dataesr/scientific-paragraphs-categorization huggingface dataset:
(D) Integration site distribution within gene bodies and their surrounding genomic regions. Each gene interval was quantified from the TSS up to the end of its coding region; this interval is considered as 100% and then normalized in bins of 20%. The surrounding genomic regions are divided in intervals of 20 kb. In panel C, statistical analyses were performed using a Fisher exact test **** P <. 0001; see supplemental Table  2  for detailed statistical comparison. collapsing chimeric reads with the same IS and homology structure, we identified 972, 394, and 131 nonredundant IS from the LN, SPL, and LIV of Zap70-deficient mice. Here, these IS data sets will be referred to as LN_ZAP70, SPL_ZAP70, and LIV_ZAP70, respectively. Fewer AAV ISs were obtained in WT mice treated with the GFP-expressing AAV8 than in Zap70deficient mice: 29 and 63 IS were identified in the LN and SPL, respectively (hereinafter referred to as LN_GFP, SPL_GFP data sets, or GFP-treated mice).
The increased frequency of short stretches of random nucleotides in the LN-and SPL-derived IS data sets compared with the others suggested the presence of a differentially regulated biological mechanism controlling the AAV integration processing in those tissues. TCR genes are the most frequently targeted genes in LN-derived mature T cells in Zap70-deficient mice Analyzing the distribution of AAV ISs within the mouse genome, we observed that, independently of the mouse background or the AAV type used, the LIV and BR data sets displayed the typical preference of AAV IS to integrate close to CpG islands and TSSs (supplemental Figure  4A-B ). In marked contrast, the LN and SPL data sets showed a peculiar distribution in CpG and TSSs, with a significantly higher integration frequency within gene bodies (91.
A single AAV IS in IgH and 2 IS in IgK (chr6) were identified in the SPL-derived data set of GFP-treated mice. In the LIV data set of ZAP70-treated mice, TCRα was the most targeted gene (N = 8, 6. 2%), followed by albumin gene (Alb, N = 4, 3. 1%) (supplemental Table  4 ). Although TCRα was targeted genes by different IS, the targeting frequency of the TCR loci was significantly reduced compared with that identified in the LN and SPL data sets of ZAP70-treated mice (Figure  3E ; P <. 0001 using Fisher exact test). Of note, 47 ISs in the ZAP70 data set were retrieved in different tissues from the same mouse: 9 ISs from mouse 101 and 37 ISs from mouse 111 were found in LN and SPL, whereas 1 IS from mouse 103 was retrieved in the LIV and SPL, suggesting the trafficking of transduced T cells to different organs.
Most of these shared IS-targeted TCR loci (38  Tcrα, 6 Tcrβ and 1 Tcrγ, N = 45, 96% frequency). In contrast, no IS in TCR loci were identified in the LIV and BR IS data sets of Mecp2-deficient mice. Neuron-and LIV-specific expressed genes, such as Ncam2, Prcc2b for the BR data set (N = 3) and Alb for the LIV (N = 9), were the most targeted (supplemental Table  4 ). Furthermore, 3 ISs in immunoglobulin H (IgH), 1 IS in IgK, and 1 IS in IgL (chr16:19061845-19260937) were found in the LIV and BR data sets of PHP-treated Mecp2-deficient mice. Overall, these data revealed a massive enrichment of AAV IS in TCR loci (α, β, and γ) in the LN and SPL of intrathymically transduced Zap70-deficient and WT mice.
01) closely resembled the conserved 7 (heptamer) and 9 (nonamer) elements of RSS signals (Figure  4C-D ). These RSSlike motifs were not found in the other IS data sets. Rather, this analysis revealed a significant enrichment for GC-rich and degenerated palindromic motifs (supplemental Figure  7 ). RSSs can be distinguished based on the 12-or 23-bp spacer lengths separating conserved heptamer and nonamer elements. The V(D)J recombination process is restricted to gene segments flanked by dissimilar RSSs following the 12 ÷ 23 rule, so that a V-to-D recombination occurs only when a 23-RSS that is downstream of a V segment is joined with a 12-RSS upstream of a D segment. 48, 49 A closer inspection of the distribution of the AAV IS within TCRs revealed that 94% and 92% of integration events of Zap70 and WT intrathymically injected mice clustered within the 3 ′ region of V segments and the 5 ′ region of J segments, hence in proximity of DSBs created by RAG enzymes recruitment to 12-RSS and 23-RSS substrates (Figure  4E -F; supplemental Figure  6 ; supplemental Table  5 6 ).
