dataesr/scientific-paragraphs-categorization huggingface dataset:
Paint sample classification by Soft Independent Modelling of Class Analogy (SIMCA) Let X be a N samples × J variables (wavenumber channels, in this case) dataset, made up of Z blocks, X z (N z × J), each one containing spectral profiles of (paint) specimens of the same unique category (i. e. , a particular chemical composition). SIMCA separately decomposes every (centred) X z array according to a Principal Component Analysis (PCA  [26, 27] ) model of appropriate dimensionality (say A z ) as: X z = T z P T z + E z ( 1 ) where T z (N z × A z ), P z (J × A z ) and E z (N z × J) denote the scores, loadings and residuals matrices resulting from the factorisation of X z. After having defined the individual class subspaces as in Equation (1), the degree of outlyingness of any new generic measurement observation, x T new (1 × J), with respect to them can be assessed in terms of the socalled reduced distance  [28]  which is calculated as: d new,z = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ( T 2 new,z T 2 lim,z ) 2 + ( Q new,z Q lim,z ) 2 √ √ √ √ (2) with T 2 new,z reflecting the  (Mahalanobis)  distance between the origin of the z-th model hyperplane and the projection of x T new onto it, and Q new,z reflecting the perpendicular (orthogonal) distance between x T new and the z-th model hyperplane.
Dataset The dataset analysed to test the proposed methodology consists of 246 spectra of spray paints (see Fig. 2A ) recorded in transmission mode by a Nicolet Continuμm infrared microscope coupled to a NEXUS 670 FTIR platform for spectroscopic measurements (Thermo Fisher Scientific, Inc. , Waltham, United States of America) within the range 4000-650 cm -1 and at a nominal resolution of 4 cm -1 (128 scans per sample). All specimens proceed from as many commercial products purchased -for the purpose of the study itself, i. e. , to estimate the variety of their composition -in a set of shops of the city of Lille, in France, and its surroundings, and manufactured by over 30 multinational companies active in the same area. Brands/family brands and colours (see the Fig. 1. Schematic representation of the basic principle of A) a discriminant and B) a classmodelling technique.
A dry paint sample was afterwards collected by means of a scalpel and a hand lens, flattened by using a 10-ton hydraulic press and deposited onto a potassium bromide (KBr) spectroscopic window. 105 of the investigated paint specimens were chemically characterised (by validating preliminary information supplied by the providers through the direct interpretation of the registered profiles) and identified as belonging to three different categories: 35 orthophtalic alkydbased paints (chemical composition #1), 52 orthophtalic alkyd-, nitrocellulose-and styrene-based paints (chemical composition #2) and 18 poly-methyl-methacrylate-based paints (chemical composition #3). No details on the constituents of the remaining 141 samples were instead available. The spectral data related to the first 105 specimens were split into a training and a test set (constituted by approximately 70% and 30% of the total number of observations available, respectively -see also Table  1 ) by means of the Duplex algorithm executed category-wise  [42].
in Fig. 5C , the signals at around 1450, 1380, 1270, 1240, 1150 and 970 cm -1 , generally attributed to the vibration modes of the polymethyl-methacrylate molecular groups. It has to be mentioned here that the developed methodology is capable of providing investigators and forensic scientists with insights about the spectroscopic similarity between databased samples (e. g. historical bodies of evidence) and newly collected ones. This would directly support them in significantly narrowing the amount of target objects with respect to which additional comparisons (in terms of other attributes like colour or production site) might be addressed. For example, in this particular case, the two training paints exhibiting the highest commonality with the test ones to which Fig. 5A  and 5B refer were found to share with them the same colour shade (brilliant blue and night blue, respectively).
The first can even be traced back to the same distribution chain. Furthermore, it is important to stress that the Mahalanobis distance between two spectral profiles (as calculated in this work) is not necessarily linked to their correlation coefficient (see Table  SM. 2 for an indicative comparison). The latter, in fact, being estimated from raw data, does not directly benefit from all the intrinsic properties of PCA modelling (which guarantees, for instance, dimensionality reduction, white noise filtering, etc. ). The same hierarchical approach was finally applied to the FTIR data measured for the paints with unknown chemical composition. Figs. 6 7 8 display the results yielded by its two sequential computational steps: out of a total number of 141 specimens, 50 were recognised as characterised by chemical composition #1, 41 as characterised by chemical ( x T new , x T nz ) = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ (tnew,z-tn z ,z ) 2 s 2 z √ , with t new,z and t nz,z being two scalars and s 2 z denoting the variance of the PCA scores of the training samples.
