dataesr/scientific-paragraphs-categorization huggingface dataset:
For the other locations, the calculated BSD agreed well with the observations. Sedimentation of OM was rather low during N 2011 , with a peak of 0. 7 μmol kg À1 d À1 TPC on day 8. The observed TL during N 2011 was 5 days. There was no uncertainty associated with the daily determination of TL during N 2011 , as both Chl a concentrations and sediment fluxes were determined every day. In S 2013 , we observed the highest peak Chl a concentrations (4. 61 (M1), 3. 03 (M3), 3. 67 (M5), 4. 40 (M9), and 5. 62 (M10) μg L À1 ) and sinking fluxes (4. 23, 3. 94, 3. 67, 5. 11, and 4. 1 μmol kg À1 48 h À1 TPC). Although this study displayed some of the shortest TLs of all data sets (6, 4, 2, 6, and 4 AE 2 days), Chl a increased very slowly over 14 days. Due to the high number of replicates in this experiment (n = 5), we were able to get a better estimate of the variability of TL among replicates.
Acknowledgments The authors thank the KOSMOS core team for the deployment and maintenance of the KOSMOS infrastructure during all experiments from 2010 to 2014. We also want to thank  Kai Schulz ,  Matthias Fischer , and  Alice Nauendorf  for their contribution of chlorophyll data;  Luana Krebs  and  Jana Meyer  for assisting with the sediment trap sampling and processing during the Gran Canaria experiment; and  Andrea Ludwig  for managing the logistics on site. We are grateful to the crews of M/V Esperanza,  R/V Alkor  ( AL376 ,  AL406 , and  AL420 ), R/V Håkan Mosby (2011609),  R/V Heinke  ( HE360 ),  R/V Poseidon  ( POS463 ), and  R/V Hesperides  ( 29HE20140924 ) for the transportation, deployment, and recovery of the mesocosms. The mesocosm studies were funded by the  Federal Ministry of Education and Research (BMBF)  in the framework of the coordinated projects  BIOACID II  ( FKZ 03F06550 ) and  SOPRAN II  ( FKZ 03F0611 ), as well as by the  European Union  in the framework of the  FP7 EU projects MESOAQUA  (grant agreement  228224 ) and  EPOCA  (grant agreement  211384 ).
MSF-managed SNCUs are integrated into district or regional hospitals: some are located in conflict settings, and most operate in remote areas where no other supporting health actors are present. The set-up of these units has been described in detail elsewhere. 9,10 Despite the differences in contexts, the care package offered is similar across all SNCUs. Patient management is carried out in accordance to standardised MSF-OCB neonatal guidelines, and paediatric guidance is provided by the MSF Headquarters. Patient data are collected and managed using a standardised e-database, allowing comparisons across sites. The present study aimed to assess, for the first time, whether this standard package of specialised neonatal care, implemented in different settings in sub-Saharan Africa and Southern Asia and integrated into different types of hospitals, performed equally well across the different contexts.
Data variables, data collection and source of data The variables collected on the neonates included sex, place of birth, resuscitation at birth, main diagnosis and outcomes (cured, left against medical advice, non-respondent and referral) by birthweight category and facility. Gestational age was not routinely assessed. Apgar scores at delivery were assessed, but were deemed unreliable. The principal diagnosis was assigned by the clinician at facility exit. Data were single-entered from the patient files into the standardised Excel-based routine database (Microsoft, Redmond, WA, USA) by trained data encoders. Data inconsistencies were observed over the different study sites. As this is not abnormal when using routinely collected data, the data were cleaned by the researchers, as inconsistencies could not be cross-checked with the original patient files.
Clinical and demographic characteristics Clinical and demographic characteristics of the admitted neonates are shown in Table  4. The majority (75%) of the admitted neonates were born in the same MSF facility where the SNCU was located, 11% were born in other health structures and 12% were born at home. The proportion of neonates born outside the facility was smaller in the group of ELBW babies. Resuscitation information was not recorded for 24% of all cases. Of all neonates with resuscitation information, 23% had received resuscitation at delivery and only 2% had received advanced resuscitation, including cardiac massage and/or adrenaline. The most common principal diagnoses were prematurity/LBW (27%), neonatal infections (21%) and asphyxia (19%). Around half of the neonates had a secondary diagnosis recorded following the same tendency as the first diagnosis (data not shown).
