dataesr/scientific-paragraphs-categorization huggingface dataset:
SBF-SEM Cells were prepared as described by Blancard and Salin (80) except for freezesubstitution medium (0. 1% potassium permanganate for 24 h followed by 0. 1% tannic acid for 24 h, and 2% osmium tetroxide with 1% uranyl acetate for 3 days). Resinembedded samples were trimmed, mounted onto stubs and placed into a Zeiss GeminiSEM300 with a fitted Gatan 3View2XP system (Gatan, Abingdon, UK). Serial images of the block face were recorded at an accelerating voltage of 1. 2 kV. All images were taken with the following scanning settings: Pixel size 10 nm, pixel time 5 µs, slice thickness 50 nm (Z-slice), image size 5000x5000. Between 700 and 900 images were collected for each run. Images were collected from Back scattered electron with a specific BSE Detector (On point -Gatan Inc. , Pleasanton, CA, USA) using the software Digital Micrograph (Gatan).
Images were combined to form a single stack. Data processing and statistical analysis: Segmentation was conducted using AMIRA (versions 5. 2. 0 Thermofisher Scientific). Manual segmentation of cells and organelles were conducted using the brush tool and was based on ultrastructural characteristics specific to each organelle. Each organelle was segmented using the organelle membrane as the outer edge. Surface models were produced for each segmented organelle. Volumes of organelles were automatically generated from the surface renderings of segmented data.
Statistical analysis Experiments were performed at least in triplicates. Statistical analyses were performed using Prism (GraphPad) software. The results are presented as mean ± S. D. Where indicated the results were subjected to two-sided student's t-test to determine statistical differences against the indicated group (Confidence interval 95% -P-value style: 0. 1234 (ns); 0. 0332 (*); 0. 0021 (**); 0. 0002 (***); <0. 0001 (****)).
Structural prediction software and sequence analysis 29 Structure predictions were performed using PSIPRED ("http://bioinf. cs. ucl. ac. uk/psipred/") (81) and prediction of mitochondrial targeting sequences by various algorythms (MitoprotII, MitoFates and iPSORT)  (82). Phylogeny analysis was performed with Geneious software using default parameters and the neighbor-joining method was used for tree calculation.
ACKNOWLEDGMENTS We thank  Keith Gull  ( University of Manchester ) for providing us the anti-Ty1 and anti-PFR antibodies and  Derrick Robinson ,  Mélanie Bonhivers ,  Elina Casas  and  Nicolas Landrein  ( University of Bordeaux ) for the pPOTv7 expression vector and extremely valuable help in the expansion microscopy experiment. Cell sorter analysis were performed at the  TBMCore facility (FACSility)  on  BD FACSAria™ III Sorter  and we thank  Atika Zouine  for data acquisition and interpretation. We also thanks  Bordeaux Imaging Center (BIC) , which is a member of the  FranceBioImaging  national infrastructure ( ANR-10-INBS-04 ), for helping us to design the ImageJ macro and microscopy acquisitions.
Our results revealed the presence of active coupling between cells. This was demonstrated before by Liu et al. [45] , but a thorough quantification of this active coupling has been lacking. Photoactivation of one cell in our doublet leads to contraction, sending a force signal. The receiver cell reacts to this signal with an active contractile response. We quantified the response of the receiver cell by comparing experimental traction force and cell stress data with an FEM model. We found, that a purely passive reaction of the receiver cell cannot account for the data and therefore concluded that the receiver cell reacts actively. This active coupling mechanism increases the spatial range a mechanical signal can travel to about 1 or 2 cell lengths according to our data. Furthermore, analysis of the cell shape showed very high symmetry of shape deformation despite the asymmetrical photoactivation.
