GenomicKB: a knowledge graph for the human genome.

Genomic Knowledgebase (GenomicKB) is a graph database for researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome with simple and efficient queries. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, including 347 million genomic entities, 1.36 billion relations, and 3.9 billion entity and relation properties. GenomicKB is equipped with a web-based query system (https://gkb.dcmb.med.umich.edu/) which allows users to query the knowledge graph with customized graph patterns and specific constraints on entities and relations. Compared with traditional tabular-structured data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities, intuitively connects isolated data matrices, and supports efficient queries for scientific discoveries. GenomicKB transforms complicated analysis among multiple genomic entities and relations into coding-free queries, and facilitates data-driven genomic discoveries in the future.

Association of spirometric restriction with mortality in the silicotics: a cohort study.

BACKGROUND: Restrictive spirometry pattern (RSP), defined as reduced forced vital capacity (FVC) in absence of airflow obstruction (AFO), is associated with increased risk of mortality in general population. However, evidence in the patients with silicosis is limited. This study was aimed to investigate the relationship between RSP and the risk of death in a silicotic cohort. METHOD: This retrospective cohort study used data from the Pneumoconiosis Clinic, Hong Kong Department of Health that containing 4315 patients aged 18-80 years and diagnosed with silicosis during 1981-2019, with a follow-up till 31 December 2019. Spirometry was carried out at the diagnostic examination of silicosis. Lung function categories were classified as normal spirometry (FEV1/FVC ≥ 0.7, FVC ≥ 80% predicted), RSP only (FEV1/FVC ≥ 0.7, FVC < 80% predicted), AFO only (FEV1/FVC < 0.7, FVC ≥ 80% predicted), and RSP&AFO mixed (FEV1/FVC < 0.7, FVC < 80% predicted). The hazard ratio (HR) and 95% confidence intervals (95% CI) were computed using a Cox proportional hazards model adjusting for age, body mass index, history of tuberculosis, smoking status, pack-years, and radiographic characteristics of silicotic nodules. RESULTS: Among the 4315 patients enrolled in the study, the prevalence of RSP was 24.1% (n = 1038), including 11.0% (n = 473) with RSP only and 13.1% (n = 565) with mixed RSP and AFO. During the follow-up period, a total of 2399 (55.6%) deaths were observed. Compared with the silicotics with normal spirometry, those with RSP only had significantly increased risk of all-cause mortality (HR = 1.63, 95% CI 1.44-1.85) and respiratory-related mortality (HR = 1.56, 95% CI 1.31-1.85). Notably, a higher risk of mortality was observed in silicotics with mixed ventilatory defects of both RSP and AFO (all-cause mortality: HR = 2.22, 95% CI 1.95-2.52; respiratory-related mortality: HR = 2.59, 95% CI 2.18-3.07) than in those with RSP only. CONCLUSION: RSP is significantly associated with increased risk of all-cause and respiratory-related mortality in the silicotics, and patients with mixed restrictive and obstructive ventilatory defect have higher risk of mortality than those with single RSP or AFO. These findings emphasize the importance of recognizing RSP in the occupational settings, especially for the silicotic patients with mixed ventilatory defect.

Integration of Metabolomic and Transcriptomic Provides Insights into Anti-Inflammatory Response to trans-10-Hydroxy-2-decenoic Acid on LPS-Stimulated RAW 264.7 Cells.

Trans-10-hydroxy-2-decenoic acid (10-HDA) is a unique fatty acid found in royal jelly that possesses potential health benefits such as anti-inflammatory. However, further research is needed to fully understand its mechanisms of action and therapeutic potential for inflammation-associated diseases. In this present study, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA-seq analyses were conducted to comprehensively analyze the in vitro anti-inflammatory effects of 10-HDA on lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Our results demonstrated that 128 differentially expressed metabolites and 1721 differentially expressed genes were identified in the 10-HDA-treated groups compared to the LPS groups. Metabolites were significantly enriched in amino acid metabolism pathways, including methionine metabolism, glycine and serine metabolism, and tryptophan metabolism. The differentially expressed genes enrichment analysis indicated that antigen processing and presentation, NOD-like receptor signaling pathway, and arginine biosynthesis were enriched with the administration of 10-had. The correlation analysis revealed that glycerophospholipid metabolism and s-adenosylmethionine-dependent methylation processes might be involved in the response to the 10-HDA treatment. Overall, the findings from this study showed that 10-HDA might involve the modulation of certain signaling pathways involved in the inflammatory response, but further research is needed to determine the safety and efficacy as a therapeutic agent.

Knee and hip arthroplasty joint surgical site wound infection in end-stage renal disease subjects who underwent dialysis or a kidney transplant: A meta-analysis.

