dataesr/scientific-paragraphs-categorization huggingface dataset:
She died at the age of 30 years of thalamic stroke. Her younger affected sister (F3:II-2; P9) presented the same clinical features (Table  1 ). She and the affected subjects from Families 1 (F1:IV-1; P1, F1:IV-2; P2) and 2 (F2:II-2; P5) were examined by dysmorphologists who noted similar facial dysmorphic features consisting of enophthalmos, a short philtrum, large incisors and diastema (Fig. 2c ). The affected patient in Family 4 (F4:VI-2; P10, Fig. 1 ) was reported to display night blindness since childhood but he pre-was diagnosed with retinitis pigmentosa (RP) at 20 years of age (Fig. 2a-b ). The cognitive deficits with global developmental delay were noticed when he was 3 years old, and some dysmorphic features were reported (Table  1 ). Analysis of datasets obtained from WES from affected individuals and their unaffected parents identified the GPATCH11 splice-site variant NM_174931.
4: c. 328+1G>T, (p. ?) in homozygosity and in compound heterozygosity with the nonsense variant c. 454C>T, (p. Arg152*) in affected subjects from consanguineous Families 1 and 3 and nonconsanguineous Family 2, respectively (Fig. 1 ). Haplotype reconstruction using exome datasets from the three families, all of whom originate from Maghreb (Tunisia, Algeria/Morocco and Algeria, respectively), identified a short (1. 27 Mb) common haplotype encompassing the splice-site variant c. 328+1G>T (Supplementary Table  1 ), supporting an ancient founder effect. In the index patient from the Family 4, originating from Portugal, analysis of WES data identified homozygosity for another splice-site variant, c. 449+1G>C, (p. ?) in GPATCH11 (Fig. 1 ). Consistent with autosomal recessive disease transmission, the parents of affected individuals in the four families carried these variants in single heterozygosity (Fig.
1 ). Whole exome sequencing in four independent families allowed the identification of 3 different novel variants in GPATCH11. The three variants were absent from controls in the genetic variation datasets (Exome Sequencing Project, 1000 Genomes Project, and Exome Aggregation Consortium) tested by Alamut Visual™. In silico predictions suggest that the consensus donor splice-site variants c. 328+1G>T (Donor loss: 0,95; SpliceAI) and c. 449+1G>C (Donor loss: 1; SpliceAI) cause skipping of in-frame exon 4 and out-of-frame exon 5, respectively. Cells from individuals carrying the c. 328+1G>T variant produce a mutant GPATCH11 protein lacking the internal portion of the GPATCH-domain. To validate the in silico predictions described above, we performed RT-PCR analysis of RNA isolated from the fibroblasts of the affected siblings in Family 1 F1:IV-1 (P1) and F1:IV-2 (P2), carrying the c.
7d, e ). Because protein degradation is often modulated by ubiquitination or other post-translational modifications, we investigated posttranslational modifications through proteomic Nextprot database (https://www. nextprot. org/entry/NX_Q8N954/proteomics?isoform=NX_Q8N954-1), which did not identify any modification of the 96-109 amino-acid deleted sequence whose loss could account for altered protein degradation. Overall, our data show that the human mutant variant in mice produces a shorter GPATCH11 protein, which results accumulated in all the retinal layers while the transcript abundance rests unaltered. Expression of the mouse GPATCH11 protein analogous to the human mutant variant affects the splicing and expression of genes related to visual perception in the retina. Whether the loss of the internal portion of the GPATCH-domain of GPATCH11 affected gene expression and/or splicing was investigated by comparing the transcriptomes of retina from 15-dayold Gpatch11 Δ5/Δ5 mice and wild-type littermates.
Genetic variation annotation and variant filtration were performed using the POLYWEB documented  31. For Family 4, WES was conducted at CeGaT GmbH in Tübingen, Germany. Here, sequencing libraries were generated using the Twist Human Core Exome Plus kit (Twist Bioscience) following the manufacturer's protocols. Paired-end sequencing on a Novaseq 6000 produced 100-base sequences. Variant filtration and prioritization were performed using an inhouse pipeline  33. Variants in GPATCH11 were identified by selecting rare sequence changes (minor allelic frequency >1%) with highest scores of pathogenicity and consistency with a recessive model of inheritance. Variants have been annotated using the GRCh38 reference genome and GenBank transcript (NM_174931. 4).
