The Contrasting Role of Water and Acid Within Organic Phase Amphiphile Aggregation

Hypothesis: Amphiphile self-assembly in non-polar media is often enhanced by polar co-solutes, as observed upon amphiphile mediated transport of water and acid into organic solution. Such co-extraction precludes understanding the individual roles of polar solutes upon self-assembly. Using this liquid-liquid extraction (LLE) system as a test-bed, we hypothesize that co-solute competition and hydrogen bond (HB) characteristics cause different size/shape distributions of assembled amphiphiles and alter self-assembly mechanisms in non-polar solvents. Experiments: Concentration dependent classical molecular dynamics simulation and intermolecular network analyses identified the correlating relationships between HB properties of H2O and HNO3 upon the aggregation of N,N,N,N-tetraoctyl-3-oxapentanediamide (TODGA), a prevalent LLE amphiphile extractant. Findings: Concentration dependent competition of hydrogen bonding fundamentally impacts amphiphile self-assembly in non-polar media. H2O bridges TODGA and enhances self-assembly, however as [H2O]org increases, preferential self-solvation leads to large (H2O)n clusters that cause TODGA clusters to sorb to the (H2O)n periphery and form extended aggregation. HNO3 restricts the (H2O)n size by disrupting the HB network. At large [H2O]org, HNO3 modulates TODGA self-assembly from extended to local aggregation. We attribute prior experimental observations to the role of water rather than co-extracted HNO3, thus providing valuable new insight into the means by which extractant aggregation can be tuned.

Southern Blight of Perennial Swallowwort (Vincetoxicum spp.) in New York

Pale swallowwort [Vincetoxicum rossicum (Kleopow) Barbar.] and black swallowwort [Vincetoxicum nigrum (L.) Moench] are invasive perennial viny milkweeds that have become prevalent across natural and managed habitats in northeastern North America. Southern blight of V. rossicum caused by the fungus, Athelia rolfsii (Curzi) C. C. Tu & Kimbr., was reported at a New York county park in 2008, resulting in a decline in V. rossicum stands. The disease outbreak and persistence of the pathogen highlighted the potential of A. rolfsii for Vincetoxicum spp. control. To better characterize A. rolfsii’s pathogenicity and biology, we studied virulence to adult Vincetoxicum spp., spatiotemporal attributes of the Southern blight epidemic at the discovery site over four years, and sclerotial survival over two years. Disease incidence and severity were high for both Vincetoxicum spp. in misting chamber experiments. The spatiotemporal spread patterns of Southern blight in V. rossicum suggest the epidemic in the first year of monitoring (2016) was already highly aggregated and that subsequent spread was limited and resulted in significant local aggregation. Sclerotial survival studies at two locations (Pittsford and Ithaca, New York) demonstrated the A. rolfsii isolates can overwinter in upstate New York and are pathogenic to Vincetoxicum spp. the subsequent season. However, shallow burial of sclerotia more rapidly reduced survival compared with placement on the soil surface. Overwinter survival of A. rolfsii sclerotia in New York is notable as this pathogen is typically associated with sub-tropical and tropical regions. Broadcast applications of the pathogen would be needed for widespread Vincetoxicum control at a site, but even restricting releases to select locations would not prevent pathogen movement off-site via water or machinery. The known risks of the A. rolfsii isolate to other broadleaf plants in natural and agricultural settings suggest a low feasibility of use for the biological control of Vincetoxicum spp.

Research On The Fractal and Percolation Characteristics of Coal-Based Porous Media For Filtration and Impregnation

In order to distinguish the difference in the heterogeneous fractal structure of porous graphite used for filtration and impregnation, the fractal dimensions obtained through the mercury intrusion porosimetry (MIP) along with the fractal theory were used to calculate the volumetric FD of the graphite samples. The FD expression of the tortuosity along with all parameters from MIP test was optimized to simplify the calculation. In addition, the percolation evolution process of mercury in the porous media was analyzed in combination with the experimental data. As indicated in the analysis, the FDs in the backbone formation regions of sample vary from 2.695 to 2.984, with 2.923 to 2.991 in the percolation regions and 1.224 to 1.544 in the tortuosity. According to the MIP test, the mercury distribution in porous graphite manifested a transitional process from local aggregation, gradual expansion, and infinite cluster connection to global connection.

