dataesr/scientific-paragraphs-categorization huggingface dataset:
The ONTIME study investigated whether early post-stroke abobotulinumtoxinA injection delays appearance or progression of upper limb spasticity (ULS) symptoms. ONTIME (NCT02321436) was a 28-week, exploratory, double-blind, randomized, placebo-controlled study of abobotulinumtoxinA 500U in patients with ULS (Modified Ashworth Scale [MAS] score ≥ 2) 2-12 weeks post-stroke. Patients were either symptomatic or asymptomatic (only increased MAS) at baseline. Primary efficacy outcome measure: time between injection and visit at which re-injection criteria were met (MAS ≥ 2 and ≥1, sign of symptomatic spasticity: pain, involuntary movements, impaired active or passive function). Forty-two patients were randomized (abobotulinumtoxinA 500U: n = 28; placebo: n = 14) with median 5. 86 weeks since stroke.
Statistical Analysis The sample size (N = 42) was chosen for exploratory purposes and not intended for definitive conclusions about efficacy. Statistical analyses were performed using SAS v9. 2. Efficacy analyses were performed on the ITT population. Primary endpoint was analyzed by Kaplan-Meier (KM) survival analysis. Median survival time and 95% CIs were estimated for each treatment group using KM product-limit estimation. Data for patients not meeting re-injection criteria at Week 28 were censored at the patient's last study visit. Treatment differences between abobotulinumtoxinA and placebo groups were tested using two-sided, stratified log-rank and Wilcoxon tests (α = 5%; spasticity status as stratification factor). p-values are for exploratory purpose only. Treatment effect for muscle tone change was assessed using analysis of variance (ANOVA; treatment and symptomatic spasticity status as fixed effects); data are shown as adjusted means, 95% CI, and corresponding p-values.
Acknowledgments: All members of the ONTIME study group are authors on this paper. The authors thank all of the patients who participated in the  ONTIME study and acknowledge the work of the study project managers at Ipsen ,  Marion Genoulaz  and  Elodie Blouquit , and the support of the clinical research organization,  EPS. In addition, the authors thank  Cassandra Hines, MSc  and  Catherine Jones of Watermeadow Medical, Macclesfield, UK  for providing medical writing and editing support, which was funded by  Ipsen Pharma , in accordance with Good Publication Practice (GPP3) guidelines.
genus (MALV or Marine Alveolates), a diverse clade of parasitoids infecting dinoflagellates, some of them responsible of toxic algal blooms  (Cachon, 1964; Park et al. , 2004). Syndinean parasites are basal to dinoflagellates, a sister group to apicomplexans such as Plasmodium falciparum and Toxoplasma gondii. Molecular barcoding has revealed the wide diversity and occurrence of Amoebophrya spp. in all marine planktonic ecosystems  (Guillou et al. , 2008; de Vargas et al. , 2015). Nevertheless, these marine alveolates remain a poorly known group when compared to other members of the marine plankton. The high-quality ribosomal RNA database SILVA encompasses today over 2,000 SSU sequences corresponding to Amoebophrya species: only 39 SSU sequences have been described today, labeled as "Amoebophrya sp.
The raw data was filtered to remove any clusters with too much intensity corresponding to bases other than the called base. RNA-Seq reads were cleaned in a three-step procedure (Figure  1 , step 1): (i) sequencing adapters and low-quality nucleotides (quality value <20) were removed, (ii) sequences between the second unknown nucleotide (N) and the end of the read were removed, (iii) reads shorter than 30 nucleotides after trimming were discarded, together with reads and their mates mapping onto run quality control sequences (PhiX genome). These trimming steps were achieved using fastx_clean (http://www. genoscope. cns. fr/ fastxtend), a home-made software based on the FASTX toolkit (http://hannonlab. cshl. edu/fastx_toolkit/). Moreover, ribosomal RNA-like reads were excluded using SortMeRNA  (Kopylova et al.
