Miracle-dz/UniFAR-FTUs huggingface dataset:
Exotic invasive species in urban wetlands: environmental correlates and implications for wetland management

Summary 1. Wetlands in urban regions are subjected to a wide variety of anthropogenic disturbances, many of which may promote invasions of exotic plant species. In order to devise management strategies, the influence of different aspects of the urban and natural environments on invasion and community structure must be understood. 2. The roles of soil variables, anthropogenic effects adjacent to and within the wetlands, and vegetation structure on exotic species occurrence within 21 forested wetlands in north-eastern New Jersey, USA, were compared. The hypotheses were tested that different vegetation strata and different invasive species respond similarly to environmental factors, and that invasion increases with increasing direct human impact, hydrologic disturbance, adjacent residential land use and decreasing wetland area. Canonical correspondence analyses, correlation and logistic regression analyses were used to examine invasion by individual species and overall site invasion, as measured by the absolute and relative number of exotic species in the site flora. 3. Within each stratum, different sets of environmental factors separated exotic and native species. Nutrients, soil clay content and pH, adjacent land use and canopy composition were the most frequently identified factors affecting species, but individual species showed highly individualistic responses to the sets of environmental variables, often responding in opposite ways to the same factor. 4. Overall invasion increased with decreasing area but only when sites > 100 ha were included. Unexpectedly, invasion decreased with increasing proportions of industrial/commercial adjacent land use. 5. The hypotheses were only partially supported; invasion does not increase in a simple way with increasing human presence and disturbance. 6. Synthesis and applications . The results suggest that a suite of environmental conditions can be identified that are associated with invasion into urban wetlands, which can be widely used for assessment and management. However, a comprehensive ecosystem approach is needed that places the remediation of physical alterations from urbanization within a landscape context. Specifically, sediment, inputs and hydrologic changes need to be related to adjoining urban land use and to the overlapping requirements of individual native and exotic species.
An Efficient Data Imputation Technique for Human Activity Recognition

The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.
Evaluation of the Performance and Cost of Cloud-Based Robot Motion Planning

We evaluate and analyze the empirical performance and financial costs of using cloud computing to accelerate robot motion planning. In prior work, Ichnowski et al. proposed a method that splits the computation of a robot’s motion plan between the robot’s own low-powered processor and a cloud-based high-powered compute-optimized server [1]. We implement this method using the compute service offered by Amazon Elastic Compute Cloud (Amazon EC2) on a Fetch robot [2] using 8 degrees of freedom to complete a task in a dynamic environment, as would be required for a consumer-based home-assistance robot. Computing across different data centers around the world, we investigate the network time elapsed during the plan-transmission process, the time taken to complete a task with the robot, and measures of planner performance including the number of vertices and edges generated for each roadmap created during the planning process. Finally, we use these results to estimate the cost model of such pay-per-use cloud services used in this new planning method to evaluate its economic feasibility and desirability compared to a traditional capital expense model.
The role of fisheries and the environment in driving the decline of elasmobranchs in the northern Adriatic Sea

Elasmobranch populations are declining worldwide, calling for urgent assessment of fishery exploitation and application of effective conservation strategies. Here, we applied a novel approach, integrating long-term time-series of landings (1945 –2012) and extensive surveys at the fish market of Chioggia, Italy, home of the major fishing fleet of the northern Adriatic Sea, to evaluate the status of elasmobranch populations and fisheries in the one of the most fished Mediterranean basins. The time-series highlight a dramatic decline in elasmobranch landings, particularly for skates and catsharks (Scyliorhinus spp.), whose current catch rates are 2.4 and 10.6% of the average 1940s levels, respectively. These data likely reflect similar large reductions in abundance, as indicated by the analysis of catch-per unit-effort timeseries. The biomass of landed skates and catsharks showed regular fluctuations that disappeared after the collapse of the landings. Elasmobranch market composition, assessed through the sampling of 11 900 specimens from 2006 to 2013, included 14 species, but was dominated by just two: Mustelus mustelus and M. punctulatus, which represented more than 60% of the catch. The proportion of sexually immature individuals was generally very high, up to 83% of landed females and 71% of landed males, depending on the species. Although some correlations were detected between landings and local hydrography or climatic indices, the analyses of landings and surveys at the fish market identified fishery exploitation as the main driver of the striking, long-term elasmobranch decline in the northern Adriatic Sea, calling for urgent management actions to improve the conservation status of these fish.
Protoplast isolation from bamboo leaves

