dataesr/scientific-paragraphs-categorization huggingface dataset:
For this updated sample, the amount of correlation decreased to 38 +7 -6 %  [15]. For this dataset, Fig. 5  (right panel) shows the most likely value of the fraction of the correlated events plotted with black dots as a function of the total number of time-ordered events (the events used in the exploratory scan are excluded). The most updated estimate of the fraction of correlating cosmic rays is 33 +5 -5 % (28 events correlating from a total of 84 events) and with 21% expected under the isotropic hypothesis  [16]. The probability of this correlation to occur by chance with isotropic distribution of arrival directions is P = 0. 006. The HiRes Collaboration  [17]  has searched for the correlation using search parameters prescribed by the Auger Collaboration in  [12]. From the 13 events with energy above the search threshold, 2 events were found in correlations, while 3.
The cosmic rays are shown in filled red (correlating events) and empty blue circles (non-correlating events). The AGNs are shown in black dots. In Fig. 6  (right panel), the number of TA events correlating with AGNs is shown as a function of the total number of events with E > 57 EeV ordered according to arrival time. The blue line stands for the expected number of random coincidences in case of a uniform distribution calculated via Monte Carlo simulation. The shaded region represent 68% CL deviations from isotropy calculated by the maximum likelihood methof of Ref. [18]. In the full TA SD data set, there are 11 correlating events out of 25, while the expected number of random coincidences is 5. 9. The probability of this correlation to occur by chance with isotropic distribution of arrival directions is P = 0.
Correlations with BLLac Another possible scenario for sources of UHECRs are BL Lacertae objects, which are a sub-class of blazars, active galaxies with beamed emission from a relativistic jet which is aligned roughly toward our line of sight. Using a technique based on the angular correlation function, a correlation of the HiRes stereo data with a subset of BL Lacs on a scale of 0. 8 • consistent with its angular resolution was found in Ref. [23]. This result was checked with a different analysis by the HiRes Collaboration  [24]  and a correlation was found for events with E > 10 EeV and the VCV catalogue. Nevertheless, data from the Pierre Auger Collaboration did not confirm this correlation  [25].
For example, given the existing bounds on the extragalactic fields  [26]  and recent estimates of the Galactic one  [27]  one can check that protons of energies of order 6 × 10 19 eV should not be deflected by more than ∼ 3 -5 • except in the direction of the Galactic center. If stronger lower bounds on deflections at corresponding energies will be derived, they would suggest that the UHECR composition is predominantly heavy. In other words, proton composition at the highest energies implies certain correlation with the LSS. There are several methods to quantify correlations of UHECRs with the LSS. The simplest one is a cross-correlation between the UHECR events and the galaxy sample used to represent the matter Fig. 7. Relative excess of pairs using data (black dots) in the case of 2MRS galaxies (left) and Swift-BAT AGNs (right).
The distributions of the mean log-likelihood (LL/N data ) thus obtained were compared to the value obtained for the data. The data are compatible with the models and differ from average isotropic expectations. The fraction f of isotropic realizations that have a higher likelihood than the data is about 0. 02 for Swift-BAT AGNs and also for 2MRS galaxies. If the exploratory scan period is included, the fraction is 2 × 10 -4 in the case of the model based on Swift-BAT AGNs, and 4 × 10 -3 with the model based on 2MRS galaxies. These studies, however, are a posteriori and do not constitute further quantitative evidence for anisotropy. A similar (but not identical) analysis has been performed by the Telescope Array collaboration making use of the surface detector data collected in the period 2008.
Project Structure European laboratories have made crucial contributions in the field of mosquito molecular biology, genetics, biology, and epidemiology and have established a number of valuable facilities to perform and supporting the use of the facility infrastructure by individual researchers. Networking activities have been aimed at improving and utilizing the facilities of the infrastructure as well as at strengthening and facilitating collaborative projects between institutions to improve, assess, and validate genetically manipulated mosquitoes for vector control. They include the development and maintenance of common facilities, such as the formation of a mosquito line repository facility by coordinating different laboratories to stock naturally occurring and genetically modified (GM) mosquito lines together with the formation of a database containing genetic and biological information of each line.
