dataesr/scientific-paragraphs-categorization huggingface dataset:
While cell area affected both genes in the same cell (and hence always induces positive correlation), we also introduced cell-specific latent variables (either cell phase or burst size) that can alter the bivariate probability distributions more flexibly. Given the ease with which these types of models can now be implemented in probabilistic programming languages such as STAN, we anticipate that the use of such latent variable models will be instrumental in describing and quantifying the underlying (but unobserved) biology that influences gene-gene relationships in multivariate single-cell measurements of gene expression. The considered models for the circadian smFISH data (Table  1 ) consisted of different mixtures of negative binomial distributions with a burst frequency that changes over time.
Methods and Protocols Cell lines and culture condition For maintenance, wild-type mouse NIH3T3 fibroblasts were cultured in Dulbecco's modified Eagle medium (DMEM, Gibco) complemented with 10% foetal bovine serum (FBS, Sigma) and 1% PSG antibiotics (Gibco). Cells were grown at 37°C, 5% CO 2 and 100% humidity and passaged every 2-3 days, until the day of the experiment. Circadian clock entrainment by temperature cycles was performed using a Memmert INCO153 incubator controlled by the Memmert Celsius 10. 0 software. We entrained the clock using temperature cycles ranging from 35. 5°C to 38. 5°C, as previously described  (Saini et al, 2012 ) and for at least 10 days prior beginning of the experiment. To maintain synchronisation after passaging, we always applied the splitting procedure at the same temperature cycle position (37°C), when the clock was at its trough.
Imaging Slides were imaged at the EPFL imaging facility (BIOP) with a Leica DM5500 wide field microscope equipped with a LED Lumencor SOLA lamp, an HCX PL APO 63× Oil objective and an appropriate set of filters (blue, green, orange and far red). We took series of about 40 z-sections with a step of 0. 3 μm, depending on the thickness of the cell layer in the field. Image processing and data analysis smRNA FISH microscopy images were processed using a custom analysis pipeline based on open-source software Ilastik1. 3. 2 and CellProfiler3. 1. 8 (Kamentsky et al, 2011; Berg et al, 2019). 1 CellProfiler was first used to generate maximum Z-stack projections (all channels) and sum Z-stack projections (Red channel only). 2 Secondly, Ilastik pixel classification projects were used to generate probability maps of dots from max Z-stack projections.
One Ilastik workflow has been used for each (Red) and (FarRed) channels using Random Forest Model trained with a mix of images from independent experiments. 3 Finally, CellProfiler was used to segment dots (dots probability maps), cells (sum Z-projections, Red channel), nucleus (max Z-projection, DAPI channel) and cytoplasms (cell minus nucleus) and related with each other. Data analysis was performed using custom R and Matlab scripts and functions. Cosinor regression of smFISH data population mean mRNA levels To model the smFISH population average mRNA levels (Fig 1B ), we performed a least-squares fit using the mean mRNA count of each replicate at all the time points. We used the following function with 2 harmonics to model to mean mRNA level m g (t) for each gene g: m g ðtÞ ¼ a 0,g 2 þ a 1,g cos 2πt 12 À ϕ 1,g þ a 2,g cos 2πt 24 À ϕ 2,g (1) Probabilistic models of smFISH data and parameter inference To model the smFISH mRNA count distributions from populations of cells, we proposed several candidate models with unique biological interpretations and estimated parameters in a Bayesian paradigm.
To infer the parameters, we will use Bayes' rule: pðθjyÞ / pðyjθÞpðθÞ: where p(y|θ) is the likelihood of observing the data given parameters θ (likelihood), p(θ) is the estimate of the parameter values before observing evidence (prior probability), and p(θ|y) is the probability of the parameters θ after the data y is observed (posterior probability). Note that in the case of normally distributed errors (a Gaussian model), the (log) likelihood reduces to a leastsquares model, but this is not applicable here for count data. In our dataset we measure the smFISH count y i,g for cell i and gene g, and each cell has an associated time t i and a measured cell area A i. To calculate the likelihood of the data for a given set of parameters θ, we multiply over a total of N measured cells and all genes pðyjθÞ ¼ Y g Y N i¼1 pðy i,g jt i ,A i ,θÞ where p(y i,g |t i ,A i ,θ) represents the probability of observing an smFISH count y i,g in a single cell given the time t i , area A i and parameters θ for a given model.