Limitations The study had some limitations. Assignment of clinical diagnoses was inconsistent across clinicians and sites. In some sites, no second or third diagnosis was recorded. The demographic and clinical information on the neonates that was captured in the database was limited. Only exit diagnoses were reported, precluding differentiation between morbidities existing at admission and morbidities acquired during hospital stay. Furthermore, outcomes were reported only at facility exit, and as no follow-up of discharged neonates was performed, no information was available about the further development of the discharged babies. A facility exit outcome is merely a proxy outcome among high-risk neonates, as neurological and other sequelae are common in babies with asphyxia and VLBW. 22 Neonates were followed up in only one single MSF project, and a scale-up of extended follow-up services is highly recommended.
Acknowledgements We sincerely thank  Mel Gable  for the skilfully drawn illustration and  Neil Brosnahan  for composing the color plates; both are volunteers at the  National Tropical Botanical Garden, Kauai, HI, USA. Frank Vorpahl  kindly sent images of the Forster drawing housed at GOET. Our thanks to  T. Deroin  for assistance with specimen dissection, to  E. Carnero-Diaz  for photography of the Forster specimen on loan to P, and to  Ken Wood  for assistance with photography of the pyrenes. The authors are grateful to curators of the following herbaria for making specimens available for study: BISH, GOET, K, NY, P, PAP, PTBG, and US. They also thank  Charlotte Taylor  ( Missouri Botanical Garden ) for her remarks which improved a previous version of the manuscript.
Electrophysiology and pharmacology. Somatic whole-cell patch-clamp recordings were obtained from stellate and pyramidal cells in layer 2 of medial entorhinal cortex. Stellate and pyramidal cells were identified by their somatodendritic morphology and their characteristic electrophysiological properties  19, 25, 26, 36, [53] [54] [55] [56]. Current-clamp recordings were acquired with a MultiClamp 700B amplifier at a 50-kHz sampling rate using custom software written in Matlab. Patch pipettes of 5-MW resistance were filled with internal solution containing (in mM): KMeSO 4 140, HEPES 10, KCl 7. 4, MgCl 2 0. 3, EGTA 0. 1, NaGTP 0. 3, Na 2 ATP 3 and sodium phosphocreatine 1. Alexa Fluor 594 (50 µM) was added to this solution to visualize cell morphology. Series resistance of the recordings was usually less than 30 MW.
gluEPSPs were first evoked by stimulating each spine individually at 300-ms intervals. We then stimulated an increasing number of synapses at short time intervals (0. 6-8 ms, as indicated), with a 10-s pause between each trial. To estimate the expected linear summation of gluEPSPs, we first shifted individual membrane potential traces according to the corresponding experimental stimulation interval (0. 6-8 ms for each trace) before computing the sum. Uncaging laser exposure time was 0. 5 ms. The laser power was adjusted to produce gluEPSPs that were comparable to sEPSPs recorded in the same cell (Supplementary Fig. 1 ). Uncaging timing and location were controlled by custom software written in Matlab. Experiments were terminated if photodamage to the dendrite was observed (for example, swelling of the dendrite) or depolarization of the membrane potential was detected.
Data analysis. Data analysis was performed with custom code written in Python 57. Nonlinearity, D, of each experiment was quantified by 3 1 100% 2 i n i i M L D n = - = × - å (1) where M i is the amplitude of the measured EPSP, L i is the amplitude of the EPSP constructed by linear summation of single synapse EPSPs, and n is the maximal number of synapses activated. Slow dendritic spikes were detected if the nonlinearity of the EPSP integral was 33% larger than the nonlinearity of the EPSP amplitude. A fast dendritic spike was defined as a step-like increase (>50%) in a plot of maximal dV/dt against the number of uncaging locations (Fig. 2  and Supplementary Fig. 3 ). To examine whether signatures of active dendritic integration can be found in grid cells in vivo, we analyzed in vivo patch-clamp recordings from MECII neurons  18.
