dataesr/scientific-paragraphs-categorization huggingface dataset:
We also investigated the effect of parasite isolate (set as a fixed effect) on EIP using a Cox's proportional hazards regression model. The effect of infection status (infected vs. uninfected) on mosquito survival was evaluated using a mixed Cox's proportional hazards regression model (package "coxme") with infection considered as a fixed effect and parasite isolate as a random intercept effect. Mosquito longevity was also analysed using two LMMs, the first specifying gametocyte density as a fixed and a random slope factor and parasite isolate as a random intercept, the second specifying sporozoite load in mosquito head/thorax as a fixed and a random slope factor and parasite isolate as a random intercept. Finally, we investigated the effect of parasite isolate (set as a fixed effect) on mosquito survival using a Cox's proportional hazards regression model.
The work described in our manuscript was funded by support from the following sources: AC received support from the  Malaria Vaccine Initiative , a program of the global non-profit PATH organization (Seattle,  USA ) and the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  733273. TL received support from the  ANR  Grant "STORM" No. 16-CE35-0007. DFDSH received support from the  JEAI IRD program  Grant No. AAP2018_JEAI_PALUNEC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In nonhuman primates (NHPs) previously primed with the ancestral D614 spike antigen (mRNA or recombinant protein), our Beta-based recombinant protein boosters have shown broad crossreactivity across SARS-CoV-2 VOCs (Alpha, Beta, Gamma, Delta, and Omicron BA. 1) and SARS-CoV-1 at early time points post-booster  7. These observations have been confirmed in two Phase 3 clinical trials (NCT04762680 and NCT05124171), demonstrating that our Beta-containing booster vaccine candidate delivered a strong immune response against VOCs, including Omicron, in adults primed with mRNA vaccines and conferred strong efficacy against symptomatic infection in adults and participants previously infected with SARS-CoV-2  3, [8] [9] [10]. In the present work, we report the durability of the crossneutralizing antibody responses in NHPs at 6 months post-booster and the effect on memory B-cells after a third immunization with various formulations, namely ancestral (D614), variant (Beta), and bivalent (ancestral + Beta) CoV2 preS dTM-AS03 in mRNA-and subunit-primed macaques.
Spike-specific memory B-cells were increased mainly in the low-responding macaques, resulting in homogeneous and robust responses post-booster. These pathogenexperienced memory B-cells induced by vaccination do not constitutively secrete antibodies but may have a critical role in controlling the viral replication when activated, such as in cases of breakthrough infections  25, 26. For COVID-19 vaccines, although serum antibody titers may wane rapidly, memory B-cells are highly durable and may contribute to protection from disease along with memory T cells  23, 27. These data provide information about alternative boosting strategies to address the risks associated with SARS-CoV-2 VOCs and waning immunity. Based on these encouraging results, Betacontaining vaccine formulations are being evaluated as booster vaccines in a clinical trial, and preliminary results confirm and extend our observations in primed NHPs by showing a superiority of the Betacontaining vaccine when compared to the ancestral D614 vaccine for the neutralization of a wide range of variants (NCT04762680, NCT04904549, and NCT05124171)  [8] [9] [10].
The CoV2 preS dTM (ancestral D614) sequence was designed based on the Wuhan YP_009724390. 1 strain S sequence, modified with 2 prolines in the S2 region, deletion of the transmembrane region, and addition of the T4 foldon trimerization domain. The CoV2 preS dTM (Beta) was designed based on the Beta (B. 1. 351) sequence (GISAID Accession EPI_ISL_1048524) and contains the same modifications. AS03 is a proprietary adjuvant system composed of α-tocopherol, squalene, and polysorbate-80 in an oil-in-water emulsion manufactured by GSK. Vaccine doses were formulated by diluting the appropriate dose of preS dTM with PBS-tween to 250 µL, then mixing with 250 µL of AS03, followed by inversion five times for a final volume of 500 µL. Each dose of AS03 contains 11. 86 mg of α-tocopherol, 10. 69 mg of squalene, and 4.
