dataesr/scientific-paragraphs-categorization huggingface dataset:
CITY PROFILE MODELING From their bibliometric analysis of documents published between 1992 and 2012, two main groups emerged: one that is techno-centric and one that advocates a more holistic approach to smart city development, both of which are ICT-driven. A second literature analysis from 1992 to 2018 led the authors to identify four main dichotomies, or eight hypotheses, as presented in Table  2. These hypotheses were tested with the analysis of four European smart cities: Amsterdam, Barcelona, Helsinki, and Vienna, which are considered as "leading examples," to identify the "strategic principles" followed. The analysis of numerous qualitative data led to the identification of a first database of city activities. A coding process was used to enrich the first database and to create a second one containing the entities associated with the activities.
Other actions, such as research on citizen participation, enriched the databases. Three classification systems were elaborated, as indicated in Table  1. The classification system relating to application domains resulted from the "services and applications" category of the first classification system. The analysis of the activity categories and application domains helped to test the hypotheses of dichotomies 1, 2 and 4; the classification of the actors or entities was related to dichotomy 3. 1. To reach urban sustainability, the city activities must be aligned with its development strategy. This can be achieved directly or by following strategic principles which will give insights for the implementation of modification of activities. Thus, the city activities should be characterized, and can be distributed in different categories (Table  1 ).
Paris example This section presents an example of the application. We selected Paris owing to the amount of available data through the DataCity program  (Numa and Ville de Paris, 2015) , which lists all of the developed projects. However, although this program is a great source of information, its aim is to enhance innovation: not all of the projects have been implemented in the city, and they may remain at the proof-of-concept stage. To obtain sufficient data, the projects developed by the DataCity program are considered as real city activities. Furthermore, activities that have been developed by the city and are available on the Paris city website (https://www. paris. fr/) have been added. In total, there are 40 activities related to Paris from the DataCity program and 15 from the city website.
An activity corresponding to the DataCity program itself is also added. The incidence matrix is constructed by attributing descriptors to each activity (Table  4 ), in a matrix of 38 rows and 56 columns. This is achieved using the information that is publicly available on the websites, and thus, may not be completely promote democratic values use of public data make public data actionable taking into account those in need a b c representative of the reality. This is particularly the case for the descriptors relating to community buildings and strategic frameworks. After uploading the matrix in the Hype app, as illustrated in Figure  5 , an initial look at the resulting simplices reveals only 35 simplices, meaning that 3 descriptors are not associated with any Paris activity. The simplices with the highest dimension are also represented; that is, the descriptors that are more often linked to the Paris activities.
More importantly, our data demonstrate that CS exacerbates the PLCH phenotype in mice, supporting the concept that CS is not only a demographic feature of PLCH but also a cofactor that is required for full expression of disease pathogenesis in the context of myelomonocytic BRAF-V600E mutations. BRAF VE mice exhibit pulmonary inflammation and disrupted DC homeostasis in the lung following CS exposure. To examine the populations and phenotypes of cells recruited to the lungs of BRAF VE mice exposed to CS, we examined single-cell suspensions obtained by whole-lung enzymatic digestion using flow cytometric analysis. There was an increase in DCs and T cells but not macrophages (Figure  2A ), suggesting that the CD68 + histiocytes that accumulate in the nodules may have a DC origin  (23). Mouse CD103 + DCs express langerin similar to that of the human CD1a + DCs found in PLCH  (24).
Examination of the major populations of conventional DCs in BRAF VE mouse lungs revealed that CS exposure specifically increased the accumulation of CD103 + and CD11b + DCs (Figure  2B ). Moreover, BRAF VE mice exhibited an increase in CD86 + mature DCs in the lungs that was further increased by CS exposure (Figure  2C ) analogous to DC accumulation in patients with PLCH  (25). Next, we assessed the functional effects of BRAF-V600E mutation and CS exposure. Pulmonary DCs isolated from BRAF VE mice secreted high levels of proinflammatory cytokines IL-6 and IL-12 upon stimulation with agonists for TLR-2 [poly(I:C)] and TLR-4 (LPS) compared with WT mice. This increased responsiveness was potentiated in DCs from BRAF VE mice exposed to CS compared with FA (Figure  2, D  and E ). Taken together, these data support a model in which aberrant DC function in PLCH is a consequence of both dysfunctional signaling due to the BRAF-V600E mutation and CS exposure.
