Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of exotic common reed (Phragmites australis) on wood frog (Lithobates sylvaticus) tadpole development and food availability

Exotic species contribute to aquatic habitat degradation. In the context of declining amphibian populations, the introduction of alien species has been the subject of numerous studies but few have been dedicated to exotic plants. We hypothesized that the establishment of the exotic common reed (Phragmites australis) in North America would lengthen larval anuran development and decrease the survival rate by modifying habitat structure, changing water characteristics, and decreasing food availability. We tested these hypotheses by studying the larval development of the wood frog (Lithobates sylvaticus) during an experiment in field enclosures. Within each enclosure, we created habitats with three different reed densities (zero, medium, and high). Tadpoles were placed in the enclosures and were followed for six weeks up to metamorphosis, during which we monitored water quality and phytoplankton composition. At the end of the experiment, tadpoles at medium and high reed densities developed more slowly than under the control. However, tadpole survival was similar across treatments. For a given developmental stage, total body length did not differ among treatments. Phytoplankton abundance varied with reed density, and groups known to be consumed by tadpoles were negatively influenced by reed density. We found no impact of reed density on pH, total phenolic concentration, or conductivity. Our results suggest that common reed establishment can influence amphibians with rapid development such as wood frogs. Though larval survival rates were similar across treatments, slower development under high reed densities implies a longer exposure to the risk of the pond drying out and to predators.
The migration of sessile organisms: a simulation model with measurable parameters

Abstract. Palaeoecologists have shown that trees migrated at rates of 100–1000 m/yr in response to post-glacial warming. In order to predict the impact of forecast anthropogenic climate changes upon forest ecosystems we need to simulate how trees may migrate in response to the changes predicted for the next 1–2 centuries. These predictions must take account of the impacts upon migration of human land-use and habitat fragmentation. We have developed a spatially-explicit mechanistic model (MIGRATE) able to simulate the migration of a single species across a realistically heterogeneous landscape. MIGRATE uses biological parameters that readily may be estimated from data in the literature or from field studies, and represents the landscape as a grid of cells, each with an associated carrying capacity. A one-dimensional version of MIGRATE has been compared both with Skellam's (1951) diffusion model and with the more recent analytical models of van den Bosch et al. (1990, 1992); despite its fundamentally different approach, MIGRATE provides comparable estimates of migration rates, given equivalent input parameters. An example is described that demonstrates the ability of the two-dimensional version of MIGRATE to simulate the likely pattern of spread of a species across a heterogeneous landscape. It is argued that MIGRATE, or models like it, will play a central role in a spatially-hierarchic modelling strategy that must be developed if we are to achieve the goal of simulating the likely response of trees, and other organisms, to both global climate change and the increasing pressures of human land-use.
Downscale MODIS Land Surface Temperature Based on Three Different Models to Analyze Surface Urban Heat Island: A Case Study of Hangzhou

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.
Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas

Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.
EZ-sketching Three-level optimization for error-tolerant image tracing

We present a new image-guided drawing interface called EZ-Sketching , which uses a tracing paradigm and automatically corrects sketch lines roughly traced over an image by analyzing and utilizing the image features being traced. While previous edge snapping methods aim at optimizing individual strokes, we show that a co-analysis of multiple roughly placed nearby strokes better captures the user’s intent. We formulate automatic sketch improvement as a three-level optimization problem and present an ef-ﬁcient solution to it. EZ-Sketching can tolerate errors from various sources such as indirect control and inherently inaccurate input, and works well for sketching on touch devices with small screens using ﬁngers. Our user study conﬁrms that the drawings our approach helped generate show closer resemblance to the traced images, and are often aesthetically more pleasing.
Mathematical Models of the Developing C. elegans Hermaphrodite Gonad

The study of growing and developing organisms is a fascinating branch of experimental biology. Once created, cells must exchange chemical and physical cues with neighboring cells in order to grow, divide, and differentiate properly. In this thesis we study portions of development of the C. elegans hermaphrodite gonad, building mathematical models of the development process. Using our models, we show that vulval precursor cells make fate decisions under a flexible program that takes advantage of inherent chemical oscillations. This flexibility allows the cells to react more sensitively to weak signaling gradients and to the actions of neighboring cells. With our mathematical models, we also show that the development of the anchor cell cannot proceed properly using the currently known decision mechanisms. We draw upon knowledge of homologous proteins in D. melanogaster to propose a modification to the current theory on anchor cell development. Our models suggest that this modified mechanism, though not yet identified in C. elegans, is sufficient to specify anchor cell fates in accordance with experimental observations. In studying our mathematical models, novel analytical techniques were developed to understand the asymptotic behavior of systems of delay differential equations.
Effect of Organic Matter Content on the Spectral Signature of Iron Oxides across the VIS-NIR Spectral Region in Artificial Mixtures: An Example from a Red Soil from Israel

