dataesr/scientific-paragraphs-categorization huggingface dataset:
5 s. When negative electrical current is applied to both rhythm generator neurons (blue area in Figure  6 ), the cycle period is prolonged to 0. 65 s per period. The cycle period is calculated using MATLAB (MATLAB 2017b, The MathWorks, Inc. , Natick, MA, USA). Each new period begins when the extensor pattern formation neuron voltage,   , crosses a threshold (i. e. ,   = -60  and    > 0). The results of the influence of external currents on the pattern formation layer is shown in Figure  6. In the first stimulus, a positive current of duration of 0. 1 s (blue area in Figure  6 ) is applied to the extensor neuron of the pattern formation layer, which is shorter than the duration of the extensor period. This result meets the first observation from the cat fictive locomotion: the influence is limited to within the ongoing step period, and does not affect the timing of the following step.
Animal Experimental Procedure We also performed animal experiments to collect rat motion data. The preparations are female Rattus norvegicus Han Wistar rats and all rats were at least 6 months old. Animal care was in accordance with German animal welfare regulations, and experimental procedures were registered with the Thuringian Committee for Animal Research (JSHK-2684-05-04/12-1). We have chosen rats as the experimental subject because their size makes them excellent for studying full body kinematics in front of our three-dimensional X-ray acquisition system. Moreover, they are easy to handle and train, and are robust against infections and heal quickly after experimental procedures. The motion data was collected through X-ray videography of rat walking on treadmills (Figure  7 ). The X-ray device (Neurostar, Siemens, Erlangen, Germany,) records at frequencies up to 2000 Hz with high resolution (1536 × 1024 dpi) high-speed cameras (SpeedCam Visario G2, Weinberger GmbH, Erlangen, Germany), enabling us to record movements precisely.
Nine rats were used in total for motion capture. The treadmills (Tetra, Ilmenau, Germany) were setup at 0. 5 m/s, and around 400 steps were analyzed for hard, medium, and soft belt runs. The motion data was analyzed to determine joint angle motions and to be used in SIMSCAN tools  [27]  as desired data to tune the simulations. with high resolution (1536 × 1024 dpi) high-speed cameras (SpeedCam Visario G2, Weinberger GmbH, Erlangen, Germany), enabling us to record movements precisely. Nine rats were used in total for motion capture. The treadmills (Tetra, Ilmenau, Germany) were setup at 0. 5 m/s, and around 400 steps were analyzed for hard, medium, and soft belt runs. The motion data was analyzed to determine joint angle motions and to be used in SIMSCAN tools  [27]  as desired data to tune the simulations.
Knee-Ankle Synergy We collected kinematic and dynamic data of rats running on a treadmill. Frame by frame tracking of X-ray video recordings of these animals revealed coordination between joints. Figure  11  shows some of the frames recorded. By applying red marks on the mid spine and all joints of the rat left hindlimb, we digitally tracked the joint motion. During normal walking, the knee and ankle joint motion profiles are similar (i. e. , both biphasic) while the hip joint motion follows its own pattern. Frame 4 to frame 5 in Figure  11  show that the hip joint is still performing flexion while the ankle and the knee begin extension. Moreover, from frame 6 to frame 7, the hip extends while the ankle and the knee flex. These observations are verified by the resulting joint rotation data extracted from these frames (Figure  12 ).
The similarity of knee and ankle motion patterns in kinematic analysis motivated us to consider the possibility of applying the same rhythm control for both the knee and ankle joints. In our previous neuromechanical model, all three joints were independently coordinated by feedback to match the desired timing between stance and swing phase  [8]. Comparing the pattern formation neuron activities of the knee and ankle joint in that model also support our hypothesis that the knee and the ankle can share the same pattern formation network to generate steady walking behavior. We tuned the intact rat model to produce steady alternating stepping with an appropriate tonic noise on the left hindlimb. Simulation data collected include joint kinematics and neural activity from all neurons. Data was processed using MATLAB.
