dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). But this hypothesis did not prevent the epidemiological molecular dynamic of the emergence and replacements of SARS-CoV-2 lineages  (Carabelli et al. , 2023). The succession of variants over time and the at least partial immune escape of the Omicron variants highlighted the need to gain a better understanding of the genomic evolution of SARS-CoV-2. At our medical site dedicated to infectious diseases (the Méditerranée Infection university hospital institute (IHU)), we performed diagnosis, virus isolation, and nextgeneration sequencing (NGS) of SARS-CoV-2 genomes (La  Scola et al. , 2020; Colson et al. , 2022). Thanks to the power of NGS, since the very beginning of the pandemic in our geographical area we were able to sequence more than 70,000 genomes (available in sequence databases).
This allowed us to decipher the genetic evolution of SARS-CoV-2. The rapid evolution of this virus does not seem to be particularly affected by the human measures taken to control viral spread  (Carabelli et al. , 2023) , and the apparent regularity of the mutations over time (https://nextstrain. org/;  Markov et al. , 2023)  raises a fundamental question about the importance of the type of evolution proclaimed by the Red Queen theory  (Van Valen, 1973) , the Court Jester theory  (Benton, 2009) , or even the Mistigri theory, which we recently reported  (Colson et al. , 2023)  (Box 1). Mutations within SARS-CoV-2 genomes were found to occur about once every 15 days  (Carabelli et al. , 2023)  based on the analysis of more than 15 million viral genomes available worldwide as well as on our own genome database  (Markov et al.
SARS-CoV-2 genome analysis SARS-CoV-2 genomes were first reanalyzed using the Nextclade tool (https://clades. nextstrain. org/;  Aksamentov et al. , 2021). The resulting file was then manually curated using Microsoft Excel software (https://www. microsoft. com/fr-fr/microsoft-365/excel). Thus, genomes covering at least 90% of the size of the reference genome of the Wuhan-Hu-1 isolate with GenBank accession number NC_045512. 2, and not obtained from a same patient and two samples fewer than 60 days apart (minimum period of time chosen by the eCDC to consider a viral re-infection (European Centre for Disease Prevention and Control, 2021) were selected. SARS-CoV-2 genomes conserved in the database analyzed in this study are available in the NCBI GenBank nucleotide sequence database  (Sayers et al.
Data Availability Statement: SARS-CoV-2 genomes are available in the GenBank sequence database (https://www. ncbi. nlm. nih. gov/genbank/,  Sayers et al. , 2023) , the GISAID sequence database (https://gisaid. org/,  Elbe et al. , 2017) , or on the website of the Méditerranée Infection university hospital institute (https://www. mediterranee-infection. com/accesressources/donnees-pour-articles/).
, 2020). During apoptosis, a pool of PtdIns(3,4,5)P 3 has also been shown to flip in the outer leaflet of the plasma membrane of mammalian cells where it serves as a recognition motif for phagocytes  (Kim et al. , 2022). These sparse data may suggest that PIs in the outer leaflet of the plasma membrane could contribute to integrating extracellular cues. To determine whether PIs are present in the outer leaflet of human platelet plasma membrane, we recently used a series of specific probes allowing to detect the seven PIs by imaging and flow cytometry techniques. A significant signal was obtained when the selective PtdIns3P-binding probes were used, while no signal could be detected using the other PIs probes. The presence of this external pool of PtdIns3P was confirmed using an anti-PtdIns3P antibody.
Parametrization Table  1  summarizes the model parameters. The description of the assigned values can be found in  Hammarlund et al. (2016)  for duration of immunity, in  Truelove et al. (2020)  for duration of infection, basic reproduction number, and reduction of Cd 1 transmission to vaccinated individuals. The host mortality rate is taken from the Institut National de la Statistique et des Etudes Economiques (INSEE) website (https://www. insee. fr/fr/outil-interactif/5367857/tableau/20_DEM/23_DME, last accessed on January 2nd, 2023). When relevant data are lacking, parameters (see below) have been calculated or chosen arbitrarily. Note that literature is contradictory in the definition of an immunizing antibody level, with threshold varying to authors. In this work, we consider an antibody level of 0.
