dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, Doucette and B22 share 97% identity, with a coverage of 76%; whereas Doucette and Anatole share 91% identity, but with a coverage of only 24%. Conversely, Anatole and B3 share 98% identity with a coverage of 91%. On this basis we propose organizing the phages into two clusters based on previously described criteria, with span-length match being the primary factor that distinguishes the two clusters (as described further below) and cluster names deriving from the Actinophage Database  [28, 29] : Cluster BW contains Doucette, B22, E6, and G4, and Cluster BV contains Anatole, E1, and B3 (Fig. 2 ). We further note that the previously characterized P. freudenreichii phage, PFR1, is more distantly related to the phages sequenced in this study and groups in a separate cluster designated BX.
Grouping of genes into phamily of related sequences and map generation was performed using Phamerator  [40]  and the database 'Actinobacteriophage_685'. Putative gene functions are listed above the genes have closely related attP sites and integrase genes and are predicted to integrate at the same attB site. Three of the Cluster BW phages (Doucette, B22, and G4) encode putative repressors (genes 33, 31, and 31, respectively) that are identical to each other, and also identical to the repressor (gp30) of Cluster BV phage B3. We therefore predict that these form a homoimmune group of temperate phages. The E6 repressor (gp30) differs from these (40% aa identity), but is identical to the repressors of Cluster BV phages Anatole and E1, likely forming a separate homoimmune group. In each of the genomes, the repressor is transcribed leftwards and divergently from an operon of rightwardstranscribed genes, in which the first is a putative cro-like gene.
There is considerable variation in the Cluster BW gene content in the Integrase/Repressor regions. G4 contains three leftwards-transcribed genes  (26, 27, 28)  of unknown function (Additional file 4: Figure  S3 ) to the left of the integrase, and there are three small genes common to Doucette  (28, 29, 30)  and B22  (26, 27, 28)  in this region that are absent from the other phages; the function of these is also unknown. E6 is unusual in that it contains a second set of divergently transcribed genes  (34 and 35)  organized as an additional repressor-cro pair of genes, suggesting that it may have complex immunity properties. It also has an insertion of a large gene  (33)  that contains a putative DNA binding motif but is otherwise of unknown function. The Cluster BW right arm genes also vary among the genomes, although they all code for a RecET-like system, a WhiB-like regulator, a single-stranded DNA binding protein, and an HNH endonuclease (Fig.
Predicted genes are shown as colored boxes either above or below each genome, representing leftwards-and rightwards-transcription, respectively. The gene numbers are shown within each colored box, and its phamily number is shown above, with the number of phamily members shown in parentheses; coloring reflects the phamily assignment. The maps were generated using the program Phamerator  [40]  and database Actinobacteriophage_685 locations. The virion structure and assembly genes are organized in the genome left arms (Anatole genes 3 -21) and arranged syntenically with structural genes of other phages with siphoviral morphologies. Several of the structural proteins (e. g. portal, prohead protease, capsid, major tail subunit) have sequence similarity to phages of other actinobacterial hosts, including Cluster N mycobacteriophages, corynebacteriophage BFK20, and microbacteriophage Min1, although amino acid sequence similarities are modest (27-45% aa identity).
Few studies have investigated the microbial composition of cheese, although, a recent analysis of the cheese-rind microbiome from over 130 samples of diverse origin has revealed group of 24 widely distributed bacterial and fungal genera  [37]. This group does not include Propionibacterium, although members of this genus were detected at low abundance in numerous samples. We therefore searched our phage genome sequences against a subset of the cheese-rind datasets, focusing on those from French cheeses, those with a natural rind (as is present on Swiss-type cheeses), and samples containing representatives from the Propionibacterium genus. From these analyses, we detected a small number of 100 bp reads with > 40% identity to our P. freudenreichii phages in five of the samples, however, coverage ranged from 0.