Imaging and optogenetic photoactivation All experiments were conducted 16 h to 28 h after seeding the cells on the sample. Then the cells were observed on an inverted Nikon Ti-E2 microscope with an Orca Flash 4. 0 sCMOS camera (Hamamatsu), a temperature control system set at 37 °C, a humidifier and a CO 2 controller. For the opto-experiments on cell doublets and singlets a Nikon 60x oil objective was used and for the opto-experiments on tissues a Nikon 40x air objective was used. The E-cadherin and vinculin staining images were taken with an Eclipse Ti inverted confocal microscope (Nikon France Instruments, Champigny sur Marne, France), equipped with sCMOS prime camera (Photometrics), a 60× objective, and a CSU X1 spinning disk (Yokogawa, Roper Scientific, Lisses, France). MetaMorph software was used for controlling the microscope (Universal Imaging Corporation, Roper Scientific, Lisses, France).
48 into the respective x-and y-components yields a system of two equations in the unknowns F ad and λ. The force F s was obtained by dividing the traction map into four quadrants and calculating the sum for each quadrant. A similar procedure as presented in  [15]. The contribution of the surface tension along the vertical fiber was estimated on TFM data as well. For the two unknowns we have λ = 1 T x F s,x -σ x L 2 (49) F ad = F s,y - T y T x F s,x -σ x L 2 , (50) such that λ and F ad can be calculated in terms of the tangent angle of the free spanning fiber at the focal adhesion.
Acknowledgements M. B. acknowledges financial support from the  French Agence Nationale de la Recherche (ANR)   MechanoSwitch project , grant  ANR-17-CE30-0032-01. U. S. S. acknowledges funding through a joint  ANR-DFG grant , project number  SCHW 834/2-1. T. B. acknowledges fundings through CNRS grants ( Actions Interdisciplinaires 2017, DEFI Instrumentation aux limites 2017, Tremplin@INP 2021 ,  PEPS CNRS-INSIS 2021 ). G. Ca. acknowledges financial support from the  ANR   SupraWaves project , grant  ANR-19-CE13-0028. This work was supported by the  Center of Excellence of Multifunctional Architectured Materials "CEMAM " (n°AN- 10-LABX-44-01 ). We would like to thank  Luis Vigetti  and  Isabelle Tardieux  for providing access to their spinning disc confocal microscope that was used to take the immunofluorescence images.
Aligning lexical glosses with target words To align the lexical glosses with the L2 translation, we use SimAlign  (Jalili Sabet et al. , 2020) , an unsupervised, multilingual word aligner which primarily computes source / target alignment links based on the similarity of the corresponding embeddings in some multilingual space. Note that our task is much simpler than word alignment in bitexts, as the lexical gloss and the translation are often in the same language (e. g. English or Spanish), meaning that similarities can be computed in a monolingual embedding space. We extract alignments from the similarity matrix with Match heuristic, as it gave the best results in preliminary experiments. Match views alignment as a maximal matching problem in the weighted bipartite graph containing all possible links between lexical glosses and L2 words.
L1 languages We consider five (out of seven) languages from the SIGMORPHON 2023 Shared Task on Interlinear Glossing  (Ginn et al. , 2023) : Tsez (ddo), Gitksan (git), Lezgi (lez), Natugu (ntu; surprise language), and Uspanteko (usp; target translation in Spanish). 7 Table  2  gives general statistics about the associated datasets; a brief presentation of these languages is in Appendix B and in  (Ginn et al. , 2023).
SimAlign settings Since the glosses and the translation are in the same language, we use the embeddings from the English BERT (bert-base-uncased)  (Devlin et al. , 2019)  when the L2 language is English and mBERT ('bert-base-multilingual-uncased') for Spanish (for Uspanteko). We stress here that our model is compatible with several target languages, SimAlign being an off-the-shelf multilingual (neural) aligner. Our preliminary experiments showed that the embeddings from the 0-th layer yielded the best alignments, especially compared to the 8-th layer, which seems to work best in most alignment tasks. A plausible explanation is that contextualised embeddings are unnecessary here because lexical glosses do not constitute a standard English sentence (for instance, they do not contain stop words, and their word order reflects the L1 word order).
