dataesr/scientific-paragraphs-categorization huggingface dataset:
Consumption of antibiotics predicts the level of stabilisation and the rate of increase of antibiotic resistance Next, we investigated which properties of the temporal trajectories of antibiotic resistance are predicted by antibiotic use. We correlated both the plateau and maximum rate of increase of the resistance trajectories with the use of the corresponding antibiotic class across countries, for bug-drug combinations for which at least five countries had data (Methods). First, we observed a positive correlation between the plateau level of resistance in the 'stable' and 'stabilising' trajectories, and the consumption of the corresponding class of antibiotics in the community for most bug-drug combinations (Figure  5A ). Overall, the mean correlation was significantly greater than 0 (mean correlation = 0.
aeruginosa  [30]. The antibiotic consumption data is subject to similar data quality considerations. To limit the inclusion of biased or poor-quality data in our analyses, we did not consider the trajectories for which none of our models fitted the data well ('poor fit' category), and we removed temporal outliers from the consumption data. Despite these concerns, most trajectories were quite smooth temporally (Figure  3 ). Furthermore, while potential biases will affect the overall estimate of resistance frequency, as long as bias remains constant in time, they will not affect the detection and categorisation of temporal trends. Although the data from the ECDC may represent one of the largest and best standardised datasets on antibiotic resistance and consumption, it is limited to European countries.
Antibiotic Consumption Raw Data Data on antibiotic consumption is publicly available from the ECDC, and consists of defined daily dose (DDD) per 1000 inhabitants by antibiotic class, country, year, sector (community, hospital care, total care) and uptake route (oral, parenteral, rectal, inhalation, implant, all). The dataset includes the same 30 countries as the resistance dataset, spans the years 1997-2016, and consists of the following classes of antibiotics: J01A (tetracyclines), J01B (amphenicols), J01C (penicillins), J01D (other betalactams), J01E (sulfonamides), J01F (macrolides), J01G (aminoglycosides), J01M (quinolones), J01R (combinations of antibacterials), J01X (others), P01A (agents against amoebiasis and other protozoal diseases), A07A (intestinal antiinfectives). A detailed description of the consumption data is made available by the ECDC  [32].
Processing of antibiotic resistance data We computed the frequency of resistant for each bug, drug, country, year, and patient type combination. Intermediate isolates were considered resistant. We compared the temporal trends in resistance in inpatients and outpatients for drug-pathogen-country combinations that had data on both types of patients. The temporal trends in outpatients and in patients were very correlated, with similar or higher levels of resistance in inpatients. Therefore we only considered resistance measured in inpatients for further analyses.
Processing of antibiotic use data For many countries and years, the total consumption of antibiotics by all routes of administration was reported. We primarily used this information when it was available. Moreover, some countries and years recorded the consumption decomposed by route of administration of antibiotics. This was most commonly oral and parenteral, and, less frequently, inhalation, rectal, implant, "other" routes. When total consumption was not available, we summed the consumption recorded over all routes to obtain an estimate of total consumption. We considered consumption both in the hospital sector and in the community. To remove all outliers for a given antibiotic class, country, sector, route of administration for each combination we removed datapoints for years not within ±3 × IQR (IQR = Inter quartile range) of the temporal median.
Inferring temporal trends in antibiotic resistance For combinations of bug, drug, country, we fitted logistic models to the temporal trends in the frequency of resistance. We only included the combinations with a time series of at least 5 years, where all years had at least 30 isolates tested for drug resistance, and in total at least 10 resistant bacterial isolates. Fitting was done with non-linear least square regression, with the function nlsLM from the minpack. lm R package, except for the flat line which was fit using lm from the base stats package  [33, 34]. The error is assumed to be normally distributed and proportional to the inverse of the sample size for each year. We fitted three models to the temporal trends data: 1. A standard logistic model with two parameters, an offset and a slope.
We additionally computed a cross-temporal correlation, whereby the consumption data were shifted by -1 and +1 year compared to the resistance data. To give additional indications that yearly variation in antibiotic resistance follows variation in consumption, we used these crosstemporal correlations. First, if the bacterial population adapts closely to the contemporaneous antibiotic use, it is expected that the "temporal adaptation" pattern formed by the crosscorrelation for time-shifts -1, 0 and +1 is maximal at 0  [24]. We counted the fraction of bug-drug combinations for which temporal adaptation was maximal at 0 and calculated the probability of this fraction under a binomial model with true probability 0. 25. Second, we compared the temporal correlation with the spatial correlation across bug-drug combinations.
