dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All data generated or analyzed during this study are included in this article and its supplementary information files. The accession number on ClinVar for the FGF14 variant reported in this paper is SCV003804286. Control DNAs can be shared at the request of any qualified investigator upon reasonable request. | (2023) 13:9737 | https://doi. org/10. 1038/s41598-023-36654-8 www. nature. com/scientificreports/.
CONFLICT OF INTEREST STATEMENT The authors have no conflicts to disclose. CGW was supported by research grants from the Veterans Health Administration, NIH/NINCDS, the American Epilepsy Society, the Epilepsy Foundation of America, the American Heart Association, the James and Debbie Cho Foundation, Wallace Laboratories, Inc. , UCB, Inc. , Johnson and Johnson, Inc. , and Eisai, Inc. These activities do not constitute conflict of interest with the reference to this article.
To this aim, we have performed long-scale all atom molecular dynamic (MD) simulations exceeding the µs time-scale of a b-cyclodextrin (BCD) encapsulated m-THPC interacting with a phosphatidylcholine (POPC) lipid bilayer surrounded by a water buffer and physiological salt concentration (Figure  1 ). The lipids have been modeled using the amberff21 force field,  42  while cyclodextrin and m-THPC force fields are the same as those used in our previous contributions. 36  All the simulations have been performed using the NAMD code  43, 44  and visualized and analyzed with VMD,  45  initial systems have been built using the Charmm-Gui web interface,  46  and two independent replicates have been performed. Extended computational details can be found in Electronic Supplementary Information (ESI). Coherently with our previous work, the 1:2 m-THPC/BCD complex is highly stable in the water bulk (see ESI), confirming a suitable internalization.
Since EEG is the most widely used technique in BCI due to its mentioned low cost, easy handling and wide availability, there are many signals used and algorithms developed to improve the implementation of EEG-based BCI  (Rashid et al. , 2020). On this issue, Kostoglou and Müller-Putz describe in their article a new estimation method for cross frequency coupling (CFC), based on Linear Parameter Varying Autoregressive (LPV-AR) models, testing this method on both simulated and data taken from patients with spinal cord injuries. This new approach might improve the decoding in motor related BCI applications for patients with this type of lesions. Importantly, systems developed in the last 30 years of BCI research have become more and more interesting for a broader community of researchers. This can be due to improvements in the reliability and usability but also to the change of focus, namely from an application for disabled to healthy users  (Blankertz et al.
Along with the samples, appropriate controls were prepared, including unstained control and compensation controls using BD™ CompBeads Anti-Mouse Ig, κ/ Negative Control (BD Bioscience, cat. no. 552843). Secondary only controls were prepared by omitting the primary Anti-beta2-adrenergic receptor Ab staining step. The analysis of the stained samples was done using a BD FACSCantoTM II flow cytometer and computer equipped with the BD FACSDivaTM software (BD Biosciences, version 6. 1. 3). Data was analysed by the FlowJo software (version 10. 6. 1).
Statistics Normality of sample distribution was assessed using the D' Agostino and Pearson omnibus normality test. Comparison between unstimulated vs stimulated condition were performed using either unpaired Mann-Whitney or unpaired t-test for data that did not or did pass normality test, respectively. For comparison between the three groups in Figures  1B, C , statistical significance was assessed using One-way ANOVA followed by Holm-Sidak post-hoc test. For comparison between the groups in Figures  2, 3 , statistical significance was assessed using Kruskal-Wallis test followed by Dunn's post-hoc test. All statistical analysis were performed using GraphPad Prism v. 6. All statistical analysis were performed using GraphPad Prism v. 6. For Figure  2  Friedman test were applied except for panel h where a two-way ANOVA test corrected by Sidak was applied.
For each channel, the range on the Y-axis is set to be equal between early and late sessions. Across channels, the mean and SD of the range on the y-axis are 5. 83 ± 5. 79, in arbitrary units (a. u. ). Figure  4  shows raw data (figure 4(b)) We examined the SNR values across all channels and sessions (supplementary figure  2(a) ), as well as the physical locations of the channels and arrays on the cortex. To compare SNR values between the period immediately following array implantation to those obtained at a later point in time, we combined data across the first two sessions (categorized as 'early sessions') and across the last two sessions ('late sessions') and compared SNR values for all 1024 channels. A one-way analysis of variance (ANOVA) was carried out, with time as a factor ('early' or 'late').
