dataesr/scientific-paragraphs-categorization huggingface dataset:
After catching the slow lorises by hand, we equipped them with 19-g VHF collars (PIP3, Biotrack, Wareham, UK). With the assistance of local field trackers, we located collared individuals using an antenna (Lintec flexible, Biotrack, Wareham, UK) and a receiver (Sika receiver, Biotrack, Wareham, UK), and recorded their location every 15 min using a handheld GPS unit (GPS62s, Garmin International, Olathe, KS, USA). For direct observations we used head torches (HL17 super spot, Clulite, Petersfield, UK) fitted with a red filter. To observe the behavior of slow lorises in chayote, we followed slow lorises for 3199 h between 17:00 and 05:00 h, from January 2014 to December 2015 (a mean of 13 ± 7 nights per month). We used all-occurrences sampling to record each instance one of the 16 focal lorises entered chayote using Coexistence Between Humans and Javan Slow Lorises in West Java JrnlID 10764_ArtID 9960_Proof# 1 -25/03/2017 AUTHOR'S PROOF U N C O R R E C T E D P R O O F Statistical Analysis Behavioral, sleep site, and ranging data did not deviate significantly from a normal distribution.
Acknowledgments We thank the villagers in Cipaganti and other parts of West Java for their time and patience in sharing their views and knowledge unreservedly with us. We thank  Indonesia RISTEK  and the regional Perhutani and  BKSDA  for authorizing the study. Amersfoort Zoo ,  Augsburg Zoo ,  Brevard Zoo, Cleveland Zoo  and  Zoo Society, Columbus Zoo ,  Conservation International Primate Action Fund  and  Margot Marsh Biodiversity Fund ,  Cotswolds Wildlife Park ,  Disney Worldwide Conservation Fund ,  Henry Doorly Zoo ,  International Primate Protection League , Little Fireface Project,  Longleat Safari  and  Adventure Park ,  Mohamed bin al Zayed Species Conservation Fund  ( 152511813 ),  Memphis Zoo ,  Nacey Maggioncalda Foundation ,  National Geographic  ( GEFNE101-13 ),  People's Trust for Endangered Species, Phoenix Zoo , the  Royal Geographical Society  (with  IBG) ,  Shaldon Wildlife Trust ,  Shepreth Wildlife Park ,  Sophie Danforth Conservation Biology Fund, Whitley Wildlife Conservation Trust , and  ZGAP  provided the funding for this project.
Drowsiness detection plays a vital role in accidents avoidance systems, thereby saving many precious lives. Many attempts were made to detect the drowsiness by the physiological features such as Electroencephalogram (EEG), Electrooculogram (EOG), and Heart Rate Variability, etc. , but a reliable index to determine the drowsiness is not yet a reality. This study contributes in identifying the drowsiness levels by an index called Drowsiness Index (DI) from the EEG signal analysis of the drivers. In this report, the EEG signal is processed to detect the behavioural patterns of the brain and drowsiness state of the drivers while performing monotonous driving for long distances. An eight-channel EEG data acquisition system is used to acquire the EEG data from thirteen male volunteers. The EEG signal is pre-processed and decomposed into various rhythms by applying a digital filter in MATLAB 2007b.
This study is devoted to the detection of drowsiness by EEG signal analysis in both time and frequency domains as shown in Fig. 2. The EEG signals were acquired by eight channel octal bio-amplifier (AD Instruments: Lab Chart 8) at a sampling frequency of 1000 Hz. The electrodes were positioned on the four lobes of the cerebral cortex, i. e. , Frontal (F3, F4), parietal (P3, P4), occipital (O1, O2) and temporal (T3, T4) lobe based on the standard 10-20 electrode system of EEG. Figure  3  shows the normalised EEG signal acquired from the parietal lobe. The raw data was pre-processed by applying Chebyshev Filter with cut-off frequencies of 0. 5 and 40 Hz. The normalised EEG signal was classified into four different rhythms namely Delta (δ: 0. 5-3. 5 Hz), Theta (θ: 4-7 Hz), Alpha (α: 8-13 Hz) and Beta (β: 14-30 Hz) by using digital filters and as shown in Fig.
