current flows northwards in the opposite direction of a cold and relatively fresh Arctic current flowing along the west coast of the bay. This circulation affects the physical and biogeochemical environment on both sides of the bay. The phytoplanktonic species composition is driven by its environment and, in turn, shapes carbon transfer through the planktonic food web. This study aims at determining the effects of such contrasting environments on ecosystem structure and functioning and the consequences for the carbon cycle. Ecological indices calculated from food web flow values provide ecosystem properties that are not accessible by direct in situ measurement. From new biological data gathered during the Green Edge project, we built a planktonic food web model for each side of Baffin Bay, considering several biological processes involved in the carbon cycle, notably in the gravitational, lipid, and microbial carbon pumps. Missing flow values were estimated by linear inverse modeling. Calculated ecological network analysis indices revealed significant differences in the functioning of each ecosystem. The eastern Baffin Bay food web presents a more specialized food web that constrains carbon through specific and efficient pathways, leading to segregation of the microbial loop from the classical grazing chain. In contrast, the western food web showed redundant and shorter pathways that caused a higher carbon export, especially via lipid and microbial pumps, and thus promoted carbon sequestration. Moreover, indirect effects resulting from bottom-up and top-down control impacted pairwise relations between species differently and led to the dominance of mutualism in the eastern food web. These differences in pairwise relations affect the dynamics and evolution of each food web and thus might lead to contrasting responses to ongoing climate change.

We monitored the spatiotemporal progression of dissolved organic carbon (DOC) and carbon monoxide (CO), along with general meteorological, hydrographic, and biological variables, in first‐year sea ice in the western Canadian Arctic between mid‐March and early July 2008. DOC and CO concentrations fluctuated irregularly in surface ice, but followed the concentration of ice algae in bottom ice, i.e., low at the start of ice algal accumulation, highly enriched during the peak‐bloom and early post‐bloom, and depleted again during sea ice melt. Vertical profiles of DOC and CO typically decreased downward in early spring and were variable in the melting season. In the presence of high bottom ice algal biomass in mid‐spring, DOC and CO exhibited high concentrations in the bottom (DOC: 563 ± 434 μmol L−1; CO: 82.9 ± 84 nmol L−1) relative to the surface (DOC: 56 ± 26 μmol L−1; CO: 16.8 ± 7 nmol L−1). Landfast ice contained higher levels of DOC and CO than drifting ice. Cruise‐mean DOC and CO inventories in sea ice were 87 ± 51 mmol m−2 and 13.9 ± 10 μmol m−2, respectively. Net productions of DOC and CO linked to the ice algal bloom were assessed to be 75 mmol m−2 and 13.2 μmol m−2. Sea ice in the study area was estimated to contribute 7.4 × 107 moles of CO a−1 to the atmosphere. This study suggests that sea ice plays important roles in the cycling of organic carbon and trace gases. Key Points DOC and CO are enriched in the bottom of Arctic landfast and drifting sea ice DOC production is a significant fraction of ice algal production Sea ice is a source of CO to the atmosphere

Imaging techniques are increasingly used in ecology studies, producing vast quantities of data. Inferring functional traits from individual images can provide original insights on ecosystem processes. Morphological traits are, as other functional traits, individual characteristics influencing an organism’s fitness. We measured them from in situ image data to study an Arctic zooplankton community during sea ice break-up. Morphological descriptors (e.g., area, lightness, complexity) were automatically measured on ∼ 28,000 individual copepod images from a high-resolution underwater camera deployed at more than 150 sampling sites across the ice-edge. A statistically-defined morphological space allowed synthesizing morphological information into interpretable and continuous traits (size, opacity, and appendages visibility). This novel approach provides theoretical and methodological advantages because it gives access to both inter- and intra-specific variability by automatically analyzing a large dataset of individual images. The spatial distribution of morphological traits revealed that large copepods are associated with ice-covered waters, while open waters host smaller individuals. In those ice-free waters, copepods also seem to feed more actively, as suggested by the increased visibility of their appendages. These traits distributions are likely explained by bottom-up control: high phytoplankton concentrations in the well-lit open waters encourages individuals to actively feed and stimulates the development of small copepod stages. Furthermore, copepods located at the ice edge were opaquer, presumably because of full guts or an increase in red pigmentation. Our morphological trait-based approach revealed ecological patterns that would have been inaccessible otherwise, including color and posture variations of copepods associated with ice-edge environments in Arctic ecosystems.