freudenreichii CIRM-BIA 139. The whole genome sequence project of P. freudenreichii contains two contigs (contigs 1 and 17; Accession numbers CCYX01000001 and CCYX01000017, respectively) with similarities to Cluster BW phages. The predicted organization of the prophage is shown aligned to phages G4 and B22 presenting them in two tiers, and organization from attL through to attR reflecting the integrated prophage structure. Segments corresponding to the two contigs are shown, which overlap in the middle of the tape measure protein gene; the reverse complement of contig 1 is shown. The prophage genes are colored according to their homologues in G4 and B22. The genes in prophage contig 1 are either shared with both phages or only with phage B22, whereas the genes at the right end of contig 1 are shared uniquely with phage G4 acnes phages lack putative lysogeny functions  [5, 35, 38, 39] , the P.
freudenreichii phages characterized here all encode putative tyrosine integrases and repressor proteins, strongly suggesting they are temperate. Our host range analyses also revealed critical differences between these two groups of phages. Whereas C. acnes phages generally show broad host ranges, infecting most laboratory and clinical C. acnes isolates, the host ranges of P. freudenreichii phages are more restricted, and many bacterial strains show high levels of phage resistance. This may result from the presence of prophages in some P. freudenreichii strains, which has been suggested experimentally  [15, 18]  and is further supported by the presence of prophages closely related to the Cluster BW phages in the fully sequenced strains, CIRM-BIA 139 and CIRM-BIA 119. In contrast, and consistent with their broad host ranges, C.
Acknowledgements We would like to thank  Yasinee Rotratsirikun  and  Rubeen Virani  for excellent technical support, and  Jessica Jeong  for assisting with the host range assays and phage lysate preparation. We also thank the  Technology Center for Genomics and Bioinformatics (TCGB)  at  UCLA  for providing sequencing services and for technical support. This study was inspired by the book Anatole by  Eve Titus.
Availability of data and materials The bacteriophage sequence data that were generated in the current study are available on GenBank, with the following accession numbers: KX620750, KX620748, KX620752, KX620751, KX620753, KX620754, KX620749. Phylogeny data have been deposited in the TreeBASE repository and are available at the following Accession URL: http://purl. org/phylo/treebase/phylows/study/TB2:S21946.
The results will also be disseminated to patients. trial registration number NCT02944929. bACkground Stroke is the second highest cause of death worldwide and the fourth leading cause of lost productivity (disability-adjusted life years) according to WHO. The annual incidence is around 130 000 new cases each year strengths and limitations of this study ► This study is the first to assess the effects of a self-rehabilitation in addition of usual treatments over a long period (6 months). ► This study will include a large sample with patients from 16 hospitals across all the country. ► The design of this study (randomised, controlled, assessor blinded study) tends to meet the highest level of evidence. ► This study would permit to apply recommendations to improve patients limitations with little additional cost to the already limited health system budget.
Settings and locations where the data will be collected. Data are recorded and managed through a dedicated web-based software (Cleanweb Telemedicine) with secured and restricted access. Access to the system is controlled for each investigator by an individual login/ password and using a secured https connection. Data are stored on a centralised server. Participants also have paper files which are anonymised and only contain the participant's unique identification code. These are stored in a dedicated storage unit in each centre. Access to the complete final trial dataset will be restricted to the Clinical Research Unit statistician who will analyse the study data for the purpose of report and publication. Subsets of the final dataset may be shared with investigators if needed for discussion or additional analyses.
statistical analysis Description and elementary analyses Quantitative data will be described by means and medians, SDs and IQRs. Nominal data will be described by percentages with 95% CIs. Patient characteristics will be described by centre and compared. Quantitative data will be compared using t-tests or non-parametric tests Open access (Wilcoxon rank-sum test) when the distribution does not approach symmetry even after suitable transformation (Tukey's ladder of powers), for instance, for the GAS T-score. Categorial data will be compared by means of χ 2 tests or Fisher' s exact tests.
