dataesr/scientific-paragraphs-categorization huggingface dataset:
Determining the size, and water content in fog particles is important to predict the attenuation. However, this information is difficult to achieve and not always available at the FSO link installation site. Therefore, researchers proposed empirical models that depend on visibility data which is widely available from meteorological stations in cities. In fact, the origin of the fog attenuation models that use visibility data, comes from the definition of atmospheric visibility itself  [17]. Visibility is defined as the distance to an object where the image distinction drops to a certain percentage of what would be if the object were nearby instead  [11] ,  [18]. Image distinction that drops to 2% and 5% is considered in visibility definition. The visibility is measured at 550 nm which represents the maximum intensity of solar spectrum.
This non-linear method tries to refine the parameters by iterative optimization procedures to compute the parameter estimates. Using the nonlinear least square regression tool in MATLAB and the measurements data, we have derived a power law decay model that properly represents the attenuation of the fog. This model is given by A ¼ k Â V ÀðaþbÞ ; 0 V 1 km; 0:65 1:55 m ( 6 ) where V is the visibility in kilometers; is the wavelength in micrometers; and k , a, and b are constants. This proposed model is consistent with our discussion in previous section considering visibility and light wavelength. The model proposes lower attenuation as visibility and/or light wavelength increases. This is shown from the model simulation in Fig. 1(h) , where 1550 nm wavelength has lower attenuation than 550 nm.
The parameters' values (k , a, and b) are derived using the nonlinear least square regression in MATLAB. In the next section, we estimate the values of these three parameters depending on a classification we make of the available measurements. Moreover, we derive a general model that can be applied without prior information about the fog event and location. Then based on prior knowledge about the fog type or the location, we propose specific parameters values (i. e. , for special cases) that improve our estimation of the signal attenuation. Therefore, once we get some information about the category of the fog event or its location, we can reduce the estimation error and improve our expectation. The first special case depends on the fog type whether it is dense or moderate. For convenient analysis and discussion in the remaining of this paper, we refer to this as class alpha (or ).
In a second step, we classify the measured data based on the location whether this has been in France, Italy, USA, or the Czech Republic. Again, for convenience, we refer to this as the class beta (or ). Our attenuation model will have specific parameters' values in each type of classification or. In a third and last step, we avoid classification of our data and try to develop a general model. The model developed for this general case corresponds to compromised settings in which we ignore or omit any prior information about the fog event. This class is referred here as compromise/unified class. For each of these three classes, we determine the optimum value of the three parameters in our proposed attenuation model. We then evaluate the performance of our model in contrast to those of existing literature models.
Fog Type Alfa-Classification In Fig. 1 , we have seven measurements, three of them were measured in moderate fog and the others are measured in dense fog. Using the nonlinear least square regression in MATLAB, the parameters' values are found and listed in Table  2. As illustrated in this table, the optimum value of k , a, and b clearly changes depending on the fog type from dense to moderate. It is reasonable to expect that much more experimental measurements, covering more accurate levels of fog, may help improving the obtained results and enhance the accuracy of our channel attenuation model. which shows slightly higher RMSE. On average, the proposed model achieves approximately 12 dB less RMSE than literature. Moreover, in order to test the goodness of fit of the proposed model, the R 2 measure is used.
Substituting (  16 ) and (  25 ) in  (24) , the distribution of the channel state is given by f h ðhÞ ¼ 1:733 lhzð1 À zÞ exp À 1 2 þ ln z 1 À z 2 (26) where z ðÀ4:343l À1 lnðhÞ À Þ=, and the channel state is limited to expðÀ0:23LÞ ! h ! expðÀ0:23ð þ ÞLÞ. Using  (25) , we can study the performance of FSO system under foggy weather in terms of average BER, and channel capacity. Because Johnson SB distribution has four parameters, finding closed form expression is difficult. We hence proceed to our performance evaluation through numerical simulation. We have used Wolfram Mathematica 10 software which uses adaptive algorithms that recursively subdivides the integration region as needed. Using this software, we solved the complex integrations numerically.
