dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis We followed the Cochrane Handbook of Systematic Reviews of Interventions to process and analyze data for meta-analysis  [47]. The results of EGs and CEs were compared after the intervention. All results are continuous variables. Meta-analysis was performed using Review Manager 5. 4 software (Cochrane). Standard mean difference and 95% CIs were used for continuous data. Subgroup analyses based on gender distribution were performed. The results were regarded as statistically significant when P<. 05  [47]. The heterogeneity test was quantified using the I 2 statistic and the chi-square P value. The I 2 statistic was considered low, moderate, or large at 25%, 50%, or 75%, respectively  [48]. A chi-square P value of. 05 or less suggests heterogeneous meta-analyzed studies  [47].
Selected Studies In total, 6410 potentially relevant studies were identified through database searching. After deduplication, the titles and abstracts of 4089 records were screened by checking the inclusion criteria; 256 studies were further screened by viewing the full texts for eligibility; 12 papers were included for the systematic review, and 10 papers were included for meta-analysis. Figure  1  shows the PRISMA flow diagram for paper selection. Five studies focused on community-dwelling older adults  [38, [49] [50] [51] [52] , 3 studies recruited older adults from care facilities  [29, 40, 53] , 1 study was conducted in hospital and home  [54] , 1 study recruited older adults in a clinical physical rehabilitation unit  [55] , and 2 studies did not report the research setting  [56, 57].
Acknowledgments We sincerely thank the library of  Central South University  for its help in the retrieval process of this study. This work was supported by the  Special Funding for the Construction of Innovative Provinces in Hunan  (grant  2020SK2055 ), the  National Key R&D Program of China  (grants  2020YFC2008503  and  2020YFC2008602 ), the  National Natural Science Foundation of China  (grant  72004237 ), the  National Key R&D Program of China  ( 2020YFC2008500 ), and the  Changsha County Science and Technology Plan Project  ( 2022037-2 ).
2g ,h and Extended Data Fig. 5 ). Overall, the massive transformation of assemblies from wakefulness to anesthesia can also be qualitatively summarized by plotting the localization of ongoing assemblies in the neuronal state space after dimensionality reduction (that is, in the space of the first three principal components (PCs) of a dataset including all assemblies and responses; Fig. 3 ). In this format, it becomes evident that assemblies observed in the awake state are essentially distinct from assemblies observed under isoflurane anesthesia. Moreover, under anesthesia, sound responses and ongoing assemblies span the same region of the neuronal state space, whereas in wakefulness they clearly span different regions (Fig. 3 ). In the awake state, spontaneous and evoked activity can thus be easily distinguished, whereas under isoflurane anesthesia, a sound response leads to population activity patterns that are extremely similar to ongoing activity.
Population events under anesthesia and in wakefulness Taking advantage of the robustness of GCaMP6s-based  20  , two-photon calcium imaging for the assignment of neuronal activity in identified neurons, we contrasted neural population activity in the auditory cortex of head-fixed mice, in the awake state (Fig. 1a ) and during light isoflurane anesthesia (Fig. 1b ). A first 10-min imaging session without auditory stimuli was followed by a 15-min session in which we presented 50 different simple and complex sounds (500 ms; Extended Data Fig. 1 ), new for the animal, each repeated 12 times and delivered in a random order. After sound presentation, another session without auditory stimuli was performed to evaluate the impact of sound presentations on ongoing activity. Then, the same protocol was repeated under anesthesia; 728 ± 180 (474-955) neurons could be imaged simultaneously in cortical layer 2/3 over a field of view of 1 × 1 mm 2 , covering about a quarter of the entire auditory cortex.
1 ). In humans, resting-state activity also displays reduced complexity and follows large-scale anatomical connectivity structures in the anesthetized state  37. Our results further indicate that, while preserving these large-scale spatial features and the activity of a large number of individual neurons, anesthesia abolishes responses that carry more precise and more sparsely encoded information about the stimulus (Fig. 4 ). Comparisons of anesthetized and awake datasets from different experiments have suggested the absence of important features of auditory responses, such as offset  38  or sustained responses  39, 40  , in the cortex in the anesthetized state. We could directly show that, under anesthesia, many neurons change or lose the specific sound response properties that they had during wakefulness.
