dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Data were analyzed using Graph Pad Prism version 7. 0 (GraphPad Software) or R (www. Rproject. org). Statistical analyses of the results were performed using either two-tailed t test or two-tailed Mann-Whitney U test, depending on the distribution of the data. Assumptions concerning the data (for example, normal distribution and similar variation between experimental groups) were examined for appropriateness before statistical tests were conducted. p values < 0. 05 were considered statistically significant. For ovarian carcinoma allografts, outliers were detected and removed according to the default options of the software used. Survival analyses were performed in R, using package survival. Differences between the survival distributions within each dataset were assessed for statistical significance using Kaplan-Meier curves and the log-rank test, with p values < 0.
GADD45B Expression Denotes Aggressive Malignant Disease Across Most Human Cancers We recently identified the product of the NF-κB-regulated gene, GADD45B, as an essential survival factor and novel therapeutic target in multiple myeloma  (24). We therefore investigated whether GADD45B was involved in any types of malignancy beyond multiple myeloma. Strikingly, in publicly available patient datasets, elevated GADD45B expression correlated with rapid disease progression in thirteen out of the top fifteen solid cancers for mortality worldwide  (36). When patients with prevalent disease subtypes were stratified at diagnosis on the basis of GADD45B expression in the tumour tissues, the patient cohorts expressing high GADD45B levels exhibited significantly shorter recurrence-free survival (RFS) and/or overall survival (OS) than the corresponding cohorts expressing low GADD45B mRNA levels (Figures  1A-1M ).
Gadd45b Loss Reduces Chemically Induced Hepatocellular Carcinogenesis The role of IKKβ/NF-κB in hepatocellular carcinogenesis is controversial  (16, 17). Indeed, in the widely used diethylnitrosamine (DEN) experimental model of HCC, hepatocytespecific Ikkb ablation was previously shown to promote, rather than inhibit, oncogenesis  (33). Therefore, to clarify the basis for the observed correlation between expression of the IKKβ/NF-κB-regulated effector, GADD45B, and aggressive disease pathology in HCC datasets (Figure  1C ), we sought to investigate whether Gadd45β was involved in this process. 15-day-old Gadd45b -/-and Gadd45b +/+ C57BL/6 males were treated with a single intraperitoneal injection of the carcinogen, DEN, according to an established protocol for HCC induction  (33). As expected, all Gadd45b +/+ mice given DEN developed typical HCCs within 9 months (Figures  2A-2E ).
Acknowledgments We thank  N. van Rooijen  for providing clodronate liposomes,  I. Marigo  and  V. Bronte  for providing the MCA-203 cell line, and  J. Dyson  for assistance with animal studies. We also thank  V. Tybulewicz ,  A. Leonardi ,  G. Melino  and  J. Behmoaras  for critical reading of the manuscript. The work was supported in part by  Cancer Research UK  programme grant  A15115 ,  Medical Research Council (MRC) Biomedical Catalyst  grant  MR/L005069/1  and  Bloodwise  project grant  15003  to G. F. , the  Associazione Italiana per la Ricerca sul Cancro (AIRC)  grants  1432  and  5172  and  MIUR PRIN  grant n°  2009EWAW4M_003  to F. Z. ,  MIUR FIRB  grant n°  RBAP10A9H9  to  E. A , and the  Associazione Italiana per la Ricerca sul Cancro (AIRC)  grant  15585  to  A. S. D. Ver. and B.
D. F. were supported by the  L'Aquila University Ph. D. program in Experimental Medicine, M. F. and D. Vec. were supported by the  L'Aquila University Ph. D program in Biotechnology. FINANCIAL SUPPORT The work was supported in part by  Cancer Research UK  programme grant  A15115 ,  Medical Research Council (MRC) Biomedical Catalyst  grant  MR/L005069/1  and  Bloodwise  project grant  15003  to G. F. , the  Associazione Italiana per la Ricerca sul Cancro (AIRC)  grants  1432  and  5172  and  MIUR PRIN  grant n°  2009EWAW4M_003  to F. Z. ,  MIUR FIRB  grant n°  RBAP10A9H9  to E. A. , and the  Associazione Italiana per la Ricerca sul Cancro (AIRC)  grant  15585  to A. S.
