dataesr/scientific-paragraphs-categorization huggingface dataset:
Instrumentation We use for this study the magnetic field investigation package onboard MAVEN (abbreviated MAG in the following), which consists of two tri-axial fluxgate magnetometers mounted at the extremity of boomlets on the spacecraft's solar panels  (Connerney et al. , 2015). It allows the measurement of the 3 components of the magnetic field with a nominal frequency of 32 Hz and an accuracy better than 0. 05%. Because the structures we are looking for in the dataset are expected to last from a few seconds up to 15-20 s  (Simon Wedlund et al. , 2022c) , the high-time resolution of the instrument is downsampled to 1 s. (SWIA) electrostatic ion analyser, which measures ion differential fluxes with a maximum temporal resolution of 4 s  (Halekas et al. , 2015). Specific modes are available depending on energy/angle scanning and telemetry modes; we considered two modes here, SWICA (SWIA Coarse Archive, 8-s resolution), suited to magnetosheath conditions, and SWIFA (SWIA Fine Archive, 4-s resolution), designed for the monitoring of solar wind ions.
(1986). We use also the ratio of the maximum-to-intermediate eigenvalues λ max /λ int (intermediate-to-minimum eigenvalues λ int /λ min ) to further constrain the shape of the variance ellipsoid into a cigar-shaped one  (Génot et al. , 2001). At this stage and using Criteria 1-4, the total number of detected 1-s duration MM-like events in the 1 Nov. 2014-7 Feb. 2021 MAVEN dataset was 2,046,533. Two examples of B-field-only detections are presented in Figs. 1 (higher solar activity conditions) and 2 (lower solar activity) as structures in green and grey. We will come back to discussing them in Sect. 2. 2. 3.
In this way, some of these discarded isolated events are not part of the usual quasi-periodic train of MM structures, and thus are more reminiscent of the so-called 'linear magnetic holes' (LMH), in the original definition of  Turner et al. (1977) , structures that we want to filter out from the database. The particular value of 30 s was chosen empirically as double the length of the longest MM structures found at Mars or Venus (see for example Simon  Wedlund et al. , 2022c; Volwerk et al. , 2008 Volwerk et al. , , 2016)) ; moreover this ensures that rotations could be calculated for trains of MM-like structures for which the 2-min windowed background magnetic field values would be representative. We then estimate how much azimuth and elevation angles fluctuate at the detected position of the candidate structure by calculating their running standard deviation ⟨σ(az, el)⟩ over a 2-min sliding interval, keeping only those structures where ⟨σ(az, el)⟩ is less than 10 knowing where the shock is can be used to automatically ensure that the structures are located in the magnetosheath.
Examples and caveats of the method Figures 1 (high solar activity conditions) and 2 (low solar activity) show examples of MM-like detections, with total magnetic field and Criteria 1-4 of Table  1. Detections matching Criteria 1-4 in coincidence are shown as vertical grey and green lines, with the green lines marking the final detections with the removal of false positives (Criteria 1-4 and 5-6 in coincidence). The events of 8 Sept. 2016 (Fig. 1 ) occur at and in the wake of a predominantly quasi-perpendicular bow shock crossing, with angle θ Bn between the normal to the shock surface and the average interplanetary magnetic field (IMF) direction of about 80 That said, other events deeper in the magnetosheath (around 18:33 and 18:36 UT) and part of the database in grey are also removed because they are isolated structures: the second of these events around 18:36 UT presents a clear B -N anticorrelation.
This shows that although the detection method using Criteria 1-6 appears quite apt at detecting MM-like structures and removing structures that are clearly not MMs, it may also ignore promising candidates. Conversely, we also expect the method to keep structures that are likely not MMs but situated in the magnetosheath (with B and N in phase). For illustration, isolated 1-s events, discarded in the final database, amount to 23, 822 additional events, which make about 14% of the final total number of events (see Table  2 ). As a consequence, on this criterion only (isolated structure), the frequency of MM-like detections in our final database could be underestimated by at least 10%. Another aspect is that several individual structures as part of trains of MMs are likely to have been missed by our B-field-only method: see for example on Fig.
