dataesr/scientific-paragraphs-categorization huggingface dataset:
13% of accuracy on the test dataset, which represents a deterioration of 0. 37% compared to the base model. However, on the evaluation dataset, the model reached 88. 80% of accuracy, which represents an improvement of 0. 44% compared to the base model. Considering the performance increase on the unseen dataset, we assess the validity of this submodel that contains 1. 46M parameters (-90. 26%) for 219. 75M FLOPs (-65. 00%). Despite the differences between technologies (NetPrune is implemented with tensorflow, not torch), the differences on the methodology to train and evaluate the models, we manually pruned 1. 76% more parameters and 14% more FLOPs compared to the results reported for the network slimming automated approach  (Liu et al. , 2017).
, 2010)  dataset. MNIST is a dataset composed of 70 000 gray-scaled images of handwritten digits. It is a dataset of ten classes composed of 60 000 train images and 10 000 test images. As shown in Table  1 , we first randomly extracted 10 000 train images to create both the validation and the evaluation datasets. The users were all asked to remove as many parameters and FLOPs as possible from an instance of Lenet-5 without dropping the accuracy below 98. 50%. We report here some of the user's strategies that we divide into two categories: the strategies to choose the layer(s) to prune, and the strategies to choose the filters to keep in the pruned layers. To choose the layer(s), the most common strategy chosen by the users was S L/B. They experimented to prune the layers one by one, in the order in which they appear in the network, from the shallowest to the deepest, with a fine-tuning step between each operation.
In general, to reach high compression ratios while maintaining the accuracy of the models, the experts had to make at least two passes over all the layers. To accelerate this process, one of our participants tried to prune a few filters in most of the layers at once, similarly to S all. Using this strategy, this participant obtained the best submodel out of all the participants, in five steps only. This submodel reached 98. 50% of accuracy on the test dataset with 91. 12% fewer parameters and 76. 52% less FLOPs. The accuracy on the evaluation dataset is 98. 46%, which represents a deterioration of 0. 42% compared to the reference model. After all the user's experiments, we observed that the accuracy on the evaluation dataset tends to drop significantly faster than the accuracy on the test dataset after a few pruning iterations.
This shows that using the test dataset to determine the scores of the filters to prune induced an overfitting phenomenon to this dataset which demonstrates the interest of the evaluation dataset (T4). Another participant suggested pruning first the layers holding the most parameters or FLOPs (S heavy_first ) to quickly reduce the network size and accelerate the successive fine-tuning steps. This user succeeded in obtaining high compression ratios quickly with this strategy, but he did not manage to maintain as high accuracy as other participants did in the last iterations. Since most of the parameters of the initial model lie in the deep fully-connected layers, pruning them first prevented the user from recovering good performance after pruning the shallow convolutional layers. Concerning the selection of filters to keep within the layers, the participants mainly used the same strategy S bottleneck we used in our experiments.
Within two hours, all of the participants learned to use Net-Prune, and obtained a smaller network that performs the same task with approximately 89. 50 ± 1. 00% fewer parameters and 68. 50 ± 8. 00% less FLOPs in 8 ± 3 iterations from the base model, with 98. 43 ± 0. 07% of accuracy on the test dataset and 98. 39 ± 0. 07% of accuracy on the evaluation dataset. All the participants found NetPrune helpful to understand the pruning process. One participant mentioned it is a helpful tool to understand the impact and contribution of each filter in the layers, and that it allows to try out different pruning strategies and understand the benefits of each one. All experts found the local CPD metric particularly important. They also insisted on the interest of using more than one metric to improve the quality of the filter selections, which justifies the interest of manual methods.
Even if radar plots are interesting to visualize a set of filters through the prism of all the importance criteria, NetPrune proposes a matrix representation to enable a comparison between metrics. Finally, NetPrune implements the Relative Confusion Matrix  (Pommé et al. , 2022)  to efficiently localize the class-wise differences between two models in the Exploration Tree view. Qualitatively, our results with NetPrune are similar to automatic approaches ones but it is difficult to fairly compare to them. First of all, there is no standard network to refer to and most of the existing works use various variants of Lenet-5 or VGG-16 architectures that do not have either the same hyperparameters or the same number of parameters. Second, we previously argued on the necessity to keep a portion of the datasets to evaluate the pruning process.