The sample was supplemented with several objects for which substantial ancillary data existed, including SMM J2135-0102. About half of the sources have been confirmed to be lensed targets from submm continuum observations (e. g. Bussmann et al. 2013). Table  1. Summary of the Galaxy sample. † : Spectroscopic redshifts adopted from  Nayyeri et al. (2016). : Redshift estimated from photometric data, using the SED of average ALESS galaxies as the template  (Ivison et al. 2016). For galaxy without spec-z information we label their names with italic fonts. The order of the table is organised by different survey fields and then by the right ascensions of the galaxies.
PACS observations and flux density measurements The original H-ATLAS parallel PACS imaging data at 100 and 160 µm have noise levels of ∼ 25 -50 mJy (almost ten times higher than our new targted observations; see  Ibar et al. 2010a; Eales et al. 2010). They were insufficiently deep to detect many of our sample, and the targets outside H-ATLAS had no coverage at these wavelengths. To complement the SPIRE photometric measurements across the flux-density peak of their SEDs and to provide stronger constraints on their rest-frame mid-infrared (MIR) emission, we obtained deep imaging observations at 100 and 160 µm with Herschel PACS. For each galaxy we obtained two cross-linked mini-scans with PACS, recording data at 100 and 160 µm simultaneously. Each mini-scan covers an area of ∼ 10 × 3 , with two orientations of 70 and 110 • , resulted to three minutes on-source and a total of 791 seconds observing time including overhead.
On average, we reach 1-σ depths of ∼ 3 and 7 mJy at 100 and 160 µm, respectively. Archival mini-scan imaging data covering SMM J2135-0102 was combined with our two scans to produce a deeper image, which is particularly useful as this field has a large number of FIR sources visible in the region around the target lensed galaxy. Information about the Herschel PACS observations is listed in Appendix A. We adopted the Herschel Interactive Processing Environment (HIPE v12; Ott 2010) to process and combine the mini-scan data using the standard pipeline scripts. To remove the 1/ f noise, a high-   (Swinyard et al. 2010; Bendo et al. 2013). Most of the 70 µm fluxes are from  Wardlow et al. (2017) , except for SDP. 81 and SDP. 130, which are measured with our PACS observations. 100 and 160 µm uncertainties include 2.
g. Frayer et al. 1998; Bothwell et al. 2013; Yang et al. 2017 ) -to each spectra, and a Gaussian function to the stacked spectra. Several FTS spectra from this dataset have been published previously (e. g. Valtchanov et al. 2011; George et al. 2013 George et al. , 2014)). Here, we include re-reductions of those data with the latest pipeline (HIPE v14. 2). The line fluxes and uncertainties are shown in Table 3 and cut-out spectral regions around the [C II] 158-µm line are shown in Fig. 3.
APEX observations and data reduction To check the [C II] flux and to resolve the line profiles of galaxies detected in our Herschel observations, we obtained groundbased observations of SDP. 11 and NA. v1. 186 with the 12-m Atacama Pathfinder EXperiment (APEX) telescope on the Chajnantor Plateau in Chile, in good (pwv < 0. 6 mm) weather conditions. We conducted the observations during the science verification phase of the band-9 Swedish ESO PI receiver for APEX (i. e. , SEPIA B9,  Belitsky et al. 2018 ) during 2016 August and September. The project number is E-097. F-9808A-2016. All observations were performed in a wobbler-switching mode. Beam throws were 2 , offset in azimuth. The focus was determined using Mars. Pointing was corrected every 30 min using nearby carbon stars, resulting in a typical uncertainty of 2 (r.
Then we bin the modeled data points with a hexagonal binning method and plot the average colors in a hexagonal box. The SPIRE colors of the sample are well within the limits defined by the models we have considered. We adopt the SED template of SMM J2135-0102, shift it to different redshifts and measure the 'observed' S 500 /S 350 and S 250 /S 350 colors. Then we plot the simulated colors as a dashed line, with their redshifts marked, to compare with the observed values. As displayed, the sample appears relatively similar to the track of SMM J2135-0102 in the observed frame, although the average colors of S 250 /S 350 and S 350 /S 500 are a little higher than those of SMM J2135-0102. This is expected due to the very high magnification factor experienced by SMM J2135-0102, which allows a less-extreme galaxy with a relatively lower SFR surface density, and therefore a likely lower dust temperature, to reach flux densities comparable to the others in our sample.
