dataesr/scientific-paragraphs-categorization huggingface dataset:
Probabilistic Tractography analysis. We used the Human Connectome Project (HCP) database that contains diffusion spectrum and T2-weighted neuroimaging data from 32 participants as reported in Deco and Kringelbach  38. A complete description of the acquisition parameters for diffusion MRI (dMRI) is described in detail on the HCP website  91. The freely Lead-DBS software package (https://www. lead-dbs. org/) provides the pre-processing described in detail in Horn et al. 92. In brief, the data were processed by using a q-sampling imaging algorithm implemented in DSI studio (http://dsi-studio. labsolver. org). A white-matter mask was computed by segmenting the T2-weighted images and co-registering the images to the b0 image of the diffusion data using SPM12. For each HCP participant, 200,000 fibres were sampled within the white-matter mask.
Acknowledgements A. E is supported by the  HBP SGA3 Human Brain Project Specific Grant Agreement 3  (grant agreement no. 945539 ), funded by the  EU H2020   FET Flagship  programme. Y. S. P is supported by  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant   896354. G. D. is supported by the  Spanish national research project  (ref. PID2019-105772GB-I00   MCIU AEI ) funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI). M. L. K is supported by the  Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ), and  Centre for Eudaimonia and Human Flourishing at Linacre College  funded by the  Pettit and Carlsberg Foundations. The study was supported by the  University  and  University Hospital of Liège , the  Belgian National Funds for Scientific Research (FRS-FNRS) , the  European Space Agency (ESA)  and the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Programme , the  BIAL Foundation , the  Mind Science Foundation , the fund Generet of the  King Baudouin Foundation , the  Mind-Care foundation and AstraZeneca Foundation , the  National Natural Science Foundation of China  (Joint Research Project  81471100 ) and the  European Foundation of Biomedical Research FERB Onlus.
2012 Offringa et al. , 2019a)) , and by instrumental effects  (Offringa et al. 2019b ). Hence, suppressing these effects during calibration is crucial for detection  (Barry et al. 2016). The calibration of the LOFAR-EoR KSP (Key Science Project) data uses the sky-based calibration approach. The processing pipeline of data has been developed and improved over a decade  (Yatawatta et al. 2013; Patil et al. 2016 Patil et al. , 2017;; Mertens et al. 2020; Mevius et al. 2022). Due to the wide field of view of LOFAR, the data need to be calibrated depending on direction to correct for different errors from the varying beam and ionospheric effects. This direction-dependent (DD) calibration step, in particular, was carried out by SAGECAL  (Yatawatta 2011 (Yatawatta , 2015 (Yatawatta , 2019)). While SAGECAL has shown excellent calibration performance, no other DD calibration code has yet been applied to LOFAR-EoR data.
DDECAL We use DDECAL as one of our DD calibration tools in the analyses of this work. DDECAL is part of the Default Preprocessing 11 The LOFAR core station field of view is ∼17. 73 deg 2 at 120  MHz (van Haarlem et al. 2013). Pipeline (DP3) processing software  (van Diepen et al. 2018)   12. DP3 performs streaming operations on an astronomical data set, such as flagging, averaging, calibration, compression, and statistical and various other corrections. DP3 is configured by providing a parameter set (parset) that defines the operations to be performed, as well as their parameters. DDECAL is implemented into DP3 with the purpose of having a flexible framework to integrate constrained calibration algorithms. At present, it integrates four algorithms: a directional solving algorithm  (Smirnov & Tasse 2015) ; a direction-iterative algorithm  (Offringa et al.
