dataesr/scientific-paragraphs-categorization huggingface dataset:
Rhythmic Degradation Regulates 35% of Rhythmically Accumulating Transcripts. We then focused on the predicted classes for all rhythmically accumulating mRNAs [6,014 mRNA profiles selected through harmonic regression, false discovery rate (FDR) < 5%] and assessed whether they fell into M2, driven by rhythmic transcription alone; M3, driven by rhythmic degradation alone; or M4, driven by both rhythmic transcription and degradation. Rhythmic transcription with constant degradation (M2) regulated 3,949 (∼65%) mRNAs (Fig. 2A ), consistent with pre-viously reported results  (7, 9). Constant transcription with rhythmic degradation (M3) regulated 1,167 mRNAs (∼20%). Genes in M3 were enriched for translation regulation, RNA transport, and mRNA catabolic processes (Dataset S2). Last, rhythmic transcription and degradation combined (M4) controlled 898 mRNAs (15%), whose functions were enriched for lipid and steroid biosynthetic processes, and circadian regulation (Dataset S2).
To assess these results using an independent approach, we applied the production-abundance test (PA test) to identify transcripts subjected to posttranscriptional regulation (9) (Dataset S3). The PA test is based on the null hypothesis of constant degradation rather than the data-driven model selection developed here, and is thus limited to the rejection of models M1/M2. The PA test requires proxies for transcription (Pol II ChIP, nascent RNAs, or pre-mRNAs), mRNA accumulation, and experimentally measured mRNA half-lives [e. g. , from mouse fibroblast cell lines  (21, 38) ]. Transcripts identified as incompatible with M1/M2 by the PA test were clearly enriched in genes classified as M3 and M4 (Fig. 2B ). Conversely, genes assigned to M3 and M4 by our method displayed overall lower significant scores by the PA test compared with M1 and M2 genes (Fig.
Estimated mRNA Half-Lives and RNA Processing Times in Mouse Liver. In addition to disentangling transcriptional and posttranscriptional contributions governing temporal mRNA profiles, our models estimated peak time and amplitude of rhythmic degradation as well as parameters that are otherwise difficult to estimate in vivo, notably mRNA degradation rate and pre-mRNA processing time (Fig. 3  A and B, Fig. S4A , and Dataset S1). mRNA half-lives in M2 and M4 (half-lives of M3 are not shown due to poor identifiability; see above) were overall short compared with the 24-h day, with a median of ∼2. 5 h, consistent with the observation that most of mRNAs continued to oscillate (3). M4 was enriched in long half-lives compared with M2, with a long-tailed distribution ranging up to 20 h (Fig. 3A ). mRNA half-lives of several core clock and clock output genes, for example, Clock, Per1/3, Nr1d1/2, and Dbp, were estimated between 1 and 2 h, which is in the range of estimates in cell culture (40) (Fig.
Median processing times of mRNAs in both M2 and M4 were found around 7 min. Overall, we estimated half-lives and processing times for over 3,000 mRNA transcripts in mouse liver (Dataset S1), including those of core clock and clock output genes (Fig. 3C ). At the genome-wide level, we observe a correlation between processing times and mRNA half-lives (Fig. S4A ), which has been previously observed in mouse dendritic cells  (22). Phases and Amplitudes of Rhythmic mRNA Degradation. Considering only well-identified mRNA degradations for M3 and M4 (Materials and Methods), we found that the estimated phases and amplitudes (Fig. 4 ) were consistent with those from the PA test (Fig. S4C ). Peak times of RD in M3 were distributed throughout the day (Fig. 4A  and Fig. S4D ) but depleted around the daynight transition at ZT12.
By contrast, peak times of mRNA degradation in M4 showed a marked peak at ZT12. Intriguingly, genes within this peak (between ZT10 and ZT16) encoded for proteins related to the endoplasmic reticulum and vesicle transport to the Golgi, such as Insig1, Sec13, Sec22b, and Sec23b (43, 44) (Dataset S4A). The amplitudes of mRNA degradation for M3 and M4 showed similar distributions (Fig. 4  and Fig. S4D ) ranging up to 16-fold, with (∼30%) showing amplitudes greater than 2-fold. Several of those transcripts with high degradation fold changes (log 2 FC > 2. 5) were linked with RNA splicing [gene ontology (GO) term analysis, P = 9e-4]. phase relationship between RS and RD for genes with identifiable and high-confidence estimated phase and amplitude (Materials and Methods) in M4. If the role of RD were to amplify amplitudes from transcription regulation, we would expect an optimal delay of 12 h between the peak times of RD and RS  (9).
