dataesr/scientific-paragraphs-categorization huggingface dataset:
1. Following our concerns, these authors generated a new dataset (version 2, V2, March 2021) on the largest 100 deltas (Max100), resorting, this time, to more cautious manual, rather than automatic, determination of change. This Max100 dataset shows a 5-fold increase in land area (from 12 to 60 km 2 /yr), but still no statistically significant relationship with the ratio Q d river/Q p river or the difference Q d river-Q p river, nor with the V1 data (Extended Data Fig. 1b, c ). This shows that improved buffer definition does not suffice to validate the finding of net delta land gain attributed to humans. Nienhuis et al. 1  uses automatic buffers that misrepresent deltas and their changes. Quantifying global delta land changes, especially in large populous deltas, requires clarity in identification and analysis.
Deltas are complex features, and examining their global-scale links with human actions faces the challenges of acquiring accurate digital datasets and rigorous modelling. This word of caution is relevant to the analysis of coastal systems and landscape geomorphology faced with increasingly large global datasets, and, hopefully, of interest to a wide audience. Plots bii, cii show subsets of their respective datasets, all with R 2 = 0. Examples of wrongly identified land changes by Nienhuis et al. 1  obtained on a selection of large deltas as a result of misplaced buffers (d, j), human-induced land transformations wrongly reported as natural land changes (e, f, h), and misidentified deltas (k). Original buffers (light grey) of Nienhuis et al. 1  are compared with our re-drawn data-check buffers (yellow) based on which only natural, and not human-transformed, delta coastal change should be calculated.
Supplementary Methods Nienhuis et al. 1  applies thresholds of water and sediment discharge (respectively >1 m 3 /s and > 0. 01 kg/s) to identify river deltas globally, and employs some filtering to exclude small basins (1 km 2 for HydroSheds, and either 50 km 2 or 1000 km 2 , based on a drainage divide altitude, for ETOPO1 grid above 60° lat) and deltas draining into fjords. Nienhuis et al. 1  determined land change for each delta from delta extent along the NOAA vectorized shoreline dataset. Nienhuis et al. 1  used Google Earth Engine  14  to retrieve surface-water changes within their selected buffer areas, but noted the potential for sizeable anthropogenic effects and therefore attempts to mask out portions of each delta that are classified as urban/artificial (class 190) by the GlobCover 12 dataset.
By selecting only land area change near the NOAA shorelines, Nienhuis et al. 1  claims that land-water conversion within delta interiors is excluded. The delta morphologies identified by Nienhuis et al. 1  are based on quantified river, wave and tidal fluxes, and additional non-quantified criteria reflecting wave and tidal influence deduced from visual observation of features (see their section: "Accuracy of delta morphology prediction") such as shoreline protrusion and deflection and a triangular shape. These are common plan-view criteria for recognizing deltas as a distinct category of river mouth. We critically examined the dataset and methodology of Nienhuis et al. 1  : (i) Firstly, we randomly selected 108 'river mouths', representing 1% of their original dataset of (Extended Data Fig.
2b ) to identify delta presence and retrieve land changes. We based this on the afore-mentioned "delta morphology prediction" criteria (including bayhead deltas), and avoiding exclusion where doubt prevailed, but discarding river mouths that had surrounding high elevation based on SRTM data (NASA SRTM Digital Elevation 30 m layer in Earth Engine). From the 108 random river mouth dataset we identify only 57 deltas, the rest being simple estuaries, non-river mouths, etc, but wrongly identified by Nienhuis et al. 1  as deltas (Extended Data Table  1 ). We carefully constructed polygons (n=44) only where deltas were present, and based on the criteria of Nienhuis et al. 1  , on the deltaic coastal fringes to obtain area changes that are representative of the coastal dynamics of each delta, excluding any interior change.
Our operator-based method relies, in line with the essence of this approach, on expert opinion and potentially arbitrary decisions, but no more than their automatic buffer method which also relies on arbitrary delta presence and buffer size definitions. A fundamental difference between the two methods is that an expert operator has the overarching and uncontestable advantage of being able to discern whether identified delta land changes are pertinent or not to the processes and forms under scrutiny. We did not draw polygons where deltas had no change or showed overwhelming anthropogenic influence. The remaining deltas are thus not relevant for change analysis and not even one pixel of land change was detected. (ii) Secondly, we checked the 100 river mouths with maximum discharge (Max100) from the V2 database (see below) of Nienhuis et al.
