dataesr/scientific-paragraphs-categorization huggingface dataset:
2A  and Table  1 ). Knockdown of cerebral Sema3D rescued spatial memory in miR-195a KO mice, while overexpression of cerebral Sema3D in mice caused significant memory and learning deficits regardless of short-term or longterm elevation of cerebral Sema3D. Both miR-195a KO mice and Drosophila with overexpression of Sema3D homology had shorter lifespan and reduced locomotor activity, while over-expression of Sema3D in rodent brain did not reduce locomotor activity. The negative impact of Sema3D on the brain was further demonstrated in human brain samples. According to the GEO datasets, we found that Sema3D mRNA levels were significantly increased with the severity of dementia in the hippocampus, prefrontal cortex, visual cortex and cerebellum. Mechanistically, we found that increased expression of Sema3D may impair autophagy and the mTOR pathway.
We designed small molecules to interfere with the binding between Sema3D and its receptor Plex-inA without interfering with other Sema3 receptors such as Nrp1 and VEGFR2. Although the clinical applications of our small molecule need to be further tested, the present study sheds light on a new methodology for prevention and treatment of dementia. Given that the human samples in the GEO databank were likely from chronic patients, it is reasonable that Sema3D was elevated in peri-hippocampal brain regions as the disease progressed. Previous studies have suggested that disruption of the prefrontal cortex and cerebellum directly led to impairment of long-term memory and spatial memory respectively  [6, 39] , which is compatible with our findings. Moreover, studies have disclosed that disconnection of the hippocampal-prefrontal cortex (PFC) circuit directly impaired cognitive functioning and ability of memory consolidation  [40] [41] [42].
Determination of whether Sema3D could disrupt the connection between the hippocampus and surrounding cortex is warranted for further investigation. We are currently optimizing the structure of small molecules to increase the efficacy and improve blood-brain barrier (BBB) penetration. Molecular docking will be experimentally evaluated using the isothermal titration calorimetry (ITC) in the future when the candidate molecule is determined. As such, the current data on small molecules interfering with Sema3D signaling has only demonstrated that continuous endeavor in discovering Sema3D inhibitors for dementia treatment is worthwhile, although we did not explore Sema3D co-receptors because this was beyond the scope of the present work. Previous knowledge of Sema3D was largely limited to the developing nervous system  [43, 44].
List of hippocampal Sema3D and Sema3A expression levels in neurodegenerative diseases and aging datasets. For each dataset, all subjects were classified according to the severity and phenotypes provided by the original article. The expression levels in all severity groups were normalized to the data of normal subjects in each correspondent dataset. MMSE: Mini-Mental State Examination. Data are presented as mean ± SEM. *p < 0. 05, **p < 0. 01 using Student's t-test. # p = 0. 080, ## p = 0. 1658 using one-way ANOVA.
Sema3D 1 ± 0. 04 1. 14 ± 0. 18 2. 49 ± 1. 19 Sema3A 1 ± 0. 004 1. 00 ± 0. 01 0. 91 ± 0. 03 GSE1297 Alzheimer's Disease Normal (Mean Incipient (Mean Moderate (Mean Severe (Mean (AD) age = 85. 33 yr; n = 9) age = 91. 86 yr; n = 6) age = 83. 38 yr; n = 8) age = 84. 00 yr; n = 7).
Sema3D 1 ± 0. 18 0. 86 ± 0. 11 1. 47 ± 0. 31 2. 16 ± 0. 60 * Sema3A 1 ± 0. 07 1. 10 ± 0. 25 1. 14 ± 0. 09 1. 26 ± 0. 22 GSE48350 Alzheimer's Disease (AD) Normal (Mean age = 62. 49 yr, n = 43) AD (Mean = 83. 05 yr, n = 19).
Sema3D 1 ± 0. 06 1. 57 ± 0. 27** Sema3A 1 ± 0. 02 1. 09 ± 0. 02 * GSE36980 Alzheimer's Disease Normal (Mean AD (Mean (AD) age = 77. 00 yr, n = 10) age = 92. 86 yr, n = 7).
Sema3D 1 ± 0. 10 0. 85 ± 0. 12 Sema3A 1 ± 0. 06 0. 99 ± 0. 10 GSE84422 Clinical Dementia Rating (CDR) CDR = 0, normal (Mean age = 85. 56 yr, n = 9) CDR = 0. 5 ~ 1 (Mean age = 84. 58 yr, n = 19) CDR = 2 ~ 3 (Mean age = 89. 41 yr, n = 17) CDR = 4 ~ 5 (Mean age = 85. 20 yr, n = 10).
