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Corylus yunnanensis (Franch.) A. Camus is a deciduous shrub, native to the Hengduan Mountain of Qinghai–Tibetan Plateau, and is an economically and ecologically important woody crop species. In the present study, one hundred and fifty trees sampled from ten populations of C. yunnanensis in Sichuan Province were investigated to assess the population genetic variation using nine SSR markers. The results revealed that C. yunnanensis has an average value of 12.111 alleles, 3.376 effective alleles, an expected heterozygosity of 0.648, and an observed heterozygosity of 0.630, presenting a relatively high level of genetic diversity. The C. yunnanensis populations in Maoxian and Wenchuan of Aba Prefecture expressed the highest value of genetic diversity, whereas the Hanyuan and Muli populations showed the lowest. Moreover, the genetic differentiation of ten C. yunnanensis populations averaged to 0.106. Correspondingly, AMOVA revealed that 87% of the total variance was accounted for the variation within populations, and only 13% was among the populations. Both UPGMA and Bayesian STRUCTURE clustering suggested that the ten C. yunnanensis populations could fall into three clusters: the Aba Prefecture population, the Ya’an population, and the population of Ganzi and Liangshan Prefecture, indicating a significant geographic distribution, which was also confirmed by the Mantel test. Our study could provide a better understanding of population genetic diversity, and serve valuable information for the genetic improvement of C. yunnanensis.

A rare instance in platinum catalysis of an in situ formed Pt-bound benzopyrylium species that undergoes [3 + 2] cycloaddition with an electron-deficient alkene tether followed by THF-enabled hydrogenation.

HighlightsThe fuzzy transplanting requirements were transformed into optimization targets.A transplanting mechanism constrained by differential noncircular gear trains was designed.The optimal software design of the transplanting mechanism was developed.The designed transplanting mechanism realized the trajectory and attitude required for rice transplanting.Abstract. The transplanting mechanism is the core working part of a rice transplanter, and its working cycle includes the stages of picking, transporting, planting, and returning. Aimed at the problems of poor working precision, poor trajectory and attitude, and low operating efficiency of the existing transplanting mechanism of rice transplanters, a transplanting mechanism with three planting arms constrained by differential noncircular gear trains was designed to avoid the side gap accumulation caused by unequal multistage speed transmission. The movement of the 2R open-chain mechanism was constrained by a differential noncircular gear train so that the connecting rod could realize the trajectory and attitude required for rice transplanting. Based on the unequal speed transmission of differential noncircular gear trains, a kinematic model of the transplanting mechanism with three planting arms was established, and the optimal software design of the transplanting mechanism was developed in a visual programming environment. Based on the agronomic requirements for rice transplanting, the optimization design targets of the transplanting mechanism were customized, the functional relationship between the optimization targets and parameters was established, and the optimization targets were embedded into the optimal software design. Through parameter optimization, a set of mechanism parameters meeting the requirements of the trajectory and attitude for rice transplanting were obtained, and the structural design of the transplanting mechanism was completed. Virtual simulation experiments and bench experiments with the transplanting mechanism with three planting arms constrained by differential noncircular gear trains showed that the transplanting mechanism accurately realized the trajectory and attitude required for rice transplanting. Keywords: 2R open-chain mechanism, Differential gear, Noncircular gear, Rice transplanter, Transplanting mechanism.

Context Prioritizing regions that facilitate connectivity among populations is an essential principle for conservation planning. However, the lack of conspicuous geographical and environmental features that constrain dispersal and geneflow throughout life history challenges the characterization of dispersal pathways within a three-dimensional marine realm. Objectives To elucidate regions of high connectivity value in the marine environment, we develop a novel approach that integrates estimates of spatial genetic structure with representation of regions of high dispersal potential for meroplankton, incorporating elements of pelagic larval and benthic adult life history. Methods Spatial patterns of connectivity were characterized using circuit theory as an inverse function oceanographic- and habitat-based resistance to movement. We integrate emergent spatial patterns of connectivity with population genetic data to account for realized patterns of geneflow across a seascape. We apply this approach to four broadly distributed species in the Northwest Atlantic. Results Estimates of resistance to gene flow revealed multiple connectivity barriers not observed in oceanographic or habitat models. Comparison of isolation-by-distance versus isolation-by-resistance revealed genetic variation was best explained by seascape resistance in three of four species, supporting the resistance-based assessments of connectivity. Our approach identified areas of high and low connectivity value for each species, with overlap generally associated with geographic pinch points and areas of low genetic exchange. Conclusions By integrating spatial interpolations of gene flow and estimated pathways for dispersal, we develop a novel area-based metric of connectivity that considers life-history based structural constraints to dispersal and observed genetic variation. Outputs from this workflow can reveal regions of connectivity for conservation planning.

Transmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, namely Prediction of TransMembrane protein Crystallization propensity (PTMC), to predict the propensity of transmembrane protein crystallization. First, we obtained several general sequence features and the specific encoded features of relative solvent accessibility and hydrophobicity. Second, feature selection was employed to filter out redundant and irrelevant features, and the optimal feature subset is composed of hydrophobicity, amino acid composition and relative solvent accessibility. Finally, we chose extreme gradient boosting by comparing with other several machine learning methods. Comparative results on the independent test set indicate that PTMC outperforms state-of-the-art sequence-based methods in terms of sensitivity, specificity, accuracy, Matthew's Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC). In comparison with two competitors, Bcrystal and TMCrys, PTMC achieves an improvement by 0.132 and 0.179 for sensitivity, 0.014 and 0.127 for specificity, 0.037 and 0.192 for accuracy, 0.128 and 0.362 for MCC, and 0.027 and 0.125 for AUC, respectively.

The hydrogen evolution effect of ZrS

Recovered archival satellite tags afford a rare opportunity to evaluate diving behavior relative to ocean conditions at fine spatiotemporal scales. Ocean temperature and mixed layer depth (MLD) were strongly related to daily and seasonal patterns in diving behavior of an adult female porbeagle shark Lamna nasus. The shark remained below MLD when waters were strongly stratified in summer or in the Gulf Stream; the daily timing of dives was associated with local sunrise and sunset. In the presence of a hurricane, diving activity abruptly increased, coincident with upwelling causing an abrupt decrease in surface temperature. Our first report of the behavioral response to a hurricane as well as the seasonal patterns in depths, occupied temperatures, and vertical speeds suggest environmental preferences of porbeagle are not static and individuals may use horizontal, as well as vertical, movement to take advantage of areas with specific characteristics.

Early detection and management of aquatic invasive species requires identification of those areas most at risk of invasion (i.e., hotspots). Here we identify present-day and future hotspots of invasion risk for marine invertebrates and algae in nearshore habitats of the northwest Atlantic and northeast Pacific using more than 12 years of monitoring data in conjunction with other occurrence data and stacked species distribution models. The stacked species distribution models predicted the general patterns of observed invasive species richness in both study areas (Atlantic: r2 = 0.52, Pacific: r2 = 0.42). In the northwest Atlantic, we identified hotspots through much of Massachusetts, New Hampshire and southern Maine, and in several bays in southwestern New Brunswick and Nova Scotia. In the northeast Pacific, much of the southern Salish Sea was identified as a hotspot, as were a few areas along the outer coast of Washington and Oregon. Projecting our species distribution modelling results to 2075 (climate scenario RCP 8.5), we found that existing hotspots are likely to expand slightly in the Atlantic, while in the Pacific existing hotspots are predicted to shift or expand, new hotspots are likely to appear, and areas with few invasive species attaining moderate invasive species richness. Our results suggest that climate change will have larger effects on the distributions of our focal invasive species on the Pacific coast compared to the Atlantic. Resultant hotspot maps provide an integrated perspective and guidance to managers tasked with prioritizing locations for monitoring and implementing policy related to marine invasive species, with projected hotspots making planning for future changes in invasion risk possible.

