dataesr/scientific-paragraphs-categorization huggingface dataset:
In silico approaches to predict and design new peptides The prediction of new peptide sequences or properties of the already described active peptides by algorithm databases has been gaining interest in the era of "big data" (Table  2 ). CPPs have been extensively studied as a delivery option for bioactive molecules that are poorly taken up by cells  (Raucher and Ryu, 2015). To the best of our knowledge, CPPsite 2. 0 is the only online database fully dedicated to experimentally validated CPPs  (Agrawal et al. , 2016). This website includes access to articles and patents with very detailed information regarding peptide sequence, length and chemical modifications, as well as the cell lines used for CPP evaluation. Many other important parameters are present in the website, which comprises 1699 unique and experimentally validated CPP sequences  (Agrawal et al.
, 2016). CPPsite 2. 0 allows the users to create their own dataset to be used by online tools dedicated to the prediction of efficient CPPs. CPPpred  (Holton et al. , 2013)  and CellPDD  (Gautam et al. , 2013)  are the two major and free web servers that have used CPPsite 2. 0 as their dataset. CPPpred is a neural network-based in silico method, which can predict CPPs ranging from 5 to 30 amino acid residues  (Holton et al. , 2013). This web server can only evaluate the input sequence, by calculating the probability of the peptide to be cell penetrating. CPPpred scores the input sequence between 0 and 1, in which a score above 0. 5 indicates that the peptide has a high chance of being cell penetrating, while a score close to 0 suggests that the peptide is very unlikely to be cell penetrating.
Due to the extensive studies of AMPs, there was a need to create AMP databases, covering peptides from prokaryotes, eukaryotes  (Seshadri Sundararajan et al. , 2012; Waghu et al. , 2016; Wang et al. , 2016) , bacteria  (Hammami et al. , 2010) , defensins  (Seebah et al. , 2007)  and peptaibols (soil fungi antiviral and antimicrobial peptides) (Table  2 )  (Whitmore, 2004). The authors of CellPDD and CPPsite 2. 0 created AntiBP2, a web server that contains the necessary tools to predict new AMPs based on peptide sequences  (Lata et al. , 2010). During literature analysis, authors found that certain amino acids are preferred at the C-or N-terminal of AMPs, a key finding for the discrimination of non-antimicrobial peptides. In response to this outcome,  Lata et al. (2010)  created a more robust model to predict new sequences.
In addition, AntiBP2 can also give information about the potential source and family of the input sequence  (Lata et al. , 2010). Antibiofilm peptides are a niche inside the AMP category that can target and destroy highly structured and organized populations of bacteria (biofilms). The first manually curated database for this class of peptides was only recently created  (Di Luca et al. , 2015). Biofilms are being recognized as the principal cause of a wide range of infections and are easily recognized in post-surgical infections on implants  (Connaughton et al. , 2014)  or in chronic infections like cystic fibrosis  (Høiby et al. , 2010). The need for new antibiotic molecules led to the creation of a web server fully dedicated to the prediction and design of antibiofilm peptides -Design Peptides Against Bacterial Biofilms (dPABPs)  (Sharma et al.
, 2016). 80 ABPs from biofilm-active AMPs database (BaAMP) were used as the positive dataset in the optimization of the implemented model in this web server. The positive dataset includes only peptides that were able to disrupt biofilms from all species tested against, which exclude those shown to be active against only one or two different strains. The aim of dPABBs is to find broad-spectrum peptides active against multiple bacterial species (Table  2 ). AVPs are another important group of peptides with therapeutic use (Table  2 ). There is a need to speed up the discovery of effective new AVPs to treat deadly human viruses, mostly because some viruses create a pandemic cycle worldwide, with the help of globalization. The Antiviral Peptide database (AVPdb) is a manually curated database dedicated to antiviral peptides, targeting 60 clinically important viruses  (Qureshi et al.
, 2014). This database comprises 2683 antiviral peptides experimentally validated, in which 624 are modified peptides. Information regarding sequence, source, targeted virus, and tested cell lines can be found in the database. Besides AVPdb, there is an anti-HIV peptide database that provides experimental records of 981 peptides targeting different steps during the life cycle of HIV  (Qureshi et al. , 2013). The same authors developed a web server that allows the prediction of potent antivirals, and the design of new peptide analogues  (Qureshi et al. , 2017). Four individual prediction models for HIV, hepatitis C virus, hepatitis B virus and human herpes virus were created, and a fifth generalist model was implemented for another 26 human viruses. The value of this web server is that it makes it possible to predict potential new antiviral therapeutics with great efficiency.
