dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: We thank the NeurImag platform and the animal facility of IPNP and their funding sources (Fédération pour la  Recherche Médicale ,  Fondation Leducq). We would also like to thank  Serge Charpak  and  Yannick Goulam  for their help with the two-photon microscope, and  Callum White  for his contribution to initial data analysis in Python. This work was supported by grants from a subaward agreement from the  University of Connecticut  with funds provided by Grant No. RG-1612-26501  from  National Multiple Sclerosis Society (NMSS) ,  Fondation pour l'aide à la recherche sur la Sclérose en Plaques (ARSEP) ,  Fondation pour la Recherche Médicale  (FRM,  EQU202103012626 ),  ANR  under the frame of the European  Joint Programme on Rare Diseases (EJP RD , project no. ANR-19-RAR4-008-03 ) and  ANR CoLD (ANR ,  ANR-20-CE16-0001-01 ).
Both the mean reversion in spending and the variability in super-utilizer status over time suggest that current approaches to identifying super-utilization based on frequency of visits are not optimal for helping policymakers reach the population who might benefit most from diversion to more appropriate care. A predictive modeling approach might better identify this population by anticipating divertible costs at the individual level. In this study, we make use of a new database of administrative records from the state of Rhode Island to develop a data-driven method for predicting divertible ED use among Medicaid recipients. Improving the availability and quality of Medicaid care while controlling costs is a key initiative of the Office of the Governor in Rhode Island  [8]. We use a machine-learning approach to predict which individuals who have no divertible ED spending in a given year will emerge as high-cost in the subsequent year.
DATA AND METHODS We use anonymized Rhode Island state administrative records housed in a secure data enclave  [9]. These records include administrative claims for 556,767 anonymous Medicaid enrollees in the state of Rhode Island between 2004 and 2014. The database also contains detailed information on these individuals' use of social benefit and insurance programs, incarceration history, quarterly wages, and demographics. Personally identifiable information has been removed from the data and replaced with anonymous identifiers that make it possible for researchers with approved access to join and analyze records associated with the same individual while preserving anonymity  [10]. Our objective was to define a panel of Rhode Island Medicaid enrollees in each quarter from 2004 and 2013; identify claims for potentially divertible Emergency Department (ED) care; define "cost emergence" as an enrollee's change in potentially divertible ED spending from zero in the 12 preceding months to non-zero in the subsequent 12 months; and predict cost emergence using information known in administrative records for the enrollee in the preceding 12 months.
Although anonymized administrative records as recent as 2015 were available for study, Medicaid expansion under the Affordable Care Act took place in Rhode Island during 2014. Therefore, we restricted the study to Medicaid enrollees in the pre-expansion period of 2004 to 2013 to prevent any confounding effects of changes in Medicaid eligibility under the Affordable Care Act. Data are available through individual data sharing agreements with each of the following Rhode Island agencies: the Department of Human Services (DHS), the Department of Labor and Training (DLT), the Department of Corrections (DOC), and the Executive Office of Health and Human Services (EOHHS). Source code for all analyses is available from GitHub at https://github. com/ripl-org/predict-medicaid-ed.
The coefficients for the extensive and intensive models are combined according to equation (3) with bootstrapped 95% confidence intervals and are available from https://github. com/ripl-org/predictmedicaid-ed/blob/main/coef. basic. csv. We estimate the LASSO model with regularized logistic regressions for equation  (1)  and regularized regressions for equation (2) using the R package gamlr, which tunes the regularization with a path descent over 100 lambda values  [18]. We select the lambda value that yields the best area under the curve of the cumulative sum of actual cost emergence when ordering individuals from highest to lowest predicted cost emergence, as measured on the validation set. This objective function optimizes the model for the policy application of prioritizing individuals for outreach by predicted cost emergence.
Furthermore, we use a bootstrap procedure called the bootstrapped LASSO, or BOLASSO for short  [19]. The advantage of this bootstrap procedure is that it consistently identifies a model in the presence of correlated variables. We run 100 bootstrap replicates of the LASSO implementations described above, resampling from  in each replicate, and select the variables occurring with non-zero coefficient in more than 90 of the 100 replicates. The selected variables are then pooled across both the extensive and intensive models to estimate a final pair of post-LASSO models for equations (  1 ) and (  2 ) with the R package glm on the BOLASSO-selected variables. The coefficients from the post-LASSO extensive and intensive models are combined according to equation (  3 ) with bootstrapped 95% confidence intervals and are available from https://github.