1  uploaded on Github (https://github. com/jhnienhuis/GlobalDeltaChange/tree/master/land_area_change). Following correspondence with these authors, they uploaded this new V2 dataset, which retrieves land changes for the 100 river mouths with the largest discharge (Max100), based on manually drawn polygons. These 100 river mouths represent 70% of global sediment discharge (Extended Data Fig. 2c ). Out of these, we identified 94 true deltas (94%). Large rivers such as the St. Lawrence, Victoria, Nelson, form estuaries, often bounded by rocky coasts, and the Volga, a true delta, experienced land change caused by water-level variability of the Caspian Sea, and thus cannot be included in this type of study. Area change was obtained based on the same methodology as mentioned above for the 108 random river mouths, first by drawing manually polygons that excluded delta-plain changes representing human occupation, and then retrieving Aqua changes with scripts.
Both these datasets (108 random river mouths and Max100 deltas) cover all the spectrum of sediment discharge and are distributed globally (Extended Fig. 2a ). Linear regression was applied to both the averaged bins and all the data. We used root-mean-square error (RMSE) and normalized-root-mean-square-error (NRMSE) to quantify errors of the regression model compared to observed values. NRMSE_sd was obtained by dividing the RMSE by the standard deviation of the data. Earth Engine scripts are provided with which users can verify each of the 108 random river mouths (https://code. earthengine. google. com/1c074d96779371d7bf20922916ca22f9) and check our Max100 delta land change values and buffers. (https://code. earthengine. google. com/24d503c385872724f0fc8fe785f2cf42).
Supplementary Discussion (i) 108 random samples Regarding the 108 random samples, too many deltas had land change caused by anthropogenic modifications (27%, Extended Data Table  1 ), indicating unsuccessful filtering by Nienhuis et al. 1  , or buffers were misplaced or captured interior delta plain change, and thus leading to an R 2 of 0 with their data (Extended Data Fig. 2c, d ). In correspondence with the authors, they expressed concerns with our analysis because it is based, they claimed, on a small (<1%), biased subset of their data that does not satisfy basic measures of statistical significance and is limited mostly to small deltas with a combined sediment flux less than 0. 5% of the global total. We reiterate that our 1% subset (of which only 0. 5% are true deltas) is randomly generated from their global dataset (Extended Data Fig.
2a, b ). We also reiterate that Nienhuis et al. 1  did not use random selections from their own dataset to verify affirmations on their global trends. (ii) Largest 100 deltas We report our findings relative to the original V1 dataset provided by the corresponding author of Nienhuis et al. 1  by email. Our checks show that there is no agreement between the V1 and V2 datasets (Extended Data Fig. 1ci ,cii) of Nienhuis et al. 1. In fact, there is more agreement between our manually drawn buffers and the V2 dataset of Max100 deltas (Extended Data Fig. 1di, dii ). This actually signifies that the V1 dataset used in the publication of Nienhuis et al. 1  is invalid and confirms our contention that automatic generation of buffers for delta land area change detection is unreliable, as shown by a number of examples from large deltas (Extended Data Fig.
2d-k ). It is interesting to note that delta lands with limited anthropogenic presence, such as the Amazon, show good agreement (Extended Data Fig. 2i ). Hence the approach of Nienhuis et al. 1  of using 40 deltas from one single geographic location, Madagascar (Extended Data Fig. 2aii ), to check the veracity of delta land areas changes is inappropriate, because anthropogenic modification of deltaic land in this site is virtually nil. Such a check should be based on a random selection of their large global dataset, and not on a statistically untenable criterion of 'coastal continuity' used by Nienhuis et al. 1  to justify the choice of Madagascar. Furthermore, bin averaging (Extended Data Fig. 1a ) is inappropriate on a dataset extremely skewed towards large deltas; the first 20 river mouths carry as much sediment as the remaining 10,828.
The dataset is noisy, but also has a few extremely large outliers. Any errors in large deltas propagate towards the entire dataset and the bin averages. The authors resort to cutting off the data where Q d river/Q p river >2 which showed a R 2 = 0 and p = 0. 0004. Nevertheless, p = 0. 98, and is therefore not significant when all data are included (Extended Data Fig. 1aii ). Clipping off some of the data is not an appropriate approach because normally, a greater increase in sediment flux would result in an even larger land gain signal and a stronger regression. Finally, we draw attention to another inconsistency in Nienhuis et al. 1  regarding calculation of delta area. Nienhuis et al. 1  approximated delta area by setting a minimum radius of 2 km for small deltas (1 km shown in the provided codes), adding buffers along the delta shorelines based on delta radius, with an additional 1 km added (shown only in codes, not in paper).
