dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Our work provides a reliable, reusable, and scalable method for the segmentation of cell images without structural FLs, with manageable annotation effort. Our results show that the proposed workflow leads to models outperforming Vanilla Cellpose on datasets with only nonstructural FLs, while requiring few annotated examples by leveraging Cellpose extended pre-training. Our results show that leveraging nonstructural (and even structurally weak) FLs in concert improves segmentation, even when the signal is very heterogeneous between cells, and some cells do not appear at all in some channels. Indeed, each channel provides some useful and potentially complementary information on nucleus and cytoplasm, which can be combined by segmentation fusion. Thus, aggregating channels together allows benefiting from complementary nonstructural FLs to outline individual cell objects.
Acknowledgments. We sincerely thank  Amritansh Sharma  for his detailed and insightful feedback on the segmentation workflow. In addition, we thank  Oliver Lai ,  Claire Repellin , and  Amritansh Sharma  for their help annotating images. Finally, we also thank  Catherine Lacayo ,  Claire Repellin ,  Oliver Lai , and  Laura Savy  for developing the cell biology tools and conducting the assays that provide the data used in this work.
Funding statement. This work was supported by grants from Région Ile-de-France through the DIM ELICIT program. The data analysis was carried out using HPC resources from GENCI-IDRIS (Grant 2022-AD011011156R2). Data availability statement. The data used in this work can be found at http://doi. org/10. 6084/m9. figshare. 21702068. The code used in this work is not publicly available as it is implemented as part of commercial research that is proprietary to Cairn Biosciences. To ease reproducibility, a pseudo-code version of this implementation is available in the Supplementary Material. Supplementary material. The supplementary material for this article can be found at http://doi. org/10. 1017/S2633903X23000168.
Current network inference algorithms fail to generate graphs with edges that can explain whole sequences of node interactions in a given dataset or trace. To quantify how well an inferred graph can explain a trace, we introduce feasibility, a novel quality criterion, and suggest that it is linked to the result's accuracy. In addition, we propose CEM-*, a network inference method that guarantees 100% feasibility given online social media traces, which is a non-trivial extension of the Expectation-Maximization algorithm developed by Newman (  2018 ). We propose a set of linear optimization updates that incorporate a set of auxiliary variables and a set of feasibility constraints; the latter takes into consideration all the hidden paths that are possible between users based on their timestamps of interaction and guide the inference toward feasibility.
We provide two CEM-* variations, that assume either an Erdős-Rényi (ER) or a Stochastic Block Model (SBM) prior for the underlying graph's unknown distribution. Extensive experiments on one synthetic and one real-world Twitter dataset show that for both priors CEM-* can generate a posterior distribution of graphs that explains the whole trace while being closer to the ground truth. As an additional benefit, the use of the SBM prior infers and clusters users simultaneously during optimization. CEM-* outperforms baseline and state-of-the-art methods in terms of feasibility, run-time, and precision of the inferred graph and communities. Finally, we propose a heuristic to adapt the inference to lower feasibility requirements and show how it can affect the precision of the result.
As a result, research has turned to inference algorithms to derive diverse types of networks which can be useful in various fields such as marketing, advertising, and politics. In advertising, for example, inference algorithms have been employed to derive the probabilities of influence between users, or the way that a specific news piece has diffused on the platform  (Gomez-Rodriguez et al. 2012). The reason why non-trivial methods such as network inference are needed to infer these types of networks is lying in the structure of the online datasets themselves. Regarding the diffusion of information through an online platform, the data we can find is limited and does not directly depict how it propagates from user to user. On Twitter, for example, given a tweet by an author and the users that retweeted it, we can get infor-mation such as the timestamps of each retweet, but we cannot know where they really retweeted it from 1.
-We show that real-world traces are not always 100% feasible given the real graph that underlies them and we propose a technique with which we can tune CEM-* to adapt to lower feasibility requirements. We evaluate to what extent tuning the inferred graph's feasibility can infer edges with better accuracy. The rest of this paper is organized as follows: in Section 2 we present related literature. In Section 3 we introduce the formulation of the problem. Section 4 presents the modeling of the problem and the learning method that we follow. Section 5 describes the datasets that we use and the methodology of the experiments. Sections 6, 7 and 8 show the results of the experiments and the comparison with other methods for the synthetic and the realworld traces respectively. Section 9 presents conclusions and future work.