The cycle period and step start time were calculated using the same method used in the deafferented model. Joint motions in each cycle were manipulated by interpolation and the mean function was used to get characteristic kinematic data. The comparison of animal joint motions with simulation characteristic kinematic data (Figure  13 ) shows some similarities in the hip and knee motions, but there are differences. The ankle joint shows the most difference; it is out of phase with the animal data in the stance phase. The core of the SIMSCAN tools used to tune the network is the penalty method of nonlinear optimization. The first priority is the During normal walking, the knee and ankle joint motion profiles are similar (i. e. , both biphasic) while the hip joint motion follows its own pattern.
Simulation data collected include joint kinematics and neural activity from all neurons. Data was processed using MATLAB. The cycle period and step start time were calculated using the same method used in the deafferented model. Joint motions in each cycle were manipulated by interpolation and the mean function was used to get characteristic kinematic data. The comparison of animal joint motions with simulation characteristic kinematic data (Figure  13 ) shows some similarities in the hip and knee motions, but there are differences. The ankle joint shows the most difference; it is out of phase with the animal data in the stance phase. The core of the SIMSCAN tools used to tune the network is the penalty method of nonlinear optimization. The first priority is the In our previous neuromechanical model, all three joints were independently coordinated by feedback to match the desired timing between stance and swing phase  [8].
Comparing the pattern formation neuron activities of the knee and ankle joint in that model also support our hypothesis that the knee and the ankle can share the same pattern formation network to generate steady walking behavior. We tuned the intact rat model to produce steady alternating stepping with an appropriate tonic noise on the left hindlimb. Simulation data collected include joint kinematics and neural activity from all neurons. Data was processed using MATLAB. The cycle period and step start time were calculated using the same method used in the deafferented model. Joint motions in each cycle were manipulated by interpolation and the mean function was used to get characteristic kinematic data. The comparison of animal joint motions with simulation characteristic kinematic data (Figure  13 ) shows some similarities in the hip and knee motions, but there are differences.
The ankle joint shows the most difference; it is out of phase with the animal data in the stance phase. The core of the SIMSCAN tools used to tune the network is the penalty method of nonlinear optimization. The first priority is the activity of motoneurons. After that, kinematic profiles were taken into consideration with the period and height of the peaks given more weight than the mean squared error between the model and the animal joint angles. Also, the simulated muscle properties in this model need to be tuned to mimic the actual rat biomechanics. Therefore, we believe the joint motion errors are understandable. Most importantly, the simulation produced steady walking, which enabled the resetting and non-resetting experiments to be performed, which is the primary purpose of this work.
This mismatch with joint motion is also found in other models  [20]. The advantage of these simplifications is much greater simulation speed, which enables more rapid tuning of parameters in the neural network using our MATLAB toolbox SIMSCAN  [27]. It is noteworthy that the presented work is useful for understanding the underlying coordination within the neural system, and not in understanding the specific activation of muscles during walking. Given enough complexity of a model, its kinematics and muscle activations can always be tuned to produce some specific motions and values. For instance, different species of quadrupeds demonstrate different kinematics, despite anatomical similarities at the skeletal, muscular, and neural levels. Despite their different walking patterns, animals can still adapt their locomotion and gait.
Electromagnetic (EM) waves, on the other hand, are tightly coupled to matter thus providing information about the material and surrounding environment being affected by the central engine  (Metzger & Berger 2012). Several astrophysical transient phenomena are thought to produce GW and EM emission strong enough to be detected by current or proposed observatories including soft gamma-ray repeaters, rapidly rotating core collapse supernovae, binary neutron star mergers and gammaray bursts. Here we focus on short gamma-ray bursts (SGRBs), now directly confirmed to arise from the mergers of compact stellar remnants to which ground-based GW detectors are most sensitive  (Abbott et al. 2017a ). This paper is limited in scope to analysis of times during O1, and focuses on the follow-up searches of data from the Fermi Gamma-ray Burst Monitor (GBM) near in time to the GW search candidates.