Plateau potentials were defined as sustained depolarizations following action potentials with a full-width-at-half-maximal amplitude exceeding 15 ms. These criteria were chosen to identify plateau potentials that closely resembled published examples (for example,  Bittner et al. 11  ). Action potentials were detected when dV/dt exceeded 30 mV/ms. We did not observe any obvious isolated plateau potentials without a preceding action potential in our data set. We analyzed a total of n = 58 recordings that produced trains of action potentials in response to depolarizing 1-s current injections. Recordings were categorized into putative dendritic and putative somatic recordings according to input resistance (R in ) and 20-80% rise time of action potentials (t 20-80 ) 6 (putative somatic: t 20-80 < 0.
Statistical significance of dendritic spike proportions was assessed using Fisher's exact test. Analyses of variance (ANOVA) were performed when more than two groups were tested. Differences were considered statistically significant when P < 0. 05. No correction for multiple comparisons was applied  58. No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in previous publications  11, 17, 18. Data collection and analysis were not performed blind to the conditions of the experiments.
ACKNOWLEDGMENTS We are grateful to  P. Latham ,  A. Packer  and  S. Turaga  for discussions and comments on the manuscript, and to  S. Chun  for assistance with histology. This work was supported by grants from the  Wellcome Trust  (M. H. ),  ERC  ( StG 678790 NEWRON  to C. S. -H. and AdG 695709 DendriteCircuits to M. H. ) and  Gatsby Charitable Foundation  (M. H. ). Publisher: NPGNY; Journal:  NN: Nature Neuroscience; Article Type: Article DOI :  10. 1038 /.
Study area The study was conducted in the Nouna region (4. 1°/3. 5°W and 12. 4°/13°N) in northern Burkina Faso (Figure  1 ). for temperature (Tn for minimum and Tx for maximum) and relative humidity (RH), all for the 1983-2011 period that was utilised for this study. The datasets were statistically homogeneous for the period under study. Monthly climatology could thus be derived for these four parameters (Figure  2 ). The height of the rainy season, characterised by maximum rainfall, minimum Tx, and maximum RH (highlighted by a red rectangle in the figure), was identified to occur from July to September. The minimum temperatures Tn occurred in December and January when the infra-red radiation from Earth is at its maximum due to prevailing clear skies (highlighted by a red circle in the figure).
The chronological series (z-series) for the different parameters were obtained by computing their mean values at the four grid-points constituting the corners of the frame of the Nouna region. Bias, spatial correlation, median absolute errors (MAE) and root-mean-squared errors (RMSE) were also been computed for each series for the region. We used an impact model to assess the impact of climate conditions on malaria risk. This approach follows the work of  Craig et al. (1999)  and  Ermert et al. (2011)  modified by  Tanser et al. (2003)  when applied in the project mapping the malaria risk in Africa (MARA). The indices for malaria diffusion were based on rainfall, temperatures (min. and max. ) and RH data computed from the impact model, with values of 0 for unsuitable conditions (U) and of 1 for suitable conditions (S).
Use of climatology The monthly rainfall forecast for thresholds >80 mm was assessed by consulting the data for the Nouna region during the 1983-2011 period. It was found that four months (June-September) had a probability of ≥0. 5 to reach this value and therefore defined as having an INDp=1. Thus, JAS were the only three months during the 1983-2011 period when the 80-mm threshold was attained. For the other months, the INDp=0 (not shown). The details for the monthly averaged correct probabilities (in %) from the model using the above prediction model can be seen in Figure  6  (red dots from the model compared to yellow dots for the actual data in the figure). Apart from June and September, the predictions from the model were quite good, confirming that the model was weak at estimating rainfall at the beginning and at the end of the rainy season.
By separating the two periods it was found that 75% of the forecasts based on the six models were correct before 2007 increasing to 80% afterwards. This result is somewhat surprising and not very encouraging for models predicting seasonal variability. Future testing of a more sophisticated method, such as filtering the Nouna data by using EOFs prior to analyses, would be useful. During the historical period of study following Craig's model, the conditions deemed favourable and those deemed unfavourable for malaria, were essentially controlled by rainfall, which are under the influences of the AMO phases. Temperature conditions  Eleven members obtained from the referral experiment  and 41 members from running the operational model  (2007) (2008) (2009) (2010) (2011). Monthly averages were computed to obtain a single rainfall time-series for the 1983-2011 period and for the above four initialisations.
