dataesr/scientific-paragraphs-categorization huggingface dataset:
5- the objective of this study was to investigate whether parental bonding is associated with attitudes toward suicide among medical college students in japan. 8-the demographic data ( age and sex ) were obtained from self -questionnaires and interviews. 14-higher scores on the care and protection dimensions reveal that participants perceive their parents to be more caring and/or protective. 39-right to suicide was significantly associated with common occurrence , unjustified behavior , and preventability / readiness to help. 43-the majority of the participants in our study agreed that anyone could commit suicide ( 88. 8% ) and that suicide is preventable ( 86. 3% ). 44-in addition , the multiple regression analysis revealed that participants who reported a higher level of maternal care thought that suicide was a common occurrence and tended to think that people do not have the right to commit suicide.
64- we estimate the temporal component of one neuron @xmath15 from spatially filtered data and then use it to extract the corresponding spatial footprint @xmath14 from the raw data. in the step of estimating @xmath14 , we re -order all frames to make nearby frames share the similar local background levels and then take the temporal differencing to remove the background signals temporally. 107-in contrast , pca -ica based detection can only detect two neurons and the calcium traces have high level of noise.
The outline of the paper is as follows. In the next section we introduce our modelbased approach and the associated penalised cost. In Section 3 we present DeCAFS, a novel dynamic programming algorithm that can exactly minimise the penalised cost. To implement our method we need estimates of the model parameters, and we present a simple way of pre-processing the data to obtain these in Section 4. We then look at the theoretical properties of the method. These justify the use of the BIC penalty, show that our method has more power at detecting changes when our model assumptions are correct than standard approaches, and also that we have some robustness to model error -in that we can still consistently estimate the number and location of the changepoints in such cases by adapting the penalty for adding a changepoint.
The DeCAFS Algorithm Algorithm 1 gives pseudo code for solving the dynamic programming recursion introduced in Proposition 1. The key to implementing this algorithm is performing the calculations in line 5, and how this can be done efficiently will be described below. Throughout we give the algorithm for the case where there is a random walk component, i. e. λ < ∞, though it is trivial to adapt the algorithm to the λ = ∞ case. As well as solving the recursion for Q t (µ), Algorithm 1 shows how we can also obtain the estimate of the mean, through a standard back-tracking step. The idea is that our estimate of µ n , μn , is just the value of µ that maximises Q n (µ). We then loop backwards through the data, and our estimate of µ t is the value that minimises the penalised cost for the data y 1:t conditional on µ t+1 = μt+1 , which can be calculated as B t (µ) in line 11.
The key condition here is (C3) which governs how accurate the model assumed by DeCAFS is to the true data generating procedure. Clearly if the model is correct then (C3) holds with α = 1. The following proposition gives upper bound on α in the the case where the covariance of the data generating model is that of a random walk plus AR(1) process, but with different parameter values to those assumed by DeCAFS in (C2). Proposition 6 Assume the noise process ζ(t) of the data generating process (C1) is equal to a random walk plus an AR(1) process. α = max c 0 ν c ν c 0 φ c φ (1 + ), c 0 ν c ν 1 + c φ c 0 φ (1 + ), c 0 η c η The following result shows that we can consistently estimate the number of changepoints and gives a bound on the error in the estimate of changepoint locations, if we use DeCAFS under an assumption of a maximum number of changepoints (the assumption of a maximum number changes is for technical convenience, though is common in similar results, e.
In Figure  6C  we explore the effect of local fluctuations in the mean by varying σ η. We see a quick drop off in performance for all methods as σ η increases, consistent with the fact that it is harder to detect abrupt changes when the local fluctuations of the mean are greater. Across all experiments, DeCAFS was the most accurate algorithm. One word of caution when fitting the full DeCAFS model, is that when σ η is large it can be difficult to estimate the parameters, as a model with a very high random walk variance produces data similar to that of a model with constant mean but high autocorrelation. Whilst the impact on detecting changes of any errors when estimating the parameters is small, it can lead to larger errors in the estimate of the signal, µ t : as different parameter estimates mean that the fluctuations in the data are viewed as either fluctuations in the noise process or in the signal.