Baselines Below, we consider three baseline approaches which handle glossing as a sequence-labelling task: • maj: a dictionary-based approach, which assigns the majority label (grammatical and lexical) seen in the training dataset to a source morpheme and fails for out-of-vocabulary morphemes; • CRF+maj: a hybrid model relying on a CRF to predict grammatical glosses and a unified lexical label (as in  (Moeller and Hulden, 2018; Barriga Martínez et al. , 2021) ). Known lexical morphemes are then assigned a lexical label according to the maj model; • BASE_ST: is the Transformer-based baseline developed by the SIGMORPHON Shared Task organisers and detailed in Ginn (2023). 9 9 https://github. com/sigmorphon/2023glossingST/ tree/main/baseline. A first observation is that system (S3), which effectively combines the information available in a dictionary and obtained via alignment in an integrated fashion (see §2.
2) greatly outperforms (S1) (only dictionary) and (S2) (only alignment), obtaining the best performance among our three variants. (S3) is also consistently better than all the baselines, with larger gaps when few training sentences are available (e. g. Gitksan or Lezgi). In comparison, the BERT-based baseline suffers a large performance drop in very low-data settings, as also reported in  (Ginn, 2023). Our CRF model also achieves competitive results compared to the best system submitted to the Shared Task, especially for the word-level scores. These scores confirm that decent to good accuracy numbers can be obtained based on some hundreds of training sentences. Note, however, that annotated datasets of that size are not so easily found: in the IMTVault  (Nordhoff and Krämer, 2022) , only 16 languages have more than 700 sentences, which is about the size of the Lezgi and the Natugu corpora.
Handling unknown morphemes Leveraging the translation in glossing opens the way to better handle morphemes that were unseen in training. Table  4  displays some statistics about unknown morphemes in test datasets. For most languages, they are quite rare, representing solely around or below 10% of all lexical glosses in the test set, with Gitksan a notable outlier  (Ginn et al. , 2023). Among those unseen morphemes, a significant proportion (from a third in Tsez up to 70% in Gitksan) of the reference lexical gloss is not even present in the translation 13 (cf. 'not in L2' in Table  4 ). Taking this into account nuances the seemingly low accuracy for unknown lexical morphemes: in Uspanteko, for instance, the system reaches an accuracy of 29. 3 when the best achievable score is about 35. To have a more optimistic view of our prediction, we 'approximate' lexical glosses with lemmas from the translation, using automatic alignments (e.
Data efficiency An important property of our approach seems to be its data efficiency. To better document this property, we report in Table  5  the morpheme-level accuracy obtained with increasingly large training data of 13 We studied the exact match with any lemmas in the translation; composed glosses are hence always considered as absent. 14 Whenever possible; if the lexical gloss has no automatic alignment, we keep the reference gloss. size (50, 200, 700, 1,000, 2,000, full) in Tsez. With 200 examples already, our model does much better than the simple baseline maj and delivers usable outputs. (S3) also has a faster improvement rate than the baseline for small training datasets (e. g. almost +7 points between 200 and 700 sentences), while maj increases by only +4 points. The return of increasing the dataset size above 1,000 is, in comparison, much smaller.
To help feature selection, we notably reduce the value of ℓ 1 to 0. 4. Pre-training results (+ IMT) are in Table 7. We note that pre-training has a negligible effect, except in some metrics, such as in Lezgi for the word level. We observed that the most important weights in the pre-trained model correspond to delexicalised pattern features that are relevant in IMTVault but not present at all in our datasets. Cross-lingual study Besides, since in our studied languages, Tsez and Lezgi belong to the same language family, we reiterate this methodology but with the Tsez dataset as a pre-training source to predict Lezgi glosses. This kinship is also explored through successful cross-lingual transfer in  (Zhao et al. , 2020). We obtain 83. 3 and 87. 1 for word and morpheme accuracy, which is very close to the performance with (and without) IMTVault despite containing fewer sentences.
Very low resource scenarios A final experiment focuses on a very low training data scenario. Save the Gitksan corpus, the test languages already represent hundreds of annotated sentences. This contrasts with actual data conditions: in IMTVault, for instance, only 16 languages have equivalent or more sentences than Lezgi (the second-lowest language in terms of training sentences in our study; cf. §5. 1). We thus focus here on (simulated) very lowresource data settings by considering only 50 sentences 16  selected from the training data. Table  8  reports the results obtained with this configuration. We observe that using the multilingual pre-training helps for all languages to a more noticeable extent than before. These three experiments confirm the potential of multilingual transfer for this task, which can help improve performance in very low-resource scenarios.
