dataesr/scientific-paragraphs-categorization huggingface dataset:
Data pre-processing and differential expression analysis We found in 2 of the 12 mice in the APPtg 10 M group a 46% lower expression levels of hsa-APPswe, a 26% lower expression of hsa-Psen1L166p and a 24% reduction in mmu-Thy1 gene, whose promoter drives the expression of the transgenes, compared to the other APPtg 10 M mice. In the absence of an explanation, these two mice were excluded from all further analyses (data not shown). miRNAs with an average of raw read counts < 5 were discarded, leaving 644 miRNAs for differential expression (DE) analysis. Non-biological variation was removed by the removeBatchEffect function from limma package 3. 22. 7 Bioconductor/R  [28] , preserving biological variation of genotype, age and their interactions in a generalized linear model for the APPtg and TAUtg dataset combined, as well as separately, while removing technical effects such as batch effect, RNA extraction group effects, RNA concentration effects.
Locomotion and anxiety-like behavior After 30 min dark adaptation, the mouse was placed in a brightly lit (465 lx) 50 × 50 cm square arena of transparent plexiglass, 1 min habituation and 10 min recording  [35]. Mice were tracked in the arena with ANY-maze tm Video Tracking System software (Stoelting Co. , IL, USA), recording total path length (locomotor behavior) and time spent and number of entries in the corners (7 × 7 cm), walls and center circle of the open field (anxiety-like behavior).
Spatial learning Mice were trained for 2 × 5 days in a 150 cm circular Morris water maze (MWM) pool filled with opaque (non-toxic white paint) water (26 °C)  [35] , where a 15 cm round platform was submerged in a fixed position 1 cm below water level. Daily training session consisted of 4 trials (15-30 min intervals) starting from 4 randomized starting positions. Mice that failed to locate the platform within 2 min, were gently guided to the platform and left there for 10s. Probe trials were conducted on day 6 and 12 during the 10-day training period, where the platform was removed from the pool and mice swam freely for 100 s. ICV injections were administered after the probe trial. Mice were tracked with EthoVision tracking equipment and software (Noldus, Wageningen, the Netherlands), calculating escape latency, path length and swimming velocity during training, and, during the probe trial, time spent in each quadrant and latency to first enter the target quadrant and target position.
Sociability/preference for social novelty test Sociability and social memory were assessed in the social preference/social novelty test (SPSN), as described in  [38]. In short, a test mouse was placed in the middle compartment of a 3-compartment transparent Plexiglas arena (94x28x30 cm), divided by manually guided sliding doors. After 5 min habituation, a stranger mouse was placed in a cylindrical cup in the left or right compartment, and the test mouse could explore all 3 compartments for 10 min during this sociability trial. Subsequently, during the social novelty test a second stranger mouse was placed in a cylindrical cup in the free compartment (left or right) and the test mouse could again explore all 3 compartments for 10 min. Using nose tracking and a virtual 5 cm perimeter around the cylindrical cups holding the stranger mouse, social exploration and distance travelled was assessed using ANY-maze tm software.
utah. edu [39]  and human AD brain miRNAseq data  [40]  as pilot data, we calculated that n = 12/group and 7 million reads/sample would enable us to detect 55% of miRNAs with a significant (p < 0. 05) 1. 3 fold change and > 75% of miRNAs with 1. 5 fold change (p < 0. 05). miRNAseq was therefore performed on 96 hippocampal samples (see Fig. 1a  for a In the age comparison, miRNA expression in all 10 month old (M) mice is compared to all 4 M mice. In the genotype comparison, miRNA expression in all transgenic (TG) mice is compared to all wild-type (WT) mice. In the age*genotype comparison, we assess which miRNAs are differentially expressed in the 10 M TG mice compared to all other groups. c) The 8 selected miRNAs that became significantly deregulated (FDRcorrected p-value< 0. 05) in the miRNAseq experiment with increasing pathology and cognitive impairment (age*genotype effect) in APPtg and TAUtg mice combined with more than 20% change.
miRNAs are progressively deregulated in the age*genotype interaction Overall, the significant (adjusted p-value after false discovery rate correction (padj) < 0. 05) observed changes in miRNA expression are modest (log2-fold change (LFC): -0. 95 -+ 2. 23), similar to what we reported in AD patients  [40]. Combining the APPtg and TAUtg dataset during differential expression analysis highlights miR-NAs that are commonly deregulated. Eight miRNAs are significantly up-regulated with more than 20% change (LFC > 0. 26) in the age*genotype interaction when both APPtg and TAUtg mice are taken together, although the pattern of deregulation of miR-451a and miR-301b-3p is less outspoken compared to the other six (see Fig. 1c ). miR-10a-5p (LFC: + 0. 76), miR-155-5p (LFC: + 1. 04) and miR-211-5p (LFC: + 1.