New sophisticated bioinformatics approaches have been developed to keep pace with unprecedented developments in sequencing technologies. New gene manipulation technologies have been developed that allow the selective targetting of mosquito genome sequences and the engineering of large mosquito populations. A confined release INFRAVEC has also promoted the formation of a mosquito line repository facility by coordinating different laboratories to stock, in their insectaries, naturally occurring and genetically modified mosquito lines together with the formation of a database containing genetic and biological information of each line. Coordination ensures that agreed standards have been implemented across different laboratories and that external users may have access to a unique collection of mosquito lines upon request through the INFRAVEC website.
Such enhanced infrastructure capacity will be mobilized towards the development of networking and research activities that have the objective to (1) generate a European risk map for viruses potentially transmitted by Aedes mosquitoes, (2) validate the cost effectiveness and the safety of GM mosquitoes for vector control, and (3) investigate how the interaction of mosquitoes with environment microbiota shapes their vectorial capacity. Networking activities will involve the strengthening of the existing multicentre mosquito repository to include the phenotypic and genotypic characterization of more than 100 mosquito lines of different genetic backgrounds as well as their genome sequencing. A database will also be created to facilitate the association of genotypic variant with biological relevant phenotypes.
Data collection A qualitative floristic exploration has been conducted in 2009 and 2010. Sampling was carried out on alternate  days. All the species observed were collected and duly identified with the help of local floras  (Bentham and Hooker 1862 -1883; Gamble and Fischer 1915 -1935; Matthew 1981 Matthew -1983;; Nair et al. 1983; Henry et al. 1987; Henry et al. 1989 ) and authentic herbaria. For all plant species documented, the binominal and author citation have been checked with International Plant Name Index (IPNI) in 2009. Voucher specimens were deposited in the Herbarium of the French Institute of Pondicherry (HIFP). Flowering and fruiting phenology were monitored and life forms were also categorized simultaneously according to their habits. The species were then classified, based upon their occurrence status (OS), into three categories namely 1.
Climbing plants, mostly belonging to Asclepiadaceae, Menispermaceae and Vitaceae recorded flowering peak in the dry months between March and August. The results of the present study are in conformity with earlier ones obtained from other regions  (Kephart 1987; Morellato and Leitao-Filho 1996). Seasonal variation in the availability of water and light should place physiological and thus evolutionary constraints on the phenologies of tropical forest plants  (van Schaik et al. 1993; Wright 1996; Lieberman 1982; Borchert et al. 2004; Hamann 2004; Zimmerman et al. 2007). Long term surface data and remote sensing measurements indicated that plant phenology has been advanced by 2 -3 days in spring and delayed by 0. 3 -1. 6 days in autumn per decade in the past 30 -80 years, resulting in extension of the growing season  (Parmesan and Yohe 2003).
This check list provides important baseline information on the successful plants that may be introduced in a damaged ecosystem or barren area for expanding the green cover in comparable regions. It will serve as a genetic resource for future references since there is a threat of genetic erosion due to global warming and climate change. As nearly 50% of the dry lands of India are devoid of forest, they can be reclaimed if treated properly by eco-restoration to develop a sustainable ecosystem and help to deal with the environmental crisis and enhance natural resources. Acknowledgments: Our sincere thanks to Mrs Sieglind D' Arcy for providing financial support through a PhD fellowship to R. Ponnuchamy. We are thankful to Mr S. Prasad and Drs B. R. Ramesh, Vincent Bonhomme and A. Stephen for taking time to give valuable suggestions on the first draft of this manuscript.
