dataesr/scientific-paragraphs-categorization huggingface dataset:
To our knowledge, no study has systematically explored the expression of the main miRNA BM genes in various brain regions or peripheral tissues of SZ individuals. In this study, we used the bioinformatic application shinyGEO to analyze gene expression from eight Gene Expression Omnibus (GEO) datasets, in order to perform differential expression analyses for eight genes encoding the canonical miRNA BM. Firstly, we compared expression of the candidate genes between healthy controls (HC) and SZ individuals in postmortem cerebral samples from seven different brain regions. Secondly, we compared the expression of the candidate genes between HC and SZ individuals in three peripheral tissues. Thirdly, we tried to replicate our findings by analyzing the expression levels of the identified differentially expressed genes in independent validation datasets.
Search and inclusion criteria of primary datasets The GEO database is a public repository, which archives and freely distributes microarray functional genomic data from control subjects and patients suffering from various disorders, along with demographic, clinical and quality data  33. GEO datasets thus constitute a valuable resource for identifying biomarkers of diseases. With the aim to identify altered expression of the miRNA BM genes both at the central and peripheral level in SZ individuals, we explored GEO database for datasets providing microarray expression results from SZ individuals and HC. Microarray datasets related to SZ, were searched in NCBI GEO database using the following search terms: "schizophrenia"[All Fields] AND "Homo sapiens"[porgn] AND "gse"[Filter] AND "Expression profiling by array" [Filter].
We only included datasets from original studies (i) involving brain or peripheral tissues samples from SZ individuals and HC and (ii) using the Affymetrix Human Genome U133 plus 2. 0 (HG-U133_Plus_2) or Affymetrix Human Gene 1. 0 ST (Human Gene 1. 0 ST) or Affymetrix Human Gene 1. 1 ST (Human Gene 1. 1 ST) arrays. Each of these three chips can technically interrogate the expression of all the candidate genes included in the present study. Moreover, Human Gene 1. 0 ST and Human Gene 1. 1 ST arrays exhibit comparable detection thresholds and are highly concordant with HG-U133_Plus_2 array  34, 35.
Included primary datasets In this study, eight independent GEO datasets were included  [36] [37] [38] [39] [40] [41] [42] [43]  , providing data from seven different brain regions relevant to SZ  [44] [45] [46] [47]  (dorsolateral prefrontal cortex (DLPFC) (BA46), anterior prefrontal cortex (BA10), parietal cortex, superior temporal cortex (BA22), hippocampus, associative striatum, and cerebellum) and three peripheral tissues (peripheral blood mononuclear cells (PBMCs), olfactory epithelium, and skin fibroblasts). The original studies from which the primary datasets were obtained are presented in Supplementary Table  1. It should be noticed that postmortem brain and peripheral tissues samples were collected from different subjects.
Search and inclusion criteria of validation datasets With the aim to replicate our main findings, validation datasets were searched in GEO database with identical search terms as those used in the first step of the study. For validation datasets, we selected GEO datasets from original studies (i) involving SZ individuals and HC, (ii) with samples from a brain region or a peripheral tissue in which altered transcription was observed in the primary datasets (i. e. , DLPFC (BA46), associative striatum, hippocampus, cerebellum, blood compartment, and olfactory epithelium), (iii) using chips able to technically interrogate the expression of the differentially expressed genes identified in the primary datasets. Additionally, included validation datasets had to meet the following criteria: (i) a primary dataset and its validation counterpart must be derived from independent samples and (ii) the sample size of the validation dataset must be equal to or larger than that of its primary counterpart.
Demographic characteristics and quality data For each primary and validation GEO dataset, the quality data of the samples and the demographic characteristics of the subjects, who provided brain or peripheral tissue are summarized in Tables  1  and 2 , respectively. For each GEO dataset providing data from postmortem brain samples, there was no significant difference in age, sex distribution, postmortem interval (PMI), and RNA integrity number (RIN) between the SZ individual and HC groups. For the brain pH, a significant difference was only observed in the anterior prefrontal cortex BA10 (p = 0. 019) and in the superior temporal cortex (p = 0. 002). For each GEO dataset providing data from peripheral tissues, there was no significant difference in age distribution between the SZ patient and HC groups.
