dataesr/scientific-paragraphs-categorization huggingface dataset:
Z-Score In order to demonstrate the ability to calculate a Z-score map from the statistical maps, the method was tested in a V/Q SPECT/CT scan of a patient with acute PE. The voxel-wise Z-score calculation followed similar registration methodology as used for database creation. Database CT was registered to the clinical case CT, and the registration applied on database mean and SD images. Normalization was performed on the clinical case SPECT according to the mean value, measured using CT lung segmentation. Finally, Z-score map was calculated at each voxel.
8% on NoAC reconstructions, and 10. 4% on AC reconstructions, respectively. Considering inferior to superior gradient, on perfusion generated maps, ISrd was -5. 4% on NoAC reconstructions, and 6% on AC reconstructions, respectively. On ventilation generated  The results of the second analysis on two randomly sampled independent subsets are presented in Table  3. No significant difference was found between non-registered database and the statistical maps.
Our study has some limitations. First, we used 81mKrypton gas for ventilation images. Our results could therefore not be extrapolated to V/Q SPECT scans performed with 99m Tc labeled aerosols. Krypton is preferred for several reasons. It has a better distribution in lungs, do not create hotspots, and has lower impact on perfusion uptake. A generation of statistical lung maps using 99m Tc labeled aerosols would be of value. Second, the methodology required deformable registration, which may affect SPECTs regional uptake. However, quantitative analysis showed consistent results between non-registered SPECTs and statistical mean maps, either on the whole dataset or on two randomly sampled independent subsets. Relative lobar lung function quantification was also consistent with previously published data  (15).
Among the enzymatic tools are enzymes today known as lytic polysaccharide monooxygenases (LPMOs), which play a major role in biomass conversion by oxidative cleavage and, thus, structural disruption of biopolymers such as chitin  1, 2  , cellulose  [3] [4] [5]  , as well as co-polymeric structures made of cellulose and hemicelluloses  [6] [7] [8]. This disruptive action boosts the depolymerizing action of glycoside hydrolases  1, 2, 5. LPMOs, classified today in families 9-11 and 13-16 of the auxiliary activities (AA) in the Carbohydrate Active enZymes database  9  , are ubiquitous enzymes with key roles in biological conversion of biomass by fungi and bacteria, but also with suggested roles in microbial pathogenicity  [10] [11] [12]. LPMOs contribute to the efficiency of modern commercial cellulase cocktails used at industrial scale  13, 14.
Prior to voltammetric measurements, Chl or chlorin e 6 was diluted ten-fold (500 µM) in potassium phosphate buffer (100 mM, pH 7. 0) supplemented with KCl as supporting electrolyte (100 mM). The voltammetric measurements were carried out using an MCS-200 potentiostat (BioLogic, France) and EC-lab software (BioLogic, France). The voltammograms were recorded using a three-electrode system (eDAQ, Australia) with a glassy-carbon electrode (GCE) of 1. 0 mm diameter as the working electrode, an Ag/AgCl (3. 4 M KCl) electrode as a reference electrode, and a platinum covered titanium wire as a counter electrode. The glassy-carbon electrode was cleaned before each measurement. It was rinsed with deionized Milli-Q water prior to polishing using a 0. 05 µm Alumina polishing slurry (eDAQ, Australia) on a wet polishing cloth.
All the potentials reported were adjusted to refer to the standard hydrogen electrode (SHE) using the conversion factor E Ag/AgCl vs. SHE = 222. 49 mV for our reference electrode  64. Of note, some previously determined potentials used in this paper were reported relative to the NHE; the difference between the two is that the SHE is appr. 6 mV lower than the NHE  65. Fluorescence measurements. Fluorescence signals were recorded using a Cary Eclipse Fluorescence spectrophotometer (Agilent Technologies) using a 2 mL quartz cuvette (Hellma Analytics, 101-QS). Data acquisition was performed at room temperature with a PMT detector voltage of 600 V. Excitation and emission spectra were acquired at respective maximum emitting and exciting wavelengths, determined iteratively and shown in Supplementary Fig.