We evaluated CCR6-dependent CCL20 responsiveness by measuring cellular cAMP levels upon stimulation with CCL20. Our data showed that CCL20 reduced forskolin-stimulated cAMP levels more potently in BRAF VE BMDCs compared with WT BMDCs, indicative of increased CCR6 responsiveness (Figure  4D ). In contrast, evaluation of CCR7 responsiveness, as defined by inhibition of cAMP levels following CCL19 and CCL21 stimulation, was not different between WT and BRAF VE BMDC (Supplemental Figure  3 ). Consistent with this finding, we also observed enhanced functional responses to CCL20 as measured by increased migration of BRAF VE BMDCs compared with WT BMDCs toward CCL20, but not CCR7, CCL19, or CCL21, using a transwell migration assay (Figure  4E  and Supplemental Figure  3 ). We postulated that the altered lung microenvironment is a key factor in the recruitment, accumulation, and activation of BRAF VE DCs in PLCH.
The BRAF/CCL7 axis was explored using the A-375 melanoma cell line (ATCC). We determined group sizes necessary for adequate statistical power using preliminary data sets. Each experiment was repeated at least 3 times independently with n of 4 or more in each group. There was no randomization used in the experiments, and we did not exclude any samples. The investigators were not blinded to group allocation during collection and analysis of the data. Measurements and counting of lung nodules and cysts were performed by 2 independent blinded lab members. Mice. C57BL/6J, CD45. 1, CD45. 2 WT mice, and BRAFV600E fl/fl mice on a C57BL/6 background were obtained from The Jackson Laboratory (Bar Harbor, Maine, USA). CD11c-CreERT mice on a C57BL/6 background were a gift from Maries van den Broek (Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland) and have been previously described  (22).
Cells were washed twice with FB and immediately analyzed by flow cytometry (Attune, Thermo Fisher Scientific). All antibodies were obtained from eBioscience, Thermo Fisher Scientific (PE-cyanine 7 antimouse CD11c [N418], APC-efluor 780 anti-mouse CD11b [M1/70], APC anti-mouse CD86 [GL1], PE anti-mouse PDCA-1 [ebio129c], anti-p-ERK [T202/Y204 clone MILAN8R]); BD Biosciences (PerCP-Cy 5. 5 anti-mouse CD103 [M290]); and Cell Signaling Technology (anti-Bcl-xL [54H6]). For p-ERK and Bcl-xL staining, BMDCs were fixed with 4% PFA for 30 minutes at room temperature followed by fixation/permeabilization with ice-cold methanol (90% final concentration) for 30 minutes. Cells were then washed twice with FB and stained with anti-p-ERK and anti-Bcl-xL. Data were analyzed using FlowJo software (v10) (FlowJo, LLC) or FCS Express V5 (De Novo Software).
After 3 hours, the number of cells in the lower chamber was determined by flow cytometry. RNA extraction and real-time quantitative PCR. Total RNA was extracted using TRIzol Reagent (Invitrogen, Thermo Fisher Scientific) and RNeasy MiniElute kit (QIAGEN). Reverse transcription was performed using the Quantitect Reverse Transcription kit (QIAGEN), per the manufacturer's instructions. Quantitative PCR with chemokine genes was conducted with the Power SYBR Green Master Mix (Thermo Fisher Scientific) with the following primers from Integrated DNA Technologies, Inc: CCL2 (Mm. PT. 58. 42151692), CCL5 (Mm. PT. 58. 43548565), and CCL7 (Mm. PT. 58. 17719534). RPL32 (forward: 5′-GAAGTTCCTGGTCCACAACG-3′; reverse: 5′-GCGATCTCGGCACAGTA-AG-3′) was used for data normalization. The quantitative PCR with mutation-specific BRAF primers was conducted using the iTaq Universal Supermix (Bio-Rad).
