dataesr/scientific-paragraphs-categorization huggingface dataset:
We carried out a statistical modelling for brightfield light adjustment to asses and correct for the possible influence of neuromelanin (NM) in human-derived RNAscope data applying a linear mixed effect model, similar as we had previously described  59, 60. Briefly, we applied a bayesian probabilistic model approach conducted in RStan 172 with the R package version 2. 21. 7, https://mc-stan. org/) using R (version 4. 2) under RStudio (version 2022. 07). We applied normal distributions for priors of all parameters. To achieve a stable non-degenerate solution priors needed to be properly informed by restricting the effective parameter space. The chosen model, detailed below, is wellinformed by the data on all its parameters and shows an acceptable fit on the age level (see posterior-predictive plots in Fig.
Stereological estimates of TH-positive neuron numbers were determined using a Leica CTR5500 microscope and the unbiased optical fractionator method (StereoInvestigator software; MBF Bioscience), similar as previously described  54, 73. The SN region throughout the whole caudo-rostral SN axis (Bregma -3. 8 to -2. 7, according to  50  was identified, using well established landmarks  78  ), and was marked as ROI for TH-positive neuron count. The ROI was marked and analysed unilaterally on each of the serial DAB-stained sections (37 for juvenile, 40 for adult mice). Sampling grid dimensions were 75 × 75 μm (x,y-axes), counting frame size was 50 × 50 μm (x,y-axes), and counting frame height was 9 or 11 μm for juvenile and adult mice, respectively. Estimated total number of TH-positive neurons (N) was calculated for each animal according to Eq.
net/Fiji]. The digital slides were processed using QuPath [https://qupath. github. io/] to label and cut out the sections of interest (37/40 consecutive SN sections covering the entire caudo-rostral axis). As for stereology, the SN ROI was identified according to the typical landmarks  50, 78  and analysed unilaterally. Processed sections (showing the ROIs) were afterwards uploaded on the Aiforia® Cloud platform [https://www. aiforia. com/] (Aiforia Technologies Oy, Helsinki, Finland) or the Wolution platform [https://console. wolution. ai/] (Wolution GmbH & Co. KG, Planegg, Germany). For both automated platforms, in case of flawed sections that could not be automatically analysed, the counts from the section before and after were averaged to prevent methodological bias (for stereology, these sections were omitted, and this information was taken account for the calculation of the stereological estimates).
After washing (three times in 1x PBS for 10 min at RT), the sections were mounted on SuperFrost® Plus glass slides (VWR) with VectaMount Permanent Mounting Medium with DAPI (Vector Laboratories). For relative quantification of fluorescence-signal intensities in cellular compartments (plasma-membrane, cytoplasm, nucleus) with DLAP-6, two similar Kv4. 3 staining protocols were utilised, adapted from ref. 97  , leading to very similar results. More precisely, one WT and one Kv4. 3 KO brain (dataset 1) was processed, and images were acquired in the Goillard lab essentially as described  97. The other brains (dataset 2) were perfused in the Goaillard lab, and further processed in the Liss lab, according to the following protocol. 50 μm free-floating coronal midbrain sections were blocked for 1 h 30 min with 5% NGS and 0.
Fluorescent images for automated TH-IF positive neuron counts and relative signal intensity quantification (DAT-IF) were acquired with a Leica DM6 B epifluorescence microscope (Leica Microsystems) using a 63X oil objective. All image parameters were set using the LAS X software (Leica microsystems) to avoid saturated pixels, and identical acquisition settings were maintained (a prerequisite for relative signal intensity quantification). TH-IF was visualized at 546 nm (exposure = 200-500 ms, gain = 1-2), DAT-IF at 488 nm (exposure = 400-600 ms, gain = 1-2), CB-IF, D2-IF and Aldh1A1-IF at 660 nm (exposure = 300-500 ms, gain = 2) and DAPI at 405 nm (exposure = 60-300 ms, gain = 1). Fluorescence lamp illumination was kept at 30%. Images were acquired as tile scans from a single plane of focus.
