dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementations Details Experiments were conducted on the URPC2019 dataset to compare our model with the state-of-the-art YOLO models, i. e. , YOLO-v4, YOLO-v4 Tiny, YOLO-v3, YOLO-v3 Tiny, and YOLO Nano. For a fair comparison, we implemented the compared networks using PyTorch and kept the same training parameters, random seed, and image pre-processing methods. Augmentations were deployed during the training phase, such as cropping, randomly flipping, affine transformation, and color jitter. The input image size is 512 × 512. The ADAM is the optimizer with weight decay 5 × 10 -4. The cosine annealing schedule  [15, 34]  was applied, and the initial learning rate is 1 × 10 -3. All the models were trained with 300 epochs and batch-size 64 from scratch. The confidence and NMS thresholds were set to 0.
Main Results The model is trained on the training set and evaluated on the validation set for model selection. The final results were tested using the testing set. Table  2  lists the results evaluated by average precision on both the testing and validation sets. Figure  7  illustrates the FLOPs-mAP curve and the Parameters-mAP curve, respectively. Overall, YOLO-v3 achieved the best performance on the URPC2019 dataset, following 1 ULO and ULO Tiny. However, the ULO and ULO Tiny models were much smaller than YOLO-v3. Moreover, the difference between ULO and YOLO-v3 in mAP was only 1. 38%. Therefore, the ULO and ULO Tiny can be more applicable to the devices in which computation resources are limited. Comparing architectures with similar network designs, the larger models often achieved better performance.
For example, YOLO-v3 outperformed YOLO-v3 Tiny, and YOLO-v4 outperformed YOLO-v4 Tiny. This is also true for ULO and ULO Tiny, as the larger models with more detection heads can provide more anchors. On the other hand, small models also significantly reduce the computational complexity, lower hardware requirements, and broaden usage scenarios. However, the model performance of YOLO-v4 in URPC2019 is not better than YOLO-v3, somewhat beyond our expectation, likely because more complex models tend to require richer data for training, and the URPC2019 dataset is still not as rich in content and sample scale as the object detection datasets for natural scenes, such as VOC  [35]  and COCO  [36]. As can be seen from the performance-computational resource curves presented in Figure  7 , our proposed ULO and ULO Tiny models have significantly smaller resource requirements and competitive performance simultaneously.
Experiments on Edge Device We deployed our models to Nvidia Jetson Nano, a 10-watt, $120 edge computing device. The details of the experiments' results are shown in Table  4. It achieves an inference speed of 5. 11 and 6. 73 FPS with the PyTorch framework for ULO and ULO Tiny, respectively. In the semi-precision mode, the performance of the model remains roughly unchanged, with some insignificant improvements in inference time, which may be related to the hardware architecture. We also observe a 30% improvement in inference time with an input size of 416 but a 10-point decrease in performance, which may be because we focus more on the impact of the model design and do not use multi-scaled inputs in training. In practical applications, adopting more training techniques, such as diverse input scales, mixup/mosaic augmentation, and using pre-training of the models on large datasets are expected to improve the performance of the models further.
Moreover, we deployed an adaptive pre-processing module for automatic image enhancement and proved it can improve the task performance in underwater object detection. At the same time, underwater images have been relatively less studied compared with natural scenes, and fewer datasets are available. Therefore, high-quality underwater detection datasets are essential for the development of related research. For this reason, we optimized some mis-annotated labels in the URPC2019 dataset, hoping to provide a more reliable reference for evaluating the underwater models. The modified annotations have been released to the public for research use.
To demonstrate that this is possible, the following steps are followed: (i) Accurate experimental voltage and current data are obtained, from which the overall instantaneous absorbed power V R ÁI is calculated. (ii) Following the work presented in Ref. 21 a simplified 1D model, termed model B, is implemented using MATHCAD software. 30 The only difference from Ref. 21 is the use of an improved model for plasma conductivity, as suggested in Ref. 31. It is important to note that model B is capable of closely reproducing all results obtained with the most complete 1D model published in the literature. 20 iii) Using model B together with accurate pressure and bubble dynamics data, it is demonstrated that the input data required for model A to satisfactorily reproduce all the available experimental data require k % 0.