Signal amplitude and peak-to-peak voltage The dataset obtained for the calculation of SNR was also used to calculate peak-to-peak voltage during visual stimulus presentation and to identify channels with high-amplitude signals, providing two additional measures of signal quality  [54]. We used automated techniques  [54, 55]  instead of manual spike sorting to avoid bias when assessing the quality of spike recordings  [56]. The methods and values used are identical to those described by Hughes et al  [54]. For each trial and electrode, we calculated the root-mean-square level of baseline activity. If the voltage signal during stimulus presentation crossed a negative threshold of -4. 5 times the root-mean-square level, the time of threshold crossing was noted and a 1. 6-ms snippet of signal was stored (starting ten samples before threshold crossing).
Snippets from an example channel (channel 40 on array 11) during early and late sessions (91 and 279 d post-implantation, respectively) are shown in figure  4(d). For each electrode, we identified the largest 2% of snippets across trials, and calculated the average waveform across these snippets. We then measured the peak-to-peak voltage of the average waveform. Electrodes with a trial-wise snippet occurrence rate of >1. 67 Hz and a peak-to-peak voltage of >30 µV in the average waveform were used for further analysis. If the peak-to-peak voltage on a given electrode was >100 µV, it was deemed to yield highamplitude spikes. Violin plots were generated to visualize changes in peak-to-peak voltage with time, using open-source Matlab scripts  [53]. We calculated the mean peak-to-peak voltage across channels for each session, and carried out a linear regression against the number of days post-implantation.
Impedance values We measured electrode impedance at 1 kHz at multiple time points during the implantation period, across 19 and 9 sessions in monkeys L and A, respectively, using the Impedance Tester function in the Central Software Suite (Blackrock Neurotech). Following perfusion of the animals and explantation of the arrays, we measured electrode impedance at 1 kHz using a nanoZ (White Matter LLC, Seattle, US). We examined impedances across all channels and sessions (supplementary figure  2(c )), and the locations of the channels and arrays on the cortex.
Results Our previous study demonstrated that a 1024channel neuroprosthesis reliably induced discriminable percepts consisting of phosphenes in monkeys  [33]. Here, we evaluated the effects of longterm implantation on the animals' health, behaviour, phosphene perception, and visual perception, to better understand the challenges associated with chronic implantation in human patients. We assessed the efficacy of the hardware, the mechanical stability and integrity of the implant, and the quality of recorded neuronal signals. After the conclusion of behavioural experiments, we examined the effects of chronic implantation on the cortical tissue including the degree of tissue damage and gliosis induced by the device. The datasets used in this paper are summarized in table 1.
Acknowledgments We thank our research technician,  Kor Brandsma , and our biotechnician,  Anneke Ditewig , for technical and surgical support; our biotechnician,  Lex Beekman ;  Matthew Self  and  Feng Wang  for assistance during surgeries;  Chris van der Togt  and  Matthew Self  for custom-written Matlab scripts;  Aitor Morales-Gregorio  for analysis scripts; and  Florian Solzbacher ,  Marcus Gerhardt ,  Stephen Hou ,  Rob Franklin ,  Sherman Wiebe ,  Vinh Ngo , and others at  Blackrock Neurotech  for scientific and technical collaborations.
Funding This work was supported by  NWO  (STW Grant Number  P15-42  ' NESTOR ';  ALW  Grant Number  823-02-010  and Cross-over Grant Number  17619  ' INTENSE '), the  European Union  ( ERC  Grant Numbers  339490  'Cortic_al_gorithms' and  101052963  ' NUMEROUS ,'  H2020 Research and Innovation programme  Grant Number  899287  ' NeuraViper '), the  Human Brain Project  (Grant Number  650003 ), and  BrainLinks-BrainTools, Cluster of Excellence  funded by the  German Research Foundation  (DFG,  EXC 1086 ), currently funded by the  Federal Ministry of Economics, Science and Arts of Baden Württemberg within   the sustainability program  for projects of the  excellence initiative.
Acknowledgments This work was supported by research grants from  EMBL  (H. A. ). The  EMBL Histology Facility  and the  Advanced Light Microscopy Facility  are acknowledged for support in sample preparation and image acquisition for histological analyses, respectively. EMBL IT Support  is acknowledged for provision of computer and data storage servers. We thank  Dmitry Molotkov  for his help in setting up the recording rig, and all the  Asari lab  members as well as Cornelius Gross and  Santiago Rompani  for many useful discussions.