Gene Expression Analysis Complete genomes of both Amoebophrya A120 and A25 strains, have been recently sequenced, assembled and annotated using a combination of deep coverage Nanopore and Illumina sequencing approaches (unpublished). The cleaned Amoebophrya A120 and A25 RNA-Seq reads were aligned against the transcripts inferred from the Amoebophrya genome annotation (26,427 genes for A120; 28,076 for A25) of their respective strains using BWA-MEM  (Li and Durbin, 2010)  with default parameters for each time point. While no significant match could be seen at T0-point (host alone), around 90% (A120) and 88% (A25) of the Amoebophrya dinospore reads were aligned on the A120 and A25 inferred transcripts, respectively. This procedure allowed distinguishing between the reads from the parasite and the host.
Functional Repertoires of Amoebophrya A120 and A25 Both Amoebophrya A120 and A25 translated inferred transcripts were assigned to functional categories using the Biomolecular Relations in Information Transmission and Expression (BRITE) functional hierarchies from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  (Kanehisa et al. , 2016)  (https://www. genome. jp/kegg/brite. html) in two steps. Briefly, KEGG mapping of both Amoebophrya proteomes was performed using BLASTp between Amoebophrya A120 and A25 proteins and the proteins included in the Eukaryotes KEGG Orthology (KO) database using an e-value ≤ 10e-5 as cutoff (8,690 and 8,360 proteins with a least one significant match were kept for further analysis). KO assignment for each Amoebophrya protein was carried out by keeping the best KO, based on cumulative bit-score and the KO with a cumulative score similar to the best one (90% of the score).
As a result, KO terms were assigned to 5,983 and 5,774 proteins in A120 and A25 respectively (23 and 21% of the entire A120 and A25 translated inferred transcripts dataset respectively). Finally, KO terms assigned to both Amoebophrya strains were classified according to BRITE functional categories  (Kanehisa et al. , 2016). A single gene product may be associated with multiple BRITE annotations within a single category. At last 5,856 (A120) and 5,774 (A25) proteins were related to a BRITE annotation.
Search for the Expression of Specific Gene Families in Amoebophrya Spp. We investigated the molecular processes involved in infection by Amoebophrya spp. To do so, selected proteins involved in invasion processes from annotated proteomes of the model apicomplexan parasites Plasmodium falciparum (Pf) and Toxoplasma gondii (Tg), were used as queries for BLASTp searches (at e-value 1e-5) using the NCBI BLAST+ 2. 2. 28 package against the Amoebophrya A120 and A25 sequences. Moreover, the Best Reciprocal Hits (BRH) were identified between each Amoebophrya predicted proteome and Pf and Tg proteomes. The resulting candidates in Amoebophrya were subsequently manually validated and their identity confirmed by BLASTp against the NR database (NCBI Resource Coordinators, 2016) and phylogenetic analysis, followed by manual expertise on the selected Amoebophrya candidates.
Correlated Gene Expression Profiles Within URG Groups Members of the URG2 and URG3 groups (beginning and end of the Amoebophrya host cell infection) were independently clustered into different sub-groups regarding the correlation of the gene expression during the entire experiment (Figure  1 , step 9). For all genes composing URG2 and URG3, the rcorr function from Hmisc package in R was computed on all expression values (RPKM) at all time points. Only expression values with a correlation with P-value up to 0. 1 were kept. Then, an hclust clustering (R) was used on the correlation values of all these upregulated genes. A cluster was validated if the mean and the median of the correlation of the genes in the cluster was higher than 0. 5. 7 and 10 sub-groups were identified in URG2 for Amoebophrya A120 and A25 respectively (Tables  S4, S5 ).
Global Gene Expression in Amoebophrya A120 and A25 The strategy followed is outlined in Figures  1A, B  (metrics in Table  S2 ; for details see Materials and Methods). A total of 48 samples encompassing a full Amoebophrya infection cycle (21 for A120 and 27 for A25) were submitted to Illumina pairend sequencing, generating 2412 M and 1889 M sequence reads per Amoebophrya A120 and A25 respectively (Tables  S2, S3 ). Cleaned sequence reads from each A120 (342 M) and A25 (160 M) samples were aligned on the gene prediction dataset of the corresponding Amoebophrya strain, yielding high-quality alignments for a significant fraction of the reads (222M for A120 and 122M for A25; Tables  S2, S3 ). After discarding sequence reads from non-accurate replicates (16 samples for A120 and 21 samples for A25, Table  S3 ; for details see Methods), read-counts were used to quantify gene expression levels (RPKM) and for pairwise differential gene expression analysis (DESeq) between each time step of the Amoebophrya A120 and A25 life cycles (Tables  S2, S4 ).