Protoplasts with a yield greater than 2.0×106 cells per 0.3 g fresh weight were isolated from leaves of four bamboo species: an herbaceous bamboo, Lithachne pauciflora, and three woody bamboos, Phyllostachys meyeri, Sasa jotanii, and Bambusa vulgaris. For aseptic protoplast isolation, folded leaves were sterilized in 70% ethanol, and the internal tissues were drawn out from the leaf sheath. The leaf material was incubated for 4 h in a modified White's medium solution containing 0.8 M mannitol (pH 5.8, 32°C) and an enzyme mixture containing 3% Macerozyme R-10, 2.5% Meicelase, and 2% Cellulase Onozuka for woody bamboos, and 2.5%, 2.5%, and 1% of these enzymes, respectively, for L. pauciflora. Protoplasts were filtered through a double-layered Miracloth funnel and then washed with 0.6 M to 0.4 M mannitol-containing White's medium (pH 5.8) prior to analysis. Protoplast viability averaged 83% as determined by FDA staining.
The wing beat of Drosophila Melanogaster. III. Control

Two major problems have to be solved by a flying animal or machine, (i) On the time average, flight force has to be produced which is sufficient to keep the body airborne and to propel it through the air. (ii) To stabilize a given position or trajectory, the vector of the generated flight force has to be controlled in its magnitude, orientation and position relative to the body. In the present study, the response of wing-beat kinematics to wind and visual stimuli was investigated in tethered flying Drosophila melanogaster . When the fly is subjected to an air stream in a wind tunnel, or to striped patterns moving in its frontal field of view, the overall shape of the wing path is altered, including variations of the wing-beat amplitude and the angles of attack. The aerodynamic forces were calculated from the kinematic data according to the quasisteady aerodynamic theory, to investigate whether this approach is sufficient to describe the control mechanisms of the fly. The stimulus-induced changes of kinematic and aerodynamic variables were compared with control reactions expected in free flight or measured during tethered flight under similar stimulus conditions. In general, the calculated flight forces are too small to account for the measured lift, thrust and torque responses to the particular stimuli, or would even increase the input stimulus instead of being compensatory. This result supports the notion that unsteady aerodynamic mechanisms are likely to play the major role in flapping flight. Following this line of thought, some kinematic responses can be qualitatively understood in terms of unsteady aerofoil action.
Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score

With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients’ characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.
Making Science Accessible to Students with Significant Cognitive Disabilities.

The publication of A Framework for K-12 Science Education (National Research Council, 2012) and the Next Generation Science Standards (NGSS Lead States, 2013) have created a need for new alternate content standards and alternate assessments in science that are linked to the new general education science standards. This article describes how a consortium of four states used Evidence-Centered Design (Mislevy, Steinberg, & Almond, 2003) and Universal Design for Learning (CAST, 2012) to develop alternate science content standards and assessments. A set of 43 alternate science content standards was created and an alternate assessment at each of three grade spans. Evidence that supports appropriateness of the alternate standards for students with SCD and fidelity of representation of the Framework is presented. One cycle of testlet/item development was conducted. Results of a pilot test (251 items; 1,606 students) are presented. Evidence for validity and accessibility of the alternate assessment is presented. Major findings include that the assessment items met accessibility, bias and sensitivity, and content requirements, and that students were able to understand and respond to assessment items. Data from a pilot assessment provided evidence of the accessibility of the standards and assessments. The implications of this work for teaching science to students with SCD and preliminary efforts to develop supports for teachers are discussed.
Anti- inflammatory and analgesic activity of Smilax chinensis

To evaluate the anti-inflammatory and analgesic activities of ethyl acetate and methanolic extracts of Smilax chinensis Linn in rodent models. The anti inflammatory and analgesic activities of ethyl acetate and methanolic extracts were studied by Carragennan induced paw edema method and eddy’s hot plate method. Smilax chinensis nsis was evaluated for anti-inflammatory action by carrageenan-induced rat paw edema. The analgesic activity was tested by eddy’s hot plate method. The ethyl acetate extract of Smilax chinensis in doses of 250mg/kg and 400mg/kg showed 68.23% and 73.53% inhibition of paw edema respectively. The methanolic extract of Smilax chinensis in doses of 250mg/kg and 400mg/kg showed 56.0% and 61.56% inhibition of paw edema respectively. The ethyl acetate and methanolic extracts from dried rhizome of Smilax chinensis Linn (Smilacaceae) revealed significant anti inflammatory and analgesic activities. These results suggest that the ethyl acetate and methanolic extracts of Smilax chinensis Linn possesses analgesic and anti inflammatory activities.
Lookback option pricing problem of mean-reverting stock model in uncertain environment