The need for an efficient and standard technique for optimal spectral sampling of hyperspectral data during the inversion of canopy reflectance models has been the subject of many studies. The objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for extracting useful features from hyperspectral data with which forest LAI can be estimated through inversion of a three dimensional radiative transfer model, the Discrete Anisotropy Radiative Transfer (DART) model. DART, coupled with the leaf optical properties model PROSPECT, was inverted with AVIRIS data using a look-up-table (LUT)-based inversion approach. We used AVIRIS data and in situ LAI measurements from two different hardwood forested sites in Wisconsin, USA. Prior to inversion, model-simulated and AVIRIS hyperspectral data were transformed into discrete wavelet coefficients using Haar wavelets.
Several model inversion techniques are available  [16, 22, 23]. Traditional inversion methods like optimization techniques iteratively adjust model parameters until the modeled reflectance 'fits' or matches measured reflectance. These iterative optimization approaches are not feasible for complex models with slow computation time when simulating reflectance over a large number of spectral bands. Another widely used inversion method is LUT-based inversion  [12, 24, 25]. They require construction of a database of pre-calculated reflectances as a function of key model parameters. Then, the spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found. The parameter combination that yields the closest spectrum in the database is considered to be the inversion solution  [26].
These studies have shown its importance as an effective tool for reducing the dimensionality of hyperspectral data while maintaining information content for a variety of applications. Banskota et al. [40]  and Pu and Gong  [41]  have also demonstrated the importance of wavelet analysis for empirical estimation of LAI in different vegetation types. The utility of the wavelet transform for model inversion has also been explored and confirmed in several geophysical and seismic studies, where it is integrated in the forward modeling process and the model is inverted in wavelet domain  [42, 43]. Such direct integration of DWT in forward modeling is also a possibility for canopy reflectance models. The transformation and inversion can also be done in a completely offline mode (independent of forward modeling) by transforming the LUT entries and remote sensing measurements into wavelet features.
The advantages of DWT in LUT inversion are threefold. First, it transforms the correlated set of spectral features into wavelet features at different scales; the information carried at different scales is less correlated because of the orthogonal nature of transformation  [39]. Second, most of the wavelet coefficients contain very low values which can be discarded by applying an appropriate threshold without losing significant information, thus affording dimensionality reduction. Third and most importantly, the wavelet transform affords a multi-resolution analysis of hyperspectral data by decomposing it into components of different spectral scales (analogous to spectral bands of different width). The sensitivity of canopy reflectance is scale dependent, as some parameters affect reflectance locally in a narrow range of spectral bands (e.
g. , chlorophyll), whereas, parameters like LAI, canopy cover and leaf angle affect reflectance over a much wider region of the spectrum. The multi-resolution analysis by discrete wavelet transform offers the possibility of re-distributing the effects of different parameters on canopy reflectance in a single band over wavelet coefficients at multiple scales. Hence, an individual wavelet coefficient might be sensitive to a reduced set of parameters, in contrast to the corresponding spectral measurement, potentially leading to an improved inversion of parameters. The main objective of this study was to assess the utility of the Haar discrete wavelet transform as a feature extraction technique for accurately estimating forests LAI through LUT inversion of DART using airborne hyperspectral data (AVIRIS).
AVIRIS Image Processing Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data used in this study (Flight ID: f080713t01 and f080714t01) were acquired in July 2008 on NASA's ER-2 aircraft at an altitude of 20 km, yielding a pixel (i. e. , spatial) resolution of approximately 17 m (16. 8-17. 0 m). The AVIRIS instrument produces 224 spectral bands (or wavelengths), with an approximate full-width half-maximum of 10 nm for each wavelength over the spectral range of 370 to 2,500 nm. AVIRIS image preprocessing involved manual delineation of clouds and cloud-shadows, cross-track illumination correction, and conversion to top-of-canopy (TOC) reflectance via atmospheric correction. Redundant bands due to overlap of detectors were also removed. Cross-track illumination effects arise from a combination of flight path orientation and relative solar azimuth.