Finally, a TcrD (chr14) more granular inspection identified some preferred nucleotide breakpoints within loop regions A and C of ITRs (Figure  5F -L), as previously reported. 13, 26, 27 V integrations are stably integrated in the genome of proliferating T cells Trdv5 TEA VD/VG >100 DG1 1800 kb JD 1-60 ED CD JG1 DG2 J G2 CG EG TcrE (chr6) VE 1-33 DE1 D E2 JE1 1-7 JE2 1-7 Trbv31 EE CE2 CE1 T T 670 kb TcrJ (chr13) VJ7 V J4 V J3 JJ3 J J2 VJ2 VJ1 JJ4 EJ4 EJ3 EJ1 E J2 VJ6 VJ5 JJ1 CJ1 CJ3 C J2 C J4 200 Our SLiM-PCR protocol was performed using sonicated genomic DNA material, which allowed for the abundance of each AAV IS to be estimated by counting the numbers of different genomic shear sites. Multiple cellular genomes in the LN and SPL data sets were associated with the same AAV IS, highlighting the stable inheritance of AAV IS by cell progeny after T-cell proliferation.
These results confirm the maintenance of integrated AAV copies, as previously reported,  35  and exclude the possibility that AAV IS occurred in T-cell receptor excision circles, episomal byproducts that originate from the excision of the delta-coding sequences during TCR recombination. Moreover, a significantly higher number of genomes per IS was observed in the LN and SPL data sets (average 31. 8 ± 2. 3 and 16. 7 ± 1. 0 genomes per IS in LNs and SPLs, respectively) than in the LIV data sets of ZAP70-treated mice (average 5 ± 0. 6; P <. 0001 using the two-tailed Mann-Whitney test; Figure  6A ), likely reflecting the higher proliferative capacity of T cells as compared with LIV cells under physiological conditions. A significantly lower number of genomes was observed in the LN and SPL data sets of GFP-treated WT mice compared to ZAP70treated mice, and this was probably because of the selective advantage conferred by the expression of the integrated therapeutic transgene in transduced T cells in the latter mice.
The integration profiles obtained from the LN and SPL of WT mice injected with a GFP-expressing AAV are similar to those obtained in the LN and SPL of Zap70-deficient mice, in which AAV ISs mainly targeted TCR loci at RSS site of V(D)J gene segments (92% in ZAP70-treated mice and 85% in WT-treated mice). These data confirmed our finding that intrathymic injection favored the trapping of the AAV genome at RAG-induced DSBs and now show that this integration is independent of the role of the transgene in the T-cell differentiation process. The limited number of AAV ISs retrieved in WT mice compared with Zap70deficient ones is due to the absence of a selection advantage conferred by the expression of the integrated transgene, which promotes the development and function of gene-corrected cells.
In addition, in physiological conditions, nontransduced T cells continuously exit the thymus after differentiation. Few AAV ISs targeting the genomic region proximal to V(D)J gene segments of IgH, IgK, and IgL genes (B-cell receptor loci) were identified in the different IS data sets. These integration events occurred exactly at the sites where the RAG recombinase complex mediates the DNA break for the rearrangement of Ig genes, suggesting that AAV can transduce B cells in the   bone marrow during B-cell receptor recombination independently from the route of vector administration and the vector capsid, albeit at a very low frequency. The lower frequency of integration events in Ig loci compared with that in TCRs can be explained by the low frequency of B cells in the thymus and the lack of a selective advantage conferred by Zap70 expression in B cells.
These findings are also consistent with our previously published data, in which low levels of B and myeloid cells expressing the therapeutic transgene were detected in AAV8-ZAP-70-treated mice during the 40-week follow-up period. 35  contrast to the LN-and SPL-derived data sets of intrathymically injected mice, AAV ISs in the LIV and BR data sets targeted highly expressed genes. Indeed, albumin was among the most targeted genes in the LIV data set of Zap70-and Mecp2-deficient mice, whereas Ncam2 and Pbbrc2 were the main targets in the BR of Mecp2-deficient mice. Preferential targeting of AAV ISs in genes with high expression levels can be explained by the higher DNA damage associated with high transcription levels, which may promote the occurrence of DNA breaks, thus favoring AAV integration.