insight. jci. orghttps://doi. org/10. 1172/jci. insight. 132048insight. jci. orghttps://doi. org/10. 1172/jci. insight. 132048insight. jci. orghttps://doi. org/10. 1172/jci. insight. 132048insight. jci. orghttps://doi. org/10. 1172/jci. insight. 132048Acknowledgments The authors would like to thank  Hans Christian Probst  ( Institute for Immunology, University Medical Centre Mainz, Germany ) and  Maries van den Broek  ( Institute of Experimental Immunology, University of Zurich, Switzerland ) for their gift of the CD11c-CreERT mice. This work was funded by support from the  NIH  ( HL119538  to MTB and  U54HL127672  to FXM) and the  Veterans Administration  ( I01BX002347  to MTB). Other support and resources were provided by the  Center for Environmental Genetics  ( NIH/ National Institute of Environmental Health Sciences ,  P30 ES006096 ) and the  Center for Clinical and Translational Science and Training (NIH ,  5UL1TR001425 ) Address correspondence to:  Michael T.
Human subjects Women with prediabetes are part of the Therapeutic Innovation in Type 2 Diabetes (IT-DIAB) prospective cohort conducted by the Endocrinology Department of the University Research Hospital of Nantes, France (clinicaltrial. gov identifier NCT01218061). This cohort received approval from local Ethic Committee of Nantes, France. Before enrollment, all patients have been clearly and fairly informed and provided written consent. The research has been conducted according to Declaration of Helsinki principles. Inclusion criteria were adults with a prediabetes status corresponding to an impaired fasting blood glucose between 110 mg/dl (6. 0 mmol/liter) and 126 mg/dl (7. 0 mmol/liter). Exclusion criteria were other endocrine disorders. Patients have been followed annually over 5 yr. Fasting blood glucose and HbA1c were assessed immediately on routine process.
Circulating LCN2 levels were measured by the LCN2 ELISA kit from R&D (MLCN20). TSE Labmaster (TSE Systems) was used for indirect calorimetry and food intake measurements. After ≥48 h acclimation to the chambers, data collected over a 48-h period were analyzed as per the manufacturer's recommendations. Body composition was determined using Bruker Minispec nuclear magnetic resonance (Bruker Optics).
RNA isolation RNA isolation, cDNA preparation, and real-time PCR analyses were performed following standard protocols. For bone tissue analysis, bone marrow cells were removed completely by extensively flushing the femurs with PBS. Trizol reagent was used for RNA extraction, a random hexamer cDNA synthesis kit (Clontech Laboratories) was used for reverse transcription PCR, and SYBR Green Master Mix (Bio-Rad Laboratories) was used for quantitative PCR. Actb was used as an internal control. Data are presented as fold change over control after normalization with Actb levels. The following primer sequences were used: Lcn2 forward: 59-AGCTTTACGATGTACAGCACCAT-39, reverse: 59-GATACCTGTGCATATTTCCCAGA-39; Actb forward: 59-GAC CTCTATGCCAACACAGT-39, reverse: 59-AGTACTTGCGCTCAG GAGGA-39; Ucp1 forward: 59-GGGCCCTTGTAAACAACAAA-39, reverse: 59-GTCGGTCCTTCCTTGGTGTA-39; Pgc1a forward: 59-CACAAAGACGTCCCTGCTCAGA-39, reverse: 59-CCTTGGGGT CATTTGGTGACTC-39; TNFa forward: 59-GAACTGGCAGAAGAG GCACT-39, reverse: 59-AGGGTCTGGGCCATAGAACT-39; IL-1a forward: 59-GCAACGGGAAGATTCTGAAG-39, reverse: 59-TGA CAAACTTCTGCCTGACG-39; IL-1b forward: 59-GCCATCCTCTGT GACTCAT-39, reverse: 59-AGGCCACAGGTATTTTGTCG-39; IL6 forward: 59-CTGCAAGAGACTTCCATCCAG-39, reverse: 59-AGT GGTATAGACAGGTCTGTTGG-39; C/EBPa forward: 59-TGTGAG TTAGCCATGTGGTAGG-39, reverse: 59-GTCATAACTCCAGTCCC TCTGG-39; PPARg forward: 59-GAAACTGGCACCCTTGAAAA-39, reverse: 59-CCCTGGCAAAGCATTTGTAT-39; Lpl forward: 59-G GGCTATGAGATCAACAAGGTC-39, reverse: 59-CACTGTGCCGTA CAGAGAAATC-39; Tgl forward: 59-TATCCGGTGGATGAAAGAGC-39, reverse: 59-CAGTTCCACCTGCTCAGACA-39; Cdkn1a forward: 59-CTGTTTCTGTAACATCCTGGTCTG-39, reverse: 59-TCGAGA AGTATTTATTGAGCACCA-39; Cdkn1b forward: 59-GCCTGTGTA TGAGAAAAATTGG-39, reverse: 59-CAACCTTTTAAGCACAGCTA C-39; Cdkn2a forward: 59-AATCCCAAGAGCAGAGCTAATC-39, reverse: 59-TTAAACAATCCAGCCATTTCC-39; Ccnd2 forward: 59-GGCTAGAGGTCTGTGAGGAACAAA-39, reverse: 59-TCACAGAGTTG TCGGTGTAAATG-39; and Cdk4 forward: 59-TGGATTTTCTTCATG CAAACTG-39, reverse: 59-GAAGAACTTCAGGAGCTCGGTA-39.
