dataesr/scientific-paragraphs-categorization huggingface dataset:
05% Tween (1× TBST) with 5% wt/vol non-fat dry milk for 1 h, and then incubated with primary antibodies in 1× TBST 5% wt/vol non-fat dry milk at either room temperature for 2 h or 4°C overnight. Primary antibodies used were: anti-V5 (1:1,000, #13202S; Cell Signaling Technology), anti-α-tubulin (1:3,000, #T9026; Sigma-Aldrich), anti-GA repeat (1:1,000, #24492-1-AP; Pro-teinTech), anti-AP repeat (1:1,000, #24493-1-AP; ProteinTech). Membranes were then washed three times for 5 min with 1× TBST and incubated with either an anti-mouse IgG-HRP-conjugate (1: 5,000, Cat. no. 172-1011; Bio-Rad) or an anti-rabbit IgG-HRPconjugate (1:5,000, Cat. no. 12-348; Millipore). ECL substrate was then added to the membrane to enable detection, and nonsaturated images were acquired using a G:BOX EF machine (Syngene) and GeneSys software (Syngene).
Cells were subsequently fixed in Glyoxal solution (pH = 5) for 30 min  (Richter et al, 2018)  and imaged with confocal microscopy (LSM 880; Zeiss, Airyscan mode) from the plane of sharp focus. From the images, using FIJI and creating a macro (https://github. com/ paoloM1990/Quantification-of-cell-fluorescent-intensity), the Mean grey values of Atto550 or Alexa647 whole-cell signals were calculated. In the case of the 37-4°C endocytosis experiment, mean grey values were log-transformed (log 10 ) only for better graph visualization.
Following an initial reverse transcription step at 50°C for 10 min and a 5 min denaturation step at 95°C, the cDNA was amplified by 39 cycles of 95°C for 10 s followed by a combined annealing/extension step at 60°C for 30 s, and subsequent melt curve analysis (to ensure primer specificity), with data collected over a temperature range of 65-95°C in 0. 5°C increments. RT-qPCR was performed on a Bio-Rad C1000 Touch Thermal Cycler. Bio-Rad CFX Manager software was used to analyse signal intensity and relative gene expression values were determined using the ΔΔCt method, with GAPDH RNA used as a reference gene. The RT-qPCR product was then visualized on a 2% agarose gel, after loading 10 μl of product along with 2 μl of 6× gel loading dye. After electrophoresis (at 120 V for 40 min), the gel visualization showed RT-qPCR products matching the expected bp size (data not shown).
Colour deconvolution for differentiating two lysosomal populations We developed custom scripts in MATLAB to produce colour deconvolution algorithms that separated "non-colocalized lysosomes" (NCLs) and "colocalized lysosomes" (CLs). NCLs corresponded to the signal of LysoTracker Green devoid of any overlapping with ATTO550-DPRs signal. CLs, instead, corresponded to the merged signal generated by LysoTracker Green-ATTO550-DPR colocalization. Briefly, eight-bit RGB time-lapse images were split into individual frames and a fuzzy colour detection algorithm was applied to identify and isolate regions of interest. Resulting images were binarised by applying a global Otsu threshold, and then de-noised using a median filter.
1% Triton X-100 in PBS and blocking of non-specific binding sites with 2% BSA. Staining with anti-LAMP1 Mouse primary mAb (1: 25, Cat. no. ab25630; Abcam) was performed overnight at 4°C. Cells were washed with 3× PBS for 5 min and incubated for 2 h with antimouse IgG F(ab) ATTO488 (H + L) (Cat. no. 2112-250UG; HyperMOL) to yield minimal linkage error  (Hust et al, 2007). Post-fixation in 4% PFA (+0. 2% glutaraldehyde) for 30 min was applied to further reduce molecule motility when needed. Coordinates were tracked with Nikon NIS-Element software for computational drift correction and, in a separate sample, tetraspeck beads (100 nm diameter; Invitrogen) were imaged as fiduciary landmarks for chromatic realignment. Dual-colour dSTORM imaging was performed under reducing condition with Tris buffer 50 mM with 10 mM NaCl (pH 8), glucose (10%), cysteamine (1 M), glucose oxidase (5 mg/ml), and catalase (4 mg/ml).