Limitations Our findings are contingent on the accuracy of the Yale definition for identifying ED visits in claims data, and of the Minnesota algorithm for classifying which of those visits were potentially treatable in primary care. We chose the Minnesota algorithm based on its reported improvements over the NYU algorithm  [20, 21] , which uses only the diagnoses codes from the visit instead of the final outcomes, such as the procedures performed and duration of the visit. Another limitation of our study is that it is restricted to the population of Medicaid enrollees. Expanding the study to a data source such as an all-payers claims database would improve the representativeness of the population by including Medicare enrollees and the privately insured. However, a strength of restricting our study to the Medicaid population is our ability to securely join claims data to additional administrative records and establish connections with other government programs, which would be challenging with an all-payers claim database.
Here we examine whether increases in low-frequency EEG power spectral density (PSD) in the delta and theta range, low-frequency cortical tracking (delta, theta and alpha) and PAC in response to rhythmic speech predict performance on multiple language and communication measures administered in later infancy and toddlerhood. EEG data was collected longitudinally at 4-, 7-and 11-months and used to predict subsequent performance in phonological, gestural and lexical measures taken from 12 to 24 months of age. It was expected that stronger cortical tracking would be predictive of better language outcomes  (26). As PAC did not show strong development with age, no strong predictions were made. However, given the DLD modelling data noted earlier  (27) , poorer PAC may be linked to worse language outcomes.
Infant participants and ethics All the data reported here is from the longitudinal Cambridge UK BabyRhythm project (122 infants recruited, 113 retained). The cohort was split into two halves based on recruitment date) to allow for confirmatory analyses of neural factors by comparing EEG data from the first and second halves of the cohort. The current report is from the second half of the sample. Results from the first half of the sample are reported in  (1). Due to participant withdrawals, missed appointments and data exclusion the final number of data points for the second half of the sample was, ~4-months (N=56), ~7-months (N=51), ~11-months (N=52). Missing data was due to missed appointments or technical issues with the data files, ~4-months (N=4), ~7-months (N=10) and ~11-months (N=7).
Other infants were excluded due to having fewer than 42 data epochs available after pre-processing, ~4-months (N=1), ~7-months (N=0) and ~11-months (N=2). The two halves of the cohort were combined for the novel language analyses reported here (language outcome data was still being collected when  Attaheri et al. , 2022 (1)  was being prepared). The full cohort provided EEG data for analysis as follows, ~4-months (N=110, aged 115. 5 ± 5. 0 days), ~7-months (N=104, aged 213. 5 ± 6. 1 days) and ~11-months (N=107, aged 333. 6, ± 5. 0 days) [mean ± standard deviation (SD)]. Infants were recruited from a medium sized city in the United Kingdom and surrounding areas via multiple means including flyers in hospitals, schools and antenatal classes, research presentations at maternity classes and online advertising.
EEG data collection Infants were seated in a highchair, approximately one meter in front of their primary care giver, within a sound-proof acoustic chamber. EEG data was recorded at a sampling rate of 1000 Hz using a GES 300 amplifier connected to an appropriately sized 64 channel infant net (Geodesic Sensor Net, Electrical Geodesics Inc. , Eugene, OR, USA). The infant was seated ~650mm away from the presentation screen and sounds were presented at 60dB (checked by sound level meter) from speakers (Q acoustics 2020i driven by a Cambridge Audio Topaz AM5 stereo amplifier) placed either side of the screen. Whilst the infant attended to the screen, the 18 nursery rhyme videos played sequentially, each repeated 3 times (54 videos, with a presentation time of 20  ' 33'' in total). If the infant lost attention to the screen an attention grabber video was played at the end of that nursery rhyme.