Acknowledgements FB is funded by  ERC   StG 949208   EvoComBac. SL is funded by the  Swiss National Science Foundation  ( PR00P3 201618 ). Data from The European Surveillance System-TESSy, provided by  Finland ,  Sweden ,  Belgium , Germany, Greece,  Ireland , Italy,  Luxembourg , Netherlands, Norway,  Portugal ,  United Kingdom , Austria, Bulgaria,  Czech Republic , Denmark, Estonia,  Spain ,  Malta , Slovenia,  France , Croatia,  Hungary , Poland, Slovakia,  Romania Cyprus , Latvia, Lithuania and released by  ECDC. The views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of  ECDC. ECDC is not responsible for conclusions or opinions drawn.
As illustrated in pancreatic tumors, for which their infiltration is predominant on the TME composition, CAFs from an heterogeneous population of cells with functional diversity ranging from tumor-promoting to anti-tumor activity  (15, 16). While both immune cells and endothelial cells are routinely targeted in cancer patients' care, targeting the fibroblastic tumor compartment did not result in any major translation in clinic yet. Knowledge describing the relationship between cancer cell clones phenotype and diversity with the TME architecture remains fragmentary. To evaluate the association between the TME and the cancer cell, different kinds of bioinformatics analyses were performed on publicly available databases such as The Cancer Genome Atlas (TCGA). In a recent article, Bagaev et al.
Example of this type of antigens are MAGE3, overexpressed in colon, brain, lung and skin cancer, and NY-ESO-1, expressed in a plethora of cancers among which sarcoma, esophageal, ovarian and prostate cancers  (27). Remarkably, PAP-loaded dendritic cell (DC)-based vaccine (Sipuleucel-T)  (28)  received FDA approval in 2010  (29)  for the treatment of castration-resistant metastatic prostate cancer. Ongoing clinical study are evaluating the interest of combining PAP-loaded DC vaccine in combination with ICB (  30 ), yet it is not possible to conclude whether Sipuleucel-T can increase ICB effectiveness in clinic. The efficacy of TTA targeting was also evaluated in a large clinical trial (NCT00480025) on NSCLC patients expressing MAGE-A3 treated with MAGE-A3 vaccine (AS15 proprietary adjuvant).
These findings might be of importance for many types of cancer characterized by gene fusion, thus important efforts have to be done in improving tools for detecting gene fusion and predicting immunogenic TSA load  (49, 50). Recently, a second family of tumor specific antigen was identified from immunopeptidomic. These are the non-mutated aberrantly expressed (aeTSA), which, emanate from the transcription and translation of non-coding genomic regions and represent the majority of the TSA pool  (40, 51). Furthermore, about 3,000 transcriptionally active endogenous retroviruses (ERVs) were identified in the TCGA dataset across multiple cancer types, and ERVs signature has been used to predict immunotherapy response  (52)  since their expression can associate with expression of aeTSA  (52)  and therefore with cancer cell innate immune signature.
Indeed, COX2 expression by the cancer cells was suggested to be an important immune escape mechanism. While PGE-2 induces blood vessel dilatation and inflammation, COX2 expression by the cancer cells was shown to inhibit T cell accumulation in the tumor mass, to favor immunosuppressive myeloid cell polarization, to impair DC and NK cell functions and to drive the expression of the indoleamine 2,3dioxygenase-1 (IDO-1) [for review see (80)]. Thus, COX2 specific inhibitors such as celecoxib were evaluated in clinic in combination with targeted therapy, chemotherapy and ICB. Unfortunately, this failed demonstrating clear benefit for the patients yet, but numerous clinical trials are still ongoing (NCT03026140, NCT03926338, NCT04188119, NCT04348747). However, non-specific COX2 inhibitor such as Aspirin and non-steroidal anti-inflammatory drugs (NSAID) are frequently administrated to cancer patients receiving ICB and retrospective analyses provided contradictory results making impossible to conclude if COX2 inhibition would be or not beneficial in combination with ICB at least for lung cancer (81, 82).