Purified products were sequenced on a 350xL Genetic Analyzer ABI and analyzed using the Sequencing Analysis v5. 2 sequence software. Quantitative analysis of U1, U2, U4, U5, U6 snRNA abundance in patient and control fibroblasts snRNAs were extracted from F1:IV-1 (P1), F1:IV-2 (P2) and control fibroblasts using the Trizol and digested through DNAse RQ1 treatment, according to the manufacturer's protocols (Qiagen and Promega, respectively). Assessment total RNA concentration and purity and first-stranded cDNA synthesis were performed as described previously. A non-reverse transcriptase (RT) reaction (without enzyme) for one sample was prepared to serve as control in RT-qPCR experiment. For RT-qPCR analysis, cDNAs were amplified using specific primers designed from the sequences of U1, U2, U4, U5, U6 snRNA and HPRT1 and RPLP0 housekeeping genes.
All primers have been previously reported  34, 35  but the U6 snRNA specific primers: Forward 5'-GCTTCGGCAGCACATATACTAAAAT-3', reverse 5'-CGCTTCACGAATTTGCGTGTCAT-3'. cDNAs (5 µL) were subjected to real-time PCR amplification in a buffer (20 µL) containing SYBR® Green Master mix (Applied Biosystems) and 0,5 μM of forward and reverse primers, on a mastercycler realplex2 (Eppendorf). Data were analysed using the realplex software (Eppendorf). For each cDNA sample, the mean of quantification cycle (Cq) values was calculated from triplicates (SD<0. 5 Cq). Expression levels U1, U2, U4, U5, U6 snRNAs were normalized by using the relation ΔΔCq relative to HPRT1 and RPLP0. Absence of amplification when using mRNA (non-RT) and water (W) as templates were controlled in each run (Cq values non-RT >30 and W=undetermined).
The goat anti-rabbit IgG-HRP was used as secondary antibody (Invitrogen; 1/4000). Immunoblots were revealed using Clarity Western ECL Substrate (BioRad) and the ChemiDoc XRS+ Imagin System (BioRad). Western blot images were acquired and analyzed with Image Lab software 3. 0. 1 build 18 (Bio-Rad). To compare GPATCH11 abundance across samples, membranes were secondarily incubated with mouse anti--ACTIN and goat anti-mouse IgG-HRP primary and secondary antibodies, respectively. The abundance of GPATCH11 relative to ß-ACTIN was estimated in each cell line by densitometry using Image Lab software (BioRad). The quantitative data are the means±SEM of three independent protein extractions and these are presented as ratio relative to -ACTIN. Data were analysed using GraphPad Prism 6 software. The significance of variations among samples was estimated using the one-way ANOVA with a post hoc Tukey's test.
The primary antibodies were labelled for 1 h at room temperature using the Alexa 555-conjugated donkey anti-rabbit (1:1000; Life Technologies) and the Alexa 488-conjugated donkey anti-mouse (1:1000; Life Technologies) secondary antibody. Nuclei were stained using DAPI (Invitrogen). Images were recorded from Spinning Disk Zeiss microscope (Zeiss) using a 40x/1. 3 Oil objective. For the analysis of GPATCH11 immunostaining, we first used a machine learning with Ilastik 12 (v1. 3. 3post3) for GPATCH11 and Cellpose2 36 nucleus detection. Then, means intensities of nuclear and cytoplasmic GPATCH11 staining we measured with Fiji 37 macro. Image figures were made through FigureJ  38. GraphPad Prism 6 software was used for statistical analyses. The mean intensity of nuclear and cytoplasmic GPATCH11 in fibroblasts of affected individuals (Ps) and controls (Cs) was compared using a one.
Generation and analysis of genome-edited hTERT-RPE1 To establish a cell line expressing a GPATCH11 protein with its CCDC-domain removed while keeping the other domains intact, we employed the CRISPR-Cas9 genome editing strategy, as previously outlined in reference  39. In brief, we designed two guide RNAs (sgRNAs) with specific targeting for the in-frame GPATCH11 exon 3, which encodes the CCDC-domain. These sgRNAs, namely Guide 5'1 (5'-TGCCTTAGCATTGGCAATCCTGG-3') and Guide 3'1 (5'-GAACAAGAAAGACGTGACATTGG-3'), were designed for exon 3 using the CRISPOR software. The selection was based on their on-target and off-target scores, accessible at (Supplementary Table  2 ). The PCR products were separated by agarose gel (2%) electrophoresis to identify samples displaying a unique band, the molecular weight of which was consistent with the homozygous deletion of exon 3.