We focused on the genes classified in M3 under AL feeding, whose rhythmic mRNA expression is solely driven by rhythmic degradation (Dataset S5). First, the majority (79%, n = 920) of these mRNAs continued to oscillate under RF with the same rhythmic parameters as under AL feeding (Fig. 6A , three top groups; see Materials and Methods for the selection of the groups), which is not surprising since such night RF only slightly perturbs the feeding rhythm  (46). Second, loss of BMAL1 did not affect the rhythmic expression of 569 out of the 920 mRNAs (62%, top group in Fig. 6A ), suggesting that a majority of rhythmic mRNA degradation does not depend on BMAL1, but may instead be regulated by systemic signals originating from feeding rhythms, light-dark, temperature, or sleep-wake cycles. Of the remaining 351 rhythmic mRNAs that were altered in Bmal1 -/-(38%, second and third groups in Fig.
6A ), 292 (32%, third group) became arrhythmic and 59 (6%, second group) remained rhythmic with shifted phases or altered amplitudes, indicating that their regulation depends on both BMAL1 and systemic cues. Interestingly, BMAL1-dependent and -independent mRNAs showed distinct phase distributions (Fig. 6B ): mRNAs whose rhythmic degradation was BMAL1-independent (and thus driven by feeding rhythms or systemic signals) were enriched in the late morning (ZT6-ZT8), while those with BMAL1-dependent degradation enriched in the late night (ZT23). GO term analysis on these two groups (SI Materials and Methods and Dataset S4 B-D) showed that the BMAL1-dependent group was enriched in regulators of actin filament polymerization or the positive regulation of WNT signaling, while genes whose degradation was driven by rhythms in feeding or systemic signals were enriched in mRNA processing and transport pathways.
Interestingly, this latter category contained genes related to poly(A) tail regulation and degradation (Pan2, Parn, Pabpc1, and Magohb) or involved in the promotion of nonsense mediated mRNA decay (Smg7, Upf3b, Ncbp2, and Etf1). Finally, we sought to identify putative regulators underlying rhythmic mRNA degradation in each group. We focused here on mRNA binding proteins (mRBPs) since mRNA rhythms were shown to be only weakly affected by perturbation of miRNA biogenesis in mouse liver  (19). Briefly, we first used known mRBP target recognition specificities (binding motifs)  (47)  to scan the 3′-UTR regions of each mRNA. Next, we modeled the rhythmic degradation rates for each transcript as a linear combination of unknown mRBP activities associated with each mRBP binding motif (SI Materials and Methods and Dataset S6)  (35, 48, 49).
Since a group of transcripts has been shown to exhibit deadenylation rhythms  (39) , a future extension of the model might explicitly consider mRNAs of different ages, and possibly infer aging parameters from measurements of poly(A) tail length distributions. In the current description, an effective rhythmic Fig. 6. BMAL1-dependent and -independent rhythmic mRNA degradation. (A) Heat map of mRNA relative expression of genes classified in M3 in mouse liver in the following conditions (biological replicates are averaged): WT ad libitum (AL WT, Right; this condition was used for the main analysis, 12 times points, 48 samples), WT restricted feeding (RF WT, Middle; 6 time points, 12 samples), and Bmal1 -/-restricted feeding (RF Bmal1 -/-; 6 time points, 12 samples). All data are from ref. 8  and provided in Dataset S5, together with the parameter estimates.
Materials and Methods For each section below, further details are included in SI Materials and Methods. Data Processing for Total RNA-Seq in Mouse Liver Around the Clock. To quantify the temporal accumulation of pre-mRNA and mRNA in mouse liver, we used total RNA-seq data from ref. 8 (GEO accession no. GSE73554), in which livers of wild-type C57BL/6J male mice between 10 and 14 wk of age, under 12-h light/12-h dark and ad libitum feeding conditions, were collected every 2 h during 1 d (four biological replicates = 48 time points in total). Modeling the Temporal Profiles of mRNA and Pre-mRNA. Here, we modeled the temporal accumulations of mRNA with the following differential equation: dmðtÞ=dt = kpðtÞ -γðtÞmðtÞ, in which m(t) and p(t) denote the temporal accumulations (concentrations) of mRNA (m) and pre-mRNA (p), respectively.
