dataesr/scientific-paragraphs-categorization huggingface dataset:
miR quantification miR quantification was performed on a Nanostring nCounterä Flex platform using the Human v3b miRNA panel which allows the simultaneous detection and quantification of 798 miRs. Briefly, direct target hybridization (without reverse transcription step) with specific probes was followed by a purification step and digital quantitative laser counting. Quality of runs was assessed with the nSolverä Analysis Software. A second run was performed for samples with low ligation and/or low spike-in controls. Data normalization was made, after exclusion of samples failing ligation, on the top 100 detected miRs. Background subtraction was done by subtracting the geometric mean of internal negative controls.
QUANTIFICATION AND STATISTICAL ANALYSIS Bioinformatic analysis The Qlucore Omics Explorer software was used to identify differentially detected miRs, to build heat maps and to perform Principal Component Analysis (PCAs). miRs with > 20 normalized counts in at least 1 patient were considered as detected. The analysis of biological pathways enrichment was performed using miRPath v3 (http://snf-515788. vm. okeanos. grnet. gr/). The target genes of miR of interest were predicted using miRTargetLink 2. 0. (https:// ccb-compute. cs. uni-saarland. de/mirtargetlink2). Target genes defined as strongly validated by miRTarget-Link 2. 0 were then analyzed using Enrichr (https://maayanlab. cloud/Enrichr/) for functional enrichment analysis including cellular component, molecular function, biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG).
Statistical analysis Statistical analyses were performed using SPSS version 23, GraphPad Prism version 7. 05 and R software version 3. 5. 3. Demographic, clinical and laboratory continuous variables were expressed as median with interquartile (IQR) range and compared using the Mann-Whitney test, while categorical variables were expressed as counts and percentages and compared using the Chi-square test or Fisher's exact test, as appropriate. For all analyses, a two-tailed p value %0. 05 was considered statistically significant. Fold change was defined as the ratio of the final value divided by the initial value. For the diagnosis of bacterial infection, miRs of interest and relevant biochemical parameters were tested in univariate logistic regression. Only variables with a p value %0. 05 were integrated in the multiple logistic regression.
For notational simplicity, the symbols π 60 , λ and γ will be used in the following instead of π(60), λ c , and γ c. The distribution of ages at diagnosis was derived from that observed in (both sexes) incident cases collected by the French network of cancer registries (FRANCIM)  [21]  during the period 1995-2009 and gathered in the FRAN-CIM common database. This database also provided the probability of administrative censoring (50%) and of loss to follow-up (3%). The survival distribution parameters in the population, fixed as λ P = 88; γ P = 11, were derived from the French general population life-tables, also for both sexes combined, for the year 2002  [19]. The underlying α in the simulated samples were attributed the values 0. 8; 1. 0; 1. 2; 1. 5; 2. 0, the first indicating some (perhaps unrealistic) protective effect, the second no effect, and the others an increased risk of non-cancer death in patients as compared with the general population.
As a consequence, the other survival parameters λ, γ, and δ, also have to change in order to fit the same dataset. Performance indicators of estimates are summarized in Table  4  for all parameters apart from λ and γ, for which we did not have reference true values. Estimates of almost all parameters presented an increasing        pattern for increasing values of θ except for α for "Breast" and δ. In order to balance the bias of β the AB of δ presented a decreasing pattern for increasing values of θ in both scenarios. The estimates of parameter α under the "Lung" scenario were the most sensitive to the value of θ, increasing from 1. 89 to 2. 81 (AB -0. 106 to 0. 815) around the true value of 2. 0. The estimates of the other parameters π 60 , and β increased respectively from 10 to 13% and from -0.
Application to real-life FRANCIM colon cancer data As an example, this method was applied to survival data of colon cancer patients recorded by FRANCIM (the French network of cancer registries). FRANCIM data are checked for quality and completeness every 4 years by an independent audit committee (Comité d'Évaluation des Registres). Life tables were provided by the National Statistics Institute (INSEE). All colon cancers diagnosed in 1995-2009 in patients aged 40-74 were included (N = 15,717 in men and N = 10,942 in women). The relative survival and the cure fraction were estimated, separately for each sex, using Model(1) and using the conventional model without the α parameter (Table  7 ). The cure assumption was already checked for these data  [21]. For both sexes, we estimated α ≅ 1. 3, with confidence intervals not including 1, therefore supporting the hypothesis of increased non-cancer mortality in colon cancer patients.
