dataesr/scientific-paragraphs-categorization huggingface dataset:
Epitope mapping Epitope mapping was carried out by ELISA using different recombinant MSLN home-made constructs corresponding to MSLN domain 1 (aa296-390, DIH-Fc), truncated domain 1 (aa 296-354, DIL-Fc), domain II/III (aa391-598, HA-His-tagged DII/ DIII) based on the putative MSLN structure  (18). All the constructs were produced in the eukaryotic system using the Gibco ™ EXPI 293 ™ Expression System Kit (Fisher Scientific) following the procedure provided by the manufacturer and purified by affinity chromatography on a GE Talon ® Superflow ™ cobalt resin column. Correct conformation of the DII/DIII protein was assessed using the SD1-Fc fusion protein described by Tang et al. (19). ELISA procedure was as described above and the concentration of nanobodies was fixed at 100 nM. Affinity measurements were performed on D1H-Fc and D1L-Fc by ELISA and data were analyzed with GraphPad Prism software.
Epitope binning MSLN nanobody were site-specifically biotinylated using sortase A-mediated conjugation (eSrtA, Addgene) and a GGGYKbiotin peptide (Pepscan) at a molar ratio of nanobody-sortag/ sortase/peptide-biotin of 1/0. 1/20. For competition experiments A1847 cells (2x105 cells/well) in PBS/BSA 1% were incubated for 1 h at 4°C with serial dilutions of MSLN nanobody and their biotinylated counterpart at their EC50. After 2 washes in PBS/BSA 1%, cells were incubated with streptavidin-Alexa FluorX ® (1/300, BioLegend) and analysed by flow cytometry. Data were analysed with GraphPad Prism software. Epitope binning was also analyzed by biolayer interferometry using an Octet R2 system (Sartorius). Biotinylated human MSLN (10 µg/ml) was immobilised on streptavidin sensors. In the first step, antibody 1 (Nbs A1 or S1 or amatuximab) in PBS (100 nM) was bound for 500 seconds to MSLN-bound biosensors.
The optical fluorescence measurements were performed with the PhotoAcquisition software (Biospace Lab), on 2 or 3 mice belonging to the same experimental group at the same time. We used identical acquisition parameters for all animals and for all time points (1, 6, and 24h). The analyses were carried out using the M3Vision software (Biospace Lab). The analysis parameters used for the 1 h time point were different from those used for the 6 and 24h time points. However, for each time point, the analytical parameters are the same for all animals. The fluorescence scales are indicated in the figures. MicroPET/CT imaging A1847 MSLN pos (10x10 5 cells) and MDA-MB-231 MSLN low (5x10 5 cells) cells in a 1/1 (v/v) Matrigel (Corning Life Sciences, Bedford, MA, USA) suspension were implanted subcutaneously in 6-week-old female NMRI-Foxn1<nu>mice (A1847, n=20; MDA-MB-231, n=20) and grown until the tumours reached an average volume of 100 -300 mm 3 (average sizes of MDA-MB-231 and A1847 tumours were 197 +/-147 and 266 +/-186 mm 3 respectively).
A catheter 26G was placed into the tail vein of the mice to facilitate a rapid radiotracer injection. For the static experiment, microPET images were acquired for 20 min, 2h after intravenous injection of the radiotracer (5 MBq/mice). Image acquisition was performed on a NanoScan PET/CT camera (Mediso, Budapest, Hungary). After each PET recording, micro-CT scans were acquired for anatomical coregistration. Region-ofinterest (ROI) analysis of the PET signal was performed on attenuation-and decay-corrected PET images using VivoQuant v. 4. 0 software (InVicron, Boston, USA) and tissue uptake values were expressed as a mean percentage of the injected dose per gram of tissue (%ID/g) ± SD. At the end of the static experiments, mice were sacrificed by cervical dislocation, and the radioactivity of individual organs was measured using a gamma counter (Wizard ™ from Perkin Elmer).
Statistical analysis Statistical analyses were performed with GraphPad Prism software (V5. 01). Unpaired two-tailed t-tests or Mann-Whitney tests were used to do a pair-wise comparison. Two-way ANOVA followed by Turkey post hoc test were used for in vivo competing experiments. All statistical tests and resulting P values are indicated in the figure panels or the figure legends. P-values below 0. 05 were considered statistically significant.