An example of this is shown in Appendix E. 1. Finally we investigate the performance of DeCAFS when its model is incorrect. First we follow  Chakar et al. (2017)  and simulate data with a constant mean between changes but with the noise process being AR(2), i. e. t = φ 1 t-1 + φ 2 t-2 + ν t. In Figure  5  we report F1 Scores for DeCAFS and AR1Seg as we vary range φ 2. Obviously as |φ 2 | increases, all algorithms perform worse, but the segmentations returned from DeCAFS are the more reliable as we increase the level of model error. Second, we consider local fluctuations in the mean that are generated by a sinusoidal process rather than the random walk model, see Figure  6B. In Figure  6A  we compare performance of DeCAFS and AR1Seg as we vary the frequency of the sinusoidal process. Again we see that DeCAFS gives more reliable segmentations in these cases.
Gene Expression in Bacilus subtilis We now evaluate DeCAFS on estimating the expression of cells in the bacteria Bacilus subtilis. Specifically we analyze data from  Nicolas et al. (2009) , which is data from tiling arrays with a resolution of less than 25 base pairs. The array contains several hundred thousand probes which are ordered according to their position on the bacterial chromosome. For a probe, labelled t say, we get an RNA expression measure, Y t. Figure  7  shows data from 2000 probes. Code and data used in our analyses, presented below, are available on forgemia : https://forgemia. inra. fr/guillem. rigaill/l2fpop_tiling_array_data. The underlying expression level is believed to undergo two types of transitions, large changes which  Nicolas et al. (2009)  call shifts and small changes which they call drifts.
Thus it naturally fits our modelling framework of abrupt changes, the shifts, between which there are local fluctuations caused by the drifts. To evaluate the performance of DeCAFS at estimating how the gene expression levels vary across the genome we will compare to the hmmTiling method of  Nicolas et al. (2009). This method fits a discrete state hidden Markov model to the data, with the states being the gene expression level, and the dynamics of the hidden Markov model corresponding to either drifts or shifts. As a comparison of computational cost for of the two methods, DeCAFS takes about 7 minutes to analyse data from one of the strands, each of which contains around 192,000 data points. Nicolas et al. (2009)  reported a runtime of 5 hours and 36 minutes to analyse both strands. A comparison of the estimated gene expression level from DeCAFS and from hmmTiling, for a 2000 base pair region of the genome, is shown in Figure  7.
Among those probes, we can count how many have a promoter nearby (within 22 base pairs). We call this M (δ). By symmetry we can define an equivalent measure for terminators. A method is better than another if for the same R(δ) it achieves a larger M (δ). Figure  8  plots M (δ) against R(δ) as we vary δ for DeCAFS and two different estimates from hmmTiling. In the case of promoters the prediction of hmmTiling is slightly better than DeCAFS for lower thresholds but noticeably worse for higher thresholds. In the case of terminators the prediction of DeCAFS are clearly better than those of hmmTiling. Given that DeCAFS was not developed to analyze such data we believe that its relatively good performances for promoters and better performances for terminators is a sign of its versatility.
Discussion There are various ways of developing the DeCAFS algorithm, that build on other extensions of the functional pruning version of optimal partitioning. For example, to make the method robust to outliers, we can use robust losses, such as the bi-weight loss, instead of square error loss to measure our fit to the data  (Fearnhead & Rigaill 2019 ). Alternatively we can incorporate additional constraints on the underlying mean such as monotonicity  (Hocking et al. 2017)  or that the mean decays geometrically between changes  (Jewell & Witten 2018 , Jewell et al. 2019). Finally, the algorithm is inherently sequential and thus should be straightforward to adapt to an online analysis of a data stream. We do not claim that the method we present in Section 4 for estimating the parameters in our model is best.
Acknowledgements This work was supported by  EPSRC  grant  EP/N031938/1 , and an  ATIGE grant  from  Genopole. The IPS2 benefits from the support of the  LabEx Saclay Plant Sciences-SPS. We thank  Pierre Nicolas  for providing the output of hmmTiling on the Bacillus subtilis data and his R code allowing us to generate Figure 8, which closely resembles Figure 4 of Nicolas et al. (2009).