Acknowledgements: the present research was done under the  French program Gestion et Impacts du Changement Climatique  ( GICC APR 2010 -N° 2100214938 ). Flore Mounier  was at the time at the  Centre National de Recherches Météorologiques (CNRM)/Météo-France. The authors would like to thank  CNES  for managing the  Paluclim  project, and  Drs. Philippe Dandin ,  Philippe Bougeaut  former directors of Météo-France Climatology and CNRM divisions. Tourre would like to thank  Dr. Sean C. Solomon  director of  Lamont-Doherty Earth Observatory (LDEO)  for supporting his research. This is LDEO contribution # 8149.
Six IR proximity sensors are installed on the front and back of the robot in order to avoid obstacles, other FishBots, or the border of the arena. A Bluetooth antenna is used for wireless communication to control the robot. An ASEBA virtual machine runs onboard each mobile robot. ASEBA  25  is an event-based architecture for the realtime distributed control of robots. The ASEBA communication protocol allows multiple robots to connect on the same network. This allows them to share global events that can be sent via a high-level control application or by the robots themselves. ASEBA, integrated with D-Bus, allows access to each robot using high-level programming languages (such as Cþþ, Python, or ROS) via a software hub called Medulla. 26 n order to increase the capabilities of the robot to mimick the locomotion of zebrafish in terms of speed and acceleration, a cascade controller (Figure  8 ) is implemented for the low-level control of each motor.
The tank is filled with water up to a level of 60 mm. The FishBots are powered using two conductive plates, one glued onto the bottom of the aquarium and one onto the support on which the robot moves. An overhead acA2040-25gm monochrome GigE CCD camera (Basler AG, Germany) with a maximum resolution of 2048 Â 2048 (pixels) and equipped with low distortion lenses CF12. 5HA-1 (Fujinon, Tokyo, Japan) grabs frames that are then processed for tracking purposes. An IR lightemitting diode is placed on top of the setup to control the RiBots. It is connected to the output of a Raspberry PI that is interfaced with the main desktop computer. The RC5 IR signals are generated using the Linux IR Remote Control (LIRC) library on the Raspberry PI. The RiBot is mounted on a base composed of a tiny carbon stick and an iron plate.
The high-level control-software tracks each RiBot and fish using frames grabbed by a camera. The same software is used to control the FishBots motion as well as the RiBots body movements. Each RiBot and each FishBot can be controlled individually. For the FishBots, the desired speed of each wheel is sent through a serial connection using Medulla integrated with D-Bus on the ASEBA network, where all the FishBots are connected and can receive or emit events. For the RiBots, the communication is only one way, where the IR RC5 signal is broadcasted to control stepper motors that drive the caudal peduncles. Thus, the zebrafish usually move by following a sequence of three steps: Step 1: Orientation. Strong caudal peduncle bending to reorient and start the propulsion toward the next goal. Step 2: Acceleration.
Experiments with the Fish-CASU Regarding the matching of locomotion, we tuned the parameters of our fish locomotion behavior implemented on the FishBot to match the data extracted from the zebrafish locomotion patterns. The final parameters of the Fish-CASU are shown in Table  2. These parameters were tuned in order to match the average linear speeds and, secondarily, the locomotion sequence curve in terms of speed and acceleration (Figures  11 12 13 ). We recorded, at 15 frames per second, the position of the Fish-CASU moving alone in the tank for 1 h using the same experimental setup and tracking software used to analyze zebrafish movements. The Fish-CASU was programmed to follow a trajectory similar to the zebrafish by following the walls and, from time to time, moving rapidly in the center of the tank.
Figure5. Architecture of the Fish-CASU vision-based closedloop control. The high-level control-software tracks each RiBot and fish using frames grabbed by a camera. The same software is used to control the FishBots motion as well as the RiBots body movements. Each RiBot and each FishBot can be controlled individually. For the FishBots, the desired speed of each wheel is sent through a serial connection using Medulla integrated with D-Bus on the ASEBA network, where all the FishBots are connected and can receive or emit events. For the RiBots, the communication is only one way, where the IR RC5 signal is broadcasted to control stepper motors that drive the caudal peduncles.