04% lower than the defined threshold (see Section 6. 2), a human would consider it if its number of parameters or FLOPs is significantly lower. Concerning the Sparklines matrix for datasets with many classes, we suggested a hierarchical visualization that follows the hierarchy of the dataset, similarly to  Elmqvist et al. (2008). The average activations and the sparklines would be computed on various class levels in the hierarchy. When the number of classes at a given level of a dataset hierarchy is still too high for the matrix to fit on screen, we suggest a color-only variant that hides the detailed information provided by the sparklines. Using our tool, we were able to prune both convolutional and fully-connected layers within a network such as VGG-16. Our tool allows the user to distinguish the important filters from the less important ones, and the type of the layer does not add any difficulty to this task.
Our experiments have shown that one can define and apply strategies to efficiently prune neural networks and learn from them. These strategies showed it is sometimes possible to reach smaller and more accurate submodels by persevering to prune a suboptimal model. As far as we know, NetPrune provides the best trade-off approach to prune a neural network with transparency and control on the process, while reaching models with high compression ratios and small accuracy drops, at the same time. NetPrune also provides a mean to control whether the output models overfit either to the training, the validation or the test datasets. In the future, we plan to improve our approach by implementing more metrics that would locally evaluate the filters that discriminate a class from all others, or that directly combine several metrics to speed-up the selection process.
An urgent need currently exists for a simple plasma test to assist clinicians with pathology assessments. A routine plasma test would enable in house testing, with existing equipment and expertise. In response, NeoNeuro has developed what they refer to as the Aptamarker platform 5. The aptamarker platform is derived from a common library of the same 4. 29E9 aptamer sequences is applied to blood samples from individuals that differ in phenotype. This approach has been used in conjunction with blood samples and data from the AIBL cohort on ten individuals with high brain amyloid, and ten individuals with low brain amyloid. Differences in the frequencies of the aptamers between the two phenotypic pools was evaluated, and a subset of eight predictive aptamers was identified. These aptamers are now referred to as Aptamarkers.
DNA library and primers The eight Aptamarkers used on the AIBL cohort 8 were used in the same manner for this analysis. The sequences of these eight Aptamarkers are provided in Table  1. Specific reverse primers were designed for each Aptamarker. A common forward primer was designed for use across all Aptamarkers: 5'-GTT CCC AGA TAC AGA C-3'. Primers were tested to ensure that each set specifically amplified the targeted Aptamarker sequences using a melt curve. The melt curve protocol followed with 10 seconds at 95°C and then 5 seconds each at 0. 5°C increments between 55°C and 95°C. Data collection was enabled at each increment of the melt curve.
Data analysis The raw data from the qPCR assays was analyzed in the same manner as described for the AIBL cohort study 5 to generate relative template proportion values (RTp). These values were preprocessed using the same scaling as on the AIBL training data. High brain amyloid deposition in patients was classified where the Abeta_42/Abeta_40 ratio was below a threshold of 0. 05. Amyloid status for the CHEDV dataset was predicted utilizing the previously identified model based on an ExtraTrees algorithms described for the analysis on the AIBL dataset 6.
The diagnostic test prediction was carried out using qPCR values from the 8 Aptamarkers selected in blood samples and the age of the patient. We compared the Aptamarker predictions to the Abeta_42/Abeta_40 ratio results with a definition that a ratio less than 0. 05 indicated high brain amyloid accumulation. The data for this comparison is provided in Table  2  and a confusion matrix summarising the data is provided in Table  3. This confusion matrix outlines the performance of Aptamarker predictions in the context of brain amyloid deposition, with the Aβ42/Aβ40 ratio serving as the reference standard. A ratio less than 0. 05 is considered indicative of high amyloid accumulation. Where P High and P Low refer to Aptamarker test predictions. High and Low refer to the amyloid ratio prediction.