Statistical analysis XLSTAT software (XLSTAT 2017: Data Analysis and Statistical Solution for Microsoft Excel. Addinsoft, Paris, France, 2017) was used to perform statistical analyses. For each included GEO dataset, demographic characteristics of subjects and quality data of samples in HC and SZ patient groups were compared using unpaired t-test (for age, postmortem interval, brain pH, and RNA integrity number), and Fisher exact test (for gender). For each tissue, we used shinyGEO application to realize differential expression analysis between SZ individuals and HC  52. shinyGEO is implemented using R [https:// www. r-project. org/] and shiny [http://shiny. rstudio. com/]. shinyGEO downloads processed gene expression datasets from GEO using GEOquery package  53  , gene expression values are log2-transformed to stabilize the variance.
Differential expression analysis in the brain tissues Compared to HC, we observed distinct altered expression patterns of the miRNA BM coding genes in the brain tissues of SZ individuals with the exception of the anterior prefrontal (BA10), parietal and superior temporal (BA22) cortices in which no alterations were found. SZ individuals exhibited a set of genes significantly upregulated in the DLPFC (BA46) (XPO1 and DICER1), the associative striatum (XPO1) and in the cerebellum (DROSHA, NUP153, DICER1, and AGO2). Moreover, altered transcription of several of the candidate genes was identified in the hippocampus (DROSHA, DGCR8, RAN, XPO5, XPO1, DICER1, and AGO2). In the validation dataset derived from DLPFC (BA46) samples, we replicated DICER1 overexpression in SZ individuals vs. HC. The detailed results are provided in Fig.
Differential expression analysis in the peripheral tissues Compared to HC, distinct altered transcription patterns of the miRNA BM coding genes were identified in the peripheral tissues of SZ individuals. SZ individuals exhibited a set of differentially expressed genes in the PBMCs (DROSHA, DGCR8, XPO5, NUP153, DICER1, and AGO2) and in the olfactory epithelium (RAN, XPO1, and AGO2). In contrast, no changes were found in the skin fibroblasts. In the validation dataset derived from whole blood samples, we replicated DICER1 overexpression and DGCR8, XPO5, AGO2 decreased expression in SZ individuals vs. HC. The detailed results are provided in Fig. 3  and Supplementary Table  5. PBMCs peripheral blood mononuclear cells, HC healthy controls, SZ individuals with schizophrenia a Unpaired t-tests and Fisher exact tests were conducted to assess group differences for continuous and discrete variables, respectively.
Discussion Compared to HC, we observed altered patterns of the miRNA BM coding genes both in brain and peripheral tissues of SZ individuals. These patterns were distinct between the different tissues. All the original studies from which are derived the included datasets used a genome-wide approach. As a principle, the expression of all genes was analyzed, including those involved in the miRNA BM. However, while some original studies have reported differential expression of these genes in their supplementary data  36, 42  , those results were not discussed in the original experiments. The present study re-analyzed the original data using a candidate-gene approach, allowing us to extend previous knowledge on schizophrenia-associated microRNA BM dysregulation to other brain regions and peripheral tissues.
More  tudies in peripheral tissues are needed to identify effective sources of biomarkers for SZ. Among individual genes whose expression was altered in the present study, a significant finding was the increased expression of DICER1 in the DLPFC (BA46), hippocampus, cerebellum and PBMCs of SZ individuals compared to HC. Moreover, in the blood compartment, we replicated DICER1 overexpression in the whole blood of SZ individuals vs. HC. It is noteworthy that DICER1 expression is age-dependent with a significant increase from young adulthood onward  65. However, since there was no significant difference between the mean age of SZ individuals and HC in the different datasets, it is unlikely that age explains our results. In the DLPFC (BA46), our results are consistent with two previous studies reporting a significant upregulation of DICER1 in the DLPFC (BA46 and BA9) of SZ individuals from an independent collection of postmortem brain samples  30, 31.
