dataesr/scientific-paragraphs-categorization huggingface dataset:
A manual shutter was placed in the illumination path in order to control the illumination period. A low-magnification water-immersion objective (Leica, 20×, N. A. =0. 7) was used for both imaging and delivering the green light activating the NpHR ion pump. The acquisition times were 10-20 seconds at 60-200 fps. The embryos did not show any sign of phototoxicity during or post acquisition. All microscopy setups used were equipped with a heating device that ensured the embryos were kept at 28°C during the imaging. The velocity of red blood cells was determined by manual tracking and kymograph analysis using ImageJ (NIH) and Matlab (MathWorks) software.
Photodiode data analysis The photodiode directly converts the displacement of the blood cell from the trap center into a different voltage. We took a 4-second time trace with an acquisition frequency of 4 kHz. We then extracted the power spectrum from that time trace. This power spectrum directly showed the heart beat frequency and also the cut-off frequency f c. This cut off frequency is directly correlated to the trap stiffness k Trap by the following equation: k Trap =2 πζ f c , where ζ is the friction of the blood cell. This trap stiffness was then used to convert the displacement extracted from the movies into forces. The theoretical hydrodynamic force applied to the blood cell can be given in first approximation by the formula F hyd = 6π ηRν, where η represents the fluid viscosity (≈2.
Plasma viscosity measurements Viscosity was measured by particle tracking a cell nucleus in the dorsal aorta in a 2,3-butanedione monoxime (BDM)-treated fish injected with gata1 MO. Fish were imaged at 20× magnification at 1000 Hz. Sub-pixel particle tracking was performed by fitting images of the nucleus to a twodimensional Gaussian using custom Matlab software. We confirmed that the motion of the nucleus reflected the motion of the entire cell, and that the cell was not tethered to the vessel wall. The histogram of the motion was then fitted to the probability distribution function of a freely diffusing particle of equivalent size; the second moment then reflects diffusion coefficient: As D = k B T(6π ηa) -1 [ where k B is the Boltzmann constant, T (303 K) is the temperature and a (5 μm) is the radius of the tracked cell], the extracted viscosity is 0.
Blood cell velocity measurements Velocities were calculated by hand or by performing a kymograph along the direction of flow. The kymograph then shows space along one axis and time along the other axis, with time step Δt given by the frame-rate (between 250 and 500 fps). Cells that move along the direction of flow leave a trace in time as they move through the vessel in space. These traces were then segmented in Matlab using the Canny edge-finding algorithm. The segmented traces consisted of an image of pixels that trace out an approximate curve. The curves were then converted into coordinates by fitting a line through each nearest neighbor segment of the curve. The fit lines are median filtered through a 3-point filter to remove spurious noise. As many cells flow through the kymograph in a given Δt, the velocity in the vessel is constructed statistically by averaging the velocity of many cells at Δt of the kymograph.
Acknowledgements We thank  Emily Steed ,  Francesco Boselli ,  Daniel Riveline  for thoughtful comments on the manuscript and  Stéphane Roth  for excellent technical help. We thank  C. Wyart ,  P. Del Bene  and  S. Schulte-Merker  for providing fish stocks. We thank the  Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC)  imaging center, in particular  Pascal Kessler ,  Marc Koch  and  Didier Hentsch  and  Sandrine Geschier  and  Sylvie Greidler  at the IGBMC fish facility.
In vivo RECORDING OF LOCAL FIELD POTENTIALS IN THE mPFC AND VENTRAL HIPPOCAMPUS LFP signals obtained from both electrodes were amplified, filtered (0. 1-3000 Hz, LP511 Grass Amplifier, Astro-Med, Germany), acquired (Micro 1401 mkII, CED, UK) and recorded on a personal computer running Signal Software (CED, UK). After reaching fully anesthetized state, 100 s of local field activity was recorded at the sampling rate of 250 Hz (for representative traces see Figures  1A-D ). After the electrophysiological protocols, a biphasic 1 mA stimulus was delivered to both electrodes. Rats were sacrificed and perfused a solution of paraformaldehyde 4%. Brains were carefully removed and sectioned in 50 µm slices. Brain slices containing the PFC and hippocampus were processed with a light Giemsa staining for determination of electrode position; the lesion caused by the biphasic stimulus identified the electrode position and the recordings were discarded whenever one of the electrodes failed the targeted position (about 12% of the recordings).
