dataesr/scientific-paragraphs-categorization huggingface dataset:
BRET signals were measured 5 min after the addition of the last compound. All experiments were performed at 37 °C and pH 7. 4 unless otherwise indicated. In all experiments, dimethylsulfoxide (DMSO) alone was used negative control (vehicle). BRET data processing and statistical analysis. Concentration-response curves, kinetics, and statistics were analyzed using the software Prism 8. 01 (GraphPad Software). Sigmoidal dose-response curves were fitted using Eq. 2: Y = Bottom + ðTop À BottomÞ 1 + 10 logEC50ÀX , [2] where X is the logarithm of agonist concentration and Y is the response; bottom is the Y value at the bottom plateau; top is the Y value at the top plateau; and Log EC 50 is the X value when the response is halfway between bottom and top. A t test corrected for multiple comparisons using the Holm-S ıd ak method was used to assess the statistical significance of the difference calculated between two set of EC 50 /IC 50 values.
3 adjusted with KOH). For all experiments, the osmolarity (measured with a cryo-osmometer type 15 L€ oser) of the external salt solution was adjusted to 300 mOsm with mannitol and pH adjusted as indicated. Cells were viewed under phase contrast using a Nikon Diaphot inverted microscope. Borosilicate glass micropipettes (GC150F-10, Harvard Apparatus, Phymep) were pulled with a DMZ-Universal puller. The pipettes had a mean resistance of 4 MΩ when measured under standard recording conditions. An RK-400 patch-clamp amplifier (Biologic) was used for whole-cell recordings. Stimulus control, data acquisition, and processing were carried out on a PC fitted with a Digidata 1200 interface, using the pCLAMP 10. 7 software (Molecular Devices). Current records were filtered using a Bessel filter at 1 kHz and digitized for storage and analysis.
The voltage protocol generation and data collection were performed with the PatchController384 V1. 6. 6 and Data Controller V1. 6. 0. The protocol consisted of two applications of external solution (control period) containing 140 mM NaCl, 4 mM KCl, 2 mM CaCl2, 1 mM MgCl2, 10 mM Hepes, and 5 mM glucose, at pH 7. 4, followed by the addition of one application of the agonist capsaicin EC 50 (100 nM) for 1 to 2 min, followed by the addition of 10 μM of the test compound (1 to 2 min), in the presence of capsaicin EC 50 , and lastly the addition of the full block 10 μM capsazepine (1 min). The current was monitored using a ramp protocol from À100 mV to +100 mV over 300 ms, from a holding potential of À60 mV, which was repeated every 20 s. In each condition, the maximum DMSO concentration at the end of the run was 0.
The research leading to these results received funding from 1)  Aquitaine Science Transfert , the  Technology Transfer Accelerator Office of New Aquitaine (France) , 2) the  French National Research Agency (ANR)  under grant agreement  ANR-19-CE44-0010-02  (the  CANALBRET  project), and 3) the  New Aquitaine regional council  under grant agreement  AAPR2020I-2019-8140410  (The  PHYSTRIG  project). We thank  Bernard Veyret  for his wise advice and proofreading of the manuscript.
Funding This research was supported by the  Chair SILVERSIGHT Agence Nationale de la Recherche  ( ANR-18-CHIN-0002 ), the  LabEx LIFESENSES  ( ANR-10-LABX-65 ), the  IHU FORe-SIGHT  ( ANR-18-IAHU-01 ), and the  Fondation pour la Recherche sur Alzheimer (FRA).
We searched for relevant literature using PubMed and Google Scholar databases. We focused on, but did not limit to, the past decade. We used combinations of two or more keywords  9  (eg, seizures, aging, lateonset epilepsy, neurovascular unit, blood-brain barrier (BBB), inflammation, glial cells, stroke, traumatic brain injury, neurodegeneration, tau-hyperphosphorylation, amyloid beta, Alzheimer's disease, and vascular dementia) to retrieve and select pertinent knowledge, including perspective views, hypotheses, and knowledge gaps. We first summarize the structural and cellular elements of the healthy NVU. Next, we focus on clinical and experimental examples supporting the involvement of NVU dysfunction, within a pro-inflammatory environment and not limited to BBB permeability, in the occurrence of seizures and epilepsy during aging.
