dataesr/scientific-paragraphs-categorization huggingface dataset:
All cells were then washed with PBS and stained with 1:10,000 Hoechst (33342, Invitrogen) diluted in phenol red-free-DMEM for 10 min at RT (or 37 C). After additional PBS washes, cells were kept in phenol red-free-DMEM for the duration of the assay. Images and analysis of Hoechst, TMRE and PicoGreen stainings were conducted with the Cellomics ArrayScan microscope according to the ''Compartmental Analysis'' BioApplication. The MMP values were extrapolated from the Cellomics measurements using the formula: TMRE (spot intensity * spot count) / PicoGreen (spot intensity * spot count). The results were calculated for each sample by subtracting the MMP value of the sample treated with FCCP and antimycin A from the MMP value of the sample under untreated conditions. The data are then presented in the paper as (TMRE / PicoGreen, a.
Electrophysiology NPCs and neuronal cells were probed on coated coverslips. An EPC-7 amplifier and Patchmaster software (HEKA) were used for patch clamp recordings. Patch pipettes, made from borosilicate glass (Science Products, Hofheim, Germany), had resistances of 3-7 MU when filled with the intracellular solution containing (in mM): potassium gluconate (110), KCl (25), NaCl (5), CaCl 2 (0. 5), MgCl 2 (1), EGTA (5) and HEPES (30). The standard extracellular solution (E1) (pH 7. 4) contained (in mM): NaCl (140), KCl (5), MgCl 2 (1), CaCl 2 (2), HEPES-NaOH (10) and glucose (10). Series and input resistances were checked throughout the whole duration of each experiment by applying À5 mV-pulses in the voltage clamp mode at À50 mV. Resting membrane potential was determined in the current clamp mode at 0 pA.
ACKNOWLEDGMENTS We thank  Dr. Sarah Doss  ( ChariteÀ Universit€ atsmedizin Berlin ) for providing the control fibroblast lines Con1 and Con2,  Clemens Edler  ( MPI-MG ) for TrakEM2 analysis,  Dr. Thomas Wiglenda  ( MDC ) for essential advice with compound screening preparation,  Franziska Seifert  ( ChariteÀ Universit€ atsmedizin Berlin ) for help in long-range PCRs,  Beate Lukaszewska-McGreal  ( MPI-MG ) for proteomic sample preparation, and  Franziska Wiedemann  ( MDC ) for cell culture and technical support. We are grateful to  Russ Hodge  ( MDC ) for proofreading the manuscript. We acknowledge support from the  MDC (Ph. D. fellowship  to  R. B. and  BOOST Idea Competition Award  to A. P. ); the  Deutsche Forschungsgemeinschaft (DFG)  ( PR1527/1-1  and  SCHU1187/4-1  to A. P. and  M. Schuelke ,  Neurocure Exc   257  to  M.
Schuelke , and  Priority Programme SPP   1784 ME2075/7-1  to J. C. M. ); the  Berlin Institute of Health (BIH)  (to A. P. and  M. Schuelke  in the form of a Ph. D. fellowship to C. L. , to J. P. and E. E. W. as a  Collaborative Research Grant , and to B. M. as Gender Equality Funds); the  Fondation pour la Recherche Medicale (FRM)  (grant  DPM20121125550  to A. L. and A. P. in the form of financial support and postdoctoral fellowship to P. L. ); the  AFM Telethon  (grant  16416  to A. P. and A. L. ); the  Fritz Thyssen Foundation  ( Az. 10. 11. 2. 160  to A. P. ); and the  Bundesministerium f€ ur Bildung und Forschung (BMBF)  (e:Bio young investigator grant.
