dataesr/scientific-paragraphs-categorization huggingface dataset:
The experimental results on videos 1, 2, 3, 4, and 5 are divided by the total number of frames 164, 151, 300, 188, and 159 respectively. It made 6 numbers of clusters for each video. From the FC8 simulation results, we have obtained 80% accuracy and 20% loss from video 1. Similarly, 90% accuracy and 10% loss from video 2. 90% accuracy, and 10% loss from video 3. 90% accuracy, and 10% loss from video 4. And 90% accuracy and 10% loss from video 5. Table  3  is regarding the accuracy comparison of FC8 with FC7 and FC. The results of the proposed method on dataset UCF101 are shown in Figure  11. The retrieving of the same vides from different colors and having the same structure. Some of the different videos are also collected from the database. This is due to the same nature of th4e videos that are similar to the input query.
Loss of Model layers in terms of MSE Mean Square Error (MSE) is a metric that fined the difference between the predicted values from the observed values between 0 and 1 in the analysis. The symbol of sigma in mathematics, the character that looks like E is called summation. That is the summation of all values, from start i=1 till n, through this we add all points from start to an end. For each point, we take the y is the predicted points for the ith observations, and the y' is the correct observed values for the i th observation. We subtract the correct observed values from the y predicted values, and calculate the square of the result. In the last  to put the summation of all the (y-y')² values, and divide it by n, n is the total number of videos in the database which will give the mean square error.
Precision and Recall Our system is evaluated on two metrics and recall. The most popular evaluated methods, these two techniques are used for the content-based video retrieval system. Precision P is calculated for the number of same elements retrieved and the number of elements retrieved from the database; it calculates the accuracy of the retrieval system. Recall R is calculated between the two values. The ratio between the number of correct elements retrieved and the total number of related elements that are presented in the database.
Time Complexity Performance The proposed system is evaluated and tested on GeForce NVidia 8 GB dedicated GPU with Windows 10 operating system is installed. MATLAB is used as a simulation and programming tool which is best suitable for rapid prototyping. Figure  13  shows the time of videos taken on GPU testing. From the first video checked total numbers of frames are 164 and take six frames as keyframes and takes a total time of 1186 seconds. From the second video checked total numbers of frames are 151 and take six frames as keyframes and takes a total time of 1120 seconds. From the third video checked total numbers of frames are 188 and take  six frames as keyframes and takes a total time of 1471 seconds. From the fourth video checked total numbers of frames are 188 and take six frames as keyframes and takes a total time of 1232 seconds.
Training and testing of the proposed system Training and testing is the major and key aspect in the CBVR system in which the real-time concept is used for the requirements of the recently taken datasets to be generated the needy output. This system is especially and particularly utilized in YouTube/Dailymotion etc. for the last two decades in which many related videos are displayed because of the contents in the queried videos. This is actually the activity of the searched video which results to display several related videos in which the same not exact copy but somehow similar activities are taking place. First of all the data are taken and then trained for testing but it is a broad term. In some systems, the data are stored in their databases i. e. , pre-stored data in which the feature models do exist.
In the new FV (Feature Vector) system the features are saved of the dataset and then this is tested against the mentioned dataset. Now, if we take a dataset that will need training and then that new dataset will be taken accordingly. These two datasets will be tested against each other in the current scenario for further processing. In other words, the new data will be taken and then tested in an efficient and effective manner with respect to the desired task. The proposed CBVR is a real-time system which needs no pre-data storage or taking data from the database. This system is actually taken the dataset as input which is trained and then it is tested. It can take new queries for that data to be processed. The data is tested against the recent taken data due to the real-time nature of the proposed system.
The new queries are used for searching for the data that is taken as input. The queries are actually used for searching in the existing database to find train/test the required data. Due to this nature of real-time, there we've not taken training & testing. The most and key factor by not taking the data for training and testing especially for storing is that this is a REALTIME system in which only the recent data are processed and it generates the required output/result from that data. FC stands for Fully Connected in the given expression which is a layer in the CNN for feature extraction providing an appropriate arrangement for segregation and classification process of images and other associated contents in the videos etc. FC6, FC7, and FC8 are layers that have diverse features in contrast with one another that is achieved by the required task.
