dataesr/scientific-paragraphs-categorization huggingface dataset:
MRI acquisition and analysis We selected those participants with available 3T MRI. The details of MRI acquisition and pre-processing are available elsewhere (http://adni-info. org/). All structural MRIs were first processed using the crosssectional cortical reconstruction stream in Freesurfer (v5. 1;http://surfer. nmr. mgh. harvard. edu) [21, 22]. For the cross-sectional analyses, after excluding subjects with lack of CSF data (n = 34) and segmentation errors (n = 33), 131 baseline MRI were included. Cortical thickness was calculated as the distance from the grey/white matter boundary closest to the grey/CSF boundary at each vertex. The longitudinal analyses were performed with the Freesurfer longitudinal stream  [23]. Specifically, an unbiased within-subject template space and image is created using all time-point MRIs available, including 1-year follow-up (n = 57), for each subject  [24].
After the exclusion of subjects with lack of baseline CSF data (n = 17), 104 were included in the longitudinal analyses. Symmetrized percent change (spc) values between baseline and two-year MRIs time-points were automatically extracted and introduced in the two stage model as implemented in Freesurfer  [25]. Briefly, the spc measure is the annual rate of atrophy between two timepoints with respect to the average thickness between them. Finally, both measures (cortical thickness and spc maps) were smoothed using a Gaussian kernel of 15mm fullwidth at half maximum.
Statistical analysis Demographic, anthropometric and cognitive variables were compared across groups using student t-tests, and χ 2 tests. Linear mixed-effects models using the Maximum Likelihood method for the change in the ADAS-Cog score were performed on the entire longitudinal database. In the primary analyses, weight variation was treated as a dichotomous variable (WLG and non-WLG). Intercept, age, sex, weight change-category, time from baseline and the interaction between time and weight change-category were included as covariates. Intercept and time from baseline were included as subjectspecific random effects. In the secondary analyses, weight changes were treated as a continuous variable (percentage with respect to baseline weight). Less than 10% of the cohort had a follow-up beyond 96 months.
Therefore, the clinical analyses were censured at month 96. The sample size for each group and the observed mean (SEM) differences in ADAS-Cog13 at each visit were reported for each WL trajectory group. The associations between weight changes with both individual baseline CSF biomarkers and AV45-SUVR were tested both using analysis of covariance -ANCOVA-(primary analysis) and regression analyses (secondary analysis). Statistical significance was set at 5% (alpha level = 0. 05). R software (version 3. 2. 5, www. r-project. org) and lme4 were used to perform the statistical analyses. To study the association of CTh and spc with weight trajectories, group comparisons were performed between the WLG and the non-WLG (primary analyses) and correlation analyses (secondary analyses) using the QDEC tool in Freesurfer.
GRANT SUPPORT This work was supported by research grants from the  Carlos III Institute of Health, Spain  (grants  PI11/02425  and  PI14/01126  to  Juan Fortea , grants  PI16/01825  and  PI13/01532  to  Rafael Blesa  and  PI11/03035  to  Alberto Lleó ) and the  CIBERNED program (Program 1 ,  Alzheimer Disease  to Alberto Lleó), jointly funded by  FondoEuropeo de Desarrollo Regional (FEDER) ,  Unión Europea , " Una manera de hacer Europa ". This work has also been supported by " Marató TV3 " grants ( 20141210  to  Juan Fortea  and  044412  to  Amanda Jiménez ) and a grant from the  Fundació Bancaria La Caixa  to  Rafael Blesa. This work was supported in part by  Generalitat de Catalunya  ( 2014SGR-0235 ). Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ).
This 'catch-up' in mortality has been noted in many other studies, including a Cochrane review  7  and analyses of the Medicare database  8  , but no satisfactory explanation for this phenomenon has emerged. The Cochrane review  7  was limited by not being able to report aneurysm-related mortality or subgroup analyses. Each trial individually has been too small to investigate the reasons for the catch-up mortality in the EVAR groups, or to answer the much discussed question of whether younger and fitter patients (or other subgroups) should be offered open repair, which is considered more durable than EVAR  9, 10. This catch-up in mortality needs to be avoided if EVAR is to outperform open repair in the longer term. To try to address some of these issues, the investigators of the four RCTs agreed to pool their data for an individual-patient data meta-analysis.