URG1 is composed of 4,349 and 4,737 genes, URG2 is composed of 3,543 and 2,540 genes and URG3 is composed of 5,287 and 4,490 genes for A120 and A25 respectively. The expression levels of genes belonging to each URG were explored all along the Amoebophrya life cycle by RPKM analysis. Expression profiles of the upregulated genes belonging to each of these URG groups displayed similar overall patterns between the two strains [Figures 4A (URG1), 4B (URG2), and 4C (URG3)]. The KEGG database was used as resource in an attempt to link the variation in Amoebophrya gene expression with the biology of the parasite. RNA-Seq reads, corresponding to 42% (93M, A120) and 47% (57M, A25) of the total reads mapping to the Amoebophrya A120 and A25 predicted proteome, were aligned to Amoebophrya genes with a KO term.
Genes Putatively Implicated in the Host-Parasite Interaction While the whole-KEGG analyses provided a general survey of the functions enriched during the Amoebophrya infection cycle, key functions likely to be implicated in the host-parasitoid interaction were specifically explored using Plasmodium falciparum and Toxoplasma gondii as references, aiming to secondarily evaluate their expression profiles to determine whether these Amoebophrya genes could be involved in such processes. well-known human apicomplexan parasites employ different extracellular adhesins for host cell recognition and invasion  (Boucher and Bosch, 2015). Some present a combination of trombospondin 1 (TSP1) and von-Willebrand factor type A (vWF) domains. The A120 and A25 transcriptomes code for 45 (A120) and 25 (A25) proteins having at least one TSP1 motif (IPR000884), and 35 (A120) and 20 (A25) proteins having at least one vWF motif (IPR002035).
Most of these Amoebophrya proteins also have additional domains (Figures  S6A-S11A ). A focus was done on the five Amoebophrya proteins having both TSP1 and vWF domains (5 in A120, 5 in A25) but their domain combinations differ significantly from those found in apicomplexans, with the possible exception of the TRAP-like protein (TLP)  (Boucher and Bosch, 2015). The expression profiles of Amoebophrya TSP1 and/or vWF coding-genes were distributed in the three URGs. While the five TSP1/vWF-containing proteins were specifically upregulated in URG2 and URG3 in both strains (A120 left and A25 right, Only one apicomplexan rhoptry-like protein  (Hakimi et al. , 2017)  was found in Amoebophrya: ROP14 (Table  S5 ). Three and two ROP14-candidate genes were identified in A120 and A25, respectively, each having at least one lipase maturation factor domain (IPR009613) and from 8 to 12 transmembrane segments (Figure  S12A ).
The expression profiles of the two A25 candidate genes and one in A120 (GSA120T00005924001) were bi-modal, with a smal peak at 6 h and a higher one at the end of the infection (24 h), clustering them into the URG3 group (Figure  S12B ). The two remaining A120 candidates had opposite expression profiles, with GSA120T00016826001 peaking at the beginning (6 h) and late (36 h) infection while GSA120T00002581001 peaked at 24 h (Figure  S12B ). As expected for alveolates, several genes encoding members of the alveolin family were identified in the transcriptomes of A120 (5) and A25 (  5 ), with sizes ranging from 285 to 984 aa and containing up to three inner membrane complex (IMCp, IPR022086) protein domains (Figure  S13A ). Very abundant in P. falciparum (12) and T. gondii (14), alveolins were therefore found in smaller repertoires in these Amoebophrya transcriptomes.
Sibling species to the Funestus, Maculatus and Leucosphyrus Groups were discriminated using allele-specific polymerase chain reaction (AS-PCR) assays adapted from Garros et al. and Walton et al. [25] [26] [27]. In case AS-PCR yielded a negative result, identification at the species level was performed by sequencing the internal transcribed spacer 2 (ITS2) DNA marker using universal primers described by Beebe and Saul  28. DNA extracts were screened for the presence of Plasmodium sporozoites using a quantitative real-time PCR (qPCR) assay targeting 18S rRNA genes adapted from Mangold et al. 29. Specificity of the signal was confirmed in all positive samples by amplifying COX genes with primers described by Cunha et al. 30. In case the confirmation assay yielded a negative result, the PCR product of the screening assay was sequenced (BioSample accessions: SAMN09845988, SAMN09845989, SAMN09845990, SAMN09845991, SAMN09845992).