Acknowledgments We thank the  Laboratoire d'Excellence (LabEx) ECOFECT ' Eco-Evolutionary Dynamics of Infectious Diseases'  ( ANR-11-LABX-0048 ) of  Université de Lyon. We thank the  Laboratoire de Biométrie et Biologie Evolutive (LBBE). This work was performed using the computing facilities of the CC LBBE/PRABI. We also thank  Nicole Guiso  for her expertise on the biology of diphtheria and for her advices in the revision of the manuscript. We also thank the three anonymous reviewers for their helpful comments that led to the improvement of the quality of this manuscript.
The explanatory variables covered a wide range of potential environmental determinants of vector resistance to insecticide or feeding behaviour : vector control, human availability and nocturnal behaviour, macro and micro-climatic conditions, landscape, etc. The resulting models revealed many statistically significant associations, although their predictive powers were overall weak. We interpreted and discussed these associations in light of several topics of interest, such as : respective contribution of public health and agriculture in the development of physiological resistances, biological costs associated with physiological resistances, biological mechanisms underlying biting behavior, and impact of micro-climatic conditions on the time or place of biting. To our knowledge, our work is the first studying insecticide resistance and feeding behaviour of malaria vectors at such fine spatial scale with such a large dataset of both mosquito and environmental data.
g. village) and time (e. g. season) in the area, with such decision-support tools important to deploy the right intervention, at the right place and time 4  (Taconet et al. 2021). In this study, we used field mosquito collections and environmental data collected simultaneously in two rural areas of West-Africa to bring elements of answer to these questions for our areas. Guided by these questions, our overall objectives were i) to assess the fine-scale prevalence and spatiotemporal heterogeneity of physiological resistances and at-risk biting behaviours of malaria vectors in these areas and ii) to better understand their drivers. To do so, we modeled a set of indicators of physiological resistances and biting behaviours (namely exophagy, early-biting, late-biting, kdr-w, kdr-e, and ace-1 target-site mutations) at the individual mosquito level using this fine-grained dataset and advanced statistical methods in an exploratory and data-driven approach.
On the other hand, algorithmic models, thanks to their ability to inherently capture complex and hidden patterns contained in the data (e. g. interactions, non-linear associations), are useful for assessing the relevance of a theory and for developing new theories or improve existing ones, through predictive analytics  (Shmueli and Koppius 2010). Predictive analytics is the process of building a model aimed at making empirical predictions and then assessing its predictive power. Shmueli and Koppius (2010)  describe six roles for predictive analytics in theory building and testing, including generation of new theory, improvement of existing theory, assessment of relevance of a theory, and assessment of level of predictability of a phenomena. In a "big data" context such as that of this study (large datasets, containing lots of observations and variables), predictive analytics is every time more used to support scientific theory development through these roles  (Karpatne et al.
Discussion In this work, we studied indicators of physiological and behavioural resistances of several malaria vectors in rural West-Africa at a fine spatial scale (approximately the extent of a health district), using longitudinal data collected in two areas belonging to two different countries, respectively 27 and 28 villages per area, and across 1. 25 to 1. 5 year. The objectives were to describe the spatial and temporal heterogeneity of vector resistance, and to better understand their drivers, at scales that are consistent with operational action. To our knowledge, our work is the first studying the heterogeneity of vector resistance at such fine spatial scale with such a large dataset of mosquito collection and potential drivers of resistance. In this discussion, we first use our results to provide elements of answers to the questions raised in introduction of this article.
[4]  for 111 In-DTPAoctreotide. Absorbed dose distributions in the kidneys were calculated for PRRT with 90 Y-, 177 Lu-, and 111 Inradiolabelled somatostatin analogues. Two dosimetry methods based on direct Monte Carlo radiation transport simulations were used for analysing absorbed dose heterogeneity. In the first method, autoradiography data were used in a 2D model of the kidney tissue sections. In the second method, a voxel lattice was implemented inside the cortex region of the MIRD19 kidney model to generate a 3D representation of the streaky uptake pattern seen in the autoradiographs. Using isodose curves and DVHs, the authors showed that the heterogeneous activity distribution considerably affects the absorbed dose distribution, generating regions within the kidney and within the cortex with significantly lower and higher doses than the average kidney absorbed dose.