Rats are allowed to spend 30 s on the escape platform before being positioned at a new starting point. Time spent to reach the platform (escape latency) was recorded in the consecutive trials. During the reference memory task, the platform was set in the same imaginary pool quadrant and rats were tested during 4 days. Data on average escape latencies to the platform on days 1-4 were analyzed as a reference memory test. To perform the reversal learning task, a PFC-dependent function  (De Bruin et al. , 1994) , on day 5, the escape platform was positioned in the opposite (new) quadrant and rats were tested in a 4-trial paradigm, similar to that described above. For this reverse-learning task, time spent swimming in the new quadrant were recorded and analyzed as a measure of behavioral flexibility.
DATA ANALYSIS AND STATISTICS The PSD of PFC and hippocampus regions, as well as the coherence between both regions, were performed on the 2-channel 100 s long LFP signals acquired at the start of the experiment for each rat. Each measure was applied on 1 s long segments and the average of all segments was considered for statistical group analysis. Both measures were assessed in a wide range of frequencies: delta (1-4 Hz); theta (4-12 Hz); beta (12-20 Hz); low gamma (20-40 Hz); high gamma (40-90 Hz). All LFP records were thoroughly inspected and those that presented significant noise corruption were excluded from further analyses. PSD and coherence were calculated with custom-written MATLAB-based programs (MathWorks, Natick, MA).
ACKNOWLEDGMENTS The authors work was supported by  FEDER  funds through  Operational program  for competivity factors-COMPETE and by national funds through  FCT-Foundation for Science and Technology (FCT)  fellowships ( João Filipe Oliveira  by  SFRH/BPD/66151/2009 ;  Luís Ricardo Jacinto  by  SFRH/BD/40459/2007 ), a  Marie Curie Fellowship  ( João Filipe Oliveira  by  PIEF-GA-2010-273936 ) and Grants from  BIAL Foundation  ( 138/2008  to  João José Cerqueira  and  61/2010  to  João Filipe Oliveira  and  Vanessa Morais Sardinha ) and  FCT  ( João Filipe Oliveira ,  Ana Lima , and  Ana Filipa Oliveira  by  PTDC/SAU-NSC/118194/2010 ;  Nuno Sérgio Dias ,  Luís Ricardo Jacinto ,  João José Cerqueira , and  Nuno Sousa  by  FCT/PTDC/SAU-ENB/118383/2010 ;  Nuno Sérgio Dias ,  Daniela Silva Ferreira ,  Joana Santos Reis ,  João José Cerqueira , and  Nuno Sousa  by  FCOMP-01-0124-FEDER-022674 ).
Both demographic data (sex, age, breed) and health related data (reasons for condemnation, condemned portions) were collected for each slaughtered animal. The large amount of data available from the Nergal-Abattoir system, nearly 2 million cattle, might be the basis of a syndromic surveillance system in France, based on meat inspection data. Indeed, syndromic surveillance is defined as the monitoring of non-specific health indicators including clinical signs, symptoms or proxy measures to enable the early identification of the impact (or absence of impact) of potential human or veterinary public health threats. It is implicit that the data are usually collected for purposes other than surveillance and, if possible, are automatically generated so as not to impose an additional burden on the data providers  [3].
Slaughterhouse data could then seem relevant for syndromic surveillance, as a complement to other existing animal health surveillance systems. In classical epidemiological surveillance, objectives are defined and relevant data are then collected to meet these objectives. In syndromic surveillance, available data, usually collected for another purpose, are used for an epidemiological surveillance objective without being able to have an impact on the way they are collected. The procedure to define a case is thus inevitably different than for classical surveillance. According to the type of meat inspection data used, different kinds of epidemiological surveillance could then be performed. Specific surveillance (i. e. surveillance with a targeted objective) is focused on the surveillance of a pre-defined disease or group of diseases whereas non-specific surveillance (i.
