dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary In this paper cloud property datasets generated within the ESA Cloud_cci project were presented. The datasets are based on passive imager measurements on board polarorbiting satellites. The measurement records have been characterized carefully and, in the case of AVHRR, been re-calibrated. Two retrieval systems were developed: (1) the Community Cloud retrieval for CLimate (CC4CL) which was applied to AVHRR as well as to the AVHRR heritage channels measured by MODIS, ATSR2 and AATSR, and (2) the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) which was applied to combined MERIS+AATSR measurements. Based on these new developments, global cloud climatologies were generated for all mentioned sensors spanning their entire life time. The datasets are named: AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR.
The cloud properties derived are cloud mask/fraction, cloud phase, cloud-top pressure/height/temperature, cloud optical thickness, cloud effective radius, liquid/ice water path and spectral cloud albedo. The data is available as pixel-based retrievals (Level-2), globally gridded composites (Level-3U) and monthly summaries of the cloud properties (Level-3C): averages, standard deviations and histograms. The OE-based uncertainty information per pixel (contained in Level-2 and Level-3U) was propagated into Level-3C data using an introduced mathematical framework (Eqs. 4 and 5). While the main characteristics of all datasets are very similar to the AVHRR-PM examples shown, it needs to be noted that some deviations exist. These are mainly introduced by differences in spatio-temporal observation frequency, remaining differences in spectral properties among the considered sensors and differences in retrieval systems, i.
e. for MERIS+AATSR dataset. Level-2 validation of cloud mask, cloud phase and cloudtop height against CALIOP revealed a probability of correct detection of cloudy and clear-sky scenes between 70 and 90 % (hit rates), with a general underestimation of cloud occurrence frequency when compared to all detected clouds in CALIOP data, which reflects the detection limitation of passive imagers. Neglecting optically very thin clouds in CALIOP data improves the agreement in terms of probability of detection and biases. For cloud phase, hit rates of 70-80 % are reached with a bias towards liquid clouds (except the MERIS+AATSR dataset) when comparing to the uppermost cloud layer of CALIOP data. When comparing against the CALIOP phase taken at a top-down cloud optical depth of 0. 15 into the cloud, hit rates increase by approximately 5 % along with a reduction in the biases.
Validating cloudtop height gives generally small standard deviations and biases for liquid clouds, while the agreement with CALIOP is lower for ice clouds. For ice clouds, a strong dependence on the reference level from which the CALIOP cloud-top height is taken is found. Biases reduce to -0. 8 to -1. 99 km when the reference CALIOP cloud-top height is taken at a topdown optical depth of 1 into the cloud top. Monthly mean liquid water path was validated against passive, microwavebased satellite data. The mean and standard deviations are relatively diverse and strongly dependent on the dataset and region. However, for most Cloud_cci datasets and considered regions agreement with the reference data within the reported uncertainty of the reference data (15-30 %) was found. The validation results presented here, as well as the very comprehensive Cloud_cci validation report  (Stapelberg et al.
, 2017) , have proven the comparability of Cloud_cci datasets with already existing datasets of the same kind. However, additionally ensuring spectral consistency and adding rigorously propagated uncertainty measures make the Cloud_cci datasets distinct from them. The evaluation process of the presented datasets has also revealed some limitations, of which the most important ones are listed in Appendix B. In the near future, the Cloud_cci retrieval systems will undergo a revision, e. g. improving the forward models and LUTs and revising the BRDF for snow and ice surfaces. Based on these developments the datasets will be reprocessed, also including more recent time periods. Along with that, the product portfolio will be extended to include broad-band radiation flux properties at the surface and TOA, which will allow several new applications such as studying the cloud radiative effect.
Applying the same retrieval system to multiple sensors also facilitates a combination of the individual datasets, ideally leading to higher quality. This will be subject to future studies. In addition to such a combination of the datasets, the consistency among them (i. e. by using the same retrieval system) will also enable studies that investigate the impact of the imaging characteristics of the different sensors on derived cloud climatologies. These imaging characteristics are, for example, the spatial resolution (1 km × 1 km for AATSR/MODIS vs. 1 km × 4 km for AVHRR GAC) as well as the observation frequency driven by the sensor swath width (2399 km for AVHRR vs. 500 km for AATSR). 1, 2, 3, 4, 5a, 6, 7, 8, 10, 11 1, 2, 3, 4, 5b, 6, 7, 10, 11 1, 2, 3, 6, 7, 10, 11 1, 2, 3, 6, 7, 10, 11 1, 2, 3, 6, 7, 9, 10, 11, 12 1, 3, 7, 10, 11, 12.