A discrimination between all polarisations states is difficult to achieve with B-field-only criteria and is outside of the scope of our study. Therefore, our method may miss a substantial amount of structures which are MMs but are non-linearly polarised in nature. Several tests have been made on reduced datasets to find the 'sweet' spot of all criteria gathered in Table  1  (see also the discussion in Simon Wedlund et al. , 2022c) to keep as many confirmed MM-like structures as possible whilst avoiding other compressional structures that are not MMs. This points overall to the difficulty of characterising such structures reliably with magnetometer-only data. In all cases, after applying our mitigation strategies, the number of structures that are captured is thus most likely a lower estimate of the frequency of linearly and non-linearly polarised MM-like structures.
Detection probability First, we apply the B-field-only criteria of Table  1  in coincidence to the whole MAVEN dataset, from November 2014 to February 2021. This yields the timing and duration of MM-like structures, noted t struct and ∆T struct , respectively. It is important to remark here that a full dip or peak of any given MM-like structure is usually longer than the total sum of 1-s detections for that structure. This arises from the fact that all the criteria of Table  1  must be simultaneously met to validate this as a detection. Consequently, ∆T struct can only be an underestimate of the total duration of a structure, which we expect to be about 25%, as evaluated from visual comparisons in a subset of events (see Figs. 1  and 2 ). Second, we define a spatial grid for the data accumulation.
, 2016) : P(i, j) = k ∆T struct k (i, j) ∆T sc (i, j) , (4) with k the number of structures found in each grid cell (i, j). In other words, P represents the percentage of observations containing a MM-like structure at any given point in the magnetosheath. The accumulation of the structures in the grid, including those crossing a cell boundary, is naturally taken into account using a bi-variate histogram accumulation. An example of spacecraft residence time and MM-like detection probabilities is shown in Fig. 3a, b. Figure  3  is discussed further in Section 3. 1. As mentioned above, because ∆T struct is a likely underestimate of the total duration of MM-like structures in the magnetometer dataset, P is also underestimated: we thus refer to it as a 'detection probability' of detecting MM-like structures.
, 2017b). This makes it all the more difficult in practice to isolate the role of a single controlling parameter from the others. For example, one way to help disentangle these effects would be to study the seasonal changes at a fixed EUV flux level or, inversely, to study a EUV flux level at a fixed seasonal bin. However, depending on the chosen binning, the event statistics may become too low for a statistically significant interpretation. We leave this for a future study, when MAVEN will have completed several additional MYs, and choose a complementary approach using the probability density function (PDF) of the total number of detected structures as a guide (see Section 3. 2. 1). Table  3  presents the general statistics of our MM-like structure database, with a total of 176, 041 events detected between 1 Nov.
2014 and 7 Feb. 2021 (last day of MY35), with a total residence time of the spacecraft in the grid of about 6. 1 Earth years. For each controlling parameter we also calculate the residence time and global observation ratio of MM-like structures. In Sect. 3, we will discuss the significance of these ratios and contrast them against the detection probability maps. We now present the statistics and the 2D cylindrical coordinate occurrence maps of MM-like structures in the magnetosheath of Mars resulting from our automatic detections described in Sections 2. 2 and 2. 3. The spatial distribution of the detected structures is shown in Fig. 3  as maps for the full dataset considered here, with a bin resolution of 0. 1 R M. For reference, we also indicate the bow shock (BS) and the induced magnetospheric boundary (IMB, sometimes referred to as 'magnetic pileup boundary') from several previous works  (Edberg et al.
Overview of the full dataset In contrast to the two main high-probability regions discussed above, we identify a region of low probabilities in the portion of the sheath behind the terminator plane and in the tail, where P < 0. 1%. We hypothesise that this is due to the plasma flow configuration which may preferentially transport the MMs almost unchanged from their birth region behind the quasiperpendicular shock down to the IMB and along it. Quantitatively, these results are in line with studies at Venus  (Volwerk et al. , 2016; Fränz et al. , 2017) , which registered detection probabilities of less than 5% on average, with maxima of occurrences taking place immediately behind the shock and close to the IMB. As in our results, these authors also pointed out increased occurrences in the magnetospheric tail.