This results in a sensitivity of 73%, specificity of 76% and overall accuracy of 75%. Table  4  provides a comparison between the Aptamarker prediction and the designation of AD by the clinicians based on the full CSF analysis and a confusion matrix for summarising AD classifications are provided in Table  5. The confusion matrix summarises the performance of Aptamarker predictions in the classification of AD. The sensitivity for this comparison was 68%, specificity 82% and overall accuracy was once again 75%. An overview of the diagnoses in relations to the Aptamarker predictions is provided in Figure  1. It is interesting to note that the three individuals predicted by the Aptamarker test as having high brain amyloid which did not have high brain amyloid all exhibited depression. This is too small a data set to speculate on any such correlations other than to note this observation for future studies.
Nucleotide alignment of Sarbecovirus genomes Complete genomes available for Sarbecovirus in June 2022 in GenBank (https://www. ncbi. nlm. nih. gov/), GISAID (https://www. epicov. org/), and NGDC (https://ngdc. cncb. ac. cn/) databases were downloaded in Fasta format. Sequences with large stretch of missing data were removed. Several genomes showing perfect identity or high nucleotide similarity (more than 99. 9% of nucleotide identity) were published for pangolin sarbecoviruses from Guangxi (5 sequences), bat sarbecoviruses from Thailand (5 sequences), Cambodia (two sequences), etc. For these clusters, a single genome was retained for our analyses. The international databases contain millions of SARS-CoV-2 genomes and hundreds of SARS-CoV genomes. For human SARS-CoV-2, we decided to include in the alignment the reference genome and one representative for each of the six variants of concern (VOC; Alpha, Beta, Gamma, Delta, Epsilon, Omicron), which were selected under NCBI Virus (ncbi.
Phylogenetic analyses Maximum likelihood analysis of the whole-genome alignment of sarbecoviruses was carried out using RAxML 8. 2. 11  [19] , different GTR+G models for the three codon-positions and non-coding regions, and 1,000 bootstrap replicates. The RAxML bootstrap trees were executed in PAUP* version 4. 0a  [20]  to construct the bootstrap 50% majority-rule consensus tree. To examine the distribution of phylogenetic support along the whole-genome alignment, the dataset was bootstrapped under the SWB program  [9]  using a window of W nucleotides (W parameter) moving in steps of 50 nt (S parameter). The window size (W) is a key parameter for SWB analyses because the amount of phylogenetic signal depends on both the number of nucleotide sites and their evolutionary rates  [9]. For that reason, we decided to perform five SWB analyses with the same step parameter (S) of 50 nt but using five different window sizes, i.
e. 400 nt, 500 nt, 600 nt, 1000 nt and 2000 nt (Table  1 ). The smallest window size (W = 400 nt) was applied to detect possible changes in phylogenetic relationships due to the recombinant origin of small genomic regions, whereas the largest window size (W = 2000 nt) was used to guarantee enough phylogenetic signal (informative sites) for bootstrap analyses. The intermediate window sizes (500, 600 and 1000 nt) were used to better interpret the differences between SWB results based on the two extreme values. In the SWB program, each window bootstrap (WB) subdataset was automatically run in RAxML  [19]  with a GTR+G model and 100 bootstrap replicates. The SWB output is a CSV file containing the bootstrap percentages (BP) calculated for each WB subdatasets and for all the bipartitions (nodes) reconstructed during the SWB analysis.
For example, the SWB400 output (SWB analysis based on a window of 400 nt) includes 1,213,380,595 BP values (595 WB subdatasets X 204,001 bipartitions), whereas the SWB2000 output includes 11,189,625 BP values (563 WB subdatasets X 19,875 bipartitions) (Table  1 ). : Bipartitions (with bootstrap percentages (BP) caculated for each WB subdatasets) obtained under the SWB program  [9] ; : Number of characters in the MRP matrix reconstructed using LFG  [9]  and SuperTRI  [21]  programs; : SWB bipartitions including at least one BP ≥ 50 selected under the BBC program using the SWB file as input  [9] ; : BBC bipartittions selected under Microsoft® Excel using the BBC file as input. The bootstrap bipartitions generated from the five SWB analyses based on different window sizes were used for SuperTRI analyses  [21]  to reconstruct the trees showing the most reliable phylogenetic relationships.
