dataesr/scientific-paragraphs-categorization huggingface dataset:
45 Using both MALDI and MALDI-2, we first screened a ∼ 1 cm 2 of tissue obtained from a treated dog which developed lesions. MALDI revealed a very low signal intensity for the drug compound (for the expected [M + H] + ion at m/z 502 and was not detected at all for [M + Na] + ion) after inspection of the averaged spectra across the analyzed tissue regions. By contrast, MALDI-2 resulted in a 270-fold improvement in signal of the [M + H] + ion (Figure  4D ). Following this preliminary test, a full consecutive tissue section was analyzed using MALDI-2, which enabled the ion distribution to be studied throughout the tissue (Figure  4E,H). The identity of the drug has been validated by MS/MS and high mass resolution experiments (data not shown). In addition to the drug distribution signal, numerous endogenous lipid compounds were also observed, including a variety of species not observed with MALDI (the full mass range spectrum is provided in Supporting Information, Figure  9 ).
DISCUSSION Concentrations of potential microbial energy sources, such as formate, acetate, CH 4 , H 2 , and DOC, in deeply sourced high-pH fluids of Mariana forearc serpentinite mud volcanoes increase systematically with distance to the Mariana Trench and with underlying slab temperature. Yet, despite these increases, there are no clear indications of metabolically active microbial populations. Downward SCOA concentrations in high-pH subsurface muds of all three mud volcanoes show no clear changes, and neither do concentrations of other potential metabolites, such as sulfate, DIC, and DOC. Though an interpretation of the dissolved CH 4 and H 2 data is confounded by potential outgassing during core retrieval, these gases also show no clear depth-related production or consumption profiles. Microbial populations, quantified by microscopic counting, are mainly around the minimum quantification limit of 20 cells cm -3.
Acknowledgements The authors would like to kindly thank  Prof. U. Bunz  for providing access to film fabrication facilities and  Prof. J. Zaumseil  for access to SEM. We kindly thank the  DFG  ( VA 991/2-1 ) for funding. This project has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC Grant  Agreement no. 714067 , ENERGYMAPS).
Biolayer Interferometry Full-length PrP C (23-230) and PrP C (23-111) protein were biotinylated by incubation with N-hydroxy-succinimideactivated biotin and free biotin was removed. The biotin-PrP C was immobilized on streptavidin-coated BioLayer Interferometry (BLI) probes (Pall ForteBio, 18-5019). Interaction with different concentrations of AZ59 in PBS in 0. 05% Tween 20 (Sigma, P7949) was detected and analyzed by an OctetRed instrument in 96 well format using ForteBio software.
05; **, P < 0. 01). n = 10-15 per group. All data are mean AE SEM, one-way ANOVA with Dunnett's comparison to APP/PS1 control IgG. (D) Meta-analysis of the final block of swims each mouse receiving WT control IgG, APP/PS1 control IgG, or APP/PS1 20 mg/ kg AZ59 performed. WT and APP/PS1 mice receiving 20 mg/kg AZ59 performed significantly better by one-way RM-ANOVA over the last twelve trials with Tukey's post hoc multiple comparisons test (****, P < 0. 0001). WT mice also performed significantly better than APP/PS1 mice receiving 20 mg/kg AZ59 (**,P < 0. 01). n = 32-39 per group, all data are mean AE SEM. (E) Meta-analysis of the final probe trial for each mouse receiving WT control IgG, APP/PS1 control IgG, or APP/PS1 20 mg/kg AZ59 was performed. WT and APP/PS1 mice receiving 20 mg/kg AZ59 spent significantly more time in the target quadrant than APP/PS1 mice receiving control IgG (***,P < 0.
Study design This study, part of a prospective multicenter clinical trial (Clinical Trials. gov: NCT03027089), ran from January 2017 to December 2017 at Chinese People's Liberation Army General Hospital, Beijing, China. All participants provided written informed consent and the study was approved by the ethics committee (S2017-015-01). Inclusion criteria were patients aged between 18 to 85 years, with lung lesions presenting as one or more cavities, nodules, masses, bronchiectasis or severe fibrotic destruction with at least two lobes of lung seen on computed tomography (CT) imaging. Exclusion criteria were as follows: (1) Antifungal treatment for over 2 weeks within 3 months of enrollment (2) Incomplete records of clinical data or examination; (3) Pregnancy or lactation; (4) Severely immunocompromised patients, including a) recent history of neutropenia (<0.