The investigation of iron oxides in soil using spectral reflectance is very common. Their spectral signal is significant across the visible–near infrared (VIS–NIR) spectral range (400–1000 nm). However, this range overlaps with other soil chromophores, such as those for water and soil organic matter (SOM). This study aimed to investigate the effect of different SOM species on red soil from Israel, which is rich in hematite iron oxide, under air-dried conditions. We constructed datasets of artificially mixed soil and organic matter (OM) with different percentages of added compost from two sources (referred to as A2 and A5). Eighty subsamples of mixed soil–OM were prepared for each of the OM (compost) types. To investigate the effect of OM on the strong iron-oxide absorbance at 880 nm, we generated two indices: CRDC, the absorbance spectral depth change at 880 nm after continuous removal, and NRIR, the normalized red index ratio using 880 and 780 nm wavelengths. The different OM types influenced the soil reflectance differently. At low %SOM, up to 1.5%, the OM types behaved more similarly, but as the OM content increased, their effect on the iron-oxide signal was greater, enhancing the significant differences between the two OM sources. Moreover, as the SOM content increased, the iron-oxide signal decreased until it was completely masked out from the reflectance spectrum. The masking point was observed at different SOM contents: 4% for A5 and 8% for A2. A mechanism that explains the indirect chromophore activity of SOM in the visible region, which is related to the iron-oxide spectral features, was provided. We also compared the use of synthetic linear-mixing practices (soil–OM) to the authentic mixed samples. The synthetic mixture could not imitate the authentic soil reflectance status, especially across the overlapping spectral position of the iron oxides and OM, and hence may hinder real conditions.
Does the Intra-Arctic Modification of Long-Range Transported Aerosol Affect the Local Radiative Budget? (A Case Study)

The impact of aerosol spatio-temporal variability on the Arctic radiative budget is not fully constrained. This case study focuses on the intra-Arctic modification of long-range transported aerosol and its direct aerosol radiative effect (ARE). Different types of air-borne and ground-based remote sensing observations (from Lidar and sun-photometer) revealed a high tropospheric aerosol transport episode over two parts of the European Arctic in April 2018. By incorporating the derived aerosol optical and microphysical properties into a radiative transfer model, we assessed the ARE over the two locations. Our study displayed that even in neighboring Arctic upper tropospheric levels, aged aerosol was transformed due to the interplay of removal processes (nucleation scavenging and dry deposition) and alteration of the aerosol source regions (northeast Asia and north Europe). Along the intra-Arctic transport, the coarse aerosol mode was depleted and the visible wavelength Lidar ratio (LR) increased significantly (from 15 to 64–82 sr). However, the aerosol modifications were not reflected on the ARE. More specifically, the short-wave (SW) atmospheric column ARE amounted to +4.4 - +4.9 W m−2 over the ice-covered Fram Strait and +4.5 W m−2 over the snow-covered Ny-Ålesund. Over both locations, top-of-atmosphere (TOA) warming was accompanied by surface cooling. These similarities can be attributed to the predominant accumulation mode, which drives the SW radiative budget, as well as to the similar layer altitude, solar geometry, and surface albedo conditions over both locations. However, in the context of retreating sea ice, the ARE may change even along individual transport episodes due to the ice albedo feedback.
Rooting depths and successional development of selected boreal forest communities

The root density and depth patterns of four boreal forest age sequences were analyzed for successional trends. Rooting depths increased with age on sandy substrates which supported aspen (Populustremuloides Michx.) and jack pine (Pinusbanksiana Lamb.) communities. Rooting depth did not change in an aspen series on fine-textured substrates or in a black spruce (Piceamariana (Mill.) BSP) series growing on organic substrates. Plant communities growing on mineral soils showed a decrease in near-surface root densities and understory vascular plant cover with increasing age. Maximum rooting was deepest on sandy substrates and shallowest on organic deposits. Roots in all stands were concentrated near the ground surface. In most cases 50% of all roots were located within 15 cm of the forest floor. Root densities in this zone ranged from 11 000 to 30 000 roots m−2 of vertical surface. Densities were approximately 4300 roots m−2 for the overall rooting zone.
Numerically Efficient Fuzzy MPC Algorithm with Advanced Generation of Prediction - Application to a Chemical Reactor