Feature analysis One positive point in using models based on CRFs relies on access to features and their correspond-ing learnt weights. We report in Appendix E ten features with the largest weight in Natugu. Among the top 1% of active features in Natugu in terms of weight, we find two features testing the gloss type b with the morpheme length: (LEX, 6+) and (LEX, 5). These indicate that longer morphemes are likely to have a lexical label. Such an analysis can also be relevant to weights learnt through multilingual pre-training. For instance, (LEX, 5) is among the top 10% features on the IMT-Vault dataset, suggesting a cross-lingual tendency of longer morphemes being lexical.
Related work Language documentation With the everpressing need to collect and annotate linguistic resources for endangered languages, the field of computational language documentation is quickly developing, as acknowledged by the ComputEL workshop. 17  Regarding annotation tools, recent research has focused on all the steps of language documentation, from speech segmentation and transcription to automatic word and morpheme splitting to automatic interlinear glossing. (Xia and Lewis, 2007; Georgi et al. , 2012 Georgi et al. , , 2013 ) use, as we do, interlinear glosses to align morphs and translations, then to project syntactic parses from L2 back to L1, a technique pioneered by  Hwa et al. (2005) , or to extract grammatical patterns  (Bender et al. , 2014). Through these studies, a large multilingual database of IMGs was collected from linguistic papers, curated, and enriched with additional annotation layers  (Lewis and Xia, 2010; Xia et al.
Conclusion This paper presented a hybrid CRF model for the automatic interlinear glossing task, which was specifically designed and tailored to work well in low data conditions and to effectively address issues due to out-of-vocabulary morphemes. We presented our main approach, which relies on analysing the translation tier, and discussed our main implementation choices. In experiments with five low-resource languages, we obtained accuracy scores that match or even outperform those of very strong baselines, confirming that accuracy values in the 80s or above could be obtained with a few hundred training examples. Using a large multilingual gloss database, we finally studied the possibility of performing cross-lingual transfer for this task. There are various ways to continue this work and improve these results, such as removing the noise introduced via erroneous alignments linkseither by marginalising over the 'origin' variable, by filtering unlikely alignments based on link poste-rior values, or by also trying to generate alignment links for function words  (Georgi, 2016; McMillan-Major, 2020).
(2022)  used the longest ecosystem COS flux measurements (5 years) made at the Hyytiälä forest, Finland, to develop an empirical parametrization of vegetation COS fluxes based on environmental drivers. This parameterization was scaled up to boreal evergreen needleleaf forest biome using the SiB4 LSM  (Haynes et al. , 2020) , leading to an increase in COS uptake in the high latitudes, compared to the one computed using the mechanistic COS model implemented in SiB3 by  Berry et al. (2013). In this context, the goal of this study is to evaluate the potential of COS to directly constrain the representation of GPP in the ORCHIDEE LSM using a data assimilation framework. This work addresses the following questions: 1. To what extent can biospheric COS flux measurements help to constrain stomatal diffusion of CO 2 and COS, and as a result, GPP? 2.
What is the impact of assimilating biospheric COS fluxes on the simulated latent heat flux (LE)? 3. How does assimilating ecosystem COS flux observations at one site along with GPP data from multiple sites impact the simulated COS, GPP, and LE fluxes over the entire biome of boreal evergreen needleleaf forests? Here, we optimize the GPP-and COS-related parameters by assimilating GPP and ecosystem COS fluxes at the Hyytiälä forest to evaluate the impact on the simulated vegetation COS and CO 2 uptakes, and LE. In particular, we focus on the constraint provided by these flux data assimilations during a severe drought event in 2006. Then, we perform multi-site assimilations to assess the changes in GPP, LE, and vegetation COS fluxes for the boreal evergreen needleleaf forest biome. Finally, we discuss necessary improvements in COS flux representation in LSMs, as highlighted in this study, and the implications of assimilating ecosystem COS fluxes to constrain GPP compared to using the empirical LRU approach.