A stronger genotype effect in TAUtg mice When comparing the effect of transgene expression alone in the combined APPtg and TAUtg data set, 23 miRNAs are affected in a statistically significant manner (padj < 0. 05, see Additional file 4: Figure  S3 ), but effect sizes are modest with maximal changes for miR-146a-5p (LFC: + 0. 36) and miR-147-3p (LFC: + 0. 34), or miR-451a (LFC: -0. 40) and miR-301b-3p (LFC: -0. 27). By assessing the transgene effects in APPtg and TAUtg mice separately, it becomes clear that TAUtg mice have more significantly differentially expressed miRNAs than the APPtg mice (24 vs 6 miRNAs; padj< 0. 05), although the effects are weak. Not only do TAUtg mice display more down-regulated miRNAs (n = 19, LFC: -0. 74 to -0. 14, for instance miR-487b-5p and miR-451a) than APPtg mice (n = 1, miR-301b-3p, LFC: -0.
We next analyze the expression of the human homologues of the 6 verified miRNAs in hippocampal material from 28 neuropathologically verified AD patients and 20 non-demented control cases, previously used in  [31]. Semi-quantitative qPCR data indicates increased miRNA expression in AD cases of hsa-miR142-5p (+ 72%), hsa-miR-146a-5p (+ 26%), hsa-miR-155-5p (+ 78%) and hsa-miR-455-5p (+ 54%; see Fig. 1e ). Hsa-miR-211-5p shows too much variability across the samples, and therefore its alteration in human patients cannot be confirmed. Hsa-miR-10a-5p is too lowly expressed in human hippocampal tissue to be detected (qPCR cycle threshold > 35). We can conclude that miR-142a-5p, miR-146a-5p, miR-155-5p and miR-455-5p are upregulated in both mouse models of neurodegeneration (albeit more strongly in APPtg mice), in an independent cohort of APPtg mice, and in AD patients, whereas the increase in miR-10a-5p and miR-211-5p expression is only found in the APPtg mouse model for AD.
Acknowledgements We thank  Veronique Hendrickx  and  Jonas Verwaeren  for animal husbandry,  Tobias Engel ,  Eva Jimenez-Matteos ,  David Henshall , and  Tom Jaspers  for help with stereotaxy,  Pierre Lau  for initial bioinformatics analyses,  Isabel Salas ,  Katrien Horré ,  Dries T'Syen  and  Anabela Duraes de Sousa Moreira  for technical support, and  Stein Aerts ,  Carlo Sala Frigerio  and  Tom Jaspers  for discussions. APPtg mice were a kind gift from  Mathias Jucker ,  DZNE, Germany. Confocal microscope equipment was acquired through a  Hercules grant   Type 1 AKUL/09/037  to  Wim Annaert.
Funding  Annerieke Sierksma  and  Bart De Strooper  are directly supported by the  Opening the Future campaign of the Leuven Universitair Fonds (LUF)  and the  Alzheimer Research Foundation  (SAO-FRA; P# 16017 ). Evgenia Salta is an  FWO and Alzheimer's Association postdoctoral fellow. Work in the De Strooper laboratory  was supported by  European Research Council (ERC)  grant  ERC-2010-AG_268675 , the  Fonds voor Wetenschappelijk Onderzoek (FWO) ,  KU Leuven ,  VIB , and a  Methusalem grant  from  KU Leuven  and the  Flemish Government ,  Vlaams Initiatief voor Netwerken voor Dementie Onderzoek (VIND, Strategic Basic Research Grant   135043 ) and the " Geneeskundige Stichting Koningin Elisabeth". Bart De Strooper  is the  Bax-Vanluffelen Chair for Alzheimer's Disease. Luc Buée  is supported by grant  ANR-16-COEN-0007.