Anne Lombes Molecular Probes Perkin Elmer Life Technologies Aureet al. , 2013 C7026 6016941 P7581 Chemicals, Peptides, and Recombinant Proteins FlexiGene DNA kit QIAGEN 51206 Matrigel Matrix Seahorse XF Cell Mito Stress Test BD Biosciences Seahorse Bioscience, 356231 103015-100 hLIF Miltenyi Biotec Agilent 130-108-156 CHIR99021 Deposited Data Cayman Chemical 13122 SB431542 Microarray results SelleckChem GEO database S1067 accession number GEO: GSE70071 Compound E Mass spectrometry proteomics data Calbiochem ProteomeXchange Consortium 15579 dataset identifier PRIDE: PXD004977 SAG Enzo Life Sciences via PRIDE partner repository ALX-270-426-M001 Poly-L-ornithine Sigma-Aldrich (Vizcaı ´no et al. , 2013) P4957 Laminin Sigma-Aldrich L2020 Thapsigargin Sigma-Aldrich T9033 (Continued on next page) Cell Stem Cell 20, 1-16.
support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology.
The definition is made more complicated still in that subsequent duplications after a speciation event results in several sequences in a species all being (in-)paralogous with each other and (co-)orthologous to their counterparts in another species. From the time that sequence data first became available, multiple independent efforts have striven to develop methods for identifying orthologs and paralogs. This has resulted in a number of publicly available and widely used resources. The Quest for Orthologs (QfO, http://questfororthologs. org) initiative has so far organized four biennial gatherings (http://questfor orthologs. org/meetings) for the research community with an interest in orthology determinations-including both developers and application users-with the aim of sharing experiences, establishing metrics, improving data exchange and comparability, as well as developing joint strategies for advancing the field.
Starting with the 2009 meeting in Hinxton (Cambridge, UK), the work centered on establishing standardized reference proteomes to benchmark different tools, developing frameworks to facilitate data exchange (e. g. OrthoXML and SeqXML,  Schmitt et al. , 2011) , and establishing collaborative working groups to keep advancing on these and other issues. While the adoption of standard formats took time, most orthology resources reported recently to provide support for OrthoXML  (Sonnhammer et al. , 2014) , thus paving the way for applications such as joint benchmarking or consensus meta-analysis servers for orthology assessment  (Pereira et al. , 2014). The second QfO meeting in 2011, also in Hinxton (Cambridge, UK), discussed the necessity of a common framework for well-curated and well-established biological datasets.
Indeed, stable versions of reference proteomes representing species from a broad taxonomic spread would greatly facilitate comparison of the results from different orthology analysis pipelines and disentangle effects of input data choices (genome versions, gene calls, taxonomic range,. ) from that of the algorithms. Since then, an increasing number of researchers and tool developers have joined the working groups in charge of the coordination and maintenance of those resources (e. g. a curated set of reference proteomes). Additionally, since orthology fundamentally relates nested histories (e. g. that of proteins) to their host histories (e. g. those of species), in many cases an accurate consensus species phylogeny is needed. Because of this mutual dependency, a QfO species tree working group, along with representatives from the community of researchers working on the 'Tree of Life' problem, was initiated in 2013 to review the congruence of species classifications.
This work resulted in an overview of consensus and disagreement with regards to species phylogeny on the medium scale  (Boeckmann et al. , 2015) , and an agreed upon species-level phylogeny for joint QfO analysis. Orthology inference methods must all cope with choosing among multiple complex evolutionary scenarios, each with a risk of conjuring up a dubious assignment. During the third Quest for Orthologs meeting in 2013 in Lausanne, Switzerland, there was a growing recognition of the need for careful benchmarking to profile and assess the performance of orthology inference methods. One theme of this discussion was how to design such benchmarks. Starting from a review of the work independently carried out by several teams in developing orthology benchmarks and curated gold standard datasets, a QfO working group began designing a shared resource for comparative evaluation of orthology calls.
First steps in synchronizing orthology with biological systematics Tree-based orthology inference presumes knowledge of the species tree. For this reason, the Quest for Orthologs species tree working group surveyed the classifications of 147 organisms of interest to the QfO, including the most well-studied model organisms, and reported areas of congruence and particularly high incongruence in the outcomes of different evolutionary history reconstruction efforts  (Boeckmann et al, 2015). Six large-scale and well-used trees of life (ToL) and species classifications were analyzed, among them the National Center for Biotechnology Information (NCBI) taxonomy  (Federhen et al. , 2012) , the Open Tree of Life  (Hinchliff et al. , 2015)  and the 16S ribosomal RNA (rRNA) database  (Yilmaz et al. , 2013).