Epitope characterisation The epitope targeted by Nb S1 was first investigated by bio-layer interferometry using streptavidin sensors pre-coated with biotinylated human mesothelin. The sensorgrams (Figure  1C ) showed that Nb S1 binds to an epitope distinct from that of Nb A1 or amatuximab and in the same way whatever the order of its addition in the reaction. As expected from previous data on Nb A1 (13, 15), Nb A1 and amatuximab recognise the same epitope which overlaps the MUC16 binding site. Competition experiments on A1847 cells using biotinylated and non-biotinylated A1 and S1 confirmed these results as no competition was observed between Nb S1 and biotinylated Nb A1 or vice versa. The immunoblotting experiment revealed that Nb S1 was able to detect human recombinant MSLN on western blotting in reducing conditions suggesting that Nb S1 recognises a linear epitope, as Nb A1 and mAb K1.
Acknowledgments We thank the  Canceropole PACA and the Plan Cancer Equipement  (# 17CQ047-00 ) for continued support in the development of the TrGET preclinical assay platform (CRCM). We are grateful to  Patrick Fourquet  for mass spectrometry analyses using the mass spectrometry facility of  Marseille Proteomics  (marseille-proteomique. univ-amu. fr)supported by  IBISA , the  Canceŕopole PACA , the  Provence-Alpes-Cote d'Azur Reǵion , the  Institut Paoli-Calmettes , and  Fonds Europeen de Developpement Reǵional (FEDER)  and to the staff of the  CRCM animal facility  for taking care of the mouse strain colonies. We would like also to thank  Mathieu Courivaud  for his technical assistance in nanobody labelling experiments and the IBDM imaging facility, member of the  France-BioImaging infrastructure , supported by the  French National Research Agency  ( ANR-10-INBS-04-01 ,  «Investments  for the future»).
Validation of the model In order to verify the reliability of COMSOL for the flow simulation within the RVF, we compared the fluid flow velocity and pressure obtained numerically with the experimental results, respectively. Table. 1 presents the operating conditions for the validation. Table. The velocity profile in the RVF module has been investigated with PIV technology in a previous study  [26]. The magnitude of the horizontal velocity component (xy-plane) along the z-axis, which is mainly in the  direction, is shown in Fig. 2  a. Different  positions were explored regarding the impeller position: leading, middle and trailing. The horizontal velocity significantly increases when close to the blades. Considering the PIV measurements as the reference, the velocity is well calculated by the CFD model at the leading edge, while it is slightly overestimated between two blades and at the trailing edge.
Sampling design Transect survey methods were used to sample the vegetation data within the four sites. At the highly grazing site, a 7-km transect was laid out, covering the periphery from the east to west of the CRZ settlement. Along this transect, 1-m 2 quadrats at 200-m intervals were placed, yielding 36 quadrats. At the site of the moderate grazing, a 4-km transect was laid out from the east to west, and 1-m 2 quadrats at 200-m intervals were systematically installed, resulting in 21 quadrats. The lightgrazing (enclosure) site was 400 m × 500 m. A 400-m transect was laid out to sample the vegetation, covering the periphery from the east to west of the enclosure. 21 quadrats of 1-m 2 each were placed along this transect at 20-m intervals. The no-grazing site was 80 m × 40 m. In the 80-m long direction, we established three parallel transects separated by 10-m intervals.
Data From each of the 1-m 2 quadrats, data on species composition and richness, ground cover and biomass were collected. The measurements were taken in September 2022 when the vegetation was at its peak flowering stage. Species composition was estimated by rating the percentage of each species abundance in the quadrats and assigned them to one of two growth forms: forbs or graminoids (henceforth 'grasses'). We did not subdivide forbs into nitrogen-fixing and non-fixing species because of the low number of nitrogen-fixing species in the area. Then, the proportion of all forbs and grass species dominance was summed up for each quadrat across the sites to estimate forbs and grass cover separately. The proportion of soil surface covered by herbaceous plants (vegetation ground cover) was also estimated visually for each quadrat.
