dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyzing mouse indirect calorimetry datasets to identify reliable parameters for circadian misalignment Misalignment between different components of the circadian systems and sleep-wake cycles or food intake behaviour as a result of genetic, environmental or behavioural factors might be an important contributor to diseases as well as how we can treat and prevent them. The mouse mutants with circadian misalignment would result in phase changes in locomotor activity and/or food intake behaviour under the light/dark cycle. We attempt to provide a systematic approach to identify genes underlying misalignment between circadian systems and behavioural factors. We thus collected available IC data from five IMPC centres: the Riken BioResource Center (RBRC), The Centre for Phenogenomics (TCP), the Institut Clinique de la Souris (ICS), the Wellcome Trust Sanger Institute (WTSI) and the Helmholtz Zentrum Munich (HMGU) and designed a workflow for systematic and unbiased analysis of circadian misalignment for more than 2,000 wild-type C57BL/6N mice and mice from 750 mutant mouse lines (Fig  1).
Baseline data from wild-type C57BL/6N were used to compare the reliability of data between centres and/or within centres to choose robust parameters of circadian misalignment. We first analyzed the activity/food intake cycle data for wild-type mice from the five IMPC centres. The total activity and food intake for each mouse was calculated and expressed as 1-hour averages over at least 21 hours according to the IMPC protocol. As expected, both activity and food intake rhythms displayed two clear peaks: a strong early evening (E) peak and a weak early morning (M) peak  [26, 27]  (Fig 2A  and 2B ). The onset time at which mouse activity or food intake behaviour transitioned from the resting state to the active state could be clearly seen. These observations suggest that the onset time and E peak phase are potentially reliable parameters for circadian behaviour despite the diversity of data from different centres.
By optimizing conditions to draw scatter plot maps, we further assessed the activity and food intake parameters using 2201 activity/rest scatter plots and 2160 food intake scatter plots at 1-hour intervals over 21 hours (ZT0: light on, ZT12: light off, plots deposited in the Cam-Su GRC database, http://gofile. me/2F1pE/RP2URKxV2) (S1 and S2 Files). The onset times and peak phases for activity and food intake were manually evaluated by observing the curve of activity or food intake. We found that the onset time points of activity and food intake were reliably detected with the lowest variance as compared to other parameters from the five different IMPC centres (S1 Fig, pink column, S1 Table ). E peak phases of activity/food intake showed a broader distribution than that of the activity onset (S1 Fig, red column, S2 Table ).
Thus, the onset time and E peak phase are reliable parameters for comparison of circadian misalignment. In addition, because light schedules in the procedure rooms are different in the five centres (light cycle, WTSI: 7:30-19:30; TCP: 7:00-19:00; ICS: 7:00-19:00; RBRC 11:00-23:00; HMGU: 6:00-18:00), our data analyses were restricted to within-centre comparisons instead of between-centre comparisons. We also limited IC data analysis to male mice because the variability of these parameters in females was great, likely due to the estrous cycles. Empirical visual assessment for onset time and peak phase is time consuming, technically demanding and subjective. To resolve these issues, we employed machine learning to develop predictive models to identify the onset time and peak phase from IC datasets.
Next, we produced various rhythmic patterns by random disturbance of the onset and peak phase. In addition, measurement noise was modelled as white noise that was added to synthetic training data. Predictive models were generated by learning these large synthetic training data sets. Furthermore, we applied predictive models to the IC datasets from 2201 mice for activity/rest and 2160 mice for food intake in five different centres. The results for the onset time and peak phase of activity and food intake for each centre were determined by SyncScreener (Fig 2C  -2H , S3 and S4 Tables). Next, we compared the results from visual assessment and machine learning algorithm (SyncScreener, S3 File) using Bland-Altman plots to determine whether the automated parameter determination was accurate compared to visual assessment  [28].
Identification of mouse mutants with impaired circadian misalignment behaviour Primary criteria: We applied the machine learning algorithm to detect the onset and peak phase of activity/food intake in mutant mouse lines. By comparing the parameters of the wildtype control mice from each centre, we screened 440 homozygous and 310 heterozygote mutant strains, representing loss-of-function of 726 unique genes (S6 and S7 Tables). We obtained a mean scatter dot curve generated from 7-8 male mice for each genotype available in the IMPC database to evaluate the onset times and peaks by SyncScreener. The distribution of phase deviations of onset and peak phases between the wild-type and mutant mouse lines at each centre are shown in Fig 4A  -4E  and S8 Table. The onset time or peak phase which falls in the tails beyond ~+/-2 s.