SAGECAL uses third-order Bernstein polynomials  (Farouki & Rajan 1988)  as the basis functions  (Yatawatta 2019). The parameter Z is a global variable shared by all frequencies in the data. The nth ADMM iteration solves Eq. (  12 ) in the following three steps for all frequencies in parallel: (J ν ) n+1 = arg min J L ν J, (Z) n , (Y ν ) n , ( 13 ) (Z) n+1 = arg min Z ν L ν (J ν ) n+1 , (Z), (Y ν ) n , ( 14 ) (Y ν ) n+1 = (Y ν ) n + ρ (J ν ) n+1 -B ν (Z) n+1. ( 15 ) Here the superscript (•) n denotes the nth iteration and ρ is a regularisation parameter that determines the level of smoothness in frequency for each iteration. For our observation, ρ ∼ 1000 is found to be optimal for 30 ADMM iterations. For more discussions about the selection of ρ, we refer readers to  Yatawatta (2015 Yatawatta ( , 2016)) ;  Mertens et al.
Direction-independent calibration The first step of calibration begins with a self-calibration  (Cornwell & Wilkinson 1981; Pearson & Readhead 1984) , and the main goal is to correct the source fluxes and build up a sky model for the DD calibration. We perform a first gain calibration on the averaged visibilities with a sky model consisting of 355 bright point sources from the NCP sky model. The gain calibration is carried out by GAINCAL in DP3. Using the usebeammodel option in GAINCAL, we apply the LOFAR-HBA beam model during calibration and use the initial sky model with intrinsic fluxes. To reduce the data volume and accelerate the calibration process we first average the data to a ten-second time resolution and gain solutions are calculated on the same timescale per sub-band with the LOFAR-HBA beam model 15.
Acknowledgements. H. G. and L. V. E. K. would like to acknowledge support from the  Centre for Data Science and Systems Complexity (DSSC)  at the  University of Groningen  and a  Marie Skłodowska-Curie COFUND  grant, no. 754315. BKG and LVEK acknowledge the financial support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement no. 884760 , " CoDEX "). Data availability: The data underlying this article will be shared upon request by the corresponding author.
The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian ( 2 norm, which can be minimised in closed form) and Laplacian ( 1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions.
• Robustness: we greatly increase the robustness of our method against remaining artefacts by modelling the registration error with Laplacian distributions that penalise its 1 norm, and solving the optimisation problem with linear programming. Moreover we use histology and reference masks on the generative model to further mitigate the impact of artefacts on the optimisation. • Computational efficiency: we greatly increase the efficiency of our method by making approximations in the optimisation that have nearly no effect on the registration accuracy as well as by using modern deep learning registration techniques. The rest of this paper is organised as follows. We introduce the proposed framework in Section 2. Experiments on two case studies are described in Section 3. A dataset with synthetic deformations is used to thoroughly test and compare different variants of the methodology in Section 3.
For intermodality registration, we use our synthesis based method presented in  Casamitjana et al. (2021). In short, this method uses a registration loss for weakly supervised image translation between domains where corresponding images exist but the spatial correspondence is not known (i. e. , as between histology and MRI). The registration loss is complemented with a structure preserving constraint based on contrastive learning, and the algorithms produces an inter-modality registration as by-product. The learning approach is used to train a total of C × (C + 1)/2 registration networks on the entire dataset, one for every possible pair of contrasts. Intermodality networks are trained with randomly selected pairs of matching images I c n , I c n at every minibatch (i. e. , with fixed n, and c = c ).
Summary The presented algorithm goes through a sequential number of steps. First, we build the graph and its associated spanning tree by finding correspondent slices between contrasts (Section 2. 1). Then, we compute the specified registrations above (Section 2. 7. 1) using the learning based approach introduced in Section 2. 6. With this information and the likelihood model of choice in hand, we use Equation 3 to solve the inference problem at each voxel in the space of the low-resolution velocity fields. Finally, we upsample the solution with linear interpolation and integrate it with the scaling and squaring technique  (Arsigny et al. , 2006)  to yield the final estimate of the latent deformation fields. The package is written in Python and made publicly available in https://github. com/acasamitjana/3dhirest.