Acknowledgements The authors thank  Jon Smith  for coordinating the production and providing the vaccine antigens for the study,  Caroline Patriarca Ruat  for project management,  Tong-Ming Fu  for coordinating the sharing of mRNA-primed macaques, and  Carlos Diaz-Granados ,  Stephen Savarino , and  Saranya Sridhar  for critical discussions on the study designs and data analysis. The authors thank  Julie Piolat  for statistical analyses support. The authors also thank  Hanson Geevarghese  and  Saili Dharadhar  ( Sanofi ) for manuscript coordination and editorial assistance, respectively. This work was done in collaboration with GSK, who provided access to, and use of, the AS03 adjuvant system. This study was funded in whole or in part by  Sanofi  and by  US  federal funds from the  Biomedical Advanced Research and Development Authority (BARDA) ,  Administration for Strategic Preparedness and Response at the US Department of Health and Human Services  under Contract #  HHSO100201600005I , and in collaboration with the  US Department of Defense Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense  under Contract #  W15QKN-16-9-1002.
We assessed the impact of the first wave of COVID-19 pandemic on non-COVID hospital admissions, non-COVID mortality, factors associated with non-COVID mortality, and changes in the profile of non-COVID patients admitted to hospital. We used the Spanish Minimum Basic Data Set with diagnosis grouped according to the Diagnostic Related Groups. A total of 10,594 patients (3% COVID-19; 97% non-COVID) hospitalised during the first wave in 2020 (27-February/07-June) were compared with those hospitalised within the same dates of 2017-2019 (average annual admissions: 14,037). We found a decrease in non-COVID medical (22%) and surgical (33%) hospitalisations and a 25. 7% increase in hospital mortality among non-COVID patients during the first pandemic wave compared to prepandemic years. During the officially declared sub-period of excess mortality in the area (17-March/20-April, in-hospital non-COVID mortality was even higher (58.
Results The study setting and population are described in detail in the "Methods" section. Briefly, the study was conducted at the Virgen del Rocío University Hospital (VRUH) (Seville, Andalusia; Spain), a large, 1500-bed tertiarycare university hospital that serves a population of more than 550,000 inhabitants. During the period of study, a total of 52,383 non-COVID patients (10,271 in 2020, and 42,112 in the 2017-19 period [13,788 in 2017; 14,271  in 2018; and 14,053 in 2019]) were admitted to the Hospital and included in the study. Anonymous patient records were extracted and analysed from the MBDS database. Hospital admissions. Sub-period 1 (corresponding to the local pandemic first wave: February 27 to June 7, 2020)  12  : in 2020 10,594 individuals were admitted to hospital (323 with COVID-19 and 10,271 without COVID); the number of admissions for years 2017-19 were 42,112 (average per year: 14,037 ± 242) (Table  1 ).
The MBDS is an administrative database that must be completed by all hospitals in Spain. The clinical variables were obtained through the hospital discharge reports and, as such, there could be some missing information and variability issues. However, the coding is carried out by experts who received specific training and it is conducted using a standardized coding regulation  35  , which reduces the probability of information bias. Secondly, the analysis was restricted to data related to one hospital only. Nevertheless, this is a large study that includes the analysis of all hospitalized patients in the largest hospital of the autonomous region of Andalusia. The comparison of the pandemic period is made with the findings observed in the previous three years in order to minimize the possible effect of factors affecting a particular year and so to reduce the risk of bias.
Methods Setting. The study was conducted at the Virgen del Rocio University Hospital (VRUH), a 1500-bed tertiarycare university hospital in Seville, Spain, which provides approximately the 10% of acute care inpatient bed capacity in the region of Andalusia, Spain. The hospital serves a population of more than 550,000 inhabitants, which may be referred for outpatient and inpatient care in all medical areas (internal medicine and specialties, surgery, orthopaedics and traumatology, emergency and critical care, mental health, paediatrics, and obstetrics/ gynaecology). In 2019, it recorded 48,765 hospital admissions, 42,854 surgical interventions and 330,142 admissions at the emergency department  36. An analysis of real-world data was conducted from Minimum Basic Data Set (MBDS) registries.
