dataesr/scientific-paragraphs-categorization huggingface dataset:
biforme, that display genetic and phenotypic variability, in contrast to the cheese clonal P. camemberti varieties  21  , and compared them to phenotypes of their sister species P. palitans. For all the experiments below, we used all available P. nalgiovense, P. salamii and P. biforme strains that we could find in public databases, from non-food environments, and similar numbers of strains, chosen at random, from dry-cured meat and from their closely related outgroup species. The Table  S1  gives the number and identity of strains used for each species, type of environment and experiment. For assessing salt tolerance, we used seven P. chrysogenum strains, 14 P. nalgiovense strains, eight P. olsonii strains, 17 P. salamii strains, 13 P. biforme strains and five P. palitans strains. Because the salt content of dry-cured meat products usually ranges between 2% to 5% of dry weight and can be much higher near the surface  111, 112  , we measured growth rates on malt extract agar medium with various salt concentrations: 2% (2g NaCl/100 mL), 10% (10g NaCl/100 mL) and 18% (18g NaCl/100 mL).
In the second one, where we tested the differences between non-dry-cured meat strains and the sister species, we included as fixed effects the clade and its interaction with salt concentration, as well as the species nested within the clade and its interactions with salt concentration. For quantifying spore number after growth at various salt concentrations, as too few spores were recovered at 18% salt concentration, we only analyzed growth data for 0%, 2% and 10% salt concentration. As spores were only counted once (after the 17-day growth experiment), we did not include any random effect in the model. We therefore fitted two linear models using as a dependent variable the log-transformed number of spores divided by the colony area to normalize the effect of growth on sporulation. For lipolysis and proteolysis analyses, we fitted two linear mixed models on the distance to the lysis zone using the day of measurement and the batch number as random effects.
Acknowledgments This work was supported by the  ERC  starting grant GenomeFun 309403 and Louis D foundation award to TG, a CNRS PEPS adaptation grant to AB and the  FUNGADAPT ANR   19 CE20 0002 ANR grant  to TG, AB and MC. YCL acknowledges a PhD grant from the  France-Taiwan joint program Campus France. We thank  Alice Feurtey ,  Fanny Hartmann  and  Fantin Carpentier  for help with genome analyses. We thank  MNHN  for granting access to strains with the help of  Manuela Lopez-Villavicencio  and  Sandrine Lacoste. We thank all the people who sent dry-cured meat casings from around the world. We thank the GenoToul platform for sequencing. We are also grateful to the  GenoToul bioinformatics platform Toulouse Occitanie  ( Bioinfo Genotoul , doi:  10. 15454/1. 5572369328961167E12 )for providing some computing resources.
Study Population and Design Adult patients presenting with pathogenic FBN1 mutations with at least one panaortic contrast-enhanced computed tomographic angiogram (CTA) in eight French MFS clinics from April to October 2018 were retrospectively included. Molecular diagnostics of all patients included in this study were performed at the Department of Genetics (Pr. Boileau, Assistance Publique-Hopitaux de Paris) using standard procedures as previously reported  [10]. All FBN1 mutations were validated using Sanger sequencing. Demographic features (sex, age, weight, height, smoking status, presence of hypertension, coronary artery disease, chronic kidney disease, history of cardiac surgery and/or AD, and history of thoracic aortic surgery), radiological findings at baseline, and clinical follow-up (FU after index CTA) data were recorded in a dedicated database.
Index CTAs represented the patients' baselines and allowed (1) the comparison of the occurrence of AD in PNAL and non-PNAL patients from birth to the baseline; (2) statistical analysis to identify factors associated with the presence of PNAL (given that PNAL could be only assessed using the index CTAs); and (3) a clinical follow-up from the index CTA to the last FU recorded, enabling analysis of the outcomes of PNAL. Missing and FU data were obtained from medical records and correspondence with the patient or referring practitioner.
Statistical Analysis Analyses were performed using the Statistical Package for the Social Sciences software, version 20 (SPSS; IBM Corporation, Armonk, NY, USA). Continuous variables are described using medians and interquartile ranges (IQRs). Categorical variables are presented as numbers and frequencies. Univariate analyses between the two groups of patients, PNAL and non-PNAL, were performed using the Mann-Whitney test for continuous variables (age at CTA) and Chi-square or Fisher's exact test for categorical variables (gender, history of cardiac surgery or AD or previous descending aortic surgery). All variables having a p value < 0. 25 in univariate testing or with previously demonstrated relationships were implemented in a logistic model for multivariate analysis. The multivariate model was summarized by the odds ratio (OR) and a 95% confidence interval (95% CI).
