dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Understanding the data In the previous section we analyzed partitional and hierarchical clustering algorithms, where the best results for the K-Means (K=3) and Average Linkage were obtained respectively, just as DS1 was the dataset that showed the best results. This section provides an analysis of the groups obtained from the K-Means algorithm performance, but Average Linkage analysis is omitted because when the dataset is high-dimensional, hierarchical clustering algorithms decompose (with some descriptive results and unreliable delivery) due to their nonlinear time complexity and high cost, so the literature shows that this kind of technique is quite effective for low-dimensional datasets  (Quiroz, Pla, Badia, & Chover, 2007; Saraçli, Doğan, & Doğan, 2013). However, to interpret the composition of groups generated by K-Means, a C.
4. 5 decision tree was used, considering that this algorithm is one of the most used for such tasks  (Corrales, Corrales, & Figueroa-Casas, 2015; Pérez, 2003). The resulting decision tree is shown in Figure  7. The DS1 dataset consists of a total of 5590 individuals of aquatic macro-invertebrates, belonging to 63 taxa, plus attributes: year, month, station code and number of individuals, as indicated in Table  2. Now, by dividing the dataset into three groups by the K-Means clustering technique, each is interpreted through the C. 4. 5 decision tree, as indicated above. Table  4  shows the la Tabla 4 muestra la distribución de casos obtenidos por el árbol de decisión C. 4. 5 para individuos macro-invertebrados acuáticos que se encuentran en la cuenca del río Piedras. Asi mismo, se clasifica el porcentaje de individuos en cada grupo, teniendo en cuenta la metodología  (Pérez, 2003) , donde el autor ha etiquetado cada macro-invertebrado con un número que indica el grado de sensibilidad a los contaminantes.
ter of dubious quality groups, due to the lack of diverse taxa of this type in the database. Moreover, for 2012 the growth of indicator species of good water and dubious quality can be observed, with a small reduction of the representatives species of water quality, generating alerts for increasing pollutants (compared to 2011) in the three sampling points, especially in the Diviso intake and Puente Carretera, where this phenomenon is most clearly noted. The behavior of groups is the same as in the previous year, where C1 represents alerts of high quality water, while the C2 and C3 alerts still represent good water quality. In the same way, from the taxonomic abundance for 2013 it is obtained that the number of indicator species of high quality continues to decline and with this the water quality of the sampling points, with the exception of the sampling point analyzed in Puente Alto, which follows an approximately constant behavior, since at this point the community of indicator species of high quality and good quality shows the same proportions during the three years analyzed.
Agradecimientos Los autores de este artículo quisieran dar las gracias a la Universidad del Cauca, el Grupo de Estudios Ambientales (GEA), el Grupo de Ingeniería Telemática (GIT), Beca Colciencias Doctorado (que fue otorgado al MSc. David Camilo Corrales) y el programa AgroCloud del proyecto RICCLISA, por el apoyo técnico-científico. tion, as well as machine-learning techniques that utilize existing knowledge to arrive at the same conclusions, by using different and less complicated methods. The objective of this study is based on the generation of alerts by cluster analysis for water quality in the Piedras river basin. For this purpose, different types of validation methods were reviewed and addressed. After analyzing the results of all the experiments, we concluded that the basins' water is of good quality at the three analyzed sampling points.
Microarray experiments MEF were incubated in normoxia or hypoxia for 72 h (1 % O 2 ) and then lysed prior to RNA isolation. RNA was assessed for integrity by using an Agilent BioAnalyser 2100 (Agilent Technologies) (RIN above 9). RNA samples were then labeled with Cy3 dye using the low RNA input QuickAmp kit (Agilent) as recommended by the supplier. Labeled cRNA probe (825 ng) was hybridized on 8x60K high density SurePrint G3 gene expression mouse microarrays. The experimental data are deposited on the NCBI Gene Expression Omnibus (GEO) under the series record number GSE63247 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE63247). Normalization of microarray data was performed using the Limma package available from Bioconductor (http://www. bioconductor. org). Inter-slide normalization was performed using quantile methods.