Imaging conditions and image analysis Embryos were dissected from gravid adults in egg buffer and mounted on a 5% agarose pad between a glass slide and a coverslip. They were incubated at 20°C until observation. Images were acquired with excitation at 515nm and/or 561nm. All image processing and data analysis were done with ImageJ and/or Python. Graphics were created with Python and Matplotlib library and assembled with Inkscape. For the time evolution of Wnt strains fluorescence intensities, we acquired stack images (slice every 1 µm) and sum-projected the first 5 µm of the ventral view after background subtraction. Embryos were imaged every 20 minutes. Average fluorescence intensity is measured in a handdrawn ROI corresponding to the embryo. This ROI is then separated into two halves (anterior and posterior) in which fluorescence intensity is measured.
Clusters are then detected along the profile as the pixels with an intensity higher than Iavg + 2*Ivar using the find_peaks function from the SciPy. signal library 47. Fluorescence Recovery After Photobleaching was performed on VBS322 strains. The anterior half of 4h old embryos were bleached with a 515nm laser (Metamorph with FRAP module). Fluorescence recovery was measured every 2 min for 16 min. CWN-2::YFP clusters were detected automatically with ImageJ plugin TrackMate (spot radius = 0. 8µm, median filtering On, threshold = 20)  26, 48. We counted the number of clusters over time in two compartments of the anterior half of 16 embryos and normalized it with the initial (pre-bleach) number of clusters. The average recovery in the compartment closest to the source was fitted with an exponential + ,1 -/ % # $ 0 with A the maximum recovery and 1 the characteristic recovery time.
After background subtraction, movies are batch processed to detect single molecules with a Laplacian of Gaussian detector. Trajectories were obtained with LAPtracker procedure. MSD curves were computed and analyzed with a Python algorithm that returns the diffusion coefficient D and anomalous exponent a after fitting with the function 452(7) = 427 ). Average diffusion coefficients are estimated from MSD curves with an anomalous exponent between 0. 9 and 1. 1. Detailed protocol and scripts are available at https://github. com/remyeltorro/Batch-SPT/tree/main/notes.
Simulations Reaction diffusion simulations were performed using SmolDyn software  28, 29, 56. Embryos were modelled as a 2D array of 12 by 8 square cells (5µm width) contained in a impermeable box modelling the eggshell. Cells were separated by 50 nm. The rightmost column of cells is the source of 4000 Wnt ligands, which are secreted from these cells in the extracellular space. They diffuse in that space with a diffusion coefficient set to 2µm 2 /s. On the membrane of all cells, we placed 40 Fz molecules which can diffuse in the membrane with a diffusion coefficient of 0.
Acknowledgements This work was funded by grants from the  Agence Nationale de la Recherche  ( ANR-14-CE11-0001  and  ANR-11-LABX-0054 ) to V. B. and  P. -F. L. Pritha Pai  was funded by a grant from  Fondation Pour la Recherche Médicale (FRM , grant  FDT201904008378 ). Imaging was performed on a PiCSL-FBI core facility (IBDM,  AMU-Marseille ) supported by the  French National Research Agency  through the ' Investments for the Future' program  ( France-BioImaging ,  ANR-10INBS-04 ). Some strains were provided by the  Caenorhabditis Genetics Center , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). The authors thank all the members of Lenne and  Bertrand labs  and  Pierre Mangeol  for fruitful discussion and reading of the manuscript. We also thank  O. Theodoly  for conceptual and technical support.
Methodology We used the last advances in the Viterbi algorithm developed by the author to incorporate external knowledge into this algorithm logic  (Baklouti, 2021). There are three agents used in our model to extract the sentiment in a tweet: Word-level tokens: We utilized the 'tokenizedDocument' function from the Text Analytics Toolbox of Matlab to extract tokens from the processed text. The phrase-level tokens in the output were formed solely with words from the Word-level tokens generated. POS tags: We attributed to each token a POS tag generated by the function 'addPartOfSpeechDetails' of the Text Analytics Toolbox of Matlab. This agent represents an external transferred knowledge to the HMM model. coeff: This is an estimation of the coefficient for a generalized linear model that predicts the sentiment category of a sentence as either negative, neutral, or positive.
