dataesr/scientific-paragraphs-categorization huggingface dataset:
Microarray datasets We used two datasets providing paired genome-wide profiles of copy number alterations and gene expression for newly diagnosed myeloma patients. The first is the IFM (Intergroupe Francophone du Myelome) dataset with 170 patients profiled in our laboratory by high-density Affymetrix 500 K SNP arrays and Exon ST 1. 0 expression arrays (GEO identifiers GSE12896 and GSE39754). Patient characteristics and clinical information have been reported previously  [2]. The second is the Dickens dataset with 71 patients profiled by Affymetrix 500 K SNP arrays and Human Genome U133 Plus 2. 0 expression arrays (GSE15695)  [21].
Primary analysis of gene expression and copy number data The gene expression and copy number profiling data were preprocessed with the dChip software  [26, 27]  to obtain expression and copy number estimates. The gene expression profiles were quantile-normalized and modelbased signals were computed  [26, 27]. Gene probe set intensity values were averaged when more than one probe sets per gene were available. To assess the impact of alternative methods for handling Affymetrix array probe sets on Dosage effect scores, we also obtained alternative expression estimates for the IFM dataset from a CDF (HuEx-1_0-st-v2,coreR3,A20071112,EP. CDF) provided by Purdom et. al. [28]. This CDF, available form aroma. affymetrix website, groups Affymetrix "core" probesets into 18,708 transcript clusters. In this setting, each exon and probe is uniquely mapped to only one transcript cluster, allowing for a unique gene expression value for each individual gene after pre-processing and eliminating the need to use avarage value for each gene.
Hyperdiploidy status Since there were no published hyperdiploidy data determined by FISH for both datasets, the myeloma hyperdiploidy status was defined using the copy number information from SNP arrays. To determine the threshold for separating samples in to two subtypes, we plotted the distribution of the median copy number of the trisomy chromosomes. We observed a bimodal distribution with two peaks at 2 and 2. 6. The HMM samples have a peak near 2. 6 (and not 3) due to the normalization effect across arrays. We chose the midway value (2. 3) between the two peaks as the cutoff to call HMM and NHMM samples from the copy number data. 77 of 170 IFM samples, and 42 of 71 Dickens samples were called as HMM, and the rest samples were called as NHMM.
Defining dosage effect score (DES) We defined a gene-wise dosage effect score (DES) to identify genes whose copy number changes modulate their own expression. The DES is the ratio between "dosage effect sample number" and "CNA sample number". The "CNA sample number" of a gene, is the number of samples in a dataset that have CNA at the gene (copy number < 1. 7 or > 2. 3, considering signal noise and normal cell contamination). The "dosage effect sample number" of the gene is defined using two steps. First, using the samples with no copy number changes at the gene (copy number within 1. 7 and 2. 3), the mean (normal_mean) and standard deviation (normal_sd) of the gene expression values is calculated. Next, we count the total number of samples that have CNA at this gene and their expression levels at the gene change more than random in the same direction as their CNA (i.
e. the samples whose gene expression value are higher than normal_mean + normal_sd for amplification samples or lower than normal_mean -normal_sd for deletion samples). The number of these samples is the "dosage effect sample number" at the gene. Being a ratio, DES ranges between 0 and 1. Larger DES indicates stronger dosage effect at the gene in the sense that a larger proportion of CNA samples have concordant expression changes. To ensure reliability, DES is computed only for genes whose CNA sample number is greater than 10% of all samples. As an example, the gene EIF3K has CNA in 55. 88% of samples in the IFM data set. Utilizing mean +/-1 SD criteria, the percent of samples with both copy number alteration and concordant expression changes are 25. 29%. EIF3K has the DES of 0. 45 (. 2559/.
Differential and functional enrichment analysis Differential expression was identified with the LIMMA Bioconductor package, and Benjamini-Hochberg multiple hypothesis correction was carried out using the R package multtest. Differential gene expression was called at the adjusted p-value < 0. 05. We used the DAVID web server for gene function enrichment analysis using gene ontology (GO) categories (GOTERM_BP_FAT, GOTERM_ CC_FAT and GOTERM_MF_FAT)  [29].