Model selection We use two different methods to estimate the predictive accuracy of all of our models: adjusted within-sample predictive accuracy and cross-validation. The starting point for our first approach is given by the elpd (expected log pointwise predictive density for a new dataset). This can in theory be estimated using leave-one-out crossvalidation, where one cell is removed from the dataset. elpd leaveÀoneÀout ¼ ∑ N i¼1 logpðy i jy Ài Þ ¼ ∑ N i¼1 log Z pðy i jθÞpðθjy Ài Þdθ which uses the leave-one-out predictive density given the data without cell i. However, this would involve retraining the model for each cell, which is not feasible with thousands of cells. As shown in  (Gelfand et al, 1992) , the log predictive density for the held-out cell y i can be estimated from the posterior samples of the full dataset using importance ratios pðy i jy Ài Þ≈ 1 1 S ∑ S s¼1 1 pðy i jθ s Þ We estimate the elpd leave-one-out with Pareto smoothing of the importance weights (Pareto-smoothed important sampling, PSIS-LOO) using the parameter samples for each model together with the PSIS-LOO python function provided with  (Vehtari et al, 2017).
Reinforcement learning (RL) has received much attention in recent years due to its adaptability to unpredictable events such as harvested energy and workload, especially in the context of edge computing for Internet-of-Things (IoT) nodes. Due to limited resources in IoT nodes, it is difficult to achieve self-adaptability. This paper studies online reactivity issues of fixed learning rate in the linear actor-critic (LAC) algorithm for transmission duty-cycle control. We propose the LAC-AB algorithm that introduces into the LAC algorithm an adaptive learning rate called Adam for actor update to achieve better adaptability. We introduce a definition of "convergence" when quantitative analysis of convergence is performed. Simulation results using real-life one-year solar irradiance data indicate that, unlike the conventional setups of two decay rate β 1 , β 2 of Adam, smaller β 1 such as 0.
Related Work Many research works in the literature of energy management in both lower-power IoT and EH-IoT exist. Roose et al. [6]  proposed a hardware solution for a sense and compress application. The method proposed loads tuning parameters obtained by offline evolutionary algorithm onto statistics (sample variance)-based runtime control. It reduces the online resources, but the runtime adaptability might not be enough in the case of unexpected events that were not in the training dataset. A stepwise online adjustment is one solution presented in  [7]  that works with a certain discrete action space, which can be a limit for applications that require continuous spaces. The use of prediction of harvested energy is also a downside, as it is likely to produce errors that can make decision-making worse.
1). The harvest-use-store scheme is adopted  [25, 26]  to provide high energy efficiency. - The wireless link quality is under the influence of path-loss and shadowing. - The workload follows the Poisson distribution. The average rate doubles after the first six months (where the algorithm is put through the test of fast adaptability/reactivity). More precisely, the system receives the average of 1. 0 pkt/min for the first six months, and it impulsively becomes twice (2. 0 pkt/min) afterwards. Figure  1  illustrates this application scenario. For the solar irradiance data, we use three kinds of datasets:.
In this case, the upper bound of the TX duty-cycle is way too large for the system as well as the application; in other words, no proper upper bound for braking duty-cycle explosion exists in the existing method. Note that, with the use of either Adam or a much smaller learning rate for the case of five-year dataset, the same divergence was observed. To resolve this issue, we suggest the use of SoB both as a performance and an upper bound index. RL algorithms are supposedly self-adaptive to environmental changes. Such changes can be characterized by their periodicity and speed. To counteract them, the system can modulate its action(s) and/or the CUI. We fix the CUI as 30 min and consider an impulsive workload change (from 1. 0 pkt/min to 2. 0 after the first six months) to shed light on the reactivity issue of fixed learning rate.
Effectiveness and Convergence of LAC-AB As observed in the previous section, the existing LAC method is limited in adaptability due to fixed learning rate. Thus, we introduce Adam to LAC to deal with this problem and call it LAC-A algorithm. In addition, as explained in Section 6, it would be possible to exclude the use of initialization bias correction terms in Adam, which leads to the LAC-AB algorithm. In this section, we use 300 simulation results and compare these approaches with different decay rate setups in terms of convergence, latency, and power failures. Note that the latency in this work is defined as the time from when a packet arrives in the data buffer until when it is successfully transmitted to the sink node. First, we use the EHD1 dataset and make a comparison among LAC-A with PA setting, LAC-A, and LAC-AB with different decay rates, through which we show more suitable setups for LAC-A/LAC-AB and the improvement in terms of latency, reactivity, and the number of power failures.