Study periods. Two periods were considered for the present study: a 130-day period corresponding to the local pandemic wave (subperiod 1: February 27 to June 7, 2020)  12  , and a 33-day period included within the latter, corresponding to the identified period of excess mortality in Andalusia according to the national monitoring report on daily mortality (subperiod 2: March 17 to April 20, 2020)  13. No official data on excess mortality was available specifically for Seville. For the region of Andalusia, official excess mortality was 21. 7%. The two periods considered in our study were selected in order to test the hypothesis that the period of excess mortality may also be the period with the largest impact on inpatient care. For each subperiod, data from patients admitted to VRUH for any cause were collected and compared with patients admitted within the same dates in years 2017, 2018 and 2019.
Collection of data and studied variables. Data from hospitalized patients were obtained from the (MBDS), a standard management tool in the Spanish National Health System for the coding of hospital admis- sions. The diagnoses at hospital discharge were coded according to the Tenth Version of the International Classification of Disease (ICD-10-EN  35  , and grouped according to the refined version (APR, "All Patient Refined") of the Diagnostic Related Groups (DRG) of the primary admission diagnosis in version 36. 0 37 , which allows a more refined classification of hospital cases by providing information on the severity of the disease and the risk of mortality. Data included demographic characteristics (age, sex, municipality of residence), variables related to the episode of hospitalisation (number of admissions during the study period, type of admission, length of stay (LOS), days spent in the intensive care unit (ICU) if applicable, severity and mortality indexes according to APR-DRG definitions, Spanish weight value of each APR DRG, Spanish APR-DRG-adjusted LOS index and in-hospital mortality), diagnoses at hospital discharge, and in-hospital or in the community cause of death (if applicable).
The population served by the VRUH was considered for each calendar year. The overall mortality data of the population served by the VRUH were obtained from the Statistical Service of the City of Seville (for the basic metropolitan health areas) and the Institute of Statistics and Cartography of Andalusia (for non-metropolitan basic health areas). Data from both sources come directly from the computerized civil registers of the Spanish Ministry of Justice. The study was approved by the Andalusian Biomedical Research Ethics Committee, VRUH, Seville, Spain. All methods were performed in accordance with the guidelines of the WMA Declaration of Helsinki. Patient records were extracted from the MBDS database in an anonymised way. Patient data were provided anonymised for analyses.
5 , and a tapering function that ensures continuity of both potential and forces at the cutoff. Here r = r ij is the distance between the particles i and j, ǫ AA = ǫ BB = ǫ AB = 1 , σAA = 0. 90 , σBB = 1. 09 ≃ 1. 2 σAA , and σAB = ( σAA + σBB )/2 ≃ 1. Masses are m A = m B = m = 1. All simulations have been conducted by using the high-performance-computing simulation code LAMMPS  50. We have considered 7 system sizes, N = 10 3 × 2 n with n = 0,. , 7 ( n = 6 excluded), ranging from the situa- tion where the entire set of the Hessian eigenvalues can be calculated comfortably (small N), to that where we are only able to obtain the INM spectrum in a limited frequency range around ≃ 0 (large N), although with high statistical accuracy. We have fixed the simulation box sizes, L, by matching the number density ρ = N/L 3 = 1 , for all systems.