Acknowledgements The authors are grateful to all study participants and team members as well as supporting staff. This study is possible thanks to the collaboration of numerous researchers, clinicians, technicians, engineers, research assistants and  Allergan laboratories. We would like each individual member of the study group to be searchable through their individual PubMed record. Collaborators Bernard Parratte ,  Théo Maulet ,  Philippe Aegerter ,  Priyanka Velou ,  Julia San ,  Yassine Omri ,  Myriem Carrier. Contributors NR and DB conceived of the study. NR and CB mostly drafted the manuscript. CB organised patient visits and performed the patients evaluations. PD, PM, CA, FP, M-EI, FCB, FM,  J-CD , PD, PG, BP-V, NB, EC, DP, N-YD-S, JR and IL participated in the design of the study and helped to draft the manuscript.
We removed the focal male without anesthetic and immediately placed it in a recording chamber for 10 min with a conspecific 1-day-old female. Copulation did not occur in our trials (1-day-old females usually do not copulate), which allowed us to record all trials for the same duration but prevented us from gathering data on mating. Recordings used an INSECTAVOX  (Gorzyca & Hall, 1987)  with internal temperature recording, band-pass filtered (Fern Developments EF5-04) at 100-700 Hz, and digitized with the software Audacity (www. audacityteam. org). We measured the interpulse interval (IPI), the sinesong frequency, and the pulse song burst duration using DataView v. 10. 6. 0  (Heitler, 2007). Pulse song was analyzed after filtering all song files (100-500 Hz frequency range). We isolated pulses by applying a Teager Energy Operator (time = 3, iteration = 4) and a Hill-Valley analysis (uphill and downhill thresholds = 50%, absolute peak height filter = 15 robust SD, max duration = 15 ms).
Furthermore, we show that the receptive fields we obtain are closer in structure to electrophysiological data reported in macaques than those predicted by normative encoding schemes (Ringach, 2002). We also demonstrate the robustness of our model to the input dataset, noise at various processing stages, and internal parameter variation. Together, our modeling results suggest that Hebbian coincidence detection is an important computational principle and could provide a biologically plausible mechanism for the emergence of selectivity to natural statistics in the early sensory cortex.
Materials and Methods Datasets. To simulate binocular retinal input, we chose two datasets-the Hunter-Hibbard dataset and the KITTI database (available at http:// www. cvlibs. net/datasets/kitti/). The main results are reported for the Hunter-Hibbard database of stereo images  (Hunter and Hibbard, 2015;  available at https://github. com/DavidWilliamHunter/Bivis under the Massachusetts Institute of Technology license, which guarantees free usage and distribution). This database consists of 139 stereo images of natural scenes relevant to everyday binocular experience, containing objects like vegetables, fruit, stones, shells, flowers, and vegetation. The database was captured using a realistic acquisition geometry close to the configuration of the human visual system while fixating straight ahead with zero elevation (Fig.
1A ). The distance between the two cameras was close to the human interocular separation (65 mm), and the two cameras were focused at realistic fixation points from 50 cm to several meters away from the cameras. The lenses had a relatively large field of view (20°), enabling them to capture binocular statistics across a sizeable part of the foveal and parafoveal visual field. Figure  1A  also shows a red-cyan anaglyph of one representative scene from the database, while Figure  1B  shows the images captured by the left and right cameras. Due to the geometry of the cameras, there are subtle differences in the two images (disparity), which provide important information about the 3-D structure of the scene. The mimicking of realistic acquisition geometry is crucial for capturing disparity statistics, which resemble those experienced by human observers.
For comparison, we also report results using the KITTI database, which uses parallel cameras, a geometry that does not correspond to the human visual system (see Fig. 7  and related discussion for more details). Input sampling. Random locations in the scenes were sampled to provide inputs to our model. In each simulation, the sampling was restricted to a specific region of interest (ROI). In this article, we present results from the following four main ROIs: foveal (eccentricities Ͻ3°); peripheral (eccentricities Ͼ6°); upper hemifield; and lower hemifield. Figure  1B  shows the foveal ROI shaded in purple. Each sample consisted of two patches corresponding to the two eyes, both centered at the same random retinal coordinates. Figure  1B  shows a random sample, with the left patch outlined in red and the right patch in green.