Confirmation of the role of Slc7a11 in circadian behaviour To confirm our screening results from the IMPC IC datasets, we generated the Slc7a11 tm1a/ tm1a mice using targeted C57BL/6N ES cells by Cam-Su Genomic Research Center  [34]   Although the onset was comparable between the Slc7a11 tm1a/tm1a and wild-type littermates in the second day, the mutant mice showed a phase advance for the declining activity phase for the next dawn (Fig 6A, red arrow). Consistent with the results of SyncScreener, the food intake was comparable between mutant and wild-type mice, including VO 2 , VCO 2 (S7 Fig). We next analyzed voluntary wheel-running activity to evaluate the onset time, and free-running period of the circadian clock for the Slc7a11 tm1a/tm1a mice. Under LD cycles, the activity onset times were significantly advanced and unstable with a larger variance in the Slc7a11 tm1a/tm1a mice compared with wild-type littermates (Fig 6C  and 6D ).
Discussion In this study, we demonstrated the feasibility of large-scale characterization of mouse mutants with impaired circadian alignment under light/dark cycles as part of an IMPC collaborative effort to generate a genome-wide catalogue of gene function. By analyzing IC datasets of 2201 C57BL/6N mice for activity/rest and 2160 C57BL/6N mice for food intake from five IMPC centres, we identified two robust parameters of circadian misalignment of behaviours. Our machine learning based SyncScreener enables fast, objective and large-scale behavioural screening of mutant mouse lines. We identified five genes (approximately 0. 66%) among 750 mutant lines that are potentially involved with circadian misalignment of activity and food intake behaviour. Previous studies have mainly focused on measuring circadian period and not misalignment as the target phenotype.
Animals Mice were housed in specific pathogen-free animal facilities. Slc7a1 1tm1a/tam1a mice were generated according to the standard protocol in CAM_SU Genomic Resource Center. Fbxl3, Zbtb20, hPER1 S730G mutant mice were generated as described previously and hPER2 S662G was generated by Dr. Xu in Fu & Ptacek lab in the UCSF. IC data from these known mutant mice were generated by Xu lab in the CAM-SU Genomic Resource Center. All mutant mice used in these studies have been described previously  [15, 16, 29, 30].
Phenotype data acquisition Indirect Calorimetry raw data was collected from five centres (WTSI, TCP, ICS, RBRC and HMGU). Five centres follow the pipeline of IMPC. IC equipment: WTSI, ICS and HMGU: TSE PhenoMaster/Labmaster CaloSys, TCP: Columbus Oxymax/CLAMS, RBRC: O'hara FWI-3002 & IA-16M, CAM-SU: Oxymax/CLAMS. Light schedules are WTSI: 7:30-19:30; TCP: 7:00-19:00; ICS: 7:00-19:00; RBRC 11:00-23:00; HMGU: 6:00-18:00. Recording in each center started and ended at different times. In the indirect calorimetry module standard measurements begin five hours before lights-off (lights off = T0) and are finished at T16 i. e. four hours after lights-on the next morning. Data analysis were restricted to within-centre comparisons between controls and mutants.
Data pre-processing We improved the accuracy of the automatic identification of the peak phase and onset time by detecting and removing the bad data points (measurement errors and environment interferences) in raw data for activity and food intake before running any analyses. First, we excluded out-of-range values that exceeded a realistic scope, such as a food intake of more than 0. 65 g and an activity higher than 4000 counts per hour during the daytime. Second, we carefully assessed the data obtained at the light-off time point ZT12 (denoted as y 12 ), particularly the data obtained by ICS, because abnormal data generated at ZT12 resulting from an unstable environment, such as unstable light intensity, may lead to an inaccurate assessment of the onset time and peak phase. If y 12 satisfied the following conditions: 1) a local maximum, 2) higher than four-fifths of the peak value (denoted as y max ), and 3) no other local maximum between ZT12 and ZT17, we considered it a bad data point and removed it from the dataset.