To control for overfitting and also redundancy of motifs, we employed regularized elasticnet linear regression [implemented in R package glmnet  (59) ]. Rhythmicity Assessment Temporal RNA-Seq Datasets. Rhythmicity in mRNA abundances for transcripts classified in M3 (Dataset S1) in different conditions was assessed using a model selection approach as described in ref. 8. GO Analysis. GO analysis was performed using the TopGO R package  (60).
ACKNOWLEDGMENTS. We thank  Amit Zeisel  and  Eytan Domany  for discussions at initial stages of the project, as well as  David Gatfield  and  Hien Du  for sharing results of unpublished experiments. This work was supported by  Swiss National Science Foundation  Grant  31003A-153340 ,  European Research Council  Grant  ERC-2010-StG-260667 ,  CycliX , a grant from the  Swiss SystemsX. ch (www. systemsx. ch)initiative evaluated by the  Swiss National Science Foundation , and by  Ecole Polytechnique de Lausanne. J. Y. benefits from the  Natural Sciences and Engineering Research Council of Canada   Postgraduate Studies Doctoral Scholarship.
Acknowledgements We are grateful to  G. Blatter  and  D. Vion  for helpful feedback on the manuscript and  T. Walter  and  P. Kurpiers  for valuable discussions. Work of H. E. T. and S. K. was supported by the  US Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering , under Award No. DE-SC0016011. A. W. C. and F. A. Y. N. S. acknowledge support from the  Winton Programme for the Physics of Sustainability. F. A. Y. N. S. also acknowledges support by the  Engineering and Physical Sciences Research Council (EPSRC). Work of A. P. , A. B. , M. C. , S. G. , Y. S. , C. E. and A. W. was supported by  ETH Zürich.
A theoretical model for isotopologues of beryllium monohydride, BeH, BeD and BeT, A P 2 to X S + 2 visible and X S + 2 to X S + 2 infrared rovibronic spectra is presented. The MARVEL procedure is used to compute empirical rovibronic energy levels for BeH, BeD and BeT, using experimental transition data for the X S + 2 , A P 2 , and C S + 2 states. The energy levels from these calculations are then used in the program Duo to produce a potential energy curve for the ground state, X S 2 , and to fit an improved potential energy curve for the first excited state, A P 2 , including a spin-orbit coupling term, a Λ-doubling state to state (A-X states) coupling term, and Born-Oppenheimer breakdown terms for both curves. These, along with a previously computed ab initio dipole curve for the X and A states are used to generate vibrational-rotational wavefunctions, transition energies and A-values.
The A state PEC was previously studied by  Le Roy et al (2006)  and is also improved upon here. Accurate ab initio calculations for the X state were performed by  Koput (2011). The fitting in Duo  (Yurchenko et al 2018)  results in a full set of accurate transitions for BeH, BeD, and BeT from a single set of  PECs, couplings, and BOB terms (Le Roy 2017). These data are used to determine temperatures in the experimental spectra. This method of using variational nuclear motion calculations with BOB terms to link isotopologue data differs from the methods based on perturbation theory generally employed up until now, see  Duxbury et al (1998)  and  Hornkohl et al (1991). These older methods use spectroscopic constants to calculate energies but cannot join isotopologue data and thus do not provide predictions for yet-to-be observed isotopologues.
The number of quantum numbers used for assignment here is 5. The thresholds for changing uncertainties and for deletion were both set to 3. The full files are given in the supplementary data. These transitions are run through the program and various unlinked spectroscopic networks of energy levels are generated. Separate networks can be joined with 'linking' transitions, e. g. joining the degenerate spin up and spin down states of the ground state with a 'transition' of zero energy from one degenerate state to another. The result of this process is to give a large set of accurate energy levels with quantum number assignments. For BeH and BeD the infrared data of  Shayesteh et al (2003)  brings together separate vibrational networks. This results in two large networks separated along the quantum number Σ, where Σ=-0.
Duo Duo (Yurchenko et al 2016) is a fully-coupled rovibronic nuclear motion code which generates rovibronic energy levels and wavefunctions for diatomic molecules from PECs, couplings between PECs, and Born-Oppenheimer correction terms This program contains an iterative fitting procedure  where PECs and other terms can be fitted to experimental data. In this way the potential form of the ground state PEC, a Morse long range type potential (Le Roy et al 2011) as given by Pitarch-Ruiz et al (2008), is refitted using energy levels output from MARVEL. A new PEC, of the extended Morse oscillator type, is fitted to the A P 2 excited state using energy levels from output from MARVEL. The A P 2 PEC is modified by the addition of an LS coupling curve and an X to A state L + (Λ-doubling) coupling curve.