In Model Predictive Control (MPC) algorithms, control signals are generated after solving optimization problems. If the model used for prediction is linear then the optimization problem is a standard, easy to solve, quadratic programming problem with linear constraints. However, such an algorithm may offer insufficient performance if applied to a nonlinear control plant. On the other hand, if a model used for prediction is nonlinear, then non–convex optimization problem must be solved at each algorithm iteration. Then the numerical problems may occur during solving it and the time needed to calculate the control signals cannot be determined. Therefore approaches based on linearized models are preferred in practical applications. A fuzzy algorithm with an advanced generation of the prediction is proposed in the article. The prediction is obtained in such a way that the algorithm is formulated as a quadratic optimization problem but offers performance very close to that of the MPC algorithm with nonlinear optimization. The efficiency of the proposed approach is demonstrated in the control system of a nonlinear chemical control plant—a CSTR (Continuous Stirred–Tank Reactor) with van de Vusse reaction.
Big spatial data for urban and environmental sustainability

ABSTRACT Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.
Female Infertility and Assisted Reproduction: Impact of Oxidative Stress

Oxidative stress (OS) occurs with an overabundance of reactive oxygen species (ROS) generation and the inability of scavengers, i.e. antioxidants, to neutralize excessive loads of ROS. OS has a role in the etiopathologenesis of many factors causing natural infertility. Infertility is a problem of great magnitude affecting 6 million American women. The etiologies of unexplained infertility and recurrent pregnancy loss remain unclear and present a scientific challenge. Oxidative stress may be a piece in this puzzle. Although investigation of the exact mechanisms by which OS causes pathological processes affecting female fertility is ongoing, research has clearly shown that the redox state affects gametes, their interactions, and the resultant embryo. OS has adverse effects on assisted fertility including IVF/ICSI and in-vitro maturation. This article addresses the role of OS in female infertility, the effect it has on assisted reproductive techniques, and OS prevention strategies including the use of in-vivo and in-vitro antioxidant supplementation.
Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models

Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.
Port Infrastructure Connectivity, Logistics Performance and Seaborne Trade on Economic Growth: An Empirical Analysis on “21st-Century Maritime Silk Road”

ABSTRACT Liang, R. and Liu, Z. , 2020. Port infrastructure connectivity, logistics performance and seaborne trade on economic growth: An empirical analysis on “21st-century Maritime Silk Road. ” In: Gong, D. ; Zhang, M. , and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 319–324. Coconut Creek (Florida), ISSN 0749-0208. This study aims to examine the impact of port infrastructure connectivity, logistics performance, and trade on economic growth. Based on World Bank Indicator data of 32 21st-Century Maritime Silk Road countries, this research conducts an empirical analysis using the Structural Equation Model. The main results are as follows: (1) Port infrastructure connectivity improvement can enhance logistics performance and economic growth, while logistics performance improvement has no significant effect on promoting economic growth. However, logistics performance plays an active intermediary role in port infrastructure connectivity promoting economic growth; (2) Logistics performance improvement can promote seaborne trade, while port infrastructure connectivity is negatively related to the seaborne trade, which may be due to the low efficiency of ports. Thus, logistics performance improvement has a dual role in promoting trade; (3) Port infrastructure connectivity and logistics performance develop simultaneously and promote each other, which can form a synergy to promote trade and economic growth.
Cloning and Characterization ofCAD1/AAF1, a Gene from Candida albicans That Induces Adherence to Endothelial Cells after Expression inSaccharomyces cerevisiae

ABSTRACT Adherence to the endothelial cell lining of the vasculature is probably a critical step in the egress of Candida albicansfrom the intravascular compartment. To identify potential adhesins that mediate the attachment of this organism to endothelial cells, a genomic library from C. albicans was used to transform a nonadherent strain of Saccharomyces cerevisiae. The population of transformed yeasts was enriched for highly adherent clones by repeated passages over endothelial cells. One clone which exhibited a fivefold increase in endothelial cell adherence, compared with S. cerevisiae transformed with vector alone, was identified. This organism also flocculated. The candidal DNA fragment within this adherent/flocculent organism was found to contain a single 1.8-kb open reading frame, which was designated CAD1. It was found to be identical to AAF1. The predicted protein encoded by CAD1/AAF1 contained features suggestive of a regulatory factor. Consistent with this finding, immunoelectron microscopy revealed that CAD1/AAF1 localized to the cytoplasm and nucleus but not the cell wall or plasma membrane of the transformed yeasts. Because yeasts transformed with CAD1/AAF1 both flocculated and exhibited increased endothelial cell adherence, the relationship between adherence and flocculation was examined. S. cerevisiae expressing either of two flocculation phenotypes, Flo1 or NewFlo, adhered to endothelial cells as avidly as did yeasts expressing CAD1/AAF1. Inhibition studies revealed that the flocculation phenotype induced by CAD1/AAF1 was similar to Flo1. Thus, CAD1/AAF1 probably encodes a regulatory protein that stimulates endothelial cell adherence in S. cerevisiaeby inducing a flocculation phenotype. Whether CAD1/AAF1contributes to the adherence of C. albicans to endothelial cells remains to be determined.