Results Differentially expressed genes between HMM and NHMM are enriched in the trisomy chromosomes A primary difference between the HMM and NHMM subtypes is the presence of three copies (trisomy) of eight specific chromosomes in HMM and normal copy numbers for them in NHMM (Figure  1 ). To check whether the trisomy chromosomes influence the expression of the genes located within them, we compared the HMM and NHMM samples of the IFM dataset for differentially expressed genes. The trisomy chromosomes contained more HMM up-regulated genes than HMM down-regulated genes (Figure  2 ). Specifically, 744 of the 4702 (15. 8%) trisomy chromosome genes are up-regulated in HMM, while only 416 of the 8134 (5. 1%) non-trisomy chromosome genes are up-regulated in HMM (Fisher's exact test p-value < 2. 2e-16).
Widespread but variable dosage effect in myeloma To understand the global and spatial patterns of how copy number alterations (CNAs) affect gene expression in myeloma, we integrated paired gene expression and copy number profiling data from two myeloma datasets. We defined a dosage effect score (DES) for each gene as the proportion of CNA samples that have concordant copy number and gene expression changes (see Methods), and then visualized the gene-wise CNA frequencies and DES in a genome-wide karyotype plot (Figures  3A  and 4A ). The plots show that the dosage effect in myeloma patients is widespread and there is an overall similar dosage effect patterns between the IFM and Dickens datasets. The impact of dosage on gene expression can be observed at most chromosomes, such as the trisomy chromosomes 9, 11 and 15, and chromosomes 13, 14 and 16, which contain hemizygously deleted regions.
DES ranges between 0 and 1, and summarizes concordant copy number and gene expression changes. A value close to 0 and 1 indicates weak and strong dosage effect, respectively. The distribution of DES in the IFM data shows a large variation of DES across genes: 58% genes have low dosage effect (DES < 0. 2, Figure  3B ), 37% have medium dosage effect (DES between 0. 2 and 0. 4), and only 5. 2% (611 genes) have high dosage effect (DES > 0. 4). For the Dickens data the distribution is similar: 64%, 30%, and 5. 5% (655) genes have low, medium and high dosage effect, respectively (Figure  4B ). The high dosage effect genes from the two datasets have 123 genes in common (Fisher's exact p-value < 2. 2e-16). The top 100 dosage-sensitive as well as resistant-genes for IFM and Dickens datasets are reported in Additional files 1 and 2.
Deletion in chromosome 13 is a prognostic factor in MM  [3]. Therefore genes showing strong dosage effect on this chromosome may be important for MM. A list of dosage sensitive genes on this chromosome is available in Additional file 3. ELOVL7, PTDSS1, POLR1D, TBC1D22A, CCNC and TCF25 are some of the interesting genes with cancer related functions identified from the DES analysis. Additional file 4 summarizes cancer related functions based on PubMed for top 50 dosage sensitive genes in both IFM and Dickens datasets.
Higher gene expression level is associated with stronger dosage effect To explain the variation of dosage effect for the genes with similar CNA frequencies, such as those in the same trisomy chromosome (Figure  3A ), we correlated gene expression levels with the DES. The motivation is that some genes are expressed at higher level than others, and genes that are not expressed in myeloma will not exhibit any dosage effect. We first ordered all the genes by their median expression levels across samples and divided the genes into five equal-size groups (Figure  5 , X-axis), and then compared the proportion of genes having low, medium and high dosage effect across the groups (Figure  5 , Y-axis). We found that for both datasets, higher expressed gene groups contain significant larger proportion of genes with high dosage effect (Cochran-Armitage p-value < 2.
Higher chromosomal CNA frequency is correlated with higher proportion of dosage sensitive genes The proportion of dosage sensitive genes (DES > 0. 4) in each chromosome also varies across chromosomes, ranging from about 2% for chromosome 17 to >12% for chromosome 13 (Figure  6A ). These proportions strongly correlate between the two datasets. Interestingly, the chromosomal proportion of dosage sensitive genes is highest for four non-trisomy chromosomes (chromosomes 13, 14, 16 and 22) and lower for the trisomy chromosomes (marked red in Figure  6A ). In contrast, the proportion of genes with medium dosage effect in each chromosome is not correlated between the two datasets (Figure  6B ). Because the pattern of recurrent chromosomal gains and losses in myeloma are conserved across different datasets (Figures  1  and 6C ), we asked whether the variation in the proportion of dosage sensitive genes across chromosomes is correlated with the myeloma CNA patterns.