Daneshmand et al. (2014)  proposed an L 1 -regularized maximum likelihood inference method for a well-known Continuous-Time diffusion model. Lokhov (2016)  introduced an approximate gradient descent approach that estimates the influence parameters using gradients of the likelihood calculated via mean-field approximation and dynamic message passing. Their formulation makes the computation tractable, but the complexity of the gradients causes slow convergence. Selecting a prior when the ground truth is unknown. Several link prediction methods extract future or missing links in datasets in which the underlying graph connecting the users is known  (Saito et al. 2008 , Bourigault et al. 2016 , Lagnier et al. 2013 , Jin et al. 2020 , Peel et al. 2022 ). However, our goal differs from these types of problems since we have to infer links in a setting where the neighborhoods of the nodes are unknown.
Datasets The general framework of CEM-* for both priors is shown in Fig. 4. To evaluate our two methods CEM-er and CEM-sbm against the ground truth and compare them with existing methods, we will use two different datasets: a synthetic and a real-world one. The synthetic dataset that we create aims to illustrate our method's efficiency when the trace includes sufficient information about the interactions between users. As we will show later, this is not always the case with real-world traces coming from OSNs, which can make the inference task even more challenging.
Synthetic dataset For the generation of synthetic social media data, we follow the code found in  Giovanidis et al. (2021). We first create a set of 100 users each of which has two random activity (posting and reposting) rates. Then, we create an SBM graph between the users, with 7 different partitions of varying sizes, that represents the friendship graph of the network. Users in the same group are connected with probability p = 0. 06 and users of different groups are connected with probability q = 0. 007. Each subgraph corresponding to a group is a random Erdős-Rényi with connection probability p. For each user, we generate a set of random timestamps, that increase according to an exponential distribution that depends on their activity rates. These timestamps represent the times they posted or reposted something.
A new entry on the Newsfeed list will push out an older entry of a random position. The Newsfeeds of the users that follow user i will then be updated accordingly. Of course, in reality, users on a social media platform may show a preference towards a specific account or topic, or even repost something outside of the scope of their followees. The random uniform selection, however, makes the simulation collect sufficient information for all the edges in the friendship graph. The simulation generates a social media trace from which we can extract all the quantities that are necessary for our method, as presented in Section 3. The detailed statistics of the synthetic dataset can be found in Table  2. The table on the left shows the statistics of the trace, whereas the table on the right shows the statistics of the ground truth graph.
Real-world data: the #Élysée2017fr dataset For the evaluation of our method on real-world data, we choose #Élysée2017fr, a publicly available dataset related to the 2017 French presidential campaign on Twitter  (Fraisier et al. 2018 ). It features 2,414,584 tweets and 7,763,931 retweets from 22,853 Twitter profiles discussing the election. Users have been manually annotated by experts with political affiliations expressing support for one of the 5 main competing parties in France: -FI: France Insoumise, far-left (Jean-Luc Mélenchon) -PS: Parti Socialiste, left-wing (Benoît Hamon) -EM: En Marche, center (Emmanuel Macron) -LR: Les Républicains, right-wing (François Fillon) -FN: Front National, far-right (Marine Le Pen) The exact timestamps of interactions between users had not been published by the creators of the dataset, therefore we had to crawl them using the Twitter API.
The statistics of the trace after the above preprocessing along with the statistics of the ground truth graph are shown in Table  2. Insufficient information in a real-world trace. From Table  2  and Fig. 5a  and 5b , we notice the main challenge in working with this dataset against the synthetic one: out of the 1,555,718 edges in the underlying friendship graph, only 45. 23% of them have a non-zero M i j value. On the other hand, the synthetic trace includes information for more than 99% of the 158 existing edges. This can be partly because, in reality, users may repost their followees with some preference, instead of randomly selecting posts from their Newsfeed as is the case in the synthetic dataset. Therefore, many users may not appear to interact with retweets even if there is a connection between them in reality.
