dataesr/scientific-paragraphs-categorization huggingface dataset:
The processor is a 32 core, 3. 1GHz Intel Haswell. For The Walrus, version 0. 19 is used and installed from pypi. The Ryser implementation is run on 4 or 32 threads. The Glynn implementation is run on a single thread. What is interesting to note is that all implementations have convergence to the theoretical performance but the factor between optimised and less optimised implementation still makes a perceptible time difference for the end-user. Based on different behaviour between Ryser and Glynn with n and potential multi-threading, Perceval has some built-in logic to switch between the two algorithms. The Stepper Back-end. This back-end takes a completely different approach. Without computing the circuit's overall unitary matrix first, it applies the unitary matrix associated with the components (see Table  1 ) in each layer of the circuit one-by-one, simulating the evolution of the state vector.
Perceval in Action In this section we provide examples of the Perceval software in use. We reproduce several photonic experiments that implement important quantum algorithms and then demonstrate a photonic quantum machine learning algorithm on a simulated photonic quantum processor. The Hong-Ou-Mandel (HOM) effect  [47]  is an interference effect between pairs of indistinguishable photons, which, when incident on a balanced beam splitter via the respective input modes, will bunch and emerge together in either of the output modes with equal probability. In 2002, Santori et al. demonstrated the HOM effect with a unique source of single photons  [48] , evidencing the indistinguishability of consecutive photons and paving the way to many developments in experimental quantum optics. In this experiment, "two pulses separated by 2 ns and containing 0 or 1 photons, arrive through a single-mode fibre.
Introduction Grover's algorithm  [2]  is an O √ N -runtime quantum algorithm for searching an unstructured database of size N. It is remarkable for providing a polynomial speedup over the best known classical algorithm for this task, which runs in time O(N ). We now provide an intuitive explanation of how this algorithm works, similar to that which can be found in the Qiskit documentation or in  [60]. We work in the n-qubit Hilbert space (C 2 ) ⊗n , which allows us to treat a database of size N = 2 n. In its standard formulation, the goal of Grover's algorithm is to look for an unknown computational basis state |T , which we will call the target state, with T ∈ {0, 1} n. Let |R be the (normalised) uniform superposition of all the N -1 computational basis states other than |T , which is orthogonal to |T.
Perceval Implementation 8 Here we reproduce the photonic demonstration of Grover's algorithm of  [62]. It uses the spatial and polarisation degrees of freedom to implement a mode realisation of the algorithm with two spatial modes. We spell out the correspondence between the marked database element and the associated quantum state in Table  4. Linear Optical Circuit. The linear optical circuit we will use in our simulation is shown in Figure  8. This circuit was experimentally realised by Kwiat et al. [62]  using bulk (i. e. free-space) optics and compiled to limit the number of optical elements introduced in the experimental setup, by moving and combining operations like phase shifts. Simulation. Here, we simulate in Perceval the linear optical circuit of  [62]  (see Figure  8 ), a photonic realisation of the quantum circuit in Figure  7 , which implements Grover's algorithm for marked elements 00, 01, 10 and 11.
Acknowledgements The authors wish to thank  Mario Valdiva  for invaluable technical support,  Arno Ricou  for code and discussions on variational algorithms, and  Jeanne Bourgeois ,  William Howard ,  Rayen Mahjoub , and  Bechara Nasr , whose internships nourished early developments on the way to this paper. Finally, the authors would like to thank  N. Quesada  and the referees for their very valuable feedback that has greatly improved the quality of the paper.
1. Problem formulation We assume that the steady state is reached when a pressure rise is observed on an ICRH antenna since the time needed for the pressure to rise on the system is much greater than the rebounding time of the desorbed particles. Indeed, in such a case, it is possible to establish a balance equation between the rate of desorption and that of pumping. Consequently, the molecular desorption balance equation is given by + R -ϕ • Sp = 0 (1) The mean speed of gas molecules v ms -1 is calculated by the Maxwell-Boltzmann distribution and is given by ⟨v⟩ = √ 8k B T (4) where, m is the mass of the desorbed molecules in [kg]. 5. 1. 2. Surfaces' molecular desorbed rate R. We rely on the X-ray photoelectron spectroscopy (XPS)  [22, 23]  data analysis, obtained for a WEST ICRH antenna representative material sample of silver-coated stainless steel, to monitor the chemical surface modifications  [10] , and relate it to the multipactor simulation results to estimate the molecular desorbed rate R.