More modern networks can do better with fewer parameters (e. g. , GoogLeNet). From the above explanation and justification, it has been revealed that FC8 Layer is much more effective than the other FC6 and FC7 Layers. Due to a high number of units, weights and connections FC8 layer has high accuracy and low loss and therefore this layer has given the best results in the entire scenario. After a successful simulation from MATLAB, the proposed model AlexNet FC8 has shown outstanding performance in contrast with FC6 and FC7. The accuracy of FC8 is greater in percentage as compared to FC6 and FC7. Furthermore, the loss of video retrieval is also much less as compared to the existing models. The proposed model FC8 has shown the best results which are considered the excessively best among other models as mentioned.
Conclusion In this work, we have proposed a new technique through which we extract the feature of contents from videos. For video retrieval, the color histogram of the video is stored in the feature vector and calculating the color histogram of selected frames. Keyframes are selected through the k-mean algorithm. From the keyframes, we are extracting color histogram features through the AlexNet model of CNN. The proposed approach is fundamentally very powerful because of the less consumption of time. The results of the system calculated through the Euclidean distance equation. The value of input query subtracting from the database query. The lowest values become the topmost result. The performance was evaluated on the equation of accuracy and loss. While the recent methods have the ability to process millions of videos in a very short time duration.
Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Regular Issue, Vol. 11 N. 2 (2022), 207-235 eISSN: 2255-2863 -https://adcaij. usal. es Ediciones Universidad de Salamancacc by-nc-nd dataset of activities because of its enormous number of classes, countless clasps, and furthermore unconstrained nature of such clasps.
Finally, a PKS4 overexpressing line showed constitutively elevated leaves and was less sensitive to wounding (Fig  3C ). Taken together these results confirm that PKS4 and PKS2 are required for shade-induced suppression of the wound-triggered inhibition of leaf hyponasty. In contrast, in the conditions tested, these genes were not required for the response to separate treatments. Expression of YUC8 was used as a positive control for this experiment. Thus PIF4, 5 and/or 7 are required for the induction of PKS4 and PKS2 by shade. By browsing published ChIP-seq datasets, we found that the PKS4 promoter is bound by PIF4 in various experimental conditions  (41, 42)  in a region containing a PBE-box (Fig  4B ). To test whether PIF4 directly regulates PKS4 expression under low R/FR conditions, we performed a ChIP-qPCR experiment using a PIF4-HA tagged line  (43).
PKS4 and PKS2 are repressed by MYCs in response to wounding To understand how PKS genes were regulated by wounding, we looked at their expression in the aos mutant from our RNAseq dataset. We observed that the repression of both PKS4 and PKS2 by wounding under sunmimicking conditions was not visible in the aos mutant (S8 Fig). We thus conclude that the impact of wounding on PKS expression depends on the jasmonate pathway. Since MYCs are the main transcription factors responsible for JA-dependent gene expression, we tested the expression of PKS4 and PKS2 upon wounding treatment in a myc234 triple mutant  (44). As observed in our RNA-sequencing results, both genes were expressed at a lower level in the petiole of wounded wild types, although the difference was significant  Graphs are represented as violin plots, which present a combination of a box plot and a kernel density plot.
Previous studies have shown that shaded plants challenged with herbivores, or more generally with pathogens, prioritize growth over defense  (18, 52). As reported by others, we also observed a dampening of the JA-dependent wounding response by low R/FR at the gene expression levels (S4D Fig). Under shademimicking conditions, less genes were differentially regulated by wounding and among regulated genes many were regulated to a reduced extent compared to control sun condition (S4D Fig). This is consistent with previous studies comparing low R/FR and MeJA treatments  (27, 53). For example, genes like LOX2 or VSP1, which are classically induced by herbivores or wounding  (54, 55) , were induced to a lower extent under shade conditions (S4D Fig)  (26). Our transcriptomic approach identified PKS genes and especially PKS4, as regulators of leaf elevation acting at the interface of shade and wounding pathways ( Interestingly, the PKS4 promoter was bound by GFP-tagged MYC2 upon MeJA treatment in a published ChIPseq dataset  (56).