Synthetic dataset The synthetic dataset consists of T1, T2 and FLAIR MRI scans from the training subset (N = 285) of the Brain Tumor Segmentation (BraTS) dataset  (Menze et al. , 2014). The T1 images with contrast ("T1c") are used as reference volumes, and the T2 and FLAIR images are used as a proxy for two histological contrasts. The BraTS scans are resampled (by the challenge organisers) to 1mm 3 isotropic resolution, and we define the z coordinate along the inferior-superior (I-S) direction. We generate synthetic 2D non-linear deformation fields using a grid of control points and B-spline interpolation  (Prautzsch et al. , 2002) , independently for each axial slice of the T2 and FLAIR images, in order to mimic geometric distortion due histological processing (further details in Section 3.
Real dataset The real dataset is a set of publicly available images from the left hemisphere from a 34-yearold donor, distributed by the Allen institute  (Ding et al. , 2016)  at http://atlas. brain-map. org/. The dataset includes a multi-echo flash MRI scan acquired on a 7T scanner at 200 µm resolution, which we use as reference for the 3D histology reconstruction. Two different histological contrasts are also available at sub-µm in-plane resolution (which we resampled to 250 µm for convenience): 641 Nissl stained coronal sections with 200 µm spacing, and 287 coronal sections with 400 µm spacing and immunostained using parvalbumin. The correspondence between histological sections is found by matching the closest pair, with errors no superior than 0. 1mm. For each section, we generate an associated tissue mask by thresholding and morphological operations.
, 2009) , we compute the projection from the subject space to MNI coordinates. Quantitative evaluation for this dataset is carried out using landmarks generated as follows. First, one salient point for each Nissl section (details below) is automatically sampled. Next, a first observer (JEI) marked the equivalent locations on the parvalbumin sections (where available) and on the resampled MRI. This generates a set of 641 pairs of landmarks for Nissl/MRI, and 287 for Nissl/parvalbumin, which can be used for quantitative evaluation. The same observer (JEI) reannotated the landmarks on a different day, for estimation of intra-observer variability. A second observer (AC) also annotated the same landmarks, for estimation of inter-observer variability. The reference landmarks on the Nissl sections were sampled in a manner that ensured both salience and uniform spatial distribution.
Metrics The synthetic dataset enables dense quantitative evaluation of the 3D reconstruction methods at the pixel level. Let φ c n (x) represent the ground truth 2D deformation field between the reference slice n and histological contrast c, and let φc n (x) be the deformation field estimated by an algorithm. To assess the performance of the presented framework, we define different performance metrics. We first define the pixel-wise error as the bivariate deviation of the estimate from the ground truth deformation field at each pixel. Hence, for slice n, contrast c, and location x, we have: e c n (x) = φ c n (x) -φc n (x). ( 10 ) Based on this error, we define two performance metrics. First, the intra-slice error, defined as a global average over all pixels and slices of the module of the pixel-wise error.
Experimental setup To generate the synthetic deformations in the BraTS dataset, we independently deform each axial slice of the T2 and FLAIR with 2D deformation fields generated as follows. First, normally distributed, low-resolution deformation fields of size 9×9×2 are generated independently for each slice, subject and modality (T2, FLAIR). Each element in the low-resolution field is an independent Gaussian variable with zero mean and a standard deviation which is constant for each slice, and which is sampled from a uniform distribution U[3, 7] (in pixels). Each deformation field is then resized to the original image size using B-Spline interpolation. We explicitly avoid using velocity fields to prevent imitating the deformation model used in the algorithm. The final volume is built by applying the deformations and resampling with bilinear interpolation.
To measure the robustness of our framework against large registration errors, we introduce outliers in the synthetic dataset. Specifically, we further distort subsets of the T2 and FLAIR slices (2%, 5%, 10% and 20%, to test scenarios with increasing number of outliers) by applying large random rotations (90 • , 180 • , or 270 • ), which lead to large errors in the nonlinear registration algorithm. Each modality is distorted independently. Evaluation is carried out only on the undistorted slices. In order to analyse the impact of the deep learning registration techniques, we consider two additional diffeomorphic registration algorithms: • NiftyReg (NR)  (Modat et al. 2012 ): we use NiftyReg with SVF parameterisation ("-vel" option), control point spacing of 8 pixels, local normalised cross correlation similarity metric and bending energy penalty term.