INTRODUCTION Forest detection has been embraced in many studies using different types of remotely sensed datasets, captured from different aerial and satellite sensor platforms. Assessment of fallen trees is an important step to characterize forest health. According to  Torres et al. (2021) , most of the studies have been applied mainly in America and Europe with the most frequently used data being multipectral imagery from Landsat sensors  (She et al. , 2015). The recently launched Sentinel-2 have shown less capability in detecting individual vegetation species  (Nzimande et al. , 2021). UAV remotely-sensed images are ideal for forest health assessment since they provide optical imagery with high geometric spatial resolution (10-40cm)  (Naik et al. , 2021). Conversely, coarse ground resolution satellite imagery from optical sensors does not allow to capture the geometric structure of fallen trees  (Naik et al.
Data Capture Unmanned Aerial Vehicles (UAV) drone imageries were captured across six sites around the lake in the early September 2021 for 5 consecutive days, see Fig. 2. The UAV drone used for the ground surveys was a DJI Phantom 4 RTK SDK, all its specification are reported in Tab. 1. Images with a mean Ground Sampling distance (GSD) of 4. 84cm and covering approximately 6. 1 square kilometers were captured across six designated sites around the Lake Nakuru National Park. The surveyed areas were mainly near the lake shorelines where a large number of degradation were observed during the flight planning stage. A total of 9,056 training image patches were generated from the six images (see Figure  3 ) with 7,590 tree samples annotated from the training sets. The fallen trees are classified into 3 classes according to their background, namely Water (W), Land (L), and Mudflat (M) as shown in Fig.
Experimental setup In this study, we use the implementation of Faster RCNN and RetinaNet provided in the Detectron2 library as previously implemented by  Wu et al. (2019). In relation to this experiment, all the annotations from the three classes, namely dead tree on Land (L), Water (W) and Mudflat (M) were sampled into two parts consisting of training/validation and testing set. From each given class of the dataset, 80% of the samples across all the missions were used for training/validation while 20% remained for testing. This particular design was adopted to cater for imbalances within the datasets across the 6 missions (Fig. 2 ). Missions 3 and 4 have particularly fewer annotations compared to the rest of the missions. Metrics We follow the Common Objects in Context (COCO) challenge for quantitative assessment, as demonstrated by  Lin et al.
DISCUSSION The model that achieves the best performances (Precision and Recall) was the RetinaNet with a setting IoU ≥ 0. 5, as shown in Tab. 2. These results corroborates with recent studies carried out by  Santos et al. (2019)  where RetinaNet outperformed two other variants, namely YoloV3 and Faster-RCNN. Similar studies by  Alon et al. (2019)  involving tree crown detection with UAV orthophotos and LiDAR point clouds had promising results with RetinaNet as well. The overall Average Precision (AP) reported with RetinaNet was 43. 85%. Usually AP or Area Under the Curve are normally used on imbalanced datasets. Datasets with low true positive rates (TPR) and high false positive (FP) and false negative (FN) rates usually produce low Precision and Recall. In the case of our UAV dataset, annotations were carried out on the fallen trees which exhibited different backgrounds, i.
e. mudflat, water and land. Class "water" had the highest detection rates with an AP. = 43. 85% due to uniformity of their background, while class "mudflat" shows the opposite behavior due to the background non-uniformity. It is well-known that a deep network's performance is impacted by the number of annotations and image patches derived from the UAV dataset, as already shown in recent studies by  Hägele et al. (2020). Although a large number of annotations are employed in this study (9,056 patches with 7,590 annotations), the performance is incomparable with tree crown detection using RetinaNet  (Santos et al. , 2019)  which reportedly achieves AP of 92. 64% on 392 image patches. We observed that two different areas might come with very different visual features and landscapes, hence problems with varying difficulty to tackle.