Approximately 8,000-10,000 frames per channel were acquired. Diffraction limited images of each channel were also acquired, providing the reference for subsequent NanoJ-SQUIRREL analysis  (Culley et al, 2018)  of image artefacts  (Fig S5C). All imaging was carried out on an inverted Nikon Eclipse Ti microscope equipped with a 100× oil immersion objective (1. 49 NA) using an Andor iXon EMCCD camera (image pixel size, 151. 57 nm). ATTO-647N and ATTO-488 were imaged using 639 and 488 nm lasers for a 10-or 20-ms exposure time. We used Nikon NIS-Elements software for both image acquisition and reconstruction. After image reconstruction, the package ChriSTORM  (Leterrier et al, 2015)  was used for translating NIS-Elements localization files into compatible files for image rendering by the open-source FIJI plugin Thun-derSTORM  (Ovesný et al, 2014).
Thus, ThunderSTORM enabled the ultimate visualization of data acquired by STORM imaging  (Fig S5A). Using the Nikon NIS-Elements software, single molecules were localised with a lateral localization accuracy of ~20 nm for 647 channel and of ~45 nm for 488 channel based on the Thompson equation  (Thompson et al, 2002) (Fig S5B). In addition, by using NanoJ-SQUIRREL FIJI plug-in, we have implemented block-wise FRC resolution mapping to provide local resolution measurements of our dSTORM dataset  (Culley et al, 2018) (Fig S5B).
Colocalization analysis in dual colour dSTORM For colocalization analysis, graphs are indicative of ~5-6 healthy iAstrocytes with five ROIs taken in regions juxtaposed to the nucleus of each cell (n = 3 biological experiments). The choice of ROIs was based by excluding artefact-rich areas with the FIJI plug-in NanoJ-Squirrel  (Culley et al, 2018). For colocalization analysis, we used the open-source software Clus-DoC  (Pageon et al, 2016)  to generate colocalization maps (Co-Loc maps) which highlighted areas of molecular interaction between the two channels with the following parameters: L(r)-r radius = 20 nm; Rmax = 500 nm; Step = 10 nm; Colocalization threshold = 0. 4; Min colocalized points/cluster = 10.
3D dSTORM Nikon NIS-Element software and a Nikon Eclipse Ti microscope were used for 3D STORM imaging. We used an astigmatic lens in the light path-directly in front of the camera. To summarise, the NIS-Element calibration algorithm was first applied to tetraspeck beads in z-stack mode (600-800 nm of z-stack). Because of the astigmatism, the point spread function of the beads changes along the z-axis; this point spread function change is used by the calibration algorithm to estimate the position along the z-axis. For each imaged channel, a new calibration was applied (although in our case we only imaged ATTO647N dye, thus one channel).
By colour deconvolution we separated "non-colocalized lysosomes" (NCLs) and "colocalized lysosomes" (CLs) to analyse displacement, speed and size. Scale bars = 1 μm. Bar graphs of mean ± SEM. Unpaired two-tailed t test with Welch's correction. ***P ≤ 0. 001. The data were collected from three independent biological replicates. (A) Figure on the left of panel (A) was created with BioRender. com under academic license.
Figure6. Alanine-rich dipeptide repeats (DPRs) undergo astrocyte-to-motor neuron propagation. (A) Schematic representation of the iAstrocytes-MNs co-culture system; the figure was created with BioRender. com under academic license. (B) Orthogonal views from confocal images show the presence of 1 µM ATTO550 DPRs (red) in the GFP-positive MNs (green); CD44 is shown in violet as the astrocytic marker. (C) Quantification of the percentage of motor neurons containing at least one DPR aggregate in the various 48 h co-culture systems. Bar graphs of mean ± SEM. One-way ANOVA with Tukey's multiple-comparisons test. ~120 neurons/condition. ns = P > 0. 05, ***P ≤ 0. 001, and ****P ≤ 0. 0001. The data were collected from three independent biological replicates. (D) Quantification of DPR levels present in the conditioned medium of healthy iAstrocytes, via spectrophotometric analysis.