Ongoing research To the best of our knowledge, there are currently only three controlled trials assessing HIFU in epilepsy that are actively recruiting patients. The second open prospective trial assesses FUS thalamotomy in the prevention of secondary bilateral tonic-clonic generalization in focal epilepsy (NCT03417297). The study started in 2018 and should be completed in 2027, with preliminary results available as early as 2025. Its target population is 10 patients with focal DRE from a single center. As opposed to the aforementioned trial, this study will assess FUS as a direct alternative to DBS in epilepsy, exploring the neuromodulatory properties of FUS. However, the desired neuromodulation will be obtained by a permanent lesion and not by neurostimulation as is the case in DBS. The third open prospective trial evaluates the safety and effects on anxiety of HIFU in patients suffering from DRE, targeting the anterior nucleus of the thalamus (NCT05032105).
In parallel, five open trials are assessing LIFU as a neuromodulation therapy in temporal lobe epilepsy (NCT03868293; NCT03657056; NCT02151175; NCT05947656; NCT04999046). In terms of technological research, there have been several recent developments in magnetic resonance imaging-compatible robotic LIFU systems for neuro-interventions using both single-element transducers  [14]  and smaller phased arrays  [15]. The goal of these devices is to improve the accuracy and repeatability of targeting using these transducers, which are less susceptible to operator movement.
The first one started in 2016 and assesses HIFU in the treatment of focal epilepsy (NCT02804230). The study should be completed in 2023, according to InSightec1, which acts as sponsor. In detail, the study assesses the effectiveness of MRgFUS in thermoablating the EZ by multiple sonications in patients with focal DRE. Broad indications related to temporal and extra-temporal EZ are considered. Its objectives are to include 20 DRE patients from four recruiting US centers.
From a large regional registry, we aimed to address the characteristics and prognosis of patients with elevated triglycerides (TG) among patients hospitalized for an acute myocardial infarction (MI). From the multicenter database of the RICO survey, all consecutive patients hospitalized for an acute MI (2001-2017) and alive at discharge were included. Among the 10,667 patients included, 17. 7% had elevated TG. When compared with patients with TG ≤ 200 mg/dL, patients with high TG ( > 200 mg/dL) were 10 years younger, had a higher BMI, were more frequently men, diabetic, and smokers. At 1-year follow-up, recurrent ischemic events were more frequent in elevated TG patients. In multivariate logistic regression analysis, high TG (OR (95%CI): 1. 356 (1. 095-1. 679)) remained an independent estimate for recurrent ischemic events, even after adjustment for confounding factors.
3  Moreover, elevated TG were associated with worse CV outcomes when compared to similar patients with normal TG. Among adults from a large database of Ontario population with lipid panels and prevalent ASCVD, increasing TG was associated with pro-gressively higher hazard of ASCVD events at approximately 3 years of follow-up. 4  However, in patients hospitalized for an acute MI, the identification of patients with elevated TGs and characterization of their risk of recurrence of ischemic events remain poorly described. The present study aimed, based on a large observational cohort collected over a 17year period, to determine the prevalence, characteristics and prognostic impact of high TG levels in patients who experience an acute MI.
Patients and methods The Côte d'Or MI observatory (RICO) is collecting data on patients hospitalized for an AMI in Côte d'Or. 5  We retrospectively analyzed all consecutive patients included between January 1 st , 2001, and June 30 th , 2017, with lipids values and follow-up at 1 year. Patients without TG assay, under fibrate treatment, who died in-hospital or were lost to followup were excluded from the study (Figure  1  suppl file ). Data collection and statistical analysis are reported as supplemental files.