For example, simulating the T-junction of the WEST ICRH antenna at 55 MHz for 10 000 ns, with the non-conditioned TEEY data curve leads to a numerical saturation in the number of electrons-a plateau region is observed. The average multipactor electrons' impact density evaluated in this case is i = 1. 284 10 14 em -2. Consequently, the multipactor dose is d = 2. 05 10 -5 Cm -2 , and the desorbed carbon atoms' percentage is: pC = 0. 5412 10 -5 %. As we consider methane molecules, we can assume that one carbon atom exists per 10 -20 m 2. The number of carbon can be expressed as: #CarbonS = SMP 10 20 , with SMP corresponding to the T-junction component. Therefore, the number of desorbed carbon by the multipactor effect is #desorbed(Carbon) S = 0. 46 10 13. And the surfaces' molecular desorbed rate is R = 4.
ORCID iDs Eva Al Hajj Sleiman https://orcid. org/0000-0003-2095-6870 At this step, we can compare the experimental pressure rise level to that caused by the multipactor phenomenon, as evaluated in section 5. 2 by the developed steady-state formulation. Although the Spark-3D software does not model any saturation mechanism, there is a good agreement between the estimated pressure rise obtained via the multipactor simulations ( 10 -3 Pa) and the experimentally measured pressure rise (10 -3 Pa-10 -2 Pa). This agreement is also evidenced when SPIS, a multipactor tool accounting for the space charge effects, is used. Therefore, the pressure rise observed on the ICRH antennas when only one antenna is active can be attributed to the multipactor phenomenon, as the pressure level caused solely by the multipactor phenomenon is within one order of magnitude of the experimentally measured pressure rise.
| CE interviews CE interviews are used to identify or generate concepts of importance to a population of interest or content for a new measure. In the current study, the initial CE interviews were designed to collect concepts (i. e. , symptoms and impacts) that were relevant and important to people with CAD. All interviews were conducted by two experienced researchers, using a semi-structured interview guide to ensure consistency while maintaining a conversational tone and encouraging spontaneity of answers. At the beginning of each interview, participants were asked openended questions about their experiences with CAD and its treatment. These were followed by targeted questions designed to ensure that potential concepts of interest were captured if not raised spontaneously. Participants were also asked to provide feedback on the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) subscale; data from this part of the study have been published in part elsewhere.
The draft items tested in each round of interview and the rationale behind any modifications are presented in Supplementary data Table  S1. Items relating to difficulty concentrating, difficulty with memory, and sleep disturbance were deleted after Round 1 due to low endorsement or lack of association with CAD. Six of 15 participants who took part in the first two rounds of interviews considered things other than CAD in their responses. Thus, for uniformity across the impact items, the phrase, "because of CAD," was added to all impact items after Round 2 and tested in Round 3. No additional concepts for inclusion in the draft item set were endorsed by the majority of participants. Two participants suggested adding dark urine and one participant each suggested adding pain and nausea, all of which are associated with reactions to cold environments.
Similarly, while the sample was geographically diverse, representing all four census regions of the United States, the majority of participants were non-Hispanic White. Future studies should be designed to include a more representative sample of the global CAD population and to limit selection bias. Additionally, data on recent or previous treatments for CAD (e. g. , rituximab use or blood transfusions) and hemoglobin levels were not systemically collected, which may have provided additional details in better understanding patients' experiences and how active their disease was at the time of the assessment. However, the CAD-SIQ was developed in accordance with industry best practice, and most symptoms and impacts included in the final item pool were identified early in the interview process, thus suggesting that concept saturation was attained, a conclusion supported by the fact that no additional concepts were identified by participants during the CD phase.