Background: To examine trends in mortality, costs and in-hospital management and outcomes of severe COPD exacerbations admitted in France. Methods: Patients hospitalized from 2007 to 2012 with COPD exacerbation as the primary diagnosis were identified from the exhaustive French medico-administrative hospitalizations database records. Four groups of severe COPD exacerbations were defined: hospitalisation in a general ward (GW) without acute respiratory failure (ARF), GW with ARF, ICU without invasive mechanical ventilation (MV), and ICU with MV. Results: A 15. 48 % increase in admissions from 113 276 in 2007 to 133 497 in 2012 was recorded. Age (+9. 9 months), gender (-2. 5 % of male) and length of stay (-0. 29 day) slightly changed while the number of ICU admissions increased markedly (+41.
-What is the key question? What are the precise trends in COPD admissions triggers and management and outcomes ? -What is the bottom line? The incidence of severe COPD exacerbations and the proportion of ICU-managed patients are increasing in parallel with total costs and mortality rates especially related to pneumonia. -Why read on? An exhaustive and consecutive national database analysis identified social determinants, cause of admissions and medical acts performed and identified great changes in the management which should be reappraised.
Study design and population This observational study used exhaustive standardized data from the national PMSI database ("Programme Médicalisé des Systèmes d'Information"), prospectively collected from 2007 to 2012. This dataset contains medico-economic information about admissions based on the International Classification of Diseases (ICD-10) classification from both public and private hospitals in France. It includes all data collected by healthcare providers before submission to the social welfare office. The study focuses on admissions for COPD from 2007 to 2012 (end of stay before January 1, 2013). Hospitalization was defined by a stay of at least one night in a hospital. All patients with a permanent address in France were included. Patients with codes for chronic airway disorders other than COPD (e.
The data collected included age, gender, length of stay, vital status at discharge, month of discharge, patient home address and hospitalization costs for the French Health Care System. The top-five diagnoses quoted as related to the exacerbation were also analyzed. The most frequently reported medical acts relevant for managing COPD exacerbations were analyzed. Costs related to each stay was computed according to French'policy which takes inflation into account. Socio-professional categories, CMU (Free Universal Health Care, corresponding to the lowest socioeconomic categories, coverage rate, average household income) in each French 'départment' were obtained from the INSEE (Institut National de la Statistique et des Études Économiques).
Because the effectives are so large, virtually all of the p values are significant which makes them less useful. Subsequently, strong confidence in p > 0. 05 was considered for non statistical significance. The statistical analysis was performed by the medical statistical department of the Montpellier University Hospital using SAS (version 9. 3; SAS Institute; Cary, NC, USA) and R (version 3. 1. 1) statistical softwares.
4 ). A monotone decrease in bronchoscopy was noticed (-22. 6 %). Seasonality of admissions and deaths are presented in Fig. 5a  and b  respectively. Data distributions were similar from 2007 to 2012. A non-uniform distribution of annual admissions (p <. 001) and deaths (p <. 001) were observed with a peak in incidence during the fall-winter season. This non-uniform distribution was also observed for admissions in each category. The seasonal peak was recorded at different dates each year, close to but not perfectly matching with FLU-peaks. Spatial distribution of age-standardized hospitalization rates in 2012 is presented in Fig. 6a. Nearly three-fold regional heterogeneities were observed as admission rates ranged from 236 per year for 100 000 inhabitants to 686 per year for 100 000 inhabitants (p <.
001). A north to south gradient was visually noticed. Regional variations from 2007 to 2012 are presented in Fig. 6b , displaying areas where COPD admissions increased the most. Regressions between age-standardized hospitalization rates and economic data of the inhabitants of the 'départment' were computerized. Lower age-standardized hospitalization rates in 2012 were associated with higher proportions of upper socio-professional categories (p =. 004). There was no significant association with the CMU (Free Universal Health Care) coverage rate (p-value = 0. 74). In contrast, when analyzing the variation of age-standardized hospitalization rates between 2007 and 2012 (areas were it increased the most), a negative correlation with average household income was found (p <. 001, Adjusted R-squared = 0.