We removed this effect by developing band-wise bilinear trend surfaces, ignoring all cloud/shadow-masked pixels, and trend-normalizing the images by subtracting the illumination trend surface and adding the image mean. Atmospheric correction of the cross-track illumination corrected images to TOC reflectance employed the ACORN5b TM software (Atmospheric CORrection Now; Imspec LLC, USA). Due to the low ratio of signal to noise at both spectral ends (366 nm-395 nm and 2,467 nm-2,497 nm), and in bands around the major water absorption regions (1,363 nm-1,403 nm and 1,811 nm-1,968 nm) those wavelength regions were dropped, resulting in a final total of 184 bands. The pixel spectra corresponding to the centers of the plot locations as described in the next section were extracted for the final 184 atmospherically corrected AVIRIS bands.
e. , LAI = L e /Ω). Hemispherical images were collected at 18 plots (60 m × 60 m) characterized by broadleaf decidous forest types. All measurements were made during the peak of the summer growing seasons in 2008 and 2010 during uniformly overcast skies or during dusk or dawn when the sun was hidden by the horizon. Images were collected in the JPEG format at the highest resolution (2,560 × 1,920 pixels) to maximize the detection of small canopy gaps. We followed the protocol of Zhang et al. [48]  to maximize the distinction between sky and leaf/woody material. At each plot, we measured L e at nine subplot locations: the plot center, 30 m from the plot center in each of the four cardinal directions, and the mid-point of each 30 m transect. Images were processed using DHP-TRAC software to estimate L e and Ω, using a nine ring configuration but selecting only the first six rings for analysis to minimize the impacts of large zenith angles on the L e retrievals and the calculation of LAI  [47, 49].
Creation of LUT Database Construction of a LUT of reflectance values for a large number of bands with a 3-D model such as DART requires an unacceptable computational time. We used an efficient strategy to populate the Cover = 100% Cover = 90% Cover = 70% Cover = 80% LUT grid with a sufficient number of DART simulations by identifying the realistic combinations of different DART parameters and their suitable ranges. The process to develop the LUT is described below. Initially, a preliminary LUT with a wide range and coarse increments for parameters was constructed for only four narrow AVIRIS bands (550 nm, 1,139 nm, 1,692 nm, and 2,208 nm). PROSPECT  [51] , which is built into the DART interface, was used to calculate leaf optical properties (reflectance and transmittance). PROSPECT calculates leaf hemispherical transmittance and reflectance as a function of four input parameters, leaf structural parameter (N), leaf chlorophyll a + b concentration (Cab, µg/cm 2 ), leaf dry matter content (DM, g/cm 2 ), and equivalent water thickness (EWT, cm).
Together with the PROSPECT parameters, the parameters that were systematically varied (free parameters) were leaf area index (LAI), leaf angle distribution (LAD), soil reflectance (SL), and crown cover (CC). The range for some of the parameters (Cab, LAI, DM, and EWT) were set based upon the databases of leaf measurements spanning all common and rare tree species collected by Serbin  [52]  in the upper Midwest and eastern US. The simulations in the LUT were then compared with the image reflectance to find the optimal ranges for individual parameters. The corresponding maximum and minimum reflectance values in image-extracted spectra were determined for each simulated band. The maximum and minimum values in the individual band were combined to form a maximum and minimum reflectance (max/min) spectrum.
Calculation of Discrete Wavelet Coefficients When computing the DWT, two input parameters are required: the choice of mother wavelet and the level of decomposition. We chose the Haar mother wavelet as it is the simplest of all available mother wavelets, and recent investigations have illustrated its effectiveness in hyperspectral data analysis  [33, 36, 39]. The decomposition level is determined by the type of wavelet and the original signal length. In this study, the level was chosen such that it is maximized (6 for 184 bands using the Haar wavelet). The DWT was implemented in Matlab (version 7. 4, The Mathworks, Inc. ) using a dyadic filter tree as described in Section 2. The hyperspectral signal in the spectral domain extracted for each pixel location was passed through a series of low pass and high pass filters.