Sema3D 1 ± 0. 01 0. 97 ± 0. 01 1. 07 ± 0. 03 1. 14 ± 0. 03** Sema3A 1 ± 0. 04 0. 99 ± 0. 03 0. 97 ± 0. 03 1. 00 ± 0. 03 GSE13162 FTLD-U Normal (Median Moderate (Median Severe (Median age = 67; n = 2) age = 71; n = 8) age = 64; n = 5).
test Datasets number Phenotype Tissue Fold change of Sema3D (Severity: Low → High) GSE33000 Alzheimer's Disease (AD) Prefrontal cortex 1. 00 ± 0. 04 (normal, mean age = 68. 6; n = 104) 1. 81 ± 0. 06*** (Young AD, mean age = 68. 6; n = 70) 2. 07 ± 0. 07*** (Old AD, mean age = 86. 7; n = 206) GSE44770 Alzheimer's Disease (AD) Prefrontal cortex 1. 00 ± 0. 03 (normal, mean age = 62. 6; n = 62) 2. 01 ± 0. 08*** (Young AD, mean age = 62. 6; n = 14) 2. 28 ± 0. 08*** (Old AD, mean age = 82. 99; n = 94) GSE44768 Alzheimer's Disease (AD) Cerebellum 1 ± 0. 04 (normal; n = 81) 1. 35 ± 0. 06*** (AD; n = 108) GSE44771 Alzheimer's Disease (AD) Visual cortex 1 ± 0. 05 (normal; n = 76) 2. 05 ± 0. 08*** (AD; n = 119) GSE185909 Alzheimer's Disease (AD) Prefrontal cortex 1 ± 0. 97 (normal; n = 7) 1. 13 ± 1. 17 (MCI; n = 10) 1.
41 ± 0. 85 (AD; n = 17) GSE131617 Alzheimer's Disease (AD) frontal cortex 1. 00 ± 0. 39 (NFT 0, normal; n = 13) 1. 04 ± 0. 56 (NFT I-II; n = 20) 1. 03 ± 0. 48 (NFT III-VI; n = 19) 1. 03 ± 0. 47 (NFT V-VI; n = 19) GSE84422 Alzheimer's Disease (AD) frontal cortex 1. 00 ± 0. 15 (CDR = 0, normal; n = 11) 0. 99 ± 0. 13 (CDR = 0. 5 ~ 1; n = 17) 1. 03 ± 0. 22 (CDR = 2 ~ 3; n = 23) 1. 03 ± 0. 47 (NFT V-VI; n = 19).
A. Experiments with simulated data The simulated data are generated similarly to  [20]  and  [24]. Specifically, 3D points are determined by randomly sampling a sphere of radius 10m. Unit directions for lines and normal for planes are generated randomly. For all experiments the average rotation error µ(δr) and the average translation error µ(δt) are computed for 1000 trials. 1) Fixed number of correspondences, increasing noise: In the first set of simulations the number of correspondences is fixed while the noise standard deviation on the generated 3D data increases from 0 to 0. 2 m. Figure  (1)  shows the results of a simulation where the rotation angle is fixed to θ = 180 • and the rotation axis and the translation are random. The number of correspondences is fixed to the minimum N = 6. For more clarity, we separate the comparison of the proposed method with closed-form algorithm from the comparison with iterative algorithms that have much more computational complexity.
3) Special case with points-to-points correspondences: Simulations with points-to-points correspondences only confirmed that Wientapper's algorithm is not able to handle this special case. In Figure  (7)  the results corresponding to this method are not displayed since the Matlab function provided by the authors does not give any results. Indeed, the polynomials equations goes from degree 4 to degree 2 (see section III-B). Note that Zhou's algorithm does not suffer from this problem and provide good results. On the contrary, Olsson's algorithm provided poor results. The proposed approach applied to this case provided the best results similarly to Briales's algorithm.
4) Comparison of computation time: In this experiment, we compare the computational time of closed-form algorithms. The number of correspondences vary from 100 to 3000. Figure  8  illustrates the results averaging the running time over 100 trials. In the current Matlab implementation the proposed approach is slower than the others closed-form methods below 1000 correspondences and faster above (e. g. around 12 milliseconds for 1000 correspondences).