2 The high proportion of patients who identified fatigue as a major symptom of CAD in this study is a new observation, and may suggest that this symptom is underreported in medical records and thus underrepresented in retrospective chart-review analyses. Most participants also reported shortness of breath and problems with concentration, while more than one-third experienced sleep disturbance. In the phase 3 CARDINAL trial (NCT03347396) of sutimlimab in CAD,.
ACKNOWLEDGMENTS The authors would like to thank all of the interviewees who participated in this study, as well as  Jun Su , former employee of  Sanofi and current employee of Astellas US LLC. , for her contributions to the conception of the study design and data acquisition. This study was funded by  Sanofi. Medical writing and editorial support for the development of the manuscript, under the direction of the authors, were provided by  Amy Watkins, PhD , and  Helen Marshall , of  Ashfield.
DATA AVAILABILITY STATEMENT Qualified researchers may request access to patient level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specifications. Patient level data will be anonymized and study documents will be redacted to protect the privacy of our trial participants. Further details on Sanofi's data sharing criteria, eligible studies, and process for requesting access can be found at: https://www. vivli. org/.
Gametocyte purification and P. falciparum genotyping Gametocyte purification was performed using a MACS ® system (Miltenyi Biotec, Bergisch Gladbach, Germany) with magnetic columns using 1 ml of serum-free blood as previously described  [28]. Genotyping was performed at seven microsatellite loci according to Anderson et al. [29]. Labelled PCR products were analysed on an ABI 3500 XL DNA genetic analyser (Applied Biosystems, Foster City, CA) at the GenSeq platform in Montpellier and alleles determined with the GeneMapper ® software (v6, Applied Biosystems). Multiplicity of infection (MOI) was defined as the maximum number of alleles at the most polymorphic locus.
Data analysis Statistical analyses were carried out using R (version 4. 0. 5). For data from the screening surveys among children, the effects of hemoglobin variant, sex, age and school were tested for parasite prevalence and infection densities. Prevalences of infection were compared using χ 2 test. Trophozoites and gametocytes densities were compared using Wilcoxon sum rank test for two group comparisons on log-transformed parasite densities and generalized linear models (GLMs) were used for multiple comparisons. P-values less than 0. 05 were considered statistically significant. Oocyst count data are overdispersed and inflated in zeros. We then applied zero-inflated negative binomial models, as they were the best fitted to our dataset. The predictor variables fitted in the models include hemoglobin status, MOI and gametocyte densities, and the blood donor was set as a random effect to control for variation between individuals.
Acknowledgements We thank the study participants and the medical team from the  Mfou Hospital  for technical assistance in sample collection. We thank the GenSeq technical facilities at the  « Institut des Sciences de l'Evolution de Montpellier  » supported by the  LabEx CeMEB , an  ANR « Investissements d'avenir » program  ( ANR-10-LABX-04-01 ). We are grateful to  Francis Essengue ,  Ballotin Fongang  and  Majoline Tchioffo  for their advice and support in the data analysis.
Study design and participants This study was reported according to CONSORT guidelines for pilot RCTs  [15]  (Additional file 1). DEXTRAIN, a pilot randomized, single-blinded trial (NCT03934073), was conducted at the GHU Paris Psychiatrie et Neurosciences hospital, Paris, France from 2018 to 2021. The study consisted of three measurement time-points: before the training (T0), immediately after (T1) and at follow-up 3 months after the end of training (T2). Inclusion criteria included: age > 18 years; patient affiliated to French social security scheme or equivalent; first symptomatic stroke (ischemic or hemorrhagic) dating back 3 months or more; mild-to-moderate gross dexterity impairment, indicated by BBT score < 52 blocks/minute and ≥ 1 block and 10° active extension of the wrist and index metacarpophalangeal joint.
