dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptional uncertainty landscape of iPSC cell differentiation to cardiomyocytes In the following, we demonstrated an application of our procedure described above to a single-cell transcriptional dataset from cardiomyocytes differentiation from human induced pluripotent stem cells (iPSCs)   et al. , 2023) ). As depicted in Figure  1b , the intermediate cell cluster (cluster 2) comprising PS-like cells have higher cell uncertainty (lower single-cell likelihood) than the other clusters. Figure  1c  and d give the moving-averaged uncertainty values for pseudotemporally ordered cells using a moving window of 10% of the total cells for both endodermal and mesodermal paths, respectively. The moving-averaged transcriptional uncertainty for the two differentiation paths follows a rise-then-fall trajectory where the peak of uncertainty coincides with the lineage bifurcation event.
The hill in the uncertainty landscape is again seen around the bifurcation event upon using a higher number of cell clusters (Extended data, S3 Figure  (Gao et al. , 2023) ). Finally, we used a different algorithm to cluster cells, specifically using a Laplacian-based clustering algorithm called single-cell interpretation via multikernel learning (SIMLR)  (Wang et al. , 2017) , to test whether the shape of the transcriptional uncertainty landscape changes with the clustering algorithm. The single-cell clusters can be interpreted as the transitional states that the differentiating cells go through. Starting with the result of SIMLR cell clustering, we then generated the lineage progression and estimated the cell likelihood values using CALISTA. The transcriptional uncertainty landscape from SIMLR cell clustering has the same shape as that in Extended data S1 Figure  (Gao et al.
, 2023) , demonstrating that the transcriptional uncertainty landscape observed above is not dependent on using CALISTA for cell clustering (Extended data, S4 Figure  (Gao et al. , 2023) ). To further elucidate the role of specific genes in shaping the transcriptional uncertainty landscape, we looked at the transcriptional uncertainty associated with individual genes. Figure  1e  depicts the NLL distribution of each gene for the five single-cell clusters. As expected, cells in cluster 2 have generally higher NLL than those in the other clusters. Figure  1e  clearly illustrates that within cluster 2, some genes show higher NLL values than the others (Extended data, S5 Figure  (Gao et al. , 2023) ). To identify the important genes related to transcriptional uncertainty, we identified genes with NLL values exceeding a threshold δ for at least 30% of the cells in each cluster, where δ is set to 3 standard deviation above the overall mean NLL for all cells and genes in the dataset (see Methods equation (  2 )).
Transcriptional uncertainty landscapes of cell differentiation We further applied the procedure above to seven additional single-cell transcriptomic datasets that were generated using scRT-qPCR (Guo et al. The use of the two-state mechanistic gene transcriptional model within CALISTA enabled us to probe into a mechanistic explanation for the observed shape of the transcriptional uncertainty landscape. Table  1  show the pairwise Pearson correlations between the cell-averaged NLL of each cluster with two biologically interpretable model parameters, namely transcriptional burst size (number of transcripts generated in each burst) and burst frequency (occurrence of burst per unit time) (Nicolas et al. , 2017) (see Methods). The Pearson correlations indicate that the single-cell gene expression uncertainty increases with higher burst size and burst frequency (p-value ≤ 0.
, 2023) ). The single-cell transcriptional uncertainty landscape again has the rise-then-fall shape, as in the other cell differentiation systems discussed above. Interestingly, the same rise-then-fall profile is also seen in the RNA velocity. As illustrated in Figure  3 , the increase and decrease of the RNA velocity preceed the transcriptional uncertainty, and the peak of RNA velocity occurs prior to those of the transcriptional uncertainty (see the Extended data S1 File for animated illustration  (Gao et al. , 2023) ). Furthermore, a gene-wise cross-correlation analysis confirms a positive correlation between RNA velocity and single-cell transcriptional uncertainty with a delay for individual genes (see the Extended data, Figure S14  (Gao et al. , 2023) ). We also compared RNA velocity and single-cell transcriptional uncertainty for another dataset from mouse hippocampal neurogenesis with a multi-branching lineage  (La Manno et al.