Moreover, it is computationally affordable for its use in industrial configurations. In our previous works, the single-velocity six-equation model was numerically integrated using the basic Fortran libraries of the CLAWPACK software  [35]. Due to its industrial interest, we have now implemented it in EUROPLEXUS code  [25]  for the simulation of metastable steam-water flows for nuclear reactor safety analyses. In this paper, we use our methodology for simulating fast depressurizations, validating it against experimental data available in the literature. The outline of the article is the following. In Section 2 we briefly describe the single-velocity sixequation two-phase flow model together with its physical and mathematical properties. Here we discuss both the homogeneous hyperbolic portion of the model and the source terms necessary for the thermodynamic equilibrium recovery.
Look-up table algorithm for liquid and vapor EoS To correctly simulate phase transition phenomena, accurate equations of state should be used. For the steam-water properties, nowadays, the most reliable EoS for water is the IAPWS-95  [60]. This is an analytical equation based on experimental data and is used for general and scientific purposes. Given that the IAPWS-95 formulation is quite cumbersome and time consuming, IAPWS also provides a separate formulation recommended for industrial use, i. e. the IAPWS-IF97  [61]. The latter consists of a set of equations for five different regions, fitting the values resulting from the IAPWS-95. In this work we rely on the IAPWS-IF97 for the water properties calculation. Generally, accurate EoS are expressed as: f = f (ρ, T ) or g = g(p, T ), (9) where: f = e -T s and g = h -T s are, respectively, the Helmholtz free energy and the Gibbs free enthalpy.
Numerical simulations of fast depressurizations In the context of nuclear safety, the Loss of Coolant Accident (LOCA) is one of the Design Basis Accidents (DBA). It represents the sudden rupture of a primary system pipe of a nuclear power plant. When it occurs, a rarefaction wave originates at the breach and moves upstream in the primary circuit. The interaction of this wave with the reactor core may lead to the mechanical damage of core components. Then, for the fluid-structure analysis, the correct calculation of the rarefaction wave amplitude is of paramount importance. The thermodynamic disequilibrium plays a crucial role on the rarefaction wave amplitude, thus, a simple HEM can not produce a correct simulation of these flows. Therefore, to improve the industrial simulation tools, the single-velocity six-equation model has been implemented in the fast dynamic code EUROPLEXUS.
Since the phenomena involving metastable states were not completely known, in the '70s-'80s, some experimental campaigns have been carried out. The experimental facilities for the industrial scenarios focused on the rapid depressurization of vessels or pipes containing water at subcooled conditions  [3, 23, 45]. In this section we use these experiments for the validation of the methodology discussed above with the EUROPLEXUS code. The benchmark is performed using the experimental data of Bartak  [3] , Edwards-O'Brien  [23]  and Super Canon test rig  [45].
Conclusions In the present work we have developed a single-velocity six-equation model for phase transition flows, and implemented it in the EUROPLEXUS code. The industrial objective of this work was to provide the EUROPLEXUS code with a single-velocity hyperbolic model able to deal with thermal and chemical disequilibria to be used for the fluid-structure interaction analysis of hypothetical accidents in nuclear reactors. This work results from the merging of two techniques previously developed by the same authors. The first technique was the fast and accurate calculation of steam-water properties  [17]. The second technique was the treatment of the relaxation processes source terms by means of EoS-independent numerical procedures  [19]. Typically, one can distinguish two kinds of two-phase flow modeling approaches: an industrial one and an academical one.
NMCS has been successfully applied to the RNA Inverse Folding problem with the NEMO program  [28]. As a follow-up to NEMO, we propose to investigate different Monte Carlo Search algorithms for this problem. The paper is organized as follows. The second section describes the Inverse Folding problem, the NEMO program by Fernando Portela  [28]  and the domain knowledge used in NEMO. The third section describes the Monte Carlo Search algorithms we have used for solving Inverse Folding problems of the Eterna100 benchmark. We present a new algorithm performing well for this problem, the GNRPA algorithm with restarts. The fourth sections details experimental results.
A puzzle consists of a given structure under the dot-bracket notation. This notation defines a structure as a sequence of parentheses and points each representing a base. The matching parentheses symbolize the paired bases and the dots the unpaired ones. The puzzle is solved when a sequence of the four nucleotides A,U,G and C, folding according to the target structure, is found. In some puzzles, the value of certain bases is imposed. Where human experts have managed to solve the 100 problems of the benchmark, no program has so far achieved such a score. The best score so far is 95/100 by NEMO, NEsted MOnte Carlo RNA Puzzle Solver  [28]. NEMO uses a level 1 NMCS for its NMCS-B. At level 1, the NMCS, in each state of the problem, will perform a certain number of playouts for each possible move.