Both dosage sensitive genes and differentially expressed genes between HMM and NHMM are enriched by protein translation and localization processes To explore the functional consequences of dosage effect, we performed functional enrichment analysis for dosage sensitive genes (DES > 0. 4, genes listed in Additional file 1 and 2 for the IFM and Dickens dataset respectively) using the DAVID gene ontology Web server. Translation, protein localization, and proteolysis processes are enriched in dosage sensitive genes from the IFM dataset (Additional file 5: Table  S1 ). Translation, protein localization and mitochondrial processes are enriched in dosage sensitive genes from the Dickens dataset (Additional file 5: Table  S2 ). Since the distinct CNA patterns in the HMM and NHMM subtypes can induce subtype specific expression changes through dosage effect, we asked how dosage effect analysis compares to differential expression analysis.
We compared 2958 differentially expressed genes between HMM and NHMM (Figure  2 ) and 611 dosage sensitive genes (DES > 0. 4) from the IFM dataset. Their overlap of 306 genes is highly significant (Fisher's exact test p-value < 2. 2e-16). Gene ontology groups enriched in HMM up-regulated genes include translation and RNA-binding, and those enriched in NHMM up-regulated genes include protein localization, proteolysis, cell cycle, RNA processing and histone modification (Additional file 5: Table  S3 ). These results suggest that differentially expressed genes between the two myeloma subtypes are mainly driven by dosage sensitive genes located in distinct CNA regions of the two subtypes.
Dosage resistant genes are enriched by the apoptosis process Compared to dosage sensitive genes, dosage resistant genes (Additional file 1 and Additional file 2) may be subjected to more stringent dosage compensatory networks such as feedback loops. Dosage resistant genes were defined as the top 20% of highly expressed genes (Group 5 from Figure  5 ) that have DES < 0. 2 (genes listed in Additional files 1 and 2). Gene ontology enrichment analysis of dosage resistant genes identified processes such as translation and organelle lumen, which are commonly enriched in both dosage sensitive and resistant genes. However, apoptosis emerged as a distinct process only enriched in dosage resistant genes in both datasets (Additional file 5: Table  S4 , Table  S5 ). Alternative pre-processing of Affymetrix Exon arrays does not impact dosage effect score substantially It is valuable to assess impact of alternative methods for handling Affymetrix Exon array probe sets on DES.
Therefore, we obtained alternative expression estimates for the IFM dataset from a CDF provided by Purdom et al. [28]. For the IFM dataset, the Pearson correlation coefficient for the mean of gene expression estimates across the samples of dChip and aroma. affymetrix normalized data is 0. 86 (12192 genes; p < 2. 2e-16) and the correlation for DES scores is 0. 81 (10960 genes; p < 2. 2e-16). Moreover, the results from functional enrichment analysis of DES scores from the IFM dataset derived using aroma. affymetrix expression estimates overlap significantly with those obtained from dChip expression estimates (Additional file 5: Figure  S1A  and S1B, Additional file 5: Table  S6  and S7 ). In addition, we can observe similar DES patterns for chromosomal locations (Figure  3  and Additional file 5: Figure  S2 ).
Although previous studies hinted at the copy number dosage effect in various cancer types including myeloma  [11, 18] , in this study we have for the first time defined dosage effect score, highlighted its variation across genes and chromosomes, and studied the potential consequence of these variations in terms of dosage sensitive genes and dosage-resistant genes. Other genomic changes such as copy-neutral loss of heterozygosity can be inferred from SNP array data, and they could affect gene expression levels. Study of these alterations can be followed up in the future. Defining a gene-wise dosage effect score (DES) allows us to compare and visualize the impact of CNAs on gene expression at the level of individual genes, across the genome and between datasets. The DES score was calculated only for those genes that have copy number alterations in at least 10% of patients and vary their expression beyond the mean expression +/-one SD, as a safeguard in the process.
