dataesr/scientific-paragraphs-categorization huggingface dataset:
8b. It is evident that the deviations of the ER estimates from unity due to the random replacements of the modeled data turned out to be much smaller than those found with real data. In the case where CO columns were not used, the maximum deviation was about 31%; there is also an upward trend (starting from the second bin) that can be explained by the factors mentioned in Sect. 2. 4. The deviations were found to be smallest (less than 12 %) in the case where AOD values had been normalized to CO columns according to Eq. (  9 ). Note that the hit rates considered can be smaller than unity not only because of model transport errors or similar errors due to misplacements of BB aerosol emission sources, but also due to many other kinds of uncertainties in the measured and simulated data. That is, our Monte Carlo experiments are likely to provide an upper limit for the effects of transport errors.
Therefore, the results shown in Fig. 8b  strongly suggest that the major features of the ER behavior found in the analysis of the real data (see, e. g. , Fig. 6a ) are unlikely due to model transport errors and associated errors in our estimates of the BB aerosol photochemical age. Third (Fig. 8c ), we investigated whether the derived increase in the ER was a general property of BB aerosol evolution in the study region, or was mostly associated with a single specific episode, like the one considered in the Supplement. To do so, we split the original dataset into two parts, such that the first part included only odd days of the study period, and the second part included only even days. This splitting allowed us to get ER estimates based on entirely different subsets of the data. To enable a straightforward comparison of the ER estimates derived from each data subset, they were obtained for the same photochemical age bins as the base case estimates shown in Fig.
6a ; that is, the estimation procedure involved Eq. (  9 ), but Eq. (  11 ) was not employed in this test. As a result, we found that the ER estimates derived from both data subsets demonstrate qualitatively similar "humped" dependencies on the BB aerosol photochemical age, although there are considerable (but not statistically significant) quantitative differences between them. Not surprisingly, withdrawing half of the data from the original dataset resulted in an increase in uncertainties in the ER estimates. Similar results (not shown here) were obtained when the original dataset was split into two parts in about the middle of the study period, such that the amount of data in the two data subsets was similar. All these results clearly indicate that the increase in the ER is not due to a haphazard feature of a single BB plume observed from satellites, but a rather general property of BB aerosol evolution in the region and period considered.
Fourth (Fig. 8d ), we examined the sensitivity of our estimates to possible systematic errors in the injection height of the BB emissions. To do so, we performed a test model run (for the STN scenario) in which the injection height of the BB emissions was increased: specifically, a factor of 2 was applied to the injection height estimates calculated in accordance with the method by  Sofiev et al. (2012) ; see Eq. (  10 ) therein. The model data for aerosol tracers and CO from that test run were then used for estimation of the ER instead of the data from the original STN run. The injection height parameterization implemented in this study can be expected to underestimate the observed height of a part of the major BB plumes reaching the free troposphere (see  Sofiev et al. , 2012, and Fig. 2d therein).
Finally, in addition to the main test cases presented above, we analyzed some other test cases that will not be discussed here in detail. In particular, we tried to ensure that the derived evolution of the ER is not an artifact of our data selection procedure (see Sect. 2. 4. 1) by using AOD values instead of CO columns in Eq. (  10 ). We also obtained test estimates of the ER by using the modeled CO columns to which no transformation involving averaging kernels was applied. These modifications of our procedure did not result in any qualitative changes in the ER behavior, either. Accordingly, we came to the conclusion that the strong increase in the ER is not an artifact of our procedure or model errors but, rather, a real property of BB aerosol evolution in the study region and period. In this section, we qualitatively discuss possible physical and chemical mechanisms that could be responsible for the distinctive features of BB aerosol evolution, which were revealed in our analysis of the MODIS AOD measurements (see Sect.
Indeed, on the one hand, our analysis indicates that when the BB aerosol simulations are based on the VBS method, optimal agreement between the simulated and measured AOD data can be achieved with smaller (by a factor of 1. 8 in our case) particulate matter emissions than when BB aerosol simulations are performed using the conventional aerosol scheme. On the other hand, as noted above, the VBS method is found to provide a more realistic representation of the effects of BB aerosol aging than the conventional aerosol scheme. Future modeling studies should involve optimization of the parameters of the VBS scheme in order to fit better the ER estimates obtained with the model data to those derived from measurements. Our results may also have implications for experimental studies of BB aerosol evolution.