Although it has been reported that pure-tone response properties are affected by anesthesia  19, 41  , the use of diverse complex sounds in our study may have magnified the effects of anesthesia, by highlighting more diluted aspects of the auditory code  42  , which seem also to be more sensitive to anesthesia (Fig. 4 ). Along the same lines, the diversity of spontaneous responses may be reduced in anesthesia due to the absence of attentional modulations and motor or multisensory inputs that impact the auditory cortex  10, 43, 44. Along with the larger diversity of sound responses, these important features of awake auditory cortical processing may contribute to the differences between spontaneous and evoked assemblies in the awake state. Thanks to large-scale imaging at single-cell resolution, our results revealed a profound change in the cortical and thalamic population dynamics at a mesoscopic scale under anesthesia, whereas activity at the individual neuron level is partially preserved (Figs.
1 2 3 4 ). This indicates that anesthesia induces network-scale effects across cortical layers (Extended Data Fig. 3 ) and thalamocortical connections (Fig. 5 ). How anesthetics such as isoflurane, which potentiates inhibition  45   Fig. 7  | Specificity of cortical neurons for evoked or ongoing activity is redistributed in anesthesia. a, Plot of the probability of being recruited in an ongoing event (spont. prob. ) plotted against the probability of responding to any sound (evoked prob. ) for 3,641 neurons in 5 mice in the awake state (left) and under anesthesia (right). The color code is defined as in the left panel. Under anesthesia, the three color-coded populations converge to a single group with strongly correlated probability of activation in spontaneous and evoked events. b, Top: time course of the mean firing rate profiles in the awake state (left) and under anesthesia (right) for each of the three groups of neurons defined in a for the awake state.
After the imaging session, atipamezole was injected intramuscularly to accelerate waking up. Zoletil was applied at a dose of 70 mg kg -1 (that is, 35 mg kg -1 of tiletamine and 35 mg kg -1 of zolazepam). This dose was sufficient for maintenance of all three animals tested under anesthesia over 45 min. Data collection and analysis were not performed blind to the conditions of the experiments. At a first level of screening, the mice with contaminated cranial windows and feeble labeling (low contrast or <200 neurons) were excluded from the experiments. At a second stage, during the preprocessing, the dataset with strong vertical motion artifacts, or feeble or absent auditory responses, were excluded.
Calcium signals processing and spike train estimation Data analysis was performed using Matlab scripts. Motion artifacts were first corrected frame by frame, using a rigid body registration algorithm. A single set of ROIs corresponding to the neurons was defined by running Autocell (https://github. com/thomasdeneux/ Autocell), a semiautomated hierarchical clustering algorithm based on pixel covariance over time  21  , on the concatenated data from the awake and anesthesia sessions. Neuropil contamination was subtracted  20  by applying the following equation: F corrected (t) = F measured (t) - 0. 7 F neuropil (t), where F neuropil (t) is estimated from the immediate surroundings (Gaussian smoothing kernel 3 , excluding the ROIs, s = 170 μm), where F is fluorescence and t time. The average neuropil signals shown in Extended Data Fig.
7  are computed by averaging F neuropil (t) across all putative neurons. We then applied the MLSpike deconvolution algorithm  22  (github. com/Mlspike) to the neuropil-corrected raw fluorescent signal, which finds the most likely spike train underlying the recorded fluorescence using a maximum-likelihood approach taking into account baseline fluorescence fluctuations. The parameters used by the algorithm were the typical time constant of calcium transient (1. 7 s) and a coefficient (range used: 4-6) adjusting for baseline drift compensation. Both these parameters were estimated to best fit the descending slope of experimental calcium spikes as well as the fluctuation of the baseline fluorescence. Time constant estimation was in accordance with the published estimations for GCaMP6s dynamics.
As it is visible from sample raster plots (Fig. 1  and Extended Data Fig. 1 ), there were periods of time where spikes were much more synchronized across the population, which could be interpreted as population events, departing from the fluctuations of an asynchronous population spiking process. To identify (1) whether those peaks of activity were above asynchronous activity baseline and thus above coincidence by chance and (2) when exactly the periods of synchronization started and ended, we applied the following algorithm: (1) The order of interspike intervals of each neuron was independently reshuffled 100×, so that 100 surrogate matrices (number of neurons × number of time frames) were created. For each of these surrogate datasets, a population firing rate was calculated. A new matrix (100 × number of time frames) was created where each row is a population firing rate for each of the 100 reshuffling trials.