5% of the French MI population had elevated TG. 7  In a multinational registry, almost 15% of patients with established CAD had high TG. 8  Moreover, in the Ontario cohort, the largest real world database to date, almost 24% of participants had elevated TG ( ≥177mg/dL). 4  Although CVD risk is increased for lower TGs levels ( > 150 mg/dL), 2019 European Society of Cardiology (ESC) / European Atherosclerosis Society (EAS) guidelines reported that the use of drugs to lower TG levels should only be considered in high-risk patients when TG are > 200 mg/dL. 9  Moreover, TG were obtained on non-fasting samples, further reinforcing the choice of a threshold not too low. Therefore, our aim was to address this population. Although patients with elevated TG were younger, CAD history and burden at coronary angiography were similar for the two groups, strongly suggesting the aggressive atherogenicity of the metabolic environment related to high TG-rich lipoproteins.
Observational cohort studies also found a stepwise-elevated ASCVD risk according to TG levels. In the large database of 196 717 subjects with prevalent ASCVD from the Ontario population, median TG was similar to our study (115 (80-159) mg/dL) and the prevalence of diabetes was progressively higher with higher TG levels. 4  At 3-years of followup, elevated TG were associated with increasing rate of the primary composite outcome, such that individuals with the highest TG ( > 354 mg/dL) had 52% higher risk than those in the lowest TG category ( < 88 mg/dL). The risk was mainly driven by the rate of recurrent ischemic events, i. e non-fatal MI/UA or revascularization and were similar among men and women, diabetics and non-diabetics, and among those with controlled vs. elevated LDL-C. In a nationwide cohort from Swedish Web-system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) study on 1 stever MI survivors, TG measured at early re-visit post MI had the strongest predictive value for CV outcomes at 4-Y follow-up, above other blood lipids (i.
Disclosure of conflict of interest MF reports having received grants, consulting fees and/or honoraria and delivering lectures for Abbott,  Amarin ,  Amgen ,  AstraZeneca ,  Ajanta ,  Kowa ,  Merck  and  Co ,  Novartis ,  Organon ,  Pfizer ,  Recordati ,  Sanofi/Regeneron ,  Servier , SMB,  Ultragenyx  and Viatris. FC reports having received non-financial support and speaking fees for  Amgen ,  MSD ,  Novartis ,  Sanofi  and  Pfizer. YC reports having received consultant or speaking fees for  Bayer ,  BMS/Pfizer ,  Boehringer Ingelheim ,  Novartis ,  Sanofi  and  Servier. MZ declare research grants from  Amarin Corp  and speaking fees from  Organon  and  Amgen. No declaration for the other authors.
Differences were largely due to changes in relative abundance of shared taxa. Then, we studied the diversity of viruses with a broad host range. We searched for viral taxa shared by the two Culex species and Aedes vexans, another disease vector, present in one of the habitats. Twenty-six out of the 163 viral taxa were found in the three mosquitoes. These taxa encompassed 14 families. A database analysis supported broad host ranges for many of those viruses, as well as a widespread geographical distribution. Thus, the viromes of mosquitoes from the same genera mainly differed in the relative abundance of shared taxa, whereas differences in viral diversity dominated between mosquito genera. Whether this new model of virome diversity and structure applies to other mosquito communities remains to be determined.
The comparison was done in the arid habitat with a limited diversity of mosquito breeding sites. This environment should favor niche overlap between mosquito species and, in turn, the interspecies transmission of generalist viruses. Despite important differences in virus diversity between the two mosquito genera, viruses with multi-genus host ranges were relatively abundant and diverse. A search in public databases showed that several generalist viruses were not only present in other mosquito species but also had a geographical distribution encompassing several continents. Overall, our results suggest that differences in virome diversity and structure can change along with taxonomic distance in the studied mosquito species and sites. Increasing taxonomic distances would lead from changes in relative abundance of shared viruses to changes in taxonomic diversity.
Bioinformatic and statistical analyses The bioinformatic analysis of sequences was done with the SnakeVir pipeline as previously described  (23). Adapters and poor-quality sequences were removed from Illumina reads with Cutadapt version 1. 6  (25). The resulting reads were removed if they mapped with BWA version 0. 7. 15  (26)  on rRNA sequences belonging to bacterial or dipteran sequences (SILVA rRNA bases : SSURefNr99 and LSURef, 18/01/2017 ; dipteran base : release 138)  (27). Then, a two-round de-novo assembly was performed using Megahit 1. 1. 2 (28) and CAP3  (29). Reads from all the libraries of the same mosquito genus were pooled and used as input in the de-novo assembly to generate a non-redundant set of contigs. A homology search was done with the resulting contigs and DIAMOND  (30)  against the NCBI nr database (publication date : 05/09/2021) (e-value threshold = 10 -3 ; minimum alignement length = 75 bp).