However, their behaviour may change when some of the assumptions are not verified, so the robustness analysis could be affected by the specific function considered. Reliable cause-of-death data from population-based sources can be used to test the distribution assumptions considered. Cause of death was not available in the data of our application. In the future this information will be standardized and included in the basic dataset of cancer registries. Age at diagnosis was the only covariate included in the model. Other variables available from population-based data, as sex, period of diagnosis or, sometimes, stage and treatment, can be included without substantially modifying the model structure. A linear effect of age, as considered in the model, can be accepted for many cancers, but can be unrealistic for others.
Reasons for different 'other causes of death' risk in patients with respect to the general population may differ. They range from causes not generated by the cancer such as smoking, genetic and other lifestyle risk factors common to cancer and other diseases, as well as to deprivation and other socio-economic factors, to factors indirectly caused by the diagnosed cancer such as side effects of cancer treatment  [29, 30]. We cannot separate these two components without detailed treatment data from clinical databases. However, our intention was to capture cure of the cancer itself, implying no further risk of progression and death due to the diagnosed cancer and following the definition proposed by Haupt in 2007 "cure from the original cancer regardless of any potential for, or presence of, remaining disabilities or side effects of treatment"  [31].
Data availability statement The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. The French colon cancer data that support the findings of this study are available from Francim but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with the permission of Francim. Code availability Details of the Stata code and R-Package curesurv used to fit the proposed corrected mixture cue model are available in the Github and Zenovo repository. The repository is called curesurvTools (https:// github. com/ Laura Botta/ cures urvTo ols and https:// zenodo. org/ record/ 75678 86).
Demographics, baseline characteristics, and diagnosis In addition to demographic data and past surgical history, BMI, American Society of Anesthesiology (ASA) score, Eastern Cooperative Oncology Group performance status, Charlson Comorbidity Index (CCI)  [16]  , biliary symptoms, serum bilirubin and CA-19. 9 level, and presence of hepatolithiasis or Clonorchis infestation were recorded. The contribution of preoperative imaging tests [computed tomography (CT), MRI, transabdominal ultrasonography, endoscopic retrograde cholangiopancreatography, endoscopic ultrasonography, endoscopic cholangioscopy, percutaneous transhepatic cholangiography] used to identify IPNB lesions was assessed.
Three-quarters of patients underwent lymph node dissection, and a median of six lymph nodes was obtained. Dissection rates and numbers of lymph nodes harvested were within the ranges established by recent recommendations for malignancies of biliary origin. Unfortunately, data on dissected lymph node stations were not available. Lymph node invasion was found in the resection specimens of 11 patients (12. 9%) with IPNB, an apparently low proportion but  [37]  similar to that described in several series, more frequent in extra-than intrahepatic IPNBs  [7, 24, 34, 35]. The study protocol did not include recording the degree of atypia found in the invaded resection margin. Given the small number of cases, the anatomical diversity of the invaded margins, and the nonassociation of the invaded resection margin with overall survival in our series, it is tempting to speculate that the degree of atypia found in the resection margin would not have provided additional information in the present study.
Quenching assay for membrane asymmetry estimation The dithionite quenching assay  (43)  was used to determine symmetric or asymmetric distribution of NBD-PE-and NBD-labeled LPS. The assay was optimized to a robust and stable protocol by Leomil et al. (in preparation). In brief, unquenched GUVs were imaged at the equatorial plane and the membrane intensity was calculated using a custom-written MATLAB code (The MathWorks, Natick, MA). For symmetric lipid GUVs, a membrane composition of POPC and 0. 5 mol % NBD-PE was employed. LPS-GUVs were doped with LPS-NBD. To quench the NBD fluorescence, 2. 5 mL sodium dithionite stock solution of 0. 1 M in 1 M Tris-HCl (pH 10) was added to a 97. 5 mL GUV sample to a final concentration of 2. 5 mM sodium dithionite. After 5 min the sample was diluted to a final dithionite concentration of 0.