54, 55 This mechanism was independent of the route of vector administration, the vector capsid, or the genetic background of the treated mice. In contrast, the presence of some IS targeting TCR loci in the LIV of ZAP70treated mice is likely due to the trafficking of transduced T cells, as demonstrated by the detection of shared IS between different tissues of the same treated mouse. G G A G C G A G C G A G T G A C T C C C C C C C T G C G C G C T C G G G C G C C G C C G G C C C C C A A A A C G T G C C C A G C G G G C G C G G G G G G C C T C G C T C A A T T T C T G G T T 0 1 5 3 0 3'- 5'- F G C C C G G G A G C G A G C G A G T G A C T C C G G A C G C G C G C C G C A C G G C C C G G G G C T T T 0 1 3 2 7 3'- G C C C A A G G C T G C G G G A C C C C C C C C C T T T T C C A A C T T G G G G G G G 5'- H C C C C C C C C C C A A G G C A G G C C C C T G G G G A A C T C C T T G G G C T T T T T C C C C C G A A A A G T T T T G A C G G C A G G C G G C G G G G G G G G G C G C C C C G G G G G C C C C 0 7 1 4 3'- 5'- I A G G G G G G G G G G G C C C C G T A A A C G G G G C C G G G G G C C C T C C C A T T T T C C C C C C C G G G A T T C C C G C G A C G G G C G C G C C G C G C G C G T T T G A C C C C C C C C T A A A A T 0 3 6 0 3 6 3'- 5'- J A A C G G G G C C A G G G G G G G G G G G C G G C G C C C G C A G C C C G C T G G A C G C G G C T T T G G A C C C C C C C C T A A A A T 3'- C G G G G G C C C T C C C A T T T T C C C C C C C G G G A T T C C 5'- The higher frequency of ITR sequences at the junction breakpoint in the LN and SPL data sets is of interest, considering the results obtained from the LIV IS data set of Zap70-deficient mice treated with the same vector whose genomic regions were interrogated using the same PCR systems.
It is likely that in this cell-specific setting, the binding of DNA damage response proteins, such as Ku70/80 heterodimer kinase (Ku), DNA-dependent protein kinase (DNA-PKs), ataxia-telangiectasia mutated kinase (ATM), and the Mre11-Rad5-Nbs1 (MRN) complex (MRE11, RAD50, and NBS1), on genomic DNA and on the free DNA ends of the vector ITRs may facilitate the joining and repair by NHEJ. 52, 56 he higher frequency of insertion of random nucleotides between the vector and chromosomal sequence in the amplified reads obtained from the LN and SPL data sets is likely due to the recruitment of the terminal deoxynucleotidyl transferase during V(D)J recombination, promoting template-independent and -dependent synthesis before ligation of the genomic and vector ITR ends. However, in the LIV and BR data sets, integration events can occur at DBSs and single-strand breaks, taking advantage of other DNA repair pathways that may involve other regions of the vector backbone.
In this paper, we propose a novel object-centric contour-aware CutMix data augmentation strategy with arbitrary-shape and size superpixel supports, which is hereafter referred to as OcCaMix for short. It not only captures the most discriminative regions, but also effectively preserves the contour details of the objects. Moreover, it enables the search of natural object parts of different sizes. Extensive experiments on a large number of benchmark datasets show that OcCaMix significantly outperforms state-of-the-art CutMix based data augmentation methods in classification tasks. The source codes and trained models are available at https:.
tour information and inaccurate label matching; (II) Deterministic selection of the most discriminative regions leads to the lack of diversification in data augmentation; (III) Additional information or modules are always required to provide attention or saliency information for discriminative region selection. To overcome the above limitations, in this work we introduce a new augmentation approach -OcCaMix, which is an objectcentric contour-aware CutMix data augmentation method with arbitrary-shape and size superpixel supports. OcCaMix can capture attention-guided contour-preserved regions with varying granularity, thus preserving the contour details of the objects in the cuts and increasing the diversification of the augmented samples. A visual comparison between our method and other representative strategies is shown in Figure  1.
(4) Finally, we propose to make a trade-off between augmentation diversification and regularization concentration, by selecting the relevant superpixels with varying superpixel granularity and contour boundaries over the attention-guided discriminative patches. The remainder of this article is organized as follows: we briefly review related work on data augmentation and attention map generation in Section 2. In Section 3 , we describe our proposed Oc-CaMix method. In Section 4 , we compare performance of different methods on several benchmark image datasets. In Section 5 , we conduct several ablation studies on the efficacy of different components of OcCaMix. Finally, Section 6 concludes the paper.