Methods The general workflow of this study is represented in Figure  1  illustrating the main steps carried out. First, sensitivity analyses over the variables of interest were conducted following prior simulations in ORCHIDEE. This led to selecting the model parameters to include in the optimizations, which were performed either at the site scale or considering multiple sites for the assimilation. Finally, the optimized simulations following these two optimization procedures are evaluated against eddy covariance data and global evaluation products. These different steps are detailed in the following sections.
Description of the Studied Sites We selected 16 boreal evergreen needleleaf forest sites from the FLUXNET network. All sites are located in North America and Europe between 40° and 68°N, with each continent featuring eight sites, as illustrated in Figure  2. Mean annual air temperatures range from -1. 4° to 6. 9°C, and mean annual precipitation ranges between 149 and 1,440 mm across these sites. Two sites are located at a high elevation: Davos (Switzerland) at 1,639 m and Niwot Ridge (United States) at 3,050 m. These 16 sites were selected because the normalized root mean square deviation (nRMSD) between the FLUXNET GPP estimates and the GPP simulated in ORCHIDEE is lower than 25%. Sites for which the nRMSD exceeds this threshold may have been impacted by processes not represented in the ORCHIDEE version used in this study, such as fires or clear-cuts, and are therefore not selected for data assimilation.
GPP and LE Global Observation Products We evaluated the simulated fluxes over the whole boreal evergreen needleleaf forest region with several global GPP and LE products. First, we used global GPP data products from the FLUXCOM version RS  (Jung et al. , 2019 (Jung et al. , , 2020) )  and  FLUXSAT version 2. 0 (Joiner et al. , 2018)  databases, which are produced by applying different machine-learning upscaling methods to FLUXNET EC measurements and remote sensing data. FLUXCOM GPP is available between 2001 and 2015, whereas FLUXSAT GPP is available from 2000 to 2020. The FLUXCOM database also provides global estimates of LE derived from the same approach as for GPP. In addition, we considered a second global product for LE from the Global Land Evaporation Amsterdam Model (GLEAM), which separately computes different components of evapotranspiration (ET) from satellite data  (Martens et al.
Data Availability Statement The eddy covariance COS flux data from Hyytiälä used for ecosystem COS flux assimilation and evaluation in this study are available in Kohonen, Tramontana, and Kolari (2022)  via https://doi. org/10. 5281/zenodo. 6940750. The soil COS flux from chamber measurements at Hyytiälä used for assimilation are published in  Sun et al. (2017)  and available via https://doi. org/10. 5281/zenodo. 322936. The FLUXNET data used for data assimilation and evaluation are available at https://fluxnet. org following the Creative Commons (CC-BY 4. 0) license  (Pastorello et al. , 2020). The FLUXCOM GPP and LE data products are freely available from the data portal of Max Planck Institute for Biogeochemistry at http://fluxcom. org. The FLUXSAT GPP data product can be downloaded from the NASA Goddard Space Flight Center at https://avdc.
Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts.
Our new implementation integrates the SQUIRREL engine to provide an automated exploration of the parameter space, yielding optimal reconstruction settings. This optimization is directly driven by the data itself and outlines the trade-offs between resolution and fidelity to the user. By highlighting the optimal parameter range and acquisition configurations, eSRRF minimizes artifacts and nonlinearity. Therefore, eSRRF improves overall image fidelity with respect to the underlying structure. The enhanced performance is verified over a wide range of emitter densities and imaging modalities. We have additionally implemented the capability to achieve full 3D resolution improvement, bypassing the original SRRF's 2D capabilities. To do so, we adapted the method to analyze the nearly simultaneous multiplane acquisition of MFM, enabling the high-speed volume observation of fluorophore fluctuations.
Here eSRRF benefits from the analysis of temporally coherent axial planes, meaning there is no time lag between axial planes. The estimation of radial fluctuations in 3D MFM data are based on the same reconstruction principles of 2D eSRRF, additionally assuming the z-axis point-spread-function (PSF) elongation. We also demonstrate a full implementation of 3D eSRRF with a comprehensive Fiji plugin and how it can facilitate fast 3D super-resolution imaging in living cells.