LUT Inversion LUT inversion required similarity matching between wavelet coefficients from plot spectra (measured) and the wavelet coefficients from simulated spectra (modeled). Similarity was assessed using a least root mean square error (RMSE) comparison of the simulated and measured coefficients according to Equation (5): ∑ where, W measured is a wavelet coefficient of measured reflectance in test dataset and W modeled is a wavelet coefficient of modeled reflectance in the LUT, and n is the number of coefficients. The solution of the inverse problem was the set of input parameters corresponding to the reflectance in the database that provided the smallest RMSE. Because of potential insufficiency in model formulation and calibration and preprocessing errors in measured reflectance, the least RMSE solution might not necessarily provide the best estimates of LAI.
According to  [32] , the best parameter retrieval is achieved when the number of solutions q ranges between 10 and 50. Consequently, for each of the 17 cases considered in the test dataset, q = {10, 20, 30, 40 and 50} closest matching LUT entries with the lowest RMSE were selected according to Equation  (5). Out of the multiple solutions, we selected the median LAI value as a final solution. In DWT, a large number of coefficients usually contain very low values (near-zero), and do not retain any useful information. For example, only 42 out of total 184 wavelet coefficients had 99. 99% of the total energy (equivalent to variance) corresponding to original reflectance in one of the plots. The rest of the coefficients (142) had very low values (cumulatively containing only 0. 01% of total energy) and might cause inversion bias.
The results also demonstrate that the different number of solutions estimated LAI with different accuracies. The differences were greatest between q = 10 and q = 30 with the latter providing the best estimates for all three datasets. This is consistent with the results obtained by Weiss et al. [32]  as they also found 20 < q < 50 as the optimum number of solutions. RMSE increased when q increased above 30 because the distribution of solutions in terms of LAI was too wide to obtain a good estimate (median value). The highest RMSE with q = 10 indicated that the solution closely matching the test dataset might not be the best solution to estimate LAI. We chose the Haar mother wavelet in this study, as it is the simplest of various mother wavelets, and has been found useful for hyperspectral data analysis in previous studies  [33, 36, 39].
Conclusion The main objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for estimating forest leaf area index (LAI) via inversion of the Discrete Anisotropy Radiative Transfer (DART) model using airborne hyperspectral data (AVIRIS). The DWT transforms the hyperspectral data into wavelet features at a variety of spectral scales. The multiscale features detect and isolate variation in the reflectance continuum not detectable in the original reflectance domain such as amplitude variations over broad and narrow spectral regions. We employed the DWT on reflectance spectra obtained from hyperspectral data to improve estimation of LAI in temperate forests. The model inversion was performed with three different datasets, the original reflectance bands, the full set of wavelet extracted features, and two wavelet subsets containing 99.
The biomarker data are therefore considered representative of the entire ZOOM study population. The post hoc clinical screening based on the NP-C SI did not yield much additional information in this study because most disease features comprising the NP-C SI itself were reflected in the ZOOM study inclusion criteria. Patients diagnosed with psychosis or early onset progressive cognitive decline and suggestive neurological symptoms therefore score highly on the SI. The proportion of patients with moderate or high scores on the SI across the diagnostic groups in this study was between 83 and 100%, which is appreciably higher than the proportion of patients scoring at this level in the much broader patient population on which the NP-C SI developmental study was based  (6). In addition, the NP-C SI threshold for high suspicion was defined for screening purposes in unselected populations, and therefore its poor predictive value in a selected adult patient sample such as that in ZOOM is not surprising  (4).
A remarkably large number of patients in the NP-C negative group in the current study had VSGP, which is associated with a high score weighting within the SI, accounting for the relatively high proportion of NP-C negative patients with high SI scores. It would be of interest to determine the final diagnoses in these patients. The data on the frequency of VSGP should be interpreted with caution. Considering the study inclusion criteria, the study population could possibly have been enriched for other neurological disorders involving oculomotor impairment, such as progressive supranuclear palsy, but no differential diagnosis data were collected. In addition, only the presence or absence of VSGP was recorded on the study CRF for each patient-no further information as to the degree of oculomotor impairment was collected.