dataesr/scientific-paragraphs-categorization huggingface dataset:
Inflammation level COX activity was measured in duplicates as described by Fujimoto and colleagues (2002), using the oxidation of 2',7'-dichlorofluorescin diacetate in the presence of arachidonate. 26 riefly, 200,000 hemocytes were resuspended in 150 µL of pre-diluted PBS (1/4) and 25 µL of this cell suspension was mixed to 150 µL of reaction media. The reaction media was composed of 50 mmol/L Tris-HCl, pH 8. 0, containing 0. 05% Tween-20, 50 µmol/L arachidonic acid, 2 µmol/L dichlorofluorescein and 0. 1 µg/mL horseradish peroxidase. The fluorescence readings were taken at 0, 3, 6 and 12 min at 25°C using 485 nm excitation and 530 nm emission wavelength (Synergy4, Gen5  V2. 05). The data were expressed in relative fluorescence units/min/mg of protein (n=30).
We propose alternative but not mutually exclusive scenarios that could account for the role of Sept2 in Drp1-dependent mitochondrial fission. Sept2 could promote Drp1 recruitment by contributing to mitochondrial constriction. Alternatively, Sept2 might stabilize productive fission complexes or act as a scaffold that promotes Drp1 recruitment or retention on mitochondria, as has been proposed for A Immunofluorescence analysis of P-MLC (green) recruitment to mitochondria (red, Tom20) in mock-treated and Sept2-depleted HeLa cells. Insets show twofold enlargements. Scale bar: 10 lm. B Colocalization of P-MLC with mitochondria was assessed with Icy software and showed no difference between mock-treated and Sept2-depleted cells (mean AE SEM). C Mock-treated or Sept2-depleted Drp1 À/À MEF cells were labeled with Mitotracker orange (red), treated for the indicated amount of time with 2 lM FCCP and stained for actin with phalloidin (green).
Insets are enlarged twofold. Scale bar: 10 lm. D Immunofluorescence analysis of the actin-binding proteins Arp3 and cofilin (green) colocalized with cytochrome c (red), and cortactin (green) colocalized with Hsp60 (red) in mock-treated or Sept2-depleted Drp1 À/À MEF cells. Scale bars: 10 lm. E Cytosol and crude mitochondria fractions were prepared from mock-treated or Sept2-depleted Drp1 À/À MEF cells and analyzed by Western blotting with the indicated antibodies. F HeLa cells were mock-treated or treated with cytochalasin D and subjected to Sept2 immunoprecipitation. Immunoprecipitates were analyzed by Western blot for Drp1 and Sept2, showing that Drp1 co-immunoprecipitation with Sept2 is not affected by cytochalasin D treatment. Source data are available online for this figure.
Imaging and image analysis Immunofluorescence and live cell imaging were performed essentially as described in  [48, 72]. For super-resolution microscopy, cells were seeded onto high-precision coverslips (Marienfeld), and after immunofluorescence, samples were mounted in Slow-Fade Gold (Invitrogen) or Fluoromount G. Super-resolution structured illumination (SR-SIM), providing an expected resolution of about 140 nm, was performed on a Zeiss LSM780 Elyra PS1 (Carl Zeiss, Germany) using 63×/1. 4 oil Plan Apo objective. Three angles of the excitation grid with five phases each were acquired for each channel and each z-plane. SIM images were processed with ZEN software for brightness/contrast adjustment. Images were aligned using 100 nm TetraSpeck beads embedded in the sample. The confocal image in Fig EV3B was acquired using the same equipment as the SR-SIM and the same pixel size to allow Huygens professional software was used for deconvolution and brightness/contrast in Fig EV3B.
CoDIM imaging was performed on a Bioaxial CoDiM 100 system equipped with 488, 561, and 640 lasers and decreases the resolution limit to about 80-100 nm. Imaris 6. 4. 1 (Bitplane) was used for 3D reconstructions. Image analysis was performed with ImageJ unless otherwise stated. For morphometric analysis on fixed cells, mitochondrial length was assessed in well-resolved mitochondria only; fissions were counted manually in live cell imaging experiments and displayed according to  [57]. To calculate the percentage of rescue, the difference in mitochondrial length between Sept2-siRNA and Sept2-rescue or Sept7-rescue is referred to the difference between Sept2-depleted and mock-treated cells (set to 100%). To quantify FCCP-induced mitochondrial fission and looping, mitochondrial elongation (long/short axis) was measured with the open-source software Icy  [73]  (protocol Mitochondria Elongation) after binarization of mitochondria.