Data and samples collection During the first visit the demographic and clinical data of patients, including underlying diseases, chronic pulmonary or systemic symptoms, details of treatments and results of CT were recorded. Meanwhile, serum samples were collected and immediately refrigerated at 4 °C for Aspergillus-specific IgG antibody assay. The residual serum samples were frozen at -80 °C for GM tests, Aspergillus-specific IgA and IgM antibody assays. C-reactive protein (CRP) tests, the peripheral total and differential white blood cell (WBC) counts, sputum direct microscopy for fungi and acid fast bacilli, and fungal, bacterial and mycobacterial culture were obtained. All patients underwent bronchoscopy and bronchoalveolar lavage fluid (BALF) and sometimes transbronchial biopsy specimens were obtained.
Statistical analysis Statistical analyses were performed using SPSS (version 22, IBM, NY, USA). The data were expressed as numbers and percentages for categorical variables and as means and standard deviations (mean ± SDs) for quantitative variables. Receiver operating characteristics (ROC) analysis was performed for Aspergillus-specific IgG assay. Area under the ROC curve (AUC) was shown with 95% Wilson confidence intervals (CI). Optimal diagnostic cut-off value for Aspergillus-specific IgG were calculated using Youden's J statistic (sensitivity+specificity-1). Normality test was applied to evaluate all quantitative variables to select the appropriate test. Comparisons between CPA group, non-CPA group, and healthy controls were done using the t-test. The McNemar χ2 test and kappa test were used to compare sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) between the assays.
Patient characteristics Between January 2017 to December 2017, 128 patients and 100 healthy persons were recruited. A total of 26 patients were excluded from this study due to the following reasons: for 7 patients we did not have complete clinical records or laboratory data because of dropout or death before diagnosis; 8 patients received antifungal therapy for over 2 weeks within 3 months of enrollment; 11 patients had severe immunodeficiency due to allogeneic stem cell transplant or T cell immunosuppressants. Eventually 102 patients were included and 42 patients were diagnosed with CPA (41. 2%). The pathogen was identified from sputum (n = 15, 35. 7%), BALF culture (n = 6, 14. 3%), lung resection surgery (n = 4, 9. 5%), bronchoscopy biopsy (n = 2, 4. 8%), percutaneous lung biopsy (n = 2, 4.
This criterion was selected principally in order to be consistent with existing internal databases. However, it must be noted that, given the 9 mm diameter of the specimen, the number of cycles to conduct the specimen to failure can be slightly superior of 100 to 1000 cycles, depending on the loading conditions. For the second type of tests, namely interrupted tests, after the achievement of requested LCF cycles at 300 • C, the specimens were fatigued to failure in air at room temperature under stress-control (R σ = 0. 2, where R σ denotes the ratio between minimum and maximum stress) at high frequency (10 Hz). The main objective of this last step is twofold: the first one is to achieve failure as rapidly as possible, the second one is to prevent any damage on fracture surfaces prior to observations by applying a positive stress ratio.
A stress amplitude of ∆σ/2 = 100 MPa was first applied until failure or 6 × 10 6 cycles at most. When necessary, this amplitude was then increased to ∆σ/2 = 120 MPa. SEM analyses of the fracture surfaces were finally conducted. Differences in fracture surfaces morphology in LCF and HCF periods allows then to provide data of both the crack depth and the crack front shape of the largest cracks as a function of the number of cycles applied during the LCF nominal solicitations.
The damage has been quantitatively assessed by determining the maximum crack depth, the surface crack length of this main crack and the total cracked area on the fracture surface. These results, reported in Table  3 , confirm the enhanced damaging effect of the SIS B signal, regardless of the parameter under consideration. Additionally, it should be mentioned that the crack shape features generated under these complex waveshapes are not much different from those of cracks produced using triangular signals with a constant strain rate of 1 × 10 -4 and 1 × 10 -5 s -1. On that basis and due to the limited number of data obtained under complex signals, it will be assumed in the following that the variation of the crack aspect ratio during the propagation in the case of complex signals is the same than in the case of these two triangular signals  [9].
