dataesr/scientific-paragraphs-categorization huggingface dataset:
46 proteins (16%) have interface residues missing in the unbound form (on average four missing residues per component). We will refer to these atoms and residues as 'missing' even though they are clearly present in the bound form. Missing atoms constituted $4% of the total interface atoms in the data set and 12. 5% for the 95 structures, with the most extreme being MAPKAP kinase 2 (PDB entry 3fyk;  Anderson et al. , 2009) , in which 52% of the interface atoms (39 of 80 interface residues) are missing. Usually the interface and non-interface residues are interspersed in a given stretch of missing residues, and there are 34 such cases (12%) with two  or more missing residues, the sequences of which can be seen in Supplementary Table  S2. Such regions undergo a disorderto-order transition upon binding to their interacting partner, and have special importance in elucidation of the structurefunction relationships of proteins (van der  Lee et al.
Of the missing residues, 49% become core interface residues (those which have atoms fully buried in the interface); however, these contribute 70% of the BSA; the BSA values of core residues missing in U exceed those of the rim with a P value of 0. 01. These observations are in conformity with a previous report  (Chakrabarti & Janin, 2002). In the 46 proteins missing one or more interface residues, the latter contribute 17% of the BSA in the bound state (190 AE 278 A ˚2 of 1017 AE 568 A ˚2); however, the distribution is rather large, ranging from $57% in the MAPK-activated protein kinase 2 (MK2) part of the assembly formed with p38 (PDB entries 3fyk and 2oza;  Anderson et al. , 2009; White et al. , 2007)  to 0. 1% in the complex formed by Ran-specific GTPaseactivating protein with GTP-binding nuclear protein RAN (PDB entries 1yrg and 1k5d;  Hillig et al.
, 1999; Seewald et al. , 2002) , with 39 and one residues, respectively, missing in the U state. In the 95 structures with missing atoms, each missing atom contributes 11. 5 AE 6. 8 A ˚2 to the BSA in the bound state, while the remaining ('non-missing') atoms each contribute 9. 4 AE 1. 5 A ˚2 (P = 3 Â 10 À21 for the two populations with 977 and 6945 atoms, respectively). Thus, at the local level the interface elements that undergo a disorder-to-order transition upon forming the complex bury somewhat more surface than other atoms in the interface. However, at the database level the effect is more marked; structures for which no interface residues are missing in the U form bury on average 786 AE 336 A ˚2 in the complex, while structures with one or more interface residues missing bury more: 1017 A ˚2, as given above (P = 0.
Grouping Ile, Leu, Met, Phe, Trp and Tyr as nonpolar and Arg, Asn, Cys, Gln, Glu, Gly, His, Lys, Ser and Thr as polar, the difference in B factors is significant The structure of the interface formed in human tissue inhibitor of metalloproteinases 2 when it forms a complex with type IV collagenase (PDB entry 1gxd;  Morgunova et al. , 2002) ; the inhibitor is denoted in cyan and the enzyme in violet. Surface representations of the proteins are displayed. The U state (PDB entry 1br9;  Tuuttila et al. , 1998)  is not shown here. The interface residues are split into two categories: the residues missing in the unbound structure are in blue and those seen in both the U and B forms are in orange. The missing segment  (183) (184) (185) (186) (187) (188) (189) (190) (191) (192)  is composed of both non-interface residues (shown in red) and interface residues (blue).
Data analysis As described in Eton et al,  18  framework analysis  23  was used on the initial Mayo Clinic interviews to identify thematic patterns in the data and to derive the preliminary conceptual framework of treatment burden. The preliminary framework (Figure  1 ) was next applied to the narrative transcripts from the subsequent HCMC interviews. Three study team members (DE, JR, and JE) independently reviewed and coded these transcripts, subsequently meeting to discuss each one and to arrive at a consensus on coded text. NVivo software (Version 10; QSR International Pty Ltd, Melbourne, Australia) was used to help organize the narrative data. For issues that were not identified by codes in the working (preliminary) framework, discussion ensued, and consensus was reached to either modify an existing code or to add a new code.