Methods In July 2013, MEDLINE, Embase and clinical trial databases were searched for randomized trials comparing open and endovascular repair of AAAs. The search terms used were: abdominal aortic aneurysm, AAA, endovascular, stent, open repair and randomized trial. From 275 reports, four eligible trials reporting mid-term follow-up were identified (Appendix S1, supporting information). The methods for the four multicentre trials included in this meta-analysis have been published previously  6, 11  -13. The EVAR-1 trial randomized 1252 patients (90⋅7 per cent men), with aneurysm diameter more than 5⋅5 cm, between September 1999 and August 2004 in the UK. The DREAM trial randomized 351 patients (91⋅7 per cent men), with aneurysm diameter at least 5 cm, between November 2000 and December 2003 in the Netherlands and Belgium.
The OVER trial randomized 881 patients (99⋅4 per cent men), with aneurysm diameter 5⋅0 cm or larger, between October 2002 and April 2008 at Veterans Affairs hospitals in the USA. The ACE trial randomized 306 patients (99⋅0 per cent men), with aneurysm diameter exceeding 5⋅0 cm, between March 2003 and March 2008 in France; seven patients withdrew consent before discharge from hospital. All patients were considered fit for open surgery under general anaesthesia and all trials used approved devices for EVAR, predominantly within the manufacturers' instructions for use, and followed up patients for a minimum of 3 years. The four data sets were merged based on fields available in the case record forms of the largest trial (EVAR-1); range checks were conducted and queries resolved with the individual trial coordinating centres.
The common baseline variables across the trials were: age, sex, history of smoking, diabetes, coronary artery disease (defined as previous stable or unstable angina or myocardial infarction), BMI, maximum aneurysm diameter, proximal aortic neck length and diameter, ankle : brachial pressure index (ABPI) and creatinine concentration, used for calculation of estimated glomerular filtration rate (eGFR) 14 but without information on ethnicity. Each trial also contained some data on hypertension, which was included in a modified Wilkins cardiovascular survival risk score  15  (Table  S1 , supporting information). The reporting of both drug use (including antiplatelet and lipid-lowering agents) and reinterventions was very different in the four trials (particularly intestinal and wound-related reinterventions following open repair).
Statistical analysis The primary analyses considered the groups as randomized within each trial. Mortality after randomization was assessed at both 30 days, in hospital and then in three defined intervals: 0-6 months, 6 months to 4 years and more than 4 years after randomization. Aneurysm-related mortality included: death from primary aneurysm rupture; within 30 days of aneurysm repair or any reintervention; and rupture after repair. Given the different times between randomization and aneurysm repair in the four trials, aneurysm-related mortality also was assessed at 30 days, 31 days to 3 years and more than 3 years after aneurysm repair. Kaplan-Meier survival curves by randomized group were generated from the combined data from all four trials, and the restricted mean life-years up to a certain time estimated by the area under the curve up to that time  16.
Logistic regression was used to compare operative (30-day) and in-hospital mortality among patients who underwent aneurysm repair, and Cox proportional hazards regression to compare total and aneurysm-related mortality and time to reintervention. A two-stage individual-patient data meta-analysis was performed. First analyses were conducted separately within each trial and then pooled time period-specific estimates were calculated using random-effects meta-analysis, Values in parentheses are percentages unless indicated otherwise; *values are mean(s. d. ), †median (i. q. r. ) and ‡rate per 100 person-years. §Ex-smokers in ACE and DREAM were defined as those smoking in the 10 years before randomization. ¶Mean and median ankle : brachial pressure index (ABPI) were almost identical. #For those who underwent aneurysm repair.
The reintervention rate was consistently higher in the EVAR groups (Table  1 ), although the data are heterogeneous and the largest trial did not report incision-related complications after open repair. It would be reassuring to learn that by using more recent EVAR devices, according to the instructions for use, coupled with more rigorous surveillance, the continuing aneurysm-related mortality in the EVAR group could be attenuated to minimize the 'catch-up' in mortality. To rely entirely on the introduction of new devices to prevent aneurysm-related mortality in the EVAR group, especially without adequate surveillance, may be unwise. Recent analyses 8 of the Medicare database support this caution. Correction of the commonest reported complication of EVAR, type II endoleak, had no effect on survival.