Acknowledgements We thank  F. Hollmann  for providing vanadium coated TiO 2 and  Zarah Forsberg  and  Gustav Vaaje-Kolstad  for helpful discussions. We thank  Jennifer Loose ,  Lukas Rieder  and  John-Kristian Jameson  at  NMBU, Ås  for providing katE, SmAA10A, and NcAA9F respectively. This work was supported by the  Research Council of Norway  through grants  262853 & 240967. This work was also supported by the  EU  in the framework of the  Marie-Curie   FP7 COFUND People Program , through the award of an  AgreenSkills  fellowship (grant  n°267196 ), and by the  French Institut National de la Recherche Agronomique (INRA).
The deposition was done after placing the RF electrode (with the hollow cathode slits) at a position 0. 5 mm from the substrate. The pressure measurements in all of these experiments were acquired outside of the electrode assembly, in the vacuum vessel. To verify that the pressure measured in the vessel corresponded well to that of the discharge area, Computational Fluid Dynamics simulations (ANSYS software) were performed using the exact chamber geometry and gas flow rates. Those simulations confirmed that for a judicious choice of electrode design, the gas distribution was uniform and the pressure drop between the slits and the measurement point was only about 7%. The results of the deposition can be seen in figure  3. Figure  3 (a) shows a photograph of a patterned deposition on the Corning Glass substrate.
In the experiments listed above, however, the overriding goal was the deposition of a homogenous layer over a large area, i. e. overcoming the localization intrinsic to the HC effect. In our implementation, the controlled localization is a benefit to be exploited, but is still governed by the same physics. To better understand the origin of this result, plasma simulations have been performed to numerically model the plasma behaviour in an individual HC. In contrast to the  experiments, the modelling was performed using only argon, with the goal of capturing some of the basic features of the plasma. The software used herein is an extension of the PHOENIX 1D code, an academic particle-in-cell (PIC) simulation algorithm, which has been previously described in  [20, 21]. The code follows typical PIC and Monte Carlo collision (MCC) algorithms  [22, 23] , but has been extended to simulate a 2D CCP system.
Climatic Variables We recorded the air temperature at the study site at 30-min intervals using a weather station (Watchdog 2700, Spectrum Technologies Inc. , United States; data provided by A. Young, Exeter University). From these data, we calculated for each 24h period the minimum, maximum, and mean air temperatures. We assessed local long-term rainfall over the 33 years, including 30 years preceding our study ; each year starting in July and ending the following June) using FLDAS_NOAH010 data provided via the online data system Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, developed and maintained by the NASA GES DISC 1   (Acker and Leptoukh, 2007). During our study, we recorded rainfall on the study site using an event logger (HOBO Pendant Event data logger, Onset Computer Corporation, United States) and tipping bucket  (Davis Instruments Corporation, United States).
Aardvark Capture and Surgery Over a 3. 5 year period, 12 individual aardvarks were darted and implanted with data loggers (see below) to record their body temperature and activity. At different timepoints throughout the study, our sample size ranged between one and five individuals for body temperature and one and four individuals for activity, as a result of aardvark deaths and failure of biologging devices (see Supplementary Table  S1  for details). In July 2012, we instrumented six aardvarks; five of these aardvarks died in March 2013 during the summer drought  (Rey et al. , 2017)  and we retrieved biologgers from four of their carcasses (A01, A02, A04, and A05). In July 2013, we recaptured the surviving aardvark (A06), and instrumented six additional aardvarks (aardvarks A07-A12). One aardvark (A10) died during the winter of 2013.
The data logger that recorded locomotor activity of the aardvark was inserted and tethered intramuscularly (Musculus transversus abdominis) using non-absorbable polyamide suture (Nylon, Scimitar Surgical Sutures, Gabler Medical, United Kingdom), to ensure that the activity records were not affected by free movement of the logger inside the animal's body. Incisions were closed using absorbable polyglycolic acid suture material (Viamac, Scimitar Surgical Sutures, Gabler Medical, United Kingdom). The wound was sprayed with a topical antiseptic (Necrospray, oxytetracycline hydrochloride: 40 mg, gentian violet: 4 mg, Animal Health Division -Bayer HealthCare, South Africa) and coated with a topical ectoparasiticide (Tick Grease, chlorfenvinphos: 0. 3%, SWAVET RSA, South Africa). After surgery, each study aardvark was released as close as possible to its capture site, where a drug to reverse the immobilisation was administered slowly intravenously (Antisedan, atipamezole hydrochloride: 0.