2. 4. 3 Analysis by the Neuro-physicians The EEG data was evaluated by three neuro-physicians who are sleep experts [I Mporas et al. ]. The experts examined the complete EEG waveforms and divided them into segments of various stages of alertness (scale 1-5) after correlating with the video recordings. A score of "1" as sleepy and the score of "5" as extremely active has been chosen. Figure  6  shows the scoring given by the experts for the EEG data recorded from a subject clearly indicates the drowsiness levels range from 1-5. These ratings are used to compare and validate the results obtained by using PRMSD and PSG. The experimental results obtained from the two methods by using RMSD & PSG (Fig. 3. 2 and 3. 3) are validated against the ratings given by the neuro-physicians (Fig. 3. 1) and comparison is made as shown in the Table  2.
ArTIFIcIAl NEurAl NETworKs bAsED clAssIFIcATIoN Artificial neural networks (ANNs) are nonlinear mapping structures in view of the capacity and working of the human brain. An artificial neural system is a computational structure that is roused by observing the process in neural systems of biological neurons in the brain. It comprises of straight forward computational units called neurons, which are exceptionally  interconnected. ANNs are presently being progressively perceived in the territory of classification and prediction, where regression model and other related statistical procedures have generally been utilised. The most generally utilised learning algorithm in a neural system is the back propagation algorithm. Figure  16  demonstrates the standard back propagation neural network. (b) (c) (d) (a) In this study, the data obtained from 13 different subjects is combined in-order to normalise any subject to subject variations, it is this data is being used in the design of neural network system for classification into active EEG and drowsy EEG.
This set of data is analysed and the following features are extracted from entire set to describe the basic features in a study. The two main features analysed here are: PRMSD and PSG helping us to quantify summaries about the sample and the measures such as maximum/minimum values of variables, mean value being the measure of central tendency and standard deviation as a measure of variability. The results of our experiment on EEG signal classification problem into drowsy or active state with the best yielding neural network are summarised in Table  4. The Mean Square Error (MSE) has the objective function to design the neural network classifiers. The experimentation results have shown optimal values for the chosen hidden neurons in hidden layer. The classifier model consists of 2 inputs, 2 hidden neurons and 1 output neuron.
coNclusIoNs EEG-based drowsiness detection was implemented in this study for a highway driving scenario in a virtual driving environment. Time-frequency domain analysis of Beta, Alpha, and Theta were carried out and two parameters PRMSD, and PSG were found to be significant (ρ < 0. 05) with a positive coefficient of correlation 0. 82-0. 96 for Beta and Alpha rhythms. The total driving profiles of all the subjects were classified into Active and Drowsy state and were validated by the subjective assessment. Drowsiness index has been derived from PRMSD and PSG of Beta and Alpha. The spectrogram of subjects shows there is a significant (ρ < 0. 05) change (p<0. 0001) in the attention levels of all the subjects, with the linear regression  A group of researchers has reported that the spectral powerbased indices γ/δ and (γ+β)/(δ+α) evidences the significant changes in the alert/drowsy transitions of 20 subjects taken from the sleep EDF (European Data Format database)  21.
AcKNowlEDGEMENTs This work is financially supported by the University Grants commission, Govt. of India under the MRP grant F. No:4-4/2015-16(MRP/UGC-SERO). The authors also thank the TEQIP (Technical Education and Quality Improvement Program) Phase-II of World Bank initiative to promote technical education in India, for extending the financial support to procure the equipment, and Software. The authors extended their sincere gratitude to the staff, Department of Physiology, Apollo Institute of Medical Sciences for extending support in Sleep Scoring from the EEG signals.
Amplification of the cochlear signal (×20,000) was achieved by a Grass P511 differential amplifier with a 100 Hz to 3. 5 kHz bandpass. CAP amplitude was measured between N 1 and P 1 , the threshold being defined as the dB SPL needed to elicit a measurable response (> 2 μV). The power spectral density (PSD) of electrical neural noise was recorded in the absence (unstimulated) or in response to sound stimulation (stimulated) using the same electrodeamplifier assembly dedicated for CAP measurement but with a 1 Hz to 30 kHz bandpass. Acoustic stimuli consisted of bursts of one third-octave band noise centered at a probe frequency varying from 1 to 32 kHz in 1/3 octave steps (2. 5-ms rise and fall, 300-ms on/300-ms off, 100 presentations per level). A single PSD estimate using Welch's method (  [25] ; pwelch function using Matlab language, 2048 samples per segment, 50% overlapped, rectangular window, sampling rate 50,000 samples/sec) was calculated on each trial during the last 200-ms of the sound stimulation, and the mean PSD was then calculated by averaging all the PSDs.