Second, our method works on a small dataset of training images. We use the SITA features of many nodes from an image instead of a single SITA feature at the root node per image. Therefore, our method learns sufficient features to be discriminative for training, whereas the number of training images can still be small (details are described in Section V). The remainder of the study is organized as follows. Section II reviews related work on texture analysis and image labeling. In Section III, we briefly introduce the basic notions and definitions of the tree of shapes and SITA. Then, in Section IV, we describe the details of the proposed texture image segmentation method. Experimental results with a synthetic texture image dataset and a real image dataset and discussion of those results are provided in Section V.
One of the supervised MRF approaches was proposed by Hirakawa et al. [29]. They proposed a patch-based method that uses a posterior probability obtained from a support vector machine (SVM) with bag-of-visual words (BoVW) histograms using more than 1,000 labeled patches for training. Another popular approach uses conditional random fields (CRFs); MRF is a generative model, whereas CRF is a discriminative model. CRF has a structural-learning property and can train the spatial structures of labels. Shotton et al. [30] ,  [31]  proposed TextonBoost for object segmentation. They introduced a novel texture descriptor, the texture-layout filter, into the CRF framework. For evaluation, they used the MSRC-21 dataset and 271 images  [31]  for training. Bertelli et al. [32] adopted a kernel-structured SVM for object segmentation.
They introduced a pairwise term to a structured SVM that is the same as the CRF framework. Their method was evaluated via 3-fold cross validation with three datasets  [33] ,  [34]. For each trial, 400, 218, and 56 images for the three datasets, respectively, are used for training. A fully connected CRF has been proposed  [35]  that uses a mean field approximation with a linear combination of Gaussian kernels for a pairwise edge potential for efficient inference. In their experiment, they used approximately 270 images on the MSRC-21  [31]  dataset and 770 images on the PASCAL Visual Object Classes (PASCAL VOC) dataset  [36]  for training. Recently, convolutional neural networks (CNNs) have been proposed for computer vision tasks that include image labeling. Farabet et al. [37]  proposed a labeling method for scene parsing.
Their approach assigns estimated labels to pixels and then refines the results using superpixels, CRFs, and optimal-purity covers on a segmentation tree. Long et al. [38]  used a fully convolutional network trained in an end-to-end manner. These two methods use the SIFT flow dataset  [39]  for evaluation, with 2,488 images used for training and 280 images used for testing. Other methods have also been proposed  [40] ,  [41] ,  [42]  using hundreds of images for training. Consequently, these CNN-based approaches have shown good performance, as long as large numbers of training images are available. It would be difficult to achieve good performance for smaller datasets. In contrast to the methods above, our proposed method works effectively with a small number of training images. In this study, we show a comparison of the proposed method with these related approaches using a small dataset of texture image segmentations.
V. EXPERIMENTAL RESULTS We tested the proposed method and compared it with existing methods using synthetic and real image datasets. if a j ≥ θ then 8: y j ← sign(w T h j + b) \\ classify a histogram. 9: else {a j < θ} 10: y j ← y P a(vj ) \\ assign label of parent node. 11: end if 12: end for 13: u l (x) = y j(x) , j(x) = argmin j {a j |x ∈ s j } \\ map label to the corresponding pixel. For the experiments, we created a synthetic dataset from the UIUC database  [45] , which comprises 25 various texture classes, each of which contains 40 images of size 640 × 480 pixels. This dataset comprises seven sub-datasets. Each subdataset includes five images containing one to three regions made of two classes. Figure  4  shows an example of a created sub-dataset containing five images and corresponding ground truth labels.
Trees of shapes of these images have sufficient nodes, 18,855 nodes per image, on average. For each subdataset, we randomly select three images for training and two for testing. In the experiments, we set sigmoid gain β = 0. 1, initial values of thresholds to 1,000, and weight for aggregating histogram w agg = 1. 0. For quantitative evaluation, we use the Dice coefficient  [46]. We used three methods for comparison: Felsenszwalb's unsupervised image segmentation  [47] , a patch-based MRF segmentation with SVM  [29] , and a fully connected CRF  [35]  with TextonBoost  [30] ,  [31].