Summary of cloud phase (CPH) validation results for Cloud_cci datasets when compared against CALIOP. Validation measures are probability of detection (POD) and bias of liquid cloud occurrence. Also given is the number of collocated pixels. The scores are separated into two cloud optical depth levels (COD lev ) representing at which top-down COD into the cloud the reference CALIOP CPH was taken. ScoreAVHRR-AM AVHRR-PM MODIS-Terra * MODIS-Aqua ATSR2-AATSR * MERIS+AATSR * Time window used for collocations was ±15 min for ATSR2+AATSR, MERIS+AATSR and MODIS-Terra. For all others a ±3 min window was used.
Summary of cloud-top height (CTH) validation results for Cloud_cci datasets when compared against CALIOP. Validation measures are standard deviation (SD) and bias. Also given is the number of collocated pixels. All scores are separated into liquid and ice clouds (both Cloud_cci dataset and CALIOP had to agree on phase) and into two cloud optical depth levels (COD lev ) representing at which top-down COD into the cloud the reference CALIOP CTH was taken.
(O'Dell et al. , 2008)er path (LWP) validation results for Cloud_cci datasets when compared against the passive, microwavebased (MW), monthly mean LWP data of(O'Dell et al. , 2008)in the period 2003 to 2008. The validation was performed for three oceanic stratocumulus regions for which the frequency of ice cloud occurrence is very small: SAF, west of Africa (10-20 • S, 0-10 • E); SAM, west of South America (16-26 • S, 76-86 • W); NAM, west of California (20-30 • N, 120-130 • W) -see text for details. Reported are the mean LWP of the MW as well as the bias and standard deviation (SD) for each Cloud_cci dataset with respect to the MW (all values in g m -2 ). In addition, the values are given in relative terms in percent in brackets. The correlation coefficient (r) is also given.
Acknowledgements. This work was supported by the  European Space Agency (ESA)  through the  Cloud_cci  project (contract no. :  4000109870/13/I-NB ). The authors would like to acknowledge the help of  NASA Goddard Space Flight Center  in providing MODIS Collection 6 Level-1 data and the help of  NOAA  and the  University of Wisconsin  for providing the AVHRR GAC Level-1 data and corresponding intercalibration coefficients for the visible and near-infrared channels of AVHRR. Edited by: Alexander Kokhanovsky Reviewed by: two anonymous referees.
1A ). This was further supported by bioinformatics and gene set enrichment analysis (GSEA) using Genomatix software and published signatures of common stem cell populations (Fig. 1B, C ; for full gene lists, see Supplemental Table  1 ;  Blanpain et al. 2004 ). To validate this signature, we performed quantitative PCR (qPCR) analysis for selected skin stem cell genes in senescent cells up to 2 wk after infection (Fig. 1D ). As before, we found increased expression of many of these, including CD34, Nestin, Lrig1, and Lgr6 as well as other hair follicle stem cell (HFSC) genes (Supplemental Fig. 1f ). Similar observations were also no-ticed in the primary control cells, which undergo senescence-like changes upon prolonged culture, including adopting a senescent-like morphology and exhibiting increased expression of p16 (Supplemental Fig.
4 ). Interestingly, here also there was increased activation of the stem cell markers CD44 and Nestin in these senescent lesions. Altogether, this Figure  1. A stem cell signature is increased in senescent cells. (A) Representative heat map of microarray analysis of HRas V12 -induced senescent keratinocytes compared with growing keratinocytes. Example genes are shown to illustrate that stem cell and senescent genes are more highly expressed in senescent cells compared with growing cells. (B) Bioinformatics analysis of genes significantly up-regulated in senescent cells (analysis performed using Genomatix software; full list of up-regulated and down-regulated genes is in Supplemental Table  1 ). (C) GSEA of stem cell genes  (Blanpain et al. 2004)   demonstrates that also in vivo in models of premalignant lesions, there is a pronounced association of stem cell markers in senescent cells.
