dataesr/scientific-paragraphs-categorization huggingface dataset:
The Open Calphad software Since 2013 there a group of dedicated scientists has been engaged in the development of a new free thermodynamic software called Open Calphad  [14]  (OC). The OC software use modern techniques to store model parameters together with the best algorithms  [15]  to minimize the Gibbs energy in order to calculate the equilibrium for a flexible set of conditions. There is also an application software interface which makes it possible to integrate OC in simulation software  [16]. The OC software has implemented the partitioning of the Gibbs energy in a new way which reduces the calculation times. Recently the OC software has also implemented the permutation of parameters in ordered FCC, BCC and HCP phases using a new technique that reduces the calculation times even more.
Summary The method to model the Gibbs energy of phases with LRO is briefly described tigether with some details of handling this in the software and databases. In the current databases many simplifications in the modeling has been made for the database management as well as to speed up the calculations. For example the  phase is often described with just 3 sublattices and in particular the ordering in FCC, BCC and HCP phases are described with a 2 sublattice model. In the case of the L1 2 phase described with 2 sublattices this leads to a very unsymmetric model and many ternary and higher order parameters must be added to compensate for this. With a 4 sublattice model this is simplified but a 4 sublattice model leads to a considerable increase the time to calculate an equilibrium. This is due to the fact that the calculation time is roughly proportional to the square of the number of constituents and with 4 sublattices the number of constituents are doubled.
A. Load Estimation in Error Free Channel A simulation using Matlab has been developed to generate the physical signal of 802. 11n based on the Orthogonal Frequency Division Multiplexing (OFDM) technique, according to WiFi 802. 11n specific parameters shown in Table  1. The length of the input signal used in our simulation is equivalent to the duration of 200 OFDM symbols in time (or 200 times the symbol duration (ts) = 3. 2 µs), where the channel load is expressed by non-zero symbols value equivalent to the time occupation of the signal (or busy time), and with null symbols value when the channel is empty (or idle time). The channels load predefined on the twelve channels is expressed as the percentage of the channel occupation time between 0% and 100% (or 0 and 1) assumed as following: 20%, 50%, 0%, 40%, 90%, 0%, 60%, 70%, 80%, 40%, 0%, 90%.
Data collection In Ludhiana city, Punjab, India, eight arterial roads, namely (1) Chima Intersection-Samrala Intersection, (2) Chima Intersection-Vishwakarma Intersection, (3) Jagraon Bridge-Jalandhar Bypass, (4) Bharatnagar Intersection-Jagraon Bridge, (5) Bharatnagar Chowk-Model Gram, (6) Bhaiwala Chowk-Shastri Nagar, (7) Ludhiana Bypass and (8) Kundan Vidya Mandir Lane, were selected for this traffic survey. These roads were selected because of the availability of vantage points for mounting cameras and variations in flow among them. A total of 16 h of traffic surveys were conducted using video-camera during peak (09:00-10:00) and off-peak hours (12:00-13:00). Pedestrian foot-over bridges were used to mount cameras as the locations provided a view of a clear road stretch of 80 m. The perspective of this road from the camera also suited the image processing software used for data extraction (TRAZER TM ).
Flow chart for extracting microscopic characteristics A MATLAB program was developed to extract traffic characteristics such as individual vehicle speeds and gaps, acceleration/deceleration, density, flow and total exposure between vehicle types. Figure  6  shows a flow chart showing the algorithm. A detailed outline of the flow chart is presented below. 8. Calculate area occupancy as the ratio of the total projection area occupied by all vehicles in a frame array to the total road trap area. 9. Calculate lateral and longitudinal gaps for vehicles that overlaps along length and width, respectively, in a particular frame. (a) This is done for all frames in a particular trajectory array. (b) The lateral gap on any side (left or right) of a vehicle is equal to the minimum of all gaps maintained by that vehicle to other vehicle on that side.