2016; Savchenko et al. 2016; Tavani et al. 2016) , nor was a similar signal found in relation to the other unambiguous GW detection in O1, GW151226  (Abbott et al. 2016e; Adriani et al. 2016; Racusin et al. 2017) , though 85% of the LIGO localization for GW151226 was out of view by Fermi, occulted by the Earth  (Racusin et al. 2017). The case for a GW candidate too low in significance to be claimed as an unambiguous detection would be strengthened by an EM detection consistent with the putative GW event. To that end, this paper reports on the search of GBM data for potential EM counterparts to any GW binary merger candidates found in the O1 data set with false alarm rate (FAR) below 10 -5 Hz by the LIGO-Virgo analyses. Two GW candidates were identified in coincidence with gamma-ray signals exceeding the GBM background: a gamma ray transient found in the follow up of GW150914, previously reported in  Connaughton et al.
(2016) , and a second GBM transient most likely due to a chance coincidence with a solar flare. The paper is organized as follows. In Section 2 we describe the data analysis methods used to identify compact binary coalescence (CBC) triggers in GW data and to identify gamma-ray transients in the GBM data. Section 3 presents the search results. Section 4 summarizes the results and conclusions, and discusses the role for GBM and LIGO-Virgo joint analysis during future observing runs.
Advanced LIGO The LIGO observatories are two modified Michelson interferometers located in Livingston, Louisiana and Hanford, Washington. Each arm of the interferometer is formed by two mirrors separated by 4 km. Passing GWs produce a strain, typically denoted as h, which changes the separation of the mirrors, inducing a relative phase difference in the light when it returns to the beam-splitter which transmits an optical signal proportional to the incident strain. The large separation between the observatories reduces the false-alarm background due to coincident instrumental or environmental contamination, and differences in time and phase of arrival constrain the sky location of astrophysical sources. For a full description see  Aasi et al. (2015). The data are analyzed for continuous  (Aasi et al.
Both pipelines estimate the background distribution of the network statistic in the absence of GWs, which is then used to map the network rank of each coincident trigger to the FAR of the search. In the remainder of this work, the FAR from the GW searches will be referred to by FAR GW. Candidates with FAR GW less than one per month exceeded the threshold for alerting EM observing partners participating in the O1 follow-up program (e. g. Abbott et al. (2016f) ). Fermi GBM has a unique data-sharing agreement with the LIGO Scientific and Virgo Collaborations, and has access to all CBC coincident triggers. Because of their different assumptions and implementation details, PyCBC and GstLAL may produce different sets of triggers from the same data. Strong CBC signals are ranked by either pipeline to correspond to sufficiently low FAR GW , although not numerically equal between pipelines.
The CBC searches were repeated when improvements to the calibration of the detectors or the configuration of the pipelines were available. The triggers employed in the joint analyses were obtained from the first analysis using the offline CBC pipelines, and an intermediate calibration of the data. Data produced with the final calibration were not available at the onset of this study. The candidate list used here was compared to that produced using the final calibration, and the only differences between the two were consistent with random noise fluctuations around the detection threshold of the CBC searches.
Advanced LIGO Sample Figure  1  shows times relative to the start of the search sample  (September 12, 2015)  for the combined CBC triggers produced by the GstLAL and PyCBC pipelines. Intervals with no triggers correspond to times when one or both detectors were unable to produce science data, or when the data were vetoed due to well-understood instrumental or environmental disturbances. With the exception of the two high-confidence detections GW150914 (green triangle)  (Abbott et al. 2016g ) and GW151226 (orange triangle)  (Abbott et al. 2016e ), and the low-significance candidate LVT151012 (purple diamond)  (Abbott et al. 2016b) , no CBC triggers were more significant than the inverse live-time of the search ∼2 × 10 -7 Hz (black horizontal line). For the targeted GBM search, the 81 subthreshold candidates with FAR GW < 10 -5 Hz ( 1 day -1 ) marked by gray circles were used as the search sample, while the 2935 blue dots (FAR GW > 10 -3 Hz) served as the background sample.