CONCLUSION In this study, we dealt with the detection of fallen Acacia xanthophloea trees, on which we evaluated two competitive CNN models, i. e. Faster-RCNN and RetinaNet using images captured by UAV with RGB Cameras on board. The networks were trained and assessed using a dataset made of 9,056 image patches and 7,590 annotations on bounding boxes. RetinaNet achieved overall Precision of 38. 9% and Recall of 57. 9%. These results indicate that RGB Cameras embedded on UAV in conjunction with deep neural networks could possibly lead to the development of operational tools for the detection of fallen Acacia xanthophloea trees on different environmental backgrounds. This could also help in carrying out demographic surveys on fallen Acacia xanthophloea trees around the Park and in similar environments.
Acknowledgements This work was supported by the  CNRS , the  Université de Bourgogne , the  Conseil Régional de Bourgogne  and the  Fonds Européen de Développement Regional (FEDER). Financial supports from  UBFC  ( I-SITE UB180013. MUB. IS_SmartTZ, PhD AD ) and by the  ANR  ( JCJC ANR-18-CE07-0015 -FIT-Fun , PhD OA) are acknowledged. Thanks is due to the PACSMUB platform for analyses (SATT SAYENS) especially  M. -J. Penouilh ,  Q. Bonnin  and  T. Régnier.
Characterization The mesostructure of the as-prepared and calcined silica films was characterized by small angle X-ray scattering (SAXS), using an Anton Paar SAXSess instrument with a Panalytical PW3830 X-ray generator at 40 kV and 50 mA, which gives a Q range between 0. 08 Å -1 and 2. 7 Å -1. Scattered X-rays (Cu K α ) were detected by a reusable Europium excitation based image plate (size: 66 × 200 mm) with a 42. 3 μm 2 pixel size. The image plate was subsequently read by a Perkin Elmer Cyclone reader using OptiQuant software. SAXS profiles were generated from the 2D image using the Anton Paar SAXSquant program. The changes of surface pressure with time were recorded by using a glass fibre (diameter: 0. 777 mm) hung from a microbalance sensor (type PS4, Nima Technology), connected to the Nima software.
Measurement of the fibre in air was used to zero the sensor. The measurement started at the point when the film formation solution was poured into the Langmuir trough with sufficient height to touch the fibre. X-ray reflectivity (XRR) and grazing incident small angle X-ray scattering (GISAXS) measurements were made using the DCD system  [42]  at the I07 beamline  [43]  at the Diamond Light Source (Didcot, Oxfordshire, UK). The X-ray energy was 12. 5 keV. Teflon troughs containing film formation solutions were placed on a sample holder and sealed using a plastic box with a Kapton window to allow the beam to go through. Helium gas flowed through the box to reduce the scattering from air. The measurements were conducted at room temperature (ca. 21 • C). Data were collected using a Pilatus 100 K detector using regions of interest for reflected intensity and background.
Data were reduced using the DAWN software package  [44] , including a geometric footprint correction for over-illumination. The data are displayed as scattering intensity against the momentum transfer, Q. The XRR measurements are sensitive to the differences in electron density normal to the surface of the growing film, while GISAXS provides structural information about the lateral surfaces  [45]. Thermogravimetric analysis (TGA) of the prepared silica films was performed on a SETSYS Evolution TGA 16/18 thermogravimetric analyser (Setaram) from room temperature up to 650 • C, at a heating rate of 1 • C/min with airflow. The TGA data are displayed as the loss of weight as a percentage against temperature in • C. Nitrogen sorption was measured at 77 K using a BELSORP instrument (BELSORP-mini Inc.
220 Å -1 ) could be hidden by the sharp peak associated with the crystalline surfactant at 0. 195 Å -1 ; or the film experiences a reorganization from a wormlike structure into a 2D-hexagonal one during drying. In previous work on surfactant templated silica films grown at the air-solution interface, the high degree of orientation of the well-ordered 2D hexagonal phase near the interface means that the (110) is not typically seen in the XRR data  [58] , however, it can be identified in the in-plane scattering measured via GISAXS  [59, 60]. The film growth and ordering of these CTAB-LPEI-silica films were therefore observed via the GISAXS patterns to determine whether the film organization is truly 2D hexagonal or more disordered. As displayed in Fig. 4 , the GISAXS pattern (collected at 70 min after the reaction started, for a solution with a SiO 2 concentration at 65.