Acknowledgements The individual-patient data meta-analysis was funded by the  National Institute for Health Research (NIHR) ,  Health Technology Assessment (HTA)  project  11/36/46  as a contract variation (Chief Investigator R. Greenhalgh). There was some support from the  Camelia Botnar Research Foundation. They had no role in the study design, in the collection, merging, analysis or reporting of data, in the writing of the report, or in the decision to submit the paper for publication. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the  HTA Programme ,  NIHR , National Health Service or the Department of Health. Disclosure: The authors declare no conflict of interest.
A widefield survey instrument will also be able to discover new TeV sources and monitor known sources. Follow-up observations with CTA can do a deep search for source variability on very short time scales, map the morphology of the sources, and constrain the spectra at lower energies than HAWC. The proposed detector can extend to energies above 10 TeV the spectra measured by IACTs. The importance of multi-messenger studies is demonstrated by the current efforts. For example, HAWC shares data with (and follows up on triggers from) many different experiments particle astrophysics including VERITAS, MAGIC, HESS, FACT, IceCube, ANTARES, SWIFT, Fermi-LAT, and LIGO. HAWC and IceCube, for example, observe the same range of energies and the same part of the sky in the northern hemisphere. The sources detected by HAWC provide excellent search candidates for neutrino emission by IceCube because proton cascades produce comparable fluxes to photons and neutrinos of similar energies.
By providing target sources, the HAWC catalog improves the sensitivity of TeV neutrino observatories such as IceCube and ANTARES (as well as future observatories such as KM3Net) by more than a factor of two. HAWC data are also being used to search for electromagnetic counterparts to the gravitational waves (GWs) observed by LIGO/VIRGO. The large field of view and high duty cycle provided by an array makes this type of detector well-suited to search for TeV counterparts to GW signals. This is particularly true because the current generation of GW observatories provide poor localization of GW events. Even when the future GW detectors go online, the localization of events in the southern hemisphere will continue to cover several square degrees.
In North Africa, Phlebotomus perniciosus is the main vector of L. infantum, the etiologic agent of zoonotic visceral leishmaniasis (ZVL)  [24]. We sought to develop a paratransgenic platform to control the transmission of L. infantum by P. perniciosus. Here, we assessed the richness of bacterial species of laboratory-reared and field-collected sand flies. We investigated the monthly variations of the bacterial diversity carried by sand flies in an endemic area of ZVL in Tunisia, during the period of Leishmania infantum transmission. We analyzed these new data within the context of previously published studies on the microbiota of sand flies.
Sequencing of 16S rDNA The 16S rDNA sequencing was carried out using the BigDye Terminator v3. 1 Cycle sequencing Kit and the ABI 3130 sequence analyzer. The partial 16S rRNA gene sequences were compared with sequences available in the ribosomal database, release 11. 4. Isolates were assigned at the species level on the basis of the 16S rRNA gene sequence similarity of the available sequences in the ribosomal database, measured by using the Seqmatch tool of RDP  [37]  (https://rdp. cme. msu. edu/). In addition, the partial 16S rDNA sequences were submitted to the BLASTn server of NCBI, using the 16S ribosomal RNA database (Bacteria and Archea) (http://blast. ncbi. nlm. nih. gov/Blast. cgi). The nucleotide similarity thresholds of the 16S rDNA sequences with the nearest neighbor were: ! 95% and 97.
Diversity analysis All the analyses were conducted with the R-vegan package, v. 2. 0-10  [39]. α-diversity was calculated using Shannon's and Simpson's diversity indices. Correspondance analysis (CA analysis) on the monthly data was carried out with the FactomineR package (https://cran. r-project. org/web/packages/FactoMineR/) using the R language (http://www. R-project. org).