The input data for this model is generated using the 'bagOfWords' function from the Text Analytics Toolbox of Matlab. This agent represents an external transferred knowledge to the HMM model. We used the Viterbi algorithm with transfer learning to extract tweet sentiment. This involved a modified version of the double-agent Viterbi algorithm. The parameters of the HMM model used were as follows: s ∈ {0, 1, 2, 3,. }, the states of the model, if the token is not selected then s = 0 else if the token is selected s > 0. For selected tokens s = 1 for the first token and the other states depend on the POS tag of the token, if the P OS ∈ {adposition, punctuation, coord -conjunction, pronoun, particle, subordconjunction} then s = 1 automatically and else the state is incremented by one s r = s r-1 + 1 A = {a coef f,P OS,i,k }, the state transition probability distribution.
Ab initio molecular dynamics DFT-based MD simulations were performed using the Quickstep module 41 of the CP2K software package, 42 using parameters already fine-tuned for ZIF-4 in previous works. 23, 25, 43  We used the hybrid Gaussian and plane wave method GPW, 41 the exchange-correlation energy was evaluated in the PBE approximation,  44  and the dispersion interactions were treated at the DFT-D3 level. 45  The multigrid system was set up with four different grids, a plane-wave cutoff for the electronic density of 600 Ry, and a relative cutoff of 40 Ry. Valence electrons were described by double-ζ valence polarized basis sets and norm-conserving Goedecker-Teter-Hutter 46 pseudopotentials, all adapted for PBE (DZVP-GTH-PBE) for H, C, and N or optimized for solids (DZVP-MOLOPT-SR-GTH) in the case of Zn.
Classical force field We employed MOF-FF for ZIFs  52, 53  and molsys, the python package behind MOF-FF for the parametrization of systems. 54  No adaptation was required for the ZIF-4 crystal, and two FF adaptations of MOF-FF for ZIFs were employed for the glasses: Zn3tetra and Zn3trig. In Zn3tetra, every three-coordinated Zn atom (denoted zn3 n3 in molsys atypes format) was treated as a four-coordinated Zn (denoted zn4 n4). In Zn3trig, zn3 n3 were treated as zn4 n4 in all their interactions with other atoms, except for the N-Zn-N angle which was changed from the tetrahedral angle (arccos(-1/3) ≈ 109. 47 • ) to 120 • , using the same spring constant. As the identification of the building units (Zn atoms and imidazolates) of the glass models could not be performed by molsys, due to large fluctuations in the bond angles and lengths as well as the presence of defects, it was carried out with our python library aMOF , 40 interfaced with molsys.
aMOF is available online on https://github. com/coudertlab/amof, where the version used for this work is v1. 0. 0. Classical MD simulations were performed using LAMMPS, 50 using identical procedures, system size and parameters than with ReaxFF excepted for the four following parameters: δt = 1 fs, T damp = 1 ps, P damp = 10 ps and τ ∆V = 500 ps.
Structural properties Structural analyses were performed using the parameters used and further detailed in a previous study,  40  using the Python library aMOF. Unless explicitly stated in the caption, properties are averaged over an (N, V, T ) trajectory of 1 ns for classical MD simulations and 60 ps for ab initio simulations. For classical MD, frames are taken every 1 ps for the radial distribution function (RDF) and bond angle distribution, and every 50 ps for pore statistics. For ab initio MD, those intervals are respectively of 0. 5 fs and 0. 5 ps. The potential of mean force (PMF)  60  is computed from the RDF g(r) through the relation F (r) = -k B T (ln g(r) -ln g max ) , where g max is the maximum of g(r) over r. N-Zn-N angle distribution is computed by taking a cutoff radius of 2.
Protocol and Search Strategy The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were applied to perform this systematic review (http://www. prisma-sattement. org, accessed on 31 March 2023). The PubMed (http://www. ncbi. mlm. nih. gov/pubmed, accessed on 31 March 2023) and Google Scholar (http://scholar. google. fr, accessed on 31 March 2023) databases were investigated to identify all relevant studies. We used an expanded MeSH to identify a maximum number of keyword synonyms. The current search was performed on 31 March 2023 by combining the following keywords: #1: "COVID-19" OR "SARS-CoV-2" #2: "Re-infection?" OR "Reinfection?" OR "Recurrent Infection" OR "Re-positive" #3: #1 AND #2.