Simulation setup A MATLAB code was written to simulate a 7-m-wide twolane road of length 5,000 m and four vehicle types namely HMVs, LMVs, 3Ws and 2Ws. As mentioned earlier, in CA models, the road is represented by a lattice of uniformly sized cells and the size of cells affects the computational time and accuracy of the model. Finer cell sizes result in higher accuracy as vehicular gaps and dimensions can be represented more accurately but it is believed that this also increases computational time as there are now more cells in lattice that needs to needs to be processed by computer at every time-step. However, authors believe that computational time is more dependent on the density of vehicles on road, number of lanes and length of road to be simulated, whereas cell size has less effect on computational time.
Similarly, if there was only one vehicle in the frame the number of interactions would be zero. Vehicles travelling side by side (with overlap [50%) and having different speeds were also considered to be involved in overtaking even if the overtaking manoeuvre could not be completed in the video. These interactions were grouped by vehicle types and expressed as the ratio of the number of vehicles interacting to that observed for that vehicle type. Here, vehicles with gap more than 60 m were not assumed to be following. Similarly, the vehicles with longitudinal overlaps were considered as overtaking or overtaken. Video-graphic data were collected on eight locations as discussed earlier. Since the objective of this paper was to develop a microscopic model that can simulate interaction rates between different vehicle types, the interaction rates were determined for eight vehicle pairs, as shown in Figs.
15  and 16. Eight locations generated a total of 64 data points. Along with these interaction rates, other traffic characteristics such as traffic composition, area occupancy, vehicle-wise maximum speed and mean lateral position on road were also measured for calibration of the model. The simulations were carried out at different area occupancies. Table  1  shows the parameters used in the model for simulation. The observed and simulated interaction rates for different vehicle pair at different locations were compared using paired sample tests. Kleijnen  [23]  suggested that the student t test can be used to verify that the expected values of x i and u i are equal. Then, t-statistic becomes t-value = d À d s d Á n À1=2 s ; ð4Þ where d is the average of n s pairs of differences in x i and u i , d is the expected value of d, s d is the estimated standard deviation of d, and n s is the sample size.
TERRA levels in the telomerase-positive cells were about 10-fold lower than in U2OS cells (Fig  3D ), an ALT cell line with high TERRA levels  [30]. TERRA levels in the ALTpositive SJ-G2 and MGBM1 cells were similar to U2OS. Interestingly, relative TERRA levels in the ALT-positive H3. 3-K27M mutant NEM165 and NEM168 cell lines were determined to be 5-fold less than in U2OS cells, while still being 2-fold higher than in HeLa cells. This indicates that in general TERRA is a good marker for ALT. However, ALT determination solely based on TERRA levels could lead to an inaccurate classification. TERRA levels have been reported to correlate with hypomethylation of the subtelomeres, where the TERRA promoter is located  [29, 54]. Accordingly, methylation at CpG sites located within one megabase (Mb) of the chromosome end was analyzed based on Illumina 450K methylation array data  [39].
These data were evaluated here in terms of the TMM status for our reference data set as defined above. Based on previous published literature we selected the following features as informative for a TMM classification (S1 Table ): (i) Loss of function of the chromatin remodeler ATRX as inferred from sequencing analysis and/or the lack of protein expression seen in immunohistochemistry (IHC) strongly correlated with ALT. (ii) Among the 7 tumor samples that harbored mutations in the H3. 3 encoding H3F3A gene and were analyzed with respect to their ALT status, 5 were found to be ALT-positive. (iii) In line with previous reports that associated TP53 mutations with ALT, 12 of the 13 C-circle-positive tumors also harbored TP53 mutations  [61] [62] [63]. (iv) Chromothripsis, the mutational 'shattering' of large parts of a chromosome has been functionally connected to dysfunctional telomeres and ALT activity  [64, 65].
In our set of reference samples we found a higher number of chromothripsis-positive samples that were non-ALT compared to ALT-positive ones. Thus, there is no association between ALT and chromothripsis. (v) TERT expression as detected by RNA-seq. In line with previous reports the number of TERT transcripts was low (S6A and S6B Figs), but their detection still correlated with the classification as 'non-ALT', further supporting the notion that in these samples telomerase is activated  [66, 67]. (vi) The DNA methylation status of a region upstream of the TERT transcription start site was used as an additional surrogate marker for TERT expression  [68]. The samples evaluated as ALT-positive showed consistently lower TERT promoter methylation compared to the 'non-ALT'-samples. Thus, the latter group or part of it maintains their telomeres via a promoter DNA methylation-linked upregulation of telomerase (S1 In summary, we defined a reference data set for TMM classification by evaluating samples with a positive C-circle signal as 'ALT' and those without C-circle signal in combination with the absence of ultra-bright foci in telomere FISH as well as samples with C250T/C228T TERT promoter mutations as 'non-ALT'.