Instead, the individual intensities have to be EPJ Web of Conferences 193,  04002 (2018)  https://doi. org/10. 1051/epjconf/201819304002 FISSION 2017 retrieved from constrained fitting of the overlapping peaks (see Fig. 5d ), a method well established in high-precision mass spectrometry  [20]. In our case, the fitting quality benefits from the outstanding properties of the CLTDs for the low-energy fission fragments after energy degradation (see above). Preceding test measurements at the tandem accelerator at the MLL Garching with stable 130 Te and 127 I ion beams, aiming at determining the energy loss difference for adjacent Z values with our set-up, provided also valuable data on the peak shapes in this mass region. Here, residual energy peaks reveal asymmetric shapes at larger degrader thicknesses, attributed to the influence of energy loss processes.
For a precise determination of fractional independent fragment yields with LOHENGRIN it is mandatory to measure systematically nuclear-charge distributions for different ionic charge states q and kinetic energies E, and follow the target burn-up during the measuring period. Due to its complexity, data analysis for extracting fragment yields is still in progress, and all data shown at present are considered as preliminary. Extended measurements were performed for masses A = 92 and 96, for several ionic charges and 5 different energies, and partly for other masses mentioned above. The isotopic fission yields of 92 Rb and 96 Y from 235 U(nth, f), 239 Pu(nth, f) and 241 Pu(nth, f) were of particular interest, since more precise data than available  [21]  have been requested recently for achieving a better understanding of the reactor antineutrino spectrum  [22, 23].
Acknowledgments We would like to thank  Norbert Laurens  ( ILL, Grenoble ) for the technical support during the preparation time and the measurement performed at the LOHENGRIN mass separator. This work was supported by the  German Research Council (DFG)  by means of the  Emmy Noether Program  and by the  German Ministry of Research and Education (BMBF)  by grant  05P15RGFAA.
Zika case data from LAC The weekly number of confirmed and suspected Zika cases within each country is reported to the Pan American Health Organization. This analysis makes use of the weekly incidence of Zika cases in 35 countries, from January 2015 to August 2017 (Additional file 1: S1). State-level ZIKV incidence data was available for Brazil and Mexico  [22]. Confirmed cases are typically identified through a positive, real-time reverse polymerase chain reaction blood test using ZIKV-specific RNA primers. Suspected cases are based on the presence of pruritic (itchy) maculopapular rash together with two or more symptoms, including fever, polyarthralgia (multiple joint pains), periarticular oedema (joint swelling), or conjunctival hyperaemia (eye blood vessel dilation) without secretion and itch  [23, 24].
Funding This work was partially supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  under ZIKAlliance Grant Agreement no. 734548  and  ZikaPLAN Grant  Agreement No 734584. RL was funded by a  Royal Society Dorothy Hodgkin Fellowship. AK was funded by a  Sir Henry Dale  Fellowship jointly funded by the  Wellcome Trust  and the  Royal Society  (grant Number  206250/Z/17/Z ). OJB was funded by a  Sir Henry Wellcome Fellowship  funded by the  Wellcome Trust  (grant number  206471/Z/17/ Z ). SC was funded by the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (grant  ANR-10-LABX-62-IBEID ), the  Models of Infectious Disease Agent Study of the National Institute of General Medical Sciences , and the  AXA Research Fund. MUGK is supported by  The Branco Weiss Fellowship -Society in Science , administered by the  ETH Zurich  and acknowledges funding from a Training Grant from the  National Institute of Child Health and Human Development  ( T32HD040128 ) and the  National Library of Medicine of the National Institutes of Health  ( R01LM010812 ,  R01LM011965 ).
Methods and analysis Data will be derived from published phase II and III EORTC trials that used the QLQ-C30 instrument, covering several cancer sites. We will use individual patient data to estimate MIDs for different cancer sites separately. Focus is on anchor-based methods. Anchors will be selected per disease site from available data. A disease-oriented and methodological panel will provide independent guidance on anchor selection. We aim to construct multiple clinical anchors per QLQ-C30 scale and also to compare with several anchor-based methods. The effects of covariates, for example, gender, age, disease stage and so on, will also be investigated. We will examine how our estimated MIDs compare with previously published guidelines, hence further contributing to robust MID guidelines for the EORTC QLQ-C30.