This may have led to a non-exhaustive evaluation of the arterial network, as the lower limbs were not frequently screened in our series. Second, CTA screening was not systematic in clinical practice. This could have resulted in a selection bias for patients with more severe aortic disease (enriched in AD), which may have influenced the estimation of aortic event occurrence in the presence of an arterial aneurysm. Third, our datasets were limited to the separate study groups: this impaired a solid statistical-based analysis of the outcomes of aneurysms and ectasias during clinical follow-up, which remained limited in our study. This also hindered the statistical power of our retrospective analysis by including potential confounding factors in our multivariate analysis. Finally, we were unable to date the occurrence of PNAL and to monitor the PNAL growth rate during follow-up.
When dedicated supportive therapy for a functioning Pan-NET syndrome is insufficient, palliative SSA treatment can be initiated. PRRT, chemotherapy, everolimus or sunitinib should generally be reserved for SSA-refractory cases [4, B]. Q9: What is the recommended follow-up in functioning Pan-NET syndromes after curative resection? In several earlier studies, patients with functioning tumours postoperatively displayed a better prognosis than NF-Pan-NET. This is believed to be related to the fact that NF-Pan-NET are usually larger at diagnosis as they do not have hormonal symptoms and are consequently discovered later. However, a recent study from the SEER database, including 2571 resected patients, contradicts this point, reporting no difference in terms of OS and cancer-specific survival. 111 Insulinoma, if localised, radically operated and without signs of malignancy nor grade 3 only need a single follow-up after 3-6 months [5, A].
Multimodal Data Acquisition (Simultaneous HD-EEG, fNIRS, and DCS Measurements) Data were recorded by a combined multimodal approach using EEG, fNIRS, and DCS (Fig. 1 ). An external transistor-transistor logic (TTL) trigger was used between the electrophysiological (EEG) and hemodynamic measurement systems (fNIRS, DCS) to synchronize the recording devices [Fig. 1(b)]. All recordings were performed in a dark room. The optical devices are time-multiplexed (i. e. , sources can be turned on and off to allow brain tissue to be illuminated for either DCS or fNIRS at any given time). All major events observed by DCS were also recorded by the EEG and fNIRS devices [Fig. 1(b)].
Neural activity measurements by high-density EEG High-density EEG was acquired using 64 electrodes to measure electrical potentials resulting from underlying neuronal activity [Fig. 2  electrodes and a Cz reference at a sampling rate of 1024 Hz, amplified by A. N. T. ® (Enschede, The Netherlands). A high-pass filter of 70 Hz and a notch filter at 50 Hz were applied. The electrode impedances were maintained below 5 kΩ. Video EEG recordings were performed during quiet arousal. Patients were monitored by video to detect artifact movements to exclude any altered data. Off-line analysis was performed using BESA research (ver. 7) and in-house MATLAB scripts for all signals. The exact timing of the onset of the electrical seizure was determined from the EEG-filtered data (0. 5 to 30 Hz) by an experienced, board-certified electroencephalographer (F.
Power spectra and time-frequency analysis The frequency power spectra were applied to the seizure-marked region to quantify the frequency content of the seizure. The seizure epoch was delineated by markers and the fast Fourier transform (FFT) was then computed for this segment. The previously calculated FFT spectrum can be redisplayed. Topographical maps of the power spectrum were calculated using BESA research. Maps of the power spectrum results were based on average referenced data. This was necessary to ensure that the electrodes were given equal weights in the nonlinear transformation to power. The selected seizure segments were preprocessed for the FFT in two steps: (1) a window function was multiplied by the data to attenuate the amplitudes at the ends to zero and (2) the number of samples was increased by interpolation (if necessary) to convert it to a power of 2.
The seizure block constituted 80% of a window of 100% width. A cosine squared (cos²) window of 20% of the total width was applied at the edges to attenuate the signal amplitude to 50% at the edges of the marked block and zero at the edges of the window. Then, we took the number of time samples in the window and selected the next higher power of 2. Using spline interpolation, the data in the window were converted to this larger number of points and the FFT applied. Time-frequency representation (TFR) was performed for frequencies between 1 and 8 Hz to characterize the changes more precisely in neuronal activity occurring during the seizure. TFR was performed according to the procedures described by Hoechstetter et al. 36  and implemented in BESA Research®. TFRs were first computed for each seizure epoch by applying complex demodulation.