Acknowledgements We thank  Mieke de Wit  for her great help on the starting of this project. Edward Elliston Farmer  ( University of Lausanne ) and people from his lab for fruitful discussions, technical advice to perform wounding experiments, input on the project as well as for providing aos and JGP seeds. We thank  Philippe Reymond  ( University of Lausanne ) for providing myc234 seeds. We thank members of the  Lausanne Genomic Technologies Facility (GTF)  from the  University of Lausanne (Switzerland)  for performing RNA-sequencing. This project was funded by the  University of Lausanne  and the  Swiss National Science Foundation  (grants no. CRSII3_154438,  310030_200318  and  310030B_179558  to C. F. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
S4B-E  and Table  S2 ). Moreover, we report the deamidation of the asparagine on position 29 of the viral N protein, a previously unknown modification for which no function has been described to our knowledge. The PTM from the proteome of cortical organoids, and how SARS-CoV-2 modulates them, represents a valuable dataset which meaning remains to be functionally investigated. In a second step, we reanalyzed our single organoid proteomic dataset using the Synapse Gene Ontologies software (SynGO)  (27). Focusing on the 180 proteins upregulated upon infection with p values < 0. 05 (no fold-change threshold applied), we found that about a third of them were part of the synaptosome (Fig. 2B -C and Table  S3 ). Quantitative image analyses of the presynaptic marker bassoon in organoids highlighted that these structures were significantly enlarged and elongated upon exposure to SARS-CoV-2.
SARS-CoV-2 particles associate and dwell at trans-synaptic sites To gain further insight into the mechanism underlying this phenomenon, we went back to our single-organoid proteomic dataset analyzed with SynGO and focused on synaptic proteins with high and consistent upregulated expression upon infection (Fig. 3A  and Table  S3 ). Among these, Latrophilin-3 (LPHN3 protein from the adgrl3 gene) caught our attention, as it is a post-synaptic G-protein coupled receptor (GPCR) involved in synapse formation and maintenance  (30) (31) (32). We found that LPHN3 was also upregulated in human brain explants by western blot analyses and immunostaining (Fig. 3B-C ). Interestingly, the protein FLRT3, the main receptor of LPHN3  (33) , was recently identified to be closely associated to the viral protein Spike in a BioID proximity labeling assay (PLA)  (34).
Image analysis and quantification Processing and image quantification were done using Bitplane Imaris v9. 7. Bassoon object were identified using absolute intensity thresholding and converted to a surface rendering model. The parameters used in this study (sphericity, volume, intensity) were automatically extracted from each object identified by intensity thresholding upon background subtraction. For virus-synapse association studies, each object was identified upon absolute intensity thresholding and converted using the "Spot" feature on the Imaris software. a region comprised between 0 and 0. 5 µm from the spot center was used to consider that a virus particle was associated with synaptic proteins. Data were extract automatically to excel files format. Graphs and statistics were performed using GraphPad Prism version 9.
Cross assignment of peptide ions abundances was performed among the samples and controls using a m/z tolerance of 10 ppm and a retention time tolerance of 42 s. Protein abundances were computed using the sum of the unique peptide abundances normalized at the peptide level using the median. To be considered, proteins must be identified in all three or four replicates in at least one condition. The imputation of the missing values and differential data analysis were performed using the opensource ProStaR software  (39). Imputation of missing values was done using the approximation of the lower limit of quantification by the 2. 5% lower quantile of each replicate intensity distribution ("det quantile"). A Limma moderated t-test was applied on the dataset to perform differential analysis. The adaptive Benjamini-Hochberg procedure was applied to adjust the p-values and False Discovery Rate.