Northern shrimp (Pandalus borealis) represents one of the most important fisheries in the Northwest Atlantic Ocean, but few studies have considered connectivity among different management units (i.e., stocks). Using a biophysical model, we investigated potential larval dispersal among North Atlantic Fisheries Organization (NAFO) divisions and its interannual variability during the long pelagic larval phase of P. borealis (∼3 months). Overall, we found a largely stable, stream-like larval connectivity system driven by the main currents that flow over both the Greenland and Canadian continental shelves, with a relatively low but consistent exchange of larvae between Greenland and Canada across the Baffin Island continental shelf. We observed highest potential settlement densities on the northwestern Greenland and Newfoundland shelves, representing retention areas that correspond to highest abundances of adult shrimp. Intermittent and variable larval exchanges of lower magnitude also occurred between populations less obviously associated with the major circulation features. Our study improves understanding of northern shrimp stock–recruitment relationships at the metapopulation level, which could help determine the appropriate spatial scale to improve management strategies.

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, these schemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community-level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the Northwest Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3-34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage distributions from environmental covariates (AUC 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important predictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage distributions in the southernmost bioregion (Scotian Shelf-Bay of Fundy) under a high emissions climate scenario (RCP 8.5). Range expansions to the northeast are projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the relatively cooler Eastern Scotian Shelf becomes more favorable. Community-level modeling provides a biotic-informed approach for identifying broadscale ecological structure required for the design and management of ecologically coherent, representative, well-connected networks of Marine Protected Areas. When combined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.

According to the abrasive morphology and distribution of sandpaper surface, the finite element model of hard anodic oxide film polishing was established by using ABAQUS finite element software, and the hard anodic oxide film and aluminum alloy matrix were defined respectively. The effects of hard anodic oxide film polishing process, polishing surface quality and polishing parameters on the polishing force were simulated. The polishing surface quality and polishing force simulated by finite element model were verified by experiments. The results show that in the polishing process, the residual stress of polishing surface is mainly distributed in the range of 0.0034mm, and the chips produced by polishing are mainly broken and long strips with microscopic morphology, and polishing mainly depends on the front part of abrasive particles in the feeding direction of sandpaper. Under the action of abrasive particles of sandpaper, the results of finite element simulation and test show that there are certain grooves on the surface of oxide film. The finite element simulation of the normal polishing force of hard anodic oxide film is consistent with the polishing test results. During the process of polishing hard anodic oxide film, the normal polishing force increases as the polishing speed increases; the normal polishing force increases as the polishing removal amount increases; the polishing removal amount parameter has a greater degree of influence on the polishing force.

An experimental study was conducted for the surface polishing of the oxide film after hard anodizing of 2D70 aluminum alloy. The surface roughness and surface morphology of aluminum alloy before, after anodizing and after polishing were analyzed, and the effects of polishing force, polishing speed, feeding amount and sandpaper grit on the polished surface roughness were investigated experimentally. The results showed that the surface roughness of 2D70 aluminum alloy decreased by 1 grade on average after hard anodizing. The hard anodized film surface has more linear protrusions, large pits and a large number of small pores. After polishing, the linear protrusions and small pores disappear, and a small number of new linear grooves appear, while the condition of large pits does not change much. The depth of small pores on the oxide film surface is within 11μm in most cases. As the polishing force, feeding amount and sandpaper grit increase, the decreasing trend of roughness value also gradually increases, and the decreasing trend of the thickness of hard anodic oxide film gradually increases. As the polishing speed increases, there is no clear trend in the decreasing trend of rough

The size of photovoltaic power generation has a certain uncertainty, which affects the quality of photovoltaic power generation, and affects the safety and stable operation of the power grid. By predicting the photovoltaic power generation, this problem can be better solved. A photovoltaic power generation power prediction method combining SOA (Seagull optimization algorithm) and BP neural network is proposed. The convergence speed of BP neural network prediction model is relatively slow, and it is easy to fall into local optimum. Optimizing its weights and thresholds through SOA can improve these situations. Through experiments, Through experimental comparison, it is found that the BP neural network method optimized by seagull algorithm has higher accuracy in predicting photovoltaic power generation.