Conclusions In this paper we used MODIS AOD measurements combined with IASI CO measurements and output data from the CHIMERE CTM to probe the effects of aging of BB aerosol in smoke plumes from Siberian wildfires. To quantify the AOD changes due to BB aerosol aging, we considered the AOD "enhancement ratio" (ER), which indicates how much the optical depth of the actual BB aerosol changes compared to the optical depth associated with a passive aerosol tracer originating from the same sources. The AOD ER has basically the same meaning as the normalized excess mixing ratio (NEMR) used in earlier studies involving in situ measurements (e. g. ,  Jolleys et al. , 2012; Vakkari et al. , 2014; Konovalov et al. , 2015) , but is defined in terms of AOD values rather than mixing ratios. The satellite-observationbased analysis allows one to obtain results for larger regions and for a larger number of BB plumes than in situ measurements.
Acknowledgements. This study was supported by the  Russian Science Foundation  (grant agreement no. 15-17-10024 ). The simulations involved in the analysis were performed with support from the  Russian Foundation for Basic Research  (grant no. 15-45-02516 ). I. B. Konovalov acknowledges travel expenses in the framework of the PARCS (Pollution in the ARCtic System -PARCS) national project. M. O. Andreae is funded by the  Max Planck Society. Edited by: C. H. Song Reviewed by: two anonymous referees.
Data availability. The MODIS AOD and IASI CO retrievals are available at ftp://ladsweb. nascom. nasa. gov/allData/51/ (LAADS, 2016) and http://ether. ipsl. jussieu. fr/ether/pubipsl/iasi_CO_uk. jsp (ESPRI, 2016), respectively. The modeled data used in this study are available upon request from the corresponding author, Igor Konovalov (konov@appl. sci-nnov. ru). The Supplement related to this article is available online at doi:10. 5194/acp-17-4513-2017-supplement.
MST assay optimization. MST experiments were performed on a NanoTemper ® Monolith NT. 115 with blue/red filters (NanoTemper Technologies GmbH, Munich, Germany). Samples were prepared in the different buffers listed in the MST buffer section and loaded into standard/premium treated capillaries. Measurements were performed at 22 °C using 20% MST power with laser off/on times of 5 s and 30 s, respectively, and 60% MST power with laser off/on times of 5 s and 20 s. All experiments were repeated three times for each measurement. Data analyses were performed using the NanoTemper ® analysis software. The K d constants between a protein and its ligand can be calculated using the saturation binding curve at equilibrium  27. The fitting function is derived from the law of mass action: Technologies (PD-1: 870 bp, Glycine-alanine-linker: 42 bp, YFP: 732 bp).
The pcDNA3. 1 Hygro plasmid was also purchased from Life Technologies. The plasmid was digested by two restriction enzymes, HindIII and EcoRI (New-England-Biolabs 2000), to allow the insertion of our mPD-1-YFP construct. The pVITRO eGFP plasmid was purchased from InvivoGen and was used for PCR amplification to obtain the eGFP amplicon. The plasmid containing hPD-L1-eGFP was purchased from GeneCopoeia (CS-GS402L-M10/pReceiver-M10). The plasmid containing the mutant variation of hPD-L1-eGFP was also purchased from GeneCopoeia (CS-GS406L-M10/ pReceiver-M10). Both sequences are based on the crystal structure of the PD-1/PD-L1 complex (PDB code: 3BIK) and are described in Supplementary Information 1. = + + - + + -. LP P L P K L P K L P P [ ] [ ] [ ] [ ] ([ ] [ ] [ ]) 4[ ] [ ] 2[ ] (1) Primer design.
With increasing PDE, the skewness of simulated data deviated from a normal distribution toward the non-normal distributions, with less effect on kurtosis and tail heaviness (Fig. 1C ; Supplemental Fig. 4 ). Importantly, the simulated data sets exhibited intermediate skewness, kurtosis, and tail heaviness compared to published translatome data sets on microarrays from NCBI GEO and EBI ArrayExpress databases  (Edgar et al. 2002; Kolesnikov et al. 2015). Overall, we have shown that translatome studies result in genuinely skewed distributions by nature and violate general assumptions imposed by conventional microarray analysis schemes. By generating simulated data presenting intermediate skew compared to the deviation that can be found in published biological data sets, we chose to not overestimate the skewness and as a consequence set the simulation in a fair and realistic environment.