, 2018). Figure  3  (bottom row) shows that like in the neurogenesis dataset earlier, the RNA velocity increases and then decreases during cell differentiation, and the change in the RNA precede that of the transcriptional uncertainty (see the Extended data, S2 File for animated illustration  (Gao et al. , 2023) ). Also, the RNA velocity peaks take place before the transcriptional uncertainty peaks. The rise-then-fall dynamic of the RNA velocity seen in the two datasets above is consistent with the view that cells engage in exploratory stochastic dynamics as they leave the progenitor state, and disengage this explorative mode as they reach toward the final cell state.
The reconstruction of the transcriptional uncertainty landscapes from 10 single-cell transcriptomic datasets of various cell differentiation processes in our study reveals a universal rise-then-fall trajectory in which cells start from a high potency state with a uniform gene expression pattern in the cell population, then progress through transitional cell state(s) marked by increased transcriptional uncertainty (i. e. , higher cell-to-cell variability), and eventually reach one of possibly several final cell states with again a uniform gene expression pattern among the cells. Furthermore, the peaks of the transcriptional uncertainty landscape colocalize with forks in the cell lineage. The rise-then-fall in cell uncertainty agrees well with other reports from different cell differentiation systems  (Han et al.
, 2018; Rebhahn et al. , 2014). Such an idea would be in congruence with the recent demonstration that, in a physiologically relevant cellular system, gene expression variability is functionally linked to differentiation  (Guillemin et al. , 2018; Moris et al. , 2018). The rise-then-fall trajectory in the transcriptional uncertainty landscape are more pronounced in some datatsets than in others. For example, in Nestorowa  (Nestorowa et al. , 2016)  and  Moignard (Moignard et al. , 2013)  datasets (see Figure  2c ), peaks in the transcriptional uncertainty landscape are less noticeable than in the other differentiation systems. We noted that cells in the Nestorowa  (Nestorowa et al. , 2016)  and  Moignard (Moignard et al. , 2013)  studies were presorted by using flow cytometry based on the expression of surface protein markers.
Software availability CALISTA tutorial is available from: https://www. cabselab. com/calista CALISTA source code is available from: https://github. com/CABSEL/CALISTA License: BSD-3-Clause license Using the CALISTA method that they have developed, Papili Gao et al. quantify single cell transcriptional uncertainty in 10 sets of single cell data associated to differentiating systems. This includes single-cell mRNA as well as single-cell RT-qPCR. The representation of the cells in the uncertainty landscape allows to visualize the differentiation trajectories and the increase in stochasticity that precedes each fate commitment. The authors also show that the transcriptional uncertainty correlates with the RNA velocity, a concept that was introduced by La Manno et al. in 2018. These results shed light on the mechanism underlying cell fate commitment, with the increase of uncertainty allowing cells to possibly engage in different cellular outcomes.
Based on eigengap heuristics (Papili Gao et al. , 2020), we grouped cells into five optimal clusters and ordered cells along the inferred linear trajectory (Extended data S9 Figure (Gao et al. , 2023)). Moussy et al. scRT-qPCR dataset. The single-cell expression dataset includes normalized Ct values for 91 genes in 435 cells captured at five distinct time points (0, 24, 48, 72, 96 h) during human cord blood-derived CD34+ differentiation (Moussy et al. , 2017). We employed CALISTA to group cells into seven clusters, reconstruct the developmental trajectory and calculate pseudotimes (Extended data, S10 Figure (Gao et al. , 2023)). Guo et al. scRT-qPCR dataset. The dataset comprises the single-cell expression values of 48 genes from 387 individual cells isolated at four distinct developmental cell stages, from 8-cell stage mouse embryos to 64-blastocyst (Guo et al.
, 2010). By applying CALISTA, we identified seven different subpopulations along the differentiation process, and the inferred lineage hierarchy pinpointed two bifurcations events at 32-and 64-cell stage (Extended data, S11 Figure (Gao et al. , 2023)). The timing of the lineage bifurcations coincides with two well-known branching points: one at 32-cell stage when totipotent cells differentiate into trophectoderm (TE) and inner cell mass (ICM), and another at 64-cell stage when ICM cells differentiate into primitive endoderm (PE) and epiblast (E). Nestorowa et al. scRNA-sequencing dataset. The dataset comprises single-cell gene expression of 1656 cells from mouse hematopoietic stem cell differentiation (Nestorowa.