The stability of functional carriers single-atom catalysts can be effectively guaranteed by using stable mineral materials to support low dimensional catalytic materials. In this paper, the theoretical calculation of electrochemical hydrogen evolution reaction (HER) of the composite functional single-atom catalysts supported by single-atom Ni was carried out using first-principles method. And the original structure of MoS

The visualization of complex geological structures can provide technical support for accurate prediction and prevention of coal mine disasters. Taking fault structure as an example, this study proposes a new digital reconstruction method to realize the visualization of geological structure. The methodology for the digital reconstruction of complex fault structures is composed by the following four aspects, including collection and fidelity of multi-physical field data of fault structure, transmission of multi-physical field data, multi-physical data normalization and digital model of fault structures. The key scientific issues of this methodology to be resolved includes in-situ fidelity of multi-field data and normalized programming of multi-source data. In addition, based on the geological conditions in Da 'anshan coal mine in the mining area of western Beijing, China, this paper makes a preliminary attempt to establish a digital model of fault and fold structures by this methodology.

Shallow-water sponges are often cited as being ‘climate change winners’ due to their resiliency against climate change effects compared to other benthic taxa. However, little is known of the impacts of climate change on deep-water sponges. The deep-water glass sponge Vazella pourtalesii is distributed off eastern North America, forming dense sponge grounds with enhanced biodiversity on the Scotian Shelf off Nova Scotia, Canada. While the strong natural environmental variability that characterizes these sponge grounds suggests this species is resilient to a changing environment, its physiological limitations remain unknown, and the impact of more persistent anthropogenic climate change on its distribution has never been assessed. We used Random Forest and generalized additive models to project the distribution of V. pourtalesii in the northwest Atlantic using environmental conditions simulated under moderate and worst-case CO2 emission scenarios. Under future (2046-2065) climate change, the suitable habitat of V. pourtalesii will increase up to 4 times its present-day size and shift into deeper waters and higher latitudes, particularly in its northern range where ocean warming will serve to improve the habitat surrounding this originally sub-tropical species. However, not all areas projected as suitable habitat in the future will realistically be populated, and the reduced likelihood of occurrence in its core habitat on the Scotian Shelf suggests that the existing Vazella sponge grounds may be negatively impacted. An effective monitoring programme will require tracking changes in the density and distribution of V. pourtalesii at the margins between core habitat and where losses and gains were projected.

As one of the most catastrophic dynamic hazards in underground coal mines, coal bursts have been a major safety concern around the world for many years. Although the coal bursts can occur in all cases of hard to soft coal if the right stress environment is created, the occurrence of coal bursts is closely related to the intrinsic mechanical properties of coal, such as the bursting proneness. In this study, a total of 27 coal specimens are selected in the open literature studies to obtain a group of fundament data, such as the mechanical parameters, four bursting proneness indices, stress-strain curves, and their geological conditions where the specimens were taken. The relationship between bursting proneness indices and the cohesion of the coal specimens is established by numerically fitting the stress-strain curves and theoretically deduction. By taking into account the coal heterogeneity, eight probability distribution functions are employed to assignment nonuniform cohesion to the numerical model and to study the influence of heterogeneity on bursting proneness. The results reveal that the coal cohesion, which combines the common advantages of the four proneness indices, can be used as bursting proneness index. In the research of heterogeneity, the coal bursting proneness will decrease with the increasing of cohesion scatter degree. The larger the cohesion scatter degree increase is, the lower the bursting proneness will be. The failure of coal specimen is more and more severe with the decrease of cohesion scatter degree. In addition, this paper provides two methods for assigning heterogeneous parameters to the numerical model. The contours of shear strain rate and plastic state between homogeneous and heterogeneous coal specimens are compared to study the failure types of coal specimens and to reveal the mechanism of violent failure in coal bursts.