Data analysis Means were expressed ± S. E. M. The significance of the group differences was assessed with a two-ways ANOVA; given significance of the group differences (p < 0. 05), a Tukey's post hoc tests were subsequently used for pairwise comparisons; Ã p<0. 05, and ÃÃ p<0. 01. Data analysis was performed using Matlab (The Mathworks Company) and its Statistics, Signal, and Image toolboxes.
Acknowledgments The authors acknowledge  Marc Lenoir  for collaborative work on experimental protocols and for constructive comments on the manuscript. Language services provided by www. stels-ol. de. This work was supported by  Inserm Grant  ( U1051-Dot 02-2014 ), by  Cochlear   France Award  ( R11055FF/RVF11006FFA ) and by the  Fondation de l'Avenir  ( Et2-675 ).
Reporter gene assay A putative promoter region was searched upstream of the start codon of chrA SO using the BPROM Softberry online software (http://www. softberry. com/berry. phtml?topic= bprom&group=programs&subgroup=gfindb). A DNA fragment corresponding to the 400 bp upstream from the ATG was amplified by PCR using S. oneidensis MR1-R chromosomal DNA, digested with XbaI and SpeI, and ligated upstream of the lacZ gene of vector pACYC184-lacZ, which was previously constructed by cloning the β-galactosidase-encoding gene lacZ from pGE593 into the vector pACYC184 (S1 Table)  [30, 31]. The ligation product was transformed into E. coli strain MC1061. The resulting plasmid called pchrA SO ::lacZ was introduced into S. oneidensis MR1-R by conjugation. Plasmid construction was checked by DNA sequencing.
Bioinformatics analysis The amino acid sequence of ChrA SO was retrieved from the MicroScope Microbial Genome Annotation and Analysis Platform (https://www. genoscope. cns. fr/agc/microscope/home/). Prediction of transmembrane helices was carried out using the TMHMM online software (http://www. cbs. dtu. dk/services/TMHMM-2. 0/). Proteins sharing homologies with ChrA SO were searched in the Bacteria kingdom using the BlastP software on the NCBI server (https://blast. ncbi. nlm. nih. gov). Representative sequences of different phyla were subsequently selected, as well as the sequences of four plasmid-encoded ChrA proteins that were previously characterized. For the phylogenetic analysis, we used the "Phylogeny. fr" software in the "one-click" mode, i. e. with the default parameters optimized by the authors (http://www.
S. oneidensis chromosome contains a chromate efflux pump encoding gene The S. oneidensis MR-1 genome contains only one gene (so0986) whose product is annotated as a putative chromate efflux pump. This gene is not located on the megaplasmid but on the chromosome. Accordingly, a Blastp search performed on S. oneidensis using well-known chromate ion transporters, such as the ChrA proteins of P. aeruginosa and C. metallidurans, only identified the SO0986 protein as a ChrA homolog. In contrast, S. oneidensis does not contain homologs of the chrB and chrF genes which sometimes co-occur with chrA. For clarity, we will refer to the SO0986 protein as ChrA SO. This protein of 383 amino acids with a molecular mass of 40. 9 kDa contains two chromate transporter regions (PF02417) and therefore belongs to the LCHR subfamily of the chromate ion transporter (CHR) superfamily.
The TMHMM program predicted that ChrA SO contains 10 transmembrane helices, which is usual for the proteins of the LCHR family. The ChrA SO protein is most likely inserted in the cytoplasmic membrane with its N-terminus oriented towards the periplasm, as schematized in Fig  1A. A Blastp search was performed using ChrA SO as query. Several hundreds of homologous proteins were identified in bacteria belonging for the vast majority of them to the Proteobacteria group, but also for some of them to the Terrabacteria group. As expected, the highest percentages of identities were found between ChrA SO and proteins from other Shewanella species. ChrA SO is also highly homologous to proteins belonging in particular to Vibrio and Pseudoalteromonas species. A phylogenetic tree was constructed using a subset of proteins taken from species representative of the different taxonomic families (Fig  1B ).
To decipher whether ChrA SO possesses a chromate reductase activity, we performed in vitro chromate reductase activity on crude extracts from the wild-type and the chrA SO mutant strains and the results show that the specific activity of chromate reductase is similar for both crude extracts (0. 78 ± 0. 1 and 0. 88 ± 0. 05 nmol reduced-Cr(VI). min -. mg -  , respectively), indicating that ChrA SO is not directly involved in Cr(VI)-reduction. The chrA SO expression is induced by chromate Since chrA SO is involved in chromate resistance, we wondered if its expression could be regulated by chromate. We thus constructed a transcriptional fusion using the lacZ gene as a reporter. The sequence upstream of the start codon of chrA SO was first analyzed with Bprom software (Softberry) to search for a putative promoter.