Acknowledgements A. Di  would like to thank the  China Scholarship Council  and the  University of Bath  for funding her PhD studies. N. Elstone thanks the  UK Engineering and Physical Sciences Research Council (EPSRC) , for a PhD studentship in the  Centre for Doctoral Training in Sustainable Chemical Technologies at the University of Bath  ( EP/L016354/1 ). The authors thank  Diamond Light Source (UK)  for the award of beamtime on beamline I07 (experiment SI52101-1), and the  ISIS Neutron and Muon Source  for beamtime on CRISP (DOI:  10. 5286/ISIS. E. RB13425 ). The authors would like to acknowledge  Dr Johnathan Rawle  for his assistance with the reduction of GISAXS data and  Dr Stephen Holt  for assistance with the experiment on CRISP. Data supporting this paper are available through the  University of Bath  research data archive system, DOI: https://doi.
Polymersomes. Quadir and Morton et al. [45], developed PEG5k-b-PPLG-Diiospropylamine (PPD) polymersomes developed. Unlike the normal PEG-b-PPLG, this polymer rearranges into a bilayer and becomes polymersomes with PEG to form an outer shell. The PPDpolymersomes are also designed to be acid pH-responsive dissociation, aiming for drug release in the acidic environment of a cancer tumor. The FRET intensity from ex vivo organs and blood samples was measured by IVIS and normalized with that of the injected dose to become the % injected dose (%ID). Thus, the semi-quantitative biodistribution data and the relative particle concentration of the intact FRET-PPDpolymersomes in a tumor-bearing mouse model were provided. The intact FRET-PPD-polymersomes were found to accumulate in the liver at 21%ID and in the tumor at 3%ID after 24 h.
4. 2. 2. 2. Nanospheres. Later, Cayre et al. [38]  demonstrated the application of a semi-quantitative FRET assay for the particle stability of squalene-gemcitabine bioconjugate nanoparticles (SQGem) in mouse liver. A pair of FRET dyes, Cy5. 5 (donor) and Cy7. 5 (acceptor), were conjugated to squalene moiety turning into squalene-Cy5. 5 (SQCy5. 5) and squalene-Cy7. 5 (SQCy7. 5). Then SQGem, SQCy5. 5, and SQCy7. 5 were all mixed and put to precipitate to form FRET-SQGem nanoparticles. The nanoparticles were intravenously injected into mice, and the fluorescence images around the liver area were taken using the IVIS. The fluorescent intensity was converted to the numerical value using Living Image software (PerkinElmer). Then, the FRET proximity ratio was calculated to determine the particle integrity.
Here, we propose an automatized binary disorder-order classification method by analyzing far-UV CD spectroscopy data. The method needs CD data at only three wavelength points, making high-throughput data collection possible. The mathematical analysis applies the k-nearest neighbor algorithm with cosine distance function, which is independent of the spectral amplitude and thus free of concentration determination errors. Moreover, the method can be used even for strong absorbing samples, such as the case of crowded environmental conditions, if the spectrum can be recorded down to the wavelength of 212 nm. We believe the classification method will be useful in identifying disorder and will also facilitate the growth of experimental data in IDP databases. The method is implemented on a webserver and freely available for academic users.
Despite significant efforts to characterize structural disorder in detail, our knowledge remains limited. Even DisProt, the largest database of manually curated, experimentally verified disordered proteins and regions  (Quaglia et al. , 2021) , only contains annotations of around 2000 proteins covering a small fraction of the predicted amount. Most of the structure characterization methods have high time and sample requirements; hence, there is a high need for fast, highthroughput, and inexpensive experimental methods to verify disorder. CD spectroscopy has been widely used to study the structure of proteins. Near-UV CD spectra in the 250-300 nm wavelength range are determined by the aromatic side chains and their environment. In disordered conformation, these side chains are accessible for the polar solvent, and their environment is averaged out resulting in a nearly zero CD signal.