Flow cytometry Epidermal cells were isolated from the dorsal back skin of adult C57Bl6/J mice or 1-to 2-d-old mice. Cultured keratinocytes were isolated through incubation with 0. 05% trypsin for 10 min at 37°C followed by inactivation of trypsin by the addition of DMEM containing 10% serum and centrifugation at 1000 rpm for 7 min. Cell suspensions were incubated for 60 min on ice with the following antibodies at the indicated dilutions: APC-conjugated or FITC-conjugated anti-CD34 (1:200; BD Pharmingen, clone RAM34), FITC-conjugated anti-α6-integrin (1:200; Serotec, CD49f clone NKI-GoH3), and APC-conjugated lineage antibody cocktail (1:200; BD Pharmingen). Keratinocytes were sorted on the basis of single cellularity, viability (DAPI), and/or CD34/CD49f status. FACS purification was performed on a FACS Aria system equipped with FACS DiVa software (BD Biosciences).
Microarray analysis Total RNA was isolated from four separate technical replicates of primary mouse keratinocytes infected with MSCV and HRas V12 at 6 dpi using an RNAEasy mini RNA extraction kit (Qiagen). RNA was subsequently labeled and hybridized to Agilent 8x60 one-color gene expression arrays. Bioinformatics analysis was performed using Genomatix software (Genomatix Software GmbH). The microarray data have been uploaded to the Gene Expression Omnibus database with accession number GSE93564.
Figure1. A stem cell signature is increased in senescent cells. (A) Representative heat map of microarray analysis of HRas V12 -induced senescent keratinocytes compared with growing keratinocytes. Example genes are shown to illustrate that stem cell and senescent genes are more highly expressed in senescent cells compared with growing cells. (B) Bioinformatics analysis of genes significantly up-regulated in senescent cells (analysis performed using Genomatix software; full list of up-regulated and down-regulated genes is in Supplemental Table1). (C) GSEA of stem cell genes (Blanpain et al. 2004)  in the gene expression profile of Ras-induced senescent keratinocytes. (NES) Normalized enrichment score. (D) qPCR analysis for skin stem cell and senescence genes in Ras-infected keratinocytes at 4-14 d post-infection (dpi) normalized to vector-infected keratinocytes at 4 dpi.
Acknowledgments We thank  Ryan Driskell  and  Fiona Watt  for technical advice,  Guillaume Filion  and  Panagiotis Papasaikas  for advice on statistics,  Pura Muñoz-Cánoves  for β-actin GFP mice, the  Centre for Genomic Regulation Bioinformatics unit  for assistance, and  Pura Muñoz-Cánoves  and  Juan Valcarcel  for critical reading of the manuscript. M. S. and B. R. were supported with fellowships from the " La Caixa " foundation. This work was funded by grants  SAF2010-18829  and  SAF2013-49082-P  (to W. M. K. ) from the  Spanish Ministry for Economy and Competitiveness , the  Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR)  from the  Generalitat de Catalunya , and  Centre for Genomic Regulation  core funding.
Semithin sections were examined with an Olympus Bx51 TF microscope (Olympus, Southend-on-Sea, UK) and digital images were captured with a CCD digital camera (Roper Scientific GmbH, Ottobrunn, Germany). Seventy-nanometer ultrathin sections stained with uranyl acetate and lead citrate were also examined in a transmission electron microscope (Hitachi H7650, Tokyo, Japan), 80 kV. To characterize the mitochondrial population in the ventral column white and gray matter intermediate zone, the electron microscopy images were analyzed using the counting and area analysis function of the FIJI software. For each mouse (n 5 3 WT and n5 3 PLP-tg 66/66 ), data have been obtained from 100 axons and 10 oligodendrocytes in both gray and white matter.
Mono-and two-dimensional electrophoresis and Western blotting Electrophoresis and Western blotting were performed as previously described  (23). Briefly, for mono-dimensional Western blot frozen spinal cords were homogenized in radioimmunoprecipitation assay buffer and sonicated for 2 minutes at 48C. Proteins of 30-50 mg were separated in polyacrylamide gels and transferred onto nitrocellulose membranes. Proteins were detected with ECL Western blotting analysis system followed by exposure to CL-XPosure Film (Thermo Scientific, Thermo Fisher Scientific, Inc. , Waltham, Massachusetts). Autoradiographs were scanned and quantified using a GS800 Densitometer and Quantity One software (Bio-Rad). For two-dimensional electrophoresis and Western blotting, frozen spinal cords were homogenized in a lysis buffer (180 mM KCl, 5 mM MOPS, 2mM EDTA, 1 mM butylated hydroxytoluene and protease inhibitors), sonicated for 2 minutes at 48C in an Ultrasonic processor UP50H and centrifuged for 5 minutes at 1000 3 g; the supernatant was collected and centrifugated again (5 minutes, 1000 3 g).