3D modelling Drillhole data provided by Mabiza Resources Ltd. was modelled in 3D space using Micromine 2014 software in order to constrain the subsurface morphology of the MUBU, and is presented in Section 5. Drillhole traces were displayed by simplifying downhole lithology data to display three lithological units: the metasedimentary hangingwall, the MUBU and the CGU. Micromine's implicit modeller was then used to extrapolate the intersection surfaces between these three units to create a 3D model of the MUBU along the southwestern margin of the MIC.
Mineralogy and petrology Thirty samples were selected for thin sectioning for petrological and mineralogical analysis at the University of Leicester. Olivine compositions were determined at the University of Leicester using a JEOL 8600 Superprobe with a wavelength dispersive system, fitted with an Oxford Instrument ED Spectrometer using Aztec software. A 30 nA current, 15 kV accelerating voltage and 5 μm beam diameter were used for all analyses. Samples containing zircons for geochronology were mapped at ZEISS's Natural Resources Laboratory in Cambridge, UK, using the Mineralogic Mining software and petrological analyser. A ZEISS Sigma VP field emission scanning electron microscope (SEM) coupled with two Bruker 6 | 30 Energy Dispersive X-ray (EDX) Spectroscopy detectors was used. A mapping analysis was selected with a step size of 10 microns.
Samples were analysed using an acceleration voltage of 20 keV at a working distance of 8. 5mm. Counts for EDX detection were consistently above 3000 with mineral classifications based on stoichiometric values. EDX calibrations were performed every hour on a Cu standard to normalise the beam alongside a brightness and contrast calibration to help limit the effects of beam drift. Thin section photomicrographs collected using the ZEISS Imager Z2N light microscope and Mineralogic maps were layered and visualised using the ZEISS Atlas correlative software.
U-Pb geochronology Samples of coarse poikilitic gabbro and ultramafic pegmatite were collected from diamond drill core MAD036 (at 155 and 216 m, respectively). Zircons were identified in thin section using Zeiss's automated Mineralogic Mining Software. Zircons were separated from the samples using conventional crushing, grinding, wet shaking table, heavy liquid, and magnetic separation methods at the Pacific Centre for Isotopic and Geochemical Research, University of British Columbia, Canada. All analytical procedures and methods for the Chemical Abrasion-Thermal Ionisation Mass Spectrometry (CA-TIMS) technique used in this study were followed from those described in detail in  Mortensen et al. (2015). Final ages assigned to the samples dated in this study are based on weighted averages of four individual 206 Pb/ 238 U ages from concordant single grain analyses.
3D modelling Assessment of drillcores from the entire strike length of the intrusion reveal that the MUBU is not as continuous at depth as it is at the surface. Due to the lack of outcrop in places, the inference of a continuous MUBU in the surface mapping (shown in Figure  2  However, down dip of the Enterprise mine, even the deepest drillholes (MAD068, 182, 184; Fig. 2 ) intersect MUBU, and as such, the down dip extent of the mineralisation has not been intersected in this area. To visualise this and the subsurface morphology of the MUBU along the southwestern margin of the intrusive complex, drillcore information from the historical exploration drilling programs was used to construct a 3D model of the MUBU using implicit modelling in Micromine software. Drillholes used in the model are shown in Figure  11A.
U-Pb geochronology Zircons were extracted from two samples of Munali igneous rocks from drillhole MAD036: a poikilitic gabbro (from a depth of 155 m); and an ultramafic phoscorite pegmatite (from a depth of 216 m). The mineralogy and texture of the samples are shown in Figure  14A-D , and typical textural association of zircons are shown in Figure  14C  and D. The results are shown as conchordia plots in Figure  14 E  and F , and the full data is presented in Table  2. The sample of poikilitic gabbro returned a CA-TIMS age of 862. 39 ± 0. 84 Ma, based on a 206 Pb/ 238 U weighted average from four concordant and overlapping zircons (Fig. 14E , Table  2 ). This is within the error of the 852 ± 22 Ma for a sample of pegmatitic gabbro from the CGU that is cited in  Johnson et al. (2007) , but provides a much more precise age.