ACKNOWLEDGMENTS This work was supported by  ANR-Epicyte ,  ANR-EpiCatcher, ANR/Era-Net Neu-Vasc  and  ANR/ HepatoBrain  to NM, and  EpilepsieNL  project number  EF 20-11  and the  European Union   Horizon 2020 WIDESPREAD-05-2020-Twinning, EpiEpiNet  (grant agreement  952455 ) to EAvV. We want to thank  Raphael Chancel  for the insightful discussion and help.
CAIDE dementia risk score CAIDE scores were calculated for each participant at baseline. 11 he CAIDE score is composed of weightings by reference to age, sex, education, systolic blood pressure, BMI, total cholesterol, physical activity and APOE4 genotype (all variables measured at baseline). The CAIDE score ranges from 0 to 18, whereby a higher score represents higher risk of future dementia. For the specific purposes of data visualisation and confirmation of interaction results only, we binarized the CAIDE score using a cutoff of ≥6, following the recommendation of its developers who adopted this cut-off in the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) study. 20 The raw CAIDE score (range 0-18) was used for all other analyses and purposes.
Quantitative measure of WMH volume WMH lesion maps were obtained using an automated script on the Statistical Parametric Mapping 8 suite (http://www. fil. ion. ucl. ac. uk/spm/) on FLAIR MRI; details described elsewhere,  25  and under online supplemental materials. WMH were segmented into lobar regions (frontal, parietal, occipital, temporal) using a region of interest template, as previously described. 25 WMH were also classified as periventricular or deep WMH based on threshold distance from ventricles (figure  2 ). WMH volumes were normalised by total intracranial volume (TIV) to account for individual differences in head size ((WMH/TIV)×100%) and underwent cube root transformation due to right-tailed skewness.
Measures of systemic inflammation Inflammation was quantified using two widely studied serum markers of systemic inflammation, C-reactive protein (CRP) and fibrinogen. 30 CRP was analysed on the Beckman Coulter AU System using a CRP latex reagent (Beckman Coulter, Brea, California, USA). As a form of sensitivity analysis, we analysed CRP data both with and without exclusion of participants with CRP exceeding 10 mg/L in separate analyses to examine the consistency of results. Core analysis was conducted with the full sample (ie, represented in tables and figures), while filtered data were used in secondary analyses. Fibrinogen was measured using the Clauss method. 31 CRP and fibrinogen data underwent cube root transformation due to skewness.
Statistical analysis All statistical analyses were conducted using R (v3. 6. 3; www. R-project. org). To test associations between the CAIDE score and baseline severity of SVD and systemic inflammation, Spearman's rank correlation was used. To further assess the relative contribution of individual CAIDE components (eg, age, hypertension, obesity) to each marker of SVD and inflammation, general linear models were fitted to each biomarker in separate models, with all CAIDE components included as predictors (results under 'Baseline associations' section). To assess whether baseline CAIDE scores were associated with longitudinal changes of SVD markers and inflammation, linear mixed-effects models were fitted using the lme4 package in R to assess changes in WMH, EPVS, CRP and fibrinogen, with CAIDE×time interaction terms as fixed effects, and subject intercept as random effects.
We consider in this paper the evolution of teleworkers' self-reported perceived job satisfaction, job stress and job productivity during the lockdown compared to before. We test our hypotheses on survey data obtained from the 'home office' module of the COVID-19 socioeconomic impacts (SEI) survey conducted between end of May and early July 2020 on around 450 employees working in Luxembourg. In Luxembourg, a general lockdown was applied between March 16 and May 3, 2020. As cross-border workers represent around 48% of the workforce employed in Luxembourg, bilateral fiscal agreements were concluded with bordering countries in order to facilitate home office. The article is structured as follows. In the next section, we present our theoretical background and literature review. Then we describe the methods we adopted in this research and the data studied.
Data We use data from the 'COVID-19 Socio-Economic Impacts (SEI) survey' conducted by LISER and the University of Luxembourg with the support of the Luxembourg National Research Fund (FNR). These data were collected between the end of May and the beginning of July 2020 with questions regarding before and during the lockdown induced by the COVID-19 health crisis in spring 2020. As described in a report presenting the SEI survey  [78] , the data collection was organized as an open-ended online survey rather than a sample drawn using a probability sampling method. This allows for the collection of an initial rapid assessment of the socio-economic effects of COVID-19 on a sample of people who are easy to contact. The survey was announced through a press release and an extensive newspaper and social media campaign.