Real and apparent discrepancies between conclusions from different methods With recent developments of the joint Quest for Orthologs benchmarking datasets and tools, this meeting renewed discussions on the extent to which, and the reasons why, different methods may produce incompatible orthologous and paralogous relationship calls. The first publication of benchmark results  (Altenhoff et al. , 2016)  provides a large-scale quantification of such discrepancies also accessible online (ttp://orthology. benchmarkservice. org/). Paul D. Thomas (University of Southern California, USA) pointed out that differences in homology clustering (e. g. alignment score cutoffs, alignment coverage requirements, or cluster tightness), could have a substantial impact on the downstream orthology inference independent of other more unique aspects of a method-the extent of which has yet to be investigated.
To improve homology inference, Mateus Patricio (EMBL-EBI, UK) presented a new library of Hidden Markov Model (HMM) profiles built upon the PANTHER  (Mi et al. , 2015)  and TreeFam  (Schreiber et al. , 2014)  databases and complemented with new profiles inferred from sequences that were Fig. 1. Nested Evolutionary Histories: Containing (e. g. species) trees (grey area) with nested (e. g. gene) trees (red/blue) inside. Semicircles denote orthologous or orthology-like relations. On the top level (top left panel), subpopulations unique to a particular ecological niche are nested within populations, and within subpopulations in turn, the histories of individual genealogies are nested. In this sense, niche segregation within a site is a form of duplication event, whereas migration to novel habitats correspond to speciation events at this level.
Furthermore, to go one step further and standardize the input of tree-based orthology methods, Thomas and Matthieu Muffato (EMBL-EBI, UK) proposed to work together on a common resource for protein family trees. The parallel approach to these efforts to improve each of the methods is to combine their output in order to obtain a consensus. For example MARIO  (Pereira et al. , 2014)  presented by Cecile Pereira (Paris-Sud University, France), combines ortholog predictions from multiple methods, input in the standard orthoXML format, with the aim of obtaining more reliable orthology calls. This method was used in to perform the most recent update of FUNGIpath (Grossetete et al. , 2010), a database for comparative analysis of fungal metabolism.
Hieranoid  (Schreiber and Sonnhammer, 2013)  achieves linear scaling to the number of species by aggregating orthologs along a species guide tree. Another approach dealing with a similar problem was presented in the form of the MMseqs many-versus-many sequence comparison software  (Hauser et al. , 2016). Still other approaches were suggested, such as making use of the nested nature of taxonomy and the transitive property of homology for more rapid identification of such sequences.
From a research infrastructure perspective, there is the recent formation of the Alliance for Genomic Resources (AGR, see http://www. alliance genome. org/), which, among other objectives, will be unifying the model organisms into a single resource using the same strategies for ortholog calling in every case. Paul Thomas and Suzanna Lewis are engaging with AGR as liaisons to the QfO Consortium members. 4 Outlook: the mosaic nature of life and its impact on evolutionary studies Alongside practical challenges such as scale and redundancy of datasets, a common trend in the discussions this year of challenges faced within orthology analysis stems from that living and evolving systems, while describable as having a tree-like history at one level, often contain subsystems with conflicting histories.
Funding This work was supported by  Spanish Ministry of Economy and Competitiveness  grant  BIO2012-37161  (to T. G. ),  Qatar National Research Fund   NPRP 5-298-3-086  (to T. G. ),  European Research Council  grant  ERC-2012-StG-310325  (to T. G. ),  National Institutes of Health (NIH)  grant  R24 OD011883  (to S. E. L. ),  U41 HG002273  (to S. E. L. and P. D. T. ),  Swiss National Science Foundation  grant  PP00P3_150654  (to C. D. ),  UK Biotechnology and Biological Sciences Research Council  grant  BB/M015009/1  (to C. D. ),  Swiss State Secretariat for Education, Research and Innovation (SERI)  funding (to B. B. and C. D. ). M. M. and M. P. acknowledge support from the  Wellcome Trust  (grant number  WT108749/Z/15/Z ) and the  European Molecular Biology Laboratory. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n  222664  (Quantomics).