The algorithm is GNRPA with P = 4, a beam of 8 and N = 100. We can see that using 8 threads gives the best results. We optimized memory in order to avoid cache misses but we were not able to have better results with more threads. Table  3  gives the number of problems solved within a fixed time limit for different algorithms. The time limits range from 1 minute to 64 minutes per problem. The parallel algorithm runs on a multicore machine. The results of the parallel program are given in the last line of table 3. The times used to stop the parallel program are the wall clock times. The last line is leaf parallel GNRPA with restarts and gives the best results within 1 hour of wall clock time, solving 92 of the 100 problems in one run. Start is 4 meaning that it starts learning after 4 playouts.
TRR (3,267 upregulated and 3,953 downregulated) (Fig. 2d , Table  2  and Supplementary Table  S3 ). Veri cation of DEGs using qRT-PCR. To validate RNA-Seq data, we randomly selected a total of 41 DEGs (18 upregulated and 23 downregulated genes) from different comparison groups to perform qRT-PCR (Supplementary Tables  S4  & S5 ). Among the 41 DEGs, 33 genes were evaluated for single comparisons, and four genes were evaluated for two different comparisons. Relative normalized expressions data were compared for three genes for CNL vs. TNL; four genes for CNR vs. TNR; four genes for CRL vs. TRL; one gene for CRR vs. TRR; seven genes for CNL vs. CRL; eight genes for CNR vs. CRR; six genes for TNL vs. TRL; and eight genes for TNR vs. TRR (Fig. 3 ). For most genes, qRT-PCR assay results showed a general trend of expression pro les observed in the RNA-Seq experiment, con rming the validity of the RNA-Seq results (Fig.
Before the of the experiment, a vernier caliper was used to measure the trunk diameter of all plants 10 cm above the soil level. At the end of treatment, nal trunk diameter data collected to calculate relative change. After completion of treatment, the survival rate of 'Nemaguard' and 'Rootpac 40' data were collected. After eight weeks of treatment, leaf samples were collected for ion analysis. Tissue samples were dried in an oven. Subsequently, a Milestone Ethos E. Z. microwave was used for the digestion of the dried leaf samples. Macro-and micronutrient elements were analyzed using Perkin Elmer Optima ICP OES. Labconco chloridometer was used for chloride content analysis. Physiological and biochemical analysis. Measurement of stomatal conductance and photosynthetic parameters were performed using Li-Cor 6400 Photosynthesis System (Li-Cor Biosciences, Lincoln, NE, USA) after eight weeks from the starting day of salt treatment.
Illumina HiSeq platform was employed for RNA sequencing and to generate 150 bp paired-end reads (Novogene Corp. Inc. , Sacramento, CA). To obtain clean sequences, raw reads were subsequently trimmed and clipped of adaptors using Trimmomatic  50. Clean reads were analyzed for quality scores, Q20 (error 1 in 100) and Q30 (error 1 in 1000), and GC contents were analyzed utilizing FASTQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads deemed to be clean were then used for further analysis. Transcriptome assembly mapping. Raw reads were stored in a left. fq. gz le and a right. fq. gz le. A shell script was written to quantify the pair-end reads using the mapping-based mode on Salmon  51. The index was created using the Prunus persica NCBIv2. 1  52  Transcriptome assembly.
The "validatemappings" ag was set so that the more sensitive selective-alignment algorithm could be used  52. The "gcBias" ag was also set to allow Salmon to correct for potential fragment-level guanine-cytosine content biases in the input data. Quanti cation les containing the read counts were then output with the. sf extension. Differential Gene Expression Analysis. The biomaRt package  53, 54  (2. 42. 0) was used to create a table of Ensembl gene ids for P. persica. Using the tximport package  55  (1. 14. 0),. sf les from Salmon were then imported and mapped to their corresponding Ensembl gene name. A matrix was created containing the sample name as the column, gene ID as the row, and the corresponding read counts as the observations. Genes with lower than 5 counts were ltered out of the data, as they would not be of much use in the analysis.