To limit the number of statistical tests with regards to secondary outcomes, P-values were only calculated for outcomes where the 95%CL of the median of group differences did not include zero. In this case, the Mood median test was used. Six tests were carried out and we did not correct for multiple comparisons given the exploratory nature of this pilot RCT. Given the group difference in age we also analyzed the percent change in BBT using gender and age normalized BBT scores  [22]. The number of patients showing clinically significant change in BBT was reported. As a posthoc, supplemental analysis using parametric statistics to analyze BBT change across time points (T0, T1, T2) was performed using linear mixed models under SPSS. We analyzed (i) BBT scores at all 3 time points including age as a fixed effect covariate and subject as random effect (Additional file 2: Table  S2 ), (ii) age and gender normalized BBT scores across all time points (Additional file 2: Table  S3 ), and (iii) normalized BBT scores with imputation of missing data from T2 (Additional file 2: Table  S4 ).
Mussels biometry Maximum length of mussels along of the anterior-posterior axis was measured using a Vernier caliper KS TOLLS® with an accuracy of ± 0. 01 mm. Total wet weight (live mussel weight) was determined using the electronic balance KERN PCB® (0. 01 g precision) after washing of sediment particles adhering to the shell and using a tissue to get rid of the attached water. The length-weight relationship of P. perna population was determined by linear regression analysis (Microsoft Excel® software) using the following equation: W=a*L b , where W is the weight in g, L the length in mm, a is the condition factor and b is the growth coefficient  (Quinn and Deriso, 1999).
Acknowledgments This work was supported by the  National Institute of Marine Sciences and Technologies (INSTM) , founded by the  Aquaculture laboratory  in the context study of  ENQuaMiB project (Elevage Nutrition Qualité Milieu Bivalves)  and supported by  EMERTOX project  ( Emergent Marine Toxins in the North Atlantic and Mediterranean: New Approaches to Assess their Occurrence and Future Scenarios in the Framework of global Environment Changes  ( H2020-MSCA-RISE-778069 ). Thanks to  MARBEC laboratory  and  IRD  for funding  Mohamed Laabir  stay in the  Aquaculture laboratory (INSTM, Monastir). The authors would like to thank Offshore fish farm staff of  Aquaculture du Sahel (AS)  who supported us and facilitated the sampling of shellfish and  Dr Idriss Ben Neila  from  Regional Center Research Veterinary of Sfax  for Mouse Bioassay analysis.
Our results also provide a demonstration of the prediction that, as phylogenetic distance increases, variation between species overwhelms variation between tissue types. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression, and use this to identify gains and losses of ovarybiased expression across these twelve species. We then use this distribution to calculate the evolutionary correlation in expression changes between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that share evolutionary trajectories, and we present these results as a dataset of new testable hypotheses about genetic roles and interactions in the function and evolution of the Drosophila ovary.
More recently,  Breschi and colleagues (2016)   [21]  demonstrated that, consistent with an evolutionary model of trait evolution, species-level variation in gene expression increases with the time since divergence from the most recent common ancestor. In addition, previous work by authors on this manuscript  [8]  showed that, while expression patterns across tissues tend to be consistent between species, lineage-specific can be interactively visualized and queried at the accompanying data visualization for this paper (visualization at https://shchurch. shinyapps. io/ hawaiian_fly_dataviz_2021 and repository at https://github. com/shchurch/hawaiian_fly_dataviz_ 2021). Raw RNA sequencing data are available at the Sequence Read Archive of the National Center for Biotechnology Information (NCBI), under BioProject PRJNA731506.
Assembled transcriptomes and DNA barcode sequences are available at GitHub, under the repository 'http:// github. com/shchurch/hawaiian_drosophilidae_ phylogeny_2021', commit 'b12cbb10'. All code and results for this manuscript are available at GitHub, under the repository 'shchurch/ hawaiian_drosophilidae_expression_2021', commit '67d8e6f'. The code to perform all agalma commands was performed in clean anaconda environment, installed following the instructions at 'https://bitbucket. org/caseywdunn/agalma'. All R commands were performed with a fresh install of R, and the session information including all package versions is available in the GitHub repository under the file 'r_session_info. txt'. The code to generate all plots as well as the text of this manuscript is available in several R scripts and Rmarkdown files at the same location.