EEG preprocessing All analyses were conducted with custom-made scripts in Matlab 2017a (The MathWorks, Inc. , Natick, MA) incorporating the EEGLab toolbox  (36). The four (channels 61, 62, 63 and 64) facial electrodes (present on the larger nets) were excluded from all analyses because they do not exist on the infant-sized EGI Geodesic sensor nets. The EEG data, from the remaining 60 channels, was filtered (pop_eegfiltnew function of EEGLab toolbox) into a broadband signal (0. 5-45 Hz for PSD and PAC, methods for acquiring PAC bands of interest further detailed in (1)) or the frequency band of interest (delta, 0. 5-4 Hz, theta, 4-8 Hz or alpha [control band] 8-12 Hz for the mTRF analyses) using zero-phase bandpass Hamming windowed FIR filters (transition band widths of 2 Hz with cutoff frequencies at -6 dB, 0-5 Hz, 3-9 Hz and 7-13 Hz respectively).
The EEG data was down sampled to 100 Hz to reduce the computational load. Next, the clean_asr EEGLab function  (36, 37)  was used to clean noise artifacts from the data by identifying and removing bad principal components via a modified PCA procedure (see  (1) ). Further bad channels were identified via probability and kurtosis and were interpolated (via spherical interpolation), if they were 3SD away from the mean (average number of interpolated channels; 4mo = 6. 9, 7mo = 6. 5, 11mo = 6. 6) before all channels were rereferenced to a 60-channel average reference. EEG responses were epoched into trials aligned to the start and ending at the completion of a nursery rhyme phrase (e. g. "Mary had a little lamb"), producing EEG responses to 83 phrases (M length ± SD: 4. 23sec ±0. 88) which were repeated a maximum of 3 times in the experiment (249 epochs in total).
mTRF auditory stimuli preprocessing The envelope of the auditory signal was extracted by taking the absolute value of the analytic signal generated by the Hilbert transform (Matlab). As the envelope of the lower frequencies is linearly relatable to the EEG signal  (39) (40) (41)  the envelope of the stimuli was filtered between 0. 5 Hz and 15 Hz (lowpass; 6 th order Butterworth filter. Highpass; 9 th order Butterworth filter). The resultant envelopes were normalised using nt_normcol (Noisetools). Finally, the stimulus envelopes were down sampled to 100 Hz to match the EEG signal.
Phase amplitude coupling (PAC) All remaining epochs after preprocessing were concatenated back into one continual EEG signal. Due to the increased susceptibility of PAC analysis to noise, manual rejection was conducted to further remove noisy periods of data from the signal. For each infant's EEG data, low-frequency phase (LFP) and high-frequency amplitude (HFA) were extracted with a zero-phase FIR filter (Hamming window), separately for all 60 electrodes. LFP centre frequencies were filtered from 2 to 8 Hz, in 1 Hz steps with a 2 Hz bandwidth, and HFA centre frequencies were filtered from 17. 5 Hz to 42. 5 Hz, in 5 Hz steps with a 5 Hz bandwidth before a normalised modulation index value was computed for each channel  (19, 42). The EEG bands were defined as delta (2-4 Hz), theta (4-8 Hz) beta  (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30)  and gamma  (30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) (44) (45).
Spectral analysis (periodogram PSD estimate) The same concatenated data sets created for the PAC analysis were also used for the periodogram Power Spectral Density (PSD) estimates, in which all epochs after preprocessing were concatenated back into one continual signal with manual rejection used to remove noisy periods of data from the signal. A one-sided PSD estimate was conducted separately for each electrode channel using the periodogram function (Matlab). The average PSD across channels was next calculated with the maximum value, within a predefined 0. 25Hz window centered on the peak of interest, taken per participant for further analysis. See (1) for a detailed description of the PSD method and analysis.
Language outcomes: statistics Multivariate linear models (using Rstudio lm function) were run separately for the PSD, mTRF, theta-delta ratio and PAC measures. Due to the inherent differences between the infantled and parent-estimated measures, separate multivariate linear models were conducted for each, providing a general measure of language abilities taken from either infant-led or parent-estimated language measures. This approach was considered most appropriate as additional variance can be extracted from an infant's overall language performance which may not be visible by looking at individual measures separately. However, we also include Bonferroni-corrected individual univariate linear models to highlight the individual contribution of each language measure to the overall infant-led or parent-estimated multivariate linear model results.