Abstract Many studies have predicted large changes in forest dynamics during the next century because of global warming. Although empirical approaches and studies based on species distribution models provide valuable information about future changes, they do not take into account biotic interactions and stand-level demographic variations. The objective of this study was to quantify the local and regional variability of the growth and regeneration of three important forest species growing often in mixed stands in Europe (Picea abies (L. ) Karst. ,  Abies alba Mill. , Fagus sylvatica) , and to assess the climatic drivers of this variability. For that purpose, we collected a large forestry data set compiling the longterm (up to 100 years) evolution of species and size distributions for 163 stands across Europe, in the mesic distribution area of these forests.
2007; Didion et al. 2011; Bodin et al. 2013). The goal of this study was to document the contribution of climate to the variability of the forest dynamics in uneven-aged European spruce-fir-beech mountain forests. For that purpose, we used a set of long-term forestry data collected in a large network of 163 stands located in 19 forests representing a large gradient of climatic conditions within the distribution area of the three species (P. abies, A. alba and F. sylvatica). From these data, we assessed the stand dynamics using two methods : first, we computed population level demographic fluxes directly from the data ; second, we calibrated the individual-based forest DVM Samsara2  (Courbaud et al. 2015) , using the inverse modeling technique developed by  Lagarrigues et al. (2015). This approach enabled us to precisely identify the effect of climate on each demographic process at the individual tree level and to factor out the multiple stand level endogenous factors (such as tree density, species composition or size structure) that affect stand dynamics and that may blur climate impacts.
Study forests and forestry data A large number of managed forests distributed across a wide geographical area were considered (Fig. 1 ). Our data were collected in forest management archives. In France, the French Forest National Office (ONF) manages a large number of public mountain forests and was therefore our main data source. Three other data sources were used in this study: a private forest management group located in the Jura mountains; the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) at Birmensdorf, Switzerland, which provided monitoring data collected in 16 permanent plots  (Zingg et al. 2009) ; and the archives of Slovenia Forest Service from which data for the Slovenian forests were derived by the University of Ljubljana (partly described by  Klopcic et al.
(2010) ). Figure  1 : Forest locations and species composition. The pie charts represent the species relative abundances. For the sake of clarity, some forests have not been labeled, see table  1  for full information about the study forests. These forestry data consist in two main elements: (1) forest inventories, in which information about tree abundance and species composition is given; (2) harvest reports, guvung information about the quantity and the size of trees harvested in the forest, usually on an annual basis. The merchantable volume is the usual unit of measurement used to quantify the tree size in these data. We therefore used this unit throughout this study and referred to it simply by "volume". All recorded forests were subjected to an uneven-aged management during the data period and during several decades before the oldest inventory.
We assumed that all dead trees were harvested and recorded in harvest reports. In the forest inventories, only trees above a diameter at breast height (DBH) threshold were monitored (7. 5, 10 and 17. 5 cm for Swiss, Slovenian and French forests respectively). To avoid a "demographic gap" when reproducing the forest history, the young stages (i. e. trees and saplings under the DBH threshold) in the initial states were simulated using Samsara2 (further information is given in the Appendix C). Finally, as there is no spatial information in the data, the tree positions were randomly drawn before each simulation. The data imputation procedures concerning spatial positions and young stages have been shown to only have moderate effects on simulation outcomes  (Courbaud et al. 2015). Initially, data were available for 274 stands (Appendix A, Table  A.