Merged images were exported as Leica Image File (LIF). For relative quantification of fluorescence-signal intensities in cellular compartments, 63X confocal quality images were acquired using a Zeiss LSM780 with the settings defined in ref. 97  for dataset 1. In dataset 2, confocal images using 100X/1. 4 NA oil objective were acquired with STEDYCON (Abberior Instruments) mounted on an Olympus BX53 upright microscope (Olympus LS), and image parameters were adjusted using the STEDYCON web-based user interface (Abberior Instruments). To avoid saturated pixels (a prerequisite for relative signal intensity quantification), photon counts from all channels were monitored in the photon count range indicator using the "fire blue" colour-map. This map indicates saturated image parts in blue over a yellow-red gradient for each individual channel.
Fluorochromes were excited using an Argon laser, and the following wavelengths were used for excitation: TH-IF at 546 nm, Kv4. 3-IF at 488 nm, and DAPI at 405 nm. The following acquisition parameters were adjusted at the STEDYCON user interface: Pixel size 78 nm; pinhole diameter 0. 71 AU; onedirectional line accumulation acquisition; laser power 10% for TH and Kv4. 3, 12% for DAPI. Images were acquired from a single plane of focus and were exported as OsmAnd Binary Maps (OBF) files. For automated counting of TH-IF positive neurons und and relative signal intensity quantification, images were converted to 8 bit RGB colour TIFF images. The Fiji software was used to label and cut out the sections of interest (~28 consecutive SN sections within the caudo-rostral axis, bilaterally), and the ROI was identified and strictly marked according to typical anatomical landmarks and the cartesian coordinates according to the mouse brain atlas for each section, similar as described for IHC/DABstained neuron counting  50, 78.
5 to 2. 5 times SD of the DAT relative fluorescence (RF) signal (depending on the different signal intensities of the different mouse cohorts; Fig. S5c ). Similarly, a threshold of mean normalised BG value for each mouse plus 1 time SD for D2, plus 4 times SD for CB, and plus 5 times SD for Aldh1A1 was used (Fig. S8 , Table  S15 ). For the generation of anatomical 2D/3D maps, the R-computational environment 174 version 4. 2. 1 with the Tidyverse package  175  , Plotly [https://plot. ly/] and RMarkdown 176 was used, and coordinates as well as DAT and TH signal intensities were systematically adjusted in order to plot the different cohorts and animals into one graph. 2D-Maps were generated by using and adjusting the x and y coordinates from each analysed TH-positive neuron, automatically provided by the DLAP-5 algorithm.
We also utilised these adjusted x and y coordinates for defining the lateral SN region as starting at >1. 5 scaled x-axis units lateral (i. e. , >377. 8 μm) from each SN hemisphere-centre (0,0; Fig. S6 , Table  S17 ). For generation of 2D and 3D gradient plots, we used the "scale ()" function to generate scaled DAT and TH RF values: similar as for the anatomical coordinates, the mean of the DAT and the TH RF intensity of all anaysed TH-positive neurons was set as 0-value and the deviation from the mean was plotted for each individual neuron, using a colour gradient. For better gradient-visualization, we excluded the highest values from the gradient-plots of (0. 3% of all neurons for DAT, 0. 005% for TH). For relative quantification of fluorescence-signal intensities in cellular compartments with DLAP-6 algorithm, OBF images were converted to 8 bit RGB colour TIFF images using the Fiji software.
com/), and Fiji (https://imagej. net/Fiji) software. Statistical tests were performed with GraphPad Prism 9. Bayesian probabilistic model was conducted in RStan with the R package version 2. 21. 7 172. Anatomical maps were plotted using R computational environment 174 version 4. 2. 1 with Tidyverse package  175  , Plotly [https://plot. ly/], and RMarkdown 176. Normal distribution was tested with D'Agostino-Pearson omnibus normality test. Correlations were performed using Pearson correlation tests, proportionality constants was calculated from the ratio between two directly corresponding values. In graphs, data are given as boxplots, showing median and whiskers representing 10th-90th percentile. For comparing one parameter only, non-parametric Mann-Whitney U tests and Kruskal-Wallis tests, with post hoc Dunn's multiple comparison was used.