Gutierrez et al. [23] Primary care EMR to extract ICD-9/ICD-10 codes directly from the patients' medical files in the BBC database from October 1998 to March 2019. Perform separate analyses using survey data collected independent of EMR during face-to-face follow-up visits to the BBC. LRTIs occurring in early childhood (0 to 2 years) significantly increased the risk of pediatric OSA at the age of 5 years (p < 0. 000). Severe preterm birth (GA ≤ 32 weeks) (p = 0. 000), maternal obesity (BMI ≥ 30) (p = 0. 001) and infant obesity (p = 0. 000) exhibited significant associations with the development of OSA. Infants with severe RSV bronchiolitis present an increased risk of OSA within the first five years of life (OR 2. 09, 95% CI 1. 12 to 3. 88, p = 0. 020). Children 2023, 10, 487 6 of 10 With regard to the viruses, seven (influenza B, influenza A, human metapneumovirus (hMPV), adenovirus, parainfluenza 3 virus, rhinovirus and coronavirus NL63) were investigated in one study  [24] , two studies only investigated EV  [24, 25]  and three studies investigated respiratory syncytial virus (RSV)  [22] [23] [24].
Risk of Bias in Included Studies Of the four studies, three were reported to be at serious risks of bias  [22, 24, 25]  and one  [23]  was reported to be at moderate risk of bias (Figure  2 ). In essence, the highest risk of bias is related to the performance measurement of outcomes (D6) (Figure  2 ). Gutierrez et al. [23]  directly from the patients' medical files in the BBC database from October 1998 to March 2019. Perform separate analyses using survey data collected independent of EMR during face-to-face follow-up visits to the BBC. Severe preterm birth (GA ≤ 32 weeks) (p = 0. 000), maternal obesity (BMI ≥ 30) (p = 0. 001) and infant obesity (p = 0. 000) exhibited significant associations with the development of OSA. Infants with severe RSV bronchiolitis present an increased risk of OSA within the first five years of life (OR 2.
Consequently, a relatively low number of children (21 with OSA versus 63 controls) were involved, and these subjects account for just a minor fraction of all children admitted to hospital during babyhood  [22]. Furthermore, within the control group, children were defined from multiple databases obtained from anterior studies. However, the authors provide no information on the prior studies used. In the case-control study, Yeshuroon-Kuffler et al. found that all (34) children in the OSA group had a history of a LRTI more than one year before the procedure, and nucleic acids of respiratory pathogens (such as rhinovirus, adenovirus and hMPV) were significantly more recurrent in their palatine tonsils. In the same way, Proenca et al. examined tonsils that were withdrawn due to diverse indications and indicated adenovirus, rhinovirus and hMPV to be the most frequent viruses in children with adenotonsillar diseases.
Imaging assessments MRI will be used to monitor focal CNS lesions and may be used to assess other pathophysiology, such as diffuse inflammation and neurodegeneration. In this multisite trial with harmonized protocols, MRI acquisition comprises isotropic sagittal 3D naive and contrast enhanced T1w, isotropic sagittal 3D FLAIR, and GRE QSM, as well as axial 2D multiband DTI, contrast-enhanced DSC, and orbital coronal 2D fat-saturated T2w and contrast-enhanced T1w MRI for brain imaging; sagittal 2D T2w and contrast enhanced T1w whole spinal cord MRI as well as axial T2w and contrast enhanced T1w MRI covering spinal cord lesions. MRI output data will be assessed by a centralized reading center for efficacy endpoints. MRI parameters of the brain, optic nerve, and spinal cord are shown in Table  2.
With diagnostic criteria established (4), there remains a need to identify markers of disease activity and predict periods of relapse in patients with NMOSD. Immunological and neurological biomarker data generated from concurrent blood and CSF samples from many participants will provide valuable insights into the inflammation and neurodegeneration mechanisms in the pathophysiology of NMOSD, and paraclinical markers such as MRI and OCT can provide useful information for disease monitoring  (18). SAkuraBONSAI will take a combined approach to evaluating the mechanistic characteristics of patients with NMOSD during the early stages of their condition, using longitudinal clinical, neurological, immunological, and imaging biomarkers to efficiently monitor the evolution of patients' disease over time with satralizumab treatment.