Bar graphs of mean ± SEM. One-way ANOVA with Tukey's multiple-comparisons test. ns = P > 0. 05, and ***P ≤ 0. 001. The data were collected from four independent biological replicates. (E) Quantification of motor neuron survival upon 48 h co-culture with iAstrocytes containing and transmitting DPRs. Bar graphs of mean ± SEM. One-way ANOVA with Tukey's multiple-comparisons test. ~120 neurons/condition. ns = P > 0. 05. The data were collected from three independent biological replicates.
Acknowledgements We thank  Dr. Matthew Stopford  for providing iAstrocytes and Hb9-GFP motor neurons;  Dr. Adrian Higginbottom  for providing 1321N1 human astrocytoma cells;  Dr. Jason King  for expertise on endocytosis;  Professor Elisabeth Smythe  for kindly providing Transferrin647 reagent;  Dr. Colin Gray  for support in confocal imaging and  Dr. Jouni Takalo  for guidance on statistical analysis. Imaging work was performed at the  Wolfson Light Microscopy Facility using the Inverted Nikon Eclipse Ti microscope at the University of Sheffield  with the grant code  MR/K015753/1. We thank the  Wolfson Foundation  for their support in funding the  Leica Confocal microscope at SiTraN. M Azzouz  is supported by the  European Research Council  grant ( ERC Advanced Award  no. 294745 ),  MRC   DPFS award  ( 129016 ),  JPND-MRC  ( MR/V000470/1 ),  ARUK award  ( ARUK-PG2018B-005 ), and  CureAP4.
L Marrone  and  M Azzouz  are supported by  Maddi Foundation and Spastic Paraplegia Foundation. L Marrone  is further funded by a grant of the  British Neuropathological Society. EF Smith was supported by a  Motor Neurone Disease Association Prize Studentship  ( DeVos/Oct13/870-892  to  KJ De Vos and AJ Grierson)  and  Motor Neurone Disease Association  project grant ( DEVOS/APR18/862-79  to KJ DeVos and AJ Grierson). KJ  De Vos  was supported by the  Medical Research Council (MRC)  ( MR/S025979/1  and  MR/M013251/1  to  KJ De Vos). L Brasseur ,  L Bousset , and  R Marroccella  were supported by the  European Commission   Joint Programme on Neurodegenerative Diseases (JPND-TransPathND ,  ANR-17-JPCD-0002-02 ). The present work benefited from the electron microscopy facility of  Imagerie-Gif  (supported by  ANR-10-INBS-04-01 ), and the  Labex   ANR-11-IDEX-0003-02.
(B, C) Colocalization analysis between all the poly-GA oligomers (B) or the poly-GA fibrils (C) and the lysosomes contained in the cortical neurons' proximal axons. By colour deconvolution we separated "non-colocalized lysosomes" (NCLs) and "colocalized lysosomes" (CLs) to analyse displacement, speed and size. Scale bars = 1 μm. Bar graphs of mean ± SEM. Unpaired two-tailed t test with Welch's correction. ***P ≤ 0. 001. The data were collected from three independent biological replicates. (A) Figure on the left of panel (A) was created with BioRender. com under academic license.
Further work We plan to use our results as forcing data in a numerical finite element model of subsurface hydrological and thermal processes implemented with the FEFLOW® program (DHI-WASY) to examine the influence of water infiltration on the thermal dynamics and degradation of mountain permafrost. We will integrate our results with information on rock temperatures using conductivity and induced polarization tomography data from an existing system that we installed. We expect this study to give unprecedented constrains on the availability of water for infiltration in high mountain rock walls and gain better understanding on their role in permafrost degradation and mechanical stability of steep fractured rock walls. Acknowledgement. This research is funded by the Agence Nationale de la Recherche Françaises (French National Agency for Research), project: ANR-19-CE01-0018 WISPER.
