dataesr/scientific-paragraphs-categorization huggingface dataset:
Parallel implementation of the DF estimator In practice, obtaining the tightest upper and lower bounds in Eqs. (  7 ) requires computing at each sample time k the distance between the measured regressor φk with respect to each of the regressors φi , i = 1,. , N in the dataset. This computation can become prohibitively expensive as the number of regressors in the dataset grows. To address this problem,  [6]  proposed an approximation of the optimal filter f c by computing the DF estimates on a grid defined over the regressor space. The estimates are stored in a look-up table, reducing the computation speed at the cost of a decrease in estimation accuracy. The problem with this approach is that increasing the regressor length m exponentially increases the amount of memory required to store the resulting look-up table.
Three kernels were implemented for each iteration of algorithm 2: 1. ψ j i = ( φ j k -φ j i ) 2 , i = 1,. , N , j = 1,. , 3m. 2. ∆ i = √ ∑ 3m j=1 ψ j i , f i = ṽi + ε + γ ∆ i , f i = ṽi -ε -γ ∆ i. 3. f = min i=1,. ,N ( f i ), f = max i=1,. ,N ( f i ). The first kernel computes, at each sample time k, the squared difference between the jth element in the regressor φk and the jth element in each regressor φi in the dataset. This computation is performed independently by 3m N threads. The second kernel computes the sum of the squared differences using a parallel reduction using N concurrent threads. The third kernel computes the minimum and maximum values also as parallel reductions using N concurrent threads. Notice that these kernels compute the 2-norm of the regressor difference, but other norms can be similarly implemented.
PCA dimensionality reduction in regressor datasets Principal Component Analysis (PCA)  [10]  is a technique used in many image processing and machine learning applications. It is used in the present context to reduce the regressor lengths in the DF datasets. The method considers the regressors as 3m-dimensional vectors and finds a change of basis that maps them into a new space where the components are ranked according to the amount of information they contain. This is achieved by constructing the covariance matrix of the regressor dataset and computing its eigenvalues and eigenvectors. The eigenvectors correspond to the independent directions where the variability of the data is maximized. The associated eigenvalues provide a measure of the information contents for each of these directions and provide a way to rank them.
The dimensionality reduction is achieved by taking a subset of the eigenvectors associated with most of the information content and using them to map the original regressors into a lower dimensional space. This mapping is performed offline on the regressor dataset and online on the measured regressor. Consider the dataset D formed by 3m-dimensional column vectors φi. Organize the N vectors into a matrix Φ ∈ R 3 m×N. The goal is to find a linear transformation Ω = Z ⊺ Φ with Z ∈ R 3 m×l , such that the reduceddimension regressors in Ω ∈ R l×N describe the data with less variables: l < 3m. Algorithm 3 presents the general approach to obtain the reduced-size dataset Ω and the transformation matrix Z using the covariance method. Notice that the parameter ζ refers to the relative information content of the principal components compared to the complete dataset.
A rule of thumb to choose this value is ζ = 0. 9. The columns in matrix Ω represent reduced-size vectors of the columns in Φ. The same transformation is used online to map the regressor φk measured at time k into the lower-dimensional space: ωk = Z ⊺ φk. N -1 B B ⊺. 5. Compute eigenvectors V ∈ R 3m×3m and eigenvalues D ∈ R 3m×1 of covariance matrix C. 6. Sort the eigenvectors V in decreasing order of associated eigenvalues and save them in matrix V s. 7. Compute the cumulative eigenvalues g j = ∑ j i=1 D i for j = 1,. , 3m. 8. Choose l as the smallest integer such that g l /g 3m >= ζ. 9. Select the first l columns of V s and save them as matrix Z. 10. Compute the matrix of reduced-size regressors as Ω = Z ⊺ Φ. 11. Save the columns of matrix Ω = [ ω1 , ω2 ,. , ωN ] in the new dataset O = {( ωi , ṽi ), i = 1, 2,.
These duty cycles will represent the period fraction that each switching device is active. In this model, we assume there is a way to measure or estimate the uncontrolled state of the diode. The design of an observer for estimating the state variables in this hybrid model is not an easy task. Therefore, we use the approach introduced in Section 4 to design direct filters for estimating the current I L 1 k from sampled noisy measurements of the input voltage u k = E k , the output voltage y k = V C 2 k and from knowledge of the controlled PWM duty cycle d k. A dataset D is obtained by applying an amplitude-modulated pseudo-random binary signal (APRBS) to the duty cycle input of the SEPIC converter in open loop and measuring the output voltage while maintaining a constant input voltage. The APRB signal maintains a random constant value throughout a given dwell time T d.