We have employed the standard scipy. linalg  52  linear algebra functions at small values of N, while for N ≥ 16000 we used RALEIGH  53  , a Python implementation of the block Jacobi-conjugated gradients algorithm for computing eigenvalues and eigenvectors of large-scale real symmetric and Hermitian problems in selected eigenvalue ranges. We have eventually estimated the INM spectra, ρ( ) , by constructing histograms of the obtained eigenvalues. (38) G r -Q(z) 1 + G r -Q(z)]�(z) P(G r ) = 0 (39) V (r)/ε = σ 12 1 r 12 - 1 r 12 c × 1 - r r c 2 2 , The work-flow we have employed is as follows. First, we integrate numerically the system equations of motion and collect at chosen time steps the coordinates of all particles (configurations). Note that the latter are instantaneous thermal configurations and not inherent configurations, i.
Participants and procedure The data for these studies come from a representative survey of the Basque Country and Catalonia conducted between November and December 2022. The surveys were commissioned to Netquest, a Spanish panel agency specialized in online data collection through respondent panels and market and opinion research services. The sample was stratified according to the distribution of the population of the main demographic variables, including gender, age, province, municipality, and population size. After excluding cases in which participants were born outside the Basque Country (88 participants excluded) or Catalonia (76 excluded), the final sample was N = 589 in the Basque Country and N = 526 in Catalonia). The sample characteristics in each of the studies can be seen in Table  2  (Study 1) and Table  6  (Study 2).
All questionnaires were administered in both the local language (Basque in Study 1 and Catalan in Study 2), and in Spanish, and informed consent was obtained from all participants. Measures used in both studies were part of larger questionnaires. The order of the measures and the order of the questions within each measure were randomized. The research has the approval of the Ethics Committee of the University of the Basque Country. The databases of the analyses carried out are available on the OSF project website [https://osf. io/p2cdy/?view_only= 02d2ec38f3d245cabead632a010d5042].
Analytical strategy Descriptive and correlational analyses were calculated using the IBM SPSS Statistic Version 23. The hypothesized links were tested using the PROCESS Macro for SPSS  (Hayes, 2013; Model 4). For mediating effects, we used a bootstrap method with 5000 repetitions to estimate the distribution of indirect effect coefficients (95% confidence interval,  Hayes & Preacher, 2014; Preacher & Hayes, 2008). To test our hypotheses, we included general inclusive linguistic victimhood and context-specific inclusive linguistic victimhood as predictors, controlling for each other, intergroup empathy as mediator, and as outcomes, a) disposition to acquire minority group's languages, b) attitudes towards maintenance of the majority group's own language, and c) attitudes towards multilingual policies to support the transformation of public administration and cultural services into a multilingual reality.
Acknowledgments This research has been carried out in collaboration with the  NGO SOS Racism-Gipuzkoa , an organization dedicated to promoting the expansion of rights for immigrants and fighting against racism and discrimination in host societies. In particular, recognize the financial support provided by the  Provincial Council of Gipuzkoa  and the  Basque Government  to cover the data collection through the panel surveys conducted. Likewise, we would like to express our gratitude for the funding received from the  University of the Basque Country  through the predoctoral scholarship held by the first author. Magdalena Bobowik  was supported with funding from the  Ramón y Cajal Program  by the  Spanish Ministry of Universities , Grant Number:  RYC2021-032887-I.
This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomicbased (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases.
More recently, methods in deep learning have also been applied to align single-cell datasets, modeled as regular grid images, both to atlases at the histological scale  [24]  as well as reference transcriptional atlases that are beginning to emerge  [25]. We should however expect even more diversity in the types and scales of data that can be measured with the rapidly developing technologies in imaging and spatial transcriptomics. These aim to detect up to thousands of genes simultaneously with spatial information, and thus, allow us to view both the micro and even nanometer scales with exquisite detail  [26]. Both the diversity and magnitude of this data pushes the limits of our ability to model such datasets as classical continuous images, discretized on regular grids. Indeed, as seen in those repositories generated in the BRAIN Initiative Cell Census Network (BICCN) and archived at the Brain Image Library (BIL), these datasets are already on the order of terabytes and will only continue to increase as technologies shift from mouse to human measurements.
