dataesr/scientific-paragraphs-categorization huggingface dataset:
PB1-F2 from Nig06 delays immune gene regulation Pathways relevant to the immune response are prevalent in the transcriptomic signature associated to PB1-F2 expression during infection. In order to evaluate the specific regulation of immune genes during infection by the 2 viruses, we filtered all the regulated immune genes of the transcriptomic analysis with the term ''immune response'' using the Gene Ontology Database (GO:0006955). As shown in Fig  4 , the global pattern of immune genes regulated by the WT Nig06 is particularly low at days 2 and 4 in comparison to the DF2 Nig06 infection. However, it displays a continuous increasing profile over the time of infection. By contrast, the number of immune genes regulated during the infection by the PB1-F2 deleted virus decreases at day 8 pi. These data closely match to the histological description of the airways inflammation and also to the severity of lung pathology observed during the infection.
PB1-F2 from Nig06 promotes tissue damage and delays alveolar macrophages activation As shown in Fig. 3A , no pathways were significantly regulated in the WT Nig06-infected mice at 2 days pi. To get insight into the critical events occurring at this time point and to identify functions that could be non-indexed in IPA software, we did a closer inspection of the genes preferentially regulated by the two viruses at day 2 pi. The scatterplot depicted in Fig. 5  illustrates the expression of the most differentially regulated genes in the 2 types of infection. Genes implicated in cell death (highlighted in purple) are regrouped in the cluster of genes specifically regulated in mice infected by the WT virus, indicative of an increase in apoptosis in the presence of PB1-F2. These results correlate well with the disturbance of alveolar macrophages function evidenced above, and also the initial observation that PB1-F2 may promote apoptosis in certain cell types, especially monocytes  [15].
The data presented were compiled from replicate RNA samples prepared from 3 to 4 individual mice and analyzed on independent microarrays. Functional categories associated to the differentially expressed genes were identified using IPA and represented as histograms. These results are presented as the negative logarithm of significance, which corresponds to the likelihood of the genes of a given pathway being found together as a result of chance, as determined by Fisher's exact test. doi:10. 1371/journal. pone. 0057894. g003 Database  [42]. The PB1-F2 from Nig06 is therefore representative of most of the PB1-F2 from HPAIV. In mouse models, HPAIV usually induce fast and high mortality with very low dose inocula, for example MLD50 of A/Vietnam/ 1203/04 virus is 3. 2 PFU in C57Bl/6 and death happens as early as 5 days pi  [13].
Microarray experiments Transcriptional profiling was performed using Agilent's Whole Mouse Genome Microarray Kit, 4644 K (G4122F). Separate microarrays were run for each experimental sample (one sample per mouse and three or four mice per time point (Days postinfection (pi) 2 and 4: n = 3; mock and day pi 8: n = 4). Arrays were hybridized according to the manufacturer's instructions and as previously described  [17]. A single color design was used to provide two comparisons: [uninfected/infected by WT Nig06 virus] and [uninfected/infected by DF2 Nig06 virus]. Identification of differentially expressed genes and functional investigation were done using GeneSpring GX 11 software (Agilent Technologies). For further analysis, data files were uploaded into the Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems).
Acknowledgments We thank  S. Van Der Werf  ( Institut Pasteur, Paris, France ) for providing the A/duck/Niger/2090/06 virus and in vitro transcribed RNA standards prepared from the cloned M gene of A/Paris/ 650/04 ,  Jean-Claude Manuguerra  ( Institut Pasteur, Paris, France ) for providing the anti-A/ Hong Kong/156/97 antibody and  Ron Fouchier  ( Erasmus Medical Center, Department of Virology, Rotterdam, The Netherlands ) for providing the pRF483 bi-directional transcription plasmid. We thank  S. Van Der Werf  for continuous support and  Huot Khun  ( Institut Pasteur, Paris, France ) for helping with histopathological analysis.