Differential expression analysis was performed using DESeq2 (1. 26. 0)  56. The package uses a model based on the negative binomial distribution to estimate variance-mean dependence of the gene count data 56. Adjusted p-values (q-values) were generated using Benjamini and Hochberg's method to control the false discovery rate 56. The signi cance cutoff was set to α = 0. 05. Genes with an adjusted pvalue lower than 0. 05 and a log 2 fold change with an absolute value greater than 2 were deemed differentially expressed (DE). If a gene satis ed both q-value ≤ 0. 05 and |log 2 (fold change)| ≥ 2, it was considered signi cantly differentially expressed. Using the differentially expressed genes, Venn diagrams and heatmaps were constructed. The Venn diagrams were made using the "VennDiagram" package in R  57.
Heatmaps were generated using the "pheatmap" package in R (https://cran. r-project. org/web/packages/pheatmap/index. html). For clustering purposes, a Z-score transformation was performed on the matrix of pooled read counts for differentially expressed genes. Gene ontology annotation and enrichment of DEGs. Gene ontology (GO) analysis the differentially expressed genes was done using the GOseq (1. 38. 0) package in R 58. We assumed that, within a category, all genes have the same probability of being chosen. This allowed us to use the Wallenius non-central hypergeometric distribution to perform the GO enrichment test. First, an overrepresentation test was performed using the DE genes as the input set. Next, the over-representation of the DE genes was tested for using an adjusted p-value cutoff of α = 0.
05. The effect size calculation was then performed by taking the p-value and performing a negative 10-base log transformation. KEGG enrichment analysis DEGs. KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www. kegg. jp/) consists of databases containing information regarding high-level functions and utilities in biological systems. KEGG pathway enrichment analysis was done using the ClusterPro ler (3. 18. 1) in R  59. To perform the enrichment, DE genes needed to be converted from their ensemble id to their Entrez id equivalent. KEGG pathways in the DEGs were then compared to pathways associated with the entire genome background using the hypergeometric distribution and adjusted for a high FDR. KEGG.
Acknowledgments This study was supported by the  Almond Board of California  (project #  Hort26-Sandhu ) and the  United States Department of Agriculture-Agricultural Research Service , National Program 301: Plant Genetic Resources, Genomics, and  Genetic Improvement  (project number  2036-13210-012-00-D ). In addition, the authors thank  Ms. Pangki Xiong  for the ion analyses. The authors also acknowledge suggestions provided by  Dr. Chaoyang Zhao  for bioinformatics analyses. Finally, the authors are grateful to  Burchell and Agromillora nurseries for providing 'Nemaguard'  and ' Rootpac 40 ' rootstocks, respectively, for salinity tolerance evaluation.
F-SIM Physical Layer The E-SSA waveform specification adopted in S-MIM and F-SIM is based on the 3GPP W-CDMA uplink waveform  [13] [14] [15]. The payload is carried by the physical layer data channel (PDCH), while pilot symbols for unknown parameter estimation (e. g. , time, frequency and phase) at the receiver and an optional signaling field providing information on the actual carrier format are carried by the physical layer control channel (PCCH). The same forward error correction (FEC) scheme (Turbo code with rate 1/3) and BPSK modulation are adopted on both channels. Similar to 3GPP W-CDMA, the two channels are spread and mapped to the I and Q components of a complex signal which is, in turn, scrambled by a complex long spreading code. As described in  [5] , differently from S-MIM, F-SIM defines four possible channel sizes: 2.
3 dB (at PER = 10 -4 ). Similar to S-MIM, F-SIM uses dual-BPSK modulation with the FEC coding rate 1/3, as shown in Figure  2. The Turbo code from 3GPP Release 99 specifications has been adopted  [13]. spreading factor of SF = 256, the burst duration varies from 32 to 240 ms (300 byte payload), and the minimum C/N for reception is -27. 3 dB (at PER = 10 -4 ). Similar to S-MIM, F-SIM uses dual-BPSK modulation with the FEC coding rate 1/3, as shown in Figure  2. The Turbo code from 3GPP Release 99 specifications has been adopted  [13]. The uplink burst composition is depicted in Figure  3  and further described hereafter. The PDCH carries the random access channel (RACH) data burst followed by a CRC. The PCCH carries physical layer signaling and reference symbols to allow coherent demodulation of the PDCH channel.