A meta-analysis study to assess the knee and hip arthroplasty joint surgical site wound infection (SSWI) in end-stage renal disease (ESRD) subjects who underwent dialysis or a kidney transplant (KT). A comprehensive literature examination till February 2023 was implemented and 1046 linked studies were appraised. The picked studies contained 5 471 898 subjects with total joint arthroplasty (TJA) at the baseline, 13 049 of them were haemodialysis or renal transplant, and 5 458 849 were control. Odds ratio (OR) in addition to 95% confidence intervals (CIs) were used to calculate the consequence of knee and hip arthroplasty SSWI in ESRD subjects who underwent dialysis or a KT by the dichotomous and continuous styles and a fixed or random model. Haemodialysis or renal transplant had a significantly higher postoperative SSWI (OR, 2.13; 95% CI, 1.73-2.62, P < .001) compared with control in TJA subjects. However, no significant difference was observed between haemodialysis and renal transplant in postoperative SSWI (OR, 0.93; 95% CI, 0.16-5.54, P = .94) and between haemodialysis or renal transplant and control in prosthetic joint infection (OR, 1.07; 95% CI, 0.25-4.55, P = .93) in TJA subjects. Haemodialysis had a significantly higher prosthetic joint infection (OR, 1.92; 95% CI, 1.21-3.03, P = .005) compared with renal transplant in TJA subjects. Haemodialysis or renal transplant had a significantly higher postoperative SSWI in TJA subjects. Also, haemodialysis had a significantly higher prosthetic joint infection compared with renal transplant in TJA subjects. Although precautions should be taken when commerce with the consequences because a low number of selected studies was picked for certain comparisons in this meta-analysis.

The Clutter Simulation of a Known Terrain by the 3D Parabolic Equation and RCS Computation.

Ground clutter data are usually generated using a statistical model, but they cannot effectively reflect the spatial distribution characteristics of ground objects. It is important in practical projects to effectively predict ground clutter when terrain data are known. In this paper, a scheme of ground clutter simulation based on the three-dimensional parabolic equation (3DPE) model is proposed. Radio wave propagation was modeled by the PE model and the spatial field distribution was solved. After the radar cross section (RCS) calculation based on space cells, the ground clutter information data were obtained. Then the radar echo was obtained and the clutter map was simulated. The simulation experimental results show that the ground clutter map simulated by the proposed method has a good reference value, meets the demand of strong clutter area prediction under known terrain conditions, and provides a theoretical basis for radar location and optimal deployment.

Association of current income and reduction in income during the COVID-19 pandemic with anxiety and depression among non-healthcare workers.

BACKGROUND: Many workers experienced income reduction during the coronavirus disease 2019 (COVID-19) pandemic, which may link to adverse mental health. AIMS: This study aimed to examine the association of current income and reduction in income during COVID-19 with anxiety and depression levels among non-healthcare workers. METHODS: This is a multi-city cross-sectional study. We used standardized questionnaires to collect information. We regrouped the current income and income reduction during COVID-19 according to the tertile and median value of each specific city. Depression, Anxiety and Stress Scales-21 item short version (DASS-21) was used to assess anxiety and depression levels. We performed multinomial logistic regression to examine the association of current and reduced income with anxiety and depression. Path models were developed to outline the potential modification/indirect effect of subsidies from government. RESULTS: Large income reduction and low current income were significantly associated with more anxiety/depression symptoms. Path analysis showed that government subsidies could not significantly alleviate the impact of reduced income on anxiety/depression. CONCLUSION: Our findings showed that large income reduction and low current income were independently associated with anxiety/depression, while these symptoms may not be ameliorated by one-off government funds. This study suggests the need for long-term policies (e.g. developing sustained economic growth policies) to mitigate negative impacts of the COVID-19.

Betaine attenuates LPS-induced downregulation of Occludin and Claudin-1 and restores intestinal barrier function.