This work was funded by the  Conseil regional d'I ˆle-de-France  through  DIM Malinf (''Domaine d'Interet Majeur Maladies Infectieuses, parasitaires et nosocomiales emergentes '', Grant#: dim100157). OL and PLH were supported by '' DIM Malinf '' doctoral fellowships awarded by the '' Conseil regional d'I ˆle-de-France ''. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The second one can also be tested by giving the patient a complex order (follow a recipe) and assess how he/she deals with the general recommendation in order to test initiation as well as execution. This type of assessment is very difficult to organize and time consuming. Following these examples the expert panel underlines: -For the assessment of BPSD objective data covering day and night behavior are needed -BPSD real time assessment must be validated in comparison to the NPI domains. -ICT devices enable the patients' performances and actions to be captured in real time and real life situations and to be accurately evaluated. This is particularly important for activities of daily living and the semi directed activities -the potential interest of serious games for assessing self initiated goal directed behaviour should be explored.
Requirements Before using ICT devices in clinical trials, several steps need to be achieved: -ICT needs to be accepted by as a valid study endpoint by Health Authorities. In fact, it seems hard to shift from a well establish end point into a surrogate marker. Unless we get this new endpoint to be approved, technologies are interesting but only as investigational technologies for clinical trial. This explains the importance of introducing ICT into clinical practice in a first step before getting it accepted as a validated clinical endpoint by drug regulators. -It is important to understand the correlation between ICT data and results obtained by classical assessment tools. -For BPSD, long-term data need to be collected (24h during one month). It is particularly important to assess whether the frequency of the behavior described by the caregiver (using the NPI) is the same as the one recorded by the ICT device.
Acknowledgements: TDK is supported by a  Royal Society University Research Fellowship , and was supported by an  Royal Astronomical Society 2010 Fellowship  for some of this work. BR and CH acknowledge support from the the  European Research Council  in the form of a Starting Grant with numbers 24067 (BR) and 240185 (CH). RM is supported by a  Royal Society University Research Fellowship. DG was supported by  SFB-Transregio 33  The Dark Universe by the  Deutsche Forschungsgemeinschaft (DFG)  and the  DFG cluster of excellence 'Origin and Structure of the Universe'  and thanks  Gary Bernstein  and  Stella Seitz  for helpful discussions. MGe, GC, GM are supported by the  Swiss National Science Foundation (SNSF). GL thanks  Wei Cui  for useful discussions. GL and BX were supported in part by the  U.
S. Department of Energy  through Grant  DE-FG02-91ER4068  and GL is also supported by the one-hundred talents program of the  Chinese Academy of Sciences (CAS). MK thanks  Liping Fu. This work was funded by a  EU   FP7 PASCAL 2 Challenge Grant. Workshops for the GREAT10 challenge were funded by the  eScience STFC Theme  and the by  NASA JPL , and hosted at the eScience  Institute Edinburgh  and by IPAC  Caltech Pasadena. We thank  Francesca Ziolkowska ,  Harry Teplitz  and  Helene Seibly  for local organization of the workshops. We thank the  GREAT10 Advisory team , co-authors of GREAT10 handbook (Kitching et al. 2012a), for discussions before and after the challenge. Contributions: All authors contributed to the writing and analysis presented. TDK was PI of  GREAT10 , defined and created the simulations, and lead the analysis.
We also tested the LUSH F121A and LUSH F121W lines, because recombinant LUSH F121A was unable to rescue the loss of spontaneous activity in OR67d neurons observed in lush mutants thereby providing a second piece of evidence linking LUSH conformational changes and OR67d neuronal firing  [24, 29]. We observed that all LUSH transgenes restored spontaneous firing of OR67d neurons (Figure  4A ). Quantification of this activity revealed minor variation in their mean firing frequencies (,1-3 spikes/s), but these differences were not statistically significant across genotypes (Figure  4B ). Importantly, LUSH D118A flies did not exhibit the elevated level of superimposed backbones of apo LUSH (grey) and cVA/LUSH (green, monomer A, conformation A only is represented; see also Figure  S2 ) (PDB IDs 1T14  [27]  and 2GTE  [29] , respectively).