We randomly generate the signals by defining minimum and maximum dwell times T d,min , T d,max for both the training and test datasets. Assuming a constant load resistance R o , the duty cycle boundary value d separating the CCM and DCM operating regions can be computed by static analysis  [12] , using Eq. (  9 ). We compute this value to show more clearly whenever transitions between CCM and DCM occur, as shown in Figs. 4  and 6. The minimum and maximum levels of the duty cycle signal applied to the converter are selected in a wide range to force operation in both CCM and DCM regions. d = 1 - 2 f pwm L 1 L 2 R o (L 1 + L 2 ) = 0. 2756 (9) Table  1  Parameters used in the simulation and experimental tests. To obtain the results presented in the following subsections, we first acquired the detailed converter current and voltage waveforms for the designed APRB signals.
These signals are then averaged and resampled at a sufficiently high rate f r s to capture the low-frequency behavior of the converter. We applied a scaling scheme to the regressor dataset as described in  [14]  to improve the estimation performance. Additionally, we used principal component analysis (PCA) to map the regressors to a lower dimensional space, reducing the computational cost. Fig. 2  presents a block diagram showing the SEPIC converter, its measured signals and the direct filter estimator. Converter Parameters SEPIC E = 20 V, L 1 = 2. 3 mH, C 1 = 190 µF, L 2 = 330 µH, C 2 = 190 µF, R L1 = 2. 134 , R L2 = 0. 234 , R o = 22 , f pwm = 20 kHz.
Simulation results A detailed noiseless simulation of the SEPIC converter is run and all the variables are sampled at f s = 1 MSa/s, and then averaged and resampled at f r s = 5 kSa/s. Multiple direct filters are designed and tested for different regressor lengths m and dataset sizes N. We developed two different direct filter implementations. The base case direct filter estimator described by Eq. (  8 ) is DF. The direct filter with PCA-reduced dataset (DFPCA) implements the same estimator, but uses a dataset formed by regressors with reduced dimension, obtained using the procedure described in Section 4. 3. We compared the performance of DF and DFPCA to other estimation approaches: extended Kalman filter (EKF), particle filter (PF), and neural networks (NN). For the model-based approaches (EKF, PF), we used the nonlinear average model of the SEPIC converter.
The PF computes the evolution of 50 particles using Euler's method for 10 subintervals on each sample step. For the data-based approaches (DF, DFPCA, NN) a dataset with N = 10000 regressors and total regressor length 3 m = 60 was obtained from simulation. We used this dataset to learn the parameters (ε, γ ) for the DF and to train a feed-forward neural network with 3m inputs and 1 output. After some tests, we achieved the best performance with 30 neurons in the hidden layer. We prepared four different datasets (DS1, DS2, DS3, DS4) using several minimum and maximum dwell times for the APRB signal, specified in Table  2. For each dataset, we prepared 20 different test runs. Fig. 3  shows the performance of the estimators for these datasets, using as error measures the relative absolute error (RAE), root relative square error (RRSE), and relative worst-case error defined as: RAE = 100∥x -x∥ 1 /∥x -x∥ 1 , RRSE = 100∥x -x∥ 2 /∥x -x∥ 2 , RWCE = 100∥x -x∥ ∞ /∥x -x∥ ∞ , where x corresponds to the measured signal, x the average, and x the estimate.
These error measures quantify several properties of each estimator: RAE indicates the mean deviation, RRSE penalizes the outliers, and RWCE indicates the maximum deviation. For all datasets, the model-based estimators have worse performance than the data-based ones. The cause is their dependence on the nonlinear average model that does not provide a good approximation of the switched linear system, particularly during transients and DCM operation. In the case of the data-based estimators, NN has better performance than DF and DFPCA in all datasets because the parameter γ in the DF represents global worst-case bounds on partial derivatives of the approximated function, while the NN locally approximates them. Notice that in most cases, the DFPCA estimate is slightly worse than the DF because the regressor mapping into a lower-dimensional space implies a certain amount of information is lost.
Experimental results We executed multiple runs by applying different APRB duty cycle signals in open loop to the SEPIC converter experimental test bench and measuring all the variables with an oscilloscope at f s = 2 MSa/s. Then we resampled the acquired signals at f r s = 4 KSa/s. We used algorithm 1 to prepare four different regressor datasets with the T d,min , T d,max values presented in Table  2. For each dataset, we prepared N = 10000 training regressors with a total length of 3 m = 60 using five 500 ms captures. We also prepared 20 test signals using three 500 ms captures for each. We designed all the estimators using the same criteria discussed in the previous subsection. Fig. 5  shows the performance comparison for all the implemented estimators. Notice that the data-based estimators (DF, DFPCA, NN) perform better than the model-based ones (EKF, PF).