From this matrix, we extracted the 99th percentile of the surrogate distribution within each time frame. The average 99th percentile across time frames was then calculated, and the final firing rate threshold was obtained by adding this average value to a local baseline estimate that aims to correct slow fluctuations of background firing rate. The baseline estimate was generated using an asymmetrical least squares smoothing algorithm with the parameters p = 0. 01 for asymmetry and λ = 10 8 for smoothness, adapted from Eilers and Boelens 26. (2) Experimental population firing rate trace was smoothed using Savitzky-Golay filtering (with order 3 and frame length 7) in Matlab to get rid of nonessential peaks (Extended Data Fig. 1e , gray trace). Local maxima for the smoothed curve that were above the previously defined threshold were retained (red dots).
The adjacent local minima around each of the local maxima were identified (blue and green circles). Time frames identified in this way were considered as the start (blue) and end (green) of population events (Extended Data Fig. 1e ). All the neurons that had at least one spike during this time interval were defined as a neuronal assembly. (3) Neuronal assemblies were then described as binary vectors of length equal to the total number of neurons in the recorded field of view. A value of 1 indicated the participation of the cell in the assembly with at least one spike over its duration and a value of 0 indicated an absence of participation. Neuronal assemblies could be detected in the same way during periods of stimulation and periods without stimulation (Extended Data Fig. 1e ). However, in most of our analyses, neuronal responses evoked by sounds were quantified irrespective of the detection of a population event after sound onset.
Dimensionality reduction The neuronal state space is defined as the space of activity (probability of responding to a sound or participating in an ongoing assembly) of all simultaneously recorded neurons. A given event is represented by a vector that has the dimensions of the neuronal population. To represent population vectors in a three-dimensional (3D) space that captures a maximum of the variance without imposing nonlinear distortions of the space, we used principal component analysis (PCA). PCA was performed on a dataset pulling all ongoing assemblies and sound responses during both anesthesia and wakefulness. Projections of population vectors on two of the first three PCs were used for displaying them in a 2D space as in Fig. 3. https://doi. org/10. 1038/s41593-022-01168-5.
Clustering of single neurons Clustering was also used to organize neurons according to the similarity of their responses to the sounds. Due to the large variability observed in many neurons, this analysis is not exhaustive but rather aims at identifying principal classes of responses within our dataset. Clustering was performed across the five cortical imaging sessions, thus including 3,641 neurons, and the seven thalamic axon imaging sessions (13,314 terminals), in both awake and anesthetized states. Each neuron was characterized by a vector of 50 elements (corresponding to the number of presented sounds), where each element contained the information about the number of responses during the trial (from 0 to 12). Pearson's correlation matrix for all neuron/ terminal response vectors was constructed by computing Pearson's correlation coefficient between them.
To identify the sounds to which every cluster was significantly tuned in different conditions (awake and anesthesia), the sound responses of every neuron within a given cluster were reshuffled over all the stimulation instances (597). The number of nonzero reshuffled responses for 12 randomly chosen instances (out of 597, corresponding to the number of presentations of the same sound during the trial) was averaged over all neurons of the cluster. We used one-way analysis of variance (ANOVA; Matlab, Mathworks), separately applied to every cluster and every condition (awake, anesthesia), to identify the sounds that had significantly higher responses than expected from the shuffled dataset. The ANOVA function also returns the particular sounds for which the response was significant.
Acknowledgements We thank  T. Deneux ,  G. Hucher  and  A. Daret  for their technical support,  M. Torao-Angosto  for help with pilot experiments and numerous colleagues for comments on analyses and figures. Funding was provided by the  European Community ,  Future and Emerging Technologies program-Human Brain Project  (no. H2020-945539  to A. D. );  Paris-Saclay University 'Initiatives de Recherches Stratégiques'-NeuroSaclay and Icode  (to B. B. and A. D. );  Agence Nationale pour la Recherche  (no. 12-PDOC-0006  to B. B. , PARADOX to A. D. );  Région Ile de France-DIM Cerveau Pensée-MULTISENSE  (to B. B. );  Fondation pour l'Audition  (nos. FPA IDA02  and  APA 2016-03  to B. B. ); and the  European Research Council  (no. CoG 770841 DEEPEN to B. B. ). B. B. thanks the  Fondation pour l'Audition  for their support to the  Institut de l'Audition.