Recently, a subclass of NSCLC stem-like cells, expressing CD133 and CXCR4, were shown to express CD38, PC-1 and CD73, enzymes that, leads to the production of high level of adenosine through the noncanonical adenosine pathway  (92). Since NSCLC patients refractory to aPD1 immunotherapy show high expression of adenosine receptor  (93) , clinical interest is aroused regarding the inhibition of adenosine signaling. In in-vivo model of melanoma and NSCLC the inhibition of adenosine receptor with specific antagonists suppresses tumor cell growth and induced the expression of PD-L1. Coherently, the coupling of aPD-L1 and adenosine receptor antagonist gave promising synergistic effects  (93). An interesting phase I clinical trial on metastatic or advanced NSCLC patients is actually ongoing to evaluate the safety of on PBF-1129, a selective Adenosine A2b receptor antagonist (NCT03274479).
Similarly, early clinical evaluation of CD73 and CD39 inhibitors are ongoing (NCT04148937, NCT05075564). Metabolism reprogramming occurring in cancer cells represent an important mechanism of TME reshaping, immune escape and resistance to immunotherapy. Thus, it might generate new therapeutic avenues to predict and enlarge immunotherapy sensitivity. Research for extending current knowledge on how cancer cell metabolism could be manipulated to boost immune activation is an expanding field, although yet it failed delivering significant improvement of patient's care. Because they are intrinsically linked together, characterization of oncogenic signaling and targeting of cancer cell metabolic subtypes needs to be integrated toward enhanced anti-tumor immunity.
Funding This work was supported by the ligue nationale contre le cancer in the context of the ARN-therpeutiques call for proposal (grant number  ATFHJ23534 ), the  Association pour la recherche contre le cancer (ARC)  (grant number  PJA2019209423 ) and the  BMS foundation  (grant number  R20024FF ). The  National Research Agnecy (ANR)  also founded this research work in the context of its support to the project  ANR-22-CE17-0004-01. All these supports were allowed to  Julien Faget. The  Labex Mabimprove supports Chiara Urcino 's salary.
22 urthermore, we conducted mediation analyses to assess whether the associations between farm milk consumption and exposure to animal sheds in infancy (4e12 months) and the risk of hay fever is mediated by gut microbiome features adjusting for centers. The mediation analysis was conducted through path analysis using the maximum likelihood test to estimate the regression parameters in Mplus 8. 5. 23 The mediating effect is reported as the proportion of the estimated indirect effect to the total effect. The statistical analyses were performed with SAS 9. 4 software (SAS Institute, Cary, NC) and Mplus 8. 5 software (Muthén & Muthén, Los Angeles, CA).
Characteristics of the study population At 10. 5-year follow-up, 778 children participated in the PASTURE study and 769 have data on hay fever. Comparing the baseline characteristics between included (n ¼ 769) and excluded children (n ¼ 364) did not show any significant difference except for maternal age at pregnancy, maternal smoking, parental education, and premature birth (Table  E1 ; available in this article's Online Repository at www. jaci-inpractice. org). Data on farm milk consumption and exposure to animal sheds at least at 1 time point (from pregnancy, 12 and 18 months, and 2, 3, 4, 5, 6, and 10. 5 years) were available for all these children. Of these, 769 children had information on hay fever at 10. 5 years. The proportion of children growing up on a farm was 47. 7%. Hay fever at 10.
jaci-inpractice. org) or incident hay fever at 10. 5 years (aOR 0. 39; 95% CI 0. 15e0. 99; P value. 05; data not shown). Because confounding by reverse causation might have biased our findings, we ran a sensitivity analysis in the subgroup of children without a family history of parental asthma and/or atopy and excluded children avoiding milk or milk products at 1 to 6 years. This did not change the inverse association with hay fever (aOR 0. 21; 95% CI 0. 06e0. 78; P value. 02; data not shown). We next assessed the association of the frequency of farm milk consumption (ie, whether frequently drinking farm milk has a dose-response effect on hay fever). The highest compared with the lowest quintile of farm milk consumption was inversely associated with hay fever (aOR 0. 37; 95% CI 0. 16e0. 84; P value.
5 years may seem surprising. This long-term association may be attributable to an earlier onset of disease. In fact, 4. 6%, 5. 9%, and 6. 7% of children with data on hay fever at 10. 5 years had already reported symptoms and/or a diagnosis of hay fever at 4, 5, and 6 years, respectively. Furthermore, early alterations of the composition of the gut microbiome may shape its subsequent development toward an adult-like compositional structure in the first 3 years of life. 26 Unfortunately, no fecal samples have been collected at later time points in the PASTURE cohort. The production of the SCFAs butyrate and propionate measured at 12 months has been reported previously as determinants of protection against atopic sensitization at 6 years. 20 n our study, no relation between the SCFAs butyrate and propionate with hay fever was found.