Results on synthetic dataset In Fig. 4  we compare different configurations of the algorithm using NR and RegNet as registration methods. RegNet, which is trained specifically for the problem at hand (as opposed to used a generic optimiser) provides better initial alignment between modalities than NR. Over the initial linear alignment, RegNet improves the intra-slice error by 49% and 42% for T2 and FLAIR modalities, respectively, compared to the 37% and 8% using NR. Moreover, estimation error variability in RegNet is consistently ∼ 5x times lower than in NR. According to these results, the dependence of standard intermodality registration algorithms (e. g. , NiftyReg) on the contrast and image appearance can be partly mitigated using learning-based approaches. Further refinement can be achieved using the presented framework with different modelling options.
Results on Allen atlas For 3D reconstruction of the histology of the Allen atlas, we use RegNet as the registration algorithm and ST3-L1 configuration with P = 4 neighbours in each of the three stacks of images; we increase P with respect to the BraTS dataset to compensate for the lower spacing between sections. Due to a number of larger gaps between sections in this dataset, this process yields a disconnected graph with 5 separate slabs. Therefore, each slab can be processed independently, which reduces the memory footprint of the algorithm. Qualitative results in the sagittal and axial planes are shown in Figures  8  and 9 , where the reconstructed volumes are resampled at 0. 25mm isotropic resolution. 3D histology stacks of both Nissl and parvalbumin contrast appear to be aligned with the reference volume at the same time that provide smooth reconstructions.
a) are not propagated to neighbouring sections (Figure  10. bc). Moreover, despite the fact that reconstruction is not accurate around histology artefacts (Figure  10. d), it can be seen that accurate accuracy is preserved away from the regions with artefacts. The correspondence between the three contrasts on a section with severe artefacts as well as the smoothness of the deformation field in plane is illustrated in Figure  4  in the supplementary material. Finally, Figure  11  shows boxplots for the intraslice errors computed from the manually placed landmarks, and compares them to the inter-and intra-observer variabilities. Even though the median error does not improve with respect to Reg-Net (as it did on the synthetic dataset, Figure  7 ), there is a small decrease in outliers (specially using NR) after refinement of the registrations with our proposed approach.
First, we assume smooth transitions between nodes in the graph. This smoothness depends directly of the regularity of the anatomy and the spacing between sections. We have presented positive results on the Allen datasets, which has spacings (200/400µm) that are comparable if not larger than those typically used in human brain atlasing (e. g. ,  Amunts et al. (2013) ;  Iglesias et al. (2015) ;  Yushkevich et al. (2021) ). Therefore, we are convinced that the smoothness assumption will be met but most datasets. We also assume the availability of an external 3D reference like MRI, which is typically acquired in most 3D histology works to avoid z-shift and banana effect  Malandain et al. (2004). This reference volume is linearly aligned to the stack of histology sections, a problem that is much simpler than estimating the in-plane nonlinear deformations; iterating between a 3D rigid body transform and the 2D non linear refinement yields very fast convergence of the 3D transform.
e. , the pairwise registrations), which can be computed in the order of seconds to a few minutes, depending on the size of the problem (number of images, number of contrasts, size of the images, etc. ). NiftyReg is between one and two orders of magnitude slower, as it iteratively optimises the deformation for every R k , such that computing the whole set of registrations take between minutes and hours. Such differences are reduced when accounting for network training times, being more efficient for larger datasets e. g. , several minutes per subjects in the BraTS dataset. These computed registrations are used to build a connected graph (i. e. all nodes are connected by at least one path) defined by the underlying spanning tree; otherwise, if some nodes remain isolated, the solution would be ambiguous and extra regularisation would be required for inference.
g. , tears, folding, cracks, inhomogeneous staining), our method is able to recover the original 3D shape with smooth transitions between slices. We have shared our results through the OpenNeuro repository (https://openneuro. org/datasets/ds003590) along with the mapping to MNI space, providing the neuroimaging community with a cross-scale link between the two atlases. In the modelling step, different assumptions are made about the observed deformation fields, such as the independence between registration noise and spatial location, or the conditional independence of the observations given the latent variables. While these assumptions are violated to different extents in different scenarios (application, base registration algorithm), the results on the two datasets have shown that our proposed method works well in practice.