However, since we did not find any significant difference in age between control subjects and SZ individuals in the different datasets used in the present study (Tables  1  and 2 ), it is unlikely that age explain our results. Second, factors inherent in postmortem brain studies, and beyond the investigator's control, might have influenced our results. (i) Treatment data were not provided for the GEO datasets. Thus, a potential impact of antipsychotic treatment on candidate gene expression could not be ruled out. In individuals with FEP, baseline circulating IL-6 levels may modulated the gene expression response of DICER1 and DROSHA to treatment with risperidone  88. Further studies are needed to replicate these results. (ii) The other concern is the effect of brain pH on our results since we observed a significant difference in brain pH between control and SZ patient groups in the anterior PFC (BA10) and superior temporal cortex.
728 1 0. 561 0. 283 0. 828 1 1 Gender (M/F) Control Schizophrenia 10/9 7/8 9/9 9/6 10/8 10/8 13/10 19/9 31/19 32/12 35/15 37/14 10/9 13/10 24/5 24/6 p-value a 0. 549 0. 493 0. 301 0. 411 0. 182 0. 132 0. 462 0. 759 Brain region Subjects (n) Age (mean ± SD) Control Schizophrenia Primary datasets DLPFC (BA46) 19 HC/15 SZ 48. 1 ± 10. 6 46 ± 8. 6 Hippocampus 18 HC/15 SZ 48. 2 ± 10. 9 45. 7 ± 8. 8 Associative 18 HC/18 SZ 48. 4 ± 10. 8 45 ± 8. 8 striatum Anterior PFC 23 HC/28 SZ 69 ± 21. 6 73. 3 ± 15. 2 (BA10) Cerebellum 50 HC/44 SZ 45. 8 ± 9. 3 43. 2 ± 9. 5 Parietal cortex 50 HC/51 SZ 45. 5 ± 9. 0 42. 6 ± 9. 9 Superior 19 HC/23 SZ 67. 7 ± 22. 2 72. 2 ± 16. 9 temporal cortex (BA22) Validation dataset DLPFC (BA46) 29 HC/30 SZ 44. 7 ± 16. 1 43. 4 ± 16. 9 DLPFC dorsolateral prefrontal cortex, PFC prefrontal cortex, BA Brodmann area, RIN RNA integrity number, HC healthy controls, SZ individuals with schizophrenia.
solution  8  , self assembly  9, 10  , interfacial properties of small particles  [11] [12] [13] [14]  , droplets in liquids  15, 16  , biological membranes  [17] [18] [19] [20] [21]  or structuration of materials  [22] [23] [24]. Numerous models  [25] [26] [27] [28] [29] [30]  have been developed to interpret experimental data and in particular angular distribution and polarization dependence of the second harmonic scattered light. The understanding of such variation allows the recovery of important properties about the symmetry  31  , the organization  9  and/or the correlation  [5] [6] [7]  of the nano-objects probed. Some of these models have also been implemented in software to compute the SHS intensity, in particular in the NLS_simulate software  32, 33  or HRS_Computing software  34.
The NLS_simulate software computes the SHS angular dependence using the Nonlinear Rayleigh Gans Debye approximation  33, 35. Those simulations are limited to spherical objects. In the HRS_computing software, the program computes the polarization resolved SHS intensity for any supramolecular structure build as a sum of correlated individual molecules. This second software is restricted to the assumption that the correlation length is small in regards to the incident wavelength. In this article, a new free python based program named PySHS 36 is presented. First, it integrates in the same package the two kind of calculation discussed above: both angular and polarization dependance SHS. Moreover, it generalizes the computational possibility to any system dimensions as no assumption about the size of the nano-object is required thank to the calculation strategy.
Indeed, PySHS has been developed in order to compute SHS of different class of nano-objects: i) uncorrelated molecules in solution in the HRS module, ii) supramolecular assemblies in the SHS module, iii) Second Harmonic Light scattered by the surface of colloidal spheres in the SPHERE module. All the calculations can be done in different experimental configurations: polarization resolved at a fixed scattered angle or angular distribution for different polarization configurations. This article presents the equation implemented in the PySHS software, and gives some computational examples useful to interpret experimental data concerning the assembly of molecular dye intercalated inside liposomes. 2) The PySHS program.