As explained by the authors, when the activity rates are not the same for all users, the performance of the model worsens. Therefore, we expect Netinf to perform worse than CEM-* in more realistic settings such as these of the synthetic dataset, in which users have different activity rates. It should be noted that Netinf requires that we set in advance the parameter k, which is the number of edges that we want to infer. For comparison, we set k equal to the number of edges of the corresponding ground truth graph. -Newman (2018): a more recent EM-based algorithm that we introduced in Section 1. As mentioned before, our algorithm is an extension of the EM formulation provided in Newman's work. The algorithm is not designed to consider hidden paths between users, thus it is not guaranteed that the inferred networks will be feasible.
Experimental settings We run the experiments on a virtual machine with 40 vCPUs and 256 GB RAM. For the solution to the optimization problem, we configure a Gurobi solver through PuLP 3 , an open-source linear programming library for Python, using the dual simplex optimization method. The parameters set θ 1 = {α, β, r, σ} and θ 2 = {α, β, p, q, σ} for CEM-er and CEM-sbm respectively are initialized uniformly at random in the range [0, 1]. As a convergence criterion for the optimization we choose the L2 norm of the difference between the values of Q, i. e. , ||Q new -Q old || < , where the threshold is set equal to 0. 001. Finally, to generate the unknown friendship graph G, we round up all edges with Q i j > 0. 5 to 1, and the rest are set to 0. We run the experiments 10 times and report the average results.
As we observe, the larger the trace, the higher the value of Recall. This was expected since bigger traces give more information which helps us derive more underlying edges. Precision presents relatively stable behavior and is higher (=0. 869) when λ = 1. Overall, we see that CEMsbm has higher performance than CEM-er in terms of Precision which reaches up to 0. 869 when λ = 1, and a slightly worse, but still competitive performance in terms of Recall (reaching up to 0. 944 for λ = 1 whereas CEM-er can reach up to 0. 954 for λ = 0). We conclude therefore that CEM-sbm is much more precise than CEM-er in the case of the synthetic dataset and can also retrieve most of the underlying edges. Different values of the hyperparameter λ. In Fig. 6b  we can see more clearly how the choice of the hyperparameter λ inside the optimization objective (Eq.
17 and Eq. 27) affects the precision of inference: for λ = 0 we get very low Precision (= 0. 024) regardless of the prior since we infer the largest number of edges possible according to the objective, which in turn results to more false positive edges. However, in this case, the Recall value is at its highest (for example 0. 954 in the case of CEM-er). In contrast, for λ = 1, we infer a graph with the smallest number of edges possible given the constraints and thus we get a considerably better Precision (= 0. 869, CEM-sbm). The Recall value, 3 https://pypi. org/project/PuLP/ in this case, is still high (= 0. 944). This can be linked to the rich information that is provided in the synthetic trace but can also be indicative of the good prediction probabilities of our method: we manage, with the help of the constraints, to infer the smallest set of edges possible (by setting λ = 1), that is precise and at the same time retrieves almost the entire ground truth graph.
141 and 0. 033 respectively, as seen in Table  4 ). Additionally, both methods infer graphs with very small average shortest paths (< 1. 5). In contrast, the ground truth has an average shortest path of 2. 57 which is closer to the value that we would expect from a real-world Twitter graph to have. Moreover, Chain infers graphs that are too dense, as seen from its maximum strongly connected component (last column, Table  5 : it includes 87% of the users, whereas    the actual value is only 11%). The above suggests that, given the synthetic dataset as input, Star and Chain infer graphs that are feasible but demonstrate properties that are far from these of the actual graph, and also, from these of a real-world graph in general. The method of Saito et al. (  2008 ) is 100% precise but produces only 8 edges, a very low number for it to be considered a sufficient solution to our problem.