Our companion paper (Paper II) on the distribution of MM-like structures at Venus further discusses these aspects with the full Venus Express dataset  (Volwerk et al. , 2022). Consequently, Mars and Venus qualitatively display a similar tendency towards higher MM detection probabilities in low solar activity conditions, a conclusion which appears in contradiction with the addition of pickup ions to the ambient plasma at higher EUV fluxes. We suggest here two phenomena that likely play complementary roles in enforcing this apparently contradictory trend. First,  Romanelli et al. (2016)  have observed an enhanced turbulence due to the presence of so-called proton cyclotron waves (PCWs) becoming much more prominent the closer to perihelion conditions. This is in turn linked to the local plasma-β, which plays a leading role in favouring the MM instability over the Alfvén Ion Cyclotron instability (typically for β ≥ 1.
Summary and conclusions In this paper, we have aimed at creating maps of detection probabilities of MM-like structures in the Martian environment, in a quest to better characterise mirror mode structures (MMs) across weakly magnetised and unmagnetised planets. Because magnetometers are a common instrument on board space missions, with similar resolution and characteristics, they are an ideal tool to compare specific low-frequency wave modes such as the ubiquitous MMs. This study is the first of two at Mars and Venus (see  Volwerk et al. , 2022 , for Paper II), using for the first time the same magnetic-field only detection criteria and the same representation at both planets. At Mars, we use the MAVEN data between 2014 and 2021 and, at Venus, the Venus Express data between 2006 and 2014. We examined how these detection probability maps in MSO coordinates change with respect to MY, atmospheric seasons (Ls) and solar EUV flux.
We emphasise several possible tracks to continue and expand on this first work. First, this work should be redone with coincident plasma/magnetometer measurements. Although we may lose somewhat in temporal resolution with respect to magnetic field measurements (SWIA has a 8-s resolution in the magnetosheath whereas SWEA can reach down to 2 s), we will gain the certitude of a B-N antiphase behaviour, a necessary characteristic of MMs. Complementary techniques such as using transport ratios to characterise the low-frequency wave environment  (Song et al. , 1994)  could also usefully be applied to the full MAVEN dataset, as shown early in the mission  (Ruhunusiri et al. , 2015). Comparing all of these results with our present ones will give a measure of the accuracy of the B-field only method over more foolproof methods and may help reinterpret past and present datasets from a new perspective.
Acknowledgements. CSW and MV thank the  Austrian Science Fund (FWF)   P32035-N36. SRM was funded by the  Swedish National Space 675 Agency  under contracts  145/19  and  79/19. The authors thank  C. Möstl ,  Z. Vörös ,  L. Preisser  and  O. Roberts  ( IWF-ÖAW, Graz ) for useful discussions with respect to bow shock crossings, Earth's turbulence, mirror modes and magnetosheath jets. The authors would like to thank  ISSI  and the  ISSI international  team #517 " Towards a Unifying Model for Magnetic Depressions in Space Plasmas " led by MV, as well as team #499 "Similarities and  Differences in the Plasma at Comets and Mars " led by  C. Götz.
Data availability. The calibrated MAVEN/MAG datasets are available from the NASA Planetary Data System (PDS) at https://doi. org/10. 17189/1414178. The FISM-P Mars Solar Spectral Irradiance model is available at https://lasp. colorado. edu/lisird/data/fism_p_ssi_mars/ and at https://doi. org/10. 17189/1517691 through NASA PDS archives. Predicted bow shock times, spatial coordinates and shock geometry (θ Bn ) for the 2014-2021 dataset (up to Mars Year 35) are available on Zenodo at https://doi. org/10. 5281/zenodo. 5725288 (Version 3, Simon Wedlund et al. , 2022a).
to fall in the middle of https://doi. org/10. 5194/egusphere-2022-634 Preprint. Discussion started: 26 July 2022 c Author(s) 2022. CC BY 4. 0 License. the distribution; however, because we removed shock substructures from the dataset (see Section 2. 2. 2) and considered only magnetosheath events, all of the events shown here are in effect in the magnetosheath and not in the solar wind. This attests to the high variability of the shock position and the limitations of a single modelled curve to represent accurately the position of the shock, a conclusion in conformity with dedicated studies (Gruesbeck et al. , 2018). Magnetic field compressional fluctuations ∆|B|/B bg for the detected events are next shown in Fig. 3c. Following Criterion (1), we consider only structures with ∆|B|/B bg ≥ 0. 15.