We present an auto-differentiable spectral modeling of exoplanets and brown dwarfs. This model enables a fully Bayesian inference of the high-dispersion data to fit the ab initio line-by-line spectral computation to the observed spectrum by combining it with the Hamiltonian Monte Carlo in recent probabilistic programming languages. An open source code, exojax , developed in this study, was written in Python using the GPU/TPU compatible package for automatic differentiation and accelerated linear algebra, JAX  (Bradbury et al. 2018). We validated the model by comparing it with existing opacity calculators and a radiative transfer code and found reasonable agreements of the output. As a demonstration, we analyzed the high-dispersion spectrum of a nearby brown dwarf, Luhman 16 A and found that a model including water, carbon monoxide, and H 2 /He collision induced absorption was well fitted to the observed spectrum (R = 10 5 and 2.
2015) , even the presence of temperature inversions  (Nugroho et al. 2017) , planetary spin  (Snellen et al. 2014; Schwarz et al. 2016) , winds  (Snellen et al. 2010; Ehrenreich et al. 2020) , C/O ratio (e. g  Giacobbe et al. 2021; Pelletier et al. 2021) , and the signature of the night-side condensation  (Ehrenreich et al. 2020 ). In addition, high-dispersion data have, in principle, the potential to constrain a planet's obliquity  (Kawahara 2012)  and the presence of exomoons  (Vanderburg et al. 2018) , although these last goals have yet to be realized. In contrast, a high-dispersion study of field brown dwarfs with R > 50, 000 remains surprisingly limited. One well-known brown dwarf dataset is the spectra of a nearby, bright brown dwarf system, the L/T transition binary Luhman 16 AB (WISE J1049-5319 AB; Luhman 2013), taken by CRIRES/VLT (2.
2018)  and CRIRES+ on VLT  (Follert et al. 2014). Given the many similarities between brown dwarfs and young, selfluminous exoplanets, the high-resolution spectroscopy of brown dwarfs is increasingly important as a prototype for exoplanet characterization. Despite the wide applicability of high-dispersion spectroscopy to exoplanets, most such exoplanetary datasets have been analyzed using the cross-correlation with a theoretical template of the planet spectrum. Recently, several efforts have been made to link the crosscorrelation to the likelihood function for Bayesian inference  (Brogi et al. 2017; Brogi & Line 2019; Gandhi et al. 2019; Gibson et al. 2020). As a fully Bayesian approach,  Gandhi et al. (2019)  proposed a combined retrieval code of low-and high-resolution spectra, HyDRA-H, and demonstrated it using a dayside emission of HD209458b as observed by HST, Spitzer, and CRIRES/VLT.
These approaches rely on a grid-based precomputation of the cross section datasets in a temperature-pressurewavenumber grid space. In this paper, we propose an approach that fits a spectral model directly from the computation of the line profile to the high-resolution data through a Markov Chain Monte Carlo (MCMC) method. Because the opacity and radiative transfer calculations are time-consuming at high dispersion, the key problem is how to reduce the size of the Markov chain required to approximate a target posterior distribution. However, the traditional Metropolis-Hasting based MCMC that uses random proposal distributions tends to be inefficient as increasing the fitting parameters in particular. Such approaches exhibit a significant drop in the MCMC acceptance rate of the proposed sampling, and/or often demonstrate a strong correlation between successive sampled states when one cannot find a proper proposal distribution in a transition kernel.
This powerful technique evaluates derivatives of most numerical functions in a straightforward manner  (Wengert 1964; Gunes Baydin et al. 2015). Unlike numerical or symbolic differentiation, the automatic differentiation decomposes a derivative of a target function into those of elementary functions based on the chain rule. Automatic differentiaion packages such as Autograd, tensorflow, and PyTorch, have been widely used in machine learning. Among them, JAX is an automatic differentiation package with accelerated linear algebra (XLA) that has recently been actively developed  (Bradbury et al. 2018). Several recent astronomical applications utilize JAX as a backend, JAXNS for nested sampling  (Albert 2020) , and optical simulation  (Pope et al. 2020). A notable feature is that JAX is compatible with modern probabilistic program languages (PPLs) such as NumPyro  (Phan et al.