Fluorescence correlation spectroscopy To assess the molecular brightness and diffusion coefficient of free TexasRed dye and TexasRed-labeled LPS, fluorescence correlation spectroscopy (FCS) measurements were performed as described previously  (47)  on a confocal microscope (Leica Microsystems TCS SP8, Wetzlar, DE) using a 63Â, 1. 2 NA water immersion objective. Samples were excited with a 561 nm DPSS laser. Fluorescence was detected between 600 and 700 nm using a HyD SMD 2 detector in photon counting mode. The measurements were performed in spot acquisition mode for 3 Â 12 times with 10 s intervals. Leica files containing recorded photon arrival times were converted to intensity time series with time binning of 0. 5 ms and subsequently analyzed using a custom-written MATLAB code. First, the autocorrelation function (ACF) of the fluorescence signal FðtÞ was calculated as follows, using a multiple tau algorithm: GðtÞ ¼ CdFðtÞdFðt þ tÞD t CFðtÞD 2 t ; (4) where dFðtÞ ¼ FðtÞ À CFðtÞD t.
Scanning FCS To determine the molecular brightness and diffusion time of TexasRedlabeled LPS in LPS-GUVs and of TexasRed-DHPE in POPC GUVs prepared via PVA-assisted swelling, scanning FCS (sFCS) measurements were performed on a Zeiss LSM880 system using a 40Â, 1. 2 NA water-immersion objective. The molecular brightness was measured to compare the quantum yield of the TexasRed fluorophore when linked to LPS and a lipid (as in TexasRed-DHPE). The probes were excited with a 561-nm diode laser. Fluorescence was detected after passing through a long-pass 571 nm bandpass filter. Data acquisition and analysis were performed as described previously  (48, 50). In brief, line scans of 128 Â 1 pixels (pixel size 41. 5 nm) were performed perpendicular to the GUV membrane with a 291 ms scan time. A total of 500,000 lines were acquired (total scan time $3 min) in photon-counting mode.
Low laser power was used ($24 mW) to minimize photobleaching and fluorescence saturation effect. Data were exported as TIF files, and then imported and analyzed in MATLAB using custom-written code  (48, 50). The analysis of the thus-obtained auto-correlation curves with a two-dimensional diffusion model resulted in the determination of the number of fluorophores in the confocal volume (N) and their apparent diffusion times. Furthermore, total fluorescence intensity at the GUV membrane and TexasRed-DHPE and LPS-TexasRed brightness were determined after background subtraction. To confirm the labeling ratio, sFCS measurements were performed on the membrane of LPS-GUVs to assess the molecular brightness of LPS-TexasRed. By applying multiple bleaching cycles, the membrane intensity was reduced down to 10% of the initial value.
Quantifying membrane fluorescence signal Confocal cross-section images of spherical GUVs were recorded using constant settings (size, 1024 Â 1024 px 2 ; scan speed, 400 Hz; bidirectional scanning mode; line average 3; pinhole, 1 Airy unit). The raw TIF images were loaded and analyzed using a custom-written MATLAB code (see Fig. S3 ). In brief, a Gaussian filter (kernel size, 2 pixels) was applied and areas of the image that should not be considered for the further analysis (e. g. , lipid aggregates, neighboring GUVs, etc. ) were excluded. After selection of the vesicle center, a radial line profile was calculated. By cubic curve fitting, the baseline of the line profile was identified. After subtraction of the baseline, the area of the line profile's peak was computed. The peak area was used to estimate to the average concentration of fluorophore in the vesicle membrane (Fig.
LPS is asymmetrically distributed across the membrane The preparation protocol as sketched in Fig. 1  suggests that LPS should be distributed exclusively at the outer GUV leaflet. However, we cannot monitor the production process directly to see whether LPS has flipped/migrated to the inner leaflet either because of improper resealing, or leakage, or stress in the membrane. To probe the actual distribution of LPS in the membrane, a quenching assay was performed based on the use of NBD-labeled LPS or PE lipids. The membrane-impermeable quencher sodium dithionite, added externally, irreversibly quenches the NBD fluorophores in the outer leaflet of the vesicle membrane. The fluorophores in the inner leaflet are not affected (Fig. 4 ). In vesicles with symmetrically distributed NBD, a fluorescence loss of 50% occurs which we confirmed on symmetric GUVs prepared with POPC and NBD-PE lipids, see data in green in Fig.
