dataesr/scientific-paragraphs-categorization huggingface dataset:
Every state of the system at a particular time is affected by the AL (the "information" of the IS is because of its ability to influence every point in the phase space). This provides a number of topological measures in the structure of this attractor landscape, namely NoEL (number of energy levels in the IS), frondosity (number of stationary nodes), criticality (sensitivity to perturbations), synchronicity (probability of all brain networks to behave in the same way) and cooperation (how some networks are influenced by others). More details about IS Reanimation de Langue Francaise (https://www. srlf. org/, reference: M-neuro-Doc): secretariat@srlf. org. A table with the values of all IS measures (the data producing the results in Table  1  and Figs  4 5 6 ) is available at https://github.
com/ fersoler/CooperativeInformationStructures. A Jupyter notebook for SageMath/Python allowing the creation and visualization of Information Structures for cooperative Lotka-Volterra systems is available at https://github. com/fersoler/ CooperativeInformationStructures. It also computes all IS measures introduced in this paper and contains an example of the calculation of mean and standard deviation for a timeseries of 295 αi values of a 7-node system. measures can be found in Methods. The IS can be computed exactly when the number of dynamical variables is low by a Lotka-Volterra Transform (LVT) procedure  [7, 13]. This simple model matches the conditions so that the invariants can be ordered in energy levels, supports an algorithm of low computational complexity, and has well-known conditions for the existence and uniqueness of its global attractor, which can be characterized in detail.
Here, we apply this precise mathematical framework to two empirical neuroimaging datasets (Liège and Paris) including BOLD fMRI resting state signals of healthy controls (HC) and two groups of post-comatose patients with disorders of consciousness (DOC) after severe brain injury: minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS), also known as vegetative state. Patients in MCS  [14]  present some behaviour that may indicate awareness (i. e. , visual pursuit, orientation to pain, or reproducible command following). Patients in UWS  [15]  show arousal (eyes opening) but unresponsive (no response to simple commands) and without signs of awareness (never presenting non-reflex voluntary movements). Yeo's parcellation  [16]  into seven brain networks and the use of LVT provide the parameters of LV equations allowing the characterisation of the instantaneous IS of the brain at each moment in time.
It is possible to look at the changes on the attractor governing the system behaviour by looking at the variability of the IS measures. The analysis is depicted in Fig 3. We have applied the above methodology to data corresponding to 147 participants (81 from Liège and 66 from Paris) including 49 healthy controls (HC, 36 from Liège and 13 from Paris), 63 in minimally conscious state (MCS, 31 from Liège and 32 from Paris) and 35 in unresponsive wakefulness syndrome (UWS, 14 from Liège and 21 from Paris). These datasets have been previously described in  [18, 19]. The diagnosis was obtained using CRS-R by trained clinicians, with some differences between both datasets. In case of Liège patients, the diagnosis was confirmed with Positron Emission Tomography (PET), to avoid misclassification of MCS � (behavioural UWS patients presenting brain activity similar to MCS, see  [20, 21] ).
Details on data acquisition and preprocessing are in S1 File. All IS measures except synchronicity were calculated at each time point and the mean (or median, in the case of criticality, which is not skewed by a small proportion of outliers) and standard deviation of each measure was calculated for each subject. The different groups (HC/ DOC and MCS/UWS) were compared using the Wilcoxon Signed-Rank test and then used as the input for machine learning classifiers. Our main interest in machine learning is not the classifiers themselves but to determine which IS measures are most sensitive to the state of consciousness. Some of the most important tasks when applying machine learning to a classification problem are the selection of the most relevant measures of the subjects to be classified, the choice of the learning algorithm and the optimisation of its parameters (the relevance of parameter optimisation to distinguish conscious from unconscious subjects is highlighted in  [22] ).
There are software packages automatising these tasks (automated machine learning). A search is conducted through several learning algorithms to obtain the classifier performing the best according to certain validation criteria. The tool we used for it is Auto-Weka  [23] , an automatic model selection and hyperparameter optimization tool. It performs a search through the classifiers available in Weka (Waikato Environment for Knowledge Analysis), a software containing a collection algorithms for data analysis and machine learning  [24]. The criterion that Auto-Weka used to select the models was performance using leave-one-out cross-validation. This strategy trains the classifier as many times (folds) as subjects are in the dataset (98 for MCS/UWS classification and 147 for HC/DOC), leaving one of them out of the training set for validation.