For example, Marien et al. [13] , developed a qPCR assay for the detection of H. illucens; and Debode et al. [14] , for the detection of T. molitor. Some investigators also looked at the applicability of light microscopy for different commercially available IMs from H. illucens, Gryllus assimilis, A. diaperinus, Bombyx mori and T. molitor  [15, 16]  or Fourier Transform Near Infrared spectroscopy approach (FTNIR) for the detection of T. molitor and A. Domesticus  [17]. However, with the fast-growing number of insect species used for the production of insect meals, the detection, identification and differentiation of insect species by qPCR or microscopy remain a challenge. In addition, the current general paucity of information on insect species in molecular reference databases further hampers any efforts to develop targeted assays.
During acid hydrolysis, tryptophan and cysteine were destroyed. After hydrolysis, the samples were cooled in cold water until room temperature was reached and centrifuged in a vacuum centrifuge to complete dryness. After centrifugation, the residues were diluted in deionized water and filtered through a syringe-driven filter. Prior to the instrumental analysis, a derivatization agent (AccQ. Tag TM , Waters, Milford, MA, USA) was added to each sample. Finally, amino acids were separated by UPLC (column: Aquity UPLC BEH C18 1. 7 µM (2. 1 × 100 mm), Waters, flowrate 0. 7 mL min -1 ) and results integrated by Empower 3 (Waters). Amino acids were quantified using standards from Thermo Fisher Scientific (product number; 20088 Rockford, IL 61105, USA). Data was analyzed and visualized using Qlucore Omics Explorer version 3.
Proteomics Bioinformatics and Data Mining Mass spectrometry data generated using the UHR-TOF (. baf files) were converted using compassXport (Bruker DataAnalysis 4. 2) and saved as mgf and mzXML files. Standard bottom-up proteomics bioinformatics analysis was conducted using the proteoQC package (version 1. 18. 1) [32]  in R (version 3. 4. 4) [33]  running in RStudio (version 1. 0. 143) [34]. In short, mgf peak lists obtained from MS/MS spectra were subjected to the X!Tandem search engine  [35]  with the following parameter settings: (i) Trypsin was set as digestive enzyme and a maximum of two missed cleavages were allowed, (ii) MS1 and MS2 tolerances were set to 10. 0 ppm and 0. 05 Da respectively, and (iii) carbamidomethylation of carbon and oxidation of methionine were set as fixed and variable modifications, respectively.
The peak lists of BSF, YW, LW and HC were searched individually against their respective UniprotKB/Swiss-Prot (release 2019_01) reference proteomes (Table  2 ). Protein identifications were inferred from peptide identifications; each identified protein had at least one associated unique peptide sequence identified at q-value equal or less than 0. 01 (equivalent to a 1% FDR). The Occam's razor approach  [36]  was applied to deal with degenerate peptides by finding a minimum subset of proteins that covered all of the identified peptides. Insect specific proteins and peptides detected across both laboratories and analyses are reported (Online resource). To compare LC-MS/MS datasets directly, independently of sequence databases, DISMS2 was used, a spectral library comparison pipeline frequently used to calculate proteome-wide distances between samples without suitable peptide/protein reference databases  [37].
In earlier work on PAP we demonstrated that data mining techniques can successfully be applied to bottom-up shotgun mass spectrometry data to obtain robust and reliable species-specific peptide markers  [12]. However, this approach requires common proteins to be identified across all species of interest  [23]. In the present work, the mass spectra obtained were analyzed using proteoQC  [51]  (mass spectra of all samples are available online), a total of 1360 peptides and 173 insect specific proteins were identified (see Tables  S3  and S4  and MassIVE dataset  [52] ). Only 269 peptides and 86 proteins were detected consistently across the two laboratories (see Figure  2 , Tables  S5  and S6  and MassIVE dataset  [52] ). As can be seen in Table  5 , the bulk of these proteins were identified in samples derived from YW; only a handful of proteins were identified in samples derived from BSF and HC and no proteins were identified in samples derived from LW (Table  5 ).