(  2 ) is with additional attention training modules. These are: SEBlock  [32]  introduces the squeeze-and-excite block to adaptively recalibrate channel-wise feature responses by explicitly modelling interdependencies between channels. CBAM  [29]  uses both the channel attention module and the spatial attention module to adaptively refine the intermediate feature map. Attention Gate (AG)  [33]  proposes to generate a spatial attention map by integrating features of the signal and the gate. PSANet  [34]  explores the local neighbourhood constraint, where each position on the feature map is connected to all others by a self-learning attention mask. In our method, we choose a simple structure to generate the attention map without an additional attention training module. Inspired by  [35]  , we used extended attention to improve robustness.
Experiments and Analysis In this section, we evaluate OcCaMix on some classification problems to determine its ability to improve generalizability. First, we present the datasets and CNN models ( Section 4. 1 ) and implementation details ( Section 4. 2 ), and then show the results for CIFAR100 ( Section 4. 3 ), CUB-200-2011 ( Section 4. 4 ), Stanford Dogs ( Section 4. 5 ), PASCAL VOC ( Section 4. 6 ). Our main goal is not to outperform the state-of-the-art results for classification, but to compare the classification performance with that obtained by different CutMix-based augmentation methods using different datasets and CNN models. All experiments were implemented and evaluated in PyTorch. The source code and trained models are available at https://github. com/DanielaPlusPlus/OcCaMix.
Datasets and Models CIFAR100. We evaluate OcCaMix with the dataset CIFAR100  [2]  on the residual network  [3]  with depth 18 (ResNet18), depth 50 (ResNet50), and a residual network of aggregated transformations with the same topology with depth 50  [37]  (ResNeXt50). We use the standard split of CIFAR100 with 50,000 images for training and 10,0 0 0 images for testing. For each model, we perform experiments with the image resolution of 32 × 32 and 224 × 224 respectively. For samples with resolution 32 × 32 , the first convolution and max-pooling kernel for all models are changed from 7 × 7 to 3 × 3. For samples with resolution 224 × 224 , all models first load the models pre-trained on ImageNet  [1]  to improve convergence efficiency. CUB-200-2011. We also evaluate OcCaMix with the dataset CUB-200-2011  [38]  on ResNet18, ResNet50, ResNeXt50 with the images in the size of 224 × 224.
Ratio λ The number of patches N and the min and max number of superpixels (q min , q max ) are three hyper parameters patch   [39]  and MobileNet-V2  [40]. For all experiments on CUB-200-2011, the models are pre-trained on ImageNet  [1]  to improve convergence efficiency. Stanford Dogs. We also evaluate OcCaMix with the dataset Stanford Dogs  [41]  on ResNet50 with the images in the size of 224 × 224. We use the standard split with 12,0 0 0 images for training and 8,580 images for testing. We change the stride from 2 to 1 for larger feature map. PASCAL VOC 1  is a dataset from the 2005 PASCAL visual object classes challenge. We select 768 color images from the four classes, with respectively motorbikes, bicycles, people, and cars in arbitrary poses. We randomly select half of the samples for training and half of the samples for testing.
Implementation Details We use the simple linear iterative clustering (SLIC) algorithm  [42]  to generate the superpixel grid map. It can be precomputed for the source image using skimage. segmentation. slic 2. The classification loss we used is the Cross Entropy loss. In the experiments with the CIFAR100 dataset, when the images have a resolution of 32 × 32 , the base augmentations for the training data are random cropping for 32 with padding 4 and random horizontal flipping. We train the networks with a batch size of 32, where the initial learning rate is 1 e -2 and is decayed by 0. 1 every 60 epochs. To improve convergence efficiency, we apply the warm-up trick for the first five epochs. When the images have a resolution of 224 × 224 , the base augmentations for the training data are random rotation and central cropping.
We train the networks with a batch size of 32, where the initial learning rate is 2 e -3 and is decayed by 0. In all experiments with the dataset Stanford Dogs, the base augmentations are random resized crop for size 224 × 224 with ratio (1,1. 3), and random horizontal flip. We train the networks with a batch size of 16. The initial learning rate is 1 e -2 and is decayed by 0. 1 every 100 epochs. In all experiments with the dataset PASCAL VOC, the training examples are randomly rotated and center-cropped for base augmentation. We train the networks with a batch size of 8, with an initial learning rate of 1 e -4. The learning rate is decayed by 0. 1 every 50 epochs. In all experiments, the baseline results are obtained with the basic data augmentation schemes for the training data. The results associated with the competing methods and our proposed method are obtained with the combination of base augmentation scheme and the proposed mixing scheme.