5 mg kg -1 , Pfizer Laboratories, South Africa). For further details about surgical procedures and data logger implantation, see  Weyer (2018). We closely observed recovery of each aardvark during the weeks following surgery. Each study aardvark was recaptured annually, and a similar procedure to that used for implants was followed to replace, or remove, the loggers and tracking transmitter. The implant sites had healed and no signs of infection from the implantation surgery were visible at recapture.
We analysed all camera trap footage manually to determine time of emergence and return to burrows. To determine whether activity loggers could reliably indicate the beginning and end of aboveground activity of aardvarks, we time-matched logger data to camera trap footage of an aardvark's times of emergence from a burrow, or an aardvark's time of return to a burrow after foraging. Occasionally after emergence from a burrow, an aardvark remained at the burrow to rest before initiating foraging; in these cases, the time of emergence was identified as the time at which the aardvark started foraging. For each data point from the activity logger, we calculated the median of six values before and six values after this data point (i. e. , median activity over a duration of 65 min). If the median exceeded 5% of the maximum activity recorded for each individual, we considered the aardvark to be "active"; if not, it was considered to be "inactive.
" We classified the time-stamp of the data point at which the 65-min median activity level changed from "inactive" to "active" to be the time of emergence, whereas the reverse change from "active" to "inactive" marked the time of the aardvark's return to a burrow. To ensure accuracy of our calculated times of emergence from and return to burrows, we validated calculated times against the actual times obtained from camera trap footage where logger data were available at the same time as camera trap footage (N = 176).
Data Analyses Some data loggers failed, thereby reducing our sample size to n = 10 aardvarks for body temperature and n = 9 aardvarks for activity data. We excluded body temperature and activity data recorded on capture days and for 2 days after capture attempts from all analyses. To account for differences in the sensitivity of individual activity loggers, each 5-min reading of activity was expressed as a percentage of the maximum activity reading (% of maximum) for each logger while implanted in a study animal  (McFarland et al. , 2013). For each aardvark over each 24-h period, we calculated the minimum, maximum, and amplitude (difference between maximum and minimum) of body temperature rhythm. To compare 24-h patterns of aardvark body temperature and activity during the hottest and coldest months of a drought year with a non-drought year, we calculated mean ± SD of hourly body temperature and activity recordings averaged across all aardvarks over 4 months of interest, namely February 2013 (drought summer), August 2013 (winter following drought), February 2014 (non-drought summer), and August 2014 (winter following non-drought summer).
This vegetation greenness index consisted of moderate-resolution imaging spectroradiometry (MODIS)-derived time-series enhanced vegetation index (EVI) data for the Gordonia duneveld vegetation  (Tokura, 2016). EVI was measured every 16 days and averaged per month. We previously have shown that EVI as an index for aardvark prey's food availability correlated with aardvark body condition  (Weyer, 2018). We tested for multicollinearity of independent variables within each model using variance inflation factors (VIF) and ensured that all VIF <2, based on a conservative cutoff  (Zuur et al. , 2010). To assess changes in activity patterns, we calculated the duration of activity between time of emergence from and return to the burrows each day. On occasion, aardvarks did not emerge from their burrows and remained inactive (see Supplementary Table  S3 ); foraging duration was assigned a zero value for these days.
For time of emergence and return, camera trap data supplemented the dataset on days when no logger data were recorded (May to September 2015). We used Python 2. 7. 13 (Python Software Foundation, United States) for the determination of daily time of emergence and time of return of aardvarks from their burrows. We used R statistical computing environment (R Core Team, 2018) with the package lme4  (Bates et al. , 2015)  to perform the GLMM analysis, and Excel 2016 (Microsoft Corporation, United States), Python 2. 7. 13 and Prism 5 (GraphPad Software, Inc. , United States) to produce graphs. We present data as mean ± SD, and considered P < 0. 05 to be statistically significant.