Machine learning is used to search for classifiers discriminating between HC/DOC and MCS/UWS. The performance of the resulting classifiers was evaluated by using leave-one-out cross-validation (Table  2 ). This requires to train the classifier as many times (folds) as samples are in the dataset, leaving each time one out for validation. The classifier determines which features provide more information within the training set and uses them to classify the validation sample. The %f columns show the percentage of folds using each measure in the best found classifiers. It is an indicator of the relevance of each parameter to identify the state of consciousness.
Low criticality values correspond to informational landscapes moving close to bifurcation points, and therefore where richer dynamic activity may be expected. The comparison between the three groups of participants indicates that the control group is the one closest to bifurcation points, while the furthest is the unresponsive group, having more robust IS and hence more trivial information landscapes. It agrees with the idea that phenomenal consciousness is realized by a particular level of brain configuration guided by   1  and validated using leave-one-out cross-validation. This strategy trains the classifier as many times (folds) as subjects are in the dataset, leaving one of them out of the training for validation. At each fold, Weka selects the most relevant measures to train the classifier and the training is validated with the patient that was left out.
According to IIT, consciousness is determined by what the system is in each state, not by how it changes through time, as PCI does. In this respect, our proposal, based on averaging IS measures over time, is more in line with IIT axioms than PCI. The usual stationarity assumption in the use of dynamical systems for whole-brain modelling raises several practical and theoretical problems. From the practical point of view, its limitations have been pointed out  [7, 44]. One of the usual practical problems is the length of the timeseries. For long data segments, it can be unrealistic to assume that all the statistics are constant throughout, while for shorter data segments, one cannot be confident that the system has explored all the states  [45]. However, our measures are designed to be applied directly to nonstationary empirical data.
PID2019-105772GB-I00 /AEI/ 10. 13039/501100011033 , financed by the  Spanish Ministry of Science, Innovation and Universities (MCIU). YSP was supported by  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant   896354. ALG and GD were supported by  Swiss National Science Foundation  Sinergia grant no. 170873. The study was further supported by the  FNRS   PDR  project (T. 0134. 21;OG, AT and SL); the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ; GD, AE, JA, OG, AT, RP and SL), and the  Luminous  project (H2020-FETOPEN-2014-2015-RIA; JA, OG, AT, RP and SL); the  European Union 's  FP7 Programme  ( FP7-HEALTH-602150 ; JA, OG, AT, RP and SL); the  H2020 Marie Skłodowska-Curie Actions  ( EU-2020-MSCA-RISE-778234 ; OG, AT and SL); and the  National Natural Science Foundation of China  (Joint Research Project  81471100 ; JA, OG, AT, RP and SL).
Overview of study design In this study, we conducted a secondary cohort analysis of a Scottish prescribing EHR dataset, as described in the following section. We identified prescribing records pertaining to asthma controller medications, and extracted pertinent information from the structured and free-text data fields, such as medication type, brand, and dose. Treatment regimens (the combination of prescribed therapies) were estimated at each day on which an individual had a prescription for at least one asthma medication (a prescription day) from the prescriptions issued to that individual in the previous 120 days. 120 days was chosen to balance sufficient time for a refill of each regimen component to have been filled, facilitating accurate treatment regimen identification, but short enough that seasonal changes in regimen (or strength of ICS, for example) could be detected.
Data The Asthma Learning Healthcare System (ALHS) dataset  [30]  recruited over half a million patients from 75 general practices in Scotland, with primary care records linked to national accident and emergency, hospital and mortality datasets using the Scottish health identification number known as the Community Health Index (CHI)  [32]. As such, we have approximately 10% population coverage  [30]. No demographic exclusion criteria were implemented, and so this cohort can be considered approximately representative of the Scottish general practice-registered population: that is, approximately 70% of the population live in urban areas, 14% in rural areas, 8% are aged 75 or o over, 15% are aged 14 and under, and 50% are female  [33, 34]. The fields available in the prescription dataset were: pseudo-anonymised patient study identifier (such as "ID0001" -allowing linkage between datasets and for observations within datasets, without revealing the identity of the individual), date of prescription, date of dispensing, medication name, BNF item code, formulation, prescribed quantity, dispensed quantity, and free-text native dose instructions.