However, the performance of the NN estimator is worse than the DF and DFPCA estimators. In particular, the RWCE measure for NN is considerably worse than the others, indicating the presence of high amplitude outliers. Fig. 6  shows large amounts of noise for Let us remark that the DF in this example takes as the only uncontrolled input u t = E corresponding to the input voltage. If load disturbances were present, the estimation performance would decrease. However, we could define the load resistance as another uncontrolled input u t = [E, R 0 ], allowing the DF to give estimates under input and load disturbances. This scenario requires including in the dataset D the typical load disturbance scenarios the system could be subject to.
Estimation performance for the parallel implementation Table  3  shows the estimation performance results of the DF and DFPCA estimators using the four experimental datasets available. We show the mean estimation performance loss of the DFPCA compared to the DF for the three error measures. The table shows the average execution time of one iteration of algorithm 2 for two different implementations: CPU running the DF estimator sequentially and GPU running the parallel implementation of the DFPCA estimator. The hardware used for these tests is the Nvidia Jetson TX2, using one of the four available ARM Cortex-A57 CPU cores running at 2. 0 GHz and the Pascal GPU running at 1. 12 GHz. The speedup of the GPU+PCA implementation compared to the base CPU case is also shown. Notice that when we applied PCA to all datasets, the estimation performance loss was less than 2% of the absolute error.
On the other hand, the algorithm implementation in the GPU using PCA allowed us to increase the computation speed more than six times. The estimation rate achieved was close to 4 kHz, corresponding to the sample time of the experimental dataset. These results show that using the reduced dimension datasets in GPU provides a computation speed gain that outweighs the estimation losses and provides a feasible implementation of the DF algorithm for real-time applications. However, the experiments performed did not use a real-time operating system (RTOS) but a general-purpose operating system (GPOS). Consequently, we observed some jitter in the estimates produced by the DF algorithm. This behavior may render the proposed method infeasible in applications where the satisfaction of hard deterministic timing constraints is required.
Dataset Mean showing that this approach provides good estimation results compared to other model-based and data-based methods. Also, we show that a real-time implementation of the direct filter algorithm is feasible on an embedded system by exploiting its parallel structure to run it on a GPU and applying PCA to reduce the size of the datasets. Future extensions of this work involve improving the computational performance of the GPU implementation with additional configurations on the Jetson embedded system such as power model, clock frequencies, CPU core isolation, memory locks, and also applying a real-time patch to the Linux kernel (PREEMPT-RT) to improve the determinism compared to the stock Linux kernel. Additional improvements can be obtained with the implementation of persistent CUDA kernels to reduce the overhead of kernel launch at each sample time  [5].
PCA Dataset Preparation Data: D Result: Z , O 1. Organize the regressors φi in dataset D into matrix Φ ∈ R 3m×N. 2. Compute the empirical mean u ∈ R 3m×1 with u j = 1 N ∑ N i=1 Φi j. 3. Subtract the deviations from the mean: B = Φuh, where h ∈ R 1×N = [1, 1,. , 1]. 4. Compute the covariance matrix C = 1.
Algorithm 3 : 3 PCA Dataset Preparation Data: D Result: Z , O 1. Organize the regressors φi in dataset D into matrix Φ ∈ R 3m×N. 2. Compute the empirical mean u ∈ R 3m×1 with u j = 1 N ∑ N i=1 Φi j. 3. Subtract the deviations from the mean: B = Φuh, where h ∈ R 1×N = [1, 1,. , 1]. 4. Compute the covariance matrix C = 1.
Interestingly, among the 190 proteins identified in  [16]  in 2 samples from hCAD samples (enriched in TNTs), 101 were also found in TNTome (listed in table  S7 ), mostly corresponding to the more abundant cellular proteins. It is possible that laser microdissection allowed a limited amount of material to be purified, and therefore few membrane proteins were identified  [16]. TNTome is rich in membrane-associated proteins, as well as in proteins linked to the cytoskeleton, in particular related to actin, as it was expected for these structures. TNTome is also abundant in ribonucleoproteins and translation-related factors (220 proteins, 19%). Some of them (as well as the proteins identified as nuclear in the databases) could be contaminants coming from cellular debris, however this fraction should not be more than 10% of the total, based on nanoFCM results.