Learning Attributed Graph Representation with Communicative Message Passing Transformer

Constructing appropriate representations of molecules lies at the core of numerous tasks such as material science, chemistry, and drug designs. Recent researches abstract molecules as attributed graphs and employ graph neural networks (GNN) for molecular representation learning, which have made remarkable achievements in molecular graph modeling. Albeit powerful, current models either are based on local aggregation operations and thus miss higher-order graph properties or focus on only node information without fully using the edge information. For this sake, we propose a Communicative Message Passing Transformer (CoMPT) neural network to improve the molecular graph representation by reinforcing message interactions between nodes and edges based on the Transformer architecture. Unlike the previous transformer-style GNNs that treat molecule as a fully connected graph, we introduce a message diffusion mechanism to leverage the graph connectivity inductive bias and reduce the message enrichment explosion. Extensive experiments demonstrated that the proposed model obtained superior performances (around 4% on average) against state-of-the-art baselines on seven chemical property datasets (graph-level tasks) and two chemical shift datasets (node-level tasks). Further visualization studies also indicated a better representation capacity achieved by our model.

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China

Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.

Citizen-Sourced Sightings and Underwater Photography Reveal Novel Insights About Green Sea Turtle Distribution and Ecology in Southern California

East Pacific (EP) green turtles (Chelonia mydas) have undergone substantial population recovery over the last two decades owing to holistic protection at nesting beaches and foraging areas. At the northern end of their range in southern California United States, green turtles have been seen in more areas and in greater numbers since 2014 than before as a result. A resident population of green turtles has established near La Jolla Shores (LJS), a protected site with daily marine tourism (e.g., kayakers, snorkelers, divers). To study this local aggregation, innovative and non-invasive methods were required because the traditional capture-recapture methods were infeasible due to public relations sensitivities. Green turtle habituation to humans at this site has created a unique opportunity for citizen-based science using underwater photography to document turtles and their surroundings. We obtained 309 usable photographs of local green turtles from members of the dive/snorkel community in LJS. Photos were taken from April 2016 to June 2019. Images were processed in Hotspotter—a patterned species instance recognition software—to identify seven individuals, five of which were consistently photographed throughout that period. These images helped infer minimum residency duration (MRD), seasonal differences in algal coverage on the carapace, habitat association, behavioral patterns, and diet. Mean MRD was 424 days (SE = 131 days, calculated from entire population, n = 7), during which turtles were active in 82.8% of the photographs; the remainder of the photographs depicted foraging (14.9%) or resting behavior (2.3%). Green turtles were seen foraging in water temperatures as low as 15.8°C, the lowest recorded temperature for foraging green turtles documented in literature. Additional opportunistic observational platforms were used to look at trends of increasing green turtle abundance in southern California since 2015 that supported the arrival of a new aggregation of green turtles in LJS. Our use of citizen-sourced photographs confirms the presence of a resident aggregation of green turtles in LJS. Existence of green turtles and other protected species in highly populated areas provide excellent opportunities to educate beachgoers and seafarers about conservation of these species. This study also highlights the value of citizen-based science in areas where traditional research techniques are ill-suited.

Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush

With the advent of big data, the use of network data to characterize travel has gradually become a trend. Tencent Migration big data can fully, dynamically, immediately, and visually record the trajectories of population migrations with location-based service technology. Here, the daily population flow data of 346 cities during the Spring Festival travel rush in China were combined with different travel modes to measure the spatial structure and spatial patterns of an intercity trip network of Chinese residents. These data were then used for a comprehensive depiction of the complex relationships between the population flows of cities. The results showed that there were obvious differences in the characteristics of urban networks from the perspective of different modes of travel. The intercity flow of aviation trips showed a core-periphery structure with national hub cities as the core distribution. Trips by train showed a core-periphery structure with cities along the national railway artery as the core. This gradually decreased toward hinterland cities. Moreover, the intercity flow of highway trips indicated a spatial pattern of strong local aggregation that matched the population scale.