BACKGROUND: The intestinal epithelial barrier, which works as the first line of defense between the luminal environment and the host, once destroyed, it will cause serious inflammation or other intestinal diseases. Tight junctions (TJs) play a vital role to maintain the integrity of the epithelial barrier. Lipopolysaccharide (LPS), one of the most important inflammatory factors will downregulate specific TJ proteins including Occludin and Claudin-1 and impair integrity of the epithelial barrier. Betaine has excellent anti-inflammatory activity but whether betaine has any effect on TJ proteins, particularly on LPS-induced dysfunction of epithelial barriers remains unknown. The purpose of this study is to explore the pharmacological effect of betaine on improving intestinal barrier function represented by TJ proteins. Intestinal porcine epithelial cells (IPEC-J2) were used as an in vitro model. RESULTS: The results demonstrated that betaine enhanced the expression of TJ proteins while LPS (1 µg/mL) downregulates the expression of these proteins. Furthermore, betaine attenuates LPS-induced decreases of TJ proteins both shown by Western blot (WB) and Reverse transcription-polymerase chain reaction (RT-PCR). The immunofluorescent images consistently revealed that LPS induced the disruption of TJ protein Claudin-1 and reduced its expression while betaine could reverse these alterations. Similar protective role of betaine on intestinal barrier function was observed by transepithelial electrical resistance (TEER) approach. CONCLUSION: In conclusion, our research demonstrated that betaine attenuated LPS-induced downregulation of Occludin and Claudin-1 and restored the intestinal barrier function.

Quinolactacin Biosynthesis Involves Non-Ribosomal-Peptide-Synthetase-Catalyzed Dieckmann Condensation to Form the Quinolone-γ-lactam Hybrid.

Quinolactacins are novel fungal alkaloids that feature a quinolone-γ-lactam hybrid, which is a potential pharmacophore for the treatment of cancer and Alzheimer's disease. Herein, we report the identification of the quinolactacin A2 biosynthetic gene cluster and elucidate the enzymatic basis for the formation of the quinolone-γ-lactam structure. We reveal an unusual ß-keto acid (N-methyl-2-aminobenzoylacetate) precursor that is derived from the primary metabolite l-kynurenine via methylation, oxidative decarboxylation, and amide hydrolysis reactions. In vitro assays reveal two single-module non-ribosomal peptide synthetases (NRPs) that incorporate the ß-keto acid and l-isoleucine, followed by Dieckmann condensation, to form the quinolone-γ-lactam. Notably, the bioconversion from l-kynurenine to the ß-keto acid is a unique strategy employed by nature to decouple R*-domain-containing NRPS from the polyketide synthase (PKS) machinery, expanding the paradigm for the biosynthesis of quinolone-γ-lactam natural products via Dieckmann condensation.

A Stenohaline Medaka, Oryzias woworae, Increases Expression of Gill Na(+), K(+)-ATPase and Na(+), K(+), 2Cl(-) Cotransporter 1 to Tolerate Osmotic Stress.

The present study aimed to evaluate the osmoregulatory mechanism of Daisy's medaka, O. woworae,as well as demonstrate the major factors affecting the hypo-osmoregulatory characteristics of euryhaline and stenohaline medaka. The medaka phylogenetic tree indicates that Daisy's medaka belongs to the celebensis species group. The salinity tolerance of Daisy's medaka was assessed. Our findings revealed that 20‰ (hypertonic) saltwater (SW) was lethal to Daisy's medaka. However, 62.5% of individuals survived 10‰ (isotonic) SW with pre-acclimation to 5‰ SW for one week. This transfer regime, "Experimental (Exp.) 10‰ SW", was used in the following experiments. After 10‰ SW-transfer, the plasma osmolality of Daisy's medaka significantly increased. The protein abundance and distribution of branchial Na(+), K(+)-ATPase (NKA) and Na(+), K(+), 2Cl(-) cotransporter 1 (NKCC1) were also examined after transfer to 10‰ SW for one week. Gill NKA activity increased significantly after transfer to 10‰ SW. Meanwhile, elevation of gill NKA αα-subunit protein-abundance was found in the 10‰ SW-acclimated fish. In gill cross-sections, more and larger NKA-immunoreactive (NKA-IR) cells were observed in the Exp. 10‰ SW medaka. The relative abundance of branchial NKCC1 protein increased significantly after transfer to 10‰ SW. NKCC1 was distributed in the basolateral membrane of NKA-IR cells of the Exp. 10‰ SW group. Furthermore, a higher abundance of NKCC1 protein was found in the gill homogenates of the euryhaline medaka, O. dancena, than in that of the stenohaline medaka, O. woworae.

Retrospective analysis of 620 cases of brain abscess in Chinese patients in a single center over a 62-year period.