PSD, mTRF, PAC and theta/delta ratio First, we present the power spectral density (using a periodogram; PSD), cortical tracking (using multivariate temporal response functions; mTRFs), phase-amplitude coupling (normalised modulation index, nMI; PAC) and theta/delta PSD ratio analyses using EEG recorded from the second half of our cohort of infants. We compare these data to the analyses from the first half of the cohort (1), and also merge the neural data to explore these neural factors in the combined full sample of 113 infants. Due to missed recording sessions, technical issues with data files and not enough trials after preprocessing, less than 113 data points are included in each analysis. Furthermore, outlier analysis was also conducted to remove extreme data points that would compromise each of the LMEMs.
Phase amplitude coupling (PAC) analysis Figure  3 , PAC replication. Violin plots of phase amplitude coupling (normalised modulation index, nMI) at 4-months (red), 7-months (green) and 11-months (blue) for the a) first half, b) second half and c) full infant cohort. The nMIs, from all significant analysis windows, were averaged for each individual infant. The PAC pairing with the maximum nMI per infant, from within the pre-defined frequency bands of interest (delta/beta, delta/gamma, theta/beta and theta/gamma; beta 2-4 Hz, theta 4-8 Hz, beta 15-30 Hz and gamma 30-45 Hz), were included in the grand average violin plot. Horizontal black and red lines represent the group mean and median respectively. Outlier analysis was conducted to remove extreme data points that would compromise the LMEM.
PSD and language outcomes The 11-months EEG data were selected for comparison to both the parent-estimated and infant-led language measures, due to the observed developmental maturation of the ~4 Hz peak. The analyses for the 4-month and 7-month EEG are provided as Tables S4 to S10. As described above, the 4. 35 Hz peak was the more robust and consistent of the two theta peaks (see Tables  1  & 2 ) and therefore was used for predicting the language outcomes (4. 05 Hz peak results are also reported in Tables  S6-S8 ). The analyses for the ~1. 92 Hz peak are also reported here. 77 infants provided complete data for all the parent-estimated measures and PSD power results, whilst 70 infants provided data for all the infant-led language measures and PSD power. Regarding global language outcomes, the multivariate models indicated that increased ~1.
mTRF and language outcomes The 11-months EEG data was selected for analyses with both the parent-estimated and infant-led language measures, due to it being the closest in time to the language measures (please see supplementary information for the earlier age groupings; these did not reach significance). 89 infants provided complete data for all the parent-estimated measures and a cortical tracking value, whilst 82 infants provided data for all the infant-led language measures and a cortical tracking value. The multivariate linear models (Table  9 ) showed that increased delta cortical tracking in response to nursery rhymes displayed a trend towards better language outcomes regarding the parent-estimated language measures (F(2,87) = 3. 00, p= 0. 055). This effect was not present for the infant-led measures, (F(5,77) = 0.
They are also consistent with a recent EEG modelling preprint based on child recordings during passive speech listening, which showed that the theta/delta power ratio was related to phonological difficulties in dyslexia and that atypical theta-gamma PAC was related to impaired language outcomes in DLD  (27). Further, these infant neural oscillatory factors were broadly consistent in terms of replicating our previous findings with the first half of the infant cohort  (1). The presence and maturation of low-frequency (<12 Hz) cortical oscillations (PSD), cortical speech tracking (mTRF) and phase amplitude coupling (PAC) in response to sung IDS was largely similar across both halves of the longitudinal Cambridge UK BabyRhythm cohort. The replication of stronger delta band tracking than theta band tracking in the youngest (4-month-old) infants suggests that delta band cortical tracking may be a key early building block for language acquisition.
Data Sharing agreement The analyses were conducted by using publicly available MATLAB toolboxes that can be downloaded at http://audition. ens. fr/adc/NoiseTools/, https://sccn. ucsd. edu/eeglab/download. php, https://github. com/mickcrosse/mTRF-Toolbox. The custom code used for the mTRF, PSD and PAC analysis can be downloaded from https://osf. io/q9s5k/. The final data included in the manuscript figures and statistics can also be downloaded from https://osf. io/q9s5k/. This data is currently being analysed with other techniques as part of the ongoing longitudinal Cambridge UK BabyRhythm project. Once this research has been completed, the raw EEG files will be uploaded to the same OSF server as above (early 2023). In the meantime, the raw data can be made available upon request.