Statistics All values are the means ± SEM. Statistical analysis were performed using the Student's t test as provided by Microsoft Excel. The p values are indicated. All categorical data used numbers and percentages. Quantitative data were presented using the median and range or mean. Differences between groups were evaluated using the chi-square test for categorical variables and the Student's t test for continuous variables. Analyses were performed using SPSS 16. 0 statistical software (SPSS Inc. , Chicago, Ill). All statistical tests were two-sided, and p values <0. 05 indicated statistical significance, whereas p values between 0. 05 and 0. 10 indicated a statistical tendency (Additional file 1).
Results High-throughput gene expression profiling of wild-type and Vdac1 -/-mouse embryonic fibroblasts We confirmed that VDAC-ΔC was produced in hypoxia in wild-type (Wt) MEF  [14]  and that the VDAC1 protein was not expressed in Vdac1 -/-MEF  [5]  (Fig. 1a ). We then analyzed the transcriptome of Wt and Vdac1 -/- MEF in normoxia or hypoxia using a mouse whole genome microarray. Only RNA transcripts showing an adjusted p value <0. 01 and an absolute log2 (fold change) >1 in at least one differential expression analysis were considered. Figure  1b  and c  recapitulates the number of genes differentially expressed when comparing the RNA transcript expression of Vdac1 -/-vs Wt MEF and the response of both cells lines to hypoxia, respectively. The data showed alteration in the gene expression profile of Vdac1 -/-MEF compared to Wt MEF in both normoxia and hypoxia (Fig.
1d, e ). Analysis of the hypoxic response identified changes between the two cell lines (Fig. 1c, d ). To understand the functional relevance of differentially expressed up-and down-regulated genes in Vdac1 -/- compared to Wt MEF, the microarray data were analyzed with Ingenuity Pathway Analysis TM (IPA TM ) software using the same cut-offs for both cells lines. The Vdac1 deficiency, highlighted terms associated with «Diseases and functions» (Additional file 2: Table  S1A ), «Canonical pathways» (Additional file 2: Table  S1B ), and «Upstream regulators» (Additional file 2: Table  S1C ) in both normoxia and hypoxia. These results pointed to alterations in programs controlling cancer through metabolic pathways modulating HIF-1, cell death and survival, as well as cell proliferation and motility.
In hypoxia in Vdac1 -/-MEF, we observed both enlarged mitochondria with abundant cristae and mitochondria similar to those in normoxia. The former may reflect the improved proliferation, thus better bioenergetics of the cells, and the later of mitochondria that had not or could not adapt. However, one question remains: how do these cells survive in normoxia with a low level of glycolysis and respiration? Fatty acid alpha oxidation by Vdac1 -/-MEF could be a possible explanation. The deficiency in Vdac1 in both MEF and RAS-transformed MEF resulted in a reduction in proliferation in normoxia. Examination of the analytic technique for assessment of RNAi by similarity (ATARiS)  [32] , a computational method to assess the effects of gene suppression on cell survival/proliferation, in the Project Achilles database  [33]  supported this result.
Acknowledgements This research was supported by grants from the  Fondation ARC ,  Fondation de France , the  ANR , the  INCA, la Ligue Nationale Contre le Cancer  (équipe labellisée LNCC), METOXIA and  Canceropôle PACA. The laboratory is funded by the  Centre A. Lacassagne, CNRS , and  INSERM. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge the excellent support of the  Nice-Sophia Antipolis Functional Genomics Platform  and the PICMI microscopy facility of IRCAN. The facility was supported financially by the  Conseil Général 06, FEDER, Ministère de lEnseignement Supérieur and INSERM. We thank  T Rudel  of the  University of Wuerzburg  for the Flag-VDAC1 plasmid and  G Rios  for microarray hybridization Mircea Ivan's research is supported by  NCI   R01 CA155332.
ϒ-secretase induces the release of a NeICD upon binding of RGMa. 15 To test whether the release of this NeICD domain into the intracellular milieu induces axonal inhibition, we expressed NeICD-GFP into temporal RGC cultures and assessed axonal length. As expected, neurons transfected with NeICD-GFP extended very short axons when compared with GFP-transfected ones (Figures  4f  and g ). In NeICD-GFP expressing neurons, axons were ~3-fold shorter than those transfected with GFP alone (77. 5 ± 9. 6 μm versus 222. 7 ± 10. 6 μm). Taken together, these data indicate that Neogenin cleavage by ϒ-secretase generates an NeICD peptide that inhibits axonal growth. The NeICD contains two domains that regulate its transport in and out of the nucleus. 15 In HEK293 cells, we have shown that deletion of the nuclear export signal (ΔNES) increases the amount of nuclear NeICD, whereas the deletion of the nuclear localization signal (ΔNLS) reduces NeICD levels in the nucleus.