Discussion Our detailed field data and supporting geochronology and petrological work allows a number of advances to be made in our understanding of the Munali deposit, but also poses a number of questions relating to: 1. The relative and absolute timings of events in a multi-stage magmatic, mineralisation and tectonic history; 2. The possible sources of magma for the multiple stages of magmatism, especially for the phoscorites, and their relation to sulfide mineralisation; 3. The physical mechanisms of sulfide emplacement, brecciation and deformation; 4. The unusual association of a magmatic sulfide assemblage of pyrrhotite-pentlanditechalcopyrite(-magnetite) with abundant carbonate and apatite. We explore each of these aspects below and provide a number of constraints on any genetic models developed for the Munali intrusion and mineralisation.
The poikilitic gabbro is comparable to the gabbros of the CGU in mineralogical composition and only the texture is distinct. Our precise U-Pb date of 862. 39 ± 0. 84 Ma for the poikilitic gabbro is within the error of the U-Pb SHRIMP zircon CGU date of 852 ± 22 Ma from  Johnson et al. (2007) , and we suggest that both the CGU and the poikilitic gabbro are related and most likely coeval, and were emplaced closer to our CA-TIMS date of 862 Ma as part of the same magmatic event. The one outlying zircon in the poikilitic gabbro dataset with a date of 865. 63 ± 2. 26 Ma may represent an inherited crystal from the main CGU. Nevertheless, we consider the mafic rocks to have formed through the same event from a common source. The ultramafic rocks postdate the CGU on geological grounds (Fig. 10B, C ).
32 The management of Consolidated Nickel Mines, especially  Simon Purkiss , are thanked for their assistance with setting up the fieldwork, providing a research funding for this project, including the MGeol projects of CLM, GAH and LAW; and for granting permission to publish. The staff of Mabiza Resources at the  Munali Nickel Mine , including  Danny Musemeka ,  Peter Munkondya , and  Matt Banda , are thanked for logistical support on site in Zambia and for facilitating underground work. Reviews by  Steve Beresford  and  John Hronsky  are acknowledged for providing positive and helpful suggestions in improving the manuscript. The geochronology was funded by a  Society of Economic Geologists Student Research Grant  awarded to GAH. Discussions in the field with  Darryl Mapleson ,  Daryl Blanks  and  Rob Moore  are acknowledged.
Electro-anatomical maps (EAMs) are commonly acquired in clinical routine for guiding ablation therapies. They provide voltage and activation time information on a 3-D anatomical mesh representation, making them useful for analyzing the electrical activation patterns in specific pathologies. However, the variability between the different acquisitions and anatomies hampers the comparison between different maps. This paper presents two contributions for the analysis of electrical patterns in EAM data from biventricular surfaces of cardiac chambers. The first contribution is an integrated automatic 2-D disk representation (2-D bull's eye plot) of the left ventricle (LV) and right ventricle (RV) obtained with a quasi-conformal mapping from the 3-D EAM meshes, that allows an analysis of cardiac resynchronization therapy (CRT) lead positioning, interpretation of global (total activation time), and local indices (local activation time (LAT), surrogates of conduction velocity, inter-ventricular, and transmural delays) that characterize changes in the electrical activation pattern.
Non-linear statistics may prevent from this but they require a large database, which is not always possible. To overcome these limitations, we propose to extract specific indices from EAMs that quantitatively define relevant aspects of the electrical activation pattern and therefore allow a better analysis of LBBB and CRT-induced changes. The main goal of this paper is to design a method to better analyze and compare EAMs, which addresses the following two tasks: (i) to establish an adequate definition of a reference system for EAMs of the two ventricles of the heart (LV and right ventricle, RV); and (ii) to propose global and local indices that allow a quantitative analysis of electrical pattern changes. The 2D disk mapping method is based on our previous work  [22] ,  [23]  which only worked on LV endocardial data.
c). The proposed analysis tools are illustrated in the context of CRT, which is a treatment used to improve cardiac pump function in patients with systolic ventricular dysfunction and heart failure by restoring a synchronous contraction between both ventricles  [28]. However, the high rate of nonresponders (around 30%) and the associated costs are still of primary concern  [29] ,  [30]. In order to optimize CRT some studies aimed at identifying the best configuration of the device, including lead positioning  [31] -  [35] , which has also been investigated with in-silico experiments  [36] ,  [37]. Despite these advances, patient selection and CRT optimization is still under debate, since the underlying mechanisms of LBBB and CRT are not fully understood. In this paper, these mechanisms are studied, both qualitatively and quantitatively, by applying the developed computational tools on endocardial and epicardial EAM data of the LV and RV acquired on an experimental swine model of induced LBBB with an implanted CRT device.