After coding of all interviews, a final review was done, and the conceptual framework was modified accordingly. Focus group data were used to 1) test the fitness of the original conceptual framework arising from the interviews; and 2) clarify any new issues not represented in the current version of the framework. This assessment was based upon an analysis of field notes and audio files obtained from the groups. 24 The facilitator (KT) and three study team members (DE, JE, and JR) took detailed notes and immediately debriefed findings after each group session. Upon completion of the first two focus group meetings, top-line reports (preliminary reports produced quickly) were distributed to two clinical members of the study team (DB and ML) for review and feedback. 24 After completion of all four focus group sessions, two team members (DE and JR) compiled the data into a saturation grid that outlined the themes discussed in each group.
Patient characteristics Thirty-two Mayo Clinic and 18 HCMC patients were interviewed for this study. Descriptive characteristics of these patients appear in Table  1. The table is reprinted from our previous report of factors that may lessen treatment burden,  20  a report featuring a complementary analysis of data from the same set of patient interviews. In brief, Mayo Clinic participants were slightly older, more formally educated, and more likely to be married or living with a partner than were HCMC participants. More racial/ethnic minorities were represented in the HCMC sample (89%) than in the Mayo Clinic sample (3%). Median number of self-reported health conditions for both samples was five. Twenty-five patients participated in one of four focus groups (five to eight participants per group).
Acknowledgments The research reported in this publication was supported by the  Sponsorship Research Board of St Mary's Hospital (Rochester, MN, USA) ;  Mayo Clinic's Center for Translational Science Activities , through grant number  UL1 RR024150  from the  National Center for Research Resources , a component of the  National Institutes of Health ; and the  National Institute of Nursing Research of the National Institutes of Health  under award number  R21NR012984.
3C ). No significant change of expression was observed for border genes present in two copies in the genome of Ts3Yah and Ms3Yah mice. Finally, genes found differentially expressed in Ts3Yah and Ms3Yah were tested by QRT-PCR. Most of the genes were validated and found to be significantly deregulated in Ms3Yah muscle as in the microarray analysis, although deregulation was only observed for Wnt4 in Ts3Yah animals (Fig. 3C ). We further analysed our microarray data with the software tool 'Gene Set Enrichment Analysis' (GSEA; http://www. broad. mit. edu/gsea) [39, 40]. This analysis helps find correlation of gene expression. The genes belonging to a defined pathway are ranked together according to their change in expression in Ts3Yah and Ms3Yah muscles compared to their diploid controls and calculating a maximum enrichment score (ES) for each gene set.
Gene expression analysis RNA extraction. Total RNA was extracted from frozen gastrocnemius muscles from Ms3Yah and Ts3Yah and their respective control diploid littermates (n = 6) using TRIzol Reagent (Invitrogen) followed by RNase-Free DNaseI (Qiagen) treatment. Microarray hybridisation and data analysis. Biotinylated cDNA were prepared from total RNAs and hybridised to Illumina's MouseWG-6 v2. 0 Expression BeadChips. Raw data was normalised by the quantile method [97], and values were log transformed. A principal component analysis was done using the GeneSpring software (Agilent Technologies) and showed that one diploid sample corresponding to the Ts3Yah control group had a completely different profile. This sample was excluded from the analysis. The data were analysed separately for Ms3Yah and Ts3Yah, comparing each set to its own littermate diploid group.
The genes expressed above the background level were determined by selecting the probe sets having a signal value above the 35th percentile of all expression values in at least one array. This corresponded to a threshold value above 90 in raw data (6. 49 in log scale). The genes differentially expressed were selected by statistical analysis (Student's t-test p<0. 05 between transgenic mice and their respective diploid littermates). Verification was done to ensure that selected probes have acceptable false discovery rate (<10%, [98]), and resampling analysis was used to make 1,000 permutations of the samples for each probe in order to calculate the false discovery rate p-value (Zoe software; http://wwwmicroarrays. u-strasbg. fr/base. php?page = analysisExpressionFilterZoeE. php). FC was calculated as a ratio of averages from transgenic and diploid signals with values <1 representing probes that are underexpressed in the transgenic (Ts3Yah or Ms3Yah) compared to diploid controls and values >1 representing probes that are overexpressed in the transgenic compared to the diploid controls.