The socio-demographic characteristics do not intervene in this part of the analysis. As our data are quantitative, our cluster analysis is conducted on the coordinated computed by the MCA. We tested different specifications of our MCA, for instance, instead of using a variable capturing the evolution, we integrated the intensity of use of each tools before and during the lockdown. We used also a variable measuring the fact that teleworkers are below the median concerning the number of daily used tools instead of their position with the respect to the total number of tools used. We retained the specification with the most consistent criteria for choosing the number of profiles in the cluster analysis. This choice was based on the cubic clustering criterion, the semipartial R square, the pseudo F statistic, the pseudo t 2 statistic and the dendogram  [89, 90].
The link between collaborative and communication digital profiles and job well-being and job productivity As shown by the main results reported in Table  3 , the analysis highlights that the digital tools profiles are related to the evolution of job satisfaction, of job stress and of job productivity and that the relationships depend on the specific profiles. For the first profile with a stable and a daily use of the four collaborative and communication digital tools, the result in Table  3  reveals that belonging to this profile increases job stress during the lockdown more than for the non-user/limited user profile (profile 3). For instance, belonging to the first profile, rather than the third one, increases the probability that the frequency of job stress has greatly increased by 0. 083 conditional on the distribution of all the model variables being what they are in the dataset.
Coimmunostaining analysis using CCL8 and IBA1 antibodies revealed that macrophages produce CCL8 in patients with F. nucleatum-positive CRC with high TSAT levels (Figure  6B ). To examine the clinical relevance of CCL8 chemokines in CRC progression, we examined CCL8 mRNA expression in patients with stage I-III CRC from The Cancer Genome Atlas database. Kaplan-Meier survival analysis revealed that high CCL8 expression was associated with poor OS in patients with CRC (P = 0. 0077) (Figure  6C ). Taken together, these results suggest that macrophage-derived CCL8 is associated with poor prognosis in patients with F. nucleatum-positive CRC with high iron status.
nucleatum at a MOI of 10 for 3 hours. RT-qPCR analysis of chemokine expression is shown. Data are presented as the mean ± SD of triplicates from a representative experiment. high or low according to the median cutoff for F. nucleatum DNA; cases with undetectable F. nucleatum DNA were defined as negative. Evaluation of TSAT and patient characteristics. We evaluated the preoperative iron status using TSAT, calculated as the ratio of serum iron to total iron-binding capacity  (41). We defined TSAT as ≥30%, which is higher than the normal range, as high levels of TSAT and TSAT <30% were considered normal. We subdivided enrolled patients according to their intratumoral F. nucleatum levels and preoperative TSAT levels. Patient characteristics according to TSAT levels in groups with high amounts of F.
05, Supplemental Table  3 ). GO analysis of differentially expressed genes was performed using the Enrichr platform (https://maayanlab. cloud/Enrichr/; Supplemental Table  4 ). Fold change rankings were used in GSEA (http://software. broadinstitute. org/gsea/index. jsp) to identify differentially regulated pathways (q < 0. 05; Supplemental Table  5 ). Gene deletion using CRISPR/Cas9 system. RELA, IKKα, IKKβ-KO THP-1 cells were generated by the CRISPR/Cas9 system  (44). The CRISPR/Cas9 guide RNA targeting guide sequences were designed using the CRISPR (http://crispor. tefor. net/). The guide sequences are listed in Supplemental Table  6. Targeting sequences were cloned into the lentiCRISPR v2 plasmid (52961; Addgene). The lentiviral transfer plasmids were cotransfected with the packaging plasmids pLenti-P2A and pLenti-P2B (LV003; Applied Biological Materials Inc.
The sequences of qPCR primers are listed in Supplemental Table  7. Data were normalized to GAPDH levels and analyzed using the ΔΔCT method. We defined a CT value of more than 40 or nonspecific amplification in the melt curve analysis as not detected. Immunoblot analysis. In brief, equal amounts of protein samples were resolved using SDS-PAGE under reducing conditions. The following antibodies were purchased and used in the present study: antibodies against IKKα (catalog 11930), IKKβ (catalog 8943), IκB (catalog 4814), pIκB (S32; 2 catalog 859), NF-κB p65 (catalog 8242), and pNF-κB p65 (S536; catalog 3033) were procured from Cell Signaling Technology; antibodies against actin (ab3280) were from Abcam; antibodies against HSP90 JCI Insight 2022;7(21):e156802 https://doi. org/10. 1172/jci. insight.