Rather than extending their foraging duration in an attempt to obtain more food, aardvarks shortened their foraging duration during the drought and winter, most notably during a winter following drought. Our data showed that, of the variables that we measured, the key driver of changes in body temperature and activity in our Kalahari aardvarks was EVI. In our GLMMs, EVI (a measure of vegetation greenness) was associated positively with minimum 24-h body temperature, and very strongly and inversely with amplitude of the 24-h body temperature rhythm, an index that we have proposed for the well-being of mammals  (Hetem et al. , 2016). EVI also was associated positively with the duration of the aardvarks' foraging activity and with the time of emergence and return to the burrows, which resulted in an increase in the proportion of diurnal activity that took place when vegetation was brown.
ACKNOWLEDGMENTS We thank  Oppenheimer Generations  (especially  Dr. Duncan MacFadyen ), Tswalu management (notably Gus van Dyk and  Dylan Smith ) and students ( Michael Daley ,  Armin du Preez ,  Kelsey Green ,  Jarett Joubert ,  Pieter le Roux ,  Prince Ngomane ,  Douglas Rattray ,  Samantha Vorster ,  Brendon White , and  Zandri White ) for enabling us to conduct this study. We thank our supporting veterinarians ( Drs. Shaun Beverley ,  Angela Bruns ,  Peter Buss ,  Michelle Miller ,  Jacques  and  Nicolize O'Dell , and  Gareth Zeiler ) and the  Central Animal Services (CAS) of the University of the Witwatersrand (notably Mary-Ann Costello)  for professional aardvark capture and implantation support. We thank  Dr. Andy Young  ( University of Exeter ) for sharing climate data of the study site.
In this work, we examined whether highly active and highly selective antibiotic peptides are also active and selective against cancer cells. We set out to investigate if the similarly potent antibacterial activity of designed highly cationic but non-homologous helical peptides is the predictor of their anticancer activity or selectivity. On the APD3 (antimicrobial peptide database  [12] ), 230 antimicrobial peptides are currently listed as anticancer peptides (ACPs) at the time of writing. Among anuran HDPs, at least 108 have anticancer activity (DADP database  [13] ). The DBAASP database of natural and artificial AMPs contains 1406 peptides, with dual antimicrobial and anticancer activity  [14]. Cancer is the major leading cause of death in the population under 70 in over 90 countries, and is expected to become the greatest obstacle to increasing longevity in the 21st century  [15].
The choice of healthy human cells differed when antimicrobial and anticancer selectivity was examined. Erythrocytes served for the SI calculation regarding bacteria, while primary dermal fibroblasts were used to calculate TI for cancer cells. We used our freely available online software tools to select for and to check the progress after each design step: Mutator tool  [25]  server: http://mutator. djpept. com/ or http://splitbioinf. pmfst. hr/mutator/. Therapeutic Index Estimator  [26]  server: http://splitbioinf. pmfst. hr/split/dserv1/. MIC-predictor server  [27] : http://micpredictor. djpept. com/ or http://splitbioinf. pmfst. hr/micpredictor/. DADP database of anuran defense peptides  [13] : http://split4. pmfst. hr/dadp/. SPLIT server 3. 5  [28]  for the predicting sequence profile of hydrophobicities, optimal hydrophobic moments, and membrane preference for amphipathic and membrane-associated helical segments (the default choice for all profiles): http://split.
One exception is our finding of the trichoplaxin AMP (now renamed as trichoplaxin-1) in the EST database belonging to the T. adhaerens  [48]. For this work, we examined some translated open reading frames from the T. adhaerens genome  [46]  code for typical tripartite structure: signal peptide, acidic propeptide, and mature antimicrobial peptide, as was found for AMP precursors of anuran and many other AMPs  [13, 49, 50]. One such peptide we found had putative AMP at its C-terminal that we named the trichoplaxin-2. Its sequence is non-homologous to trichoplaxin-1  [48]. The design of trichoplaxin-2 and its analog trichoplaxin-2a consisted of deciding where to cut the peptide N-terminal out of the longer sequence, containing predicted antimicrobial segments. The precursor candidate peptide from the gb|GR418172.