Materials In European countries, each slaughtered animal is submitted to ante and post-mortem inspection so as to guarantee food safety. From 2005 to 2010, the French Ministry of Agriculture started the Nergal-Abattoir project to collect data in real time during the slaughtering process. It involved ten cattle slaughterhouses in France that represented about 20% of cattle slaughtered in the country. Data were collected using touch screens on the slaughter lines, provided by the food business operator. Data were then transmitted through a constant data flow to the database of the French Ministry of Agriculture. The main objectives of this system were to guarantee the traceability of meat inspection results (quality assurance) and to automatically produce the mandatory condemnation reports for cattle owners.
For each animal, the database contained: identification number, date of birth and slaughter, last farm location, sex, breed, signs observed during ante-mortem inspection, reasons for condemnation and locations or absence of condemnation. From June 2005 to December 2010, 1,937,980 cattle were slaughtered in the ten slaughterhouses involved in the Nergal-Abattoir project. Cattle with missing data (n=63) were excluded. Cattle euthanized or that died in the ante-mortem inspection area, respectively 1,186 and 353 animals, were excluded from this study. Among the population of 1,937,917 cattle slaughtered without missing data, 381,186 had at least one part of the carcass condemned. The data available for each slaughterhouse did not cover the same period because the Nergal-Abattoir project did not start and finish at the same dates for each slaughterhouse, thus the number of days of available data varied from 345 to 1,698 days.
All data registered during the slaughtering process and used to create categorical variables for analyses are presented in Figure  1. Some levels of the categorical variables were grouped to avoid low numbers in levels that could create instability in data analyses (Figure  1 ). The location of the farm was used to allocate each animal to a region. Regions with a frequency lower than 1% (among condemned animals) were grouped together in an "Other regions" level. The 67 breeds, available in the database, were grouped according to production type as defined by FranceAgriMer, i. e. French national organization of agriculture products,  [7]  into the levels "dairy", "beef" and "mixed cattle". Age categories were built according to the fact that i) management practices are different according to age categories, ii) European regulation defines a specific age category for veal (animal under 8 months of age)  [8] , iii) a French observatory of livestock mortality already defined age categories in line with management practices  [9].
The ages of cattle were thus classified into six levels: [0;8 months old), [8;24 months old), [2;3. 5 years old), [3. 5;5 years old), [5;10 years old), ≥10 years old. Clinical signs observed during ante-mortem inspection were used as binary variables: presence/absence. The list of reasons for condemnation is a national mandatory list in France  [10]. Because of the database design, each condemned portion was associated with one and only one reason for condemnation. As each animal could have more than one condemned carcass portion, it could also have more than one reason for condemnation (e. g. condemnation of the liver for abscess and the heart for pericarditis). The 264 different reasons for condemnation of the Nergal-Abattoir system were merged into 57 reasons for condemnation levels according to their biological similarities or in order to compare data among slaughterhouses when the levels of detail of the reasons for condemnation were different (e.
e. observed variables. Factorial axes are constructed from active variables whereas the result interpretation is aided by supplementary variables. The supplementary variables are projected onto the vector subspace generated by the factors. The particularity of MFA is to compute a distance between individuals corresponding to a weighted sum of the separate distances induced by every set of variables. The contribution of any set of variables to the global distance depends on the dimension of the unit cloud defined by the separate Multiple Component Analysis of each set of variables i. e. a cloud with several important orthogonal variance directions has a greater influence than a one-dimensional cloud  [12, 14]. The two groups of active variables were demographic variables (sex, age category, production type) and condemnation data (reasons for condemnation and portions).
Only levels of reasons for condemnation and levels of condemned portions with percentages higher than 1% were used as active variables to avoid instability in the MFA  [15]. Six supplementary variables were used: year of slaughter, month of slaughter, farm location, presence or absence of clinical signs during ante-mortem inspection, abattoir identification number, reasons for condemnation and portions with frequencies lower than 1%. MFA was performed with the "FactoMineR" R package  [16].