The phototrophic species Rhodovastum atsumiense G2-11 acquired MGCs from an unknown alphaproteobacterial MTB by recent HGT In a systematic database search for novel MGCs, we identified several orthologs of known magnetosome genes in the recently released draft genome sequence of the culturable anoxygenic phototroph Rhodovastum atsumiense G2-11  25. This finding was unexpected as, after isolation of G2-11 from a paddy field more than 20 years ago, no magnetosome formation has been reported  26. Furthermore, no MTB has been identified so far among phototrophs or within the Acetobacteraceae family to which G2-11 belongs  26  (Fig. 1a ). Since the available genome version (Accession Number NZ_VWPK00000000) was highly fragmented (226 contigs) and the magnetosome genes were distributed over several contigs with gaps, we first re-sequenced and assembled the complete genome using long reads generated by Illumina HiSeq and Nanopore technologies.
6×AMPure XP. After library profile analysis conducted by an Agilent 2100 Bioanalyzer (Agilent Technologies) and qPCR quantification (MxPro, Agilent Technologies), the library was sequenced on an Illumina MiSeq with a MiSeq Reagent Kit v. 2 (2 × 250 bp; Illumina Inc. ). A total of 3. 27 x 106 paired-end reads were obtained. The Illumina reads were trimmed by removing lowquality nucleotides (Q < 20), sequencing adaptors and primer sequences using an internal software based on the FastX package (http://hannonlab. cshl. edu/fastx_toolkit/index. html). Reads shorter than 30 nucleotides after trimming were also discarded. For Nanopore sequencing, library preparation was done with 1 μg of the same input DNA following the 1D Native barcoding genomic DNA protocol with EXP-NBD104 and the SQK-LSK109 ligation kit (Oxford Nanopore).
The library was sequenced using a Nanopore R9. 4. 1. revD flow cell (Oxford Nanopore) and the PromethION device with the MinKNOW v. 4. 0. 5 and Guppy v. 4. 0. 11 software. A total of 309 928 reads were obtained with a N50 of 8. 07 Kb. Two hybrid assemblies were launched in parallel with Unicycler v. 0. 4. 6 (default options) and Unicycler v. 0. 4. 6 (-sc option for SPAdes) and compared to circularize chromosomes and plasmids  53. The final assembly resulted in a single chromosome 6. 48 Mb in length with a GC content of 68. 96% and 8 plasmids (from 220 Kb to 10. 69 Kb) with an average GC content of 66. 07%. Assembly completeness and contamination were estimated at 99. 5 and 0. 54%, respectively, using checkM v. 1. 0. 11  54 with a set of 336 Rhodospirillales-specific markers. The automatic annotation was performed with the MicroScope platform (https://mage.
59 available on the Galaxy server (https://usegalaxy. eu)  60. For this, the number of reads that overlap 50 nt bin fragments in the genome was counted and normalized to the number of mapped reads per million. The resulting binned counts per million (CPM) were processed as bedgraph or bigwig format files. The coverage histograms were visualized using R package Sushi v. 1. 32. 0 (Phanstiel, D. H. Sushi: Tools for visualizing genomics data. R package version 1. 16. 0. https://www. bioconductor. org/packages/release/bioc/html/Sushi. html (2019)). The fraction of reads indicating reversion to the wildtype locus by excision of the MAG cassette was calculated after mapping reads to the wildtype and the mutant reference genomes by variant calling implemented in Geneious (8. 1. 9) and checked by visual inspection.
Molecular phylogeny Since the previously built tree based on the 16S rRNA gene affiliated Rhodovastum atsumiense to the phylum Alphaproteobacteria, order Rhodospirillales, family Acetobacteraceae 26 , we investigated the evolutionary relationships between strain G2-11 and its Acetobacteraceae relatives using whole genome sequences. First the proteome of all non-redundant (1 representative species per genus) closed genomes of Acetobacteraceae (LPSN nomenclature, ranked at the order level in GTDB) were downloaded from the public repository RefSeq database at NCBI in April 2022. This database was enriched with the draft genome of the closest relatives of R. atsumiense: Acidisphaera rubrifaciens strain HS-AP3 and Rhodopila globiformis strain DSM 161. The dataset was also completed with a set of genomes from the Rhodospirillaceae family and the Azospirilllaceae family that was then used as an external outgroup based on the latest Alphaproteobacteria phylogeny  29.