2019), MCX/BlackJAX, and PyMC3 (Salvatier et al. 2016). This means that packages written in JAX can combine the HMC of these PPLs. Figure  1  shows a schematic of our auto-differentiable spectral model, exojax. We require only observational data and a molecular database as external inputs. The main modules in exojax are written in JAX, and therefore they are differentiable for parameters such as the T-P profile, volume mixing ratios, gravity, and V sin i and so on. The derivatives of these parameters are used in PPLs such as NumPyro to infer posterior distributions. exojax is an open-source package and its code is publicly available. The remainder of this paper is organized as follows. Section 2 provides a formulation of differentiable cross sections, including an auto-differentiable Voigt profile.
In this case, the standard deviation of the core broadening is expressed as β 2 = β 2 T + β 2 mic. We can also model the microturbulence by the post process after the spectrum computation at the top of atmosphere when we assume a common microturbulence velocity in the entire atmosphere, as explained in Section 4. The Faddeeva function w(z) is also known as wofz (wof-z) following the implementation of Algorithm 680 in the Transactions on Mathematical Software  (Poppe & Wijers 1990). Although wofz in scipy. special is widely used as w(z) in Python, it is not compatible with JAX 2. Therefore, we implemented the Voigt-Hjerting function compatible with automatic differentiation based on a combination of Algorithm 916 and an asymptotic representation, as explained below. To compute the Voigt-Hjerting function,  Zaghloul & Ali (2011)  proposed using its expansion, ĤM (x, a) = e -x 2 erfcx(a) cos (2xa) + 2ηx sin (ax) π e -x 2 sinc(ax/π) + 2η π -a cos (2ax)Σ 1 + a 2 Σ 2 + a 2 Σ 3 , (6) where Σ 1 = M n=1 1 η 2 n 2 + a 2 e -(η 2 n 2 +x 2 ) (7) Σ 2 = M n=1 1 η 2 n 2 + a 2 e -(ηn+x) 2 (8) Σ 3 = M n=1 1 η 2 n 2 + a 2 e -(ηn-x) 2 , ( 9 (x) = 2π -1/2 e x 2 ∞ x e -t 2 dt is the scaled complementary error function , sinc(x) = sin (πx)/(πx) is the normalized sinc function, and η ≤ 1 is a scalar control parameter (we adopted η = 0.
Collision-Induced Absorption The collision-induced absorption (CIA) of two molecules is an important continuum in the nearinfrared region for a clear-sky atmosphere. We used the HITRAN CIA database  (Richard et al. 2012; Karman et al. 2019) , which provides the absorption coefficient α CIA (T, ν) as a grid table. The optical depth was computed as follows: ∆τ cont,CIA(m,m ) = n m n m α CIA (T, ν)∆r (33) = n m n m α CIA (T, ν)H ∆P P ( 34 ) where n m = ξ m P/k B T and n m are the number densities of two molecules that collide with each other, ∆r is the physical height of the layer, and H is the pressure scale height expressed as H = k B T µm u g ( 35 ) where µ is the mean molecular weight, g is local gravitational acceleration (hereafter gravity), and m u is the atomic mass unit. The absorption coefficient is smooth in the wavenumber direction, and the change is quite negligible for the Doppler shift.
Comparison with petitRADTRANS We compared the output spectrum of our code with an existing radiative transfer code, petitRADTRANS  (Mollière et al. 2019). petitRADTRANS uses a precomputed grid model of the cross section in a pressuretemperature plane. First, we computed the cross sections of HITEMP2019/CO using exojax and created a grid model for petitRADTRANS. Therefore, in this comparison, both use the same cross section of CO. To avoid the error caused by the interpolation of the temperature and pressure grids used in petitRADTRANS, we assume a linear T -log 10 P profile with T = 1000K at log 10 (P bar) = 2 and T = 500 K at log 10 (P bar) = -10. We assume a constant mass mixing ratio of 0. 01 for CO, 0. 75 for H 2 , 0. 24 for He, and g = 10 5 cm/s 2. We also include the H 2 -H 2 and H 2 -He CIA as a continuum.