Histological analysis Pancreata were fixed in 10% neutral formalin, embedded in paraffin, and sectioned at 5 µm. Immunohistochemistry was performed using goat anti-insulin (Santa Cruz; SC7839), mouse anti-BrdU (Sigma; B2531), Cy3 rabbit anti-goat IgG (Jackson Immunoresearch Laboratories; 305-165-045), and Alexa Fluor 488 donkey anti-mouse IgG (Jackson Immunoresearch Laboratories; 715-545-150) antibodies. Zeiss fluorescence microscope was used, and Ins + BrdU + cells were counted manually using ImageJ software. For histomorphometric analysis, Osteomeasure software and a Leica DM 5000B microscope outfitted with a charge-coupled device camera (Sony) were used. Sections were stained with mouse anti-insulin (Santa Cruz; SC8033) and Vectastain Elite ABC HRP kit (Vector Laboratories; peroxidase, mouse IgG, PK6102).
Statistics All experiments were repeated at least three times unless stated in the figure legends. Statistical analyses were performed using Prism8 software. Data are presented as mean ± SEM. An unpaired or paired two-tailed Student's t test was performed for comparison between two groups and one-or two-way ANOVA for more than two groups comparisons as stated in the figure legends. Normal distribution of the data was examined using the Shapiro-Wilk test for normality. When appropriate, skewed distributed data were log-transformed to meet essential assumptions of inferential statistics. Pearson correlation coefficient was used to assess the association between LCN2 and other studied parameters in prediabetic women after log transformation of the variables. Stepwise linear regression analysis was performed to assess multivariate correlations in diabetic women using the default direction argument.
First, dependent variable LCN2 was log-transformed to satisfy distributional assumptions of linear regression. Age, BMI, glucose, insulin, HbA1c, HOMA-β, HOMA-IR, HOMA-S, and dichotomous variable diabetes coded as 0 for individuals with no diabetes and 1 for those with diabetes entered into the stepwise regression analysis. In the final model, derived from stepwise regression and based on the Akaike information criterion, age, BMI, diabetes, HbA1c, insulin, and HOMA-IR were selected as independent determinants of serum LCN2. After fitting the regression model, statistical tests were implemented for ensuring the validity of the estimates of the coefficients obtained. Using the "gvlma" package in R, we performed linear regression diagnostics for validating that our model met the assumptions of normality, independence of errors, linearity, and homoskedasticity.
Acknowledgments We thank  Dr. Lori M. Zeltser  ( Columbia University, New York, NY ) for helpful discussions;  Dr. Jonathan Barasch  ( Columbia University ) for providing the Lcn2 -/-(B) mice; and  Marina Blanchard ,  Violette Diery ,  Marielle Joliveau ,  Béatrice Lamour ,  Vincent Jacquin ,  Matthieu Pichelin ,  Yassine Zair  ( CIC Endocrino-Nutrition 1413 INSERM, Centre Hospitalier Universitaire [CHU] Nantes, Nantes, France ),  Edith Bigot-Corbel  ( Department of Biochemistry, CHU Nantes ), and  Marie-Christine Carlier  ( Département de Biochimie, Hospices Civils de Lyon, France ) for their help in data collection of the IT-DIAB study and biological parameter measurements. We also thank the Histology and  Metabolic Unit facility of the Diabetes and Endocrinology Research Center (National Institute of Diabetes and Digestive and Kideny Diseases   DK063608-07 ) and the biological resource center for biobanking ( CHU Nantes, Hôtel Dieu, Centre de ressources biologiques, Nantes, France; Bioresource Research Impact  Factor  BB-0033-00040 ).