Funding This work was supported by  Fundação para a Ciência e a Tecnologia -Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES, Portugal)  project  PTDC/BBB-BQB/3494/2014 , by  Marie Skłodowska-Curie Research and Innovation Staff Exchange (MSCA-RISE ,  European Union)  project  INPACT  (call  H2020-MSCA-RISE-2014 , grant agreement  644167 ), by the Brazilian funding agencies  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ,  Fundação de Amparo a Pesquisa do Distrito Federal (FAPDF)  and by  Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT).
knowledgebase f Peptaibol Database g Soil fungi antimicrobial and antiviral peptides (unusual amino acids included). Antiviral AVPdb h Over 2500 antiviral peptides targeting 60 viruses. AVPpred allows the prediction of new potent Detailed peptide information concerning the physicochemical properties, antivirals i structure or origin. The algorithm was developed using peptides with HIPdb j HIV inhibiting peptides. experimentally-proven antiviral activity Over 900 peptides tested in 35 cell lines. Antibiofilm BaAMP k Data for each AMP include microbial species and the biofilm conditions dPABPs is a webserver aiming to help in the against which it was tested. prediction and optimization of antibiofilm peptides l Cell penetrating CPPsite 2. 0 m Contains around 1700 unique CPP sequences, including linear and cyclic CPPpred evaluates the input sequence and scores it n peptides (modified and non-natural residues included) with different CellPDD helps in the prediction and optimization of cargos.
Setting: Phase III, open-label, randomized, multicenter study in 15 European countries (ClinicalTrials. gov, NCT01066962). Methods: Seven hundred seventy-four participants self-reported adherence (modified AIDS Clinical Trials Group questionnaire) over 96 weeks [383 RAL + DRV/r (twice daily; 5 pills/day), 391 TDF/FTC + DRV/r (once daily; 4 pills/day)]. Primary endpoint was $95% versus ,95% adherence to prescribed doses recorded (1) over the last 4 days or (2) on the visual analogue scale over the last 30 days. Results: Characteristics, except age, were similar between arms; 9% had CD4 + ,200 cells/mm 3 and HIV-1 RNA $100,000 copies per milliliter. Adherence $95% in the last 4 days (P = 0. 029) or at the visual analogue scale (P = 0. 0072) was higher with TDF/FTC + DRV/r than with RAL + DRV/r.
Study Design and Participants The NEAT001/ANRS143 trial was a phase III, openlabel, randomized trial performed in 15 European countries. The full study design and procedures are described elsewhere. 6 ight hundred five ARV-naive adults were randomized in a 1:1 ratio to receive either 2 pills of darunavir 400 mg and ritonavir 100 mg once daily combined with 1 pill of raltegravir 400 mg twice daily (5-pill twice-daily regimen) or 1 pill of tenofovir/ emtricitabine 245/200 mg fixed-dose combination once daily (4-pill once-daily regimen). In agreement with the principles of the Helsinki declaration, the Ethics Committee of each participating center approved the study protocol, and all study patients gave written informed consent. The study was registered with ClinicalTrials. gov, number NCT01066962.
6 n post hoc analyses, we also looked at the association of adherence with the primary endpoint of the NEAT001/ ANRS143 trial, which was time to virological or clinical failure. 6 Patients with failure before the first adherence assessment were excluded in these analyses. Because in NEAT001/ANRS143 higher failure rates in the RAL + DRV/ r arm were seen in those with CD4 + cells ,200 cells/mm 3 and HIV-1 RNA $100,000 copies per milliliter at baseline, we examined whether adherence was lower in this subgroup or whether, in this subgroup, the association with HIV-1 RNA suppression/virological failure differed between the randomization arms. Statistical analyses were performed using Stata Statistical Software Release 14 (StataCorp, College Station, TX).