Quantification of the number of reads mapping per contig and library was carried out with BWA 0. 7. 15  (26). Contigs with a virus as best hit (i. e. the accession with the lowest evalue found by Diamond) were screened for a potential origin from endogenous viral sequences with a Blastn against the NCBI nt database. Contigs with a non-viral best hit with a coverage above 25% were considered as potential EVEs and removed  (31). We generated an arbitrary taxon, similar to the species level, into which all the contigs with best hits associated with the same virus species were grouped. This taxon cannot be always considered as a virus species because contigs from new and closely-related viruses can be associated to the same known virus and, thus, to the same taxon. Moreover, contigs with different genes of the same genome can be clustered in different taxa if their best-hit species differs (e.
The average contig length per VTU was 3 300 bp (max/min: 20 251/404 bp, see Table  S2 -S3). To validate VTU detection in different mosquito species, contigs of each shared VTU were compared between mosquito species with Blastn. A VTU was considered as present in several mosquito species if any of its contigs in a given mosquito had percent identity above 80% at the nucleotide level with those found in another mosquito species. Taxonomic affiliation of VTUs to families was often hampered by a lack of a family rank in the taxonomy of best-hit species. Thus, VTUs were grouped into a family-like level called "cluster" as previously described  (31). Briefly, the cluster was defined as the likely family of the best-hit species based either on GenBank information or on the taxonomy of the closest relatives of the best-hit species found with Blastn searches.
Any VTU with less than 100 reads in total was removed from the dataset. A final step was carried out to limit the influence of a potential cross-contamination between libraries. This analysis focused on the VTUs with the highest number of reads, that is those VTUs that were more likely to lead to cross-contamination. For the most abundant VTUs (i. e. with a total number of reads greater to the third quartile of the distribution), we set a minimal read number per library to be considered as detected. This threshold was set at 5% of the reads in the library with the highest number of reads. Read counts were set to zero in a given library if below the threshold (i. e. , the VTU was considered not detected). The threshold can be considered as conservative as it reached over 2000 reads for certain VTUs.
Analysis of the host and geographical ranges of selected viruses A Blastn search against the Genbank database was performed to determine if viruses in our dataset had been previously found associated with other mosquito species and geographic areas. We considered that a VTU included a virus that had been previously detected if it included contigs with more than 80% identity at the nucleotide level with sequences from its best hit species, as well as a minimum alignment coverage of 90%. An analysis of the bibliography on the selected viruses was carried out in PubMed (date 04/2022) to determine their host and geographical ranges (Table  S1 ).
Again, we observed large variations in virus-like reads between libraries but variation was not explained by mosquito number (Spearman rho = -0. 26, p-value = 0. 47). Rarefaction curves suggested adequate sampling effort in terms of virus-like reads (S1B Fig). After VTU filtering, the A. vexans dataset comprised 96 VTUs, all related to arthropod viruses. Taxonomic diversity was again large, involving 32 clusters and 20 orders (S2B Fig).
To this end, we scanned GenBank and literature for records of those viruses. Host ranges of the selected viruses had a median of six mosquito species and three mosquito genera (Fig  5 ). Moreover, geographical distribution per virus had a median of six countries. Moreover, all but one of the virus species (Aedes vexans iflavirus) were found in at least an additional mosquito species and an additional country (Fig  5 ). The two mesoniviruses detected in our study stood out in host range size with at least 22 mosquito species, more than twice the number of host species in any of the other viruses. Moreover, one of the mesoniviruses, Alphamesonivirus 1, had the widest geographical distribution by far, with detections in 18 countries and five continents. Other viruses were also widespread. For example, Culex Iflavi-like virus 4 and Culex pipiens-associated Tunisia virus were found in more than ten countries and four continents.