This work was supported by the  National Institutes of Health  (grant  P01AG032959  to  S. Kousteni  and  T32  training grant  DK07328  to  S. Shikhel ). IT-DIAB was funded by an  OSEO-API (France) ISI  grant to Genfit (coordinator of the  IT-DIAB consortium)  and  CHU Nantes, France.
The McNemar test was used for diagnostic accuracy comparison. We also investigated the presence of mixed lesions as a cause of false-positive results of the centrally reduced diffusion sign. Within the tumor group (. 25% of recurrent tumor), we compared the percentages of treatment effect between patients with predominant centrally reduced diffusion and patients with other diffusion patterns using the Mann-Whitney U test. Statistical analyses were performed using GraphPad Prism software, Version 8. 2. 1 (GraphPad Software).
A longer version of the Atg7-32 motif, with 6-nt extensions on both its 5 0 -and 3 0 -ends, named ATG2700 (d [ 5 0 AT 2 CT 2 G 3 GCTGG 3 TC 3 T 2 GG 3 A 2 CTGTAT 2 G 3 TGA 2 C 2 3 0 ] ), was used to assess whether it can be recognized by the G4-specific antibody HF2 (Figure  4a ). We synthesized both Cy5-labeled ATG2700 and SS-DNA, a control that cannot fold into a G4 structure. The HF2 antibody was incubated with ATG2700 and SS-DNA in buffers with either K + , which favors G4, or Li + , which prevents G4 formation (Figure  4a, b ). We found that HF2 interacts with ATG2700 only in K + -rich conditions (Figure  4a ), without binding to the control SS-DNA. Our data thus indicate that the PQFS identified in the Atg7 gene indeed adopts a G4 structure in vitro. These findings were further confirmed with a well-established G4-binding protein, PC4.
***p=0. 0001 (t-test). Results were pooled from four independent experiments. (e-g) Cultured primary neurons were treated with a vehicle (control, cont) or with BRACO19 (2 mM) overnight. Neurons were collected and processed to measure mRNA (e) and levels of ATG7 (f,g). (e) The expression of Atg7 and Tbp was determined by qRT-PCR. ***p(Atg7)=0. 0001 (t-test). n. s. , non-significant, p (Tbp)=0. 662. Results were pooled from three independent experiments. (f) Levels of ATG7 were determined by western blotting. Actin was used as a loading control. (g) Quantification of ATG7 protein was normalized to actin from (f). ***p=0. 0001 (t-test). Results were pooled from four independent experiments. ). These data indicate that G4 ligands modulate a G4 landscape in cultured primary neurons, suggesting a mechanism of how G4 stabilization downregulates Atg7.
We analyzed fluorescence intensity of p62-GFP and discovered that PDS-treated mApple-expressing neurons exhibited a 1. 7-fold increase of p62-GFP fluorescence intensity over control neurons. Interestingly, ATG7-overexpressing neurons treated with PDS displayed 0. 7-fold reduction of p62-GFP fluorescence intensity, indicating that overexpressing ATG7 mitigates the inhibitory effects of PDS on neuronal autophagy (Figure  5 -figure  supplement 3a, b ). Similarly, ATG7 overexpression mitigates PDS-induced neurotoxicity (Figure  5 -figure supplement 3c). These data further highlight the importance of ATG7 in neuronal autophagy and survival.
One cohort of neurons co-transfected with mApple and GFP was treated with a vehicle (Apple), and the second one with 0. 5 mM PDS. Another cohort of neurons co-transfected with mApple and Pif1-GFP was treated with a vehicle (Pif1), and the second one with 0. 5 mM PDS (Pif1+PDS). Neurons were longitudinally imaged during 4 days, and risks of death were analyzed. ***p=0. 0001 (log-rank test). Results were pooled from three independent experiments. (f) Two cohorts of cultured primary neurons were co-Figure  8  continued on next page aging phenotypes in the mouse brains. Our data also suggest that anticancer drugs that target G4-DNA may accelerate brain aging and lead to early dementia.