Second, we selected only the stands with at least 30 years between the first and the last inventory. Finally, after preliminary analysis (not detailed in this paper), we excluded the stands with aberrant data (e. g. unrealistic low or high numbers of trees reported in statistical inventories). In the end, we selected 163 stands in 19 forests (Table  1 ). Table  1 : Data summary. Each line is a site, which grouped various numbers of stands (see the Material and methods section and the Appendix A for more information about site definition). Data period is the mean time between the oldest and the youngest inventories. Annual volume increment and annual recruitment rate are computed on trees whose DBH > 17. 5 cm (> 20 cm for Slovenian forest) and are formatted as "mean (min / max)". The abbreviations S, F, B and O for the species abundances mean respectively Spruce, Fir, Beech and Other species; the abundances are given in % of the total volume.
Macro-environmental data Monthly temperature and rainfall data were used to compute climatic indices. These data were extracted from the METEO FRANCE SAFRAN model  (Durand et al. 1993)  for the French and Swiss stands, and from the WorldClim data  (Hijmans et al. 2005)  for the Slovenian stands. The SAFRAN data were available annually from 1959 to 2013. These meteorological data were averaged for each forest stand over the time period that was common to the SAFRAN data and to the forestry stand data (e. g. for the Queige stands, the forestry data period was generally 1931-1980, the meteorological data were then averaged over the period. Worldclim climatic variables were averaged over the 1960-90 period. Because the meteorological data were of a too coarse spatial resolution, we corrected the monthly temperatures by the effect of elevation using linear regressions applied on climatic data subsets (moving window techniques used by  Kunstler et al.
Definition of empirical and model-based approaches We quantified the demographic processes from our data using two different approaches. The first method, thereafter named "empirical approach", consisted in estimating directly stand level demographic fluxes from our data, using population statistics. In the second one, thereafter named "modelbased approach", we first calibrated the most influent parameters of our individual-based model. We then used these calibrated parameters as demographic variables to analyze climate/demography relations. The main objective of this dual analysis was to show that the model-based approach enables to take into account the effect of stand structure (size distribution, tree density) properly and eliminates its potential confounding effect in the analysis of climate effect.
We used rather uninformative priors and ran 10,000 simulations for each stand. We thus performed 1,630,000 simulations in total (10,000 simulations x 163 stands). Each simulation consisted in reproducing the stand historical dynamics and management, starting from the oldest inventory, and then by simulating successively natural dynamics and harvests as monitored in forestry data up to the date of the youngest inventory. The youngest inventory data was used as observational data to estimate the joint posterior distributions of the species model parameters for each stand independently. The modes of posterior distributions were then used as observational data for the following analysis (see the next section). The slight modifications made in the method defined by  Lagarrigues et al. (2015)  in the course of this study are detailed in the Appendix C.
Acknowledgment We thank all data providers, in particular the French Forest National Office for its assistance while extracting data from their archives. The data compilation for the Slovenian stands, done by the University of Ljubljana, was funded by the European FP7 research project "Advanced multifunctional forest management in European mountain ranges" (ARANGE, n°289437). The simulations were carried out using computing center CIMENT located in the Grenoble University and we thank the CIMENT supporting team and Eric Maldonado from IRSTEA Grenoble for their help to fix technical issues related to the use of the simulation platform. Guillaume Lagarrigues was funded by the French Environment and Energy Management Agency (ADEME), the French Forest National Office (ONF) and IRSTEA. IRSTEA Grenoble is a part of labex OSUG@2020.
However, the precise timing of arrival of the contralateral and ipsilateral axons in the mutant VPM is difficult to assess. Although we did not find evidence for delayed arrival of the contralateral TT projections in the VPM, we cannot rule out that ipsilateral projections reach their targets earlier and hence have a competitive advantage to innervate the VPM. To determine the 3D organization of the VPM map in the Robo3 R3-5 -cKO mutants, and the origin of the GFP+ patches, we performed whole-mount imaging of brains from Robo3 R3-5 -cKO;Tau GFP mutants and heterozygous controls at P8 using iDISCO (Figure  3 )  (Belle et al. , 2014; Renier et al. , 2014). A suitable angle was determined for optimal projection of the barreloids in the thalamic whisker map onto a plane: 3D datasets were oriented at a 45 ˚oblique angle from both coronal and horizontal planes (Figure  3A , Figure  3 -figure supplement 1A and Video 2).