However, adjusting for these variables did not change the results (data not shown). Another drawback is the small number in the "only early" and "only late" exposure groups that shows protective nonstatistical significant effect on hay fever. However, using the RMLCA approach, our study could identify these small groups manifesting that these types of habits (ie, farm milk consumption or exposure to animal sheds) do exist. We performed a post hoc power calculation using SAS and considering a of 0. 05 (2-sided). For our sample size of 650 (ie, in the exposure groups "continuous consumption of farm milk" and "no consumption of farm milk"), the power of study is over 80% assuming the response probabilities ranging from 0. 02 to 0. 18 for having hay fever in children who consume farm milk and unadjusted odds ratio of 0.
Allergologie u. Immunologie, Massachusetts consultant fees from Stallergens, ALK, Aimmune Therapeutics; payment/honoria Medical Society, OM Pharma S. A. , Hanson Wade Ltd. , iKOMM GmbH, DSI from Aimmune Therapeutics (speaker at symposium at the European Academy of Dansk Borneastma Center, American Thoracic Society, HiPP GmbH & Co KG, Universiteit Utrecht, Faculteit Bètawetenschappen; patents planned, issued or pending: patent No. PCT/EP2019/085016 (Barn dust extract for the prevention and treatment of diseases) pending (Barn dust extract for the prevention and treatment of diseases) pending, royalties paid to ProtectImmun for patent EP2361632 (Specific environmental bacteria for the protection from and/or the treatment of allergic, chronic inflammatory and/or autoimmune disorders, granted The PASTURE study was supported by the European Commission (research grants QLK4-CT-2001-00250, FOOD-CT-2006-31708 and KBBE-2007-2-2-06), the European Research Council (grant 250268), Deutsche Forschungsgemeinschaft, German Center for Lung Research (DZL), Kühne Foundation, French National Programme for Hospital Research, EVO-and VTR-funding, the Academy of Finland (grant 139021, 287675, 338679, 339666), the Yrjö Jahnsson Foundation, and EU, Academy of Finland and smaller grants from Finnish foundations supporting medical research (Juho Vainio Foundation, Päivikki and Sakari Sohlberg Foundation, Finnish Cultural Foundation).
Another multicenter retrospective study including nearly 500 UC patients confirmed that disease clearance was reported in approximately 20% of cases after any induction therapy. 4 Interestingly, the achievement of disease clearance was associated with a significant reduction in the risk of hospitalization and surgery during a median follow-up of 2 years. These data demonstrate how patients with disease clearance have a better prognosis. Moreover, reaching this composite endpoint could allow to reduce the UC-related complications and the healthcare indirect costs. The term disease clearance has been coined in dermatology and is associated with a significant improvement in patients' quality of life. It is also used to directly compare drug efficacy. 5 In psoriasis, it is defined as total disappearance of skin lesions and is measured using a validated score, the Psoriasis Area and Severity Index 100.
6 Similarly, the Eczema Area Severity Index improvement = 100% (EASI-100) defines disease clearance in patients with atopic dermatitis. 7 This attempt to achieve a total and lasting remission is common to several immune-mediated disorders. To date, disease clearance also plays a role in Crohn's disease with transmural remission recognized as the main driver. 8 Disease clearance is increasingly investigated as an exploratory endpoint or assessed via post-hoc analyses in phase 3-4 trials (NCT03259334). 9 However, there are some issues that limit its wide use. Firstly, a validated and commonly accepted definition in UC is missing. Of course, disease clearance requires strict criteria proving the absence of disease. The use of validated clinical, endoscopic, and histologic scores could certainly facilitate its use and approval.
The mean percentage of protein biosynthesis was determined and normalized from duplicate wells. All values were expressed as the mean ± S. E. M. Data were fitted with Prism 7 software (Graphpad Inc. , San Diego, CA, USA) to obtain the 50% inhibitory toxin concentration (IC 50 ), i. e. , the concentration of toxin that is required to inhibit 50% of cellular protein biosynthesis. IC 50 values and protection factor R (R = IC 50 (drug)/IC 50 (DMSO)) were determined by the software's nonlinear regression "dose-response EC 50 shift equation". The goodness of fit for ricin alone (carrier) and with drug/treatment was assessed by r 2 and confidence intervals. Enhanced ricin cytotoxicity on autophagy-deficient cells was determined by 1/R, and R = IC 50 (shBECN1 or shATG5)/IC 50 (shNT).