156802 (catalog 610418) were purchased from BD Biosciences. See complete unedited blots in the supplemental material (Supplemental Figures  9  and 10 ). Immunofluorescent staining. Briefly, cells were fixed with 4% paraformaldehyde and permeabilized with 0. 1% Triton X-100. Then, cells were stained with anti-NF-κB p65 antibody (8242; Cell Signaling Technology) and Alexa Fluor 488-labeled goat secondary antibody (A-11008; Invitrogen). Images were obtained with the KEYENCE BZ-X800, and quantification was performed using a BZ analyzer (KEYENCE). Analysis of published data. OS was compared among patients with stage I-III CRC presenting high or low levels of CCL8 expression in The Cancer Genome Atlas data set accessed with the Human Protein Atlas website  (45). Downloaded clinical data (follow-up period and dead or alive) of patients with stage I-III CRC were matched to the CCL8 expression (FPKM value) in tumor tissues and analyzed using the Kaplan-Meier survival method.
Acknowledgments This work was supported by  Japan Society for the Promotion of Science (JSPS) KAKENHI  (grant  21H02764 ),  Japan Agency for Medical Research and Development (AMED) PRIME  (grant  JP22gm6210030 ),  Naito Foundation ,  The Uehara Memorial Foundation ,  Inamori Foundation ,  Daiichi Sankyo Foundation of Life Science, Japan Foundation for Applied Enzymology  (to TM), and by  JSPS KAKENHI  (grant  20H03755 , to YB). The authors thank  T. Yamauchi ,  K. Terada , and other laboratory members for technical assistance and discussion;  Core Laboratory for Medical Research and Education; Kumamoto University School of Medicine , for their technical support; and  F. Zhang  for lentiCRISPR version 2. Address correspondence to: Toshiro Moroishi, Department of Cell Signaling and Metabolic Medicine, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan.
Participants In total, 45 adults participated in our research. Two control participants were excluded due to errors during data collection. The final sample consisted of forty-three participants, 22 were diagnosed with autism spectrum disorder (ASD) without intellectual disability or language impairment, and 21 were controls participants without autism (CP). The two groups did not differ in age, gender and education (Table  1 ). All participants with ASD were diagnosed by trained clinicians, the diagnoses were confirmed with Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule, IV-module (ADOS-IV. )  [50, 51]. Twelve participants had one or more comorbid disorders (attention deficit hyperactivity disorder (5), obsessive-compulsive disorder (3), generalised anxiety disorder (2), bipolar disorder (1), depression (1), and schizophrenia (1)).
Data preprocessing and analysis Preparation of HRV data was carried out using Python 3. 7 with NumPy 1. 20. 1  [57] , pandas 1. 2. 3  [58] , and SciPy 1. 6. 1  [59]  data processing packages. Since the samples were measured at a different rate for the Polar H10 (one sample per second) and the Obimon Prox (one sample per milliseconds) devices, we resampled the Obimon data by taking the median for each second. Missing data were dropped from the analysis (9. 52% of the control group and 18. 2% of the ASD group did not have a complete HRV record). To synchronise HRV with the proximity data we needed to obtain the timestamps for each file containing the RR intervals. The first timestamp was obtained from the name of the file which indicated the start time of the recording. Since the exported files only contained the RR intervals without a timestamp for each sample, the interval values themselves were used to create the time elapsed since the first sample.
As RR intervals annotate the time between two successive heartbeats, it was possible to append the value of the RR interval to the time of the previous sample. After obtaining the timestamps, data points were replaced with the median if they indicated RR of 1200 milliseconds (ms) or above, or if their absolute Z score was higher than 2. Triggers added to the distance data (see Distance measuring) were adjusted manually if needed. HRV was estimated as the root mean square of successive RR interval differences (Root Mean Square of Successive Differences, RMSSD) since this measurement is relatively resistant to by-products caused by breathing  [60] , and can be obtained for a shorter (10 seconds) period of time  [61]. Calculations were done by the following formula (1): RMSSD ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 1 N À 1 X n i¼0 RR iþ1 À RR i À � 2 s ð1Þ Baseline HR and HRV were measured and calculated for 60 seconds (s) at baseline, and reactive HR and HRV were measured during the interpersonal distance task one minute after starting the explicit paradigm (from +60 s to +120 s).