PECs modified and fit by Duo for the X and A states of BeH, BeD and BeT are shown in figure 3. The zero point energy of these curves are: 1022. 0292 cm -1 for BeH; 742. 7911 cm -1 for BeD; 626. 2844 cm -1 for BeT. The Duo input containing the PECs, couplings and BOB terms is given for BeH, BeD and BeT in the supplementary data. Predissociation is not included in this model. Fully fitted PECs, couplings and BOB terms are used in conjunction with an ab initio transition dipole curve (Pitarch-Ruiz et al 2008) for the A to X state to generate Einstein A-coefficients for the rovibronic transitions. Duo  outputs two files for the line list of each isotopologue, one containing the list of the states involved in the transitions and the other being a list of the transitions between states and their A-values; this corresponds to the format of the ExoMol database  (Tennyson et al 2013 (Tennyson et al , 2016)).
The ExoCross calculation is repeated for rotational and vibrational temperatures varying independently from 500 to 10000 K. For each combination of vibrational and rotational temperatures a program 'diffspec' is used to integrate the area under both curves, the overlap, and the area between the curves, the difference. The metric minimized to find the best fit temperature is the difference divided by the overlap. The theoretical and experimental spectra are both normalized, in the first instance, to have Q-branch peak values of 1. 0. A multiplicative factor is then required to allow the peak values to differ. The experimental spectrum also contains a background which must be removed before matching to the theoretical spectrum, which necessitates a threshold. Also a higher weighting should be placed on fitting to the more intense parts of the experimental spectra which have the smallest experimental uncertainties in measurement.
Finally, in a comparison with the previous work on BeD spectra modeling at JET by  Duxbury et al (1998) , we conclude that a more accurate and more comprehensive model has been achieved for two reasons. Firstly, the accuracy of the transition frequency fitting is much increased in our work. Secondly, our model is built using a single dataset derived from all the experimental transition data to model the three isotopologues. Not only does this improve the accuracy of the model, but it also enables accurate predictions to be made for the sparsely observed BeT isotopologue.
Acknowledgments We would like to thank  Dr Marie Gorman  and  Grace Eickmann  for sharing their work on MARVEL for BeH, BeD and BeT with us. We would like to thank the  Engineering and Physical Sciences Research Council (EPSRC)  for studentship ( EP/M507970/1 ) and the  Culham Centre for Fusion Energy (CCFE)  for funding. This work was carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No. 633053  and from the  RCUK Energy Programme  [Grant number  EP/P012450/1 ]. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Photo-stimulation Photo-stimulation was induced using a blue LED (= 470 nm, OptoLED Lite, Cairn research, UK) collimated and coupled to the epifluorescence path of the microscope (BX51WI; Olympus). Light was delivered through a 60X (1. 0 NA) water immersion lens, centered on Layer II/III. The light intensity and waveform was controlled by the analog output of a digitizer (Digidata 1440A, Molecular Devices). Light ramps had a duration of 1-3 s, a slope of 0. 1-0. 8 mW s -1 , started at zero intensity and reached a final intensity of 0. 3-1. 6 mW s -1. The slope was adjusted in each slice to obtain a robust rhythmic activity in the gamma frequency range with a stable power for the entire duration of the stimulus. Light ramps were repeated with a 60 s interval.
Data acquisition and analysis Signals were amplified using a Multiclamp 700B patch-clamp amplifier (Molecular Devices, USA), sampled at 20 KHz and filtered at 2 KHz or 10 KHz in voltage-clamp and current-clamp mode, respectively. Voltage measurements were not corrected for liquid junction potential. Access resistance was <20 MΩ and monitored throughout the experiment. Recordings were discarded from analysis if the resistance changed by >20% over the course of the experiment. Data were analyzed using pClamp (Molecular devices, USA), Origin (Microcal Inc. , USA), MATLAB (MathWorks, USA) and custom written scripts and software.