Magnification factors Gravitational amplification values are primarily taken from  Bussmann et al. (2013)  that are derived from SMA data in preference to values derived from Near-IR imaging (e. g  Calanog et al. 2014; Dye et al. 2014) , due to the likelihood of a physical separation (and hence amplification difference) of the starburst and existing stellar population  (Fu et al. 2012; Dye et al. 2015; Hodge et al. 2016; Oteo et al. 2017). Most galaxies in the sample are strongly lensed, with a median amplification of around 10×, distributed between 1-15×. SMM J2135-0102 is an outlier with its very high amplification factor. When fitting dust SED and measuring lines ratios, we neglect the possibility of differential lensing at different wavelengths, which may bring in extra uncertainties (e.
E u is the upper level energy of this transition, Q(T ex ) is the partition function (where we adopt the Q values given by the JPL database 7 ), g I = 3 for an ortho level and g I = 1 for all any other cases. B 21 = A 21 /(2hν 3 /c 2 ), where A ul = 64π 4 ν 3 3hc 3 | µ| 2 is the Einstein coefficient for spontaneous emission, µ is the electric dipole moment and ν is the rest frequency of the line. As shown in aforementioned discussion, the non-detection of 84-µm and 79-µm lines indicates that the OH excitation is low. We therefore adopt a low excitation temperature of 9 K (comparable to the cosmic microwave background temperature, T cmb , at z = 2. 5) and derive an estimate of the OH column density: ∼ 2 × 10 15 cm -2. Assuming the OH abundance is the same as the Galactic value, X OH = 5 × 10 -6 (e.
ACKNOWLEDGEMENTS The authors are grateful to the referee for the constructive suggestions and comments. ZYZ thanks  J. D. Smith ,  Justin Spilker  for sharing data. ZYZ thanks  Donatella Romano  for very helpful discussion. ZYZ, RJI, LD, SM, IO, RDG and AJRL acknowledge support from the  European Research Council (ERC)  in the form of Advanced Grant, 321302 , COSMICISM. RDG also acknowledges an  STFC studentship. YZ thanks the support from the  NSFC  grant numbers  11673057  and  11420101002. CY was supported by an  ESO  Fellowship. US participants in H-ATLAS acknowledge support from  NASA  through a contract from  JPL. Herschel was an  ESA  space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from  NASA. PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAF-IFSI/OAA/OAP/OAT ,  LENS ,  SISSA (Italy) ;  IAC (Spain).
State characters are de ned based on well-de ned equations rather than on the individual subjective interpretation of a molecular orbital (MO). Quantitative tools can also reveal subtle trends, such as "substitution at some position by a uorine atom increases the MLCT character of a state by 5%. " Last but not least, the application of sophisticated analysis tools has allowed identifying physical phenomena such as excitonic correlation  [79, 80]  and secondary orbital relaxation  [81] , which could otherwise not be understood even qualitatively in the canonical MO picture. These wave function analysis tools are implemented in the TheoDORE package  [82] , which provides interfaces to a wide range of quantum chemistry programs and electronic structure methods. As part of this work, we report a new interface between the TheoDORE and ADF  [83]  program packages that allows for a quantitative analysis of large-scale TD-DFT computations and also provides the possibility of analyzing spin-orbit coupled states.
In addition, we use integrated analysis utilities in the Q-Chem  [71, 84]  and Molcas  [85, 86]  program packages in connection with wave function-based single-and multi-reference methods, where appropriate. The goal of this review is to present a variety of powerful ways in which wave function analysis can be applied to TMC excited-state computations. First, we shall introduce several orbital transformation techniques which can be used to represent excitations (Section 2). By means of descriptors like single-excitation contribution, charge transfer numbers, or entanglement measures we move away from orbital visualization to a purely numeric representation of the excitations and we show how these descriptors can be used to compactly depict large number of excited states (Section 3. 1 and 3.