Statistical analyses All analyses were performed with the R Statistical Software version 4. 2. 2 (R Core Team 2022). We determined the impacts of grazing intensity on species richness and composition using species accumulation curve and multivariate analyses. The grass and forb diversity was quantified based on the species × quadrat abundance matrix, the estimated species richness using the accuncomp function of the Biodiversity R package  (Kindt and Coe 2005). A sample-based rarefaction procedure was used to estimate the 95% confidence intervals and compare the patterns of plant richness among grazing intensities. To determine the impact of grazing on the species similarity, we performed an Analysis of Similarities (ANOSIM) test using the "anosim" function in the vegan package  (Oksanen et al.
2022). ANOSIM is a non-parametric test of significant difference between two or more groups, based on any distance measure  (Clarke and Ainsworth 1993) , and used for taxa-in-sample data, where groups of samples are to be compared. Then, a non-metric multidimensional scaling (NMDS) was used to group plots with similar species into separate classes using the Bray-Curtis dissimilarity matrix. The NMDS analysis was performed using the metaMDS function in the vegan package  (Oksanen et al. 2022). Stress value was used as a criterion of efficiency, where stress is the departure from monotonicity in the plot of distance in the original p-dimensional space (dissimilarity) vs. distance in the ordination space (k-dimensional space)  (Fasham 1977). A rule of thumb is that stress < 0. 05 provides an excellent representation in reduced dimensions, < 0.
In the pSEM grazing intensity was analysed as an ordinal categorical variable recoded as 0 for no grazing, 1 for light grazing, 2 for moderate grazing and 3 for high grazing intensity. The pSEM was fitted using pSEM function in the piecewiseSEM package  (Lefcheck 2016). The overall fit of the pSEM was assessed based on the Fisher's C statistic and associated p value (Lefcheck 2016).
Statistical analysis Continuous variables are presented as median + interquartile range (IQR) and were compared using an independent T-test or Mann-Whitney test. Categorical variables are presented as n (%) and were compared using a Chi-squared test or Fisher's exact test. Variables associated with p values lower than 0. 20 were included in a multiple stepwise regression with forward-backward method. The ability of the multiple logistic regression models to allow discrimination was quantified by the area under the ROC curve (AUC). P value < 0. 05 was considered as significant. All analyses were performed using SAS software (version9. 4; SAS Institute, Carry, NC). The study protocol was approved by the « Foch hospital ethical committee», Suresnes, France (1/4/2021). Protocol number: IRB00012437.
The genetic diversity of Echinococcus multilocularis (E. multilocularis) specimens isolated from patients with alveolar echinococcosis (AE), is a major field of investigation to correlate with sources of infection, clinical manifestations and prognosis of the disease. Molecular markers able to distinguish samples are commonly used worldwide, including the EmsB microsatellite. Here, we report the use of the EmsB microsatellite polymorphism data mining for the retrospective typing of Belgian specimens of E. multilocularis infecting humans. A total of 18 samples from 16 AE patients treated between 2006 and 2021 were analyzed through the EmsB polymorphism. Classification of specimens was performed through a dendrogram construction in order to compare the similarity among Belgian samples, some human referenced specimens on the EWET database (EmsB Website for the Echinococcus Typing) and previously published EmsB profiles from red foxes circulating in/near Belgium.
According to a comparison with human European specimens previously genotyped in profiles, the 18 Belgian ones were classified into three EmsB profiles. Four specimens could not be assigned to an already known profile but some are near to EWET referenced samples. This study also highlights that some specimens share the same EmsB profile with profiles characterized in red foxes from north Belgium, the Netherlands, Luxembourg and French department near to the Belgian border. Furthermore, Belgian specimens present a genetic diversity and include one profile that don't share similarities with the ones referenced in the EWET database. However, at this geographical scale, there is no clear correlation between EmsB profiles and geographical location. Further studies including additional clinical samples and isolates from foxes and rodents of south Belgium are necessary to better understand the spatial and temporal circumstances of human infections but also a potential correlation between EmsB profiles and parasite virulence.
Collection of samples Sixteen AE patients, referred from 2006 and 2021 to the University Hospital of Liege (CHU of Liege, Belgium) for diagnostic confirmation or therapeutic management, were included in the study. The samples were collected during surgery or by needle aspiration conducted to confirm a suspicious diagnosis. Samples were collected from the liver (n = 15), lung (n = 1), pleural fluid (n = 1) and bile (n = 1). Two samples were collected twice from the liver of the same patient at different times  (2011 and 2016)  and two other samples were collected from different body sites for one patient (pleural fluid and liver collected during the same intervention in 2019). This study received the approbation of the institutional ethical committee of the CHU of Liege (N • 2021/31), all data were anonymized.