Around a quarter of newly diagnosed, treatment-naïve patients experience lasting disability after failing to respond to rescue therapy following their first attack  (23). The findings of SAkuraBONSAI will advance our understanding of new-onset NMOSD and may yield information that improves the speed and accuracy of treatment decisions, ultimately minimizing disability following a first attack. However, relapse events are predicted to be rare in SAkuraBONSAI due to the efficacy of satralizumab in AQP4-IgG+ NMOSD  (6, 7, 24). As such, the dataset produced by relapsing patients is expected to be limited, restricting the scope of analyses into potential predictors of attacks. A unique feature of SAkuraBONSAI is that MRI and OCT will be assessed at regular intervals, independently of symptoms. Changes in MRI (T2w and CE-T1w lesions) and OCT (GCIPL thinning) have been reported in NMOSD subjects independent of clinical attacks  (25, 26).
A future goal in the management of NMOSD is to be able to characterize a patient's prognosis in the early phases of disease and predict treatment response  (18). Some studies show that NfL and GFAP are elevated in patients with NMOSD, particularly during periods of disease activity (15-17). An analysis using data from the Phase 2/3 N-MOmentum study found that 62 out of 215 patients with NMOSD (29%) had elevated GFAP concentrations at baseline, and that these individuals had a 3-fold increased risk of relapse vs. patients with normal GFAP levels (15). GFAP levels were found to be significantly elevated within 1 week of an attack (from 168 to 2,160 pg/ml) and correlated with attack severity (15). Results from N-MOmentum also showed that high levels of serum GFAP were associated with subclinical MRI activity in individuals who did not relapse (15); this relationship will be further evaluated prospectively in SAkuraBONSAI.
Data generated during baseline assessments may provide clinically relevant insights into the underlying pathophysiology of NMOSD in newly diagnosed patients and among those who have not responded to RTX, offering an opportunity to rapidly disseminate information generated during the cross-sectional part of the study. Data for treatment-related outcomes will be generated longitudinally and will be disseminated once the study has been completed. In conclusion, the SAkuraBONSAI study offers an opportunity to develop a greater understanding of the natural history of NMOSD, including the potential to identify imaging and molecular biomarkers that may inform the prognosis and management of patients with newly diagnosed NMOSD, while understanding how patients who do not respond to RTX therapy compare with a treatment-naïve population.
Image acquisition was controlled by the Imspector software (Abberior Instruments). The performance and spatial resolution of the microscope was checked and optimized by visualizing and overlapping the PSFs of the laser beams using 150 nm gold nano-spheres and 40 nm fluorescent beads and correcting the main optical aberrations with the SLM. The spatial resolution was 175 nm (lateral) and 450 nm (axial) in confocal mode and 60 nm (lateral) and 160 nm (axial) in STED mode.
ECS volume fraction and widths SUSHI images are single sections taken from z-stacks or time-lapse series for Fig. 1B , selected to match the x-y-z planes. Brightness and contrast were adjusted for each individual image using ImageJ (NIH) and the look-up table (LUT) was 'grays'. To measure the ECS volume fraction (VF) and widths, images were binarized using a wavelet-based software SpineJ  (Levet et al. , 2020)  (Fig. 1A ). For the VF (α), we computed the ratio of pixels representing ECS over the total number of pixels (α = N ECS /N total ) in a region of interest. For ECS widths, we analyzed 3-pixel-wide line profiles drawn across the binarized images.
Measurements of astrocytic structures and dendritic spines Images of astrocytes and dendrites are shown as maximum intensity z-projections with the LUT 'orange hot'. Areas of astrocytic cell bodies and main branches were measured by drawing a contour line around the structures and calculating their surface areas in ImageJ. The widths of astrocytic fine processes were measured on raw images in ImageJ, using the 'Plot Line Profile' function after drawing a 3-pixel-wide straight line across the structure of interest. 'Dendritic vacuoles' were visually identified and quantified by counting them in the images acquired before and after PFA application. Morphological parameters of dendritic spines were measured using the SpineJ software. The software identifies the neck region and places lines that are orthogonal to the neck axis at regular intervals of 75 nm.
minutes of PFA fixation has no detectable effects on ECS volume fraction To assess whether chemical fixation using PFA has an effect on hippocampal ECS structure, we established an experimental workflow that allowed us to compare the same sample before and after PFA fixation in a paired manner (Fig. 1A ). As a readout of the ECS structure, we analyzed the volume fraction (α) and geometric widths of the ECS. The volume fraction α is an important structural parameter of brain tissue  (Nicholson and Hrabetova, 2017) , which is defined by the ratio of the ECS volume over the total volume in a region of interest. We performed time-lapse confocal shadow imaging at 5-minute intervals before and during PFA application using Calcein to label the ACSF that the slices were maintained in (Fig. 1B ). The images were binarized using SpineJ software  (Levet et al.
