dataesr/scientific-paragraphs-categorization huggingface dataset:
In vivo anti-EV71 efficacy The anti-EV71 efficacy of suramin in vivo was assessed in 10-day-old Institute of Cancer Research mice  29  and adult rhesus monkeys,  30  as previously described. We injected 1310 7 TCID 50 (lethal dose) of the mouse-adapted EV71 strain MP10 (GenBank accession NO HQ712020, genotype C4) intraperitoneally into mice. We then injected 20 or 50 mg/kg suramin dissolved in saline, or saline alone as a placebo, intraperitoneally into the mice, twice daily for seven days. For monkey studies, we intravenously injected 1310  6. 5  cell culture infective dose 50% (CCID 50 ) EV71 FY-23 (GeneBank accession NO EU812515, genotype C4) into the monkey. We then injected 50 mg/kg suramin in saline, or saline alone as a placebo, into the monkeys intravenously on the day before virus challenge and on days 1, 3 and 5 post-challenge.
Cynomolgus monkey plasma pharmacokinetics We studied the plasma pharmacokinetics of suramin in male cynomolgus monkeys. Three monkeys were given 4. 37 mg/kg body weight suramin by intravenous bolus administration, with serial blood sample collection for up to seven days. Plasma samples were obtained by centrifugation (3000g for 10 min at 2-8 6 C). A liquid chromatography-tandem mass spectrometry method was developed for the quantification of suramin in monkey plasma. Changes in plasma concentration over time were analyzed with a non-compartmental model in WinNonlin software (version 5. 2. 1; Pharsight, Mountain View, CA, USA), with calculation of the following pharmacokinetic parameters: AUC 0-last , AUC 0-inf (AUC: area under the concentration time curve; AUC 0-last : AUC up to the last measurable concentration; AUC 0-inf : AUC curve to infinite time), half-life (T K ), maximum concentration observed (C max ), clearance (CL), volumes of distribution calculated either by the steadystate method (Vd ss ).
5 CCID 50 EV71 FY-23. In the vehicle-treated control group, more than 100 copies of EV71 genomic RNA per mL of blood were readily detected in sera at days 6 and 9 post-infection by qRT-PCR, with viremia peaking at 7 dpi. However, in the suramin-treated group, fewer than 20 copies of viral genomic RNA/mL were detected between days 6 and 9 (Figure  5B ). EV71 neutralizing antibody was negative in all serum collected before viral challenge, monkeys involved in this study do not have pre-antibody. And all serum collected at 2, 3 and 3 weeks post challenge have EV71 neutralizing antibody. These data demonstrate that suramin has a favorable pharmacokinetic and toxicity profile and inhibits EV71 replication in vivo at doses at or below the highest human dose.
Mechanism-of-action studies suggested that suramin inhibited virus entry through a mechanism similar to the antibody-mediated neutralization of virus particles. We evaluated suramin efficacy in two validated animal models. 29, 30 n mice,  29  suramin decreased mortality by 30% (Figure  5A ). In the monkey model, previously shown to be of predictive value in vaccine development,  31  the highest human dose of suramin, allometrically scaled to the monkey decreased peak viremia (Figure  5B ). This provides the first proof-of-concept that a small-molecule inhibitor can have a strong antiviral effect against EV71 in nonhuman primates. Suramin displays high levels of serum protein binding, generally considered predictive of poor therapeutic efficacy for small molecules. 50 However, our data suggest that the protein-binding features of suramin may be a key element in its anti-EV71 activity and that circulating EV71 may be neutralized by suramin the blood.
ACKNOWLEDGMENTS We thank  Dr Lianfeng Zhang  ( Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences, Beijing, China ) for performing the in vivo EV71 mouse efficacy experiment and  Dr Haiheng Dong  ( WuXi AppTec, Shanghai, China ) for carrying out the Ether-a-go-go-related gene channel assay. This research was partially supported by funding from the  National Basic Research Program of China  ( 973 program ,  2012CB724500 ),  Natural Science Foundation of China  ( 31270204 ),  Science and Technology Commission of Shanghai Municipality  ( 12431900503 ),  Li Ka Shing  foundation to  Ralf Altmeyer. Gang Zou  is supported by the  Sanofi-Shanghai Institutes for Biological Sciences Scholarship Program. Benjamin Bailly  gratefully acknowledges the award of a  Griffith University Postgraduate Scholarship  and a doctoral grant from the  Calmette program of the Insititut Pasteur International Division.