When compared with NH20000, the NH0040 is less directional. For both needle-type hydrophones, the calibration data were obtained from the National Physics Laboratory (UK) as a variable response over their entire characteristic bandwidths. (iii) Details of hydrophone mounting and operation. Apart from a very expensive fibre-optic based hydrophone used in Ref. 28, there is no hydrophone to cover the whole frequency bandwidth required by the phenomena: from Hz to tens of MHz. Therefore, the three different hydrophones presented above are all mounted using supports as shown in Fig. 4  and placed at the minimum separation from one another that allowed for the full pressure wave to be captured by each hydrophone before any reflection from the other would be detected and influence the signal. All hydrophones displayed sensitivity to the powerful electromagnetic noise generated by the operation of the pulsed power generator but, when mounted at least 0.
5 m away from the source they were capable of fully recovering, well before the pressure waves were recorded. (iv) Software and data analysis. A MATHCAD  30  program was developed to analyze the data obtained from the three hydrophones. To help with the alignment of the highly directional NH2000 hydrophone, the software was used to test agreement with the TC4034 signal in the overlapping receiving bandwidth (i. e. , from 100 to 480 kHz). As already mentioned, no hydrophone is individually capable to cover the ultrawide spectrum generated by the source, and therefore the software is used to perform the FFT of each of the pressure signals, calibrate the FFT using the corresponding frequency response data and then trim and stitch the three signals in the frequency domain, before performing the inverse FFT to obtain the pressure signal in the time domain corresponding to the entire bandwidth covered (i.
Image data To obtain land surface reflectance and land surface temperature data, we used Landsat 8 OLI/TIRS Collection 2 Level 2 products (https://www. usgs. gov/) corresponding to the study area (path 232 row 82) at 30-meter spatial resolution. The collections of Landsat ensure consistent data quality through time and across all the Landsat sensors, with sensor-specific geometric and radiometric adjustments. These collections improve substantially in the absolute geolocation accuracy of the global ground reference dataset, which improves the interoperability of the Landsat archive through time. Moreover, includes updated global digital elevation modeling sources and calibration and validation updates. We worked with images of the coldest months (i. e. from November to February) and images of the warmest months (i.
It is important to identify the high collinearity because this can result in coefficient estimates that are difficult to interpret as independent effects and/or have high SE (see review of  Zuur et al. 2009 ). We excluded variables when the coefficient r was >|0. 8|. Then, we assessed the variance inflation factor (VIFs) for any remaining collinearity on the full models from different sets and excluded variables with VIFs >5, which indicate collinearity between predictors (Heiberger 2022). All statistical analyses were performed using R version 4. 2. 1 (R Core Team 2022). We assessed the VIFs using 'HH' package  (Heiberger 2022). The models were selected with 'MuMIn' package (Barton 2022). The relative importance of predictors were calculated using 'relaimpo' package  (Grömping 2006). The graphs of each best model were performed using 'effects' package  (Fox 2003).
Various data integration methods originally designed for batch correction or cross-condition integration have been rapidly adapted to cross-species analysis  15. fastMNN is based on identifying mutual nearest neighbours (MNNs) cross datasets to correct each dimension in principle component (PC) space  18  ; harmony performs iterative clustering to impute batch correction vectors based on putative cell type centroids in PC space  19  ; LIGER uses integrative non-negative matrix factorization (iNMF) to learn shared and unique gene expression signatures between datasets, while its recent upgrade LIGER UINMF also include unshared features during iNMF to account for species-specific genes  20  ; scanorama is a panorama stitching algorithm that uses cross-dataset nearest neighbours to tackle dataset heterogeneity  21  ; scVI uses hierarchical Bayesian model with conditional distributions specified by deep neural networks to aggregate information across similar cells and genes, and to approximate the distributions that underlie observed expression values, which generates a model removing batch effects and retaining the biological signals  22  ; SeuratV4 integration uses canonical correlation analysis (CCA) or reciprocal PCA (RPCA) to identify anchors between datasets then uses dynamic time warping to align the subspaces  16.