Hence, the need remains for a modeling framework equally equipped to represent datasets in both forms of traditional continuous imagery sampled on a regular grid, and those of discrete particles with attached functional description; and for an associated registration mechanism to align objects in this framework across different functional modalities at different scales. This paper focuses on the use of mesh-based image varifolds, as described in  [27] , for simultaneously modeling molecular and tissue scale data. A subproblem covered by the mesh based image-varifold theory outlined in  [27]  is the mapping of molecular scale data to atlas coordinate systems. Image varifolds are geometric measures  [15] , which allow us to provide a single representation that supports molecular transcriptomics measurements, cell-based measurements, and tissue scale atlases.
We explicate, here, the construction of a universal method rooted in this framework for transferring molecular scale data to tissue scale "cartoon" atlases, which are devoid of gene measurements, and rather, only contain a fixed partition into structures in their description. Our solution couples coordinate system transformation via geodesic generation of minimal energy diffeomorphisms to estimation of a family of probability laws, which give for each atlas label, a distribution over molecular features that is the most reasonable explanation of the target transcriptomic dataset. Specifically, we model each atlas region as homogeneous and stationary with respect to space, giving an optimal alignment between atlas and target that maximizes similarity in distribution over features across each site in a single atlas region while minimizing the energy of the geometric deformation (diffeomorphism).
This consequently skews emphasis away from the foreground-background boundaries that almost exclusively govern image alignment and instead highlights the underlying assumptions in the architecture of the cartoon atlas, whose boundaries were initially constructed so as to maximize the homogeneity of the region. We estimate the diffeomorphism and probability laws jointly via an alternating algorithm, as explicated here, that iterates large deformation diffeomorphic metric mapping (LDDMM) with quadratic programming for minimizing the normed distance between the template and target, and as a result, yields both spatial alignment and functional correspondence between template and target. We demonstrate the efficacy of this methodology in mapping 2D sections of the ARA  [1]  to corresponding sections of both cell-independent and cellbased spatial transcriptomics datasets, both generated via the MERFISH imaging-based spatial transcriptomics technology, which yields single molecule resolution.
We present methods for sparsifying the functional transcriptome descriptions via gene selection based on mutual information with spatially discriminating variables and subsequently illustrate the stability of our estimated diffeomorphisms to choices of subsets of features. Finally, given the plethora of existing reference atlases, each of which might define a different partitioning scheme over the same area of tissue, questions of comparison and relevance of each atlas to emerging molecular and cellular signatures naturally arise  [28]. We show through the use of our methodology to map not just atlas to molecular dataset, but one atlas to another, that the correspondence yielded by our method serves as an anchor for re-examining existing ontologies and creating new ones for the future.
Natural fluctuation in gene expression over time and space coupled to the dynamics of each spatial transcriptomics technology leads each tissue section, at the molecular (1-100 micron) scale, to have a varying number of such particles with no natural ordering of particles consistently apparent between sections. To build correspondences between these datasets of point measures, we unify the molecular scales with image-like functions as has been developed for building correspondences at tissue scales in MRI  [4]. For this we represent the particles as "generalized functions"  [15]. Since they carry gene or cell image data we call them image varifolds  [27] , linking to the rich literature on the geometric measure theory of varifolds. This allows us to represent particle clouds at any scale in both spatial and imaging function dimensions.
We note landmark-based methods  [32]  that assume direct permutation correspondence between particles across images are not applicable, as a MERFISH section may have 100,000 particles requiring an unfathomable number of permutations to specify. Varifolds are defined as follows. We consider a Euclidean space in d dimensions with d = 2, 3, to which we add function dimensions represented by a set F. In spatial transcriptomics datasets, the functional dimensions represent the gene types of detected mRNA transcripts, treated as independent measures or aggregated into cells or small neighborhoods. At the finest scale, we model a discrete set of point measures (particles) reflecting the individual reads recorded by the given technology, whether they be single transcripts or distributions of transcripts in a given cell or neighborhood.