EmsB microsatellite amplification and data analysis The PCR was performed according to  Knapp et al. (Knapp et al. , 2020). The sequences of the primers used were as followed: The EmsB A: 5′-Fam-GTGTGGATGAGTGTGCCATC-3′), EmsB C: 5′-CCACCTTCCC-TACTGCAATC-3′. The EmsB A primer was 5′-labeled with 6-carboxyfluorescein 6-FAM-fluorochrome (Integrated DNA technologies, Coralville, USA), and used for PCR with the EmsB C primer. The EmsB PCR was done twice for each sample to confirm the EmsB profile reproducibility. Samples with incorrect duplicates were removed from the analysis. Each run of PCR included a calibrator that was used to check and compare electrophoresis reliability. The EmsB calibrator consists in a plasmid construction containing four EmsB microsatellite sequences  (Knapp et al. , 2017).
Capillary electrophoresis of PCR products and fragment size analysis was performed on an ABI 3500 automatic sequencer (ThermoFisher scientific, Waltham, USA). The fluorescence signal was detected by colorimetric reading. For each sample, size and height of the amplified fragments were studied using Gene mapper software (ThermoFisher Scientific, Waltham, USA). An EmsB profile is composed of several peaks from 209 to 241 bp. Euclidean distance matrix was generated from these data and the relationships between samples were established by applying the unweighted-pair group method using average linkages. A dendrogram was built in order to assess the genetic similarity among samples. The threshold of 0. 1 of Euclidean distance was used to determine the genotypic relationship among samples and define profiles (Pn), as described by  Knapp et al.
(Knapp et al. , 2020). The profiles obtained were then compared with a panel of previously genotyped samples from France, Switzerland, Austria and Germany, included in the EmsB Website for the Echinococcus Typing (EWET) data collection  (Knapp et al. , 2020; Knapp et al. , 2017)  and also compared to E. multilocularis adult worms isolated from red foxes in Belgium (n = 20), in Luxembourg (n = 13), Netherlands (n = 1)  (Umhang et al. , 2021b)  and in Northern France (n = 12)  (Umhang et al. , 2014). Previously genotyped profiles were included in the present study to compare EmsB profiles described among humans and animals. Specimens from Alaska and/or Japan were used as outgroups. The EWET database contains information about 1500 E. multilocularis specimens from different continents (mainly from 13 European countries) from adult worms (fox, cat, and raccoon dog hosts) and metacestodes (human, monkey, and rodent hosts).
Statistical analysis Correlation between the genetic profiles defined by the EmsB microsatellite and the different provinces of Belgium/Luxembourg has been assessed by a Fisher's exact test. The relationship between the EmsB profile and clinical features was studied by the ANOVA test and by the chi-square test depending on the type of variable. The results are considered significant at the 5% uncertainty level (p < 0. 05). Diversity among the EmsB profiles was evaluated using the calculation of the diversity index (D), based on the Simpson index (L), (D = 1 -L) reflecting the number and relative abundance of EmsB profiles in the studied population. The reciprocal Simpson index (1/D) has also been calculated. Statistical tests were performed using the SAS software version 9. 4.
To obtain information from a larger geographical context, we aimed to combine larger and spatially distant EmsB profiles by comparing our samples with those from the EWET database. According to a comparison made with European human samples previously studied  (Knapp et al. , 2020) , specimens were classified using a second hierarchical clustering analysis into different EmsB profiles. The different Belgian "groups" previously defined were converted into "profiles" based on the similarity within AE patient genotypes previously described  (Knapp et al. , 2020). Results are presented in Fig. 2. Similar EmsB profiles were shared among the present samples and European human patients. Nine Belgian specimens shared the P1 profile, while another sample was qualified as near-P1 (Euclidean distance (d), d = 0.