Triggering and Untargeted Search The simplest step in the joint analysis is to compare the list of times of known gamma-ray transients to the list of times of CBC candidates and search for a match. This comparison is complementary to the dedicated follow-up of known GRBs in GW data of GRBs, as reported in  Abbott et al. (2017g)  in two ways: first, here we do not impose a maximum time offset between the GW and gamma-ray candidates, and second, this study includes sub-threshold GBM candidates from the untargeted search which, in O1, were not incorporated in the LIGO-Virgo GRB follow-up searches. For the joint analysis, events found in the GBM triggering and untargeted searches during O1 are combined and correlated with the combined list of CBC candidates, searching for coincident events in the two observatories.
NC and JB acknowledge support from  NSF  under grant  PHY-1505373. SMB acknowledges support from  Science Foundation Ireland  under grant  12/IP/1288. The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
While some strains with modified spnIII loci have been detected, no sequenced pneumococcal strain has been found without an spnIII locus. SpnIII is encoded by the genes SPD_0451 through SPD_0455 in D39 (accession number NC_008533. 2) and the genes SP_0505 through SP_0510 in TIGR4 (accession number NC_003028. 3). PV results in variation through six versions of SpnIII with differing S subunits and consequent recognition sequences and was observed to occur at rates of Ͼ1%. A previous analysis found differential gene expression patterns for each active hsdS gene orientation  (17). Expression patterns were generated using non-phase-variable mutants where all but two of the TRDs had been removed. These mutants also exhibited differences in virulence in murine models of infection  (17)  and in opaque (OP)/transparent (TP) colony morphology, a phenotype previously associated with differences in the colonization and invasive potential of pneumococcal isolates  (18).
RESULTS The SpnIII restriction-modification system contains the classical type I RM system genes hsdR (restriction), hsdM (methylation), and hsdS (specificity), alongside two additional hsdS genes and a site-specific recombinase gene (creX) (Fig. 1A )  (6, 17). Only one of the hsdS genes found within the locus is a complete gene and is transcribed  (17) ; FIG 1 Active hsdS distribution in S. pneumoniae. (A) All sequenced pneumococcal isolates have an spnIII locus. However, in some strains, including G54 (GenBank accession number NC_011072), the locus appears to have undergone a deletion event; compared to D39, the G54 locus lacks the truncated hsdS= gene and the creX recombinase and shows a duplication of target recognition domain (TRD) 1. 1 with no TRD 1. 2 (TRD numbering within the arrow of the hsdS genes).
This results in G54 being limited to the active hsdS genes hsdSB and hsdSE only. (B and C) Due to recombination at the spnIII locus on the three inverted repeats, shown as hashed, dotted, and solid black boxes, the orientation of the creX recombinase can be aligned with hsdR, hsdM, and the active hsdS gene (B) or aligned with the unexpressed hsdS= and hsdSЉ genes (C). RNAseq data for an hsdSA (red)-expressing strain (B) and an hsdSB (blue)-expressing strain (C) were mapped in Artemis (45) using sorted BAM files. (D and E) Bars representing the mean percentages and SD of recombination within a minimum of 10 single colonies. Recombination events are expressed as a percentage of the total population by repeat. Recombination can occur on the 333-bp IR (dashed), the 85-bp IR (dotted), and the 15-bp IR (solid gray), and a small number are generated by Ͼ2 recombination events (checked).
A total of 10 to 15 l of the PCR product was digested according to the manufacturer's instructions, using 1 U DraI (New England Biolabs, UK), 2 U PleI (New England Biolabs, UK), and 1ϫ CutSmart buffer (New England Biolabs, UK) in a total volume of 20 l. Following digestion, each 6-carboxyfluorescein (FAM)labeled SpnIII variant has a unique size (Table  S3 ) that can be distinguished by capillary electrophoresis on an ABI prism gene analyzer (Applied Biosystems, USA). Data received from the ABI Prism gene analyzer were analyzed using Peak Scanner v1. 0 software. All experiments to determine PV at the spnIII locus were done by using colonies grown overnight on BHIA with 3% horse blood. For each experiment, a minimum of 10 single colonies were picked directly into the PCR mixture. All colony data are presented as means with standard deviations (SD).