Generation of NEM168 ATRX knockout cell lines Two candidate guide RNA sequences directed against ATRX were cloned into pX459v2. 0 as described in  [34]. The construct was transfected into NEM168 using TransIT-LT1 transfection reagent (Mirus), transfected cells were selected using 1. 5 µg/ml puromycin and single clones were assayed for indels in the ATRX gene using the Surveyor assay (IDT). Deletion of ATRX in positive clones was validated in a western blot with anti-ATRX antibody (HPA001906, Sigma).
Immunofluorescence and FISH Immunofluorescence and telomere FISH on cell lines were performed as described previously  [38]. The following antibodies were used: mouse anti-PML (1:100, Santa Cruz, sc-966) and rabbit anti-ATRX (1:500, Bethyl Labs, A301-045). Immunohistochemistry for ATRX (Sigma, HPA001906; dilution 1:750) on FFPE samples was performed as previously described  [39]. Interphase telomere FISH was performed using a PNA probe kit (Dako).
Cells were resuspended in FACS binding buffer (10 mM HEPES, 2. 5 mM CaCl 2 , 140 mM NaCl) at a final concentration of 2x10 6 cells/ml, stained with FITC annexin V (Biolegend) and propidium iodide (Invitrogen) according to the manufacturers' instructions, and analyzed by flow cytometry on a FACS Canto II (BD Biosciences). Cells that were negative for both PI and Annexin V were identified as viable cells. Annexin V positive events were characterized as apoptotic cells, whereas PI positive events were labeled as necrotic. All fractions of viable, apoptotic and necrotic cells were quantified using the WEASEL software. The percentage of induced cell death d ind was calculated as d ind = percentage dead cells treated ( )percentage dead cells control ( ) ⎡ ⎣ ⎤ ⎦ percentage viable cells control × 100%.
Whole-genome sequencing (WGS) and data analysis Whole-genome sequencing and data analysis were performed as part of the ICGC PedBrain Tumor Project, as previously described  [43]. Additional new samples (ICGC_GBM84, ICGC_GBM95, ICGC_GBM96, ICGC_GBM98, ICGC_GBM100, and cell lines NEM168, NEM165) were processed in the same way.
Construction of decision tree-based classifier To distinguish between different TMMs in pedGBM tumor samples, a classification scheme was developed. A sample with a positive C-circle signal was classified as ALT-positive, while a sample with an activating TERT promoter mutation was classified as ALT-negative (and bona fide telomerase-positive). For samples for which both features were negative or not available, a binary decision tree was constructed based on intelligent enumerating. TMMspecific features were extracted from imaging data (presence of ultra-bright telomere foci, ATRX loss of expression from immunohistochemistry staining) and combined with parameters derived from whole-genome sequencing (chromothripsis, loss of function mutations in ATRX and TP53, K27M and G34R/V mutations in H3F3A), RNA-sequencing (TERT expression), telomere qPCR (telomere content) and from methylation data from the Illumina 450K array (TERT promoter methylation).
A total of 12 samples were predicted as ALT and 17 as non-ALT (S1 Table ). It is noted that the input is calculated for any (incomplete) combination of known features from the corresponding decision tree. Finally, the method was implemented as a web-based program termed PAINT for Predicting ALT IN Tumors. It is available at http://www. cancertelsys. org/paint/index. html to predict if a sample is ALT-positive or -negative. The user can select a combination of available information for a given pedGBM sample and the predicted ALT state is returned. To assess the prediction quality, PAINT also displays the performance P, the p-value as well as the sample size used to construct the corresponding tree (see S2 Table ). In cases of nonsignificant performances (adjusted p-value > 0. 05), PAINT replaces the feature set with the subset that has a significant p-value (adjusted p-value ≤ 0.