The second case detected a P8 and a near-P8 profile. For this latter case, samples were collected respectively in 2016 and 2011 from the liver. So, we think that the sample of 2011 that is "near to P8" can be explained by DNA damages during sample conservation which can be responsible for the small variations observed by the EmsB method. To our knowledge multiple contaminations by different genotypes have been so far never described in humans' alveolar echinococcosis  (Knapp et al. , 2020). In conclusion, this is the first study addressing the genetic diversity of E. multilocularis from humans in Belgium by an EmsB microsatellite genotyping. Most of the identified profiles in Belgium were already known from previous studies performed on the EmsB microsatellite. One profile did not share any similarities with other known referenced profiles and further investigations are needed to know if this genotype is the result of artefactual variations from known profiles due to DNA damages and conservation issues or if this could be a profile not yet reported in the EWET database that can also spread into other parts of Europe.
Deep learning is based on artificial neural networks (ANNs), which are trained to capture the most essential relationship between the input and the output of an instructor model. The ANN is intended to be an efficient substitute for the original model within the range defined by the training dataset. Following this strategy, the computationally expensive Glen-Stokes model could be emulated by a simple convolutional neural network (CNN) by  Jouvet and others (2022)  with a speedup of several orders of magnitude and high fidelity levels in the case of mountain glaciers, and major benefits for inverse modelling purposes  (Jouvet, 2023). Another key asset of ANNs is that they run very efficiently on GPUs, permitting additional significant speed-ups, especially when modelling high spatial resolution domains.
However, the dependence on an instructor model makes the training of such an emulator technically difficult, not very flexible, and therefore limits its ability to generalise its validity range beyond the training data and its given spatial resolution. In recent years, physics-informed neural networks (PINNs) have emerged as a powerful approach in surrogate modelling to directly enforce physical laws (such as partial differential equations) in the learning process instead of matching datasets generated from physical models (e. g. Raissi and others, 2019). Basic PINNs are trained to minimise the residual associated with the equations and the boundary conditions  (Markidis, 2021). In contrast, variational PINNs (VPINNs) exploit the minimisation form (or equivalently the variational form) of the problem as a loss function  (Kharazmi and others, 2019) , which has the advantage of involving derivatives of lower orders compared to residuals.
Numerical model The glacier evolution model (equipped with both the ice-flow solver and the physics-informed deep-learning emulator) is implemented in the 'Instructed Glacier Model' framework (IGM, https://github. com/jouvetg/igm), which can simulate glacier time evolution (on CPU or GPU) given an initial glacier geometry and SMB forcing  (Jouvet and others, 2022). IGM is written in Python and relies on operations of the TensorFlow library to allow vector/parallel operations (such as used in neural networks) between large arrays that are computationally efficient on GPU. At each time step, IGM updates in turn the SMB (e. g. equilibrium line altitude (ELA)-based parameterisation or climate-driven PDD model), the ice flow (solved or emulated according to the user's choice) and the ice thickness by solving conservation equation (  1 ) using a first-order upwind finitevolume scheme on a regular 2D grid.
Historical phenomena often have prehistoric precedents, with this paper we investigate the potential for archaeometallurgical analyses and networked data processing to elucidate the progenitors of the Southwest Silk Road in Mainland Southeast Asia and southern China. We present original microstructural, elemental and lead isotope data for 40 archaeological copper-base metal samples, mostly from the UNESCOlisted site of Halin, and lead isotope data for 25 geological coppermineral samples, also from Myanmar. We combined these data with existing datasets (N=98 total) and compared them to the 1000+ sample late prehistoric archaeometallurgical database available from Cambodia, Laos, Thailand, Vietnam and Yunnan. Lead isotope data, contextualised for alloy, find location and date, were interpreted manually for intra-site, inter-site and inter-regional consistency, which hint at significant multiscalar connectivity from the late 2nd millennium BC.
X-ray Fluorescence (XRF) XRF was used for the OM samples' bulk elemental composition of major, minor and (some) trace elements, conducted at the Laboratoire Archéomatériaux et Prévision de l'Altération (LAPA-IRAMAT/CEA) in Saclay, France. XRF data were acquired using a NITON XL 3t GOLDD+ portable XRF analyser in 'laboratory mode' (fixed stand), with a max 40 kV accelerating voltage in the 'alloys' mode. Accuracy and precision were assessed with eleven Certified Reference Materials (Table  2 ). Good results for majors and minor components were confirmed but note that light elements at low concentrations like phosphorous, silicon, aluminium, magnesium and sulphur were not reliably detected due to non-vacuum conditions. The analyses were performed on the OM mounted and polished sections using a 3 mm beam diameter, which allowed for reliable results as long as the sample was larger than this.