We use the EHD2 and EHD3 datasets where the seasonal and weather changes are removed and the randomness of real-life solar irradiance is still retained. The analysis with the use of those datasets can be supported by the fact that the LAC algorithm is an on-policy RL and no other algorithm for capturing correlations of any two different days is used. We obtained the traces of ψ for EHD2 and EHD3 depicted in Figure  3  as well as that for EHD1. We can claim qualitatively that the ψ of LAC-A using β 1 = β 2 = 0. 4 for EHD1 dataset converges after the workload change despite its constant decrease, because the value converges to 2. 25 × 10 -2 for EHD2 (corresponding to December) and to 2. 0 × 10 -2 for EHD3 (corresponding to June) and the trace in-between can be explained by interpolation. This observation explains that the difference in the ratio of variations of the SoB and the SoC at each control interval gives rise to different optimization process.
Note that the ToF and ToR can be measured also with EHD2 dataset, but the outcome is quite similar to EHD3's, and, therefore, removed for brevity. The results are depicted in Figure  8. The number of power failures (Figure  8   The analysis on the latency in the two different workload periods was also conducted, as illustrated in Figures  9  and 10. As can be seen, the results show the opposite trends. For the first six months, the latency has a tendency to decline as β 1 increases, ranging from 3. 4 to 3. 6 min and from 10 to 12 min for the mean and standard deviation, respectively, except for β 1 ∈ [0. 5, 0. 7] with β 2 ∈ [0. 1, 0. 2]. By contrast, the rising trends are observed during the period of doubled workload, where the mean and standard deviation of latency fall between 6. 0 and 6.
2 min and between 10. 65 and 11 min, respectively. Again, the exceptions occur with β 1 ∈ [0. 5, 0. 7] with β 2 ∈ [0. 1, 0. 2]. These phenomena are explained by the effectiveness of high adaptability to each situation; higher adaptability by smaller decay rates is more suitable when the state such as SoB varies greatly (e. g. , during the last six month), and less when the state is relatively constant (e. g. , during the first six month). We ran 300 simulations and obtained ToF and ToR for both EHD2 and EHD3 dataset, since we can expect from the results in Figure  7  that the convergence speed may differ depending on the season. Figure  11  shows the ToF and ToR for the chosen sets of decay rates with the baseline values of those of the LAC algorithm using fixed learning rate. Obviously, the convergence speed improved compared to the case of LAC.
With β 1 /β 2 = 0. 3/0. 1, for instance, the ToF and ToR reduced to 7. 15 and 0. 02 days for EHD2 (or 1 December dataset) and 7. 48 and 7. 67 for EHD3 (or 1 June dataset), respectively, compared to 83. 40 and 47. 52 days for EHD2 and 60. 17 and 45. 77 days for EHD3 in using fixed learning rate in LAC algorithm. Across all the combinations, the worst ToF and ToR are still 13. 06 and 5. 67 days for EHD2 and 13. 12 and 14. 44 for EHD3, respectively. In other words, the fine-tuning and reactivity speed have improved by at least 78. 2-84. 3% and 68. 5-88. 1%. With respect to the latency and power failure, Figure  12  depicts these metrics for the chosen decay rate combinations in comparison to the LAC algorithm without Adam. The EHD1 dataset was used to obtain the results consistent with the real-world situations.
1. EHD1: Non-processed real-life one-year data from 1 June 2018 to 31 May 2019 2. EHD2: Real-life one-year data made by stacking 365 one day (1 December 2018) worth of solar irradiance data 3. EHD3: Real-life one-year data made by stacking 365 one day (1 June 2018) worth of solar irradiance dataIn each simulation study presented below, we specify which dataset was used by the notation, EHD1, EHD2, or EHD3. The uses of EHD2 and EHD3 is justified in Sections 7 and 8. 2.