Nevertheless, gene homology relationships can be obscure for species without a wellannotated genome and challenging to map for evolutionally distant species pairs. Recently, a novel method, SAMap, tackles this issue by reciprocally and iteratively update a gene-gene mapping graph and a cell-cell mapping graph to stich atlases between species  23. SAMap can lay much stronger mapping of homologous cell types across distantly species and is capable of discovering gene paralog substitution events. Nevertheless, SAMap is computationally intensive and designed for whole-body analysis, while methods optimized for large-scale integration may be more scalable to multiple datasets and more species. Overall, it remains obscure which strategy for cross-species integration can generate candid results. It is also unclear how the selection of homologous genes and integration algorithms affect the observable relationships between cells.
Hence, we expect well-integrated data to closely embed known one-to-one homologous cell types across species. We assessed the integration from two main aspects: between-species mixing and biology conservation, by computing and comparing 4 batch effect evaluation metrics or 6 biology conservation metrics, respectively (see Methods). Importantly, we developed a new biology conservation metric, Accuracy Loss of Cell type Self-projection (ALCS), to target the most unwanted artefact of cross-species integration. ALCS quantifies the degree of blending between cell types perspecies after integration, thus indicates the tendency of overcorrection of cross-species heterogeneity that may obscures species-specific cell types. We employed the self-projection concept in machine learning implemented in our previous work Single Cell Clustering Assessment Framework (SCCAF) and use the test accuracy of self-projection to indicate how well cell type labels are distinguishable from each other on a given feature representation.
Integrating embryonic development between xenopus and zebrafish Besides integrating data within one organ among adult animals, it is also of great interest to integrate the whole-body embryonic development process. In this scenario, cell types are less distinct and a continuous trajectory exists among cells from different developmental stages. Based on mapped homologous cell types and recorded hours post fertilization (HPF) between a zebrafish and xenopus embryonic development dataset  29, 30  , we examined cross-species mixing and biology conservation of various strategies' integration output. We observed that SAMap significantly outperformed others in this task with exceptional biology conservation largely attribute to a high ALCS (integrated score of SAMap: 0. 85, others: 0. 83-0. 33, Extended Data Fig.
CL is the current standard vocabulary used in both the Human Cell Atlas 33 and the EBI Single Cell Expression Atlas 34 for cell type annotation. Leveraging the hierarchical organization of CL, we developed scOntoMatch, an R package which aligns the granularity of ontology annotations of scRNA-seq datasets to make them comparable across studies (see Methods for details). Applying scOntoMatch to the heart data, we found the layer of cell type annotation granularity that was shared across any given group of species. Prior to integration, cell types were highly distinguishable from each other for all species, indicated by a test accuracy of self-projection over 0. 94 (Fig. 5A ). After integration, scVI gave the highest species mixing (batch score 0. 83-0. 7 for four scenarios, unscaled within each scenario, Fig.
4C ), followed by LIGER (0. 73-0. 63), SeuratV4 CCA (0. 71-0. 66), SeuratV4 RPCA (0. 66-0. 64), harmony (0. 66-0. 42), fastMNN (0. 64-0. 57), LIGER UINMF (0. 62-0. 57) and scanorama (0. 54-0. 49). Methods achieved good biology conservation including SeuratV4 CCA (biology conservation score 0. 99-0. 96 for four scenarios), harmony (0. 98-0. 95), SeuratV4 RPCA (0. 97-0. 95), scVI (0. 96-0. 82), scanorama (0. 95-0. 90) and fastMNN (0. 94-0. 91), while LIGER UINMF (0. 80-0. 08) and LIGER (0. 66-0. 03) experienced strong loss of biology. With the addition of species, the relative performance of different methods stayed stable and batch scores per method did not decrease significantly, suggesting that methods could stay robust when involving more datasets. In contrast, biology conservation scores dropped and cell types became more blurred when distant species were added to the integration (Fig.
Discussion We benchmarked 25 strategies for cross-species single-cell transcriptomics data integration, covering 4 ways to match genes cross-species by homology and 9 integration algorithms. Based on known homologous cell types, we used 4 metrics to evaluate the degree of species mixing and 6 for scoring biology conservation. We also developed a novel metrics, ALCS, to measure how well cell type distinguishability is preserved after cross-species integration. Generally speaking, we found that scVI achieves an overall balance between species mixing and biology conservation, while SeuratV4 methods can perform strong integration even among evolutionarily distant species. We noticed that the tailor-made cross-species integration algorithm, SAMap, can highlight core similarities between homologous cell types that are otherwise less discernible.