This study will offer a great opportunity to compare with results across several anchor-based methods. Distribution-based methods, on the other hand, rely solely on the statistical distribution of HRQOL scores (do not consider patients'/clinicians' perspective),  13 14  and have been recommended to be used as supportive evidence to anchor-based estimates. 11 n this project, we focus on the anchor-based approach, particularly in a setting where both the anchors and HRQOL scores are collected longitudinally. The data will be derived from published phases II and III European Organisation for Research and Treatment for Cancer (EORTC) trials which assessed HRQOL using the EORTC core HRQOL questionnaire, QLQ-C30. The aim of the project is to provide an evidence-based approach to determine MIDs for HRQOL scores of the EORTC QLQ-C30.
Specifically, the appropriateness of particular clinical anchors in determining MIDs will be empirically evaluated. In addition, a library of MIDs will be established on the EORTC QLQ-C30 across various patient populations, according to cancer site (melanoma, lung, brain, etc) as well as stage of disease. Osoba et al  4  provided recommendations for small (5-10 points), moderate (10-20 points) and large changes (>20) for interpreting HRQOL scores of the EORTC QLQ-C30. This was based on individual data from patients with breast and small-cell lung cancers and included four of the EORTC QLQ-C30 scales (physical, emotional, social and global health). A global patient rating of change was the anchor. Similar findings were reported by  King 8  based on comparing group differences, from multiple cancer sites, using published study results.
More recent guidelines by Cocks et al  15 16  using anchor-based methods highlighted that previous guidelines may be too simplistic in that they do not differentiate between the QLQ-C30 scales as well as between direction of change (improvement vs deterioration). These evidence-based guidelines further recommended using the lower bound as a minimal relevant threshold, arguing that large effect sizes (ES) were not always realistically achievable in all settings. In contrast to Osoba et al,  4  this project will use multiple clinical anchors using clinical variables tailored to the specific cancer disease sites that are available in our database. The guidelines of King  8  and Cocks et al  15 16  were based on meta-analyses of published studies, pooling across cancer sites, whereas we will use individual patient data to estimate MIDs for different cancer sites separately.
MEthods And AnAlysIs datasets and definition Databases used for the analysis The data will mainly be extracted from published phase II and III EORTC clinical trials. We will include only studies that collected HRQOL data at baseline and follow-up using the EORTC QLQ-C30 and supplementary EORTC questionnaire modules. Cancer types include melanoma, lung, colorectal, brain, head and neck, prostate, breast, testis, ovarian, pancreas and oesophageal cancer. Data from more recent EORTC studies, completed during this project, will also be included as well as data from non-EORTC clinical trials when available. Data will be pooled within each cancer site separately using study time (defined as days since randomisation) as the common temporal scale per patient. MIDs will be Open Access established per cancer site, with attention to robustness across the different subpopulations.
Anchor selection We hope to identify at least one suitable clinical anchor for each EORTC QLQ-C30 scale from among potential clinical factors (eg, laboratory measures, physiological measures and clinician ratings) that are available in the databases. No patient ratings of change (eg, subjective significance questionnaires) are available in the database. HRQOL scores will only be considered as anchors if valid MIDs are known. Since the QLQ-C30 yields 15 scales measuring a wide range of symptoms and functioning, the suitability of an anchor must be considered relative to specific HRQOL domains. A suitable anchor for any particular QLQ-C30 scale should fulfil several criteria. Most notably the anchor should be relevant for the disease indication, should have clear medical interpretation and clinicians should be familiar with it.
A clinical anchor will be matched to an EORTC QLQ-C30 form if their respective assessment dates are within a predefined window. This time window will be determined on a per trial basis to ensure that the underlying true associations in the data are preserved. First, a candidate list of relevant clinical variables will be assembled based on the availability within each disease site. The acceptable compliance rate (ie, availability of complete information on both the anchor and the HRQOL scale) will depend on both relative and absolute available numbers. We aim for compliance rates ≥50% and an effective sample size of at least 200 patients with repeated observations after pooling data for each cancer site separately. Thereafter, we will evaluate how well the anchors correlate with the corresponding QLQ-C30 scale at various time points of interest.