Intermodality interference between optical measurements (fNIRS, DCS) That emitter crosstalk can occur when two or more light sources are simultaneously used to actively illuminate the photodetector. To eliminate such crosstalk in our study, time division multiplexing techniques were used by enabling a single emitter during a single time slot. The optical devices were time-multiplexed (i. e. , sources can be turned on and off to allow brain tissue to be illuminated for either DCS or NIRS at any given time). All major events observed by DCS were also recorded by the EEG and NIRS devices. An external event trigger synchronized the acquisition devices. That the various measuring systems must be accurately synchronized to simultaneously measure hemodynamic activity. Synchronization was achieved by an external event trigger that produces TTL signals via a parallel port (line point terminal) using an in-house MATLAB ® code.
Optical data analysis (cerebral hemodynamic activity) The concentrations of HbO and HbR were calculated using the modified Beer-Lambert law,  41  based on the difference in absorption between two wavelengths (690 and 830 nm), using the MATLAB-based Homer2 toolbox (https://homer-fnirs. org/). Hemoglobin (HbT) concentrations were calculated by summing the HbO and HbR concentrations. A z-score-based algorithm was used to reject artifacts from optical signals. As individual features, such as skull thickness, influence signal strength, the signal was first homogenized for each participant by computing a z-score across all measurement periods for each channel. A z-score > 4 in any channel was considered to be an artifact, in which case the entire artifact time-window was excised from the data for all channels.
03 to 0. 5 Hz) using a zero-phase filter (Butterworth, order 3) to eliminate high frequency physiological noise (>0. 3 Hz, e. g. , slow drifts, arterial pulse oscillations, respiratory rate) and, for DCS, very low frequency (<0. 03 Hz) related to instruments noise. Hemodynamic changes around the time of seizure activity (time-locked to the EEG) were studied by averaging the hemoglobin concentration data within a 90-s window (-30 to 60 s from the onset of the seizure). The changes in HbO and HbR that occurred around the time of the seizure were compared to the baseline segment (lasting -30 to -20 s before T0). Finally, a grand average of hemodynamic responses HbO, HbR, and rCBF was calculated for all subjects. To compare the hemodynamic responses to the baseline (-30 to -20 s), statistical analyses (Student's t-test) were performed on the amplitude of HbO, HbR, and rCBF to highlight the effect of the seizures on the hemodynamic signals.
RESULTS Bioinformatic analysis of Mtb genome for identification of secreted proteins. Mtb possesses three different secretion systems using distinct secretion determinants (structural and/or motif-based) present on transported proteins  (13). To predict secreted proteins in silico, we analyzed the predicted Mtb H37Rv proteome using an inhouse protein analysis toolkit (PAT)  (14). First, the SignalP version 4. 1 and PredTAT software packages were used to predict the presence of known signal peptides and/or structural features necessary for secretion. In addition, transmembrane segments were inferred using either Uniprot annotations or the TmHMM prediction software. The number of predicted transmembrane segments and the position of the last transmembrane segment were also manually examined to identify signals potentially missed by the other methods.
To search for potential type VII secretion system (T7SS)-mediated secreted proteins, we first performed helix structure prediction of each protein using Psipred  (15)  and then searched for the YXXX(D/E) motif in between the characteristic two helices  (16). These data were compared with various proteomic data and model databases (ModBase, Interpro, and GO). Among the proteins identified here as potentially secreted, we studied Rv1813c, a 143-amino-acid protein (14. 9 kDa) with a predicted folded domain of unknown function. Rv1813c protein sequence features and secretion. Primary sequence analysis of the Rv1813c protein unambiguously identified a potential signal sequence (residues 1 to 28) with an upstream arginine repeat (residues 6 to 8), indicating that the protein could be exported by the Tat export system (Fig.