The ranges of a 5 and a 9 are [0,10(m 2 -min(y ave ))] and [0,10 (m 3 -min(y ave ))], and the ranges of a 1 are [(max(x onset )-min(x onset )),(max(x onset )-min(x onset ))]. The ranges of a 2 , a 6 and a 10 are all  [0, 4]. The ranges of a 4 , a 8 and a 12 are [0, 0. 15] for food intake. If there are only two peaks, m 3 is set to zero. In the end, raw average data after the first peak were fitted with multiple Gaussian functions via MATLAB's lsqcurvefit function with above initial values and parameter ranges. Accordingly, we obtained standard parameters p � * to best fit the ODE and Gaussian functions to raw average data. Next, we generated various parameter sets p ! to mimic different chorotypes. The parameters for ODE can be described as follows: 1. t 1 is the zeitgeber time of the first data point.
305 The CFD code is ANSYS Fluent 15. 0 with the SIM-PLE velocity-pressure coupling algorithm. UDF scripts have been developed to solve the additional transport equation (14 or 15) and to define the source terms (in particular the F volume force field that is applied in the 310 central region of the domain). Second-order discretization schemes are used for pressure and energy. Momentum and λ 2 equations are solved with a QUICK scheme. In the present paper, 2D flows have been considered.
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physicallyconsistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The later exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally-efficient yet compact representations to address a variety of issues, including among others adjoint derivation, model calibration, forecasting, data assimilation as well as uncertainty quantification.
g. PDE-Net  (Long et al. 2017 (Long et al. , 2018)). These previous works emphasize the connection between the underlying physics and the NN architectures. Designing or learning a NN representation for a given physical process remains a difficult issue. If the learn- * olivier. pannekoucke@meteo. fr ing fails, it may be unclear to know how to improve the architecture of the neural network. Besides, it seems irrelevant to run computationally-expensive numerical experiments on large-scale dataset to learn wellrepresented processes. The advection in fluid dynamics may be a typical example of such processes, which do not require complex non-linear data-driven representations. Overall, one would expect to accelerate and make more robust the learning process by combining, within the same NN architecture, the known physical equations with the unknown physics.
The dynamics is defined as an equation using the Eq class of PDE-NetGen, which inherits from the Eq class of sympy with additional facilities (see the implementation in Fig. 1  for additional details). The core of the NN generator is given by the NNMod-elBuilder class. This class first preprocesses the system of evolution equations and translates the system into a python NN model. The preprocessing of the diffusion equation Eq. (  3 ) presents a single prognostic function, u, and three constant functions κ 11 , κ 12 and κ 22. There is no exogenous function for this example. During the preprocessing, the coordinate system of each function is diagnosed such that we may determine the dimension of the problem. For the diffusion equation Eq. (  3 ), since the function u(t, x, y) is a function of (x, y) the geometry is twodimensional.
For the current version of PDE-NetGen, we consider Keras  (Chollet 2018 ). An example of the implementation in Keras is shown in Fig. 2 : a first part of the code is used to compute all the derivatives using Conv layers of Keras, then Keras layers are used to implement the algebraic equation which represents the trend ∂ t u of the diffusion equation Eq. (3). At the end, a python code is rendered from templates by using the jinja2 package. The reason why templates are used is to facilitate the saving of the code in python modules and the modification of the code by the experimenter. Runtime computation of the class could be considered, but this is not implemented in the current version of PDE-NetGen. For the diffusion equation Eq. (  3 ), when run, the code rendered from the NNMod-elBuilder class creates the NNDiffusion2DHeterognous class.
For the numerical experiment, the Burgers equation is solved on a one-dimensional periodic domain of length 1, discretized in 241 points. The time step is dt = 0. 002, and the dynamics is computed over 500 time steps so to integrate from t = 0 to t = 1. 0. The coefficient of the physical diffusion is set to κ = 0. 0025. The numerical setting considered for the learning is the tangent-linear regime described in  Pannekoucke et al. (2018)  where the initial uncertainty is small and whose results are shown in their Fig. 4 (a), Fig. 5 (a) and Fig. 6(a). To train the parameters (a, b, c) in Eq. (  7 ), we build a training dataset from an ensemble prediction method where each member is a numerical solution of the Burgers equation. The numerical code for the Burgers equation derives from PDE-NetGen applied on the symbolic dynamics Eq.