Concluding remarks We have reviewed a quantitative wave function analysis technique based on the concept of charge transfer numbers. Charge transfer numbers are a mathematical tool based on the transition density matrix for the analysis of excited states in terms of electronic transitions between groups of atoms. This tool-which is implemented in the TheoDORE software package and interfaced to several quantum chemistry programs-allows quantifying the localization and charge transfer character in an automatized way, without looking at any orbital. Charge transfer numbers provide signi cant advantages compared to traditional state characterization based on orbital visualization. These advantages include e cient analysis of large numbers of states, reproducibility, objectivity, and quanti cation which allows spotting chemical trends.
e. , X + Y (Equation (  1 )), are analyzed. For the purpose of analyzing spin-orbit coupled charge transfer numbers, the spin-orbit Hamiltonian was extracted from the TAPE21 le and subsequently diagonalized to yield the coe cients U I α , which were inserted into Equation  (25). The described TheoDORE-ADF interface will be made available within the next TheoDORE release along with utility scripts that help in generating the plots shown in this work.
Acknowledgments S. M. , F. P. , and L. G. gratefully acknowledge funding from the  Austrian Science Fund (FWF)  within project  I2883  and the  University of Vienna. The  Vienna Scienti c Cluster (VSC3)  is acknowledged for computational time. M. F. and C. D. acknowledge funding from the  Agence nationale de la recherche (ANR)  within project  ANR-15-CE29-0027  and the  FRC and Labex CSC  ( ANR-10-LABX-0026_CSC ). The authors also want to thank  A. Vlček  and  S. Záliš  for fruitful discussions, and the inspiring  COST action   CM1305  (ECOSTBio).
Existing ideation-tools and eco-innovation platforms Many ideation-tools have been proposed such as Eco-design Strategy wheel  [4] , BEC synergy diagram  [5]  or TRIZ-based tools  [9, 10]. These tools are mainly stimulus, mechanisms, a diagram or a wheel that help to foster the generations of useful ideas for the eco-innovation process. However, for a particular eco-innovation case, only a few stimuli, mechanisms are used and we have found a lack of interest from the community in developing ICT (Information and Communication Technologies) solutions such as web-based platform, desktop software, etc. that facilitate the identification of the appropriate ESMs to be used. Only a few works that has been proposed. Two among them are Information/ inspiration  [11]  and IDEATRIZ Innovation software  [12]  but they aims to facilitate the whole eco-innovation process instead of eco-ideation phase.
"The aim of 'Inspiration' is to encourage, inspire and educate designers, by providing them with product examples of eco-design work, to help them build up their tacit eco-design knowledge, and/ or support idea generation at the beginning of an eco-design project"  [11]. By selecting a particular product, users can see detail information about it such as description, image, functions, eco-design strategies, etc. The platform was developed a long time ago, the web interface is too simple and not really intuitive. It lacks also filter functions or navigation tools to move between eco-design cases. IDEATRIZ Innovation software is a desktop software, based on IDEATRIZ methodology proposed by Carvalho M. A et al  [12]  that aims at generating ideas that are both new and valuable to company's customer.
The most relevant ESMs are selected by platform's admin based on the voting results of all users. • Scenario 3: After logged in and consult the design brief of a particular eco-innovations, users use implemented filters and navigation functions to navigate through all eco-innovations cases existing in the database and select a set of cases that are the most related to the currently studied case. Users can see what ESMs are associated to each selected eco-innovation case in its detail page. They then classify the set of cases into different clusters according to types of issues faced by the studied case. They can leave comments in the discussion panel to explain the why a case is classified into a cluster but not the others. The most relevant ESM are then selected by the platform's admin according to the clusters established by all users.
Identification of the ESM through inspiring case studies on ALIENNOR platform With 120 implemented eco-innovation cases and 7 mechanisms, the ALIENNOR platform is notably helpful in the third approach. It facilitates the selection of inspiring cases by providing users a structured and intuitive representation of all eco-innovation cases with appropriate navigation, visualization functions. In order to identify ESMs adapted to a non-tagged case, after logged in, users consult the design brief included in its detail page (see Figure  4 ). In the next step, users select from the database a set of tagged cases (inspiring cases) that are the most related to the currently studied non-tagged case. To facilitate this selection, the platform provides users with an overview page of all tagged-cases represented in grid.