For ResizeMix  [26]  we use the same setting α = 0. 1 , β = 0. 8 as in their paper. GridMix  [27]  sets p = 0. 8 , γ = 0. 15 for all its experiments and recommends setting the size of the grid cells as the size of the feature map on the last convolutional layer. Thus, we set grid = 7 × 7 , p = 0. 8 , γ = 0. 15. Table  6  shows that our method performs best on the PASCAL VOC dataset. We outperform both the random CutMixbased methods and the attentive-guided, saliency-guided cutmixbased methods. We outperform baseline augmentation by 1. 82% for ResNet18, by 3. 91% for ResNet50, and by 2. 34% for ResNeXt50.
Visualization of Different CutMix-based Augmentation Methods Figure  8  illustrates augmentation by various CutMix-based augmentation methods using a pair of images retrieved for the dataset PASCAL VOC. Figure  8  a is the source image. Figure  8  e is the target image. Figure  8  b is the visualization of the augmentation obtained by CutMix  [10]. We can see that the captured region is a random square patch that may bring noise from the background. Figure  8 c  is the visualization of the augmentation obtained by GridMix  [27]. The inserted regions are some random square patches from the source image, which bring a lot of noise from the background and lose the contour information of the object. Figure  8  d is the visualization of the augmentation produced by random SuperpixelGrid-Mix  [12].
Conclusion In this paper we introduce OcCaMix: Object-centric Contouraware CutMix Data Augmentation, an augmentation based on a regional dropout strategy with discriminative and contour-preserving regions. We discuss the potential drawbacks of existing regional dropout strategies based on discriminative regions and propose a solution to overcome these limitations. We analyze the necessary balance between augmentation diversification and regularization concentration, and our study explores the trade-off between augmentation diversification and regularization concentration. Our method is simple but effective and does not require additional attentional training modules or pre-trained models, and no saliency information is provided. Thus, we do not need to worry about the bottleneck caused by additional attentional modules or externally provided saliency information.
1 every 40 epochs In all experiments with the dataset CUB-200-2011, the input training image is first resized to 256 × 256 , then randomly cropped to size 224 × 224 , and finally randomly flipped horizontally. The input test image is directly resized to 224 × 224. For experiments with ResNet structures, we train the networks with a batch size of 8. The initial learning rate is 1 e -3 and is decayed by 0. 1 every 50 epochs. For experiments with the EfficientNet model, we train the networks with a batch size of 16, where the initial learning rate is 5 e -3 and is decayed by 0. 1 every 50 epochs. For experiments with MobileNet-V2 model, we train the networks with a batch size of 16, where the initial learning rate is 2 e -3 and is decayed by 0. 1 every 60 epochs.
The SCAL experiment was performed on a 5 cm diameter Bentheimer plug with 20 cm length. The plug had a porosity of 24% with an absolute brine permeability of 2. 3D. Primary drainage was achieved through oil ooding using a displacement with viscous oil to target a low S wi exempted from capillary end-effect. A homogeneous S wi pro le of average 12. 1% was obtained. To make the plug mixed-wet, the same dead oil used on the anchoring wettability experiment was used to replace the mineral oil and perform 15 days of dynamic ageing process at 80°C to alter wettability of the SCAL plug. Afterwards, multi-rates water ood at 80°C was performed. Initial ow rates were sized to t Hagoort criteria  (Hagoort 1974 ) to avoid viscous ngering. It started at around 0. 3ft/day. Oil productionpressure gradient vs time, and transient to equilibrium saturation pro les through 2D X-ray imaging internal devices were acquired during the experiment and numerical interpretation was performed using the software CYDAR® to nd the best couple (Kr/Pc) matching oil production, pressure gradient and saturation pro les (transient and equilibrium).
2. 2 Ecospace spatialized food web modeling The eBos food web was modeled with a modified version of Ecopath with Ecosim (EwE 6) software  (Pauly et al. , 2000). This software can be used to model marine food webs: in a static way with Ecopath  (Pauly et al. , 2000) , in a time-dynamic way with Ecosim  (Christensen and Walters, 2004) , and temporal-spatial way with Ecospace  (Walters et al. , 1999; Christensen et al. , 2014). The EwE 6 version we used was specially modified to compute food web indices  (Nogues et al. , 2022). Following  Nogues et al. (2022)   (Halouani et al. , 2020 ). An Ecospace model was later built to spatially model the eBoS ecosystem. The eBoS model map was composed of 4,907 cells with a resolution of 0. 015° x 0. 015°, and each cell modeled a timedynamic Ecosim food web.