Miniature inhibitory synaptic events ACSF containing a high concentration of K + (~20 mM) was applied using a pressure system (puff), through a glass pipette located near the cell body of the recorded PV interneuron to depolarize axon terminals impinging the recorded neuron. High-K + puffs induced global asynchronous release of GABA that could be detected as a substantial increase in the frequency of miniature inhibitory postsynaptic currents (mIPSCs). mIPSCs were recorded during successive sequences of baseline activity and throughout puff application (3-6 s, 1 puff / minute), for at least 20 min. Miniature GABAergic events were detected using a custom written software (Detector, courtesy J. R. Huguenard, Stanford University; Supplemental figure  1 ). Briefly, individual events were detected with a thresholdtriggered process from a differentiated copy of the raw current trace.
Firing properties of layer 5 PV cells during photo-induced γ-activity Bursts of γ activity were evoked by light stimulation of ChR2-positive PN cell bodies in Layer II/III. While recording from a layer 5 PV interneuron, a simultaneous recording of a Layer II/III ChR2negative PN was used to determine the period of the γ activity. Rhythmic synaptic events evoked by light stimulation were detected using Detector (courtesy of J. R. Huguenard) as described above for mIPSCs. Spikes of Layer V PV cells were extracted using a threshold of -10 mV on the membrane potential trace. PSC cycles were measured and the timing of each spike in the PV neuron was expressed as a phase relative to the peak of each γ oscillation. Inter-spike intervals (ISI) and phase distributions were computed for each cell using custom written software (MATLAB).
Variability of firing was evaluated using the entropy of the log interval distribution  (Bhumbra and Dyball, 2004, 2010) , whereas the dispersion of peri-cycle spike times was quantified using the entropy of the corresponding phase distribution  (Bhumbra and Dyball, 2010). The Circular statistic toolbox of MATLAB was used to compute parameters of phase distributions and their associated statistical tests, as indicated in the text. Afterhyperpolarization (AHP) duration of single action potentials was measured as the 10-90% decay time setting the baseline right before the spike (5 ms window). Hence, isolated spikes were selected for this analysis since the decay time of action potential being part of doublets or burst could be contaminated by the generation of the following one. The average value of the AHP decay time of 10-20 spikes was considered for each PV cell.
Acknowledgments We thank  Frédéric Manseau ,  Pasqualina Farisello  and  Tommaso Fellin  for their initial involvement in this project and  Geeske M. van Woerden  for help with in utero electroporation. We are grateful to  Joana Lourenço ,  Javier Zorrilla de San Martin ,  Nelson Rebola  and  David DiGregorio  for critically reading this manuscript. This work was supported by  European Research Council (ERC)  under the European Community's  7th Framework Programmme  ( FP7/2007-2013) / ERC  grant agreement No  200808 ); " Investissements d'avenir "  ANR-10-IAIHU-06 ;  Agence Nationale de la Recherche  ( ANR-13-BSV4-0015-01 ,  ANR-FRONTELS  and  ANR-NanoSynDiv) ,  Fondation Recherche Médicale (Equipe FRM   DEQ20150331684 ),  NARSAD  independent investigator grant, and a grant from the  Institut du Cerveau et de la Moelle épinière (Paris)  (A.
com/batimetria/GPS-Trimble-AGP132. pdf). GPS measurements and gyro/odometer data acquisitions are realized at a 5 Hz frequency. The centimeter reference used for the evaluation of the positioning method is provided by a RTK GPS (datasheet: http://www. ctsystems. eu/Resources/Sagitta. pdf). Computing time has been measured for HC4, 3B, and our DICPS algorithm. Table  2  shows that HC4 is the fastest, but Figure  3 (b) indicates its inability to correct the heading imprecision: the heading imprecision increases linearly as the time goes by. This is a common result obtained by local consistency algorithms such as HC3  [19] , BC3  [21] , BC4  [20] , and BC5  [22]. DICPS results are observed with three window sizes Ξ (20, 40, and 100) depicted in Figure  3(b). The heading imprecision is stabilized between 10 and 35 degrees depending on the parameters.
Microorganisms and Technical Materials Experiments were performed with Pseudomonas aerugionsa PB_1, isolated from plant biomass in Poland. The strain was identified by molecular methods based on 16S rRNA gene sequencing. The nucleotide sequence of the 16S rRNA gene were deposited in the NCBI GenBank database as MF777034. The biological material was stored in Tryptic Soy Agar (TSA, Merck, Darmstad, Germany) at 4 • C. Rectangular pieces of polypropylene (PP) 70 × 25 mm (Packor Packaging, Skierniewice, Poland) were used in the adhesion test. The test material was certified by the Polish National Institute of Public Health  [35].