The proteins associated to an adjusted p-value inferior to a FDR level of 1% have been considered as significantly differentially abundant proteins. Bioinformatic analysis and data mining Twelve replicates were done for the discovery of TNT proteins. Nine over 12 were kept as being part of the TNT. The 1177 resulting proteins were sorting by quartiles according to their iBAQ value. For each of the 4 lists, proteins composition was depicted using ProteoMap tool  [78]  to visualize their weighted GO organization and protein contribution for each GO term. Also, a DAVID analysis  [79]  was done for each quartile of proteins. Functional charts and cluster were used to describe the dataset. Protein networks were visualized using STRING  [80]  and Cytoscape (cytoscape. org). Resource availability: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [1]  partner repository with the dataset identifier PXD033089.
Acknowledgements : We acknowledge the  Center for Translational Science (CRT)-Cytometry and Biomarkers Unit of Technology and Service (CB UTechS) , in particular  P. H. Commère. Thanks to  Dr. S. Charrin  for helpful discussion,  Dr. S. Lebreton ,  Ranabir Chakraborty  for critical reading of the manuscript, and all the members of the UTRAF unit for their support. We are grateful for financial support to C. Z. from  Institut National du Cancer  ( PLBIO18-103 ) and  Equipe Fondation Recherche Médicale  ( FRM EQU202103012692 ). This work was supported by  Equipe Fondation Recherche Médicale  ( FRM-EQU202103012692 ), and  Agence Nationale de la Recherche  ( ANR-20-CE13-0032 ) to C. Z. Author contributions : Conceptualization: RNM, CB, CZ; methodology: RNM, TC, ER, MM, CB; formal analysis: RNM, TC, CB; writing-original draft: RNM, CB; writing-review and editing: RNM, CB, CZ, ER, TC, MM; funding acquisition: CZ, MM; supervision: CB.
Analysis of the TNTome A. Proteomap of the 1177 proteins of the TNTome, sorted in 4 quartiles depending on their mean iBAQ. Protein accession and mean iBAQ was used to create ProteoMap, analyzed according to Gene Ontology. B. STRING physical association network for the cytoskeleton-related proteins listed in table S2. Color groups were created using Cytoscape. Green are microtubulerelated proteins, blue are intermediate filaments, orange are actin-interacting proteins. Blue and pink edges show physical interactions based on databases and experiments respectively. C. Full STRING functional association network for integral surface membrane proteins of TNTome, based on table S3. Orange are Integrin proteins, red are Ephrin receptors, dark green are Cadherins, light green are Sodium/potassium transporting ATPase ions channels, purple are monocarboxylate and amino acids transporters, and yellow are tetraspanin-related proteins.
Finally, the relatively immunoprivileged environment of the eye in general,  (5)  and the retina in particular, may protect rAAV vectors from neutralization by preexisting circulating anti-AAV antibodies. Clinical trials increasingly report adverse events in cohorts of patients injected with the highest rAAV doses. (6)  Inflammation and increased intraocular pressure were described in the high-dose cohort in a recent clinical trial of diabetic macular edema (DME, NCT04418427) patients. With the development of more effective viral vectors for retinal gene delivery, therapeutic thresholds could be reached with lower vector doses, thereby reducing the likelihood of the aforementioned adverse events. The most common strategy to optimize rAAV vector efficacy involves modification of the viral capsid via genetic engineering, using either rational design or directed evolution approaches.
Acknowledgments The authors thank all personnel at the  Boisbonne Center for Gene Therapy (ONIRIS, INSERM, Nantes, France)  for handling and care of animals in this study. We also thank the vector core of TarGeT, UMR 1089 (CPV,  INSERM and Nantes Université , http://umr1089. univ-nantes. fr)and the staff at the  Preclinical Analytics Core of TarGeT ,  UMR 1089  (PAC, INSERM and  Nantes Université). Thanks also to the  IBISA MicroPICell facility (Biogenouest) , a member of the  France-Bioimaging national infrastructure  supported by the  French national research agency  ( ANR-10-INBS-04 ). Analysis and purification of chemical compounds were performed at the  Chromatography Platform, CEISAM laboratory ,  UMR 6230 CNRS/UN ,  Nantes Université. Thanks to  Nicolas Ferry  for reviewing the manuscript and to  Owen Howard PhD  for English language editing.
This research was supported by the  Fondation d'Entreprise Thérapie Génique en Pays de Loire , the  Centre Hospitalier Universitaire (CHU) of Nantes , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and  Nantes Université , by a grant from the  French National Agency for Research  (" Investissements d'Avenir" Equipex ArronaxPlus n°ANR- 11-EQPX-0004 ) and by  Coave Therapeutics (formerly Horama ).