Statistical analysis Results are expressed as means of at least two independent experiments, and error bars represent the standard error of the mean. For multiple comparisons, data were first analyzed by one-way ANOVA on BiostaTGV (http://marne. u707. jussieu. fr/biostatgv/?module=tests), followed by pairwise comparisons with unpaired two-tailed Student's t-test on Excel (Microsoft) or Prism (GraphPad). Significance is indicated as P < 0. 05 (*), P < 0. 01 (**), and P < 0. 005 (***), ns for P > 0. 05.
Acknowledgements We would like to thank  David Ribet  for providing the Sept2-HA template and Sept7-HA and for helpful discussions,  Henry Higgs  for the kind gift of anti-INF2 antibody,  Barbara Pomili  and the C3BI open desk for help with statistic analysis,  Edith Gouin  and  Véronique Villiers  for antibody purification,  José Santos  for help with mitochondrial fractionations,  Béatrice de Cougny (Communication and Image Unit, Institut Pasteur)  for help with the schematic illustration, and  Serge Mostowy  for providing initial siRNA-treated samples and discussion. We gratefully acknowledge the kind financial support of the  Institut Pasteur (Paris) , the  France-BioImaging infrastructure network  supported by the  French National Research Agency  ( ANR-10-INSB-04 , Investments for the future), and the  Région Ile-de-France  (program DIM-Malinf).
IC 50 values positively correlated with Mcl-1 expression and negatively with Bcl-xL expression for UNBS1450, in agreement with previous data. 8 For ABT-263, we could not reach significance based on this panel; analysis of the raw data indicates a positive correlation with Bcl-2 and Bcl-xL expression, and a negative one with concomitant Mcl-1 expression. Generally, effects with ABTs were obtained with micromolar concentrations, prone to trigger resistance. We then selected U937 and TF-1 cell lines as models to investigate the synergistic potential of combination treatments. Mcl-1 inhibitor A-1210477 previously allowed characterizing Bcl-2 protein co-dependency involving Mcl-1. 11 Combination of A-1210477 and ABTs provided evidence of the co-requirement of Bcl-2/Mcl-1 expression and Bcl-xL/Mcl-1 in U937 and TF-1 cells, respectively (Supplementary Figure  1 ).
01 μM; 18 h of incubation). (f) Same analysis in AML patient lymphocytic subpopulation. (g) Western blot analysis of anti-apoptotic Bcl-2 protein expression. Synergy was estimated by using the 'response additivity' approach. Corresponding combinational index (CI) of significant interactions were computed. Statistical analyses were performed in GraphPad Prism (GraphPad Software Inc. , La Jolla, CA, USA). Significance is *P o0. 05; **P o0. 01; ***Po0. 001; ****P o0. 0001 (two-way analysis of variance; repeated measures; post hoc analyses Dunnett; Sidak). This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
3  However, with the exception of our previous finding implicating the lack of spliceosomal gene aberrations,  4  there is no systematic data elucidating the mutational landscape in primary childhood MDS. 6] [7] [8] [9]  We utilized these results for a focused mutational discovery in childhood MDS. We studied a cohort of 50 children and adolescents (28 males and 22 females) diagnosed with primary MDS 10 at a median age of 9. 0 (1. 1-17. 4) years. On the basis of paucity of gene mutations in low-risk, in contrast to high-risk MDS in adults,  [5] [6] [7]  we purposely biased our study cohort towards more advanced disease in order to increase the odds of identifying mutations. The study cohort was enriched for cases with monosomy 7 (-7) (48%) and MDS-EB (38%, Table  1 ). Germline predisposition, namely GATA2 and RUNX1 deficiency, had previously been established in one-third of our cohort (18 cases, Table  1 ).