By performing annotation transfer on shared cell types cross-species using integrated results, we observed that if complete integration is achieved, annotation transfer can be to a large extent successful even between evolutionarily distant species such as human and zebrafish. Nonetheless, cell types highly similar with each other can still be mislabelled between closely related species e. g. human and mouse. Based on a diversity of cross-species integration scenarios investigated in this study, we provide the following guidelines on choosing the most appropriate algorithm for cross-species integration: for closely-related species, scVI or harmony carries out species mixing while preserving biological heterogeneity. For evolutionarily distant species, SeuratV4 methods can achieve strong species mixing and RPCA is more scalable than CCA for larger datasets.
We anticipate that the computational tools developed in this study will aid future research to obtain the most insightful cross-species integrations by selecting the optimal strategy. We provide BENGAL, a Nextflow pipeline for cross-species scRNA-seq data integration and assessment of integration results. We propose ALCS as a cross-species integration-focused biology conservation metric to quantify the loss of cell type distinguishability. To align the granularity of cell ontology annotation across datasets, we developed an R package scOntoMatch. One key use case is to integrate between several species densely sampled in a clade to reconstruct cell type evolution. Another valuable application is to compare cell types between human and model animals, with an aim of providing molecular guidance to select the most appropriate model for a particular tissue or disease.
Datasets used in this study We downloaded raw count matrices and published annotations of the following datasets: inDrop data from human and mouse pancreas (GSE84133)  27  , snRNA-seq data from human, macaque and pig hippocampus (GSE186538)  28  , heart and aorta tissue from human (https://figshare. com/projects/Tabula_Sapiens/100973) 1 , long-tail macaque (https://db. cngb. org/nhpca/download)  3  and mouse (https://www. ebi. ac. uk/biostudies/arrayexpress/studies/E-MTAB-8810) (only no compound treatment mouse data is used)  42  and inDrops data from zebrafish (GSE112294)  29  and xenopus embryo (GSE113074)  30. We use the published cell type annotation of all these datasets and exclude cells that didn't pass quality control in the original study. For the hippocampus task, Neuroblasts, radial glialike cells and neural intermediate progenitor cell from macaque and pig are collectively annotated as "neuronal progenitor".
In the human data, we collectively annotate CA1 and SUB as "CA1 SUB", CA2, CA3 as "CA 2-3", macrophage, microglia, myeloid cell and T cell as "immune", arterial and ventricle smooth muscle cell as "smooth muscle cell", pericytes and vascular and leptomeningeal cell as "vasculature". For the zebrafish and xenopus embryo task, we used the annotations provided in the SAMap study  23  , as they have manually matched one-to-one homologous cell types using multiple lines of evidence. Transcriptomes used in the BLAST step in SAMap are downloaded from ENSEMBL, except that for the Xenopus in embryo task is downloaded from Xenbase (https://www. xenbase. org/entry/staticxenbase/ftpDatafiles. jsp). Versions are in line with the version at original publication of each dataset: pancreas task: mouse (GRCm38) and human (GRCh38); hippocampus task: human (GRCh38), macaque (Mmul_10), pig (Sscrofa11); heart task: human (GRCh38), monkey preprint (which was not certified by peer review) is the author/funder.
The BENGAL pipeline The BENGAL pipeline is built using Nextflow (v22. 04. 3) DSL2 in java (OpenJDK v11. 0. 9. 1internal). For data integration, python-based scripts were built based on Python (v3. 9. 13), Scanpy (v1. 9. 1), h5py (v3. 7. 0) and anndata (v0. 7. 5). We used python implementation of harmonypy (v0. 0. 5), scanorama (v1. 7. 2), scVI (v0. 15. 0 with pytorch v1. 12. 1 and cudatoolkit v11. 6 to support execution with Nvidia GPU), SAMap (v1. 0. 2). R-based scripts are based on R (v4. 0. 5). We used Seurat (V4. 1. 1), LIGER (v0. 5. 0) and LIGER UINMF (v1. 1. 0) and fastMNN (v1. 12. 3 from package batchelor). For SCCAF analysis, we modified the original SCCAF package and deposed our version at https://github. com/YY-SONG0718/sccaf. For batch metrics and biology conservation metrics calculation, we used scIB (v1.