The sizes of the patches varied with the ROI and can be interpreted as the initial dendritic receptive field of the V1 neurons, which was subsequently pruned by STDP. In the simulations presented in this article, the foveal patches were 3°ϫ 3°, while the peripheral patches were 6°ϫ 6°. In each simulation, 100,000 input samples were used. In Figure  3B , we present the results of our model under simulations of fixations in individuals with strabismic amblyopia. Strabismus is a misalignment of the two eyes during fixation and can lead to strabismic amblyopia if left unmanaged during childhood. While strabismic misalignment is more accurately represented by an offset of the two camera axes during dataset acquisition, in the absence of this possibility, it was approximated by using a misaligned sampling scheme.
Robustness analysis. Biological systems show a considerable resilience to factors such as noise and internal parameter variations  (Burge and Jaini, 2017). We tested the robustness of our model using the following three approaches: (1) by introducing timing jitter at both the input and the LGN level; (2) by varying a key internal parameter of our network (the ratio of the LTP rate to the LTD rate); and (3) by comparing results obtained using the Hunter-Hibbard dataset (realistic acquisition geometry) to results obtained using a dataset with a nonrealistic acquisition geometry (parallel cameras). The robustness of the model to noise was tested by adding external (image) and internal (LGN) noise to the system. This simulated timing jitter in the firing latencies from both the retina and the LGN.
01 to 100 and estimating the CI, R max 2 , population disparity tuning, and BII parameters. This simulated models where the LTD rates were from 1/100 to 100 times the LTP rates. After testing the robustness of our model to noise and internal parameter variations, the aim was to test the model on a second dataset. As mentioned earlier, the Hunter-Hibbard dataset used in all our simulations was chosen because it has an acquisition geometry close to the human visual system. Thus, for this analysis we chose the KITTI dataset, which was collected using a markedly different acquisition geometry. This dataset uses parallel cameras mounted 54 cm apart, leading to a highly nonecological, zero-vergence geometry. The aim of this analysis was to verify that the main results presented in this article are not specific to the dataset, and that the model is capable of capturing natural disparity statistics while being robust to acquisition geometry.
It is also interesting to note that, regardless of the degree of fit, the R 2 values for the left and the right eye are tightly correlated. This binocular correlation is nontrivial and cannot be attributed simply to learning from a stereoscopic dataset, as the interocular correlations in Figure  3C  shows the receptive field orientations in the left and the right eye of binocular neurons in the foveal population. Here, we define a binocular unit by using the criterion min {R left 2 , R right 2 } Ն 0. 5 (i. e. , both eyes fit the Gabor model with an R 2 value of at least 0. 5). We see that there is a strong correspondence between the orientation in the two eyes (0°and 180°are Gabors were fitted to the resulting curves, thereby giving a measure of the position and phase disparity in the receptive fields  (Prince et al.
Robustness to internal and external noise In the results reported so far, the only sources of noise are the camera sensors used to capture the stereoscopic dataset. To further test the robustness of our model, we introduced additive white Gaussian noise ϳN(0, noise ) at two stages of the visual pipeline-external noise in the image, and internal noise in the LGN layer. The noise was manipulated such that the SNR varied in steps of ϳ3 dB from Ϫ3 dB ( noise ϭ 2 ϫ signal ) to 10 dB ( noise ϭ 0. 1 ϫ signal ). Since the noise in these simulations in-duces timing jitter at the retinal and LGN stages, they are especially relevant for our model, which relies on first-spike latencies. In the first and second columns of Figure  6 , we show the behavior of the system under internal (in green colors) and external noise (red colors).
Robustness to dataset acquisition geometry All our simulations (except this section) use the Hunter-Hibbard database, which was collected using an acquisition geometry close to the human visual system. The emergent receptive fields are disparity sensitive and closely resemble those reported in the early visual system. In this analysis, we prove that this emergence is not only a property of the dataset, but also of the human-like acquisition geometry. To do this, the model was tested on another dataset (KITTI; available at http://www. cvlibs. net/datasets/ kitti/), which was collected using two widely spaced (54 cm apart), parallel cameras, which is an acquisition geometry that does not resemble the human visual system at all (Fig. 7A ). Furthermore, we present two results from this series of simulations.