Acknowledgements We are particularly grateful to the brain donors and the  German Brain Bank. We thank  Matthias Bayerle  and  Dennis Kätzel  for help with initial DAT-immunohistochemistry. This study was supported by the  German DFG  ( LI-1745/1-3 ,  GRK1789 ,  SFB1506 ), the  Austrian FWF  ( F44-12 ), the  Alfried Krupp Foundation , the  Boehringer Ingelheim Ulm University BioCenter (BIU) , a  Wellcome Trust   Collaborative Award , and a  Research Fellowship  by the  Hamburg Institute for Advanced Study (HIAS) , all to B. L. NB and JB were supported by the  International Graduate Schools of Molecular Medicine and of Aging (CEMMA) at Ulm University. MH was supported by an " Experimental Medicine" scholarship   of Ulm University.
Overview of CNNs A CNN specializes in processing data with a grid-like topology, such as an image. CNN architecture is essentially composed of two parts: feature extractor and classifier. Feature extractor layers process the original input, and classifier layers then classify the resultant features. A CNN model mainly includes Convolution layers (Conv), Pooling layers (Pool), Batch Normalization layers (BN), and Fully Connected layers (FC). A CNN can be deployed monolithically on a singleedge device or partitioned into multiple partitions. CNNs have a chain topology as each layer only depends on the previous layer's output and not on the other layers. This characteristic will help us to distribute partitions on one or multiple devices and sequentially perform inference, which will be detailed later.
A well-known CNN example is VGG16  [10] , used as an illustrative example in the rest of this article. Visual Geometry Group (VGG) is a popular and clearin-structure CNN model that includes all mainstream layer types. VGG16 (cf. Figure  1 ) is trained with 16 layers consisting of 13 convolution layers and three fully connected layers. VGG architecture has been retained in this article because it has proven to be a reference in many complex tasks such as object detection, image recognition and image classification. Noticeably, VGG16 achieves 92. 7% top-5 test accuracy in ImageNet  [11] , a dataset of over 14 million images belonging to 1000 classes. We chose VGG16 as a representative architecture of CNN models. VGG16 represents a great potential for applications in several real-world use cases such as smart factory  [12]  and autonomous vehicles  [13].
Illustrative Real Life Use Cases The hybrid partitioning strategy proposed in this article is helpful in several real-life use cases. For example, consider an AI service company sells an image recognition system linked to surveillance cameras. This system can be integrated into any embedded device that composes a typical Telecommunication company. For instance, an image recognition task can be performed on an internet gateway. Such a device can not run many CNNs by itself without optimizations. Our proposed strategy addresses resource constraints by distributing inference in time on a single device or multiple devices. Model partitioning reduces the size of the tasks assigned at a time to each device, reducing memory consumption and avoiding calculation problems. Because internet gateways can also be abruptly unplugged, all the processed data can be lost, and the inference must then be restarted from scratch.
These machines must give a real-time response. So, running the whole AI model on one machine can increase the computational load, cause memory saturation, and increase subsequent response latency. HyPS solves this problem by sequentially running a partitioned model on one or multiple machines. Indeed, the execution of one partition will be less expensive than the whole model in one block. The collected data are used locally and are not accessible by other production units. So, the proposed strategy helps the factory to improve the quality of construction pieces without slowing the production process nor causing damages to the machinery. It also permits to preserve data and model privacy since each machine only deals with specific partitions of the global model. The following sections present the proposed partitioning strategy and discuss the architecture details for HyPS implementation.
The proposed algorithm starts by going through all the model layers one by one and checks if it is possible to run it on the edge device. The program runs this process until reaching a complex layer which results in three vertical partitions for one particular complex layer: one V-partition before the first added mandatory split point, a second after the second mandatory split point and a third constituted of the complex layer itself and alone. Then, the third V-partition containing the complex layer is itself partitioned into H-partitions as small as required to fit targeted execution infrastructure. HyPS operates Hpartitioning when required and on a single layer at a time, to preserve original model performances. Algorithm 1 allows to identify the mandatory partition points for an input CNN model.