ACKNOWLEDGEMENTS CC and MD to 'Waxweiler grant for cancer prevention research' ( Action LIONS 'Vaincre le Cancer '), ThinkPinkLux and Europadonna; AG and FR to Télévie; AM and  J-YL  to  Brain Korea  ( BK21 ) PLUS and  Recherches Scientifiques Luxembourg; LBMCC to Fondation 'Recherche Cancer et Sang', Recherches Scientifiques Luxembourg , ' Een Haerz  fir kriibskrank Kanner' and Télévie; MD to  Research Institute of Pharmaceutical Sciences, College of Pharmacy ,  Seoul National University , to the  National Research Fund  by the  MEST of Korea for Tumor Microenvironment  ( GCRC 2012-0001184  grant), to  Seoul National University   Research Grant  ( 2016 ; funding number:  800-20160150 ) and to  BK21 PLUS program. We thank  Professor Véronique.
I. INTRODUCTION Beam dumps and beam stoppers are blocks of material dedicated to absorbing charged particles and consequently removing them from circulating beams. Typical issues arising from with the design of a beam dump include: heat dissipation, radioactivation, radiation damages and thermal stresses on which we focus herein. For beam energies in GeV or TeV range as in the proton synchrotron booster (PSB) and LHC respectively, the physics involved in the energy deposited is governed by the electromagnetic shower development initiated by nuclear reactions of beam particles impacting the material lattice  [1]. FLUKA  [2, 3] , a Monte-Carlo based software, is currently the standard tool used at CERN for this important application. In the context of the proton synchrotron booster used to inject the beam from the linear accelerators to the proton synchrotron (PS)  [4, 5] , it is essential to ensure the interception of high energy proton beams.
The localization of the maximum dependence reduces to Π 4. The position of the peak of stress for all Π 4 in the range ½10 -3 ; 10 2 is sought using MATLAB  [25]  and presented on Fig. 1. Two regions stand out. The first region is at approximately r ¼ 1. 414 where dynamic effects are dominant and the second near r ¼ 1. 775 for Π 4 sufficiently large which correspond to the quasistatic case. The maximum dimensional Von Mises stress in the general problem reads, σ max VM ¼ 2μ • Π c • ζðΠ 4 Þ; ð24Þ where ζ is a function of Π 4 shown on Fig. 2  and 3.
Statistical analysis The total sample size was expected to be 20 patients. This sample size was considered to allow an adequate estimation of safety events. The study was descriptive in nature and only exploratory analysis was conducted. All patients enrolled in intentionto-treat population who had a valid baseline assessment were analyzed. Mean CST and BCVA were compared at each visit with baseline values using Student's paired t-test. Mean change of the area under the curve from baseline to last value was calculated for BCVA using the trapezoidal rule divided by the total duration in days and a Student's paired t-test was used for comparison with baseline values for each group. Safety variables were analyzed using the safety population that included data from all enrolled patients who received the FAc intravitreal implant.
Circular dichroism (CD) spectroscopy Wavelength-dependent Circular Dichroism (CD) spectra were collected on a Jasco J-815 CD Spectrometer equipped with a PTC-423S single position Peltier temperature control system and counter-cooled with an Isotemp 3016S (Fisher Scientific) water bath. Samples were loaded in a Hellma 218 quartz cuvette (500 µL, 1 mm path length). A far-UV temperaturedependent wavelength scan from 185-260 nm as a function of temperature was completed for CE 1 -His 6 and E 1 C-His 6 in the absence and presence of GNPs at 0. 2 mg/mL in 10 mM sodium phosphate buffer pH 8. 0 at scan rate of 50 nm/min for a range of temperatures (25-90°C) with 3 accumulation scans. At least two batches of separately purified proteins were measured. CD data was converted into mean residue molar ellipticity (MRW) via equation [θ] MRW = θ•MW/(10•n•C•l) , where θ is in mdeg, MW is molecular weight, n is amino acid number in protein, C is concentration in mg/mL, l is path length in cm  [35].
The pellets were resuspended in 200 µL of 50 mM phosphate buffer, pH 7. 4 and kept at room temperature in the dark for release. After 10 min, the suspensions were centrifuged and the supernatant was removed and used for extraction assessment of released curcumin. This resuspension-incubation-spin-release cycle was repeated for the next eight hours at the following time points: 10, 25, 55, 85, 135, 195, 255, 315 and 495 min. Release study was performed on two different protein sample preparations to obtain the averaged release profile. Error bars on the release data represented standard error of the two sample preparations.