1| EST entry in the Library of Trichoplax adhaerens cDNA, mRNA sequences is: Molecules 2020, 25, x FOR PEER REVIEW 7 of 22 Trichoplaxin-2 (T2). Trichoplax adhaerens from the phylum Placozoa belongs to the simplest and oldest living animals  [46, 47]. This fragile and barely visible thin sheet of cells is likely to possess a powerful armament of host defense molecules, which is mostly unexplored. One exception is our finding of the trichoplaxin AMP (now renamed as trichoplaxin-1) in the EST database belonging to the T. adhaerens  [48]. For this work, we examined some translated open reading frames from the T. adhaerens genome  [46]  code for typical tripartite structure: signal peptide, acidic propeptide, and mature antimicrobial peptide, as was found for AMP precursors of anuran and many other AMPs  [13, 49, 50].
Ascaphin N-terminal GFRD was used to elongate the N-terminal of the template peptide with the ascaphin-like GIGRE motif. The reason for replacing D with E is due to the ability of the glutamine residue to decrease the toxicity of AMPs to human cells  [60]. Ascaphin is still one of the best natural anuran AMPs  [26] , although it originated from an ancient family of frogs (Ascaphidae), probably more than 100 million years ago  [61, 62]. Its N-terminal part is more important for membrane-perturbing activity  [63]. In the second design step, we used the BLASTP tool to find similar sequences to GIGREIIKKIIKKIIKKI. By far, the best hit (E value = 1 × 10 -6 ) was from the hypothetical protein from the ZYK strain of Bacillus oryziterrae, with the accession number WP_026092555  [64]. This 60 AA-long peptide sequence is predicted as an AMP with the highest possible score by the Support Vector Machine classifier from the CAMP R3 server  [31].
Effect of Charge and Helical Content on Activity For all of the nine most potent of our new peptides, we detected strong anticancer activity against prostate cancer PC-3 cells. High cationic charge (from +6 for MAPA to +9 for PEXA) and high predicted amphipathic helix (percentages from 72% for T2R1 to 86% for ZYK1) for peptides that can span the membrane in helical conformation (from 18 residues for MAPA to 23 residues for A1A) are enough to ensure the anticancer activity, regardless of very different peptide sources or   1  for peptide abbreviations. Color code follows the HeliQuest convention: yellow for hydrophobic, blue for cationic, red for anionic, grey for small amino acids, purple for uncharged polar residues, and light blue for histidine. The arrow length and direction indicate that the mean hydrophobic moment vector is fairly strong for all peptides, pointing toward the hydrophobic helix face.
The cationic twin motifs can also be the selectivity determinants. The RR and KK doublets appear 34 and 14 times, respectively, among 744 tumor-homing peptides  [98]. In a more recently updated APD3 database of all natural antimicrobial peptides, the KK doublet appears 66 times among 230 anticancer AMPs. In most cases, it is bracketed with hydrophobic residues, which results in a high predicted alpha hydrophobic moment for a sequence segment containing the KK doublet. For A1A, PEXA, MAPA, T2R1, and ZYK1 (this work), and FLEX  [22] , one or more doublets of cationic residues combine with hydrophobic residues in such a way to facilitate predicted folding into an amphipathic helical conformation. Future research might focus on the functional importance of common structural features, such as the presence of RR or KK doublets or cationic-zipper motifs of the type [K,R]-X 3 -[K,R] and [K,R]-X 6 -[K,R], in anticancer AMPs.
Figure5. The Schiffer-Edmundson wheel projection of the six best anticancer peptide antibiotics: ZYK1, A1A, MAPA, T2R1, FLEX, and PEXA, drawn by using the HeliQuest tool. See Table1for peptide abbreviations. Color code follows the HeliQuest convention: yellow for hydrophobic, blue for cationic, red for anionic, grey for small amino acids, purple for uncharged polar residues, and light blue for histidine. The arrow length and direction indicate that the mean hydrophobic moment vector is fairly strong for all peptides, pointing toward the hydrophobic helix face.
The Umbrella Visualization now takes into account both the protein topology and the glycan motions above the protein surface. The main goal of this original and unique approach is to fully understand how a glycan moiety will structurally impact the protein surface. The dynamic information collected during a MD simulation should help identify the most impacted areas and understand which parts of the protein would be accessible to other molecules such as solvent molecules or even other proteins. First, we report the main tools already available in the field of glycosylated proteins. Many of these tools focus on the study of static, crystallographic structures. Visualization softwares such as PyMol  [15]  and VMD  [16]  integrate visualization tools dedicated to the representation of glycan with the aim of better understanding their conformations.