According to the user objective, HyPS provides final output partitions ready to be deployed on the target edge device without any bottlenecks. HyPS can be used by a simple customer or can be integrated into a software program that provides an automated deploying solution of NN. Thanks to HyPS, the edge infrastructure can be covered more easily. The model structure, in addition to edge infrastructure, should be known before starting the partitioning process. Identifying these optional split points leads to making several combinations. In the case of the VGG16 structure, there are five strategic split points (five pooling layers), so there are several possible V-partitions. The pooling layers throughout the VGG16 model have different output dimensions. The pooling layers' output dimensions decrease when going closer to the final output layer.
System Setup The methodology used consists in two steps. First, partitioning the model into sequential sub-networks which can then be sent to edge nodes. The partitioning function is implemented in Python, using TensorFlow and Keras libraries. Second, after the partitioning phase, the distributed inference process is executed on a realistic collaborative edge computing testbed that consists of four Raspberry Pi's: 1 Raspberry PI 3 model B (ARM Cortex-A53 Quad-Core processor -1 GB RAM) and three Raspberry PI 3 model B+ (Broadcom BCM2837B0, Cortex-A53 64-bit SoC @ 1. 4GHz-1 GB RAM). All devices are connected within the same network via a LAN cable. The capabilities of those nodes represent typical edge devices  [17]. According to the official website of Keras, VGG16 is a large model (528 MB) and it comprises 138.
MG-MAMPOSST is a FORTRAN90 code to perform tests of General Relativity (GR) through the analysis of kinematic observations of galaxy clusters. The code solves the Jeans equation, building on the MAMPOSST method of  Mamon et al. (2013). It extends this method through new parametrisations of the gravitational potential for general families of gravity theories beyond GR aimed to explain the late-time accelerated expansion of the universe Perlmutter et al. (1999). Through appropriate input of the projected positions and line-of-sight velocities of a cluster's member galaxies, MG-MAMPOSST reconstructs the cluster mass profile and the velocity anisotropy profile in modified gravity, jointly constraining the kinematics (mass and anisotropy profile) and modified gravity parameters. The code is further supplemented with a new capability to produce weak lensing forecasts for joint kinematic and lensing analysis, offering a valuable tool for studying the nature of gravity at cluster scales.
Statement of need In the last two decades, the cosmology and astrophysics communities have developed a significant interest in tests of dark energy theories at large scales. Among the vast range of probes of modified gravity and dark energy models, galaxy clusters offer a powerful laboratory at scales where possible departures from General Relativity (GR) should become observable (e. g. ,  Cataneo & Rapetti, 2018 and references therein). Cluster mass profiles (e. g. ,  Pizzuti et al. , 2016; Sakstein et al. , 2016; Wilcox et al. , 2015)  and cluster abundances (e. g. ,  Cataneo et al. , 2016; Lombriser et al. , 2012)  have been extensively used in the literature to place stringent bounds on some popular classes of extensions of GR. In this context, we developed MG-MAMPOSST, a FORTRAN90 code capable of performing tests of gravity models using the kinematics of member galaxies in clusters.
The code is based upon the original MAMPOSST method, developed by G. Mamon, A. Biviano and G. Boué (Mamon et al. , 2013, hereafter MAM13). A public version of MAMPOSST by G. Mamon can be found at https://gitlab. com/gmamon/MAMPOSSt. Whereas the original code relies on the assumption of a standard Newtonian gravitational potential, MG-MAMPOSST implements general and viable models of gravity beyond GR, to place constraints on the space of theories and investigate the essential statistical correlations between model parameters. In addition, the code is capable of producing complementary weak-lensing forecasts for joint kinematics and lensing analysis. MG-MAMPOSST was first presented and applied to real galaxy cluster data in  Pizzuti et al. (2017) , then extended and upgraded by  Pizzuti et al.
(2021). MAMPOSST (Modelling Anisotropy and Mass Profile of Spherical Observed Systems) determines mass profiles of galaxy clusters (or in general, spherical systems in dynamical equilibrium) by analysing the internal kinematics of the cluster members. Given an input of projected positions and line-of-sight (l. o. s. ) velocities of the member galaxies, and under the assumptions of spherical symmetry and dynamical relaxation, the code solves the Jeans equation to reconstruct the gravitational potential, the velocity anisotropy profile -which measures the difference among the velocity dispersion components in each direction, and (optionally) the projected number density profile. MG-MAMPOSST extends the method to the case where the gravitational potential is explicitly modified by the presence of an additional scalar degree of freedom.