The LFG program  [9]  was used to convert the SWB output file into bootstrap log files, which were then used as inputs in SuperTRI v57  [21]  to construct a MRP (Matrix Representation with Parsimony) file. For example, the SWB400 output was converted with the LFG program into 595 bootstrap log files, and these files were further transformed into a MRP file using SuperTRI v57. In the MRP400 file, each of the 750,330 characters represents one SWB bipartition with its assigned bootstrap percentage calculated in one of the 595 window bootstrap analyses. The MRP400 file was then executed in PAUP* version 4. 0a  [20]  using 1000 bootstrap replicates of weighted parsimony (with BPs of the SWB400 analysis assigned as weights) in order to reconstruct the SuperTRI bootstrap 50%-majority-rule consensus (SB400) tree.
Construction of coloured genomic bootstrap barcodes A new method was developed in this study for constructing coloured genomic bootstrap (CGB) barcodes for a virus of special interest or the common ancestor of several viruses. A phylogenetic CGB barcode is a small image representing the genome of a virus (or an ancestral virus) in which the different colours show the best phylogenetic signals, i. e. containing the fewest number of closely-related viruses, detected in the different regions of the genomic alignment used for the analyses. Two other kinds of CGB barcodes were derived from the phylogenetic CGB barcodes: the host CGB barcodes showing the host origins of the closely-related viruses and the geographic CGB barcodes showing the geographic origins of the closely-related viruses. For this study, we choose to reconstruct CGB barcodes for the common ancestor of seven selected SARS-CoV genomes and for the common ancestor of nine selected SARS-CoV-2 genomes (see supplementary Table  S1  for the origin of the sequences used in this study).
To allow comparisons between the five BBC results, W and S parameters (window size and moving steps) were used to calculate the median positions (pos. ) for the N BPs calculated in each of the five SWB analyses. In the second step, only bipartitions including all the nine SARS-CoV-2 sequences were selected: 178 bipartitions for BBC400, 176 bipartitions for BBC 500, 175 bipartitions for BBC 600, 163 bipartitions for BBC 1000, and 132 bipartitions for BBC 2000 (Table  1 ). For each of the five BBC lists, the single bipartition including only SARS-CoV-2 sequences was removed. Then, other bipartitions were ranked in increasing order of size, from +1 (for the bipartitions including the nine SARS-CoV-2 sequences + 1 closely-related virus) to +66 (= 75 -9, for the single bipartition including all viruses of our dataset).
By this way, we were able to identify the closest virus(es) to SARS-CoV-2 in all regions of our genome alignment. All selected GRPS contained a robust phylogenetic signal (BP ≥ 70%) in at least two WB subdatasets of the BBC400, BBC500 or BBC600 results. The 5' and 3' ends of GRPS were extended using the following criteria: (i) by accepting BPs between 50% and 70% for BBC400 results; (ii) when median positions showed an average BP ≥ 50% for BBC400, BBC500 and BBC600 results; and (iii) when median positions showed an average BP ≥ 50% for all the five BBC results (BBC400, BBC500, BBC600, BBC1000, and BBC2000). This strategy was adopted for three major reasons: (i) GRPS of small lengths cannot be detected using SWB analyses based on the largest window sizes (i. e. 1000 nt and 2000 nt); (ii) due to stochastic variation in BP values, the comparisons between the three SWB analyses based on the smallest window sizes (i.
e. 400 nt, 500 nt and 600 nt) allow us to better detect GRPS of small lengths; (iii) GRPS of large size can be erroneously interrupted if we consider only SWB analyses based on the smallest window sizes because they contain lesser amounts of phylogenetic signal  [9]. It is therefore important to also make comparisons with BBC results based on the largest window sizes (i. e. 1000 nt and 2000 nt). In the fourth step, the intervals of GRPS (5' and 3' median positions in the wholegenome alignment) were written in a new CSV file for each of the 52 SWB bipartitions including SARS-CoV2 sequences in which one or more GRPS were identified. A specific colour code was chosen for each of the 52 bipartitions, and the file was used as input in the CGB program (python script) to construct the 52 phylogenetic CGB barcodes of different colours.