Camps et. al. [36]  identified that loss of POLR1D function affects cell viability in colorectal cancers. Cell cycle role of CCNC and its effect on several cancer types were reported by several authors  [37, 38]. Steen et. al. previously reported the role of NULP1 (TCG25) in cell death control and tumor growth  [39]. Additional file 4 includes literature search for the top 50 dosage sensitive genes for both IFM and Dickens dataset. A less studied phenomenon in integrative genomics is dosage resistance or compensation. Essential genes whose expression level must be tightly regulated may achieve dosage resistance through feedback loops that compensate for copy number changes  [40]. A recent study reports that amplified genes may be over-expressed, unchanged or down-regulated in cervical cancer  [41].
(i) Àx þ 1; Ày þ 1; Àz þ 1; (ii) x; y À 1; z; (iii) Àx; Ày þ 1; Àz þ 1; (iv) Àx; Ày þ 1; Àz. Data collection: CrysAlis PRO (Agilent, 2012); cell refinement: CrysAlis PRO; data reduction: CrysAlis RED (Agilent, 2012); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg et al. , 1999)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: WinGX  (Farrugia, 1999)  and publCIF  (Westrip, 2010).
Computing details Data collection: CrysAlis PRO (Agilent, 2012); cell refinement: CrysAlis PRO (Agilent, 2012); data reduction: CrysAlis RED (Agilent, 2012); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: DIAMOND  (Brandenburg et al. , 1999)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: WinGX  (Farrugia, 1999)  and publCIF  (Westrip, 2010). where P = (F o 2 + 2F c 2 )/3 (Δ/σ) max = 0. 001 Δρ max = 0. 40 e Å -3 Δρ min = -0. 25 e Å -3 Extinction correction: SHELXL97  (Sheldrick, 2008) , Fc * =kFc[1+0. 001xFc 2 λ 3 /sin(2θ)] -1/4 Extinction coefficient: 0. 294 (15).
Recent work has argued that the main factor behind successful upgrading is the consistency of the new industries with the country's latent  (and evolving)  comparative advantages  (Lin 2012). We empirically test this proposition using export data for more than 100,000 Chinese exporters over the 2000-6 period to investigate the link between export performance and the consistency of products with the local productive structure. The density of links between a product and the local product space is calculated by combining information on the local pattern of specialization and the intrinsic relatedness of the product. We thus contribute to the recent revival of literature on the role of structural transformation as an engine of economic growth  (McMillan and Rodrik 2011). The main argument is that not all products have the same degree of relatedness (and, hence, the same position in the product space); as a result, products have different potential, notably as platforms for jumping up to new economic activities  (Hausmann and Klinger 2007a; Hidalgo et al.
We define Pr(i|j) as the ratio of the number of countries with RCA in both i and j over the number of countries with RCA in i and Pr(j|i) as the ratio of the number of countries with RCA in both i and j over the number of countries with RCA in j. We calculate proximity as the minimum of those two pairwise conditional probabilities: 2 φ i,j = min[P r(i|j), P r(j|i)] (1) This bilateral relatedness φ i,j between products i and j is calculated for 5,016 products using data for 239 countries in 2000 from the BACI 3 world trade dataset  (Gaulier and Zignago 2010). 4 The matrix of these proximities characterizes the world product space. 5 Table  1  provides summary statistics of particular HS-2 product categories, and.
IV Conditionality of the relationship between product relatedness and exports Firm-ownership type and trade type heterogeneity We now assess whether the relationship between product relatedness and exports depends on the ownership type (domestic or foreign) of exporting firms and the trade regime (processing or ordinary). One interesting feature of the customs dataset is that it allows us to identify whether the export flows emanate from domestic or foreign firms, 21 and correspond to processing or ordinary trade. 22 Processing trade includes all trade from firms operating in the assembly sector that import inputs to process them in China and re-export the final products (these producers benefit from a preferential tax regime on imported inputs). In 2006, 53% of Chinese exports were from the processing-trade sector.