For both of these applications, we focus on the use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension we propose called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a nonasymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters present in the data. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data.
e. those with compositional values close to zero in one or more coordinates and those located near the vertices of the simplex. Finally, we select the number of clusters using a nonasymptotic criterion whose penalty is calibrated using the slope heuristics  (Baudry et al. , 2012; Fischer, 2011). This approach of using a K-means algorithm in conjunction with compositional data transformations is implemented in the Bioconductor package coseq, and we apply it to two practical problems: identifying groups of co-expressed genes from highthroughput RNA sequencing data, and finding patterns in the usage of stations in a bicycle sharing system. The remainder of this paper is organized as follows: in Section 2, we describe the context and data from the transcriptomic and bicycle sharing applications in greater detail.
In the current work, raw read counts were downloaded from the Digital Expression Explorer  (Ziemann et al. , 2015)  as described in  Rau and Maugis-Rabusseau (2017). The data consist of transcriptome-wide measurements in three tissues with five replicates each. As suggested by  Rau and Maugis-Rabusseau (2017) , clustering will be performed on the full set of data (after filtering, on n = 8969 genes), and visualization of resulting clusters will be done on profiles summed over each tissue. • Dynamic expression in embryonic flies:  Graveley et al. (2011)  characterized the expression dynamics of the fly using RNA-seq over 27 stages of development, ranging from early embryo to adult males and females. Raw read counts were obtained using the online ReCount resource  (Frazee et al. , 2011). We focus here on a subset of these data arising from 12 embryonic samples collected at 2-hour intervals over a 24-hour period, with a single replicate for each time point.
After filtering, we obtain a subset of n = 9524 genes. For both of the RNA-seq datasets described above, there are a large number of genes whose expression remains unchanged in different tissues or at different time points (i. e. , whose profiles are close to the center of the simplex), and a relatively small number of genes with expression highly specific to a single tissue or time point (i. e. , whose profiles are close to a vertex of the simplex); see Figure  1  for an example from the mouse neocortex data. In the remainder of the paper, we refer to the latter as observations with highly-specific profiles, and we will focus on proposing a clustering method able to highlight these small but important groups of genes.
RNA-seq data results We applied the K-means algorithm using the Euclidean distance described in the previous section to the two RNA-seq datasets described in Section 2 (referred to as the mouse or fly data, respectively), using either untransformed, CLR-transformed, or logCLR-transformed profiles for K = 2,. , 40 clusters. For each dataset and each transformation, the nonasymptotic penalized criterion described in Section 3. 2 is used to identify the appropriate number of clusters K (see Table  1 ); diagnostic plots for the calibration of the slope heuristics penalty as well as profile plots for all identified clusters under each transformation are shown in Appendix. We note that alternative model selection criteria, such as the Gap statistic and averaged Silhouette, led to models with very small numbers of clusters (2 or 3).
As such, the following results correspond to the models selected via the nonasymptotic penalized criterion. By examining the clusters of profiles identified in each dataset under each transformation, we may note qualitative differences in the results for each approach, in particular for genes with highly-specific profiles. In the case of the mouse RNA-seq data, comparisons among analagous profile clusters for untransformed, CLR-, and logCLR-transformed data indicate that untransformed and CLR-transformed data (Figure  4 , top and middle) tend to yield a larger number of small, less-variable clusters situated at the center of the simplex, corresponding to several distinct clusters representing genes that are largely nondifferentially expressed. These two strategies also tend to yield more diffuse clusters for profiles close to the vertices of the simplex (Figure  4 , bottom), particularly in genes with high expression in the SVZ tissue relative to the others.
This behavior is in fact due to the definition of the CLR transformation (see Equation  3. 1). Indeed, as illustrated in Figure  3 , the CLR transformation for coordinates close to 0 is highly sensitive to small fluctuations; for the K-means algorithm, this tends to have the effect of grouping together profiles with several zeros in common rather than those with a single non-zero coordinate in common (see for example Cluster 1 in Figure  5 , middle). In applications where it is of interest to group together profiles with common strong distinct expression, the use of untransformed or logCLR-transformed data with K-means thus appears to be a more coherent choice. To focus on a more quantitative measure of the differences among each method, we also calculate the per-cluster squared errors for each dataset using the Euclidean distance.