5 ). These findings are confirmed when the simulated data are initially skewed by a large technical variation (Supplemental Fig. 6 ). Only INCATome implementations are able to correct for technical variation without inducing an unwanted skew. To further characterize the response of each normalization method in relation to known internal controls, we assessed the RMSD of either spike-in probes or internal reference probes (ACTB and PABP). Quantile and LOESS normalizations generated significantly more dispersion in RMSD than in unnormalized data without any improvement between expected and observed values for these probes (Fig. 2B ). Conversely, INCATome allowed for a significant reduction in RMSD, thus best reproducing the true biological state for these mRNAs. Performance was also computed with receiver operating characteristics (ROC) and statistical descriptors (precision, accuracy, sensitivity, specificity).
4D ; Supplemental Fig. 13 ). The selected candidates represented each time the top changing candidates as well as mRNAs whose fold change ranged down to the limit cut off (FC > 2 and FC < 0. 5). First, a set of 20 targets identified in the Quantile method were selected. Twelve out of 20 validated with a similar fold change trend with qPCR and microarray data (60% validation concordance). In a second step, 21 targets were identified from the LOESS method. Only ten out of 21 mRNAs were confirmed as being deregulated (48% validation concordance). Finally, we selected 15 top ranked mRNAs identified only by INCATome. Among these, 11 were correctly validated as deregulated translationally (73% validation concordance). Thus, the use of INCATome in the study of the translatome of a biological data set has reinforced the evidence that INCATome outperforms the existing methods by correctly processing the data, identifying DEGs in a stringent manner and, more importantly, providing the best validation concordance on its own, even compared to candidates identified by all three approaches.
Preparation of fluorescently labeled cDNA for microarray hybridization and data analysis Microarrays used were Agilent 8×60k Human Gene Expression arrays (Agilent Technologies LDA UK Ltd. ). Equal proportions of RNA from pooled subpolysomal fractions (fractions 1-5) and pooled polysomal fractions (fractions 6-11) were fluorescently labeled, using the Agilent Low Input Quick Amp Labeling Kit, twocolor (Agilent Technologies LDA UK Ltd. ). Following hybridization, the arrays were scanned using an Agilent SureScan High Resolution scanner, and Agilent Feature Extraction software was applied to the resulting images.
Development of a new normalization procedure A novel methodology for the normalization of polysome profiling data was developed (Supplemental Fig. 1 ). It relies on the use of either Spike-In controls or internal references. Each species presents the advantage of having expected values (known concentration for Spike-In and measured polysomal distribution for the internal references such as ACTB and PABP). Thus, expected log 2 ratios are computed and a root-mean-square deviation (RMSD) algorithm is applied to each sample individually in order to converge toward the smallest RMSD residual. This assured (i) an internal normalization for each sample, (ii) the possibility to compare samples across microarrays, and (iii) the conservation of the inherently skewed nature of each sample. The R code for implementing the INCATome normalization is available as a package of the open-resource CRAN project.
Simulation of microarray data Polysome profiling of siCTRL HeLa cells was performed and the resulting subpolysomal and polysomal fractions were hybridized together onto two microarrays (dye-swap design). Preprocessing steps included background correction (LIMMA package) and dye-swap correction (averaging). Values were then sampled from the resulting data set and simulated based on a previously reported method  (Dembélé 2013)  with several modifications  (Supplemental Figs. 2, 3). Briefly, simulation was performed in order to create three replicates for two different conditions. Gene variation (λ 1 and λ 2 ), technical variability (sd TV ), and deregulation parameters (µ DE , sd DE and λ 2 ) were constant across the simulation. Conversely, the percentage of deregulation (PDE), the symmetry of deregulation (SYM), and the data noise (sd N ) ranged between (1%, 5%, 10%, 25%, 50%, 75%), (0.