RESULTS Between August 2010 and September 2011, 805 treatment-naive adults were enrolled into the NEAT001/ ANRS143 trial and randomized to RAL + DRV/r (n = 401) or TDF/FTC + DRV/r (n = 404). Of these, 10 withdrew before the first adherence assessment at week 4. Additional 21 participants (13 in the RAL-DRV/r arm and 8 in the TDF/ FTC + DRV/r arm) did not provide any adherence data after W0 and were excluded from the adherence analyses (characteristics of these patients were similar to the whole study population; data not shown). Of the remaining 774 participants, 310 (40%), 227 (29%), 96 (12%), 76 (10%), and 65 (8%) had 5, 4, 3, 2, and 1 adherence assessments, with no differences between the treatment arms. Baseline characteristics of the 774 participants retained in analysis were broadly similar between the 2 treatment groups (383 RAL + DRV/r and 391 TDF/FTC + DRV/r), except for a small difference in age.
L. K. and O. M. were supported by the BM Fonds No. 15142 and the Margaretha Hehberger Stiftung No. 15142. G. I. was supported by the AIRC 5 × 1000 (No. 10007) and by the Leukemia Lymphoma Society (SCOR 2015). The work of N. S. and P. W. was supported by the Austrian Science Fund FWF (W1241) and the Medical University of Graz through the PhD program Molecular Fundamentals of Inflammation (DK-MOLIN). R. M. , M. M. , and B. S. were funded by the FWF SFB F6105, F6106, and F6101. R. M. was additionally funded by SFB F4707.
Acknowledgements S. D. T. , L. K. , and O. M. receive funding from the  European Union 's  Horizon 2020 Marie Sklodowska Curie Innovative Training Network ALKATRAS  under grant agreement  675712 , and H. C. L. is a  Marie Curie Early Stage Researcher  within this program. S. D. T. is a Bloodwise fellow. This work was supported by the " Jubiläumsfond der Österreichischen Nationalbank " (grant No. 14856  to O. M. ), L. K. was supported by  FWF  grants  P26011  and  P29251.
In order to maximise its climb rate, a bird should fly at its 'minimum sink' speed. There are also predictions about the bank angles that birds should adopt. Pennycuick modelled the circling envelopes for soaring birds and calculated the optimal bank angle for vultures as approximately 24 deg  (Pennycuick, 1971 ; Flight software, http:// www. bristol. ac. uk/biology/media/pennycuick. c/Flight_122_ReadMe. txt). Indeed, such angles have been observed from gliders (e. g. Shannon et al. , 2002)  and in Himalayan vultures (Gyps himalayensis) flying at low altitudes  (Sherub et al. , 2016). However, the predicted 24 deg is arrived at by assuming that birds are aiming to minimise both their turn radius (and thus remain near the 'core' of the thermal with the strongest uplift) and their sink rate.
MATERIALS AND METHODS Study system Data were collected from four individual vultures [Himalayan griffon vulture, Gyps himalayensis Hume 1869, n=2; European griffon vulture, Gyps fulvus (Hablizl 1783), n=2; all >2 years] at the Rocher des Aigles falconry centre, Rocamadour, France. Here, vultures were released from their perches to fly freely three times a day (at 11:30, 13:00 and 14:00 h local time) in a protocol repeated over 3 days of data collection, totalling nine flights for each vulture (see Table  1  for a summary). This protocol provided an opportunity to quantify the flight performance of birds in semi-captive conditions, at a site with relatively good thermal soaring conditions (see  Duriez et al. , 2014 for details). Wing loading (kg m -2 ) was derived from measurements of body mass (kg) and total wing area (m 2 ) (the latter was calculated from photographs of fully extended wings on a scaled background), as turning radius increases with wing loading  (Ákos et al.
Derivation of angle using the magnetometer Acceleration and barometric pressure data were used to identify the times of take-off and landing (barometric pressure also being used to calculate altitude, see below). It is important to note that although accelerometers can be used to measure postural rotation in many terrestrial systems, they cannot be used to measure bank angle in flight, and in particular soaring flight, owing to the centripetal acceleration (see  Williams et al. , 2015). Thermal soaring flight was defined by a sustained increase in altitude (measured as a decrease in air), the presence of a consistent sine wave in the xand z-axes of the TriMag data, indicating circling behaviour  (Williams et al. , 2015) , and the distinct lack of flapping (as would be indicated by peaks in The number of flights and total flight time include all time spent in the air, all other flight parameters are specific to the thermal soaring periods.