Meta-analysis of Phlebotominae microbiota All the published data concerning bacterial species identification associated with Phlebotomus and Lutzomyia species (the only two genera for which we have data) were compiled and analyzed. Studies describing the identification of the midgut bacteria at the family, class or phylum level were not considered. To assess bacterial richness associated with the adult sand fly, data were collected without taking into account the method of bacterial isolation (culturedependent vs culture-independent) and identification (DNA sequencing of 16S rDNA, bacteriology). The overall dataset used in our analyses included ten Phlebotominae (L. cruzi  [40] , L. longipalpis  [41, 42] , L. evansi  [43] , P. argentipes  [22] , P. duboscqi  [44] , P. halepensis  [45] , P.
papatasi  [21, [45] [46] [47] [48] , P. sergenti  [45] , P. perfiliewi  [45] , P. chinensis  [49]  and P. perniciosus) and their associated microbiota for the present study. Bacterial richness is visualized through network analysis using Cytoscape (http://www. cytoscape. org/) [50]. To achieve this goal, data were extracted from our own database (focused on Phlebotominae) as CSV files, containing vertices or nodes (representing hosts and bacteria) and edges (representing links). These files were loaded into Cytoscape v 3. 4. 0, a tool specializing in graphical representation. This graph was modified to keep only one edge between host and bacteria. Bacterial nodes were colored to show their degrees of interaction with hosts. ITS-PCR analysis was used to dereplicate strain diversity among the 556 colonies.
1). The same query on the RDP database disclosed 98% similarity with Wolbachia inokumae DQ402518, which was already found in field collected P. perniciosus from Marseille, France  [51]. A search in the RDP database with the sequence obtained from the DG1 band (718 bp, Accession N˚KY322518) produced hits with various species of Ehrlichia, including 96% similarity with Ehrlichia canis-M73226. A similarity of 96% with Ehrlichia ewingii (NR-044747) was found when BLAST analysis was performed on the 718-bp DNA fragment (Fig  3 ). To our knowledge, this is the first report of the presence of Ehrlichia sp. DNA in sand fly midguts. Sand fly-associated bacteria, as revealed via meta-analysis of the literature data A meta-analysis was conducted to assess the bacterial species diversity of Phlebotomus and Lutzomyia microbiota.
This analysis included previously published studies concerning adults of seven phlebotomine sand fly species (P. argentipes, P. chinensis, P. duboscqi, P. halepensis, P. sergenti, P. papatasi, P. perfiliewi) our study reported on P. perniciosus and previously published data reported on three Lutzomyia species (L. cruzi, L. evansi, L. longipalpis)  [22, [40] [41] [42] [43] [44] [45] [46] [47] [48] [49]. Owing to the small number of studies conducted on the microbiota of Phlebotominae and the lack of information about sex in several cases, we chose to not take into account the genera of the specimen in order to highlight trends. This analysis shows that most bacteria identified from Old World sand fly species belong to the Firmicutes phylum,  Our meta-analysis shows that bacteria of the Serratia genus has been identified in almost all Old World and New World sand fly species so far studied, but Serratia marcescens was characterized only in P.
Unfortunately, no consensus protocol for microbial DNA extraction of insect-associated microbiota is currently available  [70]. Although 16S rRNA gene sequencing is highly useful with regards to bacterial classification, it has a low phylogenetic power at the species level for some genera  [71, 72]. Depending on the 16S rRNA variable region targeted and the database used to perform the taxonomic profiling, misassignation of bacterial OTU at the species level could be frequent  [73]. Nevertheless, taking into account all the above mentioned limits and pitfalls, we think that an exhaustive approach aimed at collecting a maximum of data on the microbiota of sand flies will give key information on the most commonly identified bacteria in sand fly species and those that are more specific. Our groups are interested in the development of a paratransgenic platform to control the transmission of leishmaniasis.
We generated a peak list, which was searched against the IPI human database using Mascot 2. 0 (Matrix Science Inc. , Boston, MA, USA). However, no pY-containing peptide could be identified automatically by Mascot search, but spotted instead by searching the MS/MS data set for pTyr immonium ion at m/z 216. 043  (refs 32,33)  and subsequently sequenced manually. This procedure revealed Y1196, located in the proline-rich C-terminus of SOS1, as the major putative ABL kinase target (Figure  1a ). We validated this result using two-dimensional phospho-peptide mapping that revealed a single, major phosphorylated spot derived from WT-SOS1, but not from Y1196F-SOS1 mutant (Figure  1b ). Thus, tyrosine 1196 is the major ABL phosphorylation site of SOS1 in vitro.