Re-examination of the Structural Dynamics of LUSH Given the discrepancies between the properties of transgenic and recombinant LUSH proteins, we re-examined the published LUSH structures to determine the relationship between the presence and type of ligand and the conformation of this OBP (Table  1 ). We first superposed the 12 available structures of LUSH (Figure  S2A )  [25] [26] [27] 29]. Within each asymmetric unit in LUSH crystals (except one: apo LUSH, Protein Data Bank [PDB] ID 1OOI), there are two protein molecules, referred to as monomer A and B in cVA/LUSH  [29]  (dimerization of other OBPs has been observed in crystal structures  [30] , and there is no evidence that this reflects an in vivo biochemical property of LUSH). All non-cVA bound LUSH structures (except butanol/LUSH T57A PDB 3B87) possess similar conformations of loop 116-122 (Figure  S2A ), and the cVA/LUSH complex is distinct from these structures within this region.
CGD was supported by the Post-doctoral  FICYT-PCTI Clarin Programme  from the  Government of the Principado de Asturias. CC was supported in part by the  FP7 EU project Enaromatic  ( FP7/2007-2013 , Grant Agreement No. FP7-222927 ). Research in RB's laboratory is supported by the  University of Lausanne , a  European Research Council   Starting Independent Researcher Grant  ( 205202 ), and the  Swiss National Science Foundation  ( 31003A_140869 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
25 4. 6 2. 81/4. 46 2. 70 Propanol LUSH 1OOG P4 3 1. 45 4. 6 3. 33/4. 48 4. 41 Ethanol LUSH 1OOF P4 3 1. 49 4. 6 3. 75/4. 54 3. 03 Apo LUSH D118A 2QDI P4 3 2. 0 4. 0 - - Apo LUSH Apo LUSH cVA LUSH 1OOI 1T14 2GTE P6 1 P4 3 P2 1 2 1 2 1 2. 04 1. 86 1. 40 6. 5 4. 6 8. 5 5. 33 3. 55/4. 54 4. 91/7. 70 -4. 42 8. 09 (K87 A) (K87 B) Structure PDB ID Space group Resolution (A ˚) pH Distance (A ˚): Od1/2 (D118 A) 2Nf Distance (A ˚): Od1/2 (D118 B) 2Nf.
When the input D is 0. 1 µm 2 /s, the BIC is reliable for acquisition times between 10 and 200 ms. These data indicate that the BIC is the better indicator for free Brownian motion, when compared to the AIC and AICc. It should be noted that this evaluation is solely for the model of the trajectory and not the inferred parameters, which might be subject to a bias, as discussed in reference  [12]. Confined motion in a harmonic potential (V ∝ r 2 ) This section studies the performance of the three information theory criteria for numerical Brownian trajectories that are confined in a harmonic-spring potential (V = 1/2kr 2 ), where k is the spring constant. Again, we simulated 300 trajectories under each condition and recorded the normalized histogram of decisions, which are made for each trajectory by determining which model yielded the minimal value for a criterion.
Thus, this technique supplies a rigorous and reliable tool to automatically segment and classify SMT data. Finally, this method can easily confirm previous findings about the loss of confinement of CPǫT receptor trajectories due to lipid raft disaggregation. In the previous analysis, the mean diffusion coefficient increased from 0. 063 ± 0. 01 µm 2 /s to 0. 18 ± 0. 02 µm 2 /s, while the spring constant of the confining potential decreased from 237 ± 44 k B T. µm -2 to 35. 4 ± 7. 7 k B T. µm -2  [7]. In the previous work, we have analyzed the change in motion of many receptors and concluded that their confinement reduces but does not be-come entirely free Brownian motion  [7]. Here, we have selected the most extreme trajectories from the dataset, which do switch to free Brownian motion for the sake of showing a change in the mode of motion.
This expression is the probability of going from one space-time coordinate (r 1 , t 1 ) to the next (r 2 , t 2 ) for a diffusivity D and a force F ij with a positioning noise σ. The transition probability for the free Brownian motion can be obtained from Eq. 5 by setting F ij = 0 and gives: P Brownian ((r 2 , t 2 |r 1 , t 1 )|, D) = e - (r 2 -r 1 ) 2 4(D+σ 2 /(t 2 -t 1 ))(t 2 -t 1 ) 4π (D + σ 2 /(t 2 -t 1 )) (t 2 -t 1 ) (6) The overall probability of observing a certain trajectory for a given set of variables is then computed by multiplying all the displacement probabilities between all individual points in the dataset, assuming that the motion of the molecule is a Markov process. Now that the likelihood is known, we may apply Bayes' rule: P (Q|T ) = P (T |Q)P 0 (Q) P 0 (T ) , (7) where P (Q|T ) is the posterior or a posteriori probability of the parameters, i.