Finally, to correct for the difference in quantum yields of TexasRed when bound to LPS compared with when bound to DHPE, we performed molecular brightness analysis via sFCS experiments. The data revealed that LPS-TexasRed exhibits a 15% lower quantum yield than TexasRed-DHPE (data not shown). Therefore, the molar concentration of LPS-TexasRed, as deduced from the calibration curve (Fig. 5  A) was correspondingly corrected, i. e. , n TR À DHPE 1 À 0:15. Since only 1. 2% of the total LPS for vesicle preparation was labeled with TexasRed, the total LPS concentration in the membrane was additionally corrected as n TR À DHPE 1 À 0:15 1 0:012. With the approach described above, we assessed the LPS fraction in GUVs in the outer leaflet (LPS-outside GUVs) and in GUVs with LPS in the inner leaflet (LPS-inside GUVs).
1 C ). Incubation times of 1 h, 3 h, and overnight ($19 h) were tested and the individual LPS concentrations in the vesicle membrane were calculated. Three replicates of each condition were prepared (numbers 1, 2, and 3) and 16 random vesicle images per replicate were recorded. The mean and the standard deviations are displayed to the right of the data. To see this figure in color, go online. Fig. 7 A ) was found to be 20% lower than the lipid diffusion coefficient (5. 3 5 1. 3 mm 2 s À1 , light green data in Fig. 7 A ) in the membrane of LPS GUVs. Note that, because of the presence of lipids on both sides of the membrane, the diffusion coefficient that was measured for the lipid signal originated from both the inner and outer leaflet. This decrease is understandable considering the large molecular size of LPS.
Effects of rAAV-Mediated TGF-β and FGF-2 Overexpression via PCL Film-Guided Vector Delivery on the Biological Activities of hACL Fibroblasts Monolayer cultures of primary hACL fibroblasts were finally transduced using the candidate rAAV-hTGF-β and rAAV-hFGF-2 vectors coated on pNaSS-grafted versus ungrafted PCL films to evaluate the effects of TGF-β and FGF-2 overexpression on the biological activities of these cells over time in vitro relative to control conditions in the absence of a vector coating. -β , FGF-2) expression via immunocytochemistry in hACL fibroblast cultures transduced using rAAV-coated PCL films. The PCL films were coated with rAAV-hTGF-β or rAAV-hFGF-2 or left without a vector coating before they were applied to the cultures as described in Figure  2 , and the expression of (A) TGF-β and (B) FGF-2 was examined after 21 days via immunocytochemistry (magnification ×4; scale bars: 300 µm; all representative data) with an analysis of the integrated densities using ImageJ software, as described in the Materials and Methods section.
agilent. com/en/product/real-time-pcr-%28qpcr%29/real-time-pcr-%28qpcr%29-instruments/mx3000-mx3005p-real-time-pcr-system-software/mxpro-qpcrsoftware-232751) (Stratagene Agilent Technologies), and the values were normalized to GAPDH expression via the 2 -∆∆Ct method  [28, 29]. Table  1. Primers employed in the study.
Phages and Strains Phage PBJ was isolated in our lab after two rounds of amplification from a mixed stock using P. aeruginosa strain PAO1 as the host. Original stocks of Pseudomonas phages 14-1, LKD16, and LUZ19 were kindly provided by Rob Lavigne (KU Leuven, Belgium). Pseudomonas phages (Pa collection) and phage K were obtained from the Fagenbank (Delft, The Netherlands). Phage amplification was carried out by infecting the host strain PAO1 overnight at 37 • C in Lysogeny Broth (LB). Bacterial debris was removed by centrifugation at 11,000× g (RCF) for 40 min at 4 • C. Phages were then incubated for 2 h on ice with a solution of 10% PEG-8000 and 0. 5 M NaCl and precipitated by centrifugation at 11,000× g (RCF) for 40 min at 4 • C. The preparation was mixed with chloroform, after which the aqueous phase was sterilized using a 0.
Louis, MO, USA). To achieve fluorescent labeling, phages were incubated with DBCO-AF488 (Jena Bioscience, Jena, Germany) for 1 h at 37 • C. Excess dye was washed off by washing with SM buffer in an Amicon filter (100 kDa cut-off). PAO1 expressing sfCherry was obtained by transforming PAO1 with the plasmid pUCP30T modified to express sfCherry  [23]. Bacteria were made competent by washing in 300 mM sucrose, after which the plasmid was introduced by electroporation. Selection was performed on plates containing gentamycin (30 µg/mL). S. aureus strain ATCC 19685 was kindly provided by Ana Rita Costa (Fagenbank; TU Delft, The Netherlands).