The spectral features of this dataset have the advantage of being a simple system consisting mainly of CO and H 2 O as dominant lines and CIA as continuous opacity. Clouds are considered to be located below the CIA photosphere in the observing band (22876-23453 Å) for the following reason. The absorption coefficient of CIA (H 2 -H 2 ) has a maximum value near 2. 4 µm, close to the observing band. This is approximately 10 times higher than the value in the H band. In contrast, the cloud opacity should be flat or larger on the blue side considering that most of the cloud species that can form in this temperature range do not have their absorption features in the observing band (e. g. Wakeford & Sing 2015). The fact that the H-band flux is ∼1. 5 times larger than that in the observing band  (Burgasser et al.
Opacity calculation In this demonstration, Li2015 was used for 12 C 16 O  (Li et al. 2015)  and POKAZATEL for 1 H 16 2 O (Polyansky et al. 2018) as obtained from the ExoMol database. In addition, CO in HITEMP2019 was used to observe the effect of different broadening parameters on the line shape. Modern molecular databases contain a large number of transitions. A large number of weak lines exist below the photosphere. To reduce the computational complexity for the direct LPF, we exclude weak lines that do not contribute to the emission spectrum. Such weak lines are identified by computing the layer where the line contributes the most to the emission. When this layer is below the photosphere determined by the CIA, we exclude the line from the opacity computation. A more detailed explanation is provided in Appendix G.
Model and HMC-NUTS The dataset consists of spectra from four CRIRES detectors (S1-S4). First, we fit the model to the spectrum from the first detector (S1; 22876-23002 Å). S1 contains a CO band head at 2. 29 µm. Therefore, the baseline level shortward of the CO band head wavelength can be considered to be radiation from the CIA photosphere. The original dataset is normalized by the blaze function. Therefore, we renormalized the model spectrum by the flux density at 2. 29 µm, F ν,ref , where the emission is likely dominated by the CIA. We evaluated F ν,ref using the mid-resolution spectrum from the Magellan/FIRE Prism Spectrograph (R ≈ 8, 000)  (Burgasser et al. 2013 ) that was normalized to match the K-band magnitude of 9. 44 ± 0. 07. We take the absolute flux density of F (A) λ = 3 × 10 -12 erg/cm 2 /s/µm at λ ref = 2.
An emission spectrum contains information on gravity through optical depths of both molecular lines in Equation (  38 ) and continuous opacity in Equations (  34 ) and (  35 ). Therefore, the pressure of the photosphere depends on gravity. While the temperature near the photosphere is well constrained from line strength ratios, the pressure is determined from the line pressure broadening and is more difficult to constrain. In addition, the accuracy of the broadening parameters is still limited. Instead of making gravity a free parameter, we therefore incorporate the dynamical mass constraint from astrometric observations as the prior. Garcia et al. (2017)  derived the dynamical mass of Luhman 16 A as M p = 34. 2 +1. 3  -1. 1 M J from a 31 year astrometric dataset. Lazorenko & Sahlmann (2018) updated the astrometric mass M p = 33.
HMC-NUTS setting and computational time We performed an HMC (e. g. Neal 2012; Betancourt 2017) -No-U-Turn Sampler (NUTS; Hoffman & Gelman 2011) implemented in a PPL, NumPyro  (Phan et al. 2019) , with 500 warm-up steps for adaptation phase of HMC and 1000 samples. In this demonstration, we used Python 3. 8. 5, NumPyro 0. 7. 2, and JAX 0. 2. 12 (with jaxlib 0. 1. 64 + cuda 11. 2) for the external modules and NVIDIA/DGX A100 as a GPU facility and the full analysis took 29. 5 hours (Case II; Direct LPF) and 16. 5 hours (Case II; MODIT). We set the maximum number of leapfrog steps at each iteration to be 1023 12. The median number of the steps per sample was 511; most of the iterations did not reach 1023 steps, except for the warm-up phase. The total number of the evaluated steps was 6×10 5. Hence, the mean computational time of one leapfrog step was approximately 0.