Wavelet Transform Method for Temperature and Emissivity Separation (WTTES) Wavelet Transform (WT) is a tool for dividing a dataset, function, or operator into different frequency components: high-and low-frequency wavelet coefficients. In the wavelet domain, the high-frequency wavelet coefficients of a continuum time (space) series signal usually represent noise, while the low-frequency wavelet coefficients contain the bulk of the information in the original signal  [35] [36] [37] [38]. Based on this theory, the LSEs can then be recovered from the low-frequency wavelet coefficients alone. ε r = DWT -1 (C a , db, n) (3) where ε r represents the recovered LSEs and DWT -1 is the inversion of the discrete wavelet transform function. Because the LSEs are always obtained from certain channels, a discrete wavelet transform is used instead of a continuum wavelet transform.
Simulated Data To fully evaluate the performance of the WTTES algorithm, simulated data were used in this paper, providing a comprehensive dataset that includes various possible atmosphere and surface conditions. The spectral interval used in this paper is 1 cm -1 , and the selected TIR spectral range was 800 to 1000 cm -1. In total, six MODTRAN standard atmosphere profiles were selected that include temperature, moisture, and ozone profiles  [39]. The bottom atmospheric temperature (T bottom , the near ground air temperature at approximate 1013 hPa) in the six profiles ranges from 257. 2 K to 299. 7 K, while the total water vapor (TWV) ranges from 0. 42 to 4. 08 g/cm 2. Table  1  lists the details of these profiles. After preparing the atmospheric information, the MODTRAN was used to obtain the corresponding atmospheric parameters, such as the atmospheric downwelling/upwelling radiance and the atmospheric transmittance  [29].
Field-Measured Data In addition to the simulated data, field-measured data were also used to evaluate our proposed method. The field-measured data were obtained from a field campaign that occurred over three weeks in June 2004 at the ONERA (Office National d'Etudes et de Recherches Aérospatiales) center of Fauga-Mauzac  [42]. This experiment measured 11 samples of a variety of surface types such as soil, stone, wood, sand, and even man-made materials. The LSEs of each sample were measured a minimum of 10 times in the laboratory by a BRUCKER-EQUINOX spectrometer equipped with an integrating sphere. The spectral radiances (from 700 to 3000 cm -1 ) were measured outdoors by a BOMEM (MR 250 Series) Fourier transform interferometer with a spectral resolution of 4 cm -1 and a sampling rate of 2 cm -1.
Evaluation with Field-Measured Data In this section, field-measured data are used to evaluate our proposed method, including a comparison with the ISSTES algorithm. It should be noted that, for each sample, the averages of all LSEs measured in the laboratory are taken as the actual LSEs. The LST results from the WTTES and ISSTES algorithms are shown in Figure  7. Consistent with the previous conclusions from simulated data, no obvious difference could be found between these two algorithms (Figure  7 ). According to the actual LSTs, 114 of 138 measurements have an LST difference of less than 1 K from the WTTES algorithm, while 117 measurements show the same accuracy as that from the ISSTES algorithm. The results indicate that both these algorithms provide relatively accurate LST retrievals from field-measured data.
INTRODUCTION Modern genome sequencing strategies rely strongly on the amplification of low quantities of deoxyribonucleic acid (DNA), making them highly sensitive to even small contaminations in the samples. A study by  Longo, O'Neill & O'Neill (2011) , for example, has shown almost 1/4th of non-primate genomes available in the national center for biotechnology information (NCBI) databases to be contaminated by repeated elements frequently found in human cells. Samples may furthermore be contaminated by airborne bacteria or other eukaryotes, ingested food, or symbionts living within or attached to the target organism. The detection of contaminants in genome datasets may be accomplished pre-assembly, post-assembly, or using a combination of both approaches. Pre-assembly removal of potential contaminants has the advantage of reducing the complexity of the assembly process by producing smaller and more homogenous data sets.