, 2013). Young (3 months old) and old (25 months old) mice were sacrificed, and their brains were analyzed by immunohistochemistry with the BG4 antibody. BG4-positive puncta were seen in aged mice and were very rarely seen in young mice (Figure  7b, c ). These data suggest that the G4 landscape is modulated by aging in vivo, which opens new avenues for aging research.
1 mM PDS (Pif1+PDS). One last cohort of neurons was transfected with mApple and mutant Pif1-GFP and treated with PDS (mPif1+PDS). Neurons were longitudinally imaged, and the half-life of Dendra2-LC3 of each group was analyzed, and normalized to one with respect to the control group. **p(cont vs PDS)=0. 001, **p(PDS vs Pif1+PDS)=0. 001, p (Pif1 vs Pif1+PDS)=0. 001. n. s. , non-significant, p(cont vs Pif1)=0. 1726, p(cont vs Pif1+PDS)=0. 3239, p(PDS vs mPif +PDS)=0. 4267 (one-way ANOVA). One hundred neurons per group were analyzed from two independent experiments. aged mouse brains contain numerous G4-DNA, while young brains have very few. Our data suggest that an age-associated change in DNA conformation could be a novel epigenetic-like mechanism of gene expression in aging neurons  (Kim, 2019).
CD and TDS experiments CD and UV-Vis spectra were recorded on the JASCO J-815 spectropolarimeter and the JASCO V630Bio spectrophotometer, respectively, in a 10 mm path-length quartz semi-micro cuvette (Starna). CD spectra of 3 mM of Atg7-32 and mutAtg7-32 (Eurogentec) were recorded over a range of 220-340 nm (bandwidth = 1 nm, 1 nm data pitch, 1 s response, scan speed = 500 nm. min À1 , averaged over five scans) without and with dehydrating agent (PEG200, 20% v/v; acetonitrile, 50% v/v) in 600 mL (final volume) of in 10 mM lithium cacodylate buffer (pH 7. 2) plus 10 mM KCl and 90 mM LiCl (Caco. K10). Final data were treated with OriginPro8, zeroing CD spectra at 340 nm. TDS experiments were performed with Atg7-32 and mutAtg7-32 (3 mM) recording the optical over a range of 220-340 nm at 20˚C and 80˚C in 600 mL (final volume) of Caco.
FRET-melting experiments Experiments were performed in a 96-well format using a Mx3005P qPCR machine (Agilent) equipped with FAM filters (l ex = 492 nm; l em = 516 nm) in 100 mL (final volume) of Caco. K10 with 0. 2 mM of Fam-Atg7-32-Tamra or Fam-mutAtg7-32-Tamra (Eurogentec) with 0, 1, 2 and 5 molar equivalents of PDS and BRACO-19 (i. e. , 0, 0. 2, 0. 4 and 1. 0 mM ligand). After a first equilibration step (25˚C, 30 s), a stepwise increase of 1˚C every 30 s for 65 cycles to reach 90˚C was performed, and measurements were made after each cycle. Final data were analyzed with Excel (Microsoft Corp. ) and OriginPro9. 1 (OriginLab Corp. ). The emission of FAM was normalized (0 to 1), and T 1/2 was defined as the temperature for which the normalized emission is 0. 5; DT 1/2 values, calculated as follows: DT 1/2 = [T 1/2 (DNA +ligand)-(T 1/2 (DNA alone)], and are means of three experiments.
NMR experiments Atg7-32 and mutAtg7-32 (Eurogentec) were annealed at 200 mM in a Caco. K10 by heating at 95˚C for 10 min. The samples were cooled to 4˚C (ice bath) and equilibrated at 4˚C for at least 24 hr. 1 H-NMR spectra (250 mL final volume) were acquired after the addition of DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) as internal calibration standard. NMR spectra were recorded at 298 K (4248 scans) using a 600 MHz Bruker Avance III HD spectrometer equipped with a cryogenic probe. Water suppression was achieved using excitation sculpting (pulse program: zgesgp). Final data were analyzed with TopSpin v4. 0. 6 (Bruker).