, 2010; Moignard et al. , 2013; Moussy et al. , 2017; Richard et al. , 2016; Stumpf et al. , 2017)  and scRNA-sequencing(Nestorowa et al. , 2016; Treutlein et al. , 2016), to assess the universality of the rise-then-fall feature of single-cell transcriptional uncertainty landscape during cell differentiation. The first of these datasets came from 405 cells during mouse embryonic fibroblast (MEF) reprogramming into induced neural (iN) and myogenic (M) cells(Treutlein et al. , 2016). Like the iPSC differentiation above, the lineage progression has a single bifurcation point. As depicted in Figure2a, the single-cell transcriptional uncertainty increases from the initial MEF state and reaches a peak around the bifurcation before decreasing toward two end-point cell fates. The rise-then-fall of transcriptional uncertainty in the MEF reprogramming is in good agreement with what we observed in the iPSCs differentiation above.
Higher entropy of gene expression distribution in a cell population has also been reported in the reprogramming of iPSCs (Buganim et al. , 2012). Next, we analyzed datasets from cell differentiation processes without a lineage bifurcation and with multiple lineage bifurcations. Three scRT-qPCR datasets came from differentiation systems without bifurcation, including the Richard et al. study on chicken erythrocytic differentiation of T2EC cells (Richard et al. , 2016), the Stumpf et al. study on differentiation of mouse embryonic stem cells (ESC) to neural progenitor cells (NPC) (Stumpf et al. , 2017), and the Moussy et al. study during CD34+ cell differentiation (Moussy et al. , 2017). The single-cell clustering and lineage progression by CALISTA produced the expected cell differentiation trajectory (see Extended data, S8 to S10 Figures (Gao et al.
Note that the Moussy et al. study looked at only the initial phase of the (hematopoietic) cell differentiation, and thus, it is likely that the differentiation process had not completed for the cells in the dataset. The next set of single-cell gene expression data came from differentiation systems with multi-branching lineage, including the Guo et al. study during mouse embryo development from zygote to blastocyst (Guo et al. , 2010 ),  Nestorowa et al. (2016)  and Moignard et al. (2013)  studies on hematopoietic stem cell differentiation. Figure2cshows the single-cell transcriptional landscape for each of the datasets. For the Guo et al. study, we identified seven cell clusters and identified two bifurcations in the lineage. Here, we observed two hills in the transcriptional uncertainty landscape, each coinciding with a bifurcation event in the lineage progressionone at 32-cell stage (cluster 2 to cluster 3 and 4) and another at 64-cell stage (cluster 4 to cluster 6 and 7) (see the Extended data, S11 Figure (Gao et al.
Extended data Zenodo: Extended Data for Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation. https://doi. org/10. 5281/zenodo. 7776102 (Gao et al. , 2023). This project contains the following underlying data: • Data file 1. Additional Figures and Notes (S1-S14 Fig. and S1 Note of CALISTA workflow) • Data file 2. S1 Table. Genes with high transcriptional uncertainty in Cluster 2 and 3 of Bargaje et al. (Bargaje  et al. , 2017)  data analysis. • Data file 3. S1 File. Animated illustration of RNA velocity and transcriptional uncertainty landscape in mouse hippocampal neurogenesis (La Manno et al. , 2018).
For a given real-world dataset, we fit the magnitude response of MuReNN to that of a wellestablished auditory filterbank: Gammatone for speech, CQT for music, and third-octave for urban sounds, respectively. This is a form of knowledge distillation (KD), in which the filterbank "teacher" is engineered by domain knowledge while the neural network "student" is optimized from data. We compare MuReNN to the state of the art in terms of goodness of fit after KD on a hold-out set and in terms of Heisenberg time-frequency localization. Compared to convnets and Gabor convolutions, we find that MuReNN reaches state-of-the-art performance on all three optimization problems.
e. , conservation biology  [6] , urban science  [7] , industry  [8] , and healthcare  [9]. Since these applications differ greatly in terms of acoustical content, the domain knowledge which prevails in speech and music processing is likely to yield suboptimal x natural sounds Y = |Λx| 2 Φ W x teacher student Lx(W) = cosdist |Φ W x 2 , Y Figure  1 : Graphical outline of the proposed method. We train a neural network "student" Φ W to regress the squared magnitudes Y of an auditory filterbank "teacher" Λ in terms of spectrogram-based cosine distance Lx, on average over a dataset of natural sounds x. performance. Instead, gradient-based optimization has the potential to reflect the spectrotemporal characteristics of the data at hand. Enabling this potential is particularly important in applications where psychoacoustic knowledge is lacking; e.