Because absorptive capacity is likely to be proportional to productivity, we expect the link between density and export performance to rise with firm efficiency. We consider three alternative proxies for firm-level efficiency in the customs dataset: the number of product-country pairs covered by the firm's exports; the number of products the firm exports; and the number of countries to which the firm exports. 23 These are calculated for the year 2000. Table  7  splits the sample by initial exporter productivity. We use two alternative cut-offs: the mean and the median. The estimated coefficient in the odd columns (high-productivity exporters) is always higher than that in the even columns (low-productivity exporters). 24 The product-level spillovers related to how well a product conforms to the local production structure are thus especially important for high-productivity exporters.
2 Taking the minimum of the conditional probabilities eliminates the problem that arises when a country is the sole exporter of one particular good; the conditional probability of exporting any other good given this one equals 1 for all of the other goods exported by that country. 3 This dataset, constructed using original COMTRADE data, provides bilateral trade flows. The BACI dataset is downloadable from http://www. cepii. fr/anglaisgraph/bdd/baci. htm 4 The flow dataset is constructed using an original procedure that reconciles the declarations of exporters and importers. The harmonization procedure enables us to extend considerably the number of countries for which trade data are available compared to the original dataset. 5 The product-space framework has been used in different papers on industrial policy and economic development in developing countries.
87. 14 We thank the editor for this suggestion. 15 Table  10  in the appendix verifies that our estimates are unaffected by the selectivity of our dataset, referred to in endnote 11. The results are rather similar when keeping all firm-product pairs that are active in 2006, regardless of whether they report positive export flows in 2000. 16 The weights for each product correspond to its share in the firm's residual exports. 17 We use the list established by  Wang and Wei (2010). It includes the four types of policy zones established by the Chinese government: special economic zones (SEZs), Economic and Technological Development Areas (ETDAs), Hi-Technology Industry Development Areas (HTIDA), and Export Processing Zones (EPZs). 18 The average density in a given location is computed as the weighted average of the product density in the city's export basket, with the weights being the product's export share.
19 The firm's average density is computed as the weighted average of the product density in the firm's export basket in 2000, with the weights being the product's export share. 20 In the unreported results, which are available upon request, we check that our main message holds when defining products at the 4-digit level of the harmonized system instead of the 6-digit level. 21 The data are reported separately by firm type, including foreign-owned firms, Sino-foreign joint ventures, collective firms, private firms, and state-owned firms. We consider the first two categories foreign and the other three domestic. 22 The data also refer to a third ("Others") category that covers other flows, such as aid, border trade, and consignment. Overall, this representsless than 1% of the total trade value.
Molecular Dynamics Simulations For aIIbb3 we started from the X-ray structure with PDB code 3NIG (resolution 2. 25 A ˚) and for avb3 with PDB code 3IJE (resolution 2. 90 A ˚). As b3 is very large and would have necessitated long simulation times we reduced the size of the structure examined. The GT database (http://sinaicentral. mssm. edu/intranet/research/glanzmann) shows that while a large majority of missense mutations are located in the ''head-groups'' of the two subunits, few are found at the N-terminal end of b3. Moreover, the distal extracellular b3-domain linked to the transmembrane sequence is free and being close to the a-subunit is prone to stick onto its surface during simulations leading to an abnormal complex. It was therefore decided to truncate b3 at residues 110 and 354. As a result, the extracellular N-terminal domain composing amino acids (aa) 1 to 110 and the membrane proximal C-terminal part represented by residues 354 to 466 were removed.
In the first ten years of operation, the neutron time-of-flight facility n_TOF at CERN has produced a large body of new and accurate data on neutron capture and fission cross sections, relevant to Nuclear Astrophysics and advanced nuclear technologies. In the next phase, with the construction of a second experimental area with a shorted flight path, the focus of the Collaboration will partially shift from high-resolution measurements of stable or long-lived isotopes, to high-flux measurements of isotopes of relatively short half-life, very low-cross sections, or available in very small quantities. The main results obtained so far at n_TOF and the future perspectives are here presented.