The exact orientation of the device was not known, and is likely to have differed slightly between birds and days of attachment, causing an overestimation of bank in one direction of turn and an underestimation in the other. Consequently, the data were realigned so that the crossing point between turns of opposing direction corresponded to a 0 deg angle of bank. This therefore assumed that turns of opposing direction had similar ranges in bank angle, analogous to the transformations of  Gillies et al. (2011). Centroid angles were recalculated for all flights following realignment. All subsequent analyses of bank angle were made using the re-aligned TriMag data. The processing and analysis of TriMag data were performed with the custom-built software DDMT (Wildbytes Technology Ltd, Swansea University).
The daily mean sea level pressure was taken from the nearest weather station at Lunegarde, 20 km from the study site. Each individual's circling envelope was parameterised using measured angles of bank (θ) and turn radii (r), and the lift coefficient (C L ), estimated by rearranging: The m-print that corresponds to zero bank is at the bottom of the m-sphere. Units were calibrated using this device in the field, with the camera and GPS units also attached to the platform [as these could potentially influence the magnetometer data (see  Bidder et al. , 2015) ]. r ¼ 2m ðC L Â r Â S Â sin uÞ ; ð3Þ where m is the mass of the bird (kg), ρ is the air density in 100 m bins under normal conditions and S is the wing area. Using the median C L for the bird, we then compared the envelope derived from empirical data with that predicted by Pennycuick's model in the Flight software.
Data analyses Kruskal-Wallis tests were used to assess individual variation in bank angle and associated climb rate across flights. We examined variation in airspeed with altitude using a linear mixed-effects model (LMM) with the random effects of day nested within individual ID. Individual variation in bank angle and climb rate was examined in relation to altitude. Initial inspection of the data suggested that, for each vulture, climb rate levelled off with altitude, with a breakpoint in the height at which this occurred. We therefore performed a segmented analysis to identify breakpoints in the individual-specific linear relationships between the climb rate and altitude (R software, 'segmented' package;  Muggeo, 2003). Data were restricted to ≤1000 m for the segmented analysis as birds rarely exceeded this height.
The relationship between climb rate and altitude was then compared before and after the identified breakpoint. We did not compare the results in terms of species or age (we did not believe individuals would dramatically differ in soaring performance owing to age alone given that all birds were >2 years old; see  Harel et al. , 2016a) , but focused on withinindividual trends in climb rate and bank angle, thus allowing us to examine changes in soaring behaviour through the climb. However, we did consider the effects of wing loading on soaring behaviour, as wing loading is the main morphological factor that is known to have significant impact on the limits of the circling envelope. Finally, we examined climb rate in relation to distance from the thermal core using the empirically parameterised circling envelope and data collected from a single focal individual (this being the individual where the regression analyses of V t by V a accounted for most variance).
RESULTS Overall, 34 flights were recorded across the 3 days of data collection (G. himalayensis, 9 flights each; G. fulvus, 8 flights each; Table  1 ). Flights ranged from 5. 28 to 45. 27 min (mean=20. 96±s. d. 9. 63 min). Flights performed in the first release of the day at  where an increase in climb rate occurred as birds decreased their bank angle (n=334). This trend does not hold beyond the breakpoint in any bird (Table  S2 ). 11:30 h tended to be longer and reach greater altitudes than those of subsequent releases (flight 1: 11:30 h, 27. 04±10. 06 min, 609. 84± 323. 10 m; flight 2: 13:00 h, 19. 21±6. 01 min, 424. 62±150. 38 m; flight 3: 14:30 h, 14. 44±8. 82 min, 445. 86±193. 48 m). A total of 1155 complete thermal turns were isolated for bank angle analyses (per individual: 289±70; Table  1 ).