Therefore hybrid clustering, i. e. combining several clustering methods to take advantage of their specific strengths, was performed using K-means, known for its efficiency for clustering large datasets, as a first step for HAC  [18, 19]. K-means clustering was performed on the MFA principal coordinates. The number of clusters was defined as the number of distinct principal coordinates, because similarities among principal coordinates showed that a significant number of cattle had the same principal coordinates. The number of clusters defined was then small enough to perform HAC on the centroids of the clusters. The generalized Ward's criterion was used as the aggregation criterion for HAC. It consists in aggregating two clusters in a way that minimizes intra-cluster variance and maximizes inter-cluster variance  [20].
e. point that defined the limit of the V-test values considered as highest. The variable levels identified with this process for each cluster were then used to describe the cluster using both proportion of cattle with the variable level within the cluster and the proportion of cattle with the variable level that were in this cluster. The description of the clusters was performed using the "FactoMineR" R package  [16]. We created indicators to quantify the stability of the clusters. The objective of these descriptive indicators was to identify which clusters, i. e. groups of reasons for condemnation, were commonly seen in slaughterhouses and which ones were more specific to some slaughterhouses or periods of time. The stability was evaluated by year of slaughter and by slaughterhouse through the same process, i.
The hierarchical tree suggested four possible partitions into 6, 9, 15 or 16 clusters based on the height of the HAC dendrogram (Figure  5 ). Based on the biological significance of the clusters of these partitions, the 15 clusters partition was selected. The partition was strengthened by the K-means method and each animal was attributed to its cluster (Figure  3 ). The comparison of the proportions of the levels of active and supplementary variables in each cluster and in the whole dataset made it possible to identify which   5  and 6. Clusters 1, 2, 4, 5, 7, 8, 9, 10, 12, 13, 14 and 15 contained between 1% and 2% of all the condemned cattle. Clusters 6 and 11 were larger, with respectively 4% and 6% of the condemned cattle. Cluster 3 contained almost two thirds (66%) of the condemned cattle population.
Only reasons for condemnation and portions representing at least 1% of the animals are presented. Each animal can have more than one portion of the carcass condemned but each part of the carcass can be linked to only one reason for condemnation. condemnation and by lesions associated with the liver such as macular telangiectasia, liver fluke, sclerosis, and abscess. Beef cattle, female and age categories over 5 years of age also characterized this cluster (Table  5 ). All cattle in cluster 4 (2% of condemned cattle) presented with a peritonitis lesion. "Part of the carcass" was a condemned portion that also characterized this cluster (54% of cattle in the cluster versus 13% of cattle in the whole dataset). This cluster was also characterized by female and dairy cattle. Abscess concerned 43% of cattle in the cluster versus 24% in the whole population (Table  5 ).
Cluster 5 (2% of condemned cattle) was characterized by whole carcass condemnation for 85% of cattle versus 3% in the whole dataset. The other condemned portions that characterized this cluster were viscera, tongue, kidneys, heart, liver, head, lungs, and udder. This is linked to the fact that the entire cattle carcass is defined as presented in Figure  2 , thus the condemnation of the entire animal means the whole carcass, offal, head and udder. Pleurisy, congestion and peritonitis characterized this cluster. In this cluster, 21% of cattle had presented at least one symptom during ante-mortem inspection whereas only 4% of the whole population of the study had one (Table  5 ). Cluster 6 (4% of condemned cattle) was only characterized by demographic variable levels: castrated male, mixed cattle and cattle from 2 to 3.
Space-time clustering of cattle affected by these syndromes could be performed to implement a syndromic surveillance system  [42]. b. To monitor specific diseases for which data are only available at the slaughterhouse such as fascioliasis or cysticercosis. If a database such as the Nergal-Abattoir database was implemented in all the slaughterhouses of a country, it could allow the implementation of real-time traditional surveillance with exhaustive data to detect outbreaks and analyze trends of these diseases. c. To detect an emerging disease by monitoring the percentage of cattle in each cluster defined statistically to detect changes in trends. Each new slaughtered animal will be attributed to the defined syndromes by determining its MFAderived representation and the cluster it belongs to.