(5). Using this numerical code, we generate a training dataset composed of 400 ensemble simulations of 501 time steps, where each each ensemble contains 400 members. For each ensemble forecast, we estimate the mean, variance V u and diffusion tensor ν u. Here, we focus on the development of the front where we expect the unknown term to be of key importance and keep for training purposes the last 100 time-steps of each ensemble forecast. For the training only, the RK4 time-scheme is computed as the ResNet implementation given in Fig. 5 , so to provide the end-to-end NN implementation of the dynamics. The resulting dataset involves 40000 samples. To train the learnable parameters (a, b, c), we minimize the one-step ahead prediction loss for the diffusion tensor ν u. We use ADAM optimizer  (Kingma and Ba 2014)  and a batch size of 32.
Conclusion We have introduced a neural network generator PDE-NetGen, which provides new means to bridge physical priors given as symbolic PDEs and learning-based NN frameworks. This package derives and implements a finite difference version of a system of evolution equations, where the derivative operators are replaced by appropriate convolutional layers including the boundary conditions. The package has been developed in python using the symbolic mathematics library sympy and keras. We have illustrated the usefulness of PDE-NetGen through two applications: a neural-network implementation of a 2D heterogeneous diffusion equation and the uncertainty prediction in the Burgers equation. The later involves unknown closure terms, which are learned from data using the proposed neural-network framework.
Acknowledgments The  UML  class diagram has been generated from UM-Llet Auer et al. (2003). This work was supported by the  French national program LEFE/INSU (Étude du filtre de KAlman PAramétrique, KAPA). RF has been partially supported by  Labex Cominlabs  (grant SEACS),  CNES  (grant  OSTST-MANATEE ) and  ANR  through programs  EUR Isblue, Melody and OceaniX.
Overhead Our protocol relies on Beacon and routing packets, that already exist for most of the MAC or routing protocols, and are defined either in IEEE 802. 15. 4-2015  [12]  or RFC 6550  [36]  standards. In particular, Enhanced Beacons packets keep on being generated according to the same period. Each node computes the expected wakeup time of its leaf neighbors by taking into account the instant of reception of the last EB, and the EB period in the network. The overhead generated by our solution consists of: 1. a storage flag in the data packets, indicating the emitter is in storing mode. In particular, no packet is required to maintain the nodes awake in the storing region: a node cannot sleep until all its neighbors indicated they recovered, either in EB or data packets; 2. two additional transmissions to store a packet in a neighboring leaf node (and retrieve it).
Custom scRNA-Seq Maps Cellular Distribution and Transcriptomic Signatures of S703I JAK1 To follow up on the differences across immune cell types, we performed single-cell RNA sequencing (scRNA-seq) of the patient's PBMCs. We aimed to specifically map and evaluate the impact of S703I JAK1 across immune cell subsets by relating single-cell resolution gene expression patterns to cell type and, given the mosaicism, JAK1 genotype. However, sequence data from droplet-based scRNA-seq platforms are typically restricted to 5 0 or 3 0 transcript regions and therefore do not include coverage of the S703I site (c. 2108) in JAK1 mRNA (Figure  S5A ). Therefore, using custom barcode microbeads and a modified library preparation procedure, we adapted the inDrops scRNA-seq methodology  (Klein et al. , 2015; Zilionis et al.
, 2017)  to target exon 16 (containing the S703I site) of JAK1 in addition to the standard mRNA 3 0 regions (Figure  S5B ; Method Details). We used this custom inDrops scRNA-seq to analyze the patient's PBMCs, and concurrently processed the same sample on the 10X Genomics Chromium platform (Figures  S5C  and S5D ). Clustering and manual annotation of cell types based on gene expression patterns distinguished the expected PBMC populations, with relative frequencies consistent with the corresponding CyTOF analyses (Figures  5A  and S5D ; Table  S3 ). Again, we observed a relative increase in CD56 hi NK cell frequency. Using sequence data from JAK1-targeted libraries, we assigned putative per-cell JAK1 genotypes (based on transcript sequences) to a subset of PBMCs for which transcript and read depths were sufficient in the JAK1-targeted inDrops dataset  (Figures S5E and S5F).