3/0. 1, for instance, the ToF and ToR reduced to 7. 15 and 0. 02 days for EHD2 (or 1 December dataset) and 7. 48 and 7. 67 for EHD3 (or 1 June dataset), respectively, compared to 83. 40 and 47. 52 days for EHD2 and 60. 17 and 45. 77 days for EHD3 in using fixed learning rate in LAC algorithm. Across all the combinations, the worst ToF and ToR are still 13. 06 and 5. 67 days for EHD2 and 13. 12 and 14. 44 for EHD3, respectively. In other words, the fine-tuning and reactivity speed have improved by at least 78. 2-84. 3% and 68. 5-88. 1%.
With β 1 /β 2 = 0. 3/0. 1, for instance, the ToF and ToR reduced to 7. 15 and 0. 02 days for EHD2 (or 1 December dataset) and 7. 48 and 7. 67 for EHD3 (or 1 June dataset), respectively, compared to 83. 40 and 47. 52 days for EHD2 and 60. 17 and 45. 77 days for EHD3 in using fixed learning rate in LAC algorithm. Across all the combinations, the worst ToF and ToR are still 13. 06 and 5. 67 days for EHD2 and 13. 12 and 14. 44 for EHD3, respectively. In other words, the fine-tuning and reactivity speed have improved by at least 78. 2-84. 3% and 68. 5-88. 1%.
RNAseq provides gene expression quantifications of the whole transcriptome (transcripts of more than 20,000 protein-encoding genes) or of preselected transcripts of interest (targeted sequencing), bearing the underlying hypothesis that each tumor gene expression profile mirrors the tumor aggressiveness and potential behavior in response to a particular treatment and therefore, should correlate with overall survival (OS). Since each tumor is unique in its complexity, an almost-infinite number of gene expression combinations could be expected; which drastically overcomplicates any prediction task based on RNA-seq data. Several teams recently intended to predict individual OS from RNA-seq analyses of multiple cancer types obtained from the Cancer Genome Atlas (TCGA) dataset 8 , using machine and deep learning  [9] [10] [11] [12] [13] [14]  (summarized in Supplementary Table  1 ).
With machine and deep learning, over-fitting can arise when the training dataset has greater number of dimensions (variables) than number of samples available (the size of the training set). Over-fitted models generally fail to generalize decently  17. In the case of RNA-seq, the large dimensionality (e. g. > 20,000 gene expressions) requires massive amounts of training data, which is tricky to obtain when applied to cancer and which presumably lacked to the abovementioned models (total number of samples comprised between 953 and 11,854). We hypothesized that OS prediction using supervised deep learning on pan-cancer RNAseq data as inputs would benefit from (i) starting with reducing input dimensions using prior biological knowledge and (ii) increasing the size of the training set, using a transfer learning strategy.
Gene selection We first intended to reduce the dimensions of our input dataset by selecting genes of interest, which are known to be implicated in cancer initiation, progression, dissemination or response to treatment. We merged gene lists obtained from the Molecular Signatures Database (MSigDB, relative to hallmarks of cancer)  19  , and the LM22 immune gene signatures  20. After removal of duplicates and genes associated with no expression values within our dataset, we obtained 4,499 genes (Supplementary Table  2 ).
Pilot overall survival prediction task: without pre-training We designed a pilot experiment of survival estimation starting with only tumor RNA-seq data. We retrieved all The Cancer Genome Atlas (TCGA) pan-cancer RNA-seq raw data publiclyavailable on February, 11, 2019, via recount2  21  and selected samples associated with annotated survival outcomes (Figure  2a ). We excluded uninformative patients who were censored during the first half of the total duration of the follow-up and the top 5% of patients with the longest OS, considering them cured by surgery. Altogether, we collected 6,529 RNA-seq samples from 33 tumor types fulfilling the criteria, among which 54. 8% were censored during second half of the follow-up. Based on this dataset, the pan-cancer median OS was 67. 2 months (95% confidence interval 95%CI [64.
Transfer learning: data collection for the pretraining task Since OS was highly related to tumor type (Figure  2b ), we assumed that learning to predict the organ of origin from a larger cohort could improve the overall estimation of survival duration. We therefore chose to pre-train our model on the prediction of the organ of origin from the RNA-seq expression data of the 4,499 selected genes using either healthy or tumor tissue (Figure  4a ). Healthy organs data were obtained through recount2 from the Genotype-Tissue Expression (GTEx) project  26. We additionally retrieved all the TCGA gene expression data of tumor samples (including those not associated with survival data). Each tumor type was aligned with its organ of origin (for example, kidney chromophobe, clear cell carcinoma and papillary cell carcinoma were all considered as kidney tissue).