DISCUSSION In this study, we use novel techniques to measure bank angle and turn radius using animal-attached loggers. Our method of obtaining bank angle capitalises on the inherently three-dimensional nature of magnetometry data, which can be normalised to the surface of a Spearman's rank correlation test for low and high thermal regions (significant relationships in bold) using data prior to and following the breakpoints identified from their corresponding models (Table  S2 ). ) achieved in thermal soaring in relation to the adopted turning radii (m) and average bank angles (deg). (A) The circling envelope for vulture A, the Gyps himalayensis subadult, parameterised using empirical data (grey dots) of bank angle and turning radius (n=334). With increasing radius and decreasing bank angle the bird's own sink rate decreases (labelled as negative vertical velocity).
g. Rosén and Hedenström, 2001). Our measurements of C L could have been influenced by factors that fall into three main categories: (i) methodological, (ii) environmental and (iii) behavioural (Fig. 5 ). In terms of the methodology, although a low lift coefficient may be the result of an overestimated bank angle or turning radius (the latter could result from an overestimated airspeed), the fact that our data did not cross the theoretical circling envelope supports the idea that they are accurate, as do our data checks, which resulted in an equally low lift coefficient. When it comes to behaviour, these birds were often recorded flying at airspeeds that were higher than the theoretically predicted minimum sink speeds (which is also likely linked to their conservative bank angles; see above).
Acknowledgements We would like to thank  D. Maylin  and  R. Arnaud , directors at the  Rocher des Aigles (Rocamadour, France ), and all of their staff for their patience and interest in the project. We are extremely grateful for their enthusiasm, keen interest in the project and willingness to work this data collection protocol into their daily routine. We also thank  S. Potier  and  J. Fluhr  for their help with tag deployments. DD housings were designed by P. Hopkins. H. J. W. would also like to thank  C. Rees-Roderick  for fruitful discussions.
statistical analysis All results are presented as means ± SD or percentages. Comparison of baseline characteristics among the three centers was done by one-way analysis of variance (ANOVA). Variables that changed significantly from baseline to the end of the PRP were identified by paired t-tests in cases of normality of distribution of values or the Wilcoxon test if not normally distributed. All statistical analyses were performed using SigmaPlot 11. 0 and SPSS Statistics 19 (IBM, Armonk, NY, USA). QMVC and the 6-minute walk distance (6MWD) measurements were standardized using z-scores. A z-score gives a quantified distance from the normative data in the SD in respect to the average reference performance of the population selected, which means that z-scores help us to compare the observed value of measurement to a normal population.
Modern database systems can achieve high throughput main-memory query execution by being aware of the dynamics of highly parallel hardware. In such systems, data is partitioned into smaller pieces to reach a better parallelism. Unfortunately, data skew is one of the main problems faced during parallel processing in a parallel main memory database. In some data-intensive applications, parallel range queries over a dynamic range partitioned system are important. Continuous insertions/deletions can lead to a very high degree of data skew and consequently a poor performance of parallel range queries. In this paper, we propose an approach for maintaining balanced loads over a set of nodes as in a system of communicating vessels, by migrating tuples between neighboring nodes. These frequent (or even continuous) data transfers inevitably involve dynamic changes in the partition statistics.
Introduction Memory resident database systems (MMDBS) store the data in main physical memory and thus, provide very high-speed access. Increasing main memory capacities of up to several Terabyte per server and highly parallel processing exploiting multi-core architectures dominate today's hardware environment and will shape database system technology in the near future. Over the past decade, prominent research systems such as H-Store  [27]  and HyPer  [28]  reinvigorated research into main memory and multi-core data processing. Most major database vendors now have an in-memory database solution, such as SAP HANA  [16]  and Microsoft SQL Server Hekaton  [14]. A frequently voiced problem in in-memory resident data processing is uneven distribution of data referred to as Data Skew. This well-known phenomenon can arise from natural data distributions and/or non-uniform insertions/deletions.
The main usages of our solution cconcern mainly insertions, deletions and data queries. These applications are useful in several fields, where range queries are used to extract information from a main-memory parallel database fed by sensors or other continuous data sources. Road traffic monitoring services are an example of such applications  [52, 35]  as well as Big Data applications  [8]. The captured information (car's serial number, location and so on) is inserted into a database in order to be on-line analyzed by many points and range queries. For example, a driver may send the query "return the speed observed by cameras that are between Exit 1 and Exit 10 (1 ≤ exit ≤ 10)", so that he may choose the right highway to avoid congestion down the road. A police patrolling a highway section with speed limit of 80 mph may ask the system to "return the list of cars running with a speed higher than 80 mph (speed > 80)".