In this study we test for the existence of a core suite of ovary-specific genes across species of Hawaiian Drosophilidae and describe its size and composition in relation to the described atlas of expression in D. melanogaster. The Drosophila ovary has several features  [28]  that make it a compelling organ in which to test hypotheses about expression evolution. Analyses of the FlyAtlas2 dataset  [29]  show that in D. melanogaster, more genes demonstrate highest expression enrichment in the ovary than any other adult female organ (Fig A in S1 Text). Additionally, all described signaling pathways are known to have a role in regulating ovarian development  [30]. The ovary performs several critical functions, including maintaining the germ line and manufacturing specialized egg cells, yolk, and egg-shell materials  [31].
We use the term ovary-biased and head-biased to refer to transcripts that have a positive ratio of expression between the ovary or head and carcass, regardless of magnitude. The use of expression ratios is advantageous as potential effects of variation across individuals (e. g. in transcript counting efficiency) will be present in both the numerator and denominator, and therefore canceled out  [7]. Given that the ovary and head are not equivalent body parts in terms of functional complexity (the ovary is primarily dedicated to producing oocytes, while the head contains the eyes, brain, and mouthparts, all dedicated to different tasks), we present analyses on these two body parts in parallel, and do not draw conclusions based on direct comparisons   [44] ). Divergence times estimated by Russo and colleagues, 2013  [45].
Three clades within the group are highlighted: the genus Scaptomyza, nested within the paraphyletic genus Drosophila; the Hawaiian Drosophila, which, along with Scaptomyza, make up the Hawaiian Drosophilidae; and the well-known picture-wing clade. Adjacent to tip labels are four letter species codes used throughout the manuscript. B, The experimental design used to generate the data in this manuscript. When sufficient specimens were available per species, one whole individual was used as a reference and three whole individuals were dissected into three separate tissues: the head, ovaries, and all remaining material (carcass). Reference individuals were sequenced to generate paired-end RNA reads and dissected tissues were sequenced to generate single-end RNA reads. Tissue libraries were then mapped to the assembled reference to quantify transcript expression.
between the head and ovary. In our description of results we prioritize the ovary-carcass comparison. For each analyses, we first characterized the differentially expressed genes in the ovary and head of each species individually. By comparing these to each other, and to records of ovaryenriched and head-enriched genes from D. melanogaster, we identified core suites of tissuespecific genes shared across species. We applied linear modeling to this dataset to test the overall contribution of species-and tissue-level differences to expression variation across genes, and describe the circumstances under which one is likely to dominate over the other. Finally, we used a phylogenetic analysis of expression changes over evolutionary time to identify genes likely to have gained and lost tissue-enriched expression.
Across species, we observed an average of 25. 4% to be significantly downregulated and 12. 9% of transcripts to be significantly upregulated. When comparing the head to the carcass, we observed an average of 7% of transcripts to be significantly upregulated and 7. 2% to be significantly downregulated (Fig F in S1 Text). These differences may reflect variation in the complexity and diversity of functions of the body parts being compared. We used the results of our differential gene expression analysis within species to test for the existence of a suite of genes that show consistent ovary-specific expression across species. We found a cohort of 131 genes, grouped according to BLAST sequence similarity to D. melanogaster, for which at least one transcript was significantly upregulated in the ovaries of more than ten species (Fig  2C ).
Transcripts matching these genes made up on average 24. 6% of the significantly ovary-upregulated transcripts across species, meaning roughly one quarter of ovaryspecific genes have conserved expression patterns over evolutionary time. When excluding the species S. varia, this average decreased to 17. 7%, as this species has the smallest set of ovaryupregulated transcripts, 100% of which correspond to core ovary genes. We then tested the extent to which these core ovary genes correspond to observations in well-studied laboratory Drosophila models. To accomplish this, we compared expression across Hawaiian species to reported tissue-specific expression levels from D. melanogaster  [29]. We found that Hawaiian core ovary-specific genes show nearly universal enrichment in the ovary of D. melanogaster as well, as reported in the FlyAtlas2 dataset  [29]  (Fig  2D ).