Acknowledgements The authors would like to thank all patients in France, the  US, China  and the  UK. The authors would also like to thank  Michael Acquadro  for conducting and analyzing interviews in France, his input and critical review of the manuscript and  Yujiao Wang  for conducting and analyzing interviews in China. The authors also thank  Laurence Rous, PhD ,  Emma Pilling, PhD , and  Marielle Romet, PhD  ( Synergy Pharm/Santé Active Edition ) for providing medical writing and English editing assistance, funded by  Observia.
In addition, TWIST1 protects NB cells from the pro-apoptotic effects mediated by MYCN, through the inhibition of the ARF/p53 pathway and cooperates with MYCN in NB to uphold both in vitro cell proliferation and in vivo tumor growth  14, 17. Recently, TWIST1 was also identified as a key regulator of MYCN-driven gene regulation through their cooperative binding on enhancers  17. In this study, we initially revealed the correlation between the expression of TWIST1/2 and NB clinical prognostic factors in silico on primary NB gene expression datasets and in tumor tissue microarrays. Using an in vivo model for transcriptomic analyses, we then unveiled the impact of CRISPR/Cas9-mediated TWIST1 silencing on NB tumor growth, metastatic colonization, and the reorganization of the tumor microenvironment (TME).
Results High levels of TWIST1 RNA expression are associated with poor outcomes in patients with NB. In silico analysis using the CCLE database (https://portals. broadinstitute. org/ccle) shows that NB displays the highest levels of TWIST1 expression among 40 cancer cell lines, whereas TWIST2 is barely detected (Supplementary Fig. 1a ). To evaluate whether TWIST1/2 expression correlates with patient outcomes and NB prognostic factors, we analyzed two large clinical cohorts of primary NB tumors using the R2: Genomics Analysis and Visualization Platform (http:// r2. amc. nl) (SEQC  18  , n = 498; Kocak  19  , n = 649). In both datasets, a high level of TWIST1 transcript strongly correlates with both a reduced overall survival (OS) (Fig. 1a ; Supplementary Fig. 1b ) and event-free survival (EFS) (Supplementary Fig.
1c ). Moreover, the expression of TWIST1 was more elevated in presence of disease progression (Fig. 1b ); in MNA NBs (Supplementary Fig. 1d ); and in higher stage tumors (stages 3 and 4 vs 1 and 2; stage 4 vs 4s) (Supplementary Fig. 1e ). We stratified patients of the SEQC dataset according to the level of TWIST1 expression and either the risk (HR vs low-risk (LR); (Fig. 1c ) or MYCN status (Supplementary Fig. 1f ). For HR or MNA patients, TWIST1 expression level had no impact on the EFS. Conversely, for LR cases and no-MNA tumors, a high level of TWIST1 expression was associated with a reduced outcome, likewise MNA or the HR status, hinting to a possible role for TWIST1 as a prognostic factor of adverse event for these patients. As opposed to TWIST1, in the two same datasets, higher levels of TWIST2 were associated with both a better OS and EFS in NB patients (Supplementary Fig.
To identify a TWIST1-associated gene signature relevant in primary NB we combined our ortho_1 transcriptomic analysis with RNAseq data of primary NB tumors. Using the 'R2 Platform, we first listed the genes either correlated (R positive) or anti-correlated (R negative) with TWIST1 expression in the SEQC dataset of NB tumors (n = 7737 genes with R absolute value > 0. 225). Second, we crossed this list of genes with the 2011 DE genes between SK-N-Be2c-Control and -sgTWIST1 tumors, either up-(FC positive) or downregulated (FC negative) by TWIST1. We found 763 genes in common (Fig. 5a ; Supplementary Data 4) among which we selected those that had both R and FC either positive (172 genes) or negative (317 genes). We called these resulting 489 genes the TWIST1signature (Fig. 5a ; Supplementary Data 4).