Overall, these preliminary results are consistent with past observations at Mars, be it with MGS (Bertucci et al. , 2004)  or withMAVEN (Simon Wedlund et al. , 2022c), who all noted that MM structures seemed to pile up against the IMB. This suggests that MMs, after being created upstream of their detection place, are convected down to it with the ambient plasma flow. Using the full MAVEN plasma complement and owing to the ambient plasma becoming less unstable to MMs the further away from the shock, the locus of generation of the MMs found by Simon Wedlund et al. (2022c) was inferred to be in the immediate wake of the quasi-perpendicular shock, a condition that seems to predominate in the2014-2021 MAVEN dataset   (Simon Wedlund et al. , 2022b). https://doi. org/10. 5194/egusphere-2022-634 Preprint.
•. Comparison of magnetic field and density variations is displayed in the bottom panel, with magnetic field (left axis, blue) and density (right axis, red) fluctuations being in antiphase when ∆B/|B| and ∆N/N have opposite signs. For the detections in https://doi. org/10. 5194/egusphere-2022-634 Preprint. Discussion started: 26 July 2022 c Author(s) 2022. CC BY 4. 0 License. grey in the ramp of the shock as initially captured by Criteria 1-4 but without removal of false positives, B and N appear mostly in phase at the cruder resolution of the SWIA instrument (here 8 s, with ∆B/|B| and ∆N/N having most of the time the same sign in the bottom panel). This implies a false positive detection: further applying Criteria 5-6, they are correctly removed from the final database, leaving only the structures in green.
Multiple crossings of the shock from the magnetosheath into the solar wind take place. The automatic estimate of the shock's location following Simon Wed- lund et al. (2022b) pinpoints the last great magnetic field enhancement around 19:05 UT, whereas the magnetosheath-to-solar wind position is visually closer to 18:52 UT: this is due to that automatic estimate choosing the last occurrence of a shock-like structure when crossing from the magnetosheath to the solar wind. In the solar wind after 19:05 UT, magnetic field and density seem mostly in phase: two 'linear magnetic hole' (LMHs) candidates (not shown) are first captured by Criteria 1-4. After they are removed from the database using Criteria 5-6, only MM-like structures which are roughly in B -N antiphase (around 18:39 UT) are kept.
, 2016). Noteworthy, translation defects are considered today as an important contributor of SMA physiopathology  (Gabanella et al. , 2007; Lauria et al. , 2020). In the present study, we investigated the potential involvement of NOX4 in SMA-induced spinal MN degeneration using severe type SMA-like mice at different time-points of the disease. We found that NOX4 was overexpressed in SMA mouse spinal cord and its inhibition by GKT137831/Setanaxib, a drug presently in clinical development (NCT04327089 and NCT05014672), resulted in a significant neuroprotection at the level of the MNs, likely through the activation of the pro-survival AKT/mTOR pathway. Importantly, the GKT137831/Senataxib treatment proved to act synergically with Nusinersen-like ASOs in vivo, suggesting that the pharmacological inhibition of NOX4 could represent a complementary therapeutic strategy to SMN gene-based therapies in SMA.
000, Sigma T6074) was incubated overnight at +4°C in 5% BSA constituted in TBS pH 7. 4 supplemented with 0. 05% Tween 20. The blots were then incubated with either polyclonal goat anti-mouse antibody (1:10000, Biorad 1,706,516) or polyclonal goat anti-rabbit antibody (1:10000, Jackson Immunoresearch 111-035-003). In some occasions, membranes were stripped with 100 mM b-Mercaptoethanol, 2% SDS, 62. 5 mM Tris-HCl, pH 6. 7 for 30 min at 55°C with agitation, and then blocked with 5% milk and blotted again with other primary antibodies. Bands were visualized by enhanced chemiluminescence. Intensity analysis was performed using NIH ImageJ software.
Microscopy All immunofluorescence images were collected with a CMOS camera (ORCA Flash 2. 8, Hamamatsu Photonics France, Massy, France) mounted on a Zeiss AxioObserver microscope (Z1, Carl Zeiss SAS, Le Pecq, France) using the ZEN 2012 software (Carl Zeiss SAS) with 200 (X20 Zeiss EC-Plan-Apo NA 0. 8) magnifications.