Post-translational modification analysis Raw mass spectrometry files were converted to mgf format using ThermoRawFileParser v1. 3. 4  (41)  and searched against the human complement of the UniProtKB/SwissProt protein database containing 20,397 proteins, with added common contaminants and 17 proteins from the Severe acute respiratory syndrome coronavirus 2 proteome (UP000464024; June 2022). The search was performed using the ionbot search engine (v0. 7. 0)  (42)  with open modification search settings and three expected modifications: (i) carbamidomethylation of cysteine; (ii) oxidation of methionine; (iii) and N-terminal acetylation. Results were filtered on 1% FDR using a q-value below 0. 01. A global comparison of modification levels was calculated as the ratio of ratios. In infected samples, for each modification the ratio of the number of spectra of modified versus unmodified peptides was calculated and then divided by the same ratio in the mock samples.
Electrical activity analysis using machine learning Electrical activity was collected using a 60 channel MEA. The collected raw data from the electrodes were high pass filtered over 50 Hz and then sampled at 10000 Hz. We divided electrical recording on each day from the infected and non-infected (mock) organoids into 30-time bins each and performed frequency analysis on each bin. The frequency spectrum was sampled into 300 frequency features. We used the features from 70% of the recordings to train a Random Forest Classifier to detect an infected recording time-bin from a mock organoid time bin, and used it to. CC-BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 14, 2022.
Data interpretation. Raw files were converted to. mgf peaklists using MsConvert and were submitted to Mascot database searches (version 2. 5. 1, MatrixScience, London, UK) against a human (20 342 sequences, 2021-04-20, Taxonomy ID 9606) and SARS-CoV-2 (17 sequences, 2021-04-20, Taxonomy ID 2697049) protein sequences database downloaded from UniProtKB-SwissProt, to which common contaminants and decoy sequences were added. Spectra were searched with a mass tolerance of 10 ppm in MS mode and 0. 07 Da in MS/MS mode. One trypsin missed cleavage was tolerated. Carbamidomethylation of cysteine residues was set as a fixed modification. Oxidation of methionine residues and acetylation of proteins n-termini were set as variable modifications. Identification results were imported into the Proline software (version 2.
Funding  CNRS INSB (RG) Agence Nationale de la Recherche   ANR-20-CE15-0019-01  ( RG )  Agence Nationale de la Recherche   ANR-21-CE33-0007-03  ( RG and GG) CBS2 Montpellier doctoral school (EP) FRM-ANRS MIE (RG and EP )  Agence Nationale de la Recherche   ANR-10-INBS-08-03 ;  ProFI FR2048 (CC) Isite MUSE  ( Montpellier University of Excellence ) (BC) European Union's  Horizon 2020 Programme  ( H2020-INFRAIA-2018-1 ) [823839] (LM)  Research Foundation Flanders (FWO) [G028821N] (LM) Research Foundation Flanders (FWO)  [ 1S57123N ] (TC).
17 The Helsinki Study imputed genotypes using a Finnish population-specific reference panel and the BEAGLE software. All genotype data were based on genome build hg38. Genotyping platforms and cohort-specific quality control and analysis parameters are provided in eTable 3. GWAS of stroke cases and controls were conducted within sites or within groupings of sites and then meta-analyzed. Before analysis, we removed cases if there were fewer than 40 in the analysis strata, if they could not be assigned to a genetic ancestry group, or if there was an inadequate number of controls. Filtering on these criteria left 16,730 (of 16,880) EOS cases for analysis. The overall analytic design is depicted in Figure  1. Our primary analysis was a transethnic meta-analysis. In parallel, we performed a European-only meta-analysis.