As expected, the lower hemifield population is tuned to disparities that are more crossed (negative) than the upper hemifield population (Fig. 3C ; see also Biases in natural statistics and neural populations). For comparison, the curves for the Hunter-Hibbard dataset (from Fig. 3C ) are plotted as dotted lines. The parallel geometry of the cameras in the KITTI dataset results in predominantly crossed disparities (the fixation point is at infinity, and hence all disparities are, by definition, crossed). Thus, while the upper versus lower hemifield bias (see Biases in natural statistics and neural populations) still emerges from our model, the population tunings no longer correspond to those reported in electrophysiological recordings (Fig. 3D ;  Sprague et al. , 2015).
Decoding disparity through V1 responses Recently, the effect of natural 3-D statistics on behavior has been modeled using labeled datasets in conjunction with taskoptimized filters  (Burge and Geisler, 2014)  and feedforward convolutional networks  (Goncalves and Welchman, 2017). Typically, such models are evaluated by how well they perform at classifying/detecting previously unseen data. In Figure  8A , we show the result (detection probability) of training two very simple classifiers on the activity of the converged V1 layer from our model, using RDS stimuli at 11 equally spaced disparities between Ϫ1. 5°and 1. 5°(for details, see Materials and Methods). RDS stimuli were used because they contain no other meaningful information except disparity. The first, a decoder based on linear discriminant analysis (green), performs well above chance (dashed red), especially for realistic values of disparity between Ϫ0.
Figure7. The KITTI dataset. The model was tested using a second dataset (KITTI; for details, see Materials and Methods), which had a different acquisition geometry. A, Acquisition geometry. The cameras were parallel and separated by 54 cm, a geometry that does not resemble the human visual system. A cyan-red anaglyph reconstruction of a representative scene from the KITTI dataset is also shown. B, Gabor fit. The R 2 value for the Gabor fit procedure on the left and right receptive fields of the converged neurons, with the color of the symbol coding for the R max 2 (compare to Fig. 3Afor the Hunter-Hibbard dataset). C, Horizontal disparity tuning. Horizontal disparity tunings for two populations of neurons trained on the upper (blue) and lower (red) hemifields of the KITTI database. For comparison, the curves obtained using the Hunter-Hibbard database (Fig.
This research was supported by  IDEX Emergence  (Grant  ANR-11-IDEX-0002-02 ,  BIS-VISION ) and  Agence Nationale de la Recherche Jeunes Chercheuses et Jeunes Chercheurs  (Grants  ANR-16-CE37-0002-01 ,  3D3M ) awarded to B. R. C. ; and the  Fondation pour la Recherche Medicale  (Grant FRM:  SPF20170938752 ) awarded to T. C. We thank  Simon J. Thorpe  and  Lionel Nowak  for their comments on the study. The authors declare no competing financial interests.
Under an auction mechanism, investors may prefer to wait until the end so as not to disclose their interests in buying securities, which could induce more demand. In this case, a stronger end effect may occur, shifting the dynamics of individual investments toward the end of the campaign. The current research adds to the literature on funding dynamics in equity crowdfunding by investigating how the functioning of different market mechanisms that determine the allocation of securities to investors affects the investment process. In addition to the allocation mechanisms, we examine the effects of permitting investors to comment on their investments and making the amounts pledged visible to peer investors. We test our predictions using a comprehensive dataset of individual investments made on four different portals in Germany.
Data We use data from four German equity crowdfunding portals from November 6, 2011, till August 28, 2014. The portals we consider in our analysis represent four-fifths of the German equity crowdfunding market in terms of funding volume and number of startups being financed. For Companisto and United Equity, we were able to collect all investment decisions from the portal websites for all their campaigns. The data collection for Seedmatch and Innovestment was more difficult, as these portals take investment decisions off their website as soon as the funding limit is reached. Innovestment provided us with the complete investor data for all its successful (28) and unsuccessful (17) campaigns. Finally, we hand-collected investor data for 15 of 65 Seedmatch campaigns. In total, we were able to collect investor data for 89 funding campaigns, which were run by 81 startups.