KD is the most common cause of acquired heart disease in children in developed countries. The study showed that the most differentially expressed genes were found in monocytes, with high expression of pro-inflammatory mediators, immunoglobulin receptors and low expression of MHC class II genes in acute KD, and that the percentage of CD8+ T cells is decreased in acute KD, and notably effector memory CD8+ T cells compared to healthy controls  4. Here we use scRNA-seq to sequence more than 30,000 cells from human PBMC samples to high depth of coverage (>100,000 reads/cell). We provide data from resting and stimulated PBMCs. We used lipopolysaccharides (LPS) bacteria extracts to induce an immune response and transcriptional changes. We also provide a dataset where cells have been frozen according to a demonstrated protocol for the preparation of fresh frozen human PBMCs for scRNA-seq analysis.
Briefly, the cell suspension barcoded gel beads and partitioning oil were  Data processing and analysis. The fastq and index files were processed with Cell Ranger and the human genome GRCh38 as the reference (10X Genomics annotation file refdata-gex-GRCh38-2020-A). The R package Seurat 5 version 4. 1. 0 was used for quality control and most of the analysis. In addition, we used Scrublet 6 for doublets estimation and the software tool MultiMAP 7 for certain steps of dimensionality reduction. In order to annotate the clusters described on Fig. 6 , we used the Seurat functions "FindTransferAnchors" and "TransferData" to transfer cell type labels from a reference dataset onto a new query dataset. The reference dataset in this case is a Human PBMC reference dataset included in the package Seurat (dataset "ifnb").
technical Validation After sequencing, all the samples were processed with Cell Ranger with default parameters for quality control and gene counting. For the resting and stimulated samples, we set the "-expect-cells" parameter to 8,000 and for the fresh and frozen samples we set this parameter to 6,000. We used the human genome GRCh38 as the reference (10X Genomics annotation file refdata-gex-GRCh38-2020-A). Doublets rates were estimated with the Fig. 4  UMAP plot of scRNA-seq data for the resting versus stimulated PBMC cells after integration with the RPCA method. rest = resting PBMCs, stim = stimulated PBMCs. software Scrublet 6 , using the default parameters. The Cell Ranger summary reports, which include many different quality indicators, did not report any value outside the expected ranges.
For the percentage of reads mapped to the genome and the transcriptome, the values are almost identical. The rate of doublets, estimated with the Scrublet software on the raw counting matrices, is within expected values and is slightly lower for the resting and stimulated datasets compared to the 10X Genomics reference set. The median number of mitochondrial genes, a common indicator of apoptotic cells, is well below the commonly used threshold of 10% for both the resting and the stimulated datasets. For the frozen/non frozen PBMC samples, there are four samples in total (two technical replicates for the fresh conditions and two technical replicates for the frozen conditions). All the indicators are shown in Table  2 , and are within the acceptable range. There was no special warning in the Cell ranger report.
For the fresh samples, the values are similar to the values obtained for the resting PBMC experiment. The median number of genes per cell for the fresh samples is around 2,100 with a mean number of reads per cell of 100,000 and a doublet rate of 7-8%. For the frozen samples, the mean number of genes per samples is lower, around 1,600 genes per cell, with a slightly higher mean number of reads per sample and a lower doublets rate of 3-5%. The sequencing saturation is high for all samples, with values that are between 77 and 85%. For all the samples we have a median number of mitochondrial genes below the usual threshold of 10%. Taken together, all these results demonstrate the high quality of our human PBMC scRNA-seq datasets.
Usage Notes Resting versus stimulated PBMC. For this analysis example, we filtered the two PBMC datasets to remove low quality cells. First we removed the doublets predicted by Scrublet (308 for resting PBMCs and 344 cells for stimulated). Then, we analysed the distribution of the median number of genes per cell and the percentage of mitochondrial genes per cell to define the filtration thresholds. We removed cells having a low or very high median number of genes (>200 and <7000 for resting, >200 and <6200 for stimulated PBMCs), and cells having a percentage of mitochondrial genes greater than 14%. After filtering, we were left with 6,206 cells and 7,057 cells for resting and stimulated PBMCs respectively. High-throughput single-cell transcriptomics has become a very popular and powerful tool for unbiased profiling of complex and heterogeneous cellular systems, thanks to the availability of commercialized workflows and improvements in cost and throughput  10.