This membrane could have slowed down or hindered the passage of part of the metabolites. Coherently, a higher number of DEGs was found in the present study. Of all DEGs, 991 resulted significantly deregulated at all time points, 547 were commonly down-regulated, and 426 were upregulated (Fig. 2D ), while 18 genes showed opposite regulation at T1 compared to T2 and T3 (fig. S2  and data file S2). Three genes were up-regulated at 1 hour only, with functions that could be related to the cellular rearrangements occurring upon perception of the early signals: a member of the adenosine 5′-triphosphate-binding cassette transporters, which transport compounds across cellular  (D) Venn diagrams of the overall (ALL) overlap between the three datasets (cross T1 versus controls, cross T2 versus controls, and cross T3 versus controls) and of down-and up-regulated genes separately.
Moreover, we found a heat shock factor DNA binding domain containing gene, a common domain in diatoms often found in transcription factors  (36). 5 of To pinpoint pathways deregulated during P. multistriata sexual reproduction, we performed a gene ontology (GO) enrichment analysis on the 991 genes that resulted deregulated at all time points. A total of 11 significantly enriched pathways were detected, with five terms related to photosynthesis showing the highest percentage of DEGs and the lowest adjusted P values (Fig. 2E  and data file S3). Other enriched GO terms suggested involvement of membrane proteins in the DEG dataset and pointed to a greater abundance of genes related to transcription, DNA repair, protein folding, and carbohydrate metabolic process (Fig. 2E  and data file S3). The results of this GO enrichment analysis added to the knowledge gained in previous studies  (25, 26)  and prompted a more thorough exploration of the pathways that are highly affected during sexual reproduction.
In diatoms, REC8 is absent and RAD21 seems to be the only component of the complex  (37) ; therefore, it is not possible to tell for what specific function the complex is required. Expectedly, the putatively meiosis-specific genes, Spo11-2, Mer3, Mnd1, Msh4, and Msh5, all supposed to play a role exclusively during meiosis, did not show up-regulation at T1. Gene expression datasets generated in different diatoms during sexual reproduction provide cues to identify genes that are specifically required in this process, allowing us to assign a possible function to unknown sequences that cannot be annotated on the basis of homology searches. We identified a subset of sexually induced genes (SIGs) comparing the diatom Skeletonema marinoi to P. multistriata  (39) , and the specific induction during sex for a subset of these genes was recently confirmed in the diatom S.
P. multistriata cells stopped most metabolic processes during the sexual phase; decreasing the input of photochemical energy is a way to protect cell health. This interpretation is further supported by the parallel alteration of carbon assimilation processes as we found down-regulation of all the main enzymes involved in the Calvin cycle and pentose phosphate pathway (Fig. 4A ). Exceptions are the glucose-6 phosphate dehydrogenase (13300) and one ribulose-phosphate 3-epimerase (56840) that were up-regulated although with low read counts. The down-regulation of photosynthetic carbon assimilation was mirrored by a marked down-regulation of the genes involved in both glycolytic and gluconeogenesis pathways (data file S4). The ribulose-1 5-bisphosphate carboxylase/oxygenase (RuBisCo) protein was not found in the analyzed genes, and since the MT+ reference genome (25) used for mapping reads in our transcriptomic analyses currently lacks the chloroplast genome, an ad hoc analysis was performed using other datasets (see Materials and Methods for Downloaded from https://www.