Acknowledgments We thank  Youssef Wadghiri ,  Komal Jhaveri  and  Sungheon Kim  for helpful discussions on cancer cell experiments. This work was supported by  NSF   DMR-1205384  (J. K. M. ),  Shiffrin Meyer Breast Cancer Discovery Fund  (J. K. M. ), and in part by the  GK-12 Fellows Grant   DGE-0741714  (J. A. F. ),  NIH   CA175794  (A. S. ) NSF  MRSEC Program  under Award Number  DMR-1420073  as well as the  NYU CTSA  grant  ULITR000038  from the  National Center for Advancing Translational Sciences (NCATS) ,  NIH.
Histology A transversal slice from the basal part of the LV tissue (n = 6 per group) was fixed in paraformaldehyde 4% for 24 h and paraffin-embedded after successive lavages in ethanol (70-100%) for 1 h each and incubation in xylene for 3 h. Then 5-μm sections were cut and mounted on Superfrost glass slides (Fisher Scientific). After deparaffinization in xylene and successive rehydration, sections were stained with hematoxylin-eosin. The LV wall area and the LV cross-sectional thickness (15 measurements for each section) were measured with ImageJ 1. 47v software. Sections were observed on a Leica DM4000B microscope with an Olympus DP72 camera and analyzed with CellSensDimension software.
To study the dark matter aspects of the model, we have implemented the Lagrangian eq. (  17 ) in the public code MicrOMEGAs  [24]  with the help of the FeynRules package  [25]. Our results are depicted in Fig. 4 , where we highlight the (m N 1 , sin θ N ) combinations for which the latest limits on dark matter abundance from the Planck mission  [26]  can be satisfied according to standard thermal freeze-out. The different colorings correspond to different ranges for the predicted width of the lightest heavy electron E 1 from the decay mode E 1 → N 1 W * → N 1 f f. The other parameters entering the Lagrangian eq. (  17 ) have been set to the values y N L = y t = 1 (which also amounts to a total width Γ A ≈ 40 GeV), while for the calculation of the E 1 decay width we have chosen m E 1 = 375.
Conclusion Experimental advances permitted by the Hi-C protocol pointed to the need of bridging a physical viewpoint, enlightening the functional role of 3D genome structure, with a systemic viewpoint, based on genome-wide data and network analysis. A pillar of this bridge is the development of reconstruction algorithms, in which information limited to contacts is sufficient to get a 3D representation of the data. An auxiliary though important step is to transform the contact maps into complete distance matrices. Our analysis shows that shortest-path distances, inspired by network concepts, is to date the best way to implement this step with human data, making it possible to deal with sparse chromosomal contact maps and match FISH data. The extension of ShRec3D presented here, with a tunable parameter α in the definition of the graph distances and two implementations of MDS, provides a flexible algorithm to accommodate various organisms, conditions and goals.
Acknowledgements This work has been funded by the  French Institut National du Cancer , grant  INCa_5960  (AL), the  French Agence Nationale de la Recherche , grant  ANR-13-BSH3-0007  ( AL)  and grant  ANR-15-CE11-0023-01  (JM), and  University Pierre and Marie Curie, Emergence program , grant  SU-15-R-EMR-08  (JM). The authors are grateful to  Thierry Forné  for his comments on the manuscript, and to  Michel Quaggetto  for his help with softwares. AL acknowledges the hospitality of  Jacobs University, Bremen (Germany)  during the  WE Heraeus Physics School "The physics behind systems biology " ( 6-12 July 2015 ). Received: 12 February 2016 Accepted: 7 April 2016.
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale questionanswer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the questiongeneration model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.
Introduction A major obstacle for training question-answering (QA) systems has been due to the lack of labeled data. The question answering field has focused on building QA systems based on traditional information retrieval procedures  (Lopez et al. , 2011; Dumais et al. , 2002; Voorhees and Tice, 2000). More recently, researchers have started to utilize large-scale knowledge bases (KBs)  (Lopez et al. , 2011) , such as Freebase  (Bollacker et al. , 2008) ,  WikiData (Vrandečić and Krötzsch, 2014)  and  Cyc (Lenat and Guha, 1989). 1 Bootstrapping QA systems with such structured knowledge is clearly beneficial, but it is unlikely alone to overcome the lack of labeled data. To take into account the rich and complex nature of human language, such as paraphrases and ambiguity, it would appear that labeled question and answer pairs are necessary.