Pre-training on organ prediction We pre-trained the first section DeepOS, considered as "low abstraction", on organ prediction. DeepOS is an MLP neural network, that takes gene expression values in transcripts per million (TPM) as inputs, and outputs either organ classification or survival probabilities (Supplementary Figure  2 ). DeepOS architecture comprises hidden layers composed of stacked units of dense layers, Rectified Linear Unit (ReLU) activations, dropout effect penalization, L1 and L2 regularization, and batch normalization. All the models tested reached very high validation performances to predict the organ of origin (mean hyper-parameter search accuracy = 0. 849 and standard deviation = 0. 292), with best model reaching an accuracy of 0. 9835, precision of 0. 9842, recall of 0.
Conclusions, discussion We have developed a deep learning model, DeepOS, to estimate OS duration from pancancer RNA-seq data based on a transfer learning strategy that allowed us to enlarge our pre-training dataset with healthy tissue samples. Transfer learning improved prediction performances by limiting overfitting. The pretraining task consisted in predicting the organ of origin with very high accuracy, precision and recall performances (0. 984 each on the validation set). For survival prediction, DeepOS reached a median pan-cancer C-index of 0. 72 on an independent and previously unseen test set and a mean survival AUC of 0. 76. DeepOS can output a discrete estimation of individual survival, which enables to plot Kaplan-Meier survival curves from individual predictions. Doing so, we could confirm that DeepOS survival predictions over time were not different from the ground truth in the test set (log-rank p-value 0.
388). We also validated that the pre-training step increased the performance of the model. We also found a way to use the mean square error, a classical loss function in deep learning, to train DeepOS on survival data. We used DeepSurv as an internal comparator using the same training, validation and testing data split. DeepSurv is a deep neural network trained with a Cox proportional hazards loss function, which is considered a state-of-the-art method for survival prediction  22. DeepOS significantly outperformed DeepSurv model performances (DeepSurv C-indexes were comprised between 0. 60 and 0. 61, and AUC of 0. 63 on the test set, similarly to other published models for such task; Supplementary Table  1 ). Our model has limitations. Firstly, as compared to the survival prediction task ran without pretraining, one could argue that the transfer learning strategy only slightly improved the model performances (+0.
9% on the test Cindex). However, the gain was robustly observed across all the metrics computed on the test dataset (C-index, mean AUC, log-rank p-value), which suggests that the model indeed benefited from the pre-training step. Besides, even minor upgrading in performance is challenging to obtain for C-index values above 0. 70 for such task. Our study nevertheless supports a benefice of using large tumor RNA-seq datasets with survival observation. Secondly, we noted over-optimism in DeepOS predictions for short survival durations (mainly <20 months). However, it did not negatively impact the model performances within tumor types of unfavorable prognosis. For example, the C-index within the mesothelioma and the esophageal carcinoma cohorts reached 0. 74, while median OS was below 20 months (16.
8 months each). A possible explanation is that patients with short survival have poor prognosis factors such as tumor location, poor general condition or comorbidities that are missed by using only RNA-seq data from a microscopic tumor sample. Analyzing the influence of time on models performance should be generalized to better define the application framework of deep learning models for such task. Finally, the pan-cancer TCGA RNA-seq dataset that we used for our study was built mostly upon primary tumor samples obtained from surgical resection of localized or locally-advanced diseases. This is highlighted by the observed median OS for several cancer types longer than expected for metastatic stages at diagnosis. It is nevertheless possible that DeepOS learned from the metastatic potential of the tumor samples.
60. In addition to transfer learning, DeepOS comprised methodological adaptations that we believe have permitted this upgrading. First, we transformed survival data into time interval survival probabilities, so that censored time intervals did not influence the loss function calculation. Thus, we could train on the mean squared error. We also reduced input dimensions by applying prior knowledge on the biology of cancer and immunity to limit overfitting due to irrelevant genes for our task, which contributed to improve the model predictions. Overall, our study demonstrated and/or validated that (i) predicting survival outcomes from pan-cancer RNA-seq data is feasible and can achieve decent performances, (ii) transfer learning can reduce overfitting, and (iii) partially censored survival data can be used to train supervised deep learning models with standard loss functions.