Immunocytochemistry analysis of GPATCH11 subcellular localisation and cilia formation analysis in genome-edited hTERT-RPE1 To analyse the subcellular localisation of GPATCH11 protein, Δ-CCDC hTERT-RPE1 and controls were seeded, fixed and immunostained as described previously, but only CENTRIN3 was used for the co-localisation at the centrosome. Δ-CCDC hTERT-RPE1 and controls were proceeded further for cilia formation analysis, as described previously. Data were analysed using GraphPad Prism 6 software. Data from patient and control cells were compared using the one-way ANOVA with a post-hoc Tukey's test. Error bars reflect the standard error of the mean (SEM).
Animals Generation of a mouse model expressing a GPATCH11 protein analogous to the human mutant variant To express a GPATCH11 protein analogous to the mutant variant found in humans, we employed the CRISPR-Cas9 methodology to delete Gpatch11 exon 5, which corresponds to exon 4 in humans (Fig. 5a ). To achieve this, we designed two sgRNA guides: sgRNA_DEL1 (5'-ATACCATCTCCTAAAAAAGG-3') and sgRNA_DEL2 (5'-TTCGGGTGATATCAATTATA-3') from the mouse Gpatch11 intron 4 and intron 5 sequences, respectively, using the CRISPOR software. C57BL/6J female mice (4 weeks old) were super ovulated by intraperitoneal injection of 5 IU PMSG (SYNCRO-PART® PMSG 600 UI, Ceva) followed by 5 IU hCG (Chorulon 1500 UI, Intervet) at an interval of 46-48 h and mated with C57BL/6J male mice. The next day, zygotes were collected from the oviducts and exposed to hyaluronidase (Sigma-Aldrich) to remove the cumulus cells and then placed in M2 medium (Sigma-Aldrich) into a CO2 incubator (5% CO2, 37 °C).
Western Blot analysis 15-day-old Gpatch11 Δ5/Δ5 and wild-type mice were sacrificed by cervical dislocation and enucleated. The eye globes were dissected in PBS under a microscope to recover the retina. The tissue was lysed with RIPA-PIC (Thermo Fisher Scientific) and proceeded further for Western blot analysis as described previously, but to compare GPATCH11 abundance across samples, membranes were secondarily incubated with rabbit anti-γ-TUBULIN4B and goat anti-rabbit IgG-HRP primary and secondary antibodies, respectively. GraphPad Prism 6 software was used for statistical analyses. The abundance of GPATCH11 relative to γ-TUBULIN was estimated. The significance of variations among samples was estimated using the Student's t test.
To assess rod function, dark-adapted mice were exposed to light stimulations of 0. 01, 0. 1, 1, and 3 cd. s/m2. For cone function assessment, in light-adapted conditions mice were subjected to light stimulation of 3 and 10 cd. s/m2 (light adaptation during 8 minutes to saturate/bleach the rods). The amplitudes of the "a" and "B" waves, which measure photoreceptor and bipolar cell activation, respectively, were analyzed using Diagnosys software. Statistical analysis was conducted using GraphPad Prism 6 software, and the significance of the difference in a-wave and B-wave amplitudes between agematched mutant Gpatch11 Δ5/Δ5 and wildtype mice was determined through post hoc Sidak's test following two-way ANOVA.
Longitudinal analysis of retina histology Eyes from mutant and wildtype mice, aged 15 days to 6 months, were collected following previously described procedures. The eyes were fixed in 4% PFA overnight at 4°C. Subsequently, the specimens underwent a series of steps, including washing in PBS 1X, dehydration using an ethanol gradient, embedding in paraffin, and sectioning into 4 μm thick sections. For staining, paraffin-embedded eyes were deparaffinized using Histoclear (Electron microscopy sciences) and rehydrated by incubation in baths of decreasing ethanol concentration. Subsequently, eye sections were stained with Hematoxylin-Eosin (HE) and imaged using a NanoZoomer S210 microscope from Hamamatsu Images were analyzed using NDP view software (Hamamatsu). The thickness of the outer nuclear layer (ONL) and inner nuclear layer (INL) of the retina was measured at various distances (0.
25, 0. 50, 0. 75, 1, 1. 25, 1. 50, 1. 75, and 2 mm) from the optic nerve. Three mice from each group were included in the analysis. Data were analysed using GraphPad Prism 6 software. The ONL thickness of age-matched mutant Gpatch11 Δ5/Δ5 and wildtype mice was compared using a two-way ANOVA with a post hoc Sidak's test.