B. Experiments with real data Similarly to previous work  [24] , the KITTI dataset  [33]  is used for experimental results. The current 3D points set (lidar scan k+1) is segmented to extract lines and planes and matched with the closest 3D points of the reference set (lidar scan k). There are more than 50,000 correspondences in each frame, with the majority being the point-to-plane correspondences. For simplicity, only points-to-planes correspondences are considered since they represent more than 99% of the found correspondences (i. e. points-to-points and points-to-lines correspondences are only 1% of the found correspondences). The planes are extracted from the point cloud by performing least squares fitting on k-neighbors (k = 8) around each point. If the least square error is below a threshold the points are considered to be on the same plane.
V. CONCLUSIONS This paper presents a complete and accurate solution for pose estimation from points-to-points, points-to-line and points-to plane correspondences. The proposed approach is also able to provide an accurate and robust solution to the weighted least squares problem even when considering a minimum number of correspondences and for any rotation. Simulated and experimental results show that the proposed algorithm outperforms state-of-the-art algorithms at the price of a higher computational complexity in the current Matlab implementation. Future research directions would be (i) to study if the superior accuracy comes from the choice of solver (Macaulay u-resultant instead of Gröebner basis) or from the correct handling of the quaternion constraint with the Lagrange multiplier, and (ii) to study how to reduce the computational complexity of the solver.
X-ray free electron lasers deliver photon pulses that are bright enough to observe diffraction from extremely small crystals at a time scale that outruns their destruction. As crystals are continuously replaced, this technique is termed serial femtosecond crystallography (SFX). Due to its high pulse repetition rate, the European XFEL enables the collection of rich and extensive data sets, which are suited to study various scientific problems, including ultra-fast processes. The enormous data rate, data complexity, and the nature of the pixelized multimodular area detectors at the European XFEL pose severe challenges to users. To streamline the analysis of the SFX data, we developed the semiautomated pipeline EXtra-Xwiz around the established CrystFEL program suite, thereby processing diffraction patterns on detector frames into structure factors.
Introduction The structural arrangement of atoms in matter and the nature of their chemical bonds can be deciphered by employing X-ray crystallography. This experimental technique has been pivotal in structural biology (see, e. g. ,  [1] [2] [3]  and the references therein) and contributes, to date, to approximately 85% of the structures released in the Protein Data Bank (calculated using data from  [4, 5] ). X-ray crystallography requires samples to be present in the form of crystals: these are periodic repetitions of a unique unit cell, which results in Bragg peaks upon exposure to X-rays. These encode part of the information to reconstruct the electron density of the sample. In a simplified description, assuming that the kinematic approximation holds, the overall intensity of the Bragg peaks increases with (i) the squared number of unit cells in the crystal and its degree of perfection, and (ii) the number of photons interacting with the sample (For an extensive description, the reader is referred to, e.
The processing of X-ray crystallography data commences with the reduction of raw detector frames to a unique set of structure factors  [24]. This includes finding Bragg peaks, indexing them, integrating pixel intensities in three dimensions, and averaging the symmetry-equivalent reflection observations with proper scaling. These steps have been implemented in popular software packages such as XDS  [25] , Mosflm  [26] , and DIALS  [27]. Owing to the nature of data collection, serial crystallography requires different algorithms and approaches, and includes a hit-finding step for identifying detector frames containing the signature of diffraction, which is a prerequisite for indexing the reciprocal lattice  [17]. Additionally in this case, dedicated software suites have been developed in the last decade.
Notably, DIALS has been extended to process serial crystallography data  [28] , and the CrystFEL suite  [29, 30]  has been developed. Subsequent data analysis, from structure factors to the final atomic model, requires software for crystallographic phasing, model building from derived electron densities, and its refinement and validation (For a detailed explanation, the reader is referred to, e. g. ,  [31] ). The entire crystallography data processing pipeline consists of the sequential execution of several tasks, which are often performed by different software programs. Given that the input and output data formats, as well as the user experience, might differ greatly across tools, several software pipelines have been developed with the aim of abstracting the complexity and increasing the analysis throughput and automation level  [31] [32] [33] [34].
This allows for a high degree of automation of data analysis workflows through its integration with other services provided at the EuXFEL. In this paper, we introduce EXtra-Xwiz and discuss its current status and future goals. In Section 2 we describe the EXtra-Xwiz design and architecture, followed by a step-by-step tutorial with an example of processing SFX data in Section 3. Finally, we give an outlook on planned extensions to the pipeline in Section 4.