1) Time of arrival 2) Received signal strength-based fingerprinting The RSS approach includes two main methods: the path loss lognormal shadowing model to deduce a trilateration and the fingerprinting  [12]. Basically, the RSSbased fingerprinting, as shown in Figure  2 , first collects RSS fingerprints of a scene and then estimates the location of the MT by matching online measurements with the closest possible location collected by measurements in a database  [8]. Therefore, for each possible location, ambiguity points could exist leading then to high estimation errors in standalone positioning scenario.
Denote â ≜ (â x , â y ) as the estimated location of the LMT using GPS. The estimated location is modeled as random with Gaussian distribution with variance σ 2 GPS. The Wi-Fi hotspot position is assumed to be known and without loss of generality; it could be modeled as of the LMT with a particular condition σ 2 GPS ¼ 0. As consequence, from now on, the LMT and the Wi-Fi Hotspot will be labeled as secondary anchor node (SAN) with coordinates â(â x , â y ). Because RSS fingerprintings are collected at the BS, ambiguities of the location estimation exist. In RSSbased fingerprinting, the ambiguities represent the possible locations given by the fingerprinting database of a MT, based on its measured RSS. The vector of ambiguities is represented as S = [S 0 , S 1 , …, S M ] while one point is the exact position and M points represent the ambiguities.
VI. Simulation results The aim of this section is to evaluate the hybrid positioning technique in parallel with the proposed clustering approaches. We use MATLAB© to develop the algorithms and run simulations. We consider N = 50 UMTs, uniformly distributed in a cellular area with 1 km of cell radius placing the BS (equivalently the AN) at position (0,0) and the SAN at (√3, 1) km or (√3, -1) km. The BS with position O(0,0) will be used as a reference. Without loss of generality, we assume that all MTs are uniformly distributed around a cluster head, assumed to be the first MT, within a range radius of 50 m. The simulation was run for 3,000 realizations. The number of iterations of the UKF is equal to 2 while the number of observations is equal to 5. It is worth mentioning that the positioning estimation could be improved if the number of iterations is increased.
Background: Blood donation is a major concern to the society as donated blood is lifesaving for individuals who need it. In Pakistan, the concept of voluntary blood donors is almost non-existent due to the absence of blood donor motivation and retention strategies. The healthy, active and receptive huge student population can be potential blood donor to meet the safe blood requirements. The objective of this study was to determine the knowledge, attitude and practices about blood donation among undergraduate medical students in Karachi. Methods: A cross sectional study was conducted in two public and one private medical college in Karachi from January to March 2012. A total of 600 medical students were interviewed with the help of structured questionnaire. Data analysis was performed in SPSS version 19.
Data collection procedure After approval from ethical review committee of Dow University of Health Sciences (DUHS), principal investigator and other team members of the project explained the nature and purpose of the study to all randomly selected students. Those, who agreed to participate, written informed consent was taken. Data was collected through face to face interviews during mid-semester period of one week at study area. A total of 600 undergraduate medical students were interviewed by the project team using a pre-tested, close-ended questionnaire to obtained basic information like age, gender, class year, medical college and place of residence whereas information regarding knowledge, attitude and practices of blood donation were also gathered through same questionnaire. If the randomly selected student didn't give the consent then we skipped that student and approached next student on the basis of random sampling according to Kth number.
Data analysis procedure The data was entered and analyzed by using Statistical package for Social Science (SPSS version 19) for windows. Descriptive statistics was used to determine mean and standard deviation for continuous variables like age while frequency and percentage for qualitative variables (gender, medical college, class year, place of residence etc). Scoring was done, for knowledge; each right response was given a score of 1 while a wrong or unsure response was scored 0. Total knowledge scores can can be ranged between 0-15. Knowledge scores from 0 to 9 were considered as inappropriate knowledge while knowledge scores more than 9 was considered as having appropriate knowledge regarding blood donation. Attitude towards blood donation patients was assessed using a 8-item questionnaire where attitude scores between 0 to 5 were considered as negative attitude, and scores from 6 to 8 were considered as positive attitude.