GraphPad Prism 6 software was used for statistical analyses. For immunohistochemistry analysis of proteins deparaffined eye sections were incubated in Trisodium citrate (10 mM, pH 6) and Tween (0. 05%) for 30 minutes at 95°C to achieve antigen retrieval. Subsequently, the sections were blocked for 1 hour with blocking solution containing 5% BSA in PBS. Primary antibodies were prepared in blocking solution and eye sections were further incubated overnight at 4°C in humidifying chamber. Mice eyes were immunostained using rabbit anti-GPATCH11 (1:100, Abcam) primary antibody. Sections were washed three times with PBS and incubated for 1 hour with Alexa 555-conjugated donkey anti-rabbit (IgG, 1:200, Life Technologies ThermoFisher Scientific). DAPI (Roche, Mannheim, Germany) was diluted with PBS to final 1.
25 μg/mL and used to label nuclei and sections were washed with PBS and then mounted with Fluoromount medium (Sigma) under glass coverslip. Images were recorded on Spinning Disk Zeiss microscope (Zeiss) using a 63x/1. 4 Oil objective. For the analysis, we first used machine learning for the detection of GPATCH11 and tissue detection using Ilastik  12 (v1. 3. 3post3 ). Then we measured the mean intensity of GPATCH11 with a Fiji 37 macro. Image figures were made through FigureJ  38. The mean intensity of GPATCH11 of age-matched mutant Gpatch11 Δ5/Δ5 and wildtype mice was compared using a two-way ANOVA with a post hoc Sidak's test. Data were analysed using GraphPad Prism 6 software. Mice eyes were also immunostained using rabbit anti-Blue S-OPSIN (1:100, Invitrogen), anti-M-OPSIN (1:1000, Invitrogen) and mouse anti-RHODOPSIN (1:500, Novus) primary antibody and Alexa 555-conjugated donkey anti-rabbit and 633-conjugated goat anti-mouse (1:200, Life Technologies ThermoFisher Scientific).
Immobility or being on top of an object was not considered exploration. Preference for the novel object was calculated as the time spent exploring the new object divided by the total time spent exploring both objects. The discrimination index was computed as the difference between the time spent exploring the new object and the time spent exploring the familiar object, divided by the total time spent exploring both objects. Video recordings of the behaviour were evaluated by an observer who was blinded to the identity of the mice. Behavioural data were estimated to be statistically significant when p≤0. 05 by Student's t test. Data were analyzed using GraphPad Prism v10. 0. 2 software.
On the first day (training), mice were introduced into the conditioning chamber and subjected to three foot shocks, each lasting 1. 5 seconds and delivered at intervals of 60, 120, and 180 seconds after the animals were initially placed in the chamber. Following the final shock, the mice were returned to their respective home cages, 60 seconds later. Contextual fear memory was assessed 24 hours after conditioning (testing) by reintroducing the mice to the conditioning chamber. During this 4-minute retention test, freezing behaviour was evaluated. The assessment of freezing behaviour was conducted automatically using Packwin 2. 0 software (Bioseb), with freezing defined as a period of immobility lasting at least two seconds. Behavioural data were estimated to be statistically significant when p≤0.
Open field test (OFT) This test leverages rodents' inherent aversion to well-lit areas. Each mouse was placed in the center of a 43 × 43 cm chamber specifically designed for the Open Field Test (OFT) and allotted a 30minute exploration period. Continuous monitoring of the mice during each testing session was conducted using an infrared light beam activity monitor operated by the actiMot2 Software (PhenoMaster Software, TSE). To gauge overall motor activity, we quantified the total distance traveled (ambulation). Concurrently, anxiety levels were assessed by measuring both the time spent and the distance traveled by the mice in the center of the open-field chamber compared to the periphery. Behavioural data were estimated to be statistically significant when p≤0. 05 by Student's t test. Data were analyzed using GraphPad Prism v10.
A trial concluded once the mouse successfully reached the platform and remained there for 5 seconds. Subsequently, the mice were returned to their home cages, with a 5-minute inter-trial interval between attempts. Different release points were utilized on a daily basis to prevent a consistent starting advantage. The time taken by the mice to reach the platform was used as a measure of their performance in locating the target. Behavioural data were estimated to be statistically significant when p≤0. 05 by Student's t test. Data were analyzed using GraphPad Prism.