Afterwards, the output reflections are postprocessed by the partialator program from the CrystFEL suite ("partialator run" block), which produces a unique set of structure factors ("SF" file). Reflections can be split by EXtra-Xwiz into custom subsets ("partialator split" block) using conditions derived from the input data (see text for details). Automatic execution of EXtra-Xwiz and harvesting of metadata can be obtained by exploiting the DAMNIT tool ("DAMNIT" block). Adapted from  [43]. The first step of the EXtra-Xwiz pipeline is to provide the data in a format suitable for processing by CrystFEL. CrystFEL can read diffraction data stored in the Crystallographic Binary Format (CBF)  [48]  or the Hierarchical Data Format v. 5 (HDF5)  [49]. For the latter, various standards are supported, including NeXus  [50, 51]  and the native format of the Coherent X-ray Imaging Data Bank (CXIDB)  [52].
Due to technical requirements, data from each module of the X-ray detectors at the EuXFEL are saved to separate files. As this process is not compatible with CrystFEL, the library EXtra-data  [53]  is used to generate a single HDF5 file with a "virtual" layout (virtual data set file or VDS) containing references to the relevant original EXDF data. This processing step, as illustrated in Figure  2 , is represented as a block "Data preparation" in Figure  1  and corresponds to the section "[data]" in the configuration file. To cope with the variable spatial configurations of detector modules, CrystFEL uses a generalized detector representation -the geometry file  [29]. This describes the position of each detector pixel in the laboratory coordinate system, as well as the pixel size and the X-ray beam energy.
After the indexing step, the cell_explorer graphical user interface (GUI) will be launched to display the histograms of the resulting lattice parameters. This allows users to determine the cell parameters with a fit and to save them into a unit cell file, which will be expected by EXtra-Xwiz to continue processing the data as shown in Figure  1. The main processing and reduction of the SFX data in EXtra-Xwiz is performed with the indexamajig tool from CrystFEL. It is used for finding the location of Bragg peak candidates in each detector image, indexing the patterns, and integrating peak intensities. For each image frame, CrystFEL writes information on the identified Bragg peak candidates, lattice parameters found by the auto-indexing algorithm, and integrates the intensities into a so-called stream file in the form of a plain text.
A session of the CrystFEL GUI can be stored into a project file, and EXtra-Xwiz provides a command-line tool xwiz-import-project for importing parameters from such a file into the EXtra-Xwiz configuration file. This procedure is represented by the "CrystFEL GUI proj. " block in Figure  1. The most time-consuming step of processing SFX data is indexing. Due to the independent nature of the SFX frames, portions of collected data sets can be processed separately, which allows for parallelization. EXtra-Xwiz automatically distributes indexamajig jobs on the local HPC cluster Maxwell that is operated with the Slurm [54] batch-queue system. The results are then concatenated into a single stream file. This is represented by the "HPC management" block in Figure  1. In the configuration file, the name of the Slurm partition, as well as the number and expected duration of the distributed tasks, have to be specified under the "[slurm]" section.
After indexing, reflection intensities from the whole data set are merged for each symmetrically unique reflection. This task is accomplished by the CrystFEL partialator program, which can determine scaling factors for individual measurements, correct for partiality, and postrefine the model parameters for each crystal  [55]. This process is usually repeated in an iterative procedure to bring the individual measurements into agreement  [30, 55]. The partialator program operates on the content of the stream file and therefore does not require the initial SFX data. This data analysis step corresponds to the "partialator run" block in Figure  1  and the "[merging]" section of the EXtra-Xwiz configuration file. Merged reflections are stored in the hkl file format as plain text, which can be imported into most structure-solution packages.
EXtra-Xwiz produces such files either using the frame pattern information specified by a user or by automatically utilizing information from a diode, which records the signal from the pump laser. This procedure is represented by the "partialator split" block in Figure  1. The parameters for the splitting have to be specified in an optional "[partialator_split]" section of the EXtra-Xwiz configuration file. Merged reflection hkl files, as well as FOMs, are produced for each subset, as well as for the overall data set. The execution of EXtra-Xwiz can be automatically triggered as soon as experimental data become available by exploiting its integration with the software DAMNIT  [56]. Furthermore, DAMNIT provides a graphical interface for the EXtra-Xwiz results.