Acknowledgments: The present work was part of the research program of the  Netherlands Centre for One Health. The authors would like to thank  Rob Lavigne  and  Ana Rita Costa  for supplying phage stocks and host strains. We thank  Sayali Gorivale  ( KU Leuven ) for her help in sequencing the genome of phage PBJ. We thank  John Lambris  for providing compstatin. We thank  Carla Gosselaar-de Haas  ( UMCU ) for producing the complement-targeting nanobodies and the anti-WTA human IgM antibody,  Loutje van der Sman  ( UMCU ) for producing PBJ-AF488, and  Dani Heesterbeek  ( UMCU ) for producing the AF405-tagged antibody bH6. We thank  Jos van Strijp  ( UMCU ) for the critical reading of the manuscript.
[4]  Although cervical screening services are commonly available in HIV clinics across SSA, implementation practices are rarely described and routinely collected data to monitor global WHO targets for CC elimination in WLHIV are rarely reported. To address this gap, we qualitatively assessed the implementation of CC prevention services at the facility/site level. In addition, using patient-level data we populated steps in the prevention and care cascade for WLHIV attending HIV clinics with fairly evolved CC prevention programmes across SSA.
Study design and setting We conducted a facility-based survey between November 2020 and July 2021. The study sites were selected HIV clinics in four African regions participating in the International epidemiology Databases to Evaluate AIDS (IeDEA). The IeDEA is a global network gathering and analysing routinely collected clinical data from children, adolescents and adults living with HIV across 240 HIV treatment and care sites (https://www. iedea. org/ ). We included 30 clinics offering CC prevention services on-or off-site. The IeDEA regional principal investigators identified sites that had fairly evolved CC screening programmes for participation in the survey.
Study participants We collected patient-and site-level data for HPV vaccination and cervical screening in four populations as described below: a) HPV vaccination ii) Girls and/or adolescents and young women living with HIV eligible for HPV vaccination: according to site's eligibility criteria b) Cervical screening iii) Women in care: Women living with HIV 15 years old or older, who had at least one HIV medical care visit during the index year iv) Women eligible for cervical screening: Women living with HIV who met the site-specific CC screening eligibility criteria These harmonised definitions of girls and women in care allowed for data comparisons across sites in different countries.
Survey development We constructed a two-level survey based on the International Agency for Research on Cancer CANscreen5 tool (https://canscreen5. iarc. fr/) and the WHO Toolkit for Cervical Cancer prevention and control programmes. [20] Firstly, we organised a meeting with the IeDEA principal investigators, data managers, the CANscreen5 and WHO toolkit development team members, to discuss: the scope of the study, study population, site eligibility, and index years for data collection. Secondly, the lead author (SLA) visited six of the 30 participating sites to discuss the survey objectives and questions with CC control programme teams, and revised the survey based on their input. The revised survey was programmed into Research Electronic Data Capture (REDCap 9. 8. 2), a web-based application used to create databases and projects.
We offered the survey in English and French. At the site-level, we performed a qualitative assessment of CC prevention and care services. We assessed the availability of HPV vaccination and CC prevention services and organized the selected indicators into six domains: 1) respondent and site characteristics (e. g. facility location); 2) HPV vaccination (e. g. cost of vaccination); 3) CC screening, diagnosis and treatment (e. g. screening guideline availability); 4) data collection and aggregation systems (e. g. electronic system availability); 5) evaluations and audits (e. g. assessment of health information system within the last ten years); and 6) decision and referral support systems (e. g. availability of monitoring and evaluation plan). At the patient-level, we analysed data from routine care offered to WLHIV in these sites.
Survey piloting and data collection Between May and August 2020, we piloted the survey at two sites in West and East Africa and revised following feedback. Target respondents for the site-level survey were CC prevention and control programme managers or health personnel directly involved in CC screening activities. For the survey of patient-level data, the data manager was the preferred respondent. We invited respondents via email using automatically generated links to survey forms. Sites with challenges using REDCap 9. 8. 2 printed the forms, filled them in and submitted scanned copies through a secured email server. One researcher (SLA) manually entered scanned responses into REDCap 9. 8. 2 and another (MD) checked the entries. Site investigators were also able to check accuracy of their site data and communicated any queries to the lead author.