ACKNOWLEDGMENTS This work was supported by grants from  Retina France ;  Fondation JED Belgique ;  Fondation Visio  to JMR and IP. Fondation des Aveugles de France , PhD international  Institut Imagine  to AZ and by grant #  310030_204285s  from the  Swiss National Science Foundation  to CR. JMR is member of the European Reference Network for Rare Eye Diseases (ERN-EYE), which is co-funded by the  Health Program   of the European Union  under the  Framework Partnership  Agreement  n°739534. We thank the  Transgenesis, Neurobehavioural  and metabolism,  Necker Bioimage Analysis, Cell imaging, Histology , Cytometry, Genomic and Proteomic platforms for valuable discussions and help. A special thank for  Nicolas Cagnard  for the submission to BioStudies. The authors would like to acknowledge all patients and their families for participating in this study.
monoclonal anti-acetylated α-TUBULIN (Sigma Aldrich; 1:2000) and rabbit polyclonal anti-PERICENTRIN (1:5000, Abcam) primary antibodies overnight at 4°C, and Alexa 488-conjugated donkey anti-mouse (1:1000 Life Technologies) and Alexa 555-conjugated donkey anti-rabbit (1:1000; Life Technologies) secondary antibodies for 1 h at room temperature, respectively. Images were recorded from Spinning Disk Zeiss microscope (Zeiss) using a 40x/1. 3 Oil objective. Mean numbers of ciliated cells were calculated from >100 cells each from the four individual controls, in three independent experiments. Cilia lengths were measured from the same immunofluorescent images. Mean values were calculated from >100 cells in three independent experiments for each cell line for each cell line. Data were analysed using GraphPad Prism 6 software.
Development priorities Multiple challenges lie ahead in terms of software development and methods, in particular because more complex levels of theory are needed in order to improve the predictive power of first-principles methods. The formalisms involved are much more memory and computationally intensive, hence exascale resources are needed to tackle these problems. The following priorities will be tackled by different groups within the ABINIT developer community. Theories extending the DFT through the inclusion of the exact electronic exchange have appeared in the last two decades. One important priority is to go beyond ground-state (GS) properties by implementing response function calculations within DFPT with such exact electronic exchange-based functionals. Another ambition is to apply accurate beyond-DFT methodologies based on Green's functions,-specifically the GW approach-to problems and systems currently out of reach with state-of-the-art computers and software.
For this reason, most of the highlevel logic needed to drive these ABINIT workflows is implemented by a python driver that generates input files with optimized MPI input variables and orchestrates the submission of the different tasks while providing automatic error handlers and restart capabilities. A natural continuation of this effort is to extend the present implementation to automatically compute electron-phonon related quantities such as superconducting properties, temperature-dependent band structures, and transport properties including polaronic effects. These new studies will require a high degree of synergy between the HTC infrastructure and the HPC part of ABINIT, as elucidated in  [8]  in which dynamical quadrupoles (third-order derivatives of the energy, recently implemented in the DFPT module) were found to be crucial for accurate computations of carrier mobilities in semiconductors.
It is one among numerous libraries developed in the NOMAD Centre of Excellence Roadmap (https://nomad-coe. eu), a European Union Horizon 2020 project towards exascale (more on this topics in sections 6 and 7). More extensive use of libraries will require refactoring some sections of code to leverage the different interfaces. (4) Low-level abstraction layer A significant portion of new high-level developments in ABINIT is usually done by researchers with a background in physics or chemistry rather than in computer science. To bridge the gap between the two worlds, we have introduced an abstraction layer exposing an easy-to-use API to perform the typical tasks occurring in ab initio calculations while hiding the internal implementation. In the hidden layer, algorithms can be implemented in different languages and this allows computer scientists to fully exploit the capabilities of modern hardware, in particular graphics accelerators.
Reaching the exascale will also need complementary improvements in high-throughput workflows. In addition to these axes, ABINIT build system and documentation will be improved, especially for exascale architectures. Indeed, the production of an optimized ABINIT executable needs to link advanced libraries (e. g. ELPA, SCALAPACK, libraries for GPU) and use specialized compilation options (e. g. OpenMP in conjunction with threaded libraries for BLAS/FFT). Few users are expert in such technicalities, and a suboptimal version of ABINIT might be built. The integration with package managers such as EasyBuild and Spack will be improved, so that system administrators can easily build and deploy different optimized versions (pure-MPI, MPI + OpenMP, GPU, etc. ).
The use of strictly localized, quasi-orthogonal basis functions further ensures that the matrices are sparse and well conditioned; the overlap matrix has a low spectral width and the ratio of the band gap to the spectral width of the Hamiltonian is relatively high. These properties lead to huge efficiency gains when using diagonalization-free methods as implemented in our CheSS  [12]  and NTPoly libraries  [13]. BigDFT is now a mature and reliable code with a wide variety of features. A comprehensive overview of its capabilities is available in our recent review  [11]. BigDFT uses dual space Gaussian type norm-conserving pseudopotentials including those with non-linear core corrections. It is parallelized using a combination of MPI and OpenMP and has support for GPU acceleration. BigDFT's flexible Poisson solver can handle a number of different boundary conditions including free, wire, surface, and periodic (orthorhombic only).