A lookback option is an exotic option that allows investors to look back at the underlying prices occurring over the life of the option, and to exercise the right at assets optimal point. This paper proposes a mean-reverting stock model to investigate the lookback option in an uncertain environment. The lookback call and put options pricing formulas of the stock model are derived, and the corresponding numerical algorithms are designed to compute the prices of these two options.
Cloning and expression of Phanerochaete chrysosporium cellobiohydrolase ( cbhI. 2)

The Phanerochaete chrysosporium ME446 cbhI.2 cDNA was cloned and expressed in Escherichia coli using a pET system. Pulse-labelling revealed the expression of an induced protein of approximately 50- 60 kDa that reacted with anti-P. chrysosporium ME446 CBH antibodies. The expressed protein remained undegraded in vivo for 135 min but was biologically inactive in vitro.
Notes on mixed Teichmüller motives

As a higher genus version of universal mixed elliptic motives by Hain and Matsumoto, we consider mixed Teichmuller motives as certain motivic local systems on the moduli space of pointed curves. We show that the category of mixed Teichmuller motives is equivalent to a full subcategory of a certain product category of mixed Tate motives over Z and universal mixed elliptic motives. Furthermore, we show that unipotent fundamental torsors for the universal open curve give rise to a pro-object in the category of mixed Teichmuller motives. Our results can give a realization of motivic correlators proposed by Goncharov.
Milk – A new source for bioactive phospholipids for use in food formulations

The fat globules of milk are surrounded by a membrane, which contains a triple layer of phospholipids. Concentrated cream is thus a very good raw material for further processing in order to make a powder, enriched in phospholipids. A powder can in this way easily be produced with 20% phospholipids using only common dairy processes such as centrifugation and membrane filtration. Milk phospholipids differ in compositions from existing commercial sources such as soybean lecithins and egg lecithins particularly with respect to the content of two bioactive phospholipids namely sphingomyelin (about 24%) and phosphatidylserine (12%). Sphingomyelin is an active agent controlling the intestinal uptake of cholesterol and triacylglycerols in human nutrition. Intake of phosphatidyl serine has clinically documented effects in maintaining cognitive performance like memory and stress control. A milk phospholpids enriched product thus offers a 100% natural alternative to the well-established, semi-synthetic head-group exchanged phosphatidyl serine from soy lecithin now existing on the market for functional foods formulations.
Robust Sequential Path Planning Under Disturbances and Adversarial Intruder

Provably safe and scalable multi-vehicle path planning is an important and urgent problem due to the expected increase of automation in civilian airspace in the near future. Although this problem has been studied in the past, there has not been a method that guarantees both goal satisfaction and safety for vehicles with general nonlinear dynamics while taking into account disturbances and potential adversarial agents, to the best of our knowledge. Hamilton-Jacobi (HJ) reachability is the ideal tool for guaranteeing goal satisfaction and safety under such scenarios, and has been successfully applied to many small-scale problems. However, a direct application of HJ reachability in most cases becomes intractable when there are more than two vehicles due to the exponentially scaling computational complexity with respect to system dimension. In this paper, we take advantage of the guarantees HJ reachability provides, and eliminate the computation burden by assigning a strict priority ordering to the vehicles under consideration. Under this sequential path planning (SPP) scheme, vehicles reserve "space-time" portions in the airspace, and the space-time portions guarantee dynamic feasibility, collision avoidance, and optimality of the paths given the priority ordering. With a computation complexity that scales quadratically when accounting for both disturbances and an intruder, and linearly when accounting for only disturbances, SPP can tractably solve the multi-vehicle path planning problem for vehicles with general nonlinear dynamics in a practical setting. We demonstrate our theory in representative simulations.
Salt tolerance of two wheat – Agropyron junceum disomic addition lines