, M. L. and A. E. generated and identified autophagy-deficient HFF cell lines; N. M. assisted in using a Cytation 5 cell imaging multi-mode reader (BioTek) to evaluate cytotoxicity; J. -C. C. provided compound Retro-2. 1; Y. W. , A. F. , A. E. and J. B. analyzed the data; Y. W. , A. F. , A. E. , J. B. and D. G. wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the joint ministerial program of R&D against CBRNE risks, CEA and under Contracts ANR-18-CE18-0016 (LeishmaStop), ANR-20-CE18-0016 (SMERSEC) and ANR-20-SEBM-0001-01(PLANT). SIMoS and SCBM are members of the Laboratory of Excellence LERMIT, supported by a grant from the Agence Nationale de la Recherche (ANR-10-LABX-33). A. F. is a research associate of the FRS-FNRS.
1 kW x cm -2 at the beam center). For the A555 dye, 561 nm laser (1. 8 kW x cm -2 at the beam center) was used in combination to the 532 nm laser. During data acquisition, a continuous focus system (ZDC, Olympus) was used to maintain a constant focal plane. To correct for chromatic aberration, 100 nm TetraSpeck TM microspheres (Thermofisher, T7279) embedded in the imaging buffer without the scavenging system were imaged in the same conditions as used during acquisition. Image stacks were treated using imageJ. To remove background signal, a temporal median filter was applied on images with a 100 frames interval  60  using the GDSC Single Molecule Light Microscopy (SMLM) ImageJ Plugins (https://github. com/aherbert/gdsc-smlm). After the Gaussian fitting step, we performed drift correction using either gold nanoparticles as fiducial markers, or cross-correlation procedure in a few cases where fiducials could not be identified.
We next proceeded with grouping of localizations originating from single molecules detected in subsequent frames to avoid artifactual clustering. To do so we used the following parameters: maximum distance -one-pixel size and trace length -infinite (length of acquisition) as described by Jimenez et al  61. Additionally, out-of-focus localizations were filtered based on the value of the standard deviation of the Gaussian fit being superior to 200 nm. To allow registration of sequential acquisitions in two-color experiments, the drift displacement of the first channel was applied on the second channel using ChriSTORM (https://github. com/cleterrier/ChriSTORM). The chromatic aberration correction was performed using the Detection of Molecules (DoM) plugin v. 1. 2. 1 for imageJ (https://github.
com/ekatrukha/DoM_Utrecht). Finally, estimation of image resolution was performed using Fourier Ring Correlation (FRC) on the whole image using the NanoJ-SQUIRREL imageJ plugin  62. Voronoï tessellation analysis was computed to quantify cluster size using SR-Tesseler  63, 64  , and for co-localization analysis Spearman's rank correlation and Manders' coefficients were computed using Coloc-Tesseler  63, 64. Segmentation of the clusters and Manders' coefficients were obtained by applying a threshold based on one time the average density (δ) of the whole dataset. The Spearman's rank analysis was performed on the whole cell and specific areas of 2. 6 μm². Control experiments were performed to obtain reference values. An artificially created positive control was obtained by splitting localizations from even and odd frames of an image stack.
Finally, the beads were washed two times and suspended in PBS to 1% w/vol beads. Uniform lipid coating was checked by flow cytometry. Beads were added to J774 macrophages at a ratio of 10:1 (beads:J774), for 1 h at 37 °C, 5% CO2 in serum-free DMEM. J774 cells were then washed and harvested with 0. 25% trypsin/EDTA and kept on ice before analysis by flow cytometry. To assess the global lipid mobility reduction within DPPC in comparison to POPC bilayers, we monitored the mobility of the fluorescent PS analog TopFluorPS by FRAP experiments performed with a spinning disk confocal microscope (Olympus & Andor, M4D cell imaging platform, ISBG). Data were analyzed with EasyFRAP-web  69. Statistics and reproducibility. The details of each quantification during superresolution image processing are included in the Methods section.