A first step may be filtering according to kmer-coverage or according to per-read guanine-cytosine (GC) contents  (Schmieder & Edwards, 2011)  or applying more advanced binning techniques based on oligonucleotide composition  (Teeling et al. , 2004; McHardy et al. , 2007). This bears the risk of also removing genomic sequences from the target organism, for example, repeated elements or regions resulting from recent horizontal gene transfers, and of missing contaminants with similar properties as the target genome. A complementary approach is to search for potential contaminants based on sequence similarity. Here the main limitation lies in the quality and completeness of the reference databases. Furthermore, sequence similarity searches may be time-consuming due to the amount of raw data to treat and are classically limited to smaller sets of contaminants such as vectors or individual species (e.
g. , to screen for human DNA contaminants), or ribosomal sequences. However, the recent development of fast search and classification algorithms that do not rely on marker genes and are dedicated to the analysis of (mainly microbial) metagenomes, such as RITA  (MacDonald, Parks & Beiko, 2012) , KRAKEN  (Wood & Salzberg, 2014) , or Kaiju  (Menzel, Ng & Krogh, 2016 ) (see  Sangwan, Xia & Gilbert, 2016  for a recent review), could also be applied to standard genome datasets with the aim of identifying contaminations prior to assembly. Just as for kmer-and GC contents-based approaches the limitation remains that foreign sequences recently integrated into the host genome, may be falsely removed. Post-assembly cleaning of genomic sequences is frequently performed once manual annotation reveals the presence of contaminants  (Cock et al.
, 2010; Olsen et al. , 2016) , and does not impact non-contaminated scaffolds. As for pre-assembly approaches, criteria to remove sequences are properties such as GC contents and read coverage  (Kumar et al. , 2013)  or sequence similarity  (Cock et al. , 2010; Collen et al. , 2013; Olsen et al. , 2016). As mentioned above, GC-and coverage-based approaches will fail to detect contaminants that resemble the target organism with respect to these characteristics, while the performance of sequence-similarity based approaches is strongly dependent on the quality of the reference database. A key advantage of post-assembly approaches, however, is that they allow for the detection of recent horizontal gene transfer events as long as these elements are embedded into scaffolds containing also host DNA.
METHODS Two approaches are frequently used for the sequence-similarity based detection of potential contaminants: one is to perform searches with nucleotide sequences (either as blastn against a nucleotide database or blastx against a protein database), but if such searches are carried out with the entire scaffolds, in our experience, they may be biased by highly conserved regions, which frequently have very little discriminatory power (transposons, virus insertions etc. ). Alternatively, protein-based searches may be performed with all predicted proteins of a genome against a reference database (frequently the NCBI non-redundant (nr) protein database or uniref90). Based on these results, scaffolds for which most proteins have best hits with expected contaminants are removed. The advantage of the protein-based approach is that, if there are several predicted proteins on a scaffold, then all of them can be analyzed independently, and the results combined to reach a conclusion.
Furthermore, protein sequences are more conserved than nucleotide sequences thus making it easier to find hits. The main disadvantage, however, is that this approach depends on the available protein predictions. Not all DNA encodes proteins, and especially bacterial contaminations in eukaryote genomes frequently lack protein predictions. This lack can be partially explained by the heavy reliance of gene calling software on ribonucleic acid sequencing (RNAseq) data, which, in turn usually includes PolyA-selection during library preparation, removing bacterial messenger RNA from the final dataset. Here we developed and used a pipeline called Taxoblast that constitutes a compromise between both approaches. We worked with nucleotides to eliminate the impact of protein predictions, but in a first step (Fig.
1 ) each scaffold of the S. latissima genome was split into small fragments of, for example, 500 base pairs (bp). Then, each of these fragments was compared individually to a reference database. This way each sequence fragment had the same weight in the analysis, that is, analyses were not biased towards few highly conserved elements as may have been the case for blast searches with a single long query sequence. Internal tests aiming to identify bacterial sequences in algal genomes have exhibited very little differences between blastx searches against the NCBI protein nr (non-redundant) database and blastn/discontinuous megablast searches against the NCBI nucleotide nt database, except that blastx searches were significantly slower. Regular (continuous) megaBLAST searches are designed specifically for highly similar sequences with >95% sequence identity  (McGinnis & Madden, 2004)  and were not sufficiently sensitive to detect potential bacterial contaminants in the genome of brown algae; this algorithm, may, however, be preferable when attempting to detect well-known contaminants such as human DNA.