clustering was carried out on significantly deregulated genes (p<0. 05) with Cluster 3. 0 software [99] using Euclidian distances to calculate the distances between the genes and between the samples. The calculated distances were then clustered by complete linkage clustering. The red-to-green colour scale represents the mean-adjusted expression values, where red corresponds to higher expression and green to lower expression. Gene Set Enrichment Analysis (GSEA) was used to test sets of related genes that might be coordinately deregulated in Ts3Yah and Ms3Yah gastrocnemius muscles  [40, 41]. The whole transcriptome output lists of Ts3Yah and Ms3Yah differentially expressed genes, together with their diploid controls, were submitted to the GSEA tool, and the functional gene sets containing curated biological pathways from the MSigDB C2 curated database were tested  [40].
mtDNA quantification by quantitative real-time PCR DNA was isolated from muscle tissue, and quantitative PCR using the TaqMan technology was performed in triplicate to determine the relative quantity of the mitochondrial DNA marker cyclooxygenase 2 (Cox2) and the genomic DNA marker myxovirus resistance 1 (Mx1) gene. mtCOX2 and Mx1 primers were purchased from Sigma-Aldrich and TaqMan MGB probes for mtCOX2 and Mx1 from Applied Biosystem. PCR conditions were as follows: (1) 50°C for 2 minutes, (2) 95°C for 10 minutes, (3) 95°C for 15 seconds, and (4) 60°C for 1 minute (steps 3 and 4 were repeated 50 times). The results were calculated from the difference in threshold cycle (ΔCT) values for mtDNA and nuclear-specific amplification. Data were expressed as mtDNA in transgenic mice relative to diploid control groups.
SDH and immunofluorescence staining on frozen muscle sections Tibialis anterioris muscles and soleus tissues were collected from 5 to 7 mice aged 4 to 6 months per genotype and immediately frozen in 5 minutes and 3 minutes, respectively, in isopentane cooled in liquid nitrogen (-190°C). A total of 10 μm thick serial sections were obtained using a cryostat (Leica CM3050) at -25°C. The sections were processed for succinate dehydrogenase (SDH) staining (S1 Text). Alternatively, type I fibres in tibialis and soleus were characterised by MyHC-I immunostaining using the primary monoclonal mouse antibody against type I (M844, Sigma) as described in S1 Text. Quantification was done by counting muscle fibres using the ImageJ software (W. Rasband, NIH; http://rsb. info. nih. gov/ij/) on at least three consecutive sections.
Statistics Unless otherwise stated, statistical analyses were performed comparing two groups, the Ts3Yah or Ms3Yah and their diploid control littermates, using Student's t-test when appropriate or the nonparametric Mann-Whitney rank sum test. For tests requiring serial recordings, data were analysed by two-way ANOVA using the SigmaPlot software. Data are presented as mean ± sem. Significant threshold was set for p<0. 05.
Acknowledgments We thank  Feijun Luo ,  Laurence Jeronne ,  Caroline Colombier ,  Francoise Levavasseur , and  Valérie Nalesso  for their participation in the creation of the Ts3Yah and Ms3Yah models. We are also grateful to  Ivana Prokic ,  Thanuja Gali Ramamoorthy , and  Daniel Metzger  for their help in muscle histological analysis. We thank members of the research group, of the  IGBMC laboratory, of the ICS, and of the AnEUploidy consortium  for their helpful comments (www. aneuploidy. org). We are grateful as well to the animal caretakers of the ICS.
The project was supported by the  French National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg  and the  "Centre Europeen de Recherche en Biomedecine" , the " Fondation Jerome Lejeune ", and the  European commission  ( AnEUploidy  project  LSHG-CT-2006-037627  to YH, MD and SEA). The region has a peripheral impact on locomotor activity by altering muscle function. laboratory of SEA was supported by grants from  The Swiss National Science Foundation  ( 144082 ), the  EU   AnEUploidy  project ( LSHG-CT-2006-037627 ), and the  ERC  ( 249968 ). This study been supported by  French state funds through the "Agence Nationale de la Recherche " under the  frame programme Investissements d'Avenir labelled   ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07   PHENOMIN.