A progressive but massive replacement of chest X-rays by CT scans has been recorded while less spirometries and bronchoscopies were performed. The main strengths of the present study rely on the exhaustiveness of data collection, largely resulting from enforced legal reporting obligations. Additionally, we have limited the classification bias by means of a strictly standardized patient identification algorithm which is strict and identical for all and has already been validated  [17] , with a view to sideline differential diagnoses and stays for which patients presenting COPD had not been admitted because of an exacerbation. Lastly, the fact that this study spans a period of 6 consecutive years makes its longitudinal quality a crucial strength potentially generalizable to most westernized countries.
Weaknesses inherently associated with such studies (lack of clinical data, missing values of pulmonary function tests, etc. ) impose not to over-interpret any of those findings. Nonetheless, since 2007, a threepronged quality control approach is applied to all dossiers (involving the coding doctor in charge of the patient, the dedicated doctor of the institution and the payer), which is the reason why we started our analysis at this period. We selected stays where COPD was quoted as the primary diagnosis only to limit the risk of coding bias especially in non specialized centres; subsequently our findings are quite specific of the disease but probably underestimated. Biases related to the epidemiological nature of the database are incontrovertible why trends assessed during six consecutive years likely represent the most meaningful part of the present study.
Interestingly, the regions where the incidence increased the most are not the same and it can be hypothesized that occupational COPD is progressively vanishing. Data obtained from the monitoring of climatic and air pollution variables will probably be important to cross with the present epidemiological data in order to identify potential warnings usable at the individual level. We found a relationship with socioeconomic data, a finding largely reported before. More unexpected, COPD admissions increased over the 6-year period of the study in the wealthiest areas, suggesting a change in the profile of COPD toward a richer, more feminine and less occupationally-exposed population. Seasonality of admissions and deaths were reported before  [25]. In the present data base, the peak of admission was disconnected from the FLU-peak some but not all years, and this represents a critical field of investigation.
Here we describe methods for the successful genetic manipulation of LUHMES cells in order to create targeted protein knock-out, disease-causing point mutation knock-in, and large tag knock-in cell lines. The combination of targeted mutagenesis with rapid generation of mutant neuronal cells provides a potentially valuable tool for neuroscience. These manipulated cell lines may complement in vivo datasets as disease phenotypes obtained using mouse models can be coupled with electrophysiological and biochemical data from human neurons in order to bridge the gaps between disease causing mutations, neuronal malfunction and whole organism pathophysiology. The ease of high-throughput differentiation of this cell line in 96-well plates opens the door towards drug screening programs like those already being pursued using hiP-SCs  (Cao et al.
Two rounds of mutagenesis PCRs were used to incorporate first the R306C point mutation, and then two PAM-abolishing point mutations for two separate sgRNAs to create the final targeting vector. Tissue culture LUHMES cell (ATCC cat# CRL-2927, RRID CVCL_B057, kind gift from Dr Tanja Waldmann) tissue culture medium and methods were as described in Reference  (Scholz et al. , 2011)  with some minor alterations. All vessels were coated in poly-L-ornithine (PLO) and fibronectin overnight at 37°C. Proliferating LUHMES cells were seeded at 2x10 6 cells/T75 every 2 days. For differentiation, 2. 5x10 6 cells were seeded in a T75 for the first 2 days of the protocol and on day 2 cells were seeded as follows: 8x10 6 cells/T75 and 0. 15x10 6 cells/coverslip. During differentiation a half-media change was performed on day 6 and neurons were harvested for protein or fixed for immunostaining on day 9.
Other microscopy Phase contrast images were taken on an Eclipse TS100 inverted microscope (Nikon) using QCapture Pro software version 5. 1. 1. 14 (QImaging). UV fluorescence was used to take GFP pictures of cells. The IncuCyte microscope (Essen Biosciences) was used to take phase contrast and GFP fluorescent images and ZOOM software (Essen Biosciences) was used to extract and export the images.