Finally, we compared our approach with traditional approaches  [2, 4, 11] , our previous results  [21] , and state-of-the-art methods  [13, 19, 20]  on AR face database  [23]  and obtained the best results. Our experiments also suggested that, in comparison with weighting based method  [20] , occlusion exclusion (i. e. , weighting as 0 or 1) is more appropriate to handle the occlusion problem in face recognition. The rest of this paper is structured as follows. First, we review the related works in Section 2. Then, the proposed approach is described in Section 3. Section 4 presents the experimental results and analysis. Finally, we draw the conclusion and discuss future directions in Section 5.
OA-LBP  [21]  Occlusion analysis + LBP (our preliminary work). presence of occlusions. Liao and Jain  [16]  incorporated the SIFT descriptor into the SRC framework to achieve alignment free identification. Yang et al. [17]  proposed a robust sparse coding (RSC) method which seeks the maximum likelihood estimation (MLE) solution of the sparse coding problem for non-Gaussian/Laplacian occlusions in an iterative manner. Even though the SRC based methods achieve significant identification results on occluded faces from standard face databases (i. e. , AR face database  [23] ), the prerequisite of those methods relies on the large number of training samples of each identity with sufficient variations. But in many practical face recognition scenarios, the training samples of each subject are often insufficient (the "curse of the dimensionality"  [24]  problem, in the extreme case, only one template face per subject is available).
Besides the occlusion detection module (which was introduced in  [21] ) which can detect the presence of occlusion in patchlevel, we adopted GPM-MRF to detect occlusion in pixel-level to facilitate later recognition. We then propose a customized corruption-tolerant local descriptor selective LGBPHS which summarizes features from nonoccluded pixels for efficient face representation and recognition. Unlike  [11, 20] , our approach applies occlusion exclusion (by assigning weights as 0 or 1) based on our explicit occlusion analysis. Our results demonstrate that occlusion exclusion is more efficient than occlusion weighting, since weighting based methods still preserve some information from the occluded region. In addition, because the proposed occlusion analysis is an independent module from the face matching part and no model learning step (such as Eigenface  [2] , Fisherface  [3]  or SRC  [13] ) is required in our approach, the proposed method is not limited by the number of training samples.
Experimental Results and Analysis To evaluate the proposed approach, we performed a set of experiments on AR face database  [23]  and compared our result against those of seven different methods including Eigenface  [2] , LBP  [4] , OA-LBP  [21] , LGBPHS  [11] , KLD-LGBPHS  [20] , S-LNMF  [19] , and RSC  [17]. Among the selected methods, KLD-LGBPHS, S-LNMF, and OA-LBP (our previous work) are the state-of-the-art works which explicitly exploit automatic occlusion analysis (whereas Part-PCA  [18]  is based on manual annotation) to improve face recognition according to our survey in Section 2. LBP and LGBPHS are selected to represent the locally emphasized methods without explicit occlusion analysis. Because RSC reports the most recent and very competitive result among all SRC based methods  [17] , we select it as the representative algorithm of SRC based methods for comparison.
Experimental Data and Setup. For our experimental analysis, we considered the AR face database  [23]  which contains a large number of well-organized real-world occlusions. The AR database is the standard testing set for the research of occluded face recognition, and it is used in almost all literature works  [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]. It contains more than 4000 face images of 126 subjects (70 men and 56 women) with different facial expressions, illumination conditions, and occlusions (sunglasses and scarf). Images were taken under controlled conditions but no restrictions on wearing (clothes, glasses, etc. ), make-up, hair style, and so forth were imposed to participants. Each subject participated in two sessions, separated by two weeks (14 days) of time.
The original image resolution is 768 × 576 pixels. Some examples of face images from the AR face database are shown in Figure  5. Using eye and nose coordinates, we cropped, normalized, and downsampled the original images into 128 × 128 pixels. For occlusion detection, we randomly selected 150 nonoccluded faces, 150 faces occluded with scarf, and 150 faces  For face recognition, the face images are then divided into 64 blocks as shown in Figure  6. The size of each block is 16 × 16 pixels. The selective LGBPHS is extracted using the operator LBP 2 8,2 (using only uniform patterns, 8 equally spaced pixels on a circle of radius 2) on the 40 GMPs, yielding feature histograms of 151040 bins. To test the proposed algorithm, we first selected 240 nonoccluded faces from session 1 of the AR database as the templates images.