( ) 5 Here is the second order hyperpolarisability of the molecule j located at the position expressed in the mesoscopic frame. The hyperpolarisability tensor components in the different frames are linked according: The transformation matrix is: Finally, the bracket in formula (3) means the average over all the orientation of the scattered molecule and is implemented by a numerical integration in the PySHS program. ( ) 9 To perform the I SHS calculation, the user needs to specify the position and orientation ( of each molecule in the mesoscopic frame. Two ways are implemented to perform the I SHS calculation: the first way implemented in the SHS program of PySHS package explicitly computes I SHS according to equations (3-9). The second way implemented in the SHSlinear program, uses the approach detailed in these references  6, 23, 31  and in the HRS_Computing software  34  , approximates the exponential term in equation (  5 ) by: (10)   This last calculation is only valid in the domain where is small compared to 1, ie if the characteristic size of the molecular assembly is small compared to the incident wavelength.
Taking the molecular hyperpolarizability, the position and orientation of each molecule in the supramolecular assembly as input parameters, the SHS and SHSlinear programs can compute the polarization resolved plot or the scattered angle plot. For the polarization plot, the outputs of the program are the coefficients , , and , defined in this reference  31. Indeed, the SHS intensity exhibits the equivalent following dependencies with the input polarization angle : (11) (12) The link between these two descriptions are given by: (13) (14) (15) The full expression of the , , coefficients are given in SI. For an angular dependency study, the output of the program is the dependency for different combination of polarization state ( ): ( °); (9 °); ( °); (9 °).
2. 3) Calculation for the surface of colloidal sphere in solution: SPHERE module PySHS package offers the possibility to compute the SHS intensity of homogeneous spheres in solution. To perform this calculation, an effective nonlinear susceptibility  25  is introduced, which represents the nonlinear response of the entire sphere surface: (16)   where is the surface second order nonlinear susceptibility of the sphere. Using the same formalism as above, The SHS intensity is expressed with the same equation (3) where is now expressed as: The SPHERE program in the PySHS package takes the surface second order nonlinear susceptibility and the radius of the sphere as input parameters and computes the same quantities as define above in equations (11-14)   3) Results and discussion section Some results illustrating the possibilities given by the PySHS package are presented and compared with experimental data.
This part focuses on the SHS polarization plot in the right angle direction for spherical supramolecular assemblies in order to interpret experimental results concerning the organization of molecular dyes inclusion into the membrane of liposomes. The Figure  3  shows the polarization plot computed with the programs implemented in PySHS package for a 50 nm radius sphere. An input file containing the position and orientation of 100 molecules equally spaced onto the sphere surface and radially oriented was generated by the script provided in the package. The other input parameters of the calculations are the refractive index set to 1. 33, the incident wavelength set to 800 nm and the hyperpolarizability of the molecule which is assumed to have only the component to be no zero. The simulations b) and c) give exactly the same results at the 3rd digit after the decimal point although the two approaches are not the same.
It just shows that 100 dipoles equally spaced onto a sphere is equivalent to an homogenous sphere. We have also tested the case of few dipoles spaced onto a sphere, and we found the same results as given in this previous work  31  concerning the evolution of I 2 and I 4 parameters with the number of dipoles onto the sphere. When this number N is superior to 10, these quantities reach the asymptote of the homogeneous sphere given by the sphere program. To reproduce the experimental data presented in Figure  4 , a microscopic model taking into account both the effect of the order/disorder of the assembly onto the colloidal object and the shape of the colloidal object is implemented. Some scripts provided in the PySHS package generates randomly the position and orientation of the molecules with a normal distribution characterized by its standard deviation  around the radial direction, for spheroid objects characterized by radius a and c.
Notes. The authors declare no competing financial interest. The PySHS package is available here: https://www. researchgate. net/profile/Pierre-Marie_Gassin/publication/342742567_PySHS_V11_A_Python_Open_Source_Software_For_Se cond_Harmonic_Scattering/data/5f72fe6d458515b7cf565a6d/PySHS-V1-1. zip Funding Sources.
support for this work by the ANR project CAMOMILS (ANR-15-CE21-0002) is greatly acknowledged. ACKNOWLEDGMENT We warmly thank Pierre-Francois Brevet for fruitful regular discussions about simulation and 37. Gassin, P. -M. ; Bellini, S. ; Zajac, J. ; Martin-Gassin, G. , Adsorbed Dyes onto Nanoparticles: Large Wavelength Dependence in Second Harmonic Scattering. J. Phys. Chem. C 2017, 121 (27), 14566-14571.