Some benefits from position resolution The position resolution of AGATA can be used for the determination of nuclear structure observables with a new degree of sensitivity, giving access to new physics opportunities. Few selected examples from the Legnaro and GANIL campaigns will be given in the following. The left tail consequent to inefficiency in charge collection due to neutron damage is evident in the spectra in red (no correction applied) and it is significantly reduced in the spectra in blue, obtained after correction for neutron damage (resolution for the sum of all segments from 5. 9 keV to 2. 9 keV after correction. The data are taken during the Legnaro campaign (picture taken from  [9] ).
Data analysis Microarray analysis. The microarray data of KDM5A-/-MEFs stably transduced with either empty vector (EV), KDM5A WT or KDM5A-H483A mutant (MT) were used for the analysis. Raw profiling data were normalized with affy::RMA and further processed using the Limma R package  (59). A list of differentially expressed genes in cells overexpressing KDM5A WT compared to control (EV) cells were called using a FDR <0. 1. RNA-seq data analysis. Sequencing reads were aligned to the mouse reference genome (version mm9) using Bowtie2  (60). Splice-junction reads were handled by creation of a pseudo-splice genome, similar to the strategy utilized in RSEQTools  (61)  and aligned using Bowtie2 with standard parameters. Reads were filtered post-alignment for a MAPQ score greater than or equal to 30.
The number of exon reads for all RefSeq genes was counted using Subread  (62)  and differential expression analyses were performed using DESeq2 (FDR <0. 05, paired analysis)  (63). Clustering of RNA-sequencing data was performed using Mfuzz  (64) , only including genes with a membership value >0. 5 for subsequent analysis. For both microarray and RNA-seq data, functional enrichment analysis was performed with HOMER (65) using pathways related to metabolism from the KEGG database annotation  (66, 67)  and WikiPathways  (68). ChIP-seq data analysis. ChIP-seq data sets were mapped to the mm9 genome with STAR (69) set to not map across splice junctions (-alignIntronMax 1 -outSJfilterIntronMaxVsReadN 0). Tag directories for each ChIP-seq library were generated using HOMER  (65) , where KDM5A and KDM5C ChIP-seq tag counts were normalized to 10M reads.
For analyses of promoter features, we retrieved 2,742 promoters containing TATA boxes from the Eukaryotic Promoter Database (EPDnew)  (72). We overlapped these promoter sequences with the respective gene groups and considered a gene as having a TATA box in the promoter if it was found within ±100 bp of the TSS of the RefSeq-annotated gene. We retrieved the position of 16,026 CpG islands from the UCSC genome browser  (73)  and considered a gene as having a CpG-island promoter when the TSS of the RefSeq-annotated gene overlapped a UCSC CpG island. A group of 17,515 RefSeq-annotated promoters were included as a control. Statistics. Two-tailed Student's t-test was used to determine significance in qPCR analyses. The P-value for the difference between data in box plots was computed using Wilcoxon rank sum test.
(D) Box plot showing log2 normalized KDM5A signal at day 2 (as tags per kb) in the KDM5A-occupied promoters of all analyzed genes (gray), as well as cell cycle genes from the KEGG database annotation that are either induced (green) or repressed (red) from day 0 to day 2 of differentiation. (E) ChIP-seq profiles of KDM5A at the Cdc6, Cdc20, E2f1, Plk1, and Ccna2 loci at day 2. C and D: P-value: # P <0. 05, Wilcoxon rank sum test as indicated. (F) ChIP-qPCR of KDM5A (left) and KDM5C (right) binding at promoters of selected pro-proliferative genes in control (blue) and KDM5 KD (red) cells presented as percent recovery of input. P-value: *P <0. 05, **P <0. 01, and ***P <0. 005, positive targets versus negative control (neg. control) as well as control versus KDM5 KD, Student's two-tailed t-test.
ACKNOWLEDGEMENTS The authors thank  Dr Patrick Seale ,  University of Pennsylvania  for kindly providing the immortalized brown preadipocytes utilized for this study and  D. William Kaelin ,  Dana-Farber Cancer Institute, Harvard Medical School  for contributions to the microarray data. Furthermore, the authors thank members of the  Mandrup laboratory  for experimental support and fruitful discussions. FUNDING  Danish Independent Research Council | Natural Science ;  Novo Nordisk Foundation ; research grant from the  Danish Diabetes Academy  supported by the  Novo Nordisk Foundation ; grant from the  VILLUM Foundation  to the  VILLUM Center for Bioanalytical Sciences at University of Southern Denmark ;  American Cancer Society   Research Scholar Grant  [ RSG-13-384-01-DMC  to Q. Y. ]. Funding for open access charge:  Danish Independent Research Council | Natural Science.