The current version of the code supports up to seven parameters, with two parameters defining the mass profile, one or two parameters for the velocity anisotropy profile, one for the number density profile, and finally, two parameters related to the modified gravity framework. Each parameter can be either treated as free in the fitting procedure, or it can be assigned pre-defined values. In the original code of MAM13 there are several possible choices for the modelling of the dark matter mass profile in the ΛCDM scenario. MG-MAMPOSST adds new parametrizations to handle popular modifications of gravity. At the moment, all the implemented non-standard profiles rely on the Navarro-Frenk-White (NFW,  Navarro et al. , 1997)  mass density profile to model the matter density distribution, which has been shown to provide a good description of simulated and observed galaxy clusters, both in GR and in modified gravity (see e.
g. ,  Peirani et al. , 2017; Umetsu, 2020; Wilcox et al. , 2016). Nevertheless, other mass models will be included in upcoming versions of the code. Since the mass profile, or equivalently, the gravitational potential, enters only in the expression of the radial velocity dispersion, Equation 1, the implementation of new models can be performed directly by the user, modifying the subroutines in the auxiliary file MAM. f where the above equation is involved straightforwardly. In particular, the functions called sr2int(alr) and fa(tlog) are the only parts of the MG-MAMPOSST code where parametrisations for the mass profile appear. The code is equipped with the two most popular and observationally viable classes of dark energy models beyond GR based on a single, extra scalar field. These correspond to the so-called "chameleon" models and the "Beyond Horndeski/DHOST" models.
Functionality and Design A complete run of the MG-MAMPOSST code is based upon several correlated files which store the input/output information. In particular: • gomamposstopt_x. inp contains the names and locations of the input data file and of the input parameter file, as well as the names and locations of the output files. Each of them can be customised by the user. • data/datphys. dat is the input data file, structured as a table where the number of rows coincides with the number of data points. The first column is the projected radius in units of kpc, the second and third columns represent the l. o. s. velocities and the associated errors in units of km/s. • input_pars/pars_test. txt is the input parameters file, where one can select the number of free parameters and their guess values, the models of the various kinematic components (gravitational potential, number density profile and velocity anisotropy profile) and other relevant physical quantities for the MG-MAMPOSST analysis.
• Options. txt contains additional options required by MG-MAMPOSST, e. g. how to explore the parameter space (fixed grid of values or MCMC) and the details of the lensing simulation in modified gravity. • Output/MaxLik. dat is the main output. It is organized as a table where each row indicates the values of the parameters for a given point in the six-dimensional parameter space and the corresponding value of the logarithm of the Likelihood/Posterior. The MG-MAMPOSST run further produces additional output files, stored in the Output folder and, optionally, a plot of the marginalized posteriors for the free parameters when the MCMC exploration mode is selected. For some MG and GR mass models, the code also outputs a plot of the best-fit mass profile along with its associated one and two-sigma uncertainties.
The plot generation requires the Python getdist package  (Lewis, 2019). In Figure  1  an example of a typical output plot is shown for MCMC sampling in the case of DHOST model of gravity with five free parameters. The run has been performed by using the sample dataset shipped with the code; the test of execution described in the documentation and the README should produce almost the same figure. For a complete description of the code's basic usage and functionalities, see the code's documentation in the repository or on arXiv  (Pizzuti, Saltas, Biviano, et al. , 2022).
Acknowledgements Lorenzo Pizzuti  acknowledges support from the  Czech Academy of Sciences  under grant number  LQ100102101. Lorenzo Pizzuti  is partially supported by a 2019 " Research and Education " grant from  Fondazione CRT. The OAVdA is managed by the  Fondazione Cle'ment Fillietroz-ONLUS , which is supported by the  Regional Government of the Aosta Valley , the  Town Municipality of Nus and the "Unite' des Communes valdotaines Mont-E'milius. Ippocratis D. Saltas  is supported by the  Czech Grant Agency (GACR) , under grant number  21-16583M. The authors further acknowledge all the developers of the free FORTRAN routines used in MG-MAMPOSST. Credits are given in the header of each routine.