On the given data set (240 training faces, with 3 templates for each identity), robust sparse coding (RSC) yields relatively low identification rates (86. 25%, 56. 67%, and 30%). Comparing the results on the test sets of faces with sunglasses and scarves, we notice that most methods (except for PCA) are more sensitive to sunglasses than to scarf. This is an interesting phenomenon which is in agreement with the psychophysical findings indicating that the eyes/eyebrows region plays the most important role in face recognition  [32].
Robustness to Other Facial Variations. We compared our proposed approach against OA-LBP and S-LNMF  [19]  using similar protocol under the more challenging scenario in which the gallery face images are taken from session 1 of AR database while the test sets are taken from session 2. Note that the two sessions were taken at time interval of 14 days. The comparative results of our approach against OA-LBP and S-LNMF are illustrated in Table  3. The results in Table  3  clearly show that our proposed approach outperforms OA-LBP and S-LNMF in all configurations showing robustness against sunglasses, scarves, screaming, and illumination changes. The robustness of our approach to illumination changes and drastic facial expression is brought by the use of local Gabor binary patterns, while the occlusion detection module significantly enhances the recognition of faces occluded by sunglasses and scarves even with time elapsing.
To compute the PCA subspace, we consider a training dataset consisting of feature vectors from both occluded and nonoccluded image patches. Let us denote the feature vectors from the nonoccluded patches by   and let us denote the feature vectors from the occluded patches by. The training dataset  can be formed as:  = {  1 ,   2 ,. ,   /2 ,   /2+1 ,. ,  2  }, where  is the size of the training dataset. The eigenvectors associated with the  largest eigenvalues of ( -)( -)  (the covariance matrix of.
0364 ± 0. 0016 rad  [5]. The LHCb collaboration has measured the weak phase in the combination of B 0 s → J/ψ K + K -and B 0 s → J/ψ π + π -decays to be 0. 07 ± 0. 09 (stat) ± 0. 01 (syst) rad  [6]. A recent analysis of B 0 s → J/ψ π + π -decays using the full LHCb Run I dataset of 3. 0 fb -1 has measured the CP -violating phase to be 0. 070 ± 0. 068 (stat) ± 0. 008 (syst) rad  [7]. These measurements are consistent with the SM and place stringent constraints on CP violation in B 0 s -B 0 s oscillations  [8]. The CP -violating phase, φ s , in the B 0 s → φφ decay is expected to be small in the SM. Calculations using quantum chromodynamics factorisation (QCDf) provide an upper limit of 0. 02 rad for |φ s |  [1] [2] [3]. Triple-product asymmetries are formed from T -odd combinations of the momenta of the final state particles.
The invariant mass sidebands are defined to be inside the region 120 < |m K + K -K + K --m B 0 s | < 300 MeV/c 2 , where m K + K -K + K -is the four-kaon invariant mass. Separate BDTs are trained for data samples collected in 2011 and 2012, due to different data taking conditions in the different years. Variables used in the BDT consist of the minimum and maximum kaon p T and η, the minimum and the maximum p T and η of the φ candidates, the p T and η of the B 0 s candidate, the minimum probability of the kaon mass hypothesis using information from the RICH detectors, the quality of the four-kaon vertex fit, and the χ 2 IP of the B 0 s candidate. The BDT also includes kaon isolation asymmetries. The isolation variable is calculated as the scalar sum of the p T of charged particles inside a region defined as ∆ϕ 2 + ∆η 2 < 1, where ∆ϕ (∆η) is the difference in azimuthal angle (pseudorapidity), not including the signal kaon from the B 0 s decay.