Due to their recent divergence, these viruses have very similar genomes. Indeed, pairwise nucleotide distances are comprised between 0. 04% and 0. 28%. We also found very similar distances for most other closely-related viruses supported by high MBP values: between 0. 11% and 0. 41% for the seven SARS-CoV genomes; 0. 46% between Rmac1 and Rmac279; 0. 76% between Rf1 and Rf273. All these pairwise distances are the smallest calculated for our dataset. In contrast, the two other virus pairs supported by MBP400/2000 ≥ 70 were found to be more distant: 2. 0% between Ra7909 and RstYN04, and 12. 5% between RhGB01 and RhKhosta2. Interrelationships between the seven human and two mink SARS-CoV-2 sequences were not robust in the WGB tree of Figure  2 , except the basal position of HsRef (BPWG = 92) which was supported by one GRPS (pos.
indicate the percentages of exclusive ancestry found in bat sarbecoviruses (A), host taxa (B), and geographic regions (C). To assess sampling efforts, the grey histograms show the proportions of bat viruses used in our dataset for the host taxa and geographic regions of interest, respectively. Several regions of the SARS-CoV genome were found to be exclusively related to RfYNLF/31C, a virus found in Rhinolophus ferrumequinum. These regions represent 17. 9% of the whole-genome alignment and include several dispersed fragments in ORF1ab, including two fragments in the RdRp gene (RfYNLF/31C; pos. 13,301-13,900 and 14,451-15,300), as well as almost the complete ORF7a (RfYNLF/31C; pos. 27,601-27,950). The genomic contribution of the other species is much lower (≤ 3. 3%), except for Rhinolophus sinicus (13.
Recently, a new Sarbecovirus genome, RspSC2018 (GenBank accession: MK211374), was described from an unidentified bat at the species level collected from an unknown locality in Sichuan province  [31]. Since Illumina reads were not deposited in any of the international Sequence Read Archive (SRA) databases, it was impossible to know from which host species the virus was extracted. Moreover, the quality of the virus genome assembly cannot be verified, which may also compromise some future evolutionary studies. Our poor knowledge of horseshoe bat taxonomy also hinders understanding of their role as Sarbecovirus reservoir. For instance, some taxonomists have proposed to split R. ferrumequinum into two different species: R. ferrumequinum in Europe and West Asia, and Rhinolophus nippon in East Asia  [32].
Data Availability Statement: The whole-genome alignment, the five SWB output files, the five Su-perTRI MRP files, the five SB trees (SB400, SB500, SB600, SB1000, and SB2000), all the 5,594 GB barcodes, and all the 303 CGB barcodes constructed in this study are available in the Open Science Framework (OSF) platform at https://osf. io/XXXXX/ (the link will be provided for the final version). The SWB, BBC, CGB and LFG programs are available at https://github. com/OpaleRambaud/GB_bar-codes_project.
Introduction Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores reporting on pH, fluorescence resonance energy transfer (FRET), binding, and metabolism. (1) (2) (3) (4) (5)  FLIM is increasingly used in biology and biophysics applications because the fluorophore lifetime can be used to provide functional contrast in images. However, the use of quantitative, easy, fast, and interactive analysis of large FLIM datasets has been until now hampered by the lack of suitable open-source software. The most intuitive method to analyze FLIM data is to fit the complex fluorescence decay curve measured in every pixel of the image with a multi-exponential function. (3, 5)  Several companies have developed commercial closed-source packages for multi-exponential FLIM analysis that usually support their own file formats  (6)  and recently some free and open-source software options have been developed for exponential fitting such as FLIMfit,  (7)  FLIM-FRET analyzer,  (8)  Flimview  (9)  , and FLIMJ.
(10)  An alternative to multi-exponential curve fitting that has been gaining popularity is the phasor approach  (11) (12) (13) (14) (15) (16) (17)  as it reduces the complexity and calculation time of the analysis while providing a unique visualization option. (5, (17) (18) (19) (20)  The phasor approach is a fit-free technique in which the fluorescence decay from each pixel is transformed with a fast Fourier transform (FFT) into a point in two-dimensional phasor space. The main advantages of phasor analysis are that it provides a visual distribution of the molecular species by clustering pixels with similar decays, it allows for color mapping of the pixels of the FLIM images, and it is linear in terms of non-interacting molecular species. (15, 16, 21, 22)  Phasor analysis of FLIM data is increasingly used in several fields of biomedical research  (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40)  and is particularly useful for metabolic imaging with the endogenous biomarkers NAD(P)H and FAD to map their complex autofluorescence distributions in live tissues in the context of different physiopathological processes.