Protein identification Protein identification was performed in the Proteomics Unit of the Vall d'Hebron Institut de Recerca (VHIR). Briefly, after excision from gel, proteins were reduced with 10 mM DTT and alkylated with 55 mM iodoacetamide. Enzymatic digestion was performed with trypsin following conventional procedures  (21). After evaporation and re-dissolution in methanol/water (1:2 v/v), 1% acetic acid digests were analyzed by matrix-assisted laser desorption/ionization reflectron time-of-flight (MALDI-reTOF MS). The MALDI-reTOF MS analysis of the samples was performed using a Voyager DE-PRO MALDI-reTOF mass spectrometer (Applied Biosystems, Foster City, CA). Swiss-Prot and GenBank data bases were used for the protein identification from the peptide mass fingerprinting from MALDI-reTOF MS.
Analysis of mitochondria morphology Control and PMD patient fibroblasts were fixed with 4% PFA and mitochondria were stained with the anti-Tom20 antibody and nucleus with DAPI. Cells were imaged by a Leica TCS SL Laser scanning confocal spectral microscope (Leica Microsystems Heidelberg, GmbH, Mannheim, Germany) with a 633 oil immersion objective. To observe individual mitochondria, we acquired z-stack images in series of eight slices per cell with thickness 0. 4 lm per slice. Confocal images were processed using the NIH-developed Image J software. Cells were thresholded to select mitochondria and binary (black and white) images were generated. Then, the perimeter, average size and the circularity of each mitochondria were quantified using the Analyze Particle function.
Inner mitochondrial membrane potential quantification by flow cytometry Treated cells were washed with PBS 1X and incubated with 50nM of TMRE in pre-warmed PBS 1X for 30 minutes at 378C. Next, cells were trypsinized, centrifugated at 1000 3 g for 5 minutes and resuspended in pre-warmed PBS 1X. All samples were captured in a FACS Canto TM recording 5000 cells for each condition and genotype tested. Histograms showing the percentage of depolarized cells were obtained after gating live cells. Data were analyzed with FlowJo Tree Star software.
In some neurodegenerative disorders as Alzheimer Disease (AD), an altered balance in mitochondrial fission and fusion leading to mitochondrial and neuronal dysfunction has been revealed in brain and fibroblasts, with different protein expression level pattern in each tissue  (78, 79). To investigate whether the fission and fusion balance was altered in the PMD model, we measured levels of the main proteins involved in this process: dynamin related protein 1 (DRP1), fission protein 1 (Fis1), optic atrophy 1 protein (OPA1), mitofusin-1 (MFN1) and mitofusin-2 (MFN2), in the spinal cords of PLP-tg 66/66 mice. Unexpectedly, we found a significant induction of Fis1 protein level while mitochondria presented increased quantity and size in the spinal cords of PLP-tg 66/66 mice (Figure  3D ). These data suggest malfunction in mitochondrial dynamics, whereby fission is initiated with proper Fis1 induction, but fails to engage the subsequent steps, resulting in fission arrest and thus, increased mitochondrial size.
Protein oxidation was not observed at 2 weeks of age (data not shown), indicating a phenomenon worsening with time and correlating with the onset of neurological damage. Thus, in PLP-tg 66/66 mice, mitochondrial impairment is linked to an increase in oxidative stress. Bioenergetic failure in PLP-tg 66/66 spinal cords We next studied the possible consequences of protein oxidation on energetic homeostasis by quantifying ATP content in PLP-tg 66/66 mice spinal cords at 6 weeks of age. We found that ATP was reduced by 40% in these samples (Figure  5A ), a direct indication of bioenergetic failure. We also measured the activity of pyruvate kinase (PK) to test the effect of decreased ATP levels on glycolysis. PK activity was significantly increased in PLP-tg 66/66 mice spinal cords (Figure  5B ), suggesting an unsuccessful attempt to offset the mitochondrial ATP deficit by increasing glycolytic activity.