Data Collection Process Four researchers independently checked the relevant articles and data extraction. All discordant results were discussed to reach a consensus between researchers. The type of study, country, and setting where studies were performed, the number and proportion of reinfection cases, demographic information (age and sex), comorbidities and vaccination status, the time between two episodes of infection, symptoms, imaging findings, and severity outcomes (hospitalization and transfer to intensive care unit and death) were extracted from the included articles. When available, risk factors for reinfection were reported or calculated.
Statistics All statistical analyses were performed using the open-source software R [R Core Team] R: A language and environment for statistical computing from the R foundation for statistical computing, based in Vienna, Austria, in 2020. URL: [http://www. r-project. org, accessed on 31 March 2023]. In the present study, the heterogeneity I2 > 50% was identified as representing substantial heterogeneity  [11]. The pooled effect size and 95% confidence interval (95% CI) were given using the random effect model. A funnel plot was applied to estimate publication bias. We excluded all studies from the analysis where there was no event in either arm, according to the Cochran guide  [12]. The most frequent variant circulating at the time of reinfection in each country was assessed using the CoVariants website (https://covariants.
Acknowledgements We thank  Martin Ackermann ,  Franz J. Weissing  and  Simon van Vliet  for comments on earlier versions of this manuscript. SMG and GSvD were supported by a  Starting Independent Researcher Grant  ( 309555 ) of the  European Research Council  and a  Vidi fellowship  ( 864. 11. 012 ) of the  Netherlands Organization for Scientific Research. SMG was supported by a  James S. McDonnell Foundation   21st Century Science Initiative Understanding Dynamic and Multi-scale Systems Postdoctoral Fellowship Award  ( 2020-1456 ) and a  Rubicon grant  ( 019. 201EN. 041 ) from the  Netherlands Organization for Scientific Research.
METHODS Individual patient-level data collected from 1994-2015 were pooled from 11 paediatric HIV cohort networks: Baylor International Pediatric AIDS Initiative (BIPAI); European Pregnancy and Paediatric Infections Cohort Collaboration (EPPICC); the IeDEA Consortium, comprising IeDEA Asia-Pacific, IeDEA Central Africa, IeDEA East Africa, IeDEA Southern Africa, IeDEA West Africa, and Central and South America and the Caribbean network for HIV epidemiology (CCASAnet); International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) 219C and P1074; Optimal Models (ICAP at Columbia University); and the Pediatric HIV/AIDS Cohort Study (PHACS). The data represent a range of care settings, including dedicated research cohorts and programmatic services. There was no overlap in the coverage of the networks, therefore no duplication of participants amongst networks.
Exclusions from modelling due to insufficient height data or missing characteristics at ART initiation varied by region and were most likely in East & Southern Africa (excluding Botswana & South Africa) and least likely Asia-Pacific (Supplementary Tables S1-S2, http://links. lww. com/QAD/C891). ALWPHIV excluded were born slightly more recently (and less likely to have height data in later adolescence) and were slightly younger at ART initiation than those who were included, with these differences most pronounced in Europe and North America (Supplementary Tables S1-S2, http://links. lww. com/QAD/C891). Among the 3,230 females and 1493 males included, there were 94,009 and 52,029 height measurements recorded respectively, with a median 27[interquartile range; (IQR)18,38) per female and 33[IQR 23,44] per male.
Median age at latest height measurement was 13. 9[IQR 12. 8,15. 2] years for females and 15. 3[IQR 14. 6,16. 4] years for males, though there was some variation by region (Supplementary Table  S3 , http://links. lww. com/QAD/C891). Median age at ART initiation was 7. 7[IQR 6. 0,8. 9] years in females and 8. 0[IQR 6. 6,9. 1] years in males; adolescents from the sub-Saharan regions started ART at older ages than in other regions (Table  1 ). Median HAZ, WAZ and BMIz at ART initiation varied by region and all were higher in Europe and North America than elsewhere; HAZ and WAZ was lowest in Asia-Pacific (Table  1 ). Average growth curves, estimated using SITAR models fitted separately to each region, are shown in Figure  2. SITAR models fitted on the complete dataset (with all regions combined) explained 98% of the variation in individual growth trajectories of both males and females (Supplementary Figure  S1 , http://links.