22 We then tested whether genetic predisposition to VTE, as measured by a polygenic risk score (PRS), was more prominently associated with EOS compared with LOS. For this purpose, we generated a VTE PRS for individuals from the EOSC and LOS subset from SiGN based on a large prior GWAS of VTE  23  using PRSice software. 24 The VTE PRS included 255 SNPs using a GWAS p-value threshold of 1 × 10 -5 (see eMethods, links. lww. com/ WNL/C245). We tested the association between the VTE PRS score with stroke in the European ancestry sample using logistic regression with 10 principal components for ancestry and sex included as covariates. Effect sizes between EOS and LOS stroke were compared using a Wald test (see eMethods). We identified several disorders and plasma biomarkers (LOS, VTE, and plasma levels of von Willebrand factor [VWF] and factor VIII) for which associations at the ABO locus have previously been reported and then used the coloc software  25  to assess evidence that the same causal SNPs associated with EOS also drove associations with the second trait.
J. Cárcel-Márquez  is supported by an  AGAUR  Contract (agència de gestió d'ajuts universitaris i de recerca; FI_DGR 2019, grant number  2019_FI_B 00853 ) co-financed with  Fons Social Europeu (FSE). J. Danesh  reports grants, personal fees and nonfinancial support from  Merck Sharp & Dohme (MSD) , grants, personal fees and nonfinancial support from  Novartis , grants from  Pfizer  and grants from  AstraZeneca  outside the submitted work. J. Danesh  sits on the  International Cardiovascular and Metabolic Advisory Board  for Novartis (since 2010); the  Steering Committee of UK Biobank  (since 2011); the  MRC International Advisory Group  (ING) member, London (since 2013); the  MRC High Throughput Science 'Omics Panel Member, London  (since 2013); the  Scientific Advisory Committee for Sanofi  (since 2013); the  International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis ; and the  Astra Zeneca Genomics Advisory Board  (2018).
The Juno spacecraft has been collecting data to shed light on the planet's origin and characterize its interior structure. The onboard gravity science experiment based on X-band and Ka-band dual-frequency Doppler tracking precisely measured Jupiter's zonal gravitational field. Here, we analyze 22 Juno's gravity passes to investigate the gravity field. Our analysis provides evidence of new gravity field features, which perturb its otherwise axially symmetric structure with a time-variable component. We show that normal modes of the planet could explain the anomalous signatures present in the Doppler data better than other alternative explanations, such as localized density anomalies and non-axisymmetric components of the static gravity field. We explain Juno data by p-modes having an amplitude spectrum with a peak radial velocity of 10-50 cm/s at 900-1200 μHz (compatible with groundbased observations) and provide upper bounds on lower frequency f-modes (radial velocity smaller than 1 cm/s).
The gravity and ring data are mutually consistent because the rings are exquisitely sensitive to f-modes and completely insensitive to p-modes. The evidence for normal modes at Saturn motivates a similar search in Juno's data. Previous numerical simulations  17  , based on the amplitude spectrum of Gaulme et al. 9  , predicted that Jupiter's normal modes should be observable in Juno's Doppler data. In this work, we focus on internal oscillations, or normal modes, as a possible cause of the unexplained accelerations and show that temporal variations in the gravity field of Jupiter caused by normal modes are compatible with Juno's Doppler data. The best-fit amplitude spectrum has a peak radial velocity of 10-50 cm/s at 900-1200 μHz (p-modes regime), while lower frequency, f-modes has to be smaller than 1 cm/s.
Results The unambiguous identification of single normal modes from Juno Doppler data is not possible if the observed accelerations arise from multiple modes. In addition, Juno's orbit is far from ideal to map a timevarying gravity field, because, as a consequence of the large eccentricity, relevant observations are concentrated to a few hours around perijove, repeating every 53. 5 days over different Jovian longitudes. Since typical normal modes periods range from 10 min to two hours, the short observation window (6-8 h) limits the observability of timevarying phenomena. We, therefore, focus our analysis on the identification of spectral amplitudes compatible with the observed signatures in Juno's Doppler data. After having selected an empirical model describing those amplitudes, we carry out the analysis of Juno's Doppler data by varying the model parameters and identifying the region of the parameter space that shows maximum compatibility with the observations, according to the Akaike information criterion (AIC)  18.