This method yields values of 7. 3 ± 0. 4 and 17. 8 ± 0. 9 events in the 2011 and 2012 datasets, respectively. The amount of Λ 0 b → φpK -decays is estimated directly from data by changing the mass hypothesis of the final-state particle most likely to have the mass of the proton from RICH detector information. This method yields 52 ± 19 and 51 ± 29 Λ 0 b → φpK -events in the 2011 and 2012 datasets, respectively. In order to correctly determine the number of B 0 s → φφ events in the final data sample, the four-kaon invariant mass distributions are fitted with the B 0 s → φφ signal described by a double Gaussian model, and the combinatorial background component described using an exponential function. The peaking background contributions are fixed to the shapes found in simulated events. The yields of the peaking background contributions are fixed to the numbers previously stated.
This consists of the sum of a Crystal Ball function  [34]  and a Gaussian to describe the B 0 → φK * 0 reflection and a Crystal Ball function to describe the Λ 0 b → φpK -reflection. Once the BDT requirements are imposed, an unbinned extended maximum likelihood fit to the four-kaon invariant mass yields 1185 ± 35 and 2765 ± 57 B 0 s → φφ events in the 2011 and 2012 datasets, respectively. The combinatorial background yield is found to be 76 ± 17 and 477 ± 32 in the 2011 and 2012 datasets, respectively. The fits to the four-kaon invariant mass are shown in Fig. 1. The use of the four-kaon invariant mass to assign signal weights allows for a decay time dependent fit to be performed with only the signal distribution explicitly described. The method for assigning the signal weights is described in greater detail in Sec.
A first-order polynomial is then fitted to the distribution of σ t i versus σ t true , with parameters denoted by q 0 and q 1. The calibrated per-event decay time uncertainty used in the decay time dependent fit is then calculated as σ cal i = q 0 + q 1 σ t i. Gaussian constraints are used to account for the uncertainties on the calibration parameters in the decay time dependent fit. Cross-checks, consisting of the variation of an effective single Gaussian resolution far beyond the observed differences in data and simulated events yield negligible modifications to results, hence no systematic uncertainty is assigned. The results are verified to be largely insensitive to the details of the resolution model, as supported by tests on data and observed in similar measurements  [6]. The effective single Gaussian resolution is found from simulated datasets to be 41.
Angular acceptance The geometry of the LHCb detector and the momentum requirements imposed on the final-state particles introduce efficiencies that vary as functions of the helicity angles. Simulated events with the same selection criteria as those applied to B 0 s → φφ data events are used to determine this efficiency correction. Efficiencies as a function of the three helicity angles are shown in Fig. 3. Acceptance functions are included in the decay time dependent fit through the 15 integrals (Ω)f k (Ω)dΩ, where f k are the angular functions given in Table  1  and (Ω) is the efficiency as a function of the set of helicity angles, Ω. The inclusion of the integrals in the normalisation of the probability density function (PDF) is sufficient to describe the angular acceptance as the acceptance factors for each event appear as a constant in the log-likelihood, the construction of which is described in detail in Sec.
Decay time acceptance The impact parameter requirements on the final-state particles efficiently suppress the background from numerous pions and kaons originating from the PV, but introduce a decay time dependence in the selection efficiency. The efficiency as a function of the decay time is taken from B 0 s → D - s (→ K + K -π -)π + data events, with an upper limit of 1 ps applied to the D - s decay time to ensure topological similarity to the B 0 s → φφ decay. After the same decay time-biasing selections are applied to the B 0 s → D - s π + decay as used in the B 0 s → φφ decay, B 0 s → D - s π + events are reweighted according to the minimum track transverse momentum to ensure the closest agreement between the time acceptances of B 0 s → φφ and B 0 s → D - s π + simulated events. The denominator used to calculate the decay time acceptance in B 0 s → D - s π + data is taken from a simulated dataset, generated with the B 0 s lifetime taken from the value measured by the LHCb experiment  [37].
Systematic uncertainties The most significant systematic effects arise from the angular and decay time acceptances. Minor contributions are also found from the mass model used to construct the event weights, the uncertainty on the peaking background contributions, and the fit bias. An uncertainty due to the angular acceptance arises from the limited number of simulated events used to determine the acceptance correction. This is accounted for by varying the normalisation weights within their statistical uncertainties accounting for correlations. The varied weights are then used to fit simulated datasets. This process is repeated and the width of the Gaussian distribution is used as the uncertainty. A further uncertainty arises from the assumption that the angular acceptance does not depend on the algorithm used for the initial flavour assignment.