Analysis plan Having assigned the treatment steps, we measured the time spent on a treatment step before changing, stratified by whether the change was a step up or down in intensity. An individual's final treatment step change was omitted from these calculations as the time to change was censored (the duration before change after the end of the study was not known). The rate of change and the frequency of changes in regimen, moving with and between treatment steps, was also assessed. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines  [38]  were used to guide the presentation of the methodology and results used herein, and to ensure that no important information had been omitted. R scripts for data processing and analysis are available at https://github.
Asthma medication data cleaning As described in the prescription processing report presented in Appendix F, 4,450,709 of the 41,433,707 prescriptions in the dataset (10. 7%) were identified as eligible asthma medications, after having removed prescriptions with dates outside of the extracted study period (n = 673), prescriptions which noted they should be deleted (n = 39), prescriptions which did not match any asthma brand names or ingredients (n = 36,264,262), prescriptions which were excluded based on formulation (n = 716,472), and prescriptions which matched non-asthma indication brand names (n = 1332). Multiple prescriptions on one day were condensed such that there was only one BTS/SIGN step or regimen per person on a single day, resulting in 2,880,546 records. Additionally, records before June 1st, 2009, were excluded to allow a run-in period of regimen estimation from staggered refills, resulting in 2,772,818 records for 157,503 unique individuals.
[44] ; an algorithm for the extraction of prescription data from the free-text prescribing fields which was applied automatically to all research datasets extracted from the Scottish Prescribing Information System (PIS). A substantial limitation of their approach is that it does not adapt well to the nuances of asthma pharmacotherapy, such as combined therapy inhalers, and has not been made available for researchers to adapt. When both methods had managed to obtain values for the number of doses per day, the amount to take at each dose, and the strength of the medication, the agreement was between 99. 6 and 99. 9%. Our methods consistently resulted in lower missingness (before imputation): 13% versus 10% for daily dose frequency, 13% versus 11% for dose quantity, and 62% versus 8% for dose strength.
The most common phrases which were not translated (no information extracted) were "Morning and night" (equalling two daily dose times), "[n] inhalations" or "[n] inspirations" (equalling n units of inhaled medication to be taken at each dose time), and ICS + LABA medications such as Seretide and Symbicort which were commonly listed without the unit (i. e. , "SERETIDE 250"). Finally, an important strength of this analysis is the transparency of our methods. The mapping from prescription records to time-varying BTS step estimate described herein was derived using Scottish electronic health records, which contain rich free-text dose directions, often not available in other UK and international research datasets. The text-processing components of the methods are not always possible to perfectly implement in other settings, therefore, however by providing detailed descriptions of the rule-based imputation process it is possible to implement robustly in lieu of this data.
To detect possible inversion events due to CTG-repeat excision and then reinsertion in the reverse orientation, we performed qPCR analyses using as positive control a previously described cell clone derived from DM1 edited cells carrying a repeat inversion in the mutated allele (clone C12). 16 The results revealed that inversions occurred with a frequency of 0. 6%-3. 5% in EF1-Crispr in GM/DM and in CK8-Crispr in DM (Figure  2C ). Collectively, these data indicate that, in addition to the expected CTG-repeat deletions, other editing events occur at on-target regions, such as inversions, possibly big deletions (encompassing the PCR-primer binding sites), and other undetected events. The sum of the total editing outcomes is close to the efficiency estimated by ribonuclear foci analysis (see below).