The Gulf of Maine (GoM) is currently experiencing its warmest period in the instrumental record. Two high-resolution numerical ocean models were used to downscale global climate projections to produce four estimates of ocean physical properties in the GoM in 2050 for the “business as usual” carbon emission scenario. All simulations project increases in the GoM mean sea surface temperature (of 1.1 °C–2.4 °C) and bottom temperature (of 1.5 °C–2.1 °C). In terms of mean vertical structure, all simulations project temperature increases throughout the water column (surface-to-bottom changes of 0.2 °C–0.5 °C). The GoM volume-averaged changes in temperature range from 1.5 °C to 2.3 °C. Translated to rates, the sea surface temperature projections are all greater than the observed 100-year rate, with two projections below and two above the observed 1982–2013 rate. Sea surface salinity changes are more variable, with three of four simulations projecting decreases. Bottom salinity changes vary spatially and between projections, with three simulations projecting varying increases in deeper waters but decreases in shallower zones and one simulation projecting a salinity increase in all bottom waters. In terms of mean vertical structure, salinity structure varies, with two simulations projecting surface decreases that switch sign with depth and two projecting increases throughout the (subsurface) water column. Three simulations show a difference between coastal and deeper waters whereby the coastal zone is projected to be systematically fresher than deeper waters, by as much as 0.2 g kg–1. Stratification, 50 m to surface, is projected to increase in all simulations, with rates ranging from 0.003 to 0.006 kg m–4 century–1 which are lower than the observed change on the Scotian Shelf. The results from these simulations can be used to assess potential acidification and ecosystem changes in the GoM.

Transmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, namely Prediction of TransMembrane protein Crystallization propensity (PTMC), to predict the propensity of transmembrane protein crystallization. First, we obtained several general sequence features and the specific encoded features of relative solvent accessibility and hydrophobicity. Second, feature selection was employed to filter out redundant and irrelevant features, and the optimal feature subset is composed of hydrophobicity, amino acid composition and relative solvent accessibility. Finally, we chose extreme gradient boosting by comparing with other several machine learning methods. Comparative results on the independent test set indicate that PTMC outperforms state-of-the-art sequence-based methods in terms of sensitivity, specificity, accuracy, Matthew's Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC). In comparison with two competitors, Bcrystal and TMCrys, PTMC achieves an improvement by 0.132 and 0.179 for sensitivity, 0.014 and 0.127 for specificity, 0.037 and 0.192 for accuracy, 0.128 and 0.362 for MCC, and 0.027 and 0.125 for AUC, respectively.

This paper explores continuous pressure wave characteristics and flow features in a periodic transient flow induced by a rotary pressure wave generator. A three-dimensional CFD model based ...

The stability of functional carriers single-atom catalysts can be effectively guaranteed by using stable mineral materials to support low dimensional catalytic materials. In this paper, the theoretical calculation of electrochemical hydrogen evolution reaction (HER) of the composite functional single-atom catalysts supported by single-atom Ni was carried out using first-principles method. And the original structure of MoS2, amorphous structure and S-vacancy structure are studied. Through the analysis and discussion of electronic properties, adsorption energy and active sites, it is found that Ni@Amorphous MoS2-FeS has excellent effect of hydrogen evolution in acidic environment, ΔG H is 0.312 eV, and the other two structures supporting Ni single-atom also have excellent HER properties in a wide range of pH. This design broadens the research idea of single-atom catalysts carriers and provides a new direction for the research and development of electrocatalytic materials.

In 2012, 2014, and 2015 anomalous warm events were observed in the subsurface waters in the Scotian Shelf region of eastern Canada. Monthly output from a high resolution numerical ocean model simulation of the North Atlantic ocean for the period 1990–2015 is used to investigate this phenomenon. It is found that the model shows skill in simulating the anomaly fields derived from various sources of data, and the observed warming trend over the last decade. From analysis of the model run it is found that the anomalies originate from the interaction between the Gulf Stream and the Labrador Current at the tail of the Grand Banks (south of Newfoundland). This interaction results in the creation of anomalous warm/salty (or cold/fresh) eddies that travel east-to-west along the shelfbreak. These anomalies penetrate into the Gulf of St. Lawrence, onto the Scotian Shelf, and into the Gulf of Maine via deep channels along the shelfbreak. The observed warming trend can be attributed to an increase in the frequency of creation of warm anomalies during the last decade. Strong anomalous events are commonly observed in the data and model, and thus should be considered as part of the natural variability of the coupled atmosphere-ocean system.

Correction for ‘THF-enabled PtBr2-catalyzed desymmetric hydrogenative [3 + 2] cycloaddition of 2-alkynylbenzaldehyde-tethered cyclohexadienones’ by Rui Hu et al., Org. Chem. Front., 2022, https://doi.org/10.1039/d2qo00593j.