RNAseq analysis was done by using a MiSeq desktop processor (Illumina, USA) and the ScriptSeq complete kit (bacteria) (CamBio, UK), which includes an rRNA depletion step. Raw data were trimmed using Trimmomatic-0. 32 and aligned using BWA-Mem and samtools. Expression data were generated using Rockhopper v2. 0. 3  (44)  with D39 as the reference genome (GenBank accession number NC_008533. 2). UV sensitivity assay. Cells were grown to an OD 590 of ϳ0. 2 in 50 ml TSB. Cultures were centrifuged at 4,000 rpm for 10 min to pellet cells. Cells were resuspended in 20 ml of a 0. 9% NaCl solution. A 10-fold serial dilution of cells was plated prior to UV exposure to determine total cell numbers. Cells in 0. 9% NaCl were placed in a sterile 90-mm petri dish and exposed to a UV source producing 10 J/min for 45 s.
ACKNOWLEDGMENTS The work was funded by  BBSRC  grant  BB/N002903/1  to M. R. O. , N. J. C. , and  S. D. B. and  MRC  grant  MR/M003078/1  to  M. R. O. N. J. C. is also supported by a  Sir Henry Dale fellowship , jointly funded by  Wellcome  and the  Royal Society  (grant number  104169/ Z/14/Z ). We acknowledge the contribution of the sequencing facility Nucleus and the proteomic facility PNACL of the  University of Leicester. We declare no competing interest with respect to the work performed in the manuscript.
, 2016). Where long-term records are available, bioeconomic modeling approaches can sample many years of weather observations and climate information, and can provide insight that may address the stochasticity challenge. The main weakness of modelbased impact estimates, however, is that their realism can be limited by the ability of the models to capture farmer decisions and resulting economic impacts, particularly when weakly grounded in empirical data. In most instances, globally and in Africa, these modeling approaches have been applied in a stylized manner to hypothetical uses of information; realistic evaluation was often not the objective  (Meza, Hansen, & Osgood, 2008). However, this is not an intrinsic limitation of the approach-and though bio-economic modeling tools tend to have demanding data requirements, it is feasible for analyses to be well grounded in empirical data, as is often the case for ex-ante evaluation of agricultural production technologies.
ACKNOWLEDGMENTS We thank  Sarah Henly-Shephard ,  Kristin Lambert ,  Eliot Levine , and  Alison Rose  for comments that improved the paper; the contributions of Elisabeth Gawthrop and  Francesco Fiondella  in developing the graphic are also appreciated. Thanks are due as well to the participants of the February 2017 workshop on climate services evaluation, held in conjunction with the fifth  International Conference on Climate Services , who reviewed an initial draft, and to those who participated in a June 2018 session at the Adaptation Futures conference. C. V. and E. C. were funded by the  Climate Information Services Research Initiative , made possible by the generous support of the American people through the  United States Agency for International Development, Agreement Num   AID-OAA-A-16-00072ber.
027). Additionally, a retrospective analysis evaluating ZNF217 expression levels in primary breast tumor of ER+/HER2-/LN0 breast cancer patients treated with adjuvant ET enabled the identification of poorer responders prone to earlier relapse (P = 0. 013), while ZNF217 did not retain any prognostic value in the ER+/HER2-/LN0 breast cancer patients who did not receive any treatment. Altogether, these data suggest that ZNF217 expression might be predictive of clinical response to ET.
Study Design This was a prospective neoadjuvant ET study on breast cancers expressing the estrogen receptor (ER+) and having a clinical size exceeding 2 cm (T2). This study has been approved by the local ethics committee (Institut du Cancer de Montpellier, France). Patients were informed that their data could be used for research; all the patients signed an informed consent form and the study was conducted in accordance with the Declaration of Helsinki principles. A total of 111 patients were treated for 4 months with neoadjuvant ET (letrozole 2. 5 mg/day or tamoxifen 20 mg/day), before being subjected to resection surgery (see Supplementary Material). The response to treatment was evaluated by monitoring the evolution of a biological marker of proliferation (Ki-67) before (initial tumor) and after 4 months of ET.