We show that the existing measures used to characterize the node's position do not take into account all aspects of the community-related external connectivity of a node. Moreover, we object the assumption of universality of the thresholds applied to the measures in order to distinguish the different node roles. The dataset we use constitutes a counterexample showing the original thresholds are not relevant for all systems. We then explain how to tackle these limitations. We first introduce three new measures to characterize the external connectivity of a node in a more complete and detailed way. We then describe an unsupervised approach aiming at identifying the node roles without using fixed thresholds. Finally, we apply our method on the Twitter network to determine the position of social capitalists, and show they occupy specific roles in the network.
Data and Tools We analyze a freely-available anonymized Twitter followerfollowee network, collected in 2009 by Cha et al. [5]. It contains about 55 million nodes representing Twitter users, and almost 2 billion directed links corresponding to followerfollowee relationships. We had to consider the size of these data when choosing our analysis tools. For community detection, we selected the Louvain method  [2] , because it is widespread and proved to be very efficient when dealing with large networks. We retrieved the C++ source code published by its authors, and adapted it in order to optimize the directed version of the modularity measure, as defined by Leicht and Newman  [20]. Empirical benchmarks show that our adapted version performs better than the original one on directed network. All the role measures, that is Guimerà & Amaral's original measures, their directed variants (section 3.
The whole source code is freely available online 2. Because we are dealing with 55 millions of objects and only 8 attributes, we know that a lot of local maximums exist while minimizing the within-cluster sum of squares with the k-means algorithm. In this paper, we are specifically looking 2 https://github. com/CompNet/Orleans for assessing the social capitalists visibility in the Twitter network. To achieve this goal, we do not necessarily need to get the best partition of the role space. We are actually looking for a partition in which clusters are well separated and interpretable according to Guimerà & Amaral's terminology. With such a partition, it is possible to look at the specific roles held by social capitalists in these clusters and to determine their actual visibility.
Those of high in-degree are classified as kinless or connectors hubs, depending on their ratio r. This shows the efficiency of their strategies, which lead to a high visibility for a vast part of the network, not only for their own community. The most direct perspective for our work is to assess its robustness. In particular, it is important to know how the stability of the detected communities and clusters affects the identified roles. In this study, our aim was to assess the social capitalist visibility, which is independant of this goal. Furthermore, the very large size of the data prevented us to do so efficiently. On a related note, we want to apply our method to other smaller systems, in order to check for its general relevance. The method itself can also be extended in two ways. First, it would be relatively straightforward to take link weights into account (although this was not needed for this work).
Search strategy The search strategy for this review aims to retrieve results of both high sensitivity and high specificity to the review question  [33]. Defining searches of high specificity, or those that find a larger proportion of relevant studies within search results, without sacrificing the comprehensiveness allowed by broader searches of lower sensitivity, was facilitated by the repeated testing of search strings in the databases Web of Science (WOS) and CAB Abstracts to determine the effects of including or excluding specific words and phrases. Search strings were composed of population, exposure, and location terms derived from the PECOs in section 2, and combined using the following Boolean operators: (P 1 OR P 2 OR P 3 …) AND (E 1 OR E 2 OR E 3 …) AND (L1 OR L 2 OR L 3 …), where P stands for population, E for exposure, and L for location terms.
For sources other than the three bibliographic databases, should any individual search yield >10,000 records, outcome terms will be added to the search string to further refine results. Where search engines do not support the use of Boolean operators, we will conduct simplified searches using key population, exposure, and location terms, as well as apply relevant topic filters where available. These will be fully documented for source. The full list of population, exposure, location, and outcome terms can be found in Additional file 1.