Experiments on the #Élysée2017fr dataset Next, we will work with real-world data that, as seen in Section 5. 1. 2, have different properties from the synthetic dataset, making the inference process more challenging. Table  7 : Converged values for parameters α, β given |T elysee | = 5,000,000 lines as input. CEM-* parameters (1 -α * ) β * CEM-er (λ = 0) 0 1. 19e-10 CEM-er (λ = 1) 1. 32e-11 2. 46e-12 CEM-sbm (λ = 0) 5. 55e-16 1. 07e-10 CEM-sbm (λ = 1) 0. 004 0. 001.
More specifically about Precision, we notice a slight drop as the size of the trace increases. This makes sense, since we infer more edges the more data we get, and therefore we are more likely to make errors. The drop is milder when λ = 1 and more noticeable when λ = 0. Different values of the hyperparameter λ. From Fig. 9b  we notice that high values of λ given a constant trace size (= 5 million lines) correspond to higher values of Precision. Here, we observe a trade-off between Precision and Recall, which was not evident in the synthetic dataset: in CEM-sbm for example, the lowest Precision(= 0. 213) corresponds to the highest Recall value(= 0. 185) when λ = 0 and a lower Recall value (= 0. 074) corresponds to a higher Precision (= 0. 478) when λ is set to 1. Therefore, we see that depending on our goal, we can choose to prioritize Precision over Recall and vice-versa.
This can be controlled by the correct selection of the hyperparameter λ. Difference between priors. In contrast to the synthetic dataset case, from the above figures we notice that CEM-er and CEM-sbm present more similar behavior. This is largely due to the properties of the trace itself: we have relatively sparse information on the edges between users that belong to different communities (we observe only 18. 95% of the existing inter-edges as seen in Table  2 , in contrast to the 98. 25% of the positive inter-edges in the case of the synthetic dataset). This makes sense since, in reality, users between different communities interact less often, so it is less likely that they will appear in a trace when we collect it. Therefore, the benefit of using the SBM instead of the ER prior cannot be easily made obvious given the specific trace that we have at hand.
34% of all users), which is bigger than the corresponding size in the real graph (= 93. 28%). Given that the inferred graph by Chain is half the size of the real graph, this high percentage suggests that it is more densely connected than we would expect from a real graph. What is more, in a real-world graph, most nodes have a relatively small degree, but some of them will have a noticeably larger degree, being connected to many other nodes. However, in Chain, we do not notice this phenomenon. As was the case in the synthetic dataset evaluation, the method of  Saito et al. (2008)  generates only a few edges (= 768) and is therefore not feasible. The method of Saito et al. (  2008 ) may be again relatively precise, but presents no strongly connected component, has a very low average out-degree (= 0.
Controlling feasibility through β As expected, since 2017 (the year that the dataset was created), some Twitter profiles have been deleted or set to private. In addition, users may have retweeted a tweet/episode outside the scope of their followees (e. g. , through Twitter search, recommendation algorithms, Twitter trends, etc. ). As a result, the #Élysée2017fr trace is not 100% feasible given the ground truth friendship graph. In other words, the current view of the friendship graph does not explain all the episodes in the selected trace; in fact, it can only explain 49% of them. We can therefore control the feasibility of our result to match the feasibility of the trace given the ground truth through the parameter β: for an inferred graph to be feasible, we want the false positive utilization rate β, i.
Evaluation with no ground truth Overall, we notice that feasibility is proved beneficial and can increase the quality of the inferred graph. For example, the methods with the lowest feasibility rates  (Saito et al. , Netinf, Newman)  infer graphs with low predictive quality and present statistics that are far from those of the real-world graph. The benefit of CEM-* over other methods is especially apparent when we have collected sufficient data between edges, as was the case in the synthetic dataset case. Consequently, when the underlying friendship graph is not available, which is often the case in graph reconstruction problems, the feasibility rate of the inferred graph could be an effective indicator of a method's performance. However, feasibility not a sufficient condition for better prediction results.