Furthermore, we calculated RMSSD around time points where interpersonal distance data were reported. To calculate RMSSD for each explicit condition, eight local minimums of the distance data were determined from data recorded by Obimon Prox. These eight time points indicate the shortest distances between the participant and the experimenter, corresponding to the time point when the reported distance was reached. RMSSD was calculated for an interval starting 10 seconds prior to reaching the reported distance.
Funding: This research was supported by the  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 , DN); the  Hungarian Scientific Research Fund  ( OTKA K 128016 , DN); the  Hungarian Scientific Research Fund  ( OTKA PD 124148 , KJ); the  Janos Bolyai Research Scholarship  of the  Hungarian Academy of Sciences  (KJ);  New National Excellence Program of the Ministry for Innovation and Technology (HU) , ( U ´NKP-19-2-I-ELTE-332 , AG); the  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 , DN) and  Higher Education Institutional Excellence Programme of the Ministry of Human Capacities in Hungary , within the framework of the  Neurology thematic program   of Semmelweis University  (JMR). The funders had no role in study design, data collection, and analysis, the decision to publish, or preparation of the manuscript.
To address these main concerns, we developed a novel modelbased approach (Fig. 4A ) consisting in using the dynamics of the Multisensory Correlation Detector units (audio and visual inputs, MCD CORR , and MCD LAG outputs) in response to the audiovisual sequences presented in both tasks to explain MEG activity. Specifically, encoding models have been successfully applied to EEG and MEG recordings to understand speech processing  [37] [38] [39] [40] [41]. The basic idea is that brain activity recorded with temporallyresolved neuroimaging can be decomposed as the convolution of the stimulus inputs and a filter. The filter, also known as "temporal response function", is usually unknown, and leastsquare fitted with a linear model. The quality of the resulting encoding model is then assessed with a cross-validation procedure with the model being fitted on one part of the data and evaluated on the unseen part.
It located this pattern in bilateral posterior superior temporal gyri, consistent with auditory cortical sources. When applied to the visual-only responses, a temporal response function that took the visual input components of the Multisensory Correlation Detector (MCD V ) as predictor was able to predict the observed MEG activity (⍴ = 0. 11, p < 0. 001). Cluster-based permutations revealed a significant bilateral posterior cluster of predicted sensors. Source estimations located the pattern in bilateral occipital cortices. These results validate the use of the temporal response function methodology to capture basic auditory and visual evoked responses, as well as their implementation in the Multisensory Correlation Detector. Having ensured that the model-based temporal response functions provided coherent results for the unisensory localizers, we tested the experimental data on the causality and temporal order judgments.
7 ). The results show that the temporal response functions are stable across the two halves. This rules out the possibility that this activity reflects decisional processes, as decisional processes are supposed to happen only late in the sequence presentation. One further limitation could be that 4 out of 6 stimuli are perceived close to the 50/50 point in the temporal order judgment blocks. It could be the case that the ambiguity of these stimuli for this task is so great that the signals being measured in the brain are uncertainty plus guess, rather than temporal order computations per se. To rule out this hypothesis, we replicated the analysis in a reduced dataset comprising only the stimuli that are ambiguous in the temporal order judgment (see Supplementary Fig. 8 ). The results show that the temporal response functions remain stable in this reduced dataset and that the results remain unchanged compared to the complete dataset.
The time interval between the end of a stimulus and the auditory cue was randomly chosen between 800 and 1200 ms. The order of the stimuli was pseudo-randomized, so that none of the sequences were presented more than three times consecutively. Stimuli. Stimuli were designed and presented using Matlab (R2012a, Mathworks Inc. ) with Psychtoolbox-3 62 on a PC (Windows XP). Six audiovisual stimuli were used in the experiment and each consisted of a one-second sequence of 5 auditory bursts and 5 visual flashes (see Supplementary Table  1 ). Sounds were 10 ms bursts of pink noise, whose intensity linearly ramped-on for the first half of the time, and off for the second half. The sampling frequency used was 44. 1 kHz. The sound pressure level was set to a comfortable hearing level, around 70 dB, for all participants.