Results were expressed as a number of infective bites/person/month. The exophagy index (EI) was defined as the outdoor HBR divided by the sum of the indoor and outdoor HBRs. The cow-biting index (CBI) was defined as CBR divided by the sum of the HBR and the CBR. Confidence Intervals (CIs) for Poisson counts (HBR, CBR and EIR) were estimated using the exact method of the poisson. conf. int() function in the epitools package version 05-10 of R software version 3. 3. Binomial CIs were estimated for proportions (SI, EI and CBI) using the exact method of the binom. confint() function in the binom package version 1. 1-1 of R software version 3. 3. Species-specific proportions were used to compute HBR, CBR, EI and CBI estimates at the species level if ≥30 samples in the corresponding Anopheles Group were genotyped for each value of the grouping variable.
Malaria vectors The contribution of six Groups of Anopheles to malaria transmission was determined by processing 56872 samples collected by HLC in qPCR Plasmodium. Human malaria parasites P. falciparum and P. vivax were detected in 106 Anopheles specimens that belonged to four Groups of Anopheles species (Funestus, Maculatus, Leucosphyrus and Barbirostris) (Supplementary File 1, Table  S4 ). Both P. falciparum and P. vivax were detected in the Funestus, Leucosphyrus and Maculatus Groups, whereas only P. vivax was detected in the Barbirostris Group. One specimen of the Funestus Group was co-infected   S11 ). The data on secondary vectors (e. g. Barbirostris and Annularis Groups) are more difficult to interpret because they probably represent a mix of several sibling species. Leucosphyrus members were the most anthropophagic and endophagic species (mean CBI=0.
I approve the current version after fixing three minor points: Page 8: "The proportion of malaria vectors collected indoors between 09:00 pm and 05:00 am was 29% (range= 15-48% according to the species). Conversely, 64% of the infected specimens were collected out of doors, before 09:00 pm and/or after 05:00 am (Figure  5 )". Based on the data in Figure  5 , 29% should be 36%; the last sentence should be rephrased as for Conversely, 64% of the infected specimens were collected out of doors, before instance: " either or 09:00 pm and after 05:00 am. Page 13: "In this study, only 36% of the Plasmodium-infected mosquitoes were collected indoors between 09:00 pm and 05:00 am (when and where people are expected to sleep under a bed-net). This proportion might have been as malaria vectors were already active at 06:00 underestimated pm and/or at 06:00 am, suggesting that the exposure stretched out of the collection time".
References Page 10, paragraph starting with "average values". I do not follow the reasoning from "The lowest HBR measured on a single collector. " until the end of this paragraph. What exactly do you mean by "mean HBR measured on single collectors" ? Usually when using HCL, more than one collector is involved per class (indoor versus outdoor) located in different houses to avoid bias linked to the person or the surrounding of the house. We therefore combined data from several collectors within a village over a define time scale, as here month. It may be just a matter of presenting, arguing the data presented in this paragraph. I feel it may need some clarification. The value of the entomological indices are longitudinal and spatially hierarchized. Therefore it is possible to describe the data at different level of aggregation ranging from hourly measurements on single individuals (mosquito collectors) to average values obtained by pooling the entire dataset.
Mean values collated at the village and survey level are indeed useful to describe the data independently of bias linked to the person, the surrounding of the house or the night of collection, were reported in the first lines of the paragraph. Further insights into the characteristics of the entomological indices were be obtained by looking at different level of aggregation. For example, the range of HBR collated at the village and survey level was 13 to 2611 bites/person/month whereas the range of non-collated HBR estimates (number of mosquito collected during one night nd the range of HBR collated at the village and survey level was 13 to 2611 bites/person/month whereas the range of non-collated HBR estimates (number of mosquito collected during one night by a single mosquito collector) was 0 to 289 bites/person/night (8670 bites/person/month), further documenting the high abundance of malaria mosquitoes sometime observed in the area of the study.