Data Processing with EXtra-Xwiz via Example This section describes an example of processing SFX data from hen egg-white lysozyme (HEWL) microcrystals collected by the SPB/SFX instrument using the AGIPD detector (run 30, proposal 700000). Only basic knowledge of the Unix commands and environment are expected from the reader. Lines starting with a "$" indicate commands which should be executed in a Unix shell. For readers who are not users of the European XFEL, the Virtual Infrastructure for Scientific Analysis (VISA)  [57]  service can be used, and additional instructions are provided in Section 3. 2. Access to the Maxwell cluster is exclusive to EuXFEL users, and detailed instructions on how to connect to it can be found in the EuXFEL Data Analysis user documentation  [58]. In general, a user with an active account can connect to one of the interactive cluster nodes: $ ssh <user name>@max-exfl-display.
For the purpose of reproducing the SFX analysis, EXtra-Xwiz supports a list of different versions of the CrystFEL suite, which can be selected in the "[crystfel]" section: [crystfel] version = '0. 10. 2' Currently, recent major CrystFEL versions are available, as well as a "maxwell_dev" option, which corresponds to the constantly updated installation of the latest CrystFEL version. The geometry and unit cell parameters files should be provided to the pipeline in the "[geom]" and "[unit_cell]" sections, respectively: [geom] file_path = "agipd_p700000_r0030. geom" [unit_cell] file_path = "hewl. cell" A representative detector frame is shown in Figure  2b. The detector modules are positioned in the laboratory frame layout with the use of the EXtra-geom library  [62]. In the event that the unit cell of the sample is not known prior to the analysis, it can be generated by setting the option file_path = "none" as described in Section 2.
During the EXtra-Xwiz session, after the indexing step, an interactive cell_explorer session will start. At this point, the user is expected to determine the cell parameters from the histograms of the indexing results (as explained in  [30] ), and to then save them into a unit-cell file, which will be requested by EXtra-Xwiz. This procedure is illustrated in Figure  3. The parameters for Bragg peak identification and indexing using the indexamajig program have to be specified in the "[indexamajig_run]" configuration block: [indexamajig_run] resolution = 4. 0 peak_method = "peakfinder8" peak_threshold = 800 peak_snr = 5 index_method = "mosflm" integration_radii = "2,3,5". min_peaks = 10 extra_options = "--no-non-hits-in-stream" The documentation regarding all of the indexamajig options can be found in  [46].
In the current state of EXtra-Xwiz, not all of these options are covered by the default configuration file parameters, and if any of such options are required for data processing, they can be specified in the string using the extra_options parameter. Data processing with indexamajig is the most time-consuming step of the whole pipeline, but the computations are usually performed in parallel on multiple nodes of the Maxwell cluster. Cluster partition, the number of nodes to use in parallel and maximum expected duration of the individual jobs, should be specified under the "[slurm]" section of the configuration file: [slurm] partition = "upex" n_nodes_all = 20 duration_all = "10:00:00" For testing the pipeline on a small subset of data (e. g. , a hundred frames) without exploiting the Slurm jobs scheduler, it is advised to select the "local" partition.
As already mentioned in Section 2, for such a separation, the partialator requires an additional input file that accordingly labels each frame of the input data. EXtra-Xwiz can generate such a file according to parameters specified in the "[partialator_split]" block. Let us assume that the machine only delivers at 1/8th of the 4. 5 MHz maximum repetition rate, and the detector is configured to record only these pulses. The sample is illuminated by infrared light at every third delivered pulse (i. e. , the 24th pulse assuming a 4. 5 MHz operation), thereby resulting in the following labels: "pump_on pump_off pump_off pump_on. ". The latter can be set in the configuration as follows: [partialator_split] execute = true mode = "by_pulse_id" [partialator_split. manual_datasets] pump_on = {start=0, end=-1, step=24} pump_off = [{start=8, step=24}, {start=16, step=24}] Any user-defined set of labels can be specified with a corresponding list of inclusive range-like dictionary objects or pulse ID values.
Click "Create a new instance"; 3. Click "Search for experiments", and select the proposal "p700000-SFX on Hen eggwhite lysozyme, AGIPD detector"; 4. Click on the "EXtra-Xwiz_Crystals2023" environment; 5. Choose the virtual hardware; 6. Create the instance. There is no need to load any additional modules; the tutorial described above applies, except for the distribution of computations on the HPC cluster, which is not accessible using VISA. Because of that, the following should be used: [slurm] partition = "local" [indexamajig_run] n_cores = 1. Be careful, as this would result in a very slow processing of the input data. Therefore, instead of running EXtra-Xwiz on the entire run, a preselection of "good frames" can be used by specifying the following in the "[data]" section: [data]. frames_list_file = "indexed_p700000_r0030.