The transient nature of SOS1 knocked down and Y1196F reexpression, which were counter selected and lasted only for a limited number of passages (similar data were obtained using CRISPR-SOS1-KO K562 cells-not shown), prevented us to assess whether also in vivo we could restore leukemogenesis. To overcome this limitation, we employed a murine model in which the loss of Sos1 can be induced by tamoxifen-mediated CRE recombination (herein on referred to as Sos1 fl / fl ). 41 bone marrowderived cell (BMDC) from control and Sos1 fl / fl mice were retrovirally infected with EGFP-p210-BCR-ABL-fusion gene-expressing retroviral vectors (MigBCR-ABLp 210 ), treated with tamoxifen to induce Sos1 deletion and tested in vitro and in vivo to assess BCR-ABL transforming and leukemogenic ability (Figure  5 ).
K562 cells are bipotent progenitor cells, which are blocked in the differentiation program due to the expression of the BCR-ABL. 50 Thus, removal of SOS1 may induce these cells to acquire erythrocyte lineage properties, suggesting that SOS1 is required for cell proliferation and possibly for the maintenance of a more undifferentiated state rather than controlling apoptosis. In summary, our data reveal a novel molecular mechanism through which the poorly studied SOS1 RAC-GEF activity becomes activated by BCR-ABL, contributing to its pathogenic, transforming functions. They further suggest that targeting this interaction and signaling axis might be beneficial in combination with BCR-ABL pharmacological treatment to prevent or delay the onset of BCR-ABL resistance, at least in part, by reducing the proliferation potential of leukemic progenitors.
ACKNOWLEDGEMENTS We thank  A Bachi  ( IFOM ) for critically reading the manuscript. This work has been supported by the  Associazione Italiana per la Ricerca sul Cancro  ( AIRC 10168  and  18621 ), the  Italian Ministries of Education-University-Research  ( MIUR-PRIN--2009X23L78 ), the  International Association For Cancer Research  ( AICR-09-0582  and  14-0335 ), the  CARIPLO Foundation  ( 2010-0737 ) and the  European Research Council  ( Advanced-ERC268836 ). ALI was supported by a Grant from der  German Jose Carreras Stiftung  ( DJCLS R14/22 ) and a Grant from the  Government Baden-Württemberg (MWK). CM was supported form a  AECC fellowship, Spain.
The combinatorial complexity of GRNs limits the applicability of this analysis to motifs comprising more than four nodes, and complimentary ways of analyzing networks are important to better understand how larger-scale topology is associated with GRN function  (4, 8, 9). In this article, we use a methodology inspired by motif analysis to study the behaviour of a particular class of network configurations that we call linear regulatory chains (LRCs). Our approach exploits the theoretical power of mathematical and statistical analysis to determine the expected behaviour of LRCs and to derive predictions that we then test on biological datasets available in the literature to Nucleic Acids  Research, 2017, Vol. 45, No. 12 7079 obtain a better understanding of the selection pressures acting on GRNs.
For the purpose of our mathematical analysis, we define LRCs as linear chains of one-way regulation in which each node interacts with at most one node downstream and one node upstream. A given interaction can be either inhibitory or activating. Each LRC starts at the top layer (no transcriptional input) and ends at the bottom layer (no transcriptional output) of the respective GRN. Transcription factors that are only regulated by feedback loops are also considered at top layer. In the GRNs analysed in this study, there are very few transcription factors in this category. We relax this definition when studying real GRN datasets, and define LRCs as linear chains of genes which form a causal chain of transcriptional interaction, without placing restrictions on the number of connections to any given node.
The procedure described along with all the other operations on the networks were implemented using R version 3. 2. 2 and the 'igraph' package version 1. 0. 1  (16). The code is available as a supplementary file. To perform the statistical analysis of the embeddedness of chaotic motifs with the aim of assessing the probability of interaction of the chaotic motifs with the longest LRCs of the network we employed a simple statistical model that could avoid many of the mathematical and computational complications arising from the comparison of real networks with a randomized null model. In particular, we focused on the behaviour of the TF directly upstream of the chaotic motifs, which will be referred to as U. In all of the GRNs considered in this article, U is the third gene of the longest LRCs.