Methods Providing the global description of the villages, as done in the J. Vector ecology Paper Study sites: (ref14), would save time, rather than going to the published document as well as possibly useful for the discussion. Was the MDA still active during the whole entomological survey? Were the house randomly chosen? Were the preliminary data (ref14) included Entomological surveys: in the analysis? Consider simplification: instead "6 sites" use 5 houses plus one cow collection. Verify the accuracy of the sentence for mosquito collection under the net covering the cow. Expressing SI / 1000 mosquito can be confusing. I would keep with the classical SI Data analysis: expressed as %. The following sentence "Entomological inoculation rate (EIR) was defined as the number of positive in qrtPCR Plasmodium divided by the corresponding number of person-nights of collection, adjusted over the proportion of collected mosquitoes that were analyzed by qrtPCR Plasmodium" is fully unclear to me.
Biodiversity of the Anopheles entomo-fauna From my understanding of the Methods, the total person-nights should be 4200 and total cow-nights 420. Did I miss something? "Potential malaria vectors": could you provide your definition of those? A species table as sup data could be useful for non-specialist of Asian mosquito vectors. In figure  2  "other species" what does this include? Fig2: why not commenting the comparative results for HLC and CBC and among the villages. Homogenize between fig 2 A, Legend, Table  1  and main text : CBC or CBT. "Results of the molecular identification are presented in the Table  1. ".
The "Kayin" state is barely mentioned in the report, bringing confusion when done ie: appearing only on page 13 beside the title and the abstract with "Therefore, baseline intensity of malaria transmission in hotspot villages from the Kayin state is likely to be higher than that reported in this study" Reviewer's concern was addressed by changing "Thailand-Myanmar border" to Kayin state were possible and by mentioning more often "Kayin state" in the main text. Lastly, if there is no real discussion on the difference similarity among the 4 villages, why attracting with the "4 villages" in the title? We agree with the reviewer that a more detailed explanatory analysis on the heterogeneity of malaria transmission in the four villages would have been valuable. However, in the absence of appropriate datasets, this analysis is not possible.
1, not 0. 01. Mean Pf-EIR for Funestus Group has been corrected. Also in this table and followings, could not really follow the calculation from HBR and SI to the EIR, from the values of the former indexes, possibly as the mean values were calculated across the different months and/or villages. This might need to be specify at some point. The raw number used for the calculation was provided in all tables. Unless specified otherwise, the calculation was made using the entire dataset. It would be also informative to provide the raw data as supp file for the identification of the species for the Plasmodium positive samples. Reviewer's suggestion was addressed by presenting the raw data of identification of the species for positive samples in the Supplementary File 1, Table  S4. Plasmodium I would suggest to specify in the sup file that the calculated LOD of 6 Pf sporo or 4 Pv sporo are indeed per mosquito, according to your method.
In addition, please indicate the oocyst detection method, PCR? And how the midgut were preserved? A statement on oocysts detection method (PCR) and midgut preservation has been added in the Methods section of the revised manuscript in order to address reviewer's comment. Table  3  could be placed as supp data. Table  3  has been moved to supplemental data as per reviewer suggestion. First sentence: "overall", be more precise as taking into account all mosquitoes captured by First sentence: "overall", be more precise as taking into account all mosquitoes captured by HLC… We are thankful to the reviewer for pointing this inaccurate wording. The word "overall" has been changed to "taking into account the whole dataset". Nevertheless, again I cannot obtain the same numbers for both HBR and CBR, though very closed.
As said above, this might be the most important analysis. However, I still have some difficulties with the calculation in Table  4  and 5 , At what level were the mean calculated? The mean was calculated using the entire dataset for the corresponding grouping variable (village in the table 4 and season in the table 5) using the definition provided in the Method section. Raw data used for the calculation of entomological indices estimates were added to all tables to help the reader understanding the details of the calculation. Because you have all these nice data as monthly collection (see sup file 1) , why not comparing HBR per species (or even group) and EIR for each village, over time. This will clearly show who and when most transmission occurs and easier to visualize as graphs rather than tables as Table  4  and 5.
Acknowledgments We thank all study participants and communities from  Htoo Pyin Nyar ,  Tar Au Ta ,  Ka Nu Hta  and  Htee Kaw Taw villages  for their participation and support to the study. SMRU  is part of the  Mahidol Oxford University Research Unit , supported by the  Wellcome Trust of Great Britain. We thank to the reviewer for approving the version 3 of our manuscript. Page 8: "The proportion of  malaria vectors collected  indoors between 09:00 pm and 05:00.