In theory, convnets may approximate any finite impulse response (FIR), given a receptive field that is wide enough; but in practice, gradient-based optimization on nonconvex objectives yields suboptimal solutions  [12]. On the other hand, the parametric approaches enforce good time-frequency localization, yet at the cost of imposing a rigid shape for the learned filters: cardinal sine (inverse-square envelope) for SincNet and Gabor (Gaussian envelope) for LEAF. Our goal is to overcome this dilemma by developing a neural audio model which is capable of learning temporal envelopes from data while guaranteeing near-optimal time-frequency localization. In doing so, we aim to bypass the explicit incorporation of psychoacoustic knowledge as much as possible. This is unlike state-of-the-art convnets for filterbank learning such as SincNet or LEAF, whose parametric kernels are initialized according to a mel-frequency scale.
Then, given a model Φ W with weights W, we evaluate the dissimilarity between teacher Λ and student Φ W as their (squared) spectrogram-based cosine similarity Lx(W). The distance of student and teacher in this similarity measure can be computed via the L 2 distance after normalizing across frequency bins f , independently for each time t. Let | Φ W x 2 and Y denote these normalized versions of student and teacher, then Lx(W) = cosdist |Φ W | 2 , Y = 1 2 T /2 J t=1 F f =1 | Φ W x 2 [f, t] -Y[f, t] 2 , ( 1 ) where F is the number of filters. We seek to minimize the quantity above by gradient-based optimization on W, on a real-world dataset of audio signals {x1. xN }, and with no prior knowledge on Λ.
Music A variable-Q transform (VQT) with Mj = 12 frequency bins per octave at every level. The VQT is a variant of the constant-Q transform (CQT) in which Q is decreased gradually towards lower frequencies  [24] , hence an improved temporal resolution at the expense of frequency resolution. Urban A third-octave filterbank inspired by the ANSI S1. 11-2004 standard for environmental noise monitoring  [25]. In this filterbank, center frequencies are not exactly in a geometric progression. Rather, they are aligned with integer Hertz values: 40, 50, 60; 80, 100, 120; 160, 200, 240; and so forth. We construct the Synth teacher via nnAudio  [26] , a PyTorch port of librosa  [27] ; and Speech, Music, and Urban using the Large Time-Frequency Analysis Toolbox (LTFAT) for MATLAB  [28].
Datasets Synth As a proof of concept, we construct sine waves in a geometric progression over the frequency range of the target filterbank. Speech The North Texas vowel database (NTVOW)  [29]  contains utterances of 12 English vowels from 50 American speakers, including children aged three to seven as well as male and female adults. In total, it consists of 3190 recordings, each lasting between one and three seconds. Music The TinySOL dataset  [30]  contains isolated musical notes played by eight instruments: accordion, alto saxophone, bassoon, flute, harp, trumpet in C, and cello. For each of these instruments, we take all available pitches in the tessitura (min = B0, median = E4, max = C♯8 ) in three levels of intensity dynamics: pp, mf, and ff. This results in a total of 1212 audio recordings.
Benchmarks For each audio domain, we randomly split its corresponding dataset into training, testing and validation subsets with a 8:1:1 ratio. During training, we select 2 12 time samples from the middle part of each signal, i. e. , the FIR length of the filters in the teacher filterbank. We train each model with 100 epochs with an epoch size of 8000. Table  1  summarizes our findings. On all three benchmarks, we observe that MuReNN reaches state-of-the-art performance, as measured in terms of cosine distance with respect to the teacher filterbank after 100 epochs. The improvement with respect to Conv1D is most noticeable in the Synth benchmark and least noticeable in the Speech benchmark. Furthermore, Figure  2  indicates that Gabor1D barely trains at all: this observation is consistent with the sensitivity of LEAF with respect to initialization, as reported in  [32].