Cell treatment with Cholesterol oxidase Where mentioned, we incubated cells with 20 U/mL cholesterol oxidase (Calbiochem) in HBSS+10mM HEPES for 30 minutes. A cholesterol quantification kit (Invitrogen) was used to determine successful cholesterol digestion on lyzed cells before and after incubation. The data has been previously used to examine membrane-protein interactions  [7].
g. , the case of nosocomial infections in hospitals). In such contexts, the design and evaluation of prevention measures or containment policies is naturally based on targeting specific classes of individuals. For example, it is possible to prioritize the vaccination of a certain class of individuals (e. g. , nurses in a hospital  (35) ), whereas no simple policy can be based on a list of specific individuals. Therefore, it is important to build data representations that yield accurate results at the level of groups of individuals and that, at the same time, can be generalized in order to support generic conclusions on the relative efficacy of different strategies, and in particular of strategies targeting only high risk groups. Here we put forward such a representation and we validate its appropriateness and usefulness in a case study of structured population: to this aim, we leverage a high-resolution dataset on the face-to-face proximity of individuals in a hospital ward, collected by the SocioPatterns collaboration by using wireless wearable sensors.
The population is structured in different classes that correspond to different roles in the hospital ward. The collected data, described in detail in Ref. (33) , include the detailed timeordered sequence of individuals' contacts between all subject pairs. We consider various representations of the hospital ward data, corresponding to different types and degrees of aggregation. At the most detailed level, the raw data can be viewed as a dynamic contact network, where all the available information on the interactions between pairs of individuals (contact times and durations) is explicit. We then construct static networks that project away the temporal structure of the contacts but retain the identity of each individual and the structure of her contact network. At a coarser level, we consider the customary contact matrix representation of the data, which aggregates along the class dimension: All individuals belonging to the same role class are grouped together, and the contact rate between two individuals of given classes is given by the corresponding element of the contact matrix.
Data The dataset we use  (33)  describes the contacts of 119 individuals in a general pediatric ward of the Bambino Gesù Children's Hospital in Rome, Italy, over a period of one week  (9-16 November 2009). The ward under study is located in the Department of Pediatrics and is physically separated from other wards and facilities of the Hospital. It has 44 beds arranged in 22 rooms with 2 beds each and mostly admits children with acute diseases who do not require intensive care or surgery. Admitted patients are accompanied by one person (parent or tutor) who spends the night in the same room. Contacts, defined as close proximity interactions, were measured by using wearable badges that engage in bidirectional low-power radio communication  (7, 8)  and can assess the face-to-face proximity of two individuals.
The study was approved by the Ethical Committee of the Bambino Gesù Hospital  (33). As described in  (33) , during the data collection period all healthcare workers (HCWs) on duty, patients, parents or tutors were invited to participate in the measurements. HCWs participated in several meetings to receive information about the study and all procedures were approved by the Ethical Committee of the hospital. Only 5 HCWs, 1 patient and 1 parent declined. Some proximity sensing badges assigned to patients, parents or tutors were not continuously detected by the receiving infrastructure and were missing for more than 25% of the time they were assigned to the individuals. These badges were excluded from the dataset, after assessment of the robustness of the data statistical properties with respect to this filtering procedure (see  (33)  for details).
Overall, the data describes the contacts of 119 individuals, among which more than 90% of the HCWs who were on duty in the ward during the chosen week. These 119 participants were associated with the following role classes: 37 patients (P), 20 physicians (D), 21 nurses (N), 10 ward assistants (A), i. e. , health care workers in charge of cleaning the ward and distributing the meals, and 31 caregivers (C), who comprise tutors and non-professional visitors. As data was collected during the pandemic period when several patients with H1N1 infection were hospitalized, HCWs applied standard operating procedures to prevent transmission of infection including hand hygiene, face mask, wearing gowns and gloves. Specific guidelines are however permanently adopted in the study hospital to prevent cross transmission of respiratory infections, so that the contact patterns were not specifically altered during the data collection period.