Impaired mitochondrial dynamics of PMD fibroblasts subjected to fission stimuli The above data support that mitochondrial fitness is impaired in PMD. Potential mechanisms for this include an increase in the proportion of defective/damaged mitochondria as a consequence of impaired mitochondrial dynamics and quality control. We chose to verify this hypothesis in the human PMD fibroblasts used above. We first performed immunoblots against the fusion/fission machinery components: DRP1, Fis1, OPA1, MFN1 and MFN2. We found a significant increase of DRP1 and MNF2 protein levels while Fis1 level was decreased, suggesting a general dysregulation of the fission and fusion process (Figure  9A ). Then, control and PMD patient fibroblasts were stained with Tom20 antibody and mitochondria were visualized by fluorescence microscopy.
E. Simulation Platforms Several tools have been created to simulate the performance of a 6TiSCH network. NS-3 is the most widely adopted tool for network simulation. It provides a large database of protocols, tools and propagation models to emulate almost any kind of network. There are several ongoing efforts to add support for IEEE802. 15. 4 TSCH and 6TiSCH 10. The 6TiSCH simulator 11 is a Python-based high-level simulator maintained by the 6TiSCH working group. It allows students vto benchmark the performance of a network implementing the full 6TiSCH stack. OpenSim is the emulator which is part of the OpenWSN project. That is, the OpenWSN firmware can be compiled as a Python extension module, and run within OpenVisualizer, the OpenWSN software running on the gateway computer. In this setup, the same firmware runs, allows debugging and performance estimation without requiring real hardware.
F. Miscellaneous Tools The channel-hopping nature of a 6TiSCH networks makes it challenging to capture over-the-air traffic, as typical sniffers can only listen to a single channel at a time. Beamlogic's sniffer 12 listens to all 16 frequencies in the 2. 4 GHz band simultaneously, thus allowing to capture all packets on air. The Argus project https://github. com/openwsn-berkeley/argus builds upon it to allow co-located students to share one sniffer through the Internet. Recent versions of Wireshark 13 come with 6TiSCH dissectors, including IEEE802. 15. 4-2015 and the 6TiSCH 6top protocol. These dissectors are developed and maintained by the OpenWSN project team 14. 10 https://github. com/EIT-ICT-RICH/ns-3-dev-TSCH 11 https://bitbucket. org/6tisch/simulator/ 12 http://www. beamlogic. com/ 13 https://www.
ACKNOWLEDGMENTS This work is partially supported by the  EU H2020 F-Interop  and  H2020  ARMOUR projects, Inria through the DIVERSITY associate team, the  Spanish Ministry of Economy  and the  FEDER regional development fund  through the  SINERGIA  project ( TEC2015-71303-R ), the  Center for Cyber-Physical Systems  and the  Internet of Things at the University of Southern California.
We extend the application of LBP descriptor to disparity map in order to incorporate disparity information in image representation by simply concatenating the two descriptors (LBP from gray-scale image and LBP from disparity map). This produces a new descriptor named D-LBP. The integration of disparity information in image representation provides depth information which should be helpful for place recognition, especially in complex environment situation. Indeed, image description using features LBP and HOG and the depth information will permit reducing perceptual aliasing problems related to visual place recognition. As will be shown in our experiments, features combination permits achieving better recognition performance than single feature. Also the performance of place recognition is compared with the state-of-the-art FAB-MAP algorithm: the achieved  1 scores on four tested datasets using our approach are better than those resulting from FAB-MAP method.
1). (iii) A speeding-up of the place recognition method is achieved by approximating the Euclidean distance between features with hamming distance over bit vectors obtained by locality sensitive hashing (see Section 5. 3). The rest of this paper is organized as follows. Firstly, the LBP descriptor and several of its variants as well as HOG feature are introduced in Section 2. Then, in Section 3, the proposed approach is described in detail. Section 4 deals with the presentation of the tested database and the used performance evaluation parameters. The obtained results are presented and discussed in Section 5. Finally, conclusions and future works close this paper (Section 6).
Final Matching Validation. For a given current image pair 𝐼 test  , the validation of matching candidate from the training database is based on the ratio SS  , calculated as follows: SS 𝑖 = 𝐷 𝑖,𝑚1 𝐷 𝑖,𝑚2 , (22) where  ,1 and  ,2 are, respectively, the first and second minimum distances between the current image multifeature V test  As said before, the lower the distance is, the more similar the images are. The potential matching candidate is the image   (the one giving the lower distance with the testing image). However, if the second best matching candidate provides a distance very close to the first one, this means that the matching algorithm provides two confused solutions. In this case, we propose to ignore the matching result and consider that the testing image has no matching image. For that, a threshold Th is applied to the ratio SS  , which takes its values in the range [0 1].