This work was supported by grants to UVN from the  ANR  ( ERA   NET NEURON ,  ANR-17-NEU3-0005 ,  ANR-17-CE37-0011 ),  Fondation pour la Recherche Médicale ,  Human Frontier Science Program  ( RGP0041/2019 ),  European Research Council   Synergy grant  ( ENSEMBLE , # 951294  to UVN) and a PhD fellowship from  Bordeaux Neurocampus Graduate Program  (to AI). SB received funding from  Horizon 2020 program  under the  Marie Sklodowska-Curie Grant  # 794492 , as well as from the  Fonds AXA pour la Recherche ,  AXA Banque Direction Banque Patrimoniale et ses donateurs. MA received funding from  Japan Society for the Promotion of Science (JSPS). The authors thank  R. Sterling  for preparing PFA solutions, constructing sample holder and ensuring safety measurements for handling PFA solutions. We thank  F.
RNA Extraction and RT-PCR RNA was extracted from SV40-transformed H9c2 cells, mouse brain, dorsal root, and heart after homogenization using Precellys homogenizer associated with a Cryolys cooling system to ensure homogenization at 4 • C for tissues followed by Tripure reagent solution (Roche), following the manufacturer's protocol. RNA was treated with DNAse I Amplification grade to remove any DNA contamination. cDNA synthesis was performed using PrimeScriptTM RT Reagent Kit (Perfect Real Time; Takara) as specified by the manufacturer. TRPV1-specific primers shown in Table  1  were designed using Primer-blast software (https://www. ncbi. nlm. nih. gov/tools/primer-blast/; accessed on 8 September 2023) and tested for efficiency. All PCR reactions were run in the CFX96 C1000 system supplied by Bio-Rad.
A cover slide was finally added. Fluorescent immunostaining was measured using a Nikon Eclipse Ti confocal microscope. The microscope was equipped with a 60× oil immersion objective. Images were processed with NIS software (Nikon). Alexa-488 was excited at 488 nm and Alexa-647 at 642 nm. Their respective emitted fluorescent lights were collected at wavelength 525/50 nm using a GaAsP detector and at wavelength 700/50 nm using a PMT detector.
#A6964) were pooled, and cell mortality was checked with propidium iodide (PI) by flow cytometry (Fortessa X-20). A total of 10,000 events in triplicates were recorded. Data were represented as a percentage of positive PI cells and were analyzed using DIVA Software (BD Biosciences, San Jose, CA, USA). Based on the literature, different conditioning strategies were used during the H/R sequence  [55]. As resiniferatoxin (RTX) and 5 -iodoresiniferatoxin (iRTX) were prepared diluted in DMSO (1/10,000), our negative control was obtained with DMSO, which did not affect the percentage of dead cells compared to without DMSO (control condition; Figure  S1A ). Preconditioning (pre-C) was induced by applying the TRPV1 agonist or antagonist 30 min before the sequence of H/R. Postconditioning (post-C) was generated by applying a TRPV1 agonist or antagonist at the onset of reoxygenation.
Statistical Analysis Using GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA), the D'Agostino & Pearson omnibus normality test verified normality. Data are represented as scatter plots where the line is the median ± interquartile range. The Kruskall-Wallis test with Dunns posthoc test assessed statistical analyses of multiple groups. Differences were considered significant when the p-value was <0. 05.
Acknowledgments: The authors address special thanks to  Murielle Robert  and  Andréa Kaczmarczyk  for their administrative support, to  Anaïs Corneloup  for her technical support, and to  Elisabeth Errazuriz  and  Christel Cassin  at the  CIQLE platform (Lyon, France ) for the electron microscopy. We express special thanks to  Melanie Paillard  for proofreading the manuscript. We are grateful to  Roger Tsien  ( University of California, San Diego, CA, USA ) and  Maria Teresa Alonso  ( University of Valladolid, Valladolid, Spain ) for the various biosensors.