Motivations In avionics equipment, embedded software is usually a set of tasks that are executed under the control of a scheduler, and that interact with thirdparty libraries through pre-defined API, e. g. libc, mathematical library. Each task is associated to local deadlines, that come up periodically or are triggered by external events. Ensuring that each task will meet its deadline requires the following analyses: A task-per-task evaluation of individual needs for computation time. That stage is called "Worst Case Execution Time Evaluation" (WCET). The leading idea consists in exploring jointly hardware and software's most unfavorable behaviors. A global verification that scheduling will grant all tasks their needed amount of computation time before they reach their deadlines. That can be performed through simulation or offline tests.
Nevertheless, sanity checks do exist in the binary and the WCET analysis tool cannot differentiate them from the regular nominal control flow. This is illustrated on Figure  5  and Figure  6. Figure  5  provides an overview of CFG computed from a binary code that performs a Fibonacci computation. We used a non-optimized recursive Fibonacci implementation provided as an example with aiT  [14]. CFG were also computed by aiT with a version for MPPA many-core processor developed by Kalray. While it is not appearing in the original source code, for each recursion, the program checks for a possible overflow, responsible for most of the Fibonacci function control, and the complexity of the CFG depicted on Figure  5. Assuming that the domain usage analysis has already being performed, these sanity checks are useless and will never be triggered.
That means they consider WCET evaluation method as fixed and alter the hardware and/or software environment to make them applicable. Several approaches have extended existing WCET evaluation methods to take interferences into account on a closed set of tasks. Nowotsch  [36]  proposes the notion of Interference Sensitive WCET (isWCET), which extends the analysis flow implemented in aiT. It alters the processor's model with an interference penalty that is obtained by gathering a profile of resource usage for each task. That operation is also performed by aiT. In  [37] , Bin proposes a set of benchmarks that stress selected components on a processor. Then she defines the notion of signature over a component of the processor, that roughly corresponds to the level of interferences inside the component with regard to the amount of incoming traffic.
g. ,  Widlowski et al. , 2013) ; and 3) to mimic in situ measurement protocols in an effort to a) characterise their inherent uncertainties with respect to their target variables at coarser spatial scales, e. g. Jonckheere, Nackaerts, Muys, van Aardt, and Coppin (2006) , b) optimize sampling schemes and measurement devices for specific biomes, test sites, and/or illumination conditions, and c) support validation efforts of satellite-derived essential climate variables as suggested by the Global Climate Observing System, e. g. ,  Bojinski, Verstraete, Peterson, Richter, Simmons, and Zemp (2014). Efforts to assess the quality of remotely sensed data (whether real or simulated) inevitably require access to a reliable reference. However, this in itself is not sufficient to determine whether a candidate method or dataset is actually good enough for a particular task or not.
To make that decision also requires information on how close an item must match the reference, or better still, how often the candidate method must fall within a predefined tolerance interval from the reference in order to be declared acceptable. This type of quality evaluation is deeply rooted in the manufacturing sector and also in legal metrology where it is referred to as conformity testing (e. g. ,  JCGM-106, 2012). Over the years these communities have developed methods and rules (of thumb) allowing to explicitly account for or to limit the influence of uncertainty in either the reference and candidate methods or datasets. This body of knowledge is also applicable to a remote sensing context, where the idea of compliance testing is gradually gaining in prominence due to the regular publication of accuracy criteria for quantitative remote sensing products of relevance in a climate change context (e.
RAMI-IV actual canopy scenarios In an effort to increase the realism of test scenes used in 3D radiative transfer simulations, RAMI-IV proposed a small number of actual canopy scenarios. These highly detailed canopy representations were based on available inventory data from a series of test sites described in Table  1. In total, six different canopy scenarios covering deciduous and coniferous environments, regular and clumped tree patterns, as well as summer and winter conditions, were presented. For each canopy scenario, the RAMI website (http://rami-benchmark. jrc. ec. europa. eu/) provided the necessary structural and spectral information to assign a colour, shape, location and orientation to every individual leaf, twig, needle and branch within a nominally 1 ha sized area. While this wealth of detail was not necessarily usable by all of the participating 3D models, it was a direct corollary of the RT community's request to increase the realism of the virtual plant environments used in RAMI.