2112Del (Figure  1B ). The c. 1084C>T variant is a nonsense mutation, whereas the single-nucleotide deletion c. 2112Del is predicted to induce a translational frameshift. Both mutations were predicted to produce premature stop codons: p. (Arg362Ter) and p. (Arg704serfsTer7), respectively, leading either to the complete absence of the protein, or to the production of a truncated and non-functional protein. The two mutations are therefore most-likely deleterious (Figure  1B ). Both variants were absent in our control cohort and their minor allele frequencies (MAF), according to the gnomAD v3 database, were 6. 984x10 -5 and 0 respectively. The presence of these variants and their homozygous state were verified by Sanger sequencing, as illustrated in Figure  1. Taken together, these elements strongly suggest that mutations in the CCDC146 gene could be responsible for the infertility of these two patients and the MMAF phenotype.
These examples illustrate the complexity of function of some multiple-domain proteins. The fact that CCDC146 can localize to multiple subcellular compartments suggests that it also contains several domains. Interestingly, a PF05557 motif (Pfam mitotic checkpoint protein, https://www. ebi. ac. uk/interpro/protein/UniProt/E9Q9F7/) has been identified in the mouse CCDC146 sequence between amino acids 130 and 162. Proteins belonging to the "mitotic spindle checkpoint" monitor correct attachment of the bipolar spindle to the kinetochores. The presence of this motif likely explains the ability of CCDC146 to localize to cell cycle-dependent subcellular compartments containing tubulin. However, the most important structural motifs identified in CCDC146 are the coiled-coil domains. Although coiled-coils play a structural role in a variety of protein interactions, their presence in CCDC146 remains mysterious, and how they contribute to its function remains to be elucidated.
to P. F. R. , the Direction Générale de l'Offre de Soin (DGHOS) for the program PRTS 2014 to P. F. R. , the Fondation Maladies Rares (FMR)-grant ''Whole genome sequencing of subjects with Flagellar Growth Defects (FGD)'' financed by for the program Séquençage à haut débit 2012 to P. F. R. and the European Research Council (ERC) ACCENT Starting Grant 715289 to P. G. CE17-0014) U-ExM: 1/400 WB: 1/2500 High Affinity (HA) Rabbit Cell Signaling Technology 3724 U-ExM: 1/100 High Affinity (HA) Rabbit Sigma Aldrich H6908 U-ExM: 1/200 Dpy19L2 Rabbit ** IF: 1/100 Target Fluorophore Reference Dilution Goat anti-Rabbit Alexa Fluor 488 Jackson 111-545-144 IF: 1/800 Target Host Reference ImmunoResearch Dilution U-ExM: 1/250 Goat anti-Mouse species DyLight 549 Jackson 115-505-062 IF: 1/400 CCDC146 Rabbit Atlas Antibodies ImmunoResearch HPA020082 IF: 1/200 Goat anti-Guinea Pig Alexa Fluor 647 Invitrogen A-21450 U-ExM: 1/200 IF: 1/800 Centrin, 20H5 Goat anti-Rabbit Mouse Alexa Fluor 568 Merck Life Technologies 04-1624 A11036 IF: 1/200 U-ExM: 1/250 γ-tubulin Goat anti-Mouse Mouse Alexa Fluor 488 Santa Cruz Biotechnology Life Technologies sc-17787 A11029 IF: 1/500 U-ExM: 1/250 Goat anti-Rat Alexa Fluor 549 Jackson 112-505-175 IF: 1/800 ImmunoResearch β-tubulin Guinea pig Geneva Antibody Facility AA344-GP IF: 1/500 Goat Anti-Rat HRP conjugate Merck AP136P 1/10 000 β-tubulin Rabbit Cell Signaling Technology 2128 IF: 1/100 PCM1 (G-6) Mouse Santa Cruz Biotechnology sc-398365 IF: 1/200 *α-tubulin and β-tubulin were used together and noted as α+β-tubulin α-tubulin Mouse Geneva Antibody Facility AA-345 U-ExM: 1/250 **Dpy19l2 antibodies are polyclonal antibodies produced in rabbit that were raised against β-tubulin * RSKLREGSSDRPQSSC and CTGQARRRWSAATMEP peptides corresponding to amino acids 6-21 and 21-IF: 1/500 AA-344 POC5 36 of the N-terminus of mouse Dpy19l2, as described in [76].