6) with 10% D 2 O for the lock. 1 H chemical shifts were directly referenced to the methyl resonance of dextran sulfate sodium (DSS), while 13 C and 15 N chemical shifts were referenced indirectly to the absolute 15 N/ 1 H or 13 C/ 1 H frequency ratios. All NMR spectra were processed and analyzed with GIFA. Backbone and Cb resonance assignments were made using standard HNCA, HNCACB, CBCA(CO)NH, HNCO, and HN(CA)CO experiments performed on the 15 N, 13 C-labeled Rv1813c 28-143 sample. NOE cross-peaks identified on 3D [ 1 H, 15 N] nuclear Overhauser effect spectroscopy (NOESY)-HSQC (mixing time, 120 ms) were assigned through automated NMR structure calculations with CYANA 2. 1, whereas NOE on 3D [ 1 H, 13 C] NOESY-HSQC were assigned manually. Backbone w and c torsion angle constraints were obtained from a database search procedure on the basis of backbone ( 15 N, H N , 13 C9, 13 Ca, Ha, 13 Cb) chemical shifts using the program TALOS1  (46).
Hydrogen bond restraints were derived using standard criteria on the basis of the amide 1 H/ 2 H exchange experiments and NOE data. When identified, the hydrogen bond was enforced using the following restraints: ranges of 1. 8 to 2. 0 Å for d(N-H,O) and 2. 7 to 3. 0 Å for d(N,O). This established the final list of restraints, from which values redundant with the covalent geometry have been eliminated. The 30 best structures (based on the final target penalty function values) were minimized with CNS 1. 2 according the RECOORD procedure  (47)  and analyzed with PROCHECK  (48). The root mean square deviations (RMSDs) were calculated with MOLMOL  (49). All statistics are given in Table  S1. The chemical shift table was deposited in the BMRB data bank (accession number 7NHZ). Antibodies. The following primary antibodies were used in this study: mouse anti-myc (Invitrogen, catalog no.
Finally, the coverslips were mounted with Prolong Gold Antifade (catalog no. P36934, Thermo Fisher Scientific). The slides were examined under a Leica TCS SPE confocal microscope equipped with a 40Â/1. 15 or 63Â/1. 33 ACS APO oil-immersion objective or a Zeiss LSM880 AiryScan confocal microscope equipped with a 40Â/1. 4 or 63Â/1. 4 Oil Plan-apochromat DIC objective. Fluorescence images were adjusted for brightness, contrast, and color balance using ImageJ software. Flow cytometry analysis of JC-1 and MitoSox-stained cells. For MitoSox red staining of HeLa cells, 2. 5 Â 10 5 cells resuspended in CPBS buffer (PBS, 2. 67 mM KCl, 0. 5 mM MgCl 2 , 0. 7 mM CaCl 2 , and 0. 1% glucose) were incubated in 5 mM MitoSox red. After 20 min at 37°C with shaking, the cells were washed twice in CPBS buffer before FACS analysis.
ACKNOWLEDGMENTS Flow cytometry and microscopy analyses of uninfected cells were performed at the Montpellier RIO imaging facility of the  University of Montpellier , member of the national infrastructure  France-BioImaging , supported by the  French National Research Agency  under program  ANR-10-INBS-04 ,  "Investments for the Future. "The Centre de Biochimie Structurale  acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)  with grant  ANR-10-INSB-05-01. The following reagents were obtained through  BEI Resources ,  NIAID ,  National Institutes of Health : monoclonal anti-M. tuberculosis GlnA ( Gene Rv2220 ), clone  IT-58  ( CBA5 ) (produced in vitro), NR-44103, and polyclonal anti-M. tuberculosis FtsZ ( Gene Rv2150c ) (antiserum rabbit). M. M. , A.
Transmission measurement model As illustrated in Figure  1 , two types of transmission measurements are considered, which correspond to the two experimental configurations tested in this study. In a given experiment, the setup is made of M slabs illuminated by the X-ray source. Behind each slab, a detector is placed to measure the fluence (the detector response function is ideal), which gives us M measurements from which we can infer the X-ray source spectrum. Each slab can be made of K layers of different materials (left side of Figure  1 ) or just one material (right side of Figure  1 ). In what follows, the detector spectral response D(E) is not accounted for and taken as unity. For each measurement, the forward transmission model is given by the Beer-Lambert law. Given an X-ray source with spectrum S(E), the transmitted intensity I of a measurement writes where l is the thickness and μ(E) the linear attenuation coefficient (taken from the NIST database  [13] ) of the material used for this measurement.