As was observed previously, the rate of PAP protein identification is mainly related to the size of the UniprotKB/Swiss-Prot databases  [51]. Despite the importance of insects for agricultural ecosystems, many insect species lack up-to-date genomic data  [53] , which is a challenge for the development and application of insect specific molecular tools. When there is a paucity of genomics information, direct comparisons of mass spectra can be performed  [37]. We recently demonstrated that using compareMS2, an approach for the direct pairwise comparison of tandem mass spectra  [25] , allowed for the species and tissue specific differentiation of PAP of bovine, ovine, porcine and avian origin  [12]. Contrary to the identification of peptides and proteins using database dependent search algorithms, tools like compareMS2 and the recently published DISMS2  [37]  make use of the bulk of high quality tandem mass spectra rather than relying on selected few.
Conclusions In conclusion, our results confirmed the suitability of insect meals as a source of proteins for future fish feed formulations. A nitrogen-to-protein conversion factor of ~4. 21-5. 0 may be more suitable than the usual factor of 6. 25, when quantifying total protein content of insect meals derived from BSF, YM, LW and HC. The different sample preparation approaches employed in the present work strongly affected total protein yield and identification rates in bottom-up proteomics. To a lesser extent also the direct spectral comparisons were affected by the different protein extraction and digestion procedures employed; yet the DISMS2 algorithm was able to successfully differentiate insect meals according to their taxonomic groups. It is therefore of interest to further explore the use of database agnostic approaches for regulatory uses which aim for the identification and differentiation of the species origin of insect-based feed ingredients.
Introduction Zika virus (ZIKV) infection during pregnancy is a known cause of microcephaly and other congenital and developmental anomalies. In the absence of a ZIKV vaccine or prophylactics, principal investigators (PIs) and international leaders in ZIKV research have formed the ZIKV Individual Participant Data (IPD) Consortium to identify, collect and synthesise IPD from longitudinal studies of pregnant women that measure ZIKV infection during pregnancy and fetal, infant or child outcomes. Methods and analysis We will identify eligible studies through the ZIKV IPD Consortium membership and a systematic review and invite study PIs to participate in the IPD meta-analysis (IPD-MA). We will use the combined dataset to estimate the relative and absolute risk of congenital Zika syndrome (CZS), including microcephaly and late symptomatic congenital infections; identify and explore sources of heterogeneity in those estimates and develop and validate a risk prediction model to identify the pregnancies at the highest risk of CZS or adverse developmental outcomes.
We will use participant-level data to develop and externally validate clinical risk prediction models to facilitate the identification of pregnancies that are most likely to result in ZIKV-related adverse fetal or infant outcomes and longer-term developmental delays. Open access standardisation and cross-national partnerships to inform the public health response to emerging pathogens Formation of the ZIKV IPD Consortium The ZIKV IPD Consortium is a global collaboration designed to streamline the international response to ZIKV. To facilitate cross-country analyses and a coordinated response to ZIKV, representatives from WHO, PAHO, the US Centers for Disease Control and Prevention (CDC), the National Institutes of Health (NIH), the National Institute of Allergy and Infectious Diseases (NIAID), Institut national de la santé et de la recherche médicale (INSERM), Institut Pasteur, and the networks of Fundação Oswaldo Cruz (Fiocruz), Grupo de Pesquisa da Epidemia da Microcefalia (MERG)/ZikaPlan, ZIKAlliance, ZIKAction, the Consortium for the Standardization of Influenza Seroepidemiology (CONSISE) and International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) have developed a standardised protocol for cohorts of pregnant women and their infants exposed to ZIKV to facilitate the proposed IPD-MA; identified existing or planned cohorts and prospectively introduced cohort principal investigators (PI)s and MOH officials to the methodological and public health benefits related to IPD-MA in the context of Zika.
Information sources ZIKV IPD Consortium We anticipate that most eligible studies will have been identified through the efforts of the ZIKV IPD Consortium. The Consortium is an international initiative that is meant to include the PIs from all planned, ongoing or completed ZIKV longitudinal studies at the time of this review. We have searched clinical trials and ZIKV-related databases  63  (online supplementary table  3 ) to identify existing or planned longitudinal studies. We have circulated the list of ongoing or planned ZIKV-related longitudinal studies of pregnant women to MOH Officials in countries with autochthone ZIKV transmission and to PIs of ZIKV cohorts and asked them to update the list as necessary.