JAK1 Expression Is Restricted to a Single Allele across Immune Cells Given the mosaicism of a heterozygous mutation, we expected to observe some cells (i. e. , homozygous WT JAK1) containing only WT JAK1 transcripts and others (i. e. , heterozygous S703I JAK1) containing both WT and S703I JAK1 transcripts. However, we found that expression of the 2 alleles seemed almost mutually exclusive, as very few cells expressed both transcripts, as opposed to the ~50% that was expected given our genomic estimates (Figures  1D  and 5F  from our dataset containing heterozygous variants exhibited the expected biallelic distribution (Figure  5F , right panel). This result suggests that JAK1 may be subject to monoallelic bias, a pattern that has only recently been recognized in a fraction of the autosomal transcriptome  (Borel et al.
Data and Code Availability The raw data for experiments performed, including the PCR, multiplex ELISA, mass cytometry, flow cytometry, scRNaseq data, are available upon request from the lead contact. For privacy concerns of the study participants, raw scRNA-Seq data are not available, and the complete data files from whole exome sequencing will be restricted to the variants in Table  S1. scRNA-Seq gene x cell matrices, python scripts and associated Seurat code (R-based) used for the tailored analysis of JAK1-specific scRNA-Seq are available at https://github. com/jorgcalis/JAK1-alleles-pipeline.
Variant analysis All samples were collected with informed consent in accordance with IRB-approved protocols (Study ID# IF2349568). For wholeexome sequencing, DNA was isolated (QIAGEN Cat No 69504) from Ficoll-isolated granulocytes from whole blood of the proband and her healthy parents. Library preparation, sequencing (150 bp paired-end reads) and alignment for whole-exome sequencing were performed with the Genewiz exome-sequencing package. Potential disease-causing variants were investigated by Ingenuity Variant Analysis (QIAGEN). Hight-quality variants were identified by filtering as follows: exclusion of common variants (> 0. 1% allele frequencies in public databases); retention of variants in coding regions resulting in substitutions, premature stops, frameshifts or altered splicing; exclusion of variants with CADD scores below MSC thresholds.
Mass cytometry Whole blood was collected, by venipuncture, into sodium heparin vacutainer tubes. For immunophenotyping, blood was immediately stained and processed for mass cytometry. For intracellular staining, whole blood was stimulated by incubation for 15 minutes with the cytokines indicated and was then immediately stabilized with Proteomic Stabilizer PROT1 (SmartTube) and frozen at À80 C. Similarly, for inhibition with ruxolitinib and tofacitinib, blood was treated for 4 hours with 500 nM inhibitor and was then stimulated by incubation for 15 minutes with 1000 IU/mL IFNa, before stabilization and freezing. Frozen samples were thawed according to the manufacturer's recommended protocol. The thawed samples were washed with barcode permeabilization buffer (Fluidigm) and barcoded with Fluidigm's Cell-ID 20-Plex Pd Barcoding Kit.
02% saponin and 30 nM OsO4 (ACROS Organics) for 30 minutes at room temperature. Samples were then washed and acquired immediately after staining. Samples were washed once with PBS+0. 2% BSA, once in PBS, and once in CAS buffer (Fluidigm). They were and resuspended at a concentration of 1 million cells per mL in CAS buffer containing a 1/20 dilution of EQ beads (Fluidigm). Following routine instrument tuning and optimization, samples were run at an acquisition rate of < 300 events per second on a Helios mass cytometer (Fluidigm) with a modified wide-bore injector (Fluidigm). FCS files were then normalized and concatenated with Fluidigm acquisition software, and the barcoded samples were deconvoluted with a MATLAB-based debarcoding application (''Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm'').
The FCS files were then uploaded to Cytobank for analysis. Cell events were identified as Ir191/193-positive and Ce140-negative events. Doublets were excluded on the basis of Mahalanobis distance and barcode separation and with the Gaussian parameters acquired with Helios CyTOF software. Downstream data analysis was performed on Cytobank, by both tSNE analysis and biaxial gating of immune populations, as shown in Figure  S3. Mean signal intensities were calculated, and relative induction was determined by normalization relative to the mean for healthy control samples.