Our aim in the present work was to revise this observation and work out an automatized method for improved identification of IDPs by CD spectroscopy. We collected a larger reference set of CD spectra of ordered globular proteins and disordered polypeptide chains based on our own measurements, data downloaded from the protein CD database (PCDDB)  (Whitmore et al. , 2017) , and collected from the literature. Starting with the double-wavelength plot, we applied various algorithms searching for an optimal method to identify disordered proteins from the spectral information gathered by CD spectroscopy. We examined the number and values of wavelengths needed for accurate disorder detection. To find the optimal method, the robustness regarding the sensitivity for incorrect concentration determination and experimental noise were also taken into account.
CD Spectroscopy Synchrotron radiation CD (SRCD) spectra were recorded at the DISCO beamline of SOLEIL French synchrotron facility  (proposal Nos. 20181890, 20191810, and 20200751 ). Samples at 5-7 mg/ml were measured in CaF 2 cells with path lengths of 6-20 μm. In total, 6-12 scans were accumulated in the 175-270 nm or 180-270 nm wavelength range depending on the sample absorption; 1 nm data steps with a lock-in time constant of 300 ms and integration time of 1,200 ms were used. After baseline subtraction, the spectrum was corrected with the CSA calibration  (Chen and Yang, 1977). Protein concentration was determined by directly measuring the absorbance of the CD sample and buffer reference at 205 and 214 nm  (Kuipers and Gruppen, 2007; Anthis and Clore, 2013). For the case studies, CD experiments were carried out on a Jasco J-810 spectropolarimeter (Japan Spectroscopic Co.
Mathematical Models Used for Disordered-Ordered Binary Classification For disordered-ordered classification, the following built-in models of the MATLAB Classification Toolbox were used. Tree: A binary classification decision tree is a learning method, where internal nodes represent the inspection of a predictor, branches show the outcome of the inspection, and leaf nodes represent class labels. Based on the number of leaves, we categorized trees as "simple" and "medium. " The maximum number of leaves is 4 in a simple tree and 20 in a medium tree. Support vector machines: SVMs are methods which use a subset of training data to create a decision function. The data points in this subset are called support vectors. We used different kernel functions for our models: linear and radial basis function (RBF).
Steps of Finding the Optimal Classification Method We aimed to classify proteins based on two or three data points. Therefore, we implemented classifiers that consider either a pair or a triplet of wavelengths, and perform classification by using CD values at the given wavelengths. Wavelength pairs and triplets consisted of wavelengths with a minimum pairwise difference of 3 nm in the 175-250 nm wavelength range. To develop the disorder determination method in a certain wavelength range, we used all proteins whose spectra covered the studied range. Leave-one-out cross-validation error rates were calculated by summing misclassified proteins and dividing their number by the total number of proteins. Error rates were determined separately for disordered and ordered proteins and for the total dataset.
The robustness of each method was also tested. We simulated the effect of inaccurate concentration measurement by rescaling the amplitude of test spectra and examined the sensitivity of methods to the scaling factor in the range of 0. 5-2. Furthermore, the dependence of the methods' accuracy on noise was evaluated. Noise was added independently to each CD value using random values from normal distribution (µ = 0 M -1 cm -1 , σ = 0. 1 M -1 cm -1 or σ = 0. 05 M -1 cm -1 ). The effect of noise was calculated by averaging the results of 1,000 simulations. When picking the best classifiers, the global error, error on disordered structure, preferably higher wavelengths for analysis, and the robustness were considered. MATLAB scripts used in the present study are provided in the Supplementary Material.
Reference Dataset of IDPs and Ordered Proteins To investigate the problem of distinction between disordered and ordered protein structures based on CD data alone, we collected the CD spectra of IDPs and proteins with ordered structures from various sources. In total, 140 high-quality SRCD spectra in a wide wavelength range from 175 or 180 nm of globular native proteins were downloaded from the protein CD databank (PCDDB)  (Whitmore et al. , 2017). The spectra of 9 globular native proteins, 2 amyloid fibrils, and 26 disordered polypeptides were the result of our SRCD measurements. These include IDPs, such as ERD14 (early responsive to dehydration) plant chaperone and its variants  (Murvai et al. , 2021) , histone-lysine N-methyltransferase constructs, artificial peptides designed for maximal disorder, and β-structure-rich globular proteins, such as dUTPase and SH3 domains that have CD spectra similar to disordered proteins.