The fossil record shows that iguanian lizards were widely distributed during the Late Cretaceous. However, the biogeographic history and early evolution of one of its most diverse and peculiar clades (acrodontans) remain poorly known. Here, we present the first Mesozoic acrodontan from Africa, which also represents the oldest iguanian lizard from that continent. The new taxon comes from the Kem Kem Beds in Morocco (Cenomanian, Late Cretaceous) and is based on a partial lower jaw. The new taxon presents a number of features that are found only among acrodontan lizards and shares greatest similarities with uromastycines, specifically. In a combined evidence phylogenetic dataset comprehensive of all major acrodontan lineages using multiple tree inference methods (traditional and implied weighting maximumparsimony, and Bayesian inference), we found support for the placement of the new species within uromastycines, along with Gueragama sulamericana (Late Cretaceous of Brazil).
Introduction The phylogenetic position of Iguania (chameleons, agamas, iguanas and New World lizards such as anoles) within Squamata is conflictive between morphological  [1, 2]  and molecular or combined datasets  [3] [4] [5] [6] [7] , being recovered as a nearly diverging branch of the Squamata tree in the former, and nested with snakes and anguimorph lizards (monitors, gila monsters, glass 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons. org/licenses/by/4. 0/, which permits unrestricted use, provided the original author and source are credited.
Phylogenetic procedures Character scores from the new taxon and Gueragama sulamericana  [21]  were added to a dataset comprised of morphological and molecular characters  [34] , and analysed under traditional maximum-parsimony (MP), implied weighting maximum-parsimony (IWMP), and Bayesian inference. Although a larger morphological dataset is available  [25] , it contains far less mandibular characters than  [34] , and only one character would have been scored for the new taxon described herein. The resulting dataset includes 30 taxa, with 22 mandibular morphological characters and 1968 molecular characters of mitochondrial data from ND2 and adjacent loci. Out of these, 544 molecular characters were identified as invariable sites (27% of total number of sites) and were removed for the MP analyses.
Therefore, 22 morphological and 1424 molecular characters (total of 1446 characters) were analysed under MP. The MP analyses were run in TNT v. 1. 1 64-bit (no taxon limit)  [35]  using 1000 RAS and tree search with TBR (no gains in the sampling of local optima were obtained using the New Technologies algorithms). IWMP is designed to downweight highly homoplastic characters  [36, 37]  and was also used to analyse the dataset in TNT, using a K = 3. 0. A third analysis using Bayesian inference was conducted in MrBayes v. 3. 2. 5 [38]. Morphological data were analysed under two distinct model parameters for morphological data: Mk + F81 + Γ and Mk + F81 + LN, whereas the molecular data were analysed under a GTR + I + Γ model (the best substitution model for this molecular dataset  [34] ). All remaining parameters were set with the default options of MrBayes.
The number of generations was set to 1 000 000, with six chains and three swaps per cycle (sample frequency = 1000 and burn in fraction of 0. 25). The Bayes factor based on harmonic means of the model likelihoods  [39, 40]  was calculated in order to evaluate whether a gamma (Γ ) or a lognormal (LN) distribution had a better fit to the morphological data. As recent studies indicate a LN distribution of rate variation might be preferred to the most widely used gamma distribution  [41, 42]. However, no significant differences were observed-2 log e (B 10 ) < 2-and therefore only the analysis using a gamma distribution (default option) is reported herein. In datasets with relatively small numbers of taxa (less than 50 taxa-such as the one herein), Bayesian inference has been consistently demonstrated to provide greater accuracy and efficiency in finding the correct trees when evolutionary rates vary among branches for molecular characters  [43] [44] [45] , which is a biologically sound assumption for both morphological and molecular characters  [46] [47] [48] [49] [50].