0, 0. 1, 0. 2, 0. 3, 0. 4, 0. 5, 0. 6, 0. 7, 0. 8, 0. 9, 1. 0), and (0. 1, 0. 4), respectively. Each simulation was then subjected to several normalization procedures: Quantile, LOESS, INCATome (Spike-In), and INCATome (ACTB/PABP)  (Bolstad et al. 2003; Smyth and Speed 2003). Finally, each simulation was subjected to INCATome (ACTB/PABP) normalization and carried over for comparisons across different statistical tests: Welch's T-Test, model fitting LIMMA, RankProd, and SAM  (Tusher et al. 2001; Hong et al. 2006; Ritchie et al. 2015). The R code for simulating translatome data is available on request by emailing the corresponding author.
ACKNOWLEDGMENTS We thank  Nick Burgoyne  for his contribution to the initial method development. This work was funded by grants from the  Medical Research Council  programme (T. S. , L. A. W. , R. S. , A. B. , T. P. , and A. E. W. ), the  Wellcome Trust  (X. P. ), and the  Core Medical Research Council Toxicology Unit  (C. J. and K. D. ) Received December 22, 2016; accepted July 27, 2017.
Literature Review of the CC problem To our knowledge, the CC problem (as defined above) was first addressed by  Doreian et al. (Doreian and Mrvar 1996a)  (although not under this name), who provided a heuristic solution method for analyzing structural balance in signed social networks. Their method, implemented in software Pajek  (De Nooy et al. 2011) , consists of a simple greedy neighborhood search procedure which requires the number of clusters in the desired solution. Having a document clustering problem in mind, Bansal et al. (Bansal et al. 2002)  formalized the unweighted version of the CC problem and also discussed its NP-completeness proof, whereas the weighted version of the problem was addressed in  (Demaine et al. 2006). Integer linear programming (ILP) can be used to solve the CC problem optimally, but only when the number of data points is small.
As mentioned in Section 2. 1, the CC problem is related to the clique partitioning problem. The authors in  (Brusco and Köhn 2009)  developed a neighborhood search heuristic (known as NS-R) for the clique partitioning problem, which is similar to the one proposed in this section. NS-R includes the basic principles of random perturbation and pursuit of a local optimum through a local search procedure also present in ILS. Our ILS algorithm to solve the CC problem reuses the constructive and local search modules of GRASP, as well as its multistart nature. We have also added a procedure that creates perturbations in the best current solution, creating a multistart Iterated Local Search (ILS) algorithm called ILSM ultiStartCC. Multistart provides an additional approach to diversify the obtained solutions, restarting the ILS algorithm with a new randomized initial solution any time a search region has been largely explored.
In order to improve the local search procedure, we replaced the simple neighborhood traversal with a Variable Neighborhood Descent heuristic; -VNS metaheuristic  (Mladenović and Hansen 1997) : Variable Neighborhood Search consists of a well-established standard metaheuristic for combinatorial optimization. Based on our ILS algorithm, we have implemented VNS by doing the following changes to Algorithm 1: line 3: replacing "P * ← ∅" and "I(P * ) = ∞" by "P * ← ConstructiveP hase(G, α)"; line 5: replacing "P ← ConstructiveP hase(G, α)" by "P ← P * ". -Doreian Mrvar Method, implemented in Pajek software 6  (De  Nooy et al. 2011 ): consists of a relocation algorithm for partitioning signed graphs. In simple terms, a given partition (randomly generated at each iteration) is optimized to get as much as possible positive edges inside clusters and negative edges between clusters.
In the local optimization procedure, vertices can be moved from one cluster to another or pairs of vertices can be interchanged between clusters. For each such change, the criterion function is evaluated. If a relocating change leads to a decrease of the criterion function, the new partition is retained and the process continues until the criterion function cannot be lowered. If the procedure finds several optimal solutions, all of them are reported. More details are available in  Doreian and Mrvar (1996b) ; -VOTE/BOEM heuristic  (Elsner and Schudy 2009) : provides good correlation clustering solutions on datasets far too large for ILP to scale. The algorithm is composed of two phases: (1) the VOTE algorithm adds each vertex to the cluster that minimizes the correlation clustering objective; and (2) the best one element move (BOEM) algorithm repeatedly makes the most profitable and the best element move until a local optimum is reached.