At D0 most cells were not proliferating (less than 3% BrdU-positive nuclei) and post-mitotic, since they started to express myogenin (MYOG) (data not shown). Myotubes were then fixed and processed for FISH and for MYOG staining. In both GM and DM the number of foci-negative nuclei increased significantly from 8-fold to more than 30-fold following DOX treatment for 7 days (Figure  3B ). In proliferating fibroblasts infected with EF1-Crispr the number of foci per nucleus decreased in DOX medium, paralleled by an increase in the percentage of foci-negative nuclei (Figure  3C ). Foci number was analyzed also in cells infected with EF1-Crispr and CK8-Crispr and induced to differentiate into myotubes. In cells infected with EF1-Crispr, both total nuclei and MYOG-positive nuclei were analyzed (Figures  3D  and 3E ), while in cells infected with CK8-Crispr only MYOG-positive nuclei were counted (Figure  3F ), Cas9 being expressed at higher levels in differentiated cells (Figure  1C ).
Off-target analysis Potential off-targets were identified by interrogating with four publicly available software tools: Cas-OFFinder (http://www. rgenome. net/casoffinder),  58 COSMID (https://crispr. bme. gatech. edu),  56 CRISPR-OFF (https://rth. dk/resources/crispr/crisproff),  55 and CRISTA (http:// crista. tau. ac. il/findofftargets. html). 57 Results of the different algorithms were intersected for each sgRNA and ranked decreasingly by number of hits. Sequence variation analysis by next-generation sequencing was performed using gene-specific primers (Table  S3 ) comprising also standard Illumina adapters for library preparation as described for the on-target analysis. For each genomic DNA sample, PCR amplicons were generated as described for the on-target analysis, without addition of DMSO.
ACKNOWLEDGMENTS This study was supported by  Telethon-Italy  to G. F. and F. M. (no. GGP19035 ) and  AFM-Téléthon  to F. M. and G. F. (no. 23054 ), F. M. was also supported by the  Italian Ministry of Health  (" Ricerca Corrente " and  RF-2019-12368521 ),  EU-CardioRNA COST  Action  CA17129 , and  EU  COVIRNA agreement # 101016072. M. I. , J. B. , and D. B. are recipients of fellowships funded by  Telethon-Italy. A. P. is supported by the  Italian Ministry of Health  ( SG-2019-12368989 ). We are grateful to  J. Chamberlain  and  G. Cossu  for providing precious reagents.
Aging is a process of progressive change. To develop biological models of aging, longitudinal datasets with high temporal resolution are needed. Here we report a multi-omics longitudinal dataset for cultured primary human fibroblasts measured across their replicative lifespans. Fibroblasts were sourced from both healthy donors (n = 6) and individuals with lifespan-shortening mitochondrial disease (n = 3). The dataset includes cytological, bioenergetic, DNA methylation, gene expression, secreted proteins, mitochondrial DNA copy number and mutations, cell-free DNA, telomere length, and whole-genome sequencing data. This dataset enables the bridging of mechanistic processes of aging as outlined by the "hallmarks of aging", with the descriptive characterization of aging such as epigenetic age clocks.
Here we focus on bridging the gap for the hallmark mitochondrial metabolism. Our dataset includes measurement of healthy cells, and cells subjected to over a dozen experimental manipulations targeting oxidative phosphorylation (OxPhos), glycolysis, and glucocorticoid signaling, among others. These experiments provide opportunities to test how cellular energetics affect the biology of cellular aging. All data are publicly available at our webtool: https://columbia-picard. shinyapps. io/shinyapp-Lifespan_Study/.
have enabled researchers to generate high-dimensional datasets across multiple modalities that illuminate the complex molecular landscape of biological aging. These data have been combined with machine-learning methods to develop molecular "clocks" that track chronological age and mortality risk with remarkable accuracy  14, 15  , and to model complex systems-level processes  16, 17. The clocks and related measures make possible measurements of biological processes of aging in free-living humans. But their connections to the hallmarks of aging remain unclear. Research is needed to elucidate fundamental mechanisms that cause aging-related changes, and that drive aging-related declines in resilience and increased vulnerability to disease, disability, and mortality. The ideal approach to meeting this research need would be to longitudinally monitor a population of individuals over their entire lifespan, taking regular measures of many metrics at frequent intervals.