We defined mesozooplankton biogeography in the North American Arctic to elucidate drivers of biodiversity, community structure, and biomass of this key component of the Arctic marine ecosystem. A multivariate analysis identified four mesozooplankton assemblages: Arctic-oceanic, Arctic-shelf, Coastal-Hudson, and Labrador Sea. Bathymetry was a major driver of the distribution of these assemblages. In shallow waters, Cirripedia and the copepod Pseudocalanus spp. dominated the Coastal-Hudson and Arctic-shelf assemblages, which showed low species richness (19) and biomass (0.28 and 1.49 g C m−2, respectively). The Arctic-oceanic assemblage occupied the entire North American Arctic, except for shallow breaks in the Canadian Arctic Archipelago downstream of sills blocking the Atlantic Water layer circulation below a depth of 200 m. This assemblage showed high copepod biomass (4.74 g C m−2) with a high share of Calanus hyperboreus, C. glacialis, and Metridia longa. In habitats below 200-m depth, C. hyperboreus represented 68% of the copepod biomass, underscoring its role as a keystone species in this ecosystem. Strong numerical representation by the boreal-Atlantic C. finmarchicus and Oithona atlantica stressed the strong Atlantic influence on the subarctic Labrador Sea assemblage on the northwestern Labrador Sea slope. The mixed Arctic-Atlantic composition of the Labrador Sea mesozooplankton resulted in high species richness (58) and biomass (5.73 g C m−2). The low abundance of Atlantic and Pacific taxa in the areas influenced by Arctic currents did not alter the Arctic status of the Arctic-oceanic, Arctic-shelf, and Coastal-Hudson assemblages. This study identifies hotspots of mesozooplankton biomass and diversity in Central Amundsen Gulf, Lancaster Sound, North Water Polynya and Baffin Bay, known for their high biological productivity and concentrations of vertebrate predators. The continental-scale zooplankton mapping furthers our understanding of the importance of bathymetry and ocean circulation for ecological connectivity in a vast and complex portion of the Arctic marine ecosystem.

The Green Edge initiative was developed to investigate the processes controlling the primary productivity and fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797∘ N, 63.7895∘ W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea-ice cover from the surface to the bottom (at 360 m depth) to better understand the factors driving the PSB. Key variables, such as conservative temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured at the ice camp. Meteorological and snow-relevant variables were also monitored. Here, we present the results of a joint effort to tidy and standardize the collected datasets, which will facilitate their reuse in other Arctic studies. The dataset is available at https://doi.org/10.17882/59892 (Massicotte et al., 2019a).

PurposeMechanisms of circulatory failure are complex and frequently intricate in septic shock. Better characterization could help to optimize hemodynamic support.MethodsTwo published prospective databases from 12 different ICUs including echocardiographic monitoring performed by a transesophageal route at the initial phase of septic shock were merged for post hoc analysis. Hierarchical clustering in a principal components approach was used to define cardiovascular phenotypes using clinical and echocardiographic parameters. Missing data were imputed.FindingsA total of 360 patients (median age 64 [55; 74]) were included in the analysis. Five different clusters were defined: patients well resuscitated (cluster 1, n = 61, 16.9%) without left ventricular (LV) systolic dysfunction, right ventricular (RV) failure or fluid responsiveness, patients with LV systolic dysfunction (cluster 2, n = 64, 17.7%), patients with hyperkinetic profile (cluster 3, n = 84, 23.3%), patients with RV failure (cluster 4, n = 81, 22.5%) and patients with persistent hypovolemia (cluster 5, n = 70, 19.4%). Day 7 mortality was 9.8%, 32.8%, 8.3%, 27.2%, and 23.2%, while ICU mortality was 21.3%, 50.0%, 23.8%, 42.0%, and 38.6% in clusters 1, 2, 3, 4, and 5, respectively (p < 0.001 for both).ConclusionOur clustering approach on a large population of septic shock patients, based on clinical and echocardiographic parameters, was able to characterize five different cardiovascular phenotypes. How this could help physicians to optimize hemodynamic support should be evaluated in the future.