Dataset We use the SimpleQuestions dataset  (Bordes et al. , 2015)  in order to train our models. This is by far the largest dataset of question-answer pairs created by humans based on a KB. It contains over 100K question-answer pairs created by users on Amazon Mechanical Turk 3  in English based on the Freebase KB. In order to create the questions, human participants were shown one whole Freebase fact at a time and they were asked to phrase a question such that the object of the presented fact becomes the answer of the question. 4 Consequently, both the subject and the relationship are explicitly given in each question. But indirectly characteristics of the object may also be given since the humans have an access to it as well. Often when phrasing a question the annotators tend to be more informative about the target object by giving specific information about it in the question produced.
Modeling the Source Language A particular problem with the model presented above is related to the embeddings for the entities, relationships and tokens, which all have to be learned in one way or another. If we learn these naively on the SimpleQuestions training set, the model will perform poorly when it encounters previously unseen entities, relationships or tokens. Furthermore, the multi-relational graph defined by the facts in SimpleQuestions is extremely sparse, i. e. each node has very few edges to other nodes, as can be expected due to high ratio of unique entities over number of examples. Therefore, even for many of the entities in SimpleQuestions, the model may perform poorly if the embedding is learned solely based on the SimpleQuestions dataset alone. On the source side, we can resolve this issue by initializing the subject, relationship and object embeddings to those learned by applying multi-relational embedding-based models to the knowledge base.
In the extreme case where the subject and object of a triple only appears once in the dataset, the learned embeddings of the subject and object will be semantically meaningless. This happens very often in SimpleQuestions, since only around 5% of the entities have more than 2 connections in the graph. Thus, by applying TransE directly over this set of triples, we would eventually end up with a layout of entities that does not contain clusters of semantically close concepts. In order to guarantee an effective semantic representation of the embeddings, we have to learn them together with additional triples extracted from the whole Freebase graph to complement the SimpleQuestions graph with relevant information for this task. We need a coarse representation for the entities contained in SimpleQuestions, capturing the ba-sic information, like the profession or nationality, the annotators tend to use when phrasing the questions, and accordingly we have ensured the embeddings contain this information by taking triples coming from the Freebase graph 5  regarding: 1.
A single placeholder token for all question types could unnecessarily limit the model. We therefore also experiment with another model, called the Multi-Placeholder (MP) model, which uses 60 different placeholder tokens such that the placeholder for a given question is chosen based on the subject category extracted from the relationship (e. g. contained by is classified in the category location, and so the transformed question would 6 We use the tool difflib: https://docs. python. org/2/library/difflib. html be Which forest is <location placeholder> in?). This could make it easier for the model to learn to phrase questions about a diverse set of entities, but it also introduces additional parameters, since there are now 60 placeholder embeddings to be learned, and therefore the model may suffer from overfitting.
Training Procedure All models were implemented using Theano  (Theano Development Team, 2016). To train the neural network models, we optimized the log-likelihood using the first-order gradient-based optimization algorithm Adam  (Kingma and Ba, 2015). To decide when to stop training we used early stopping with patience  (Bengio, 2012)  on the METEOR score obtained for the validation set. In all experiments, we use the default split of the SimpleQuestions dataset into training, validation and test sets. We trained TransE embeddings with embedding dimensionality 200 for each subject, relationship and object. Based on preliminary experiments, for all neural network models we fixed the learning rate to 0. 00025 and clipped parameter gradients with norms larger than 0. 1. We further fixed the embedding dimensionality of words to be 200, and the hidden state of the decoder RNN to have dimensionality 600.
google. com/ p/word2vec/. tiple placeholders are shown in Table  4. The neural network models outperform the templatebased baseline by a clear margin across all metrics. Template-based baseline is already a relatively strong model, because it makes use of a separate template for each relationship. Given enough training data this suggests that neural networks are generally better at the question generation task compared to hand-crafted template-based procedures, and therefore that they may be useful for generating question answering corpora. Furthermore, it appears that the best performing models are the models where TransE are trained on the largest set of triples (TransE++). This set contains, apart from the supporting triples described in Section 4. 3, triples involving entities which are highly connected to the entities found in the Sim-pleQuestions facts.