Grey literature Discussions with subject experts indicated the presence of significant archives of grey literature that would be valuable for inclusion. Some of these will be retrieved from institutional searching (see above). However, in order to capture published and grey literature that may not have been indexed electronically either in the bibliographic databases or institutional databases and website, we will contact subject specialists for additional peerreviewed and grey literature that they believe to be relevant to answering the review question. A hand-search of the forestry collections of the Bodleian library at the University of Oxford will also be conducted with the help of specialist librarians a.
A checklist will be used to examine the quality elements in each article for inclusion, although no overall quality score will be calculated. Instead, each element will be assessed independently and reported upon as part of an appraisal of the quality of the evidence base. Relevant articles will be appraised using the following criteria: Clarity of study site selection criteria -Is the choice of study site selection clear and justified? This decision will be based on the explanations provided by study authors regarding a study site's relevance in answering research questions, and is particularly important for the selection of case and control sites in terms of their comparability. Sources of data -Are the sources of data reliable, complete and available in the article? The reliability of data will be assessed based on the authors' acknowledgement of potential biases and if triangulation is performed to ascertain research results.
Data extraction and presentation The aim of the data extraction is to assess the existence of socio-economic, environmental and other changes on the target population due to the wood fuel production, processing, trading, consumption, and policy implementation. To extract information from selected studies, tables will be designed to compile quantitative and qualitative data from each of the relevant studies selected. Table  3  summarises the main data categories that will be included in the map to provide information about the evidence base, knowledge gaps and possible future systematic review questions. Where insufficient data are provided, we will contact authors to acquire additional data. To present the evidence base, we will provide a database b and supporting narrative of all the relevant articles that have been reviewed, summarising and presenting descriptive statistics in tables, graphs and charts on quantity, type, focus, study location, and target population of reviewed articles.
Apoptosis was determined by FACS analysis of DNA fragmentation of propidium iodide-stained nuclei (right panel). Data represent mean + S. E. M. of three independent experiments performed in triplicate; **Po0. 001. (c) U87MG cells were transduced with a vector containing cFLIP S or empty vector (Ctrl). Expression of cFLIP was assessed by western blotting (left panel). Cells were treated with 5 μg/ml Drozitumab and/or 4 μM BV6 for 72 h. Cell viability was determined by MTT assay and is expressed as the percentage of untreated controls (middle panel). Apoptosis was determined by FACS analysis of DNA fragmentation of propidium iodide-stained nuclei (right panel). Data represent mean + S. E. M. of three independent experiments performed in triplicate; **Po0. 001. (d) U87MG cells were transduced with control vector (shCtrl) or vectors containing two different shRNA sequences against cFLIP (shcFLIP1, shcFLIP2).
Expression of cFLIP was assessed by western blotting (left panel). Cells treated with 0. 3 μg/ml Drozitumab and/or 4 μM BV6 for 72 h. Cell viability was determined by MTTassay and is expressed as percentage of untreated controls (middle panel). Apoptosis was determined by FACS analysis of DNA fragmentation of propidium iodide-stained nuclei (right panel). Data represent mean + S. E. M. of three independent experiments performed in triplicate; **Po0. 001 This finding is in line with previous reports showing that cFLIP is a negative regulator of cell death induced by Smac mimetic or genotoxic drugs that cause depletion of IAP proteins. 25, 31 y comparison, cFLIP L and cFLIP S have recently been reported to differentially regulate cell death in the absence of cIAPs in keratinocytes. 32 Whereas cFLIP L blocked RIP1/ FADD/caspase-8 complex formation and cell death, cFLIP S promoted these events.
(a) Glioblastoma cells were treated for indicated times with 3 μM (A172) or 4 μM (U87MG) BV6. Expression of cIAP1 and XIAP was analyzed by western blotting. GAPDH served as loading control. (b) Glioblastoma cells were treated for 3 h with 0. 3 μg/ml (A172) or 5 μg/ml (U87MG) Drozitumab and/or 3 μM (A172) or 4 μM (U87MG) BV6. TNFα mRNA expression was determined by RT-PCR analysis. Fold change of TNFα mRNA levels is shown. Data represent mean + S. E. M. of three independent experiments performed in triplicate; *Po0. 05. (c) Glioblastoma cells were pretreated or not for 1 h with 100 μg/ml Enbrel and then treated for 72 h with Drozitumab at 0. 3 μg/ml (D54) or 10 μg/ml (U118MG) and/or BV6 at 1 μM (U118MG) or 2 μM (D54). Apoptosis was determined by FACS analysis of DNA fragmentation of propidium iodide-stained nuclei.