Two wheat – Agropyron junceum disomic addition lines homoeologous to groups 2 and 5 were tested for tolerance to salt. The experiments included germination and growth to maturity at various concentrations of sodium chloride (NaCl). The results were compared with those of wheat lines tetrasomic for chromosomes 2A, 2B, 2D, 5A, 5B, and 5D and also with the wheat parent 'Chinese Spring', and the salt-tolerant 'Chinese Spring' – A. junceum amphiploid. The addition of homoeologous group 2 chromosomes reduced the tolerance to salt relative to 'Chinese Spring' in every case. The order of tolerance was ranked as 'Chinese Spring' > 2J disomic addition line > tetra 2A = tetra 2D > tetra 2B. The addition of wheat group 5 chromosomes was either equal to 'Chinese Spring' or worse with respect to tolerance to salt. However, the disomic addition line for 5J showed considerable tolerance to salt and at 200 mol m−3 NaCl produced a similar response to that of the amphiploid in producing fertile tillers. Both produced viable.
Graph Summarization Methods and Applications

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.
Diversity of SHV and TEM β-Lactamases in Klebsiella pneumoniae: Gene Evolution in Northern Taiwan and Two Novel β-Lactamases, SHV-25 and SHV-26

ABSTRACT A total of 113 blood culture isolates of Klebsiella pneumoniae from 10 hospitals in northern Taiwan were studied for SHV and TEM β-lactamase production. blaSHVwas amplified from all isolates by PCR. TEM-type resistance, was found in 32 of the isolates and was of the TEM-1 type in all isolates. SHV-1, -2, -5, -11, and -12 and two novel enzymes were identified. These novel enzymes were designated SHV-25 and SHV-26 and had pIs of 7.5 and 7.6, respectively. Amino acid differences in comparison to the amino acid sequence of blaSHV-1 were found at positions T18A (ThrACC→AlaGCC), L35Q (LeuCTA→GluCAA), and M129V (MetATG→ValGTG) for SHV-25 and at position A187T (AlaGCC→ThrACC) for SHV-26. The results of substrate profiles and MIC determinations showed that the novel enzymes did not hydrolyze extended-spectrum cephalosporins, rendering the isolates susceptible to these agents. Inhibition profiles revealed that the 50% inhibitory concentration for SHV-26 was higher than those for SHV-1 and SHV-25, resulting in an intermediate resistance to amoxicillin-clavulanic acid. Forty-nine ribotypes were identified, suggesting that major clonal spread had not occurred in any of the hospitals. According to the amino acid sequence, SHV β-lactamases in Taiwan may basically be derived through stepwise mutation from SHV-1 or SHV-11 and further subdivided by four routes. The stepwise mutations initiated from SHV-1 or SHV-11 to SHV-2, SHV-5, and SHV-12 comprise the evolutionary change responsible for extended-spectrum β-lactamase (ESBL) production in Taiwan. The stepwise mutations that lead to a non-ESBL (SHV-25) and the β-lactamase (SHV-26) with reduced susceptibility to clavulanic acid are possibly derived from SHV-11 and SHV-1, respectively. The results suggest a stepwise evolution of SHV β-lactamases in Taiwan.
Identifying Effective Variables Using Mutual Information and Building Predictive Models of Sulfur Dioxide Concentration with Support Vector Machines

Sulfur dioxide (SO 2 ) is an issue of increasing public concern due to its recognized adverse effects on human health. Therefore, accurate SO 2 prediction models are very important tools in developing public warning strategies. The goal of this study is to identify the relevance of meteorological and air pollutant variables using a classical and widely used measure of dependence, Shannon's Mutual Information (MI), and to build an accurate SO 2 prediction model using the relevant variables as inputs. Specifically, features ranked by MI measure are tested on how much joint predictive power they have of the target using a popular machine learning tool, support vector machines (SVM), and in comparison to multilayer perceptron (MLP), which is the most commonly used machine learning tool in previous studies for the prediction and analysis of air pollutants. It was found that the SVM model gave a higher correlation coefficient (r) and less root mean squared error (RMSE) than MLP for both test and validation sets. The predictive model used 6 input variables for both data sets as the relevant features for maximum SO2 concentration prediction at time t+1, which are the average SO2, maximum SO2, outdoor temperature (OT), average nitrogen dioxide (NO2), average ozone (O3), and average wind speed at time t. The results of this study indicate that MI can be used efficiently in determining the importance of input variables in the prediction of SO2 concentration and SVM is a popular machine learning tool well suited for use in air pollution modeling.