Our scientific aim was to derive a method for estimating BVOC emissions for present and future climate scenarios that would be capable of considering both atmosphere and land surface processes as well as land-atmosphere interactions that impact vegetation. Our objective was to therefore develop a modelling system for BVOCs based on MEGANv2. 1 that would be flexible enough to use a variety of meteorological forcing datasets, e. g. , present day reanalyses and output from climate models for future scenarios. Furthermore, this modelling system would have to be capable of simulating impacts on vegetation arising from atmosphere-land interactions. In this study, we have therefore chosen to implement MEGANv2. 1 within the SURFEXv8. 1 (Surface Externalised) model, which is a land surface modelling platform developed by Météo-France in cooperation with the scientific community.
As shown in Figure  1 , each grid box in SURFEX is represented by 4 adjacent tiles: nature, urban areas, sea or ocean and lakes. The final fluxes are the average of the fluxes calculated over nature, city, sea/ocean and lake, weighted by their respective fraction (S l , S t , S n , S s ). SURFEX contains four principal surface schemes: ISBA for the nature tile  (Calvet et al. , 1998) , TEB for urban areas  (Masson, 2000) ,  FLAKE Mironov et al. (2010)  for lakes and SEA for sea and oceans. SURFEX can also simulate aerosol chemistry and surface processes, and can be used for assimilation of surface variables (Le Moigne, 2018). To define the surface coverage, SURFEX uses ECOCLIMAP-II, which is a 1km global database of land covers made by CNRM (Centre National des Recherches Météorlogiques, in French)  (Faroux et al.
This allows key land surface parameters used by MEGAN, i. e. leaf area index and soil moisture, to be calculated at a more precise scale. Thus, activity factors are individually calculated for each patch. This approach allows for a more accurate representation of biogenic emissions in the context of climate change and their impact on air quality. In the coupled model the estimation of biogenic fluxes of various species was carried out based on 16 vegetation types extracted from the ECOCLIMAP-II database  (Faroux et al. , 2013). Each vegetation type from ECOCLIMAP-II was mapped to its corresponding defined in CLM4. the three plant functional types corresponding to the different types of shrubs were introduced within ECOCLIMAP-II by assigning the shrub patch to a specific geographical area based on a given latitudinal range.
CAMS-GLOB-BIOv1. 2 is a 0. 5°× 0. 5°spatial resolution dataset obtained with ERA-interim meteorology, the vegetation cover is based on the CLM4 16 PFTs The ALBERI dataset is a bottom-up inventory of isoprene emissions developed in the frame of the ALBERI project funded by the Belgian Science Policy Office  (Opacka et al. , 2021). Isoprene emissions are estimated by the MEGANv2. 1 model, coupled with the canopy environment model MOHYCAN (Model for Hydrocarbon emissions by the CANopy)  (Wallens, 2004 )  (Bauwens et al. , 2018). The model was driven by the ERA-interim meteorological fields, vegetation description was provided from satellite-based Land Use/Land Cover (LULC) datasets at annual timesteps. The LULC datasets are based on the MODIS PFT dataset and are adjusted to match the tree cover distribution from the Global Forest Watch (GFW) database  (Hansen et al.
Northern temperate regions are only active during boreal summer with a maximum contribution of 24% in July. The contribution of southern temperate regions follows a cyclical pattern, with a maximum in austral summer (6% in reference simulation). Finally, the Arctic is characterised by a very low activity, which is due to the unfavourable weather conditions and relatively low vegetation cover. The monthly variation in isoprene emissions is strongly influenced by the emitting regions contribution throughout the year. As already mentioned, southern tropical regions are active throughout the year for all isoprene datasets, with particularly high contributions during November/December and lower contributions during June/July. As shown in Figure  7 , southern tropical regions account for approximately 49% of annual isoprene emissions in SURFEX-MEGAN and CAMS-GLOB-BIOv2.
SURFEX-MEGAN isoprene sensitivity tests In order to analyse isoprene emission variation linked to MEGAN's driving parameters, 3 sensitivity tests were conducted. As stated in  (Guenther et al. , 2012)  isoprene emissions depend on various meteorological and environmental parameters as well as the model assumptions. In this study, we have investigated isoprene emission sensitivity to meteorology using 2 different additional meteorological datasets (both IFS and MERRA) (S1), analysed isoprene emissions with a different set of emission potentials (S2), and studied the impact of soil moisture on isoprene emission (S3). Table  4  summarises the list of sensitivity tests performed in this study, along with a description of each test setup. The impact of each sensitivity test was examined on the global and regional scales by analysing the annual isoprene emission contribution from nine geographical regions defined in the GlobEmission project (www.