Computational environment The algorithms described in the previous section were implemented in ANSI C++ and MPI (OpenMPI) for message passing. All experiments were performed (with exclusive access) on a cluster with 42 nodes, each one with two Intel Xeon Quad-Core 2. 66GHz processors and 16Gb of RAM under Linux (Red Hat 5. 3). The ILP formulation presented in Section 2 is coded in Xpress Mosel 3. 2. 0 with solver Xpress Optimizer 21. 01. 00. The CPU time limit is set to 1 hour for the ILP formulation. Additionally, all heuristic outcomes represent the average of 25 independent runs.
Sequential ILS vs. Doreian Mrvar Method The Doreian Mrvar Method (implemented in Pajek software) presents good-quality results (with respect to solution value and average time spent) for the smallsized literature instances in (i) and also for random social networks of up to 50 vertices  (Figueiredo and Moura 2013). However, for larger instances, it presents poor performance. Table  4  Sequential ILS for the CC Problem, here named SeqILS, vs. Doreian Mrvar Method vs. V OT E/BOEM results obtained on Slashdot signed graphs. BestSol is the average value of the best imbalance found within the time limit, after 25 executions of each algorithm. For this benchmark, we compared our ILS procedure with Doreian Mrvar Method to solve the Slashdot instances in set (i). We have obtained average results of 25 executions (Table  4 ).
The software Pajek (version 4. 09) 15 was invoked with the following configuration: Doreian Mrvar Method, starting from a random partition (1-mode), 100 repetitions, α = 0. 5, minimum number of vertices in clusters = 1. Pajek's Doreian Mrvar Method accepts only one stopping criterion: number of iterations. Since an iteration of this method is fundamentally different from an iteration performed by ILS, the number of iterations is not a sound basis for comparison of both algorithms. Therefore, as an alternative, we have set the timelimit parameter of ILS to 2 hours and the number of repetitions of Mrvar Method to 100, which was enough for this method to either reach the same solution value of ILS, or exceed its time limit with an inferior solution value. In this way, we were able to compare both solution values and execution times in a fair manner.
United Nations General Assembly (UNGA) Voting Data As noted in the introduction, some authors have applied different signed graph clustering methods to networks of international alliances and disputes. Traag and Bruggeman (2009)  analyzed international relations taken from the Correlates of War  (Stinnett et al. 2002 ) data set, while  Macon et al. (2012)  showed how three different network representations could be used to identify voting groups in UNGA annual sessions. Likewise, our work is based on the voting on resolutions in the United Nations General Assembly (UNGA), separated by year. Having applied the sequential ILS algorithm using the UNGA instances listed in Section 4. 2-(ii) as input, we give a taste of social network analysis that can be done with the obtained CC and SRCC results.
A complete documentation and conservation of a historic timber roof requires the integration of geometry modelling, attributional and dynamic information management and results of structural analysis. Recently developed as-built Building Information Modelling (BIM) technique has the potential to provide a uniform platform, which provides possibility to integrate the traditional geometry modelling, parametric elements management and structural analysis together. The main objective of the project presented in this paper is to develop a parametric modelling tool for a timber roof structure whose elements are leaning and crossing beam frame. Since Autodesk Revit, as the typical BIM software, provides the platform for parametric modelling and information management, an API plugin, able to automatically create the parametric beam elements and link them together with strict relationship, was developed.
, 2015)  and towers  (Leonov et al. 2015)  has been geometrically re-built from remote sensing and surveying data. On the other hand, Finite Element Method (FEM) based computational software can conduct structural analysis by directly introducing 3D geometry files obtained by reality-based modelling  (Armesto et al. , 2009). So, the 3D geometry model can be directly utilized to structurally analyse the reliability of a timber beam frame structure. Reality-based modelling and reverse engineering provide the asbuilt model about the heritage for further structural analysis. Often, the auxiliary property and dynamic information is relatively neglected owing to the limitation of knowledge management. Meanwhile, the modelling and analysing is usually conducted separately in different platforms. The gap between the geometry modelling and structural analysis needs a 3D information model and a unified platform to manage the geometry, relationship, material and even changing information to perform structural analysis.