Patient population and study design CHARISMA is a prospective, multicenter cohort study designed to collect data on clinical practice concerning the ablation approach to various arrhythmias. The study complies with the Declaration of Helsinki; the locally appointed ethics committee approved the research protocol, and informed consent was obtained from all patients. From June 2021 to June 2022, 212 consecutive patients referred for paroxysmal and persistent AF ablation and undergoing their first high-resolution mapping and ablation procedure with a novel CF-and LI-featured catheter in  16  European centers were included. Of these, twenty cases (out of 45) with complete information were analyzed from our previous pilot study  (9). All patients were followed up at the same hospital, from the time of first ablation to the last followup visit.
Contact force The ablation catheter used in the current study, in addition to measuring LI from a local electric field generated at the tip of the catheter, has the ability to monitor real-time CF. The force applied to the tip electrodes is transferred to inductive sensors via a spring. The signal change measured by the inductive sensors is then converted to a 3-dimensional force vector by means of known spring dynamics. The target CF was 5-40 g, at the operator's discretion. The following data on each first-pass ablation point were collected: power, minimum CF, maximum CF and mean CF. In addition, the CF range during the applications was calculated by subtracting the minimum CF from the maximum CF at the ablation point.
PDZ domain-containing proteins are thought to be involved in the organization and localization of supramolecular signaling complexes in the cells  (14). Among DGKs, type II (DGKδ, η and κ) and type IV (DGK ζ and ι) carry a putative C-terminal tail segment that conforms to the class I (DGKη, κ, ζ, ι) or class II (DGKδ) PBM consensus sequences (class I: [S/T-X-Φ-$], class II: [Φ-X-Φ-$], class III: [E/D-X-Φ-$] where Φ is any hydrophobic amino acid, X is any amino acid and $ denotes the free COOH terminus, according to the Eukaryotic Linear Motif database (15)). Although PDZ-PBM interactions may be important in the regulation of all PBM-containing DGKs, only some PDZ-mediated interactions of the DGKζ and ι enzymes have been characterized in depth so far. It has been shown that the PBM of DGKζ binds to the PDZ domains of syntrophins and together they form a ternary complex with dystrophin in cell bodies and dendrites in neurons (16).
ProfAff database Both nHU-MS and AP-MS measure interactions with full-length proteins across the proteome, yet the utilized motifs are expected to bind only with PDZ domain-containing proteins in a direct manner. To compare the binding profiles of the PBMs of DGKs across the entire human PDZome in the most sensitive manner, we analyzed the recently measured PDZ-PBM affinity interactomic map (available on the ProfAff server,  (25) ). These data consist of equilibrium binding constants of >65,000 PDZ-PBM interactions that were measured between purified PDZ domains and synthetic 10-mer C-terminal PBM peptides by high-throughput holdup method (25). The binding constants for DGKκ (type II) and DGKζ (type IV) were determined against 97% of all the PDZ domains of the human proteome (258 out of 266, Table  S4 ).
83, Fig. 3C ) with almost the same binding constants (Pearson correlation coefficient R 2 =0. 88, two-tailed t-test p<0. 0001, Fig. 3D ). In the case of DGKη we observed low binding affinities (all measured pK d values were below 5) just as in the case of DGKκ (Fig. 3E ). No overlap between binders (common binders n=8, Jaccard similarity=0. 2) or significant correlation between the affinities of the DGKκ and DGKη PDZ partners were identified (R 2 =0. 30, twotailed t-test p=0. 16, Fig. 3F ). We compared the fragmentomic affinities of the DGKζ peptide from the ProfAff database and the affinities obtained for full-length proteins in the nHU experiment (Fig. S1  and 2D ). Due to the quantitative nature of the nHU experiment, even affinity values below the binding threshold may be meaningful, but due to the low affinities we lose statistical power.
Evolutionary conservation analysis of the PBM-like sequences in the DGK family The PDZ domains are evolutionary highly conserved modules of the proteome, and their interactions can create significant selection pressure leading to evolutionarily conserved PBMs  (29). Therefore, we investigated the degree of conservation of PBMs among DGK proteins, which may indicate their binding ability to PDZ proteins. We collected DGK sequences across all the phylogenetic tree by sampling all major taxa equally and taking the Lifemap tool as a guide  (30). First, 176 sequences were collected from 39 species of different taxa using the Uniprot database. The domain architecture in the DGK family is highly conserved from multicellular organisms (as an example, we present here the domain structures of DGKs from human and brachiopod Lingula unguis, Fig.