In addition, it was hoped that the actual canopy scenarios might permit documenting the impact that different levels of canopy abstraction may have on the simulated radiative properties of a canopy, e. g. ,  Widlowski et al. (2013) , as well as on the response of virtual sensors placed within these modelled plant environments. It is imperative to note that the RAMI-IV actual canopy scenes are not identical to the real world environments that gave rise to the data on which the reconstructions are based. This is because even the most intensive field campaigns are not yet able to gather spatially-resolved information on the spectro-directional and architectural characteristics of all objects within (and adjacent to) a particular test site, e. g. ,  Kuusk, Kuusk, and Lang (2009) ;  Schneider, Leiterer, Morsdorf, Gastellu-Etchegorry, Lauret, Pfeifer et al.
Architectural reconstructions The virtual trees of the RAMI test cases Järvselja Pine Stand (HET07), Summer Järvselja Birch Stand (HET09) and Winter Järvselja Birch Stand (HET15) were generated on the basis of site-specific allometric equations for tree height, crown radius and crown length  (Kuusk, Nilson, Paas, Lang, & Kuusk, 2008; Kuusk, Lang, & Kuusk, 2013). However, the number of unique virtual tree reconstructions was much smaller than that counted at the actual test sites. More specifically, the available tree height range was discretized into a small number of height intervals, for which a single virtual treecentred on the origin of a Cartesian coordinate systemwas then constructed on the basis of the available average architectural information and the corresponding tree libraries of the xfrog software  (Lintermann & Deussen, 1999).
12 ha thus reducing the effective tree density to 919 tree by hectare. Despite these apparent differences between actual field inventory and RAMI-IV tree densities, the tree count statistics were maintained within the central 1 ha of the virtual canopy scenes. This applies thus also to any other statistics that require normalisation by the stand area, e. g. , the leaf area index. The virtual trees of the Ofenpass Pine Stand (HET08) scene were generated with the xfrog software  (Lintermann & Deussen, 1999)  using tree crown information extracted from LiDAR observations over the test site, e. g. ,  Morsdorf, Kötz, Meier, Itten, and Allgöwer (2006) ;  Morsdorf, Meier, Kötz, Itten, Dobbertin and Allgöwer (2004). Similar to the Järvselja scenes, the available tree height range of the several hundred live Mountain Pine trees was subdivided into 12 height intervals for each of which a single model tree was then built.
(2008). Here, individual trees were constructed with the arbaro software  (Weber & Penn, 1995)  on the basis of field data collected by  Somers et al. (2009)  in a citrus grove near Wellington, South Africa and calibrated by comparing measured and simulated gap fraction distributions. The generated 10 unique models of the Citrus sinensis L. species were randomly rotated around their vertical axis before being positioned at circa 2 m intervals in rows separated by approximately 4. 5 m in an effort to resemble the actual orchard spatial distribution. About 11% of the potential tree positions were omitted in order to mimic the percentage of missing trees in the real orchard. The rows were parallel to the Y-axis of the scene which corresponded to having an azimuth of 90°when counted counterclockwise from the X-axis.
The background was assumed free of grass and completely flat. The second actual canopy scene with a regular planting pattern was the Short Rotation Forest (HET16). It featured 11924 poplar clones, which occurred every 0. 45 m along double rows separated by about 3. 55 m from each other  (Zenone et al. , 2008). The rows in each double row were separated by roughly 0. 75 m. Six unique representations of 1 year old Pegaso plants were generated with the arbaro software  (Weber & Penn, 1995). These model plants were rotated in a random manner around their vertical and then distributed according to the double-row planting scheme described above. Overall, 2% of the potential tree positions were omitted to match the percentage of missing trees in the original test site. As with the other actual canopy scenes, the background was assumed completely flat and the rows were oriented parallel to the Y-axis of the scene which corresponded to having an azimuth of 90°when counted counterclockwise from the X-axis.