Analysis The empirical contact data are analyzed and characterized in Ref. (33). In order for the present paper to be self-contained, we report here the contact matrix measured in the hospital ward as well as the newly introduced contact matrix of distributions (CMD). The simulated properties of epidemic spread, based on different contact pattern representations, are quantified and compared by means of several indicators that we compute over an ensemble of stochastic realizations of the epidemic process. We focus in particular on the number of recovered individuals at the end of the spreading process: its distribution provides important information on the expected magnitude of the epidemics and can hence be used to evaluate the effectiveness of interventions. We study the impact on this distribution of the choice of the initial seed (initial infectious individual) and compute the distributions of attack rates for each role class, as such measures can be used to design and evaluate containment policies targeting specific roles or groups of individuals.
Results The dataset we use provides information on approximately 16,000 contact events, recorded during one week, among 119 individuals: 37 patients (31. 1% of the total number of individuals), 31 caregivers (26. 1%), 20 physicians (16. 8%), 21 nurses (17. 6%), and 10 ward assistants (8. 4%). Table  2  reports the contact matrix defined on role classes: the element at row X and column Y is the average over all pairs of individuals x in X and y in Y of the total time that x has spent in contact with y during the measurement week, normalized to obtain daily contact durations. Table  3  reports the parameters of the contact matrix of distributions introduced above. As shown in the Additional file 1, we fit a negative binomial distribution to each empirical distribution of cumulated contact durations between members of classes X and Y and set the (X,Y) entry of the matrix to the values of the two fitted parameters.
Discussion We have introduced and validated in a case study a novel way of summarizing and using in simulation high-resolution contact data that describe the interactions between individuals in a structured population. We based our work on a high-resolution time-resolved dataset on the face-to-face contacts of individuals in a hospital ward. We have defined several representations of the contact data that correspond to different levels of aggregation, from the high-resolution dynamical information on all individual contacts (DYN), passing through several intermediate representation that include individualbased static networks (HOM and HET) and contact matrices (CM, CMB and CMD), to the coarse homogeneous mixing description (FULL). In particular, we have defined a new representation in terms of a contact matrix of negative binomial distributions (CMD) that has the simplicity of a contact matrix formulation but takes into account the heterogeneity of contact durations between individuals.
We then used the two representation to simulate the epidemic spread within the hospital community. Each representation was evaluated by comparing the final size of the epidemics, the impact of the seed role class, and the final attack rates in each role class against simulations based on the much richer network-based representations (HET and DYN), which retain several heterogeneities and correlations of the empirical dataset. Our results show that the usual contact matrix representation leads to an overall strong overestimation of the outbreak probability and of the attack rate. Moreover, the relative risks of individuals belonging to different classes are not correctly estimated from the simulations performed with the usual contact matrix. On the other hand, the contact matrix of distributions (CMD) affords a very good qualitative and quantitative agreement with the spreading patterns obtained with the heterogeneous network representation (HET), even though the CMD representation contains significantly less information on the contact network structure than HET (see Table  1 ).
These results are somewhat different from the ones obtained in Ref  (48)  for the case of contacts at a conference. This difference might stem from various causes: first, conference attendees mix more than individuals in a hospital and the structure of the corresponding contact network is thus different from the hospital case  (33). Moreover, the present hospital dataset is longer than the 2-days dataset used in Ref  (48) , hence dynamical effects at multiple timescales may play a stronger role. The difference between the simulations performed on the HET and DYN representations are larger for faster propagations, consistently with the results of Ref. (49) , according to which a very fast (deterministic) spreading phenomenon can result in very different patterns on static and dynamic network representations.
It is clearly possible to consider intermediate temporal aggregation levels that include some information on the activity schedules, for instance at the daily scale. We have thus built daily contact networks, as well as daily contact matrices and daily contact matrices of distributions: these representations take into account the fact that not all individuals are present every day, and that the contact patterns can vary from one day to the next. The results of SEIR spreading simulations, presented in the Additional file 1, are interestingly very close to the ones of representations aggregated at a weekly timescale, showing that weekly averaged data contain substantially enough information to correctly evaluate mitigation and containment measures. We finally note that the study of time-resolved networks is still in its infancy  (50).