Translating features cross-species by gene homology We used ENSEMBL multiple species comparison tool (version 106), accessed via biomaRt (v2. 46. 3), to annotate gene homology and translate ENSEMBL gene id cross-species. We chose ENSEMBL to be the primary database for gene homology annotation, since there is manually curated homology, in addition to the automated annotation pipeline, it is a popular choice among previous and current cross-species scRNA-seq studies and it is broadly accessible for biologists from various disciplines. We developed scOntoMatch, an R package which aligns the granularity of ontology annotation of scRNA-seq datasets to make them comparable across studies. Cell type terms can be robustly described by cell ontology (CL), a structured controlled vocabulary for cell types in animals  33.
Cell ontology (CL) is a species-neutral ontology systems that describes the relationships between cell type terms, such as ancestor-descendant, and it covers cell types from prokaryotes to mammals. scOntoMatch performs annotation alignment by matching descendant terms to ancestor terms so that every cell type term is a leaf node in the directed acyclic graph (DAG) created by all cell types and their ancestors from all the input datasets. Taking any number of single cell datasets with ontology labels, the algorithm first trims the cell type tree in each dataset so that there is no ancestor-descendant relationship between cell types within the dataset. The main step is to identify an ontology matching among the datasets, in which direct matching and mapping from descendants to ancestors are performed to find the last common ancestor (LCA) term.
Construct concatenated count matrix cross-species Starting from raw count matrices of scRNA-seq data from different species, the BENGAL pipeline first identifies gene groups composed of one-to-one, one-to-many or many-to-many orthologs and species-specific genes using ENSEMBL multiple species comparison tool. One-to-one orthologs are directly matched across species. On top of that, one-to-many or many-to-many orthologs within each gene group are added and matched by selecting those with higher average expression level calculated using scanpy. pp. calculate_qc_metrics, or with stronger homology confidence defined in ENSEMBL using available attributes among: orthology. confidence, Gene. order. conservation. score, Whole. genome. alignment. coverage, query. gene. identical. to. target, gene. identical.
Batch removal metrics Species mixing is assessed by the relative reduction of batch metrics before and after integration. We compute the principal component regression (PCR) score, batch average silhouette width (ASW) score, graph connectivity (GC) score and batch local inverse Simpson's index (LISI) score using the scIB framework. All metrics are computed on the PCA embedding for methods returning a pseudocount matrix and the output embedding for methods returning an embedding. We did not use kBET, since cross-species batch effect is strong while kBET being highly sensitive. In all the tasks, kBET score per cell type stays highly significant after integration, thus cannot inform the relative performance of different strategies. (1) PCR score: assume that there is batch effect in the dataset, then the heterogeneity caused by batch shall be captured by some principal components (PC) in the PC representation of the dataset.
Regression of the batch covariant will then significantly correlate with some PCs. The sum of all variance contribution by batch from all PCs (Var(C|B) gives an approximation of the level of batch effect in the dataset. PCR score is the scaled Var(C|B) before and after data integration. 𝑉𝑎𝑟(𝐶|𝐵) = + 𝑉𝑎𝑟(𝐶|𝑃𝐶 ! ) × 𝑅 " # !$% (𝑃𝐶 ! |𝐵) 𝑃𝐶𝑅 = 𝑉𝑎𝑟(𝐶|𝐵) &'( -𝑉𝑎𝑟(𝐶|𝐵) &)*+ 𝑉𝑎𝑟(𝐶|𝐵) &'( PCR range from 0 to 1, with 0 means no change of batch contribution and 1 means complete removal of batch contribution after integration. (2) Batch ASW: silhouette measures the relationship between with-in cluster distance of a cell and the between-cluster distance of that cell to the closest cluster. The batch ASW is computed based on the absolute silhouette of batch labels per cell i of cell type j, then averaged across M cell types.