Treatment with 5 ng/ml TNFα and BV6 for 72 h served as positive control for Enbrel. Data represent mean + S. E. M. of three independent experiments performed in triplicate; *Po0. 05. (d and e) A172 cells were transduced with shRNA vector against TNFR1 (shTNFR1) or control sequence (shCtrl). Expression of TNFR1 was analyzed by western blotting (d, upper left panel). A172 cells were treated with indicated concentrations of Drozitumab and/or BV6 for 72 h, cell viability was determined by MTT assay and is expressed as percentage of untreated controls (d, lower left panel). A172 cells were treated with indicated concentrations of Drozitumab and/or BV6 for 72 h and apoptosis was determined by FACS analysis of DNA fragmentation of propidium iodide-stained nuclei (e). Treatment with 5 ng/ml TNFα and 3 μM BV6 for 72 h served as positive control for TNFR1 knockdown.
Acknowledgements. We thank  A. Ashkenazi  for providing Drozitumab, and  K. Deshayes  and  K. Zobel  for providing BV6 (all from  Genentech Inc. ,  South San Francisco, CA, USA) ,  C. Hugenberg  for expert secretarial assistance,  D. Brücher  and  F. Cay  for expert technical assistance, and  J. Cinatl  for help with microscopy. This work has been partially supported by grants from the  Deutsche Forschungsgemeinschaft, European Community (ApoDecide)  and  IUAP VII  (to S F).
The elution of proteins was monitored by UV detection (280 and 320 nm). The protein-containing fractions were analyzed by SDS-PAGE and western blot (Data S2). The positive fractions were pooled and diafiltrated on 3-kDa Amicon â Ultra system (Amicon, Millipore, Molsheim, FRANCE). A MALDI-TOF analysis was performed to verify the molecular mass of the produced protein (Data S2). Proteins were then lyophilized and preserved at À20°C for further experiments. Antifungal activity against Fusarium oxysporum sp. melonii Liquid growth inhibition assays were conducted in a microplate using half strength Potato Dextrose Broth containing a Fusarium oxysporum sp. melonii spore suspension at 10 4 spores. mL À1 and 10 lg. mL À1 tetracycline as previously described  (Marqu es et al. 2009 ). To observe the hyphae, the spore suspension was grown with a sublethal concentration of AhPDF1.
cerevisiae strain (MATa, his3D1, leu2D0, met15D0, ura3D0) together with the HIS3+ pFL38H vector using the lithium acetate/single-stranded carrier DNA/ polyethylene glycol method  (Gietz and Woods 2002). Assaying zinc tolerance was performed in three different ways in the yeast S. cerevisiae. Firstly, drop tests were performed on agar selective modified YNB medium, as previously described  (Shahzad et al. 2013). Secondly, growth of isolated yeast colonies was also analyzed, starting from 100 lL of a A 600 nm = 0. 01 yeast solution homogeneously plated on a Ø 9 cm petri dish containing the selective modified YNB medium. Pictures of the isolated colonies were taken with a SZX16 Olympus stereozoom microscope and the area of colonies was measured using Image J software  (Schneider et al. 2012).
To test HAC1 mRNA splicing, a RT-PCR was performed according to  Gardarin et al. (2010)  with 1 lL of cDNAs and 20 PCR-cycles. The primers used are given in Data S4. 1. The PCR products were analyzed by gel electrophoresis (0. 5% TAE, 1% agarose, ethidium bromide). To evaluate the expression levels of KAR2 and HRD1 genes, Real-Time RT-PCRs were performed in 384-well plates with the LightCycler â 480 Real-Time PCR System (Roche Diagnostics GmbH, Meylan, France) using SYBR Green to monitor cDNA amplification. ACTIN and TUBULIN were used as internal controls for normalization. For each gene, specific primer pairs were designed (Data S4. 2) and the PCR efficiency (E) of each primer pair was determined after the analysis of five serial 1:20 dilutions of cDNA. Relative expression ratios of target transcripts were then calculated according to Pfaffl method  (Pfaffl 2001).