Financial support for this work by the ANR project CAMOMILS (ANR-15-CE21-0002) is greatly acknowledged. ACKNOWLEDGMENT We warmly thank Pierre-Francois Brevet for fruitful regular discussions about simulation and 37. Gassin, P. -M. ; Bellini, S. ; Zajac, J. ; Martin-Gassin, G. , Adsorbed Dyes onto Nanoparticles: Large Wavelength Dependence in Second Harmonic Scattering. J. Phys. Chem. C 2017, 121 (27), 14566-14571.
Modern life science relies heavily on fluorescent microscopy and subsequent quantitative bio-image analysis. The current rise of graphics processing units (GPUs) in the context of image processing enables batch processing large amounts of image data at unprecedented speed. In order to facilitate adoption of this technology in daily practice, we present an expert system based on the GPU-accelerated image processing library CLIJ: The CLIJ-assistant keeps track of which operations formed an image and suggests subsequent operations. It enables new ways of interaction with image data and image processing operations because its underlying GPU-accelerated image data flow graphs (IDFGs) allow changes to parameters of early processing steps and instantaneous visualization of their final results. Operations, their parameters and connections in the IDFG are stored at any point in time enabling the CLIJ-assistant to offer an undo-function for virtually unlimited rewinding parameter changes.
Furthermore, to improve reproducibility of image data analysis workflows and interoperability with established image analysis platforms, the CLIJ-assistant can generate code from IDFGs in programming languages such as ImageJ Macro, Java, Jython, JavaScipt, Groovy, Python and C++ for later use in ImageJ, Fiji, Icy, Matlab, QuPath, Jupyter Notebooks and Napari. We demonstrate the CLIJ-assistant for processing image data in multiple scenarios to highlight its general applicability.
Introduction The availability of image analysis algorithms exploiting the computational power of graphics processing units (GPUs) to a broader audience boosts the need for accessible tools for building GPU-accelerated image analysis workflows; in the life sciences and in adjacent imaging-dependent research fields. Typically, designing data analysis procedures utilizing GPUs involves expertise in programming and knowledge of GPUspecific programming languages such as the Open Computing Language (OpenCL)  [1]. We demonstrate how one can construct complete image analysis workflows without writing a single line of OpenCL by assembling workflows from operations provided by the CLIJ framework  [2]. We called the user interface CLIJ-asssistant because it allows interactive design of image data flow graphs (IDFGs) in ImageJ  [3]  or Fiji  [4] , while guiding the user with automatic suggestions.
Methods Image data flow graph We implemented the CLIJ-assistant under the hood of the established ImageJ user-interface so that ImageJ users can adopt it without changing their habits to much. In the background, CLIJ-assistant manages an image data flow graph (IDFG): Data flow graphs are a technical ab-Figure  1 : An IDFG allows assembly of image processing workflows and display of intermediate results in real-time. The presented graph takes a 4D image stack (a), pushes the current frame to the GPU memory (b), applies a top-hat filter for background subtraction (c), spot detection (d), thresholding (e), binary AND (f) spot labeling (g) and labeled spot extension (h). To facilitate examination of intermediate results, maximum intensity projections for intermediate results of pushing (i), background subtraction (j), thresholding (k) and label extension (l) are shown.
Our approach is similar to the user-interfaces of Icy  [5]  and Knime  [6]. An advantage is that intermediate results of the whole graph are always images which can be updated instantly in ImageJ's user-interface while the user changes parameters. On the other hand, this limits available functionality compared to Knime and Icy data flow graphs: CLIJ-assistant focuses on image processing and analysis. In order to further optimize performance, the image stacks are kept in the GPU's memory while minimizing pushing and pulling image data to/from GPU memory. Configuration dialogs of multiple steps can be open on screen. If the user changes a parameter, the affected operation and all subsequent operations are computed. In order to visualize the state of the graph, IDFG windows appear differently than standard ImageJ windows by their frame color, visualizing the graph's execution state: A window with a red frame indicates that the shown image is invalid and will be computed as soon as intermediate results higher in the graph hierarchy are available.