One major pathway surfacing as a critical and possibly primary driver of aging biology is mitochondrial metabolism  23. Omics-based discovery studies consistently converge on mitochondria as a major molecular signature of biological aging  9, 24. Beyond providing ATP for all basic cellular functions, mitochondria produce signals that can trigger multiple hallmarks of aging  25. In humans  26, 27  and animals  [28] [29] [30]  , mitochondrial respiratory chain dysfunction dramatically shortens lifespan, further suggesting a primary causal role of mitochondria as a "timekeeper" and driver of the biological aging process. To examine longitudinal aging trajectories and quantify the influence of mitochondria on canonical and exploratory aging markers in a human system, we generated a multi-omics, longitudinal dataset across the replicative lifespan of primary human fibroblasts from several healthy and disease donors (Fig.
1 ). These data include genomic, epigenomic, transcriptomics, and protein-based measures along with bioenergetic and mitochondrial OxPhos measures. The relatively high temporal resolution of measurements allows for non-linear modeling of molecular recalibrations in primary human cells, as recently shown for DNA methylation in a pilot cellular lifespan study  31. In this cellular lifespan system, the rate of biological aging appears to proceed at a rate ~40-140x faster than in vivo (i. e. , in the human body), such that 200-300 days in vitro corresponds to multiple decades of human life  31. In addition to the rich descriptive data in multiple donors, this dataset includes experimental conditions with metabolic manipulations targeted to mitochondria, allowing investigators to directly test the influence of mitochondrial metabolism on human molecular aging signatures.
5 µM of each primer and 20 ng of DNA. The PCR products were analyzed on a 1% agarose gel electrophoresis. NGS Libraries were generated using an enzymatic DNA fragmentation approach using Ion Xpress Plus Fragment Library Kit. Library were diluted at 100 pM before sequencing and pooled by a maximum of 25 samples. Sequencing was performed using an Ion Torrent S5XL platform using Ion 540 chip TM. Signal processing and base calling were done by the pre-processing embedded pipeline. Demultiplexed reads were mapped according to the mtDNA reference sequence (NC_012920. 1) before being analysed with a dedicated homemade pipeline including eKLIPse  56  (https://github. com/dooguypapua/eKLIPse) using the following settings: ) and nuclear (nDNA, B2M) amplicons, as described previously  57. The reaction mixture included TaqMan Universal Master mix fast (life technologies #4444964), 300 nM of custom design primers and 100 nM probes: • ND1-Fwd: 5′-GAGCGATGGTGAGAGCTAAGGT-3′ • ND1-Rev: 5′-CCCTAAAACCCGCCACATCT-3′ • ND1-Probe: 5′-HEX-CCATCACCCTCTACATCACCGCCC-3IABkFQ-3′ • B2M-Fwd: 5′-CCAGCAGAGAATGGAAAGTCAA-3′ • B2M-Rev: 5′-TCTCTCTCCATTCTTCAGTAAGTCAACT-3′ • B2M-Probe: 5′-FAM-ATGTGTCTGGGTTTCATCCATCCGACA-3IABkFQ-3′ The samples were cycled in a QuantStudio 7 flex qPCR instrument (Applied Biosystems) at 50 °C for 2 min, 95 °C for 20 sec, 95 °C for 1 min, 60 °C for 20 sec, for 40 cycles.
Of the 360 samples, 352 had an RNA integrity number (RIN) score >8. 0, a A260/A280 ratio between 1. 8-2. 2, and no detectable levels of DNA. For cDNA library preparation, 1,500 ng of RNA at 50 ng/μl was processed using Ribo-Zero Gold purification (QIAseq FastSelect -rRNA HMR Kit #334387) and NEBNext ® Ultra TM II RNA Library Prep Kit (Illumina #E7770L). cDNA was sequenced using paired-end 150 bp chemistry on a HiSeq. 4000 instrument (Illumina, single index, 10 samples/lane, Genewiz Inc). Sequencing depth was on average 40 million reads per sample. Post-sequencing QC (multiQC, v1. 8) excluded 6 more samples, for a final sample set of 345. Sequenced reads were then aligned using the pseudoalignment tool kallisto v0. 44. 0  58. This data was imported using txi import ('tximport' , v1. 18. 0, length-scaled TPM), and vst normalized (''DEseq2' , v1.