Using the same SEQC dataset, we analyzed the clinical significance of the signature, and observed that genes correlated with TWIST1 in NB patients and upregulated by TWIST1 in ortho_1 tumors (R and FC positive) mostly had an elevated level of expression in high-risk, more advanced stages and MNA tumors. In addition, these tumors displayed a low level of expression of genes downregulated in the TWIST1-signature (Fig. 5b ). Finally, an elevated expression level of the TWIST1-signature was associated in the SEQC and Kocak datasets with a poor OS and EFS for both the complete patient cohorts and the sub-cohorts without MNA (Fig. 5c ). Among the top deregulated genes in the TWIST1-signature, several have crucial roles during embryonic development, in particular for the correct development of the nervous system (BMP7, FGF2, DTNA, MATN2, PCDHA1, PMP22, and SCL1A3).
Moreover, most of the top upregulated genes are involved in the organization of both TME (PDGFRA, VCAN, BMP7, and FGF2) Fig. 1  TWIST1 RNA expression is associated with poorer outcome of NB patients and displays an opposite protein expression profile in a NB tissue microarray. a-c Analysis of TWIST1 expression in the SEQC dataset of primary NB tumors. a Kaplan-Meier OS curve associated with TWIST1 expression. Expression cutoff: 44. 441. b Box-and whisker plots showing the expression of TWIST1 in relation to disease progression. c Kaplan-Meier EFS curves showing the stratification of patients of the SEQC dataset according to the risk classification (high-risk: HR; low-risk: LR) and TWIST1 expression (high or low). d TWIST1 and TWIST2 protein expression was analyzed by IHC using a NB TMA containing 97 tumor sections: 72 primary tumors, 25 matched metastases and 44 matched control normal tissues, adrenal glands, and sympathetic ganglia (SG).
This TWIST1-tumor-stroma signature is composed by 77 DE genes, 33 up-and 44 downregulated (Fig. 7a ; Supplementary Data 5). Several play a pivotal role in the regulation of focal adhesion (EGFR, ITGA11, ITGA6, and PDGFRB); cell migration (COL5A1, ITGAV, ITGB3, PDGFRB, and TGFB1); proliferation (FGF1, FIGF, and IFI16); angiogenesis (ACKR3, ACVRL1, EGFL7, FGF1, FGFR2, and FIGF); and inflammatory and immune responses (NGFR, TNF, TNFRSF1A, TNFRSF1B, TNFRSF4, TNFRSF9, TNFSF12, TNFSF13, and TNFSF4). A high level of expression of the TWIST1-tumor-stroma signature was associated with a poor OS and EFS of NB patients in both the SEQC (Fig. 7b ) and the Kocak datasets (Supplementary Fig. 7a ). To validate the tumor-stroma signature at the protein level and further characterize TWIST1-mediated alterations in cell-cell communication, we analyzed the secretome of SK-N-Be2c-Control and -sgTWIST1 cells in vitro by HPLC/Tandem MS using their conditioned media (CM) containing both secreted proteins and extracellular vesicles released by tumor cells.
5  Identification of a TWIST1-associated gene signature correlating with poor prognosis in NB. a Heatmap showing 763 common genes either correlated or anticorrelated with TWIST1 in NB patients and DE in the ortho_1 tumors, and volcano plot showing the distribution of the 489 genes of the TWIST1-signature according to their log2(FC) in SK-N-Be2c ortho_1 tumors and R values in the SEQC dataset. The binary side color bar going from green to red indicates DE genes anti-correlated (R < -0. 225, green) or correlated (R > 0. 225, red) with TWIST1 in the SEQC dataset. The black bar shows the genes that have both FC and R values either positive or negative representing the TWIST1-signature, and the gray bar the genes that have opposite FC and R values (not included in the signature). b Heatmap hierarchical clustering showing different expression pattern relative to TWIST1-signature genes generated using the R2 Platform (http://r2.
amc. nl). Columns represent patients annotated in the SEQC cohort; the 489 genes are clustered hierarchically along the left y-axis. Clinical criteria taken into consideration (risk groups, tumor stages, and MYCN amplification status) are indicated on the top by color codes. The heat map indicates in red, blue, and white a high, low, and a medium level of gene expression (z-score), respectively. The blue-white-red color bars depicted at the bottom of the heatmap represent the z-score of TWIST1_Up and TWIST1_Down gene sub-lists of the signature, as well as for the zscore of the whole signature (weighted). c Kaplan-Meier OS and EFS survival curves according to the expression level of the TWIST1-signature in both the SEQC and Kocak datasets, in the complete cohorts and in sub-cohorts of patients without MNA (no-MNA).