Furthermore, we observe that feasible graphs are usually closer to the underlying graph compared to non-feasible graphs. When the trace is 100% feasible given the ground truth, as is the case in the synthetic dataset, we find that feasibility is a necessary condition for the inferred graph to be as close to reality as possible. In the case of the #Élysée2017fr graph, where the trace is only 50% feasible, we observed that Precision improves as we force the feasibility of the inferred graph to be closer to the real percentage (through the parameter β). We conclude therefore that, for higher Precision, the feasibility of the trace given the inferred graph should match the feasibility of the trace given the true graph. However, if we cannot be sure about the ground truth's feasibility, we still suggest starting working with β = 0, since, as we saw, it still returns better, and more realistic networks than other inference methods.
Ex-GBH chicks were more likely to explore their new environment than CT chicks during the open-field test. In addition, they had an increased fattening and blood triglycerides level, whereas their food consumption was similar to CT chicks. Quantitative PCR on the chemerin system and FASN in chicks livers indicate a transcriptional activity in favor of fatty acid synthesis, and lipidomic analysis on chicks abdominal adipose tissue reveal a global increase in monounsaturated fatty acid and a global decrease in polyunsaturated fatty acids. Seven genes involved in the synthesis of fatty acids were identified with the open access LIPIDMAP software, and their disturbance in Ex-GBH chicks was confirmed via qPCR. Taken together, these results suggest that the progeny of hens temporarily exposed to a GBH are more likely to fatten, even with a balanced diet.
Lipidomic Metabolism The lipidomic datasets were analyzed by an openly available bioinformatics tools LIPIDMAP (https://lipid maps. org/biopan) to produce a graphical representation of the results. BioPAN takes an input file in CSV (comma-separated values) format containing quantitative data. The file structure should be as follows: -The first row contains sample labels (e. g. , "wild_-type_1," "wild_type_2," "control_1," "control_2"). The file must contain at least 2 conditions (e. g. , "wild_type" and "control") and 2 samples per condition. -The first column contains lipid molecular species. The lipid subclasses recognized by BioPAN are provided on the website. -The second column contains molecular concentration quantification. After loading a dataset, LipidLynxX2 is launched in the background.
Statistical Analysis The GraphPad Prism software (version 8) was used for all analyses. All data are reported as means § standard error of mean. Bartlett's test was run to test the homogeneity of variance, and normal distribution was verified by the Shapiro-Wilk test. We performed one t-test or one-way analysis of variance (ANOVA) to compare the different means, when appropriate. When the ANOVA indicated significant effects at P < 0. 05, the means were analyzed by using the Fisher's test. Stars (*) correspond to the unpaired t-test significance (*P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001).
A recent study performed on human serum reports disturbances of the lipidomic profile following to an occupational exposure to glyphosate leading to potential severe health effects  (Zhang et al. , 2023). Beside the effect for the health of the chick itself, a change in fatty acid content in farm animals must be taken seriously, as the relative amount of saturated, mono, and PUFA must range in the proper interval for a safe and healthy human consumption  (Mili cevi c et al. , 2014). Opensource software LIPIDMAP allows to make predictions on the expression levels of enzymes involved in the synthesis of fatty acids. Several enzymes were predicted to be affected (ELOV3 and FADS2 were predicted to be downregulated, and FADS1, SCD1, ELOVL2, ELOVL1, 2, 3, 5, and 6 were predicted to be upregulated), which was confirmed by the qPCR analysis on chicks abdominal adipose tissue.
Bioinformatics processing Primary analysis has been performed with our in-house developed tool SysISTD (SysFate Illumina Spatial transcriptomics Demultiplexer: https://github. com/SysFate/SysISTD). SysISTD takes as entry paired-end sequenced reads (fastq or fastq. gz format), and two TSV files, the first one containing the sequence of the molecular barcodes associated to the rows or column in the printed arrays and the second file presenting the physical position architecture of the spatial barcodes. SysISTD search for the Gibson sequence (regex query), then for two neighboring barcodes. Paired-reads presenting these features were aligned to the human genome (hg19) with Bowtie2, and known transcripts were annotated with featureCounts  12. As outcome, SysISTD generated a matrix presenting read counts associated to physical coordinates in the array in columns and known transcripts ID in rows.