Real-time analysis of the syndromic time series with anomaly detection algorithms could then be performed to implement a syndromic surveillance system. 2) Define monthly, quarterly or annual indicators: a. At herd level for farmers, so as to highlight poor practices and then enable them to implement corrective actions. b. At herd or département level, for veterinary services to focus their controls on high risk herds/areas. c. At slaughterhouse level, for monitoring the quality of the slaughtering process. Our results showed the various implications of slaughterhouse data for surveillance as well as for herd management and optimization of veterinary services controls. This should encourage increasing use of meat inspection data. Even though these datasets are complex, our study showed that it is possible to define groups of underlying reasons for condemnation and portions, taking into account both demographic and lesion data on slaughtered cattle.
1 C (recorded over a 20-day period during February 2000). Fluctuating temperature profiles followed a sinusoidal increase during the day, and a negative exponential decrease at night, with minimum and maximum temperatures reached at 6:00 AM and 2:00 PM, respectively. Experimental climatic conditions were maintained for experimental mosquitoes in KBF115 incubators (Binder, Tuttlingen, Germany). Internal data loggers recorded temperatures in all incubators. Additionally, HOBO data loggers recorded temperatures on an hourly basis in the two incubators with fluctuations. Actual air temperatures that mosquitoes were exposed to during experiments were consistent (±0. 3 C) with that programmed at each point during the day. The mean temperature for the small DTR treatment incubator was 26. 71 C, and for the large DTR temperature averaged at 26.
After collection, all samples were frozen at -80 C for later analysis by indirect fluorescence assay. We also collected the whole bodies (without separation of heads) of dead females daily and tested them for infection status. Results from analysis of dead mosquitoes were included in our survival analyses. Wing lengths. Female wings were removed at the same time as heads and bodies were separated, and mounted on microscope slides with double-sided tape. Photographs of the wings were taken and measurements of each wing were made, in millimeters, using AutoMontage (Syncroscopy, Frederick, MD) software. Wings were measured from the distal end of the alula to the most posterior point of the R 3 vein, minus the fringe. Damaged wings or wings that were mounted imperfectly were excluded from analysis.
Data analysis. All data was analyzed by using JMP software version 10 (SAS Institute Inc. , Cary, NC). Vector competence was analyzed by nominal logistic regression of the infection or dissemination status as a full-factorial function of temperature and DPE, and carton nested within temperature and DPE. To improve the statistical model, factors with a P value 0. 5 were removed sequentially from the model, except for the nested carton effect, which was required to test the effects of temperature and DPE. Survival was analyzed by assessing the longevity of females exposed to an infectious blood meal by using deaths throughout the duration of the experiment, as well as right-censored mosquitoes that were killed on the days we collected mosquitoes for testing midgut infection and dissemination (sampling days).
At day 27, there was only a single mosquito tested from the large DTR (which was uninfected). There-fore, we did not consider this data point in the pairwise analyses for that time point. Survival. A total of 186 females died during the experiment and 526 were sampled at scheduled time points and were therefore right-censored. We identified an overall significant effect of DTR on survival (χ 2 = 6. 85, degrees of freedom [df] = 2, P = 0. 032) (Figure  1A ). Lowest survival was for females held under large fluctuations, with a mean adult lifespan of 20 days, relative to females under a constant temperature (23 days) and small fluctuations (21 days). We detected an overall effect of infection on survival (χ 2 = 25. 17, df = 1, P 0. 0001) (Figure  1B ), in which females that were uninfected, although they had been exposed to the virus, had a lower survival than those that were infected.
Acknowledgments: We thank  W. K. Reisen  for continued support of our use of containment facilities, laboratory space, and fruitful discussions;  P. S. Ward  for use of microscopes for wing measurements;  A. Ponlawat  for collecting and sending mosquito eggs;  N. H. Willits  for statistical advice; and two anonymous reviewers for constructive comments on an earlier version of this manuscript. This research benefited from discussions with  M. B. Thomas ,  K. P. Paaijmans , and working group members in the  Research and Policy for Infectious Disease Dynamics Program of the Science and Technology Directorate, Department of Homeland Security , and the  Fogarty International Center, National Institutes of Health. Financial support: The study was supported by grant  EF-0914384  from the  Ecology of Infectious Disease Program of the National Science Foundation.