Preliminary analyses a. Descriptive tabulation of the distribution of anchors and the EORTC QLQ-C30 scales will be made by trial, and pooled across trials. If insufficient variation is present or missing data are substantial in any anchor or scale, its inclusion in further analyses will be re-evaluated. b. As a first step to establish the validity of an anchor, correlations between the anchors and their corresponding QLQ-C30 scales will be calculated using all matched anchor/HRQOL scale pairs, regardless of time point. Scatter plots of the correlations will be inspected to gain greater understanding of bivariate distributions. The correlations will be calculated taking potential confounding factors into account (eg, treatment, gender, age, disease stage, country, trial, etc), to investigate the robustness of the associations in the overall population.
handling of missing data Missing HRQOL data We will cross-check compliance with the protocol schedule and verify the reasons for missing data. A cross-tabulation of the clinical anchors with HRQOL compliance will be made. We will evaluate the proportion of missing HRQOL forms per category of the anchor and also check if subjects with missing HRQOL forms differ systematically from those with complete HRQOL data. If systematic differences are found, a panel of methodological experts will be consulted to suggest appropriate sensitivity analyses (eg, imputation techniques) to check the robustness of the estimated thresholds.
ConClusIon In this project, we will determine MID for HRQOL scores of the EORTC QLQ-C30, using empirical individual patient data. The main focus is on the anchor-based approach. We aim to construct multiple anchors per QLQ-C30 multiple-item or single-item scale and apply and compare results from several anchor-based methods as recommended in the literature. 11 19 Figure  1  presents a flow diagram summarising the key data component, the clinical anchor construction procedure and the main statistical methods which will be applied in this project.
A strength of our research is its integral combination of both clinical and methodological expertise. The findings will ultimately improve the interpretation of the QLQ-C30 scale scores in clinical trials by providing empirical guidelines for relevant improvements and deteriorations. Each year, there are over 5000 newly registered downloads of the EORTC quality-of-life measures. The information from our research will be of added value to all its users (eg, pharma and academic) since a frequent issue raised by regulators and trial sponsors is an understanding of MID. The main limitations of this project are that anchorbased MIDs can only be estimated for QLQ-C30 scales for which a suitable anchor is available in the database. Also, the available anchors rely exclusively on clinical observations or interpretations.
Unfortunately, patient ratings of change (eg, subjective significance questionnaires) are not available in the study database. We will consider using other HRQOL scores as a way to include the patient's perspective if valid MIDs are known for the given HRQOL scores. Overall, this project will supplement previously published research by using individual patient data to estimate MIDs for different cancer sites separately, hence, further providing evidence to robust and practical MID guidelines for the EORTC QLQ-C30.
Acknowledgements We thank the members of the various EORTC disease groups and their clinical investigators, and all the patients who participated in the trials that we shall be analysing. Contributors AB, CC, DEE, MTK, MG, MAGS, GV and H-HF: contributed to the conception and design of the study. MG, MAGS, YB, GV and H-HF: revised the proposed methodology for clinical plausibility. ZJM,  J-FH , CC, KC, DEE, JM and MTK: provided critical input on the proposed statistical analysis. ZJM and CC: drafted the protocol. All the authors read and corrected the drafts and approved the final version.
challenges of developing and launching new drugs, led to many pharmaceutical companies exiting the field  2. A major limitation of traditional high-throughput screening (HTS) approaches is that only a finite amount of chemicals in a limited number of conformations are available in any given HTS library. As this chemical space limitation will hardly be overcome, novel approaches are needed to tackle the rising problem of bacterial resistance to current treatments. Our study provides a framework for which such novel strategies can be developed and further adapted to use. With sheer amounts of high-throughput genomic, structural and transcriptomic data from many important bacterial pathogens openly available in biological databases, an approach based on multidimensional data integration towards pinpointing new drug targets represents a more rapid and cost-effective strategy than traditional screening techniques.
ncbi. nlm. nih. gov/bioproject/) under accession no. PRJNA78291. Subcellular localization was predicted for each protein using PSORTb v3. 0. 2 running in Bacterial, Gram-negative mode  31. Generation of structural homology-based models. 394 unique crystal structures for K. pneumoniae proteins were retrieved from the Protein Data Bank (PDB)  32. For all remaining predicted peptide sequences, we tried to build homology-based models using our previously developed structural genomic pipeline  33. Protein sequences were used as input for PSI-BLAST searches (parameters -j 3 -e 1e-05)  34  against the UniRef50 (UniProt trimmed at 50% redundancy)  35. Once a position-specific scoring matrix (PSSM) was obtained, this PSSM was used to search against the PDB95 (non-redundant PDB at 95% level) using PSI-BLAST (parameter -e 1e-05).