Systematic review We will perform a systematic search of biomedical databases for published longitudinal studies and protocols. The search strategy is based on Medical Subject Headings (MeSH) and text-based search terms for ZIKV, pregnant women, infants and children. The search strategy was developed in collaboration with an information scientist and adapted for the following electronic databases: Embase (Medline), Embase (Ovid) and SCOPUS (see online supplementary text 1 for the search strategy for Embase (Medline and Ovid). We also will search the additional databases listed in online supplementary table  3  and review the reference lists of published systematic reviews and the list of studies produced by a living systematic review of ZIKV studies conducted by the University of Bern  64  to identify additional studies.
Collection of study-level data We will contact the PIs of eligible studies identified through either the ZIKV IPD Consortium or the electronic searches to invite them to take part in the IPD-MA and ask them to provide a copy of their study protocol. We will develop and pilot an electronic data extraction form to record study-level characteristics for all eligible studies, regardless of whether study PIs agree to participate in the IPD-MA. Two reviewers will independently review protocols and study-related publications to extract data on study design; study population; enrolment, follow-up and laboratory procedures; assay and specimen type; criteria used to define ZIKV infection and timing of infection and exposure, cofactor and outcome ascertainment for all eligible studies. We will ask study PIs for clarification if there are outstanding questions or disagreements regarding study-level data.
step 2. Collection, review and synthesis of deidentified, participant-level data We will contact the PIs and authors of studies that meet our inclusion criteria to request deidentified, participant-level data on select variables and the associated surveys and data dictionaries or codebooks. If study data have been imputed, we will request both the original and imputed data so that we can apply consistent imputation methods across studies and review the imputed dataset for validation purposes. To reduce the burden on individual studies and ensure clear documentation of all steps in the creation of the synthesised dataset, we will use the study codebooks or data dictionaries to develop study-specific code in the statistical language used by the study data manager that selects only the study variables required for the proposed analyses and removes information that could be used to identify individual participants.
The study's data manager will apply the code to the original dataset. The deidentified, participant-level data will be transferred from the study site to Emory University, which will serve as the WHO data synthesis partner centre, using secure file transfer protocol and will be protected on a secure server with standard encryption and by the Emory University firewall. Data synthesis-related decisions will be reviewed by a ZIKV IPD Consortium membership and will be recorded using Jupyter Notebook. 65 Researchers who are unable or unwilling to provide their participant data after at least four attempts at contact by the project team over a period of 6 months will be excluded from the IPD-MA and we will report the reason for their exclusion. When IPD are not available for a given study, we will extract study-level effect estimates from any publications to compare study-level estimates from all eligible studies, whether or not they provide data for the IPD-MA.
Using continuous variables will allow us to test the sensitivity of results to the application of different cutoffs and the reference standards used to generate Z-scores. Definitions for miscarriage, fetal loss and other pregnancy outcomes vary across countries. We will explore the sensitivity of project findings to different outcome definitions. Assessing the integrity of deidentified, participant-level data We will review the distribution of variables to identify potential outliers and to assess the proportion missing within each study. We will discuss the distribution of key variables with the study data manager to identify and address any inconsistencies. If there has been a publication related to a given longitudinal study, we will attempt to replicate table 1 presented in the publication and will resolve any inconsistencies with the data manager.
We will consider random slopes for certain covariates to allow for between-study variation in covariate effects across studies. Given the difficulty in assessing the total df in mixed models, we will apply bootstrapping to assess the approximate confidence intervals of the pooled interaction terms. We will present the analysis of effect measure modifiers in accordance with the revised STROBE guidelines. 91 ased on our review of research protocols for planned or ongoing cohort studies, we expect to include data from longitudinal studies with different enrolment criteria, exposure and outcome ascertainment, diagnostic assays for prior-infections or coinfections and measurement of important cofactors. We will include measures of study-level sources of heterogeneity (eg, diagnostic assay, outcome definitions) as covariates in the one-stage regression to assess the variance explained by these factors.