BACKGROUND: Despite advances in laboratory diagnostics, antibiotic regimens, and neurosurgical techniques, brain abscess (BA) remains a potentially fatal infectious disease. This study analyzed clinical and epidemiological aspects of BA in Chinese patients treated at a single center during a 62-year period. METHOD: We retrospectively analyzed 620 BA patients treated at Tianjin Medical University General Hospital, Tianjin, PR China from 1952 to 2014. Because of the initiation of imaging technology use in 1992, and other specific changes, we analyzed data over three study periods: 1952-1972, 1980-1991, and 2002-2014. Information including incidence, sex, age, community distribution, BA size and location, therapeutic method, prognosis and outcome of BA patients was collected and evaluated. RESULTS: Our study included 620 BA patients. The percentage mortality significantly decreased from 22.8 % in 1952 to 6.3 % in 2014 (p < 0.001). Although the incidence of BA was higher in males than females, there was no significant change in the male/female incidence ratio over time: 2.5 in 1952-1972, 2.6 in 1980-1991, and 2.2 in 2002-2014. The cryptogenic infection incidence significantly increased over time (p < 0.001). The number of positive bacterial cultures significantly decreased over the three study periods (p < 0.01). CONCLUSIONS: The prognosis of patients with BA has gradually improved over the past 62 years in Tianjin, China. This may be because improvements in neurosurgical techniques, cranial imaging, and antimicrobial regimens have facilitated less invasive and more precise neurosurgical procedures.

Hierarchical Sparse Learning with Spectral-Spatial Information for Hyperspectral Imagery Denoising.

During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches. All patches can be regarded as consisting of clean image component, Gaussian noise component and sparse noise component. The first term is depicted by a linear combination of dictionary elements, where Gaussian process with Gamma distribution is applied to impose spatial consistency on dictionary. The last two terms are utilized to fully depict the noise characteristics. Furthermore, the sparseness of the model is adaptively manifested through Beta-Bernoulli process. Calculated by Gibbs sampler, the proposed model can directly predict the noise and dictionary without priori information of the degraded HSI. The experimental results on both synthetic and real HSI demonstrate that the proposed approach can better suppress the existing various noises and preserve the structure/spectral-spatial information than the compared state-of-art approaches.

Increasing anti-Aß-induced neurotoxicity ability of Antrodia camphorata-fermented product with deep ocean water supplementary.

BACKGROUND: Antrodia camphorata is proven to probably inhibit the neurotoxicity of amyloid ß-peptide (Aß), known as a risk factor toward the development of Alzheimer's disease. Deep ocean water (DOW), drawn from an ocean depth of more than 200 m, has proven to stimulate the growth and metabolite biosynthesis of fungi owing to its rich minerals and trace elements. Based on these advantages of DOW, this study used statistical response surface methodology (RSM) to investigate the effects of DOW on the growth and anti-Aß-induced neurocytotoxicity ability of A. camphorata. RESULTS: The results showed that DOW was useful for increasing the biomass of A. camphorata and enhancing its neuroprotective capability. The anti-Aß40-induced neurocytotoxicity ability of filtrate was increased via raising the mycelium-secreted components. Furthermore, the anti-Aß40-induced neurocytotoxicity ability of mycelium was also increased by the DOW-stimulated intracellular antioxidants. Using 80% DOW concentration, initial pH 3.3 and 20% inoculum size as the optimal culture conditions of A. camphorata significantly stimulated the biomass and mycelium-mediated Aß40-induced cell viability from 302 ± 14 mg per 100 mL and 49.2 ± 2.2% to 452 ± 33 mg per 100 mL and 65.0 ± 7.4% respectively. CONCLUSION: This study indicated that DOW could be used as a promising supplementary for the production of A. camphorata secondary metabolites with strong antioxidant activity to protect neuron cells from damage based on Aß stimulation cytotoxicity. © 2016 Society of Chemical Industry.

The inner opercular membrane of the euryhaline teleost: a useful surrogate model for comparisons of different characteristics of ionocytes between seawater- and freshwater-acclimated medaka.

The inner opercular membranes of the brackish medaka, Oryzias dancena, have numerous ionocytes, similar to the gill epithelia. By histological observation, this study demonstrated that it is possible to investigate the cellular morphology and function of ionocytes in the opercular membrane. The mitochondria-rich ionocytes in the opercular membranes were traced using rhodamine 123 and a cytochrome c oxidase IV antibody in vital and fixed situations, respectively. To validate different morphologies of seawater (SW)-type and freshwater (FW)-type ionocytes of the opercular membrane of euryhaline brackish medaka, a method of dual observation including immunofluorescence staining and subsequent scanning electron microscopy was used. The apical morphologies of SW- and FW-type ionocytes were hole and flat opening, respectively. In addition, the microvilli were found on the apical surface of the FW-type ionocytes. The SW-type ionocytes exhibited basolateral Na(+), K(+), 2Cl(-) cotransporter and the apical cystic fibrosis transmembrane conductance regulator. In contrast, in the apical region of FW-type ionocytes, Na(+), Cl(-) cotransporter and villin 1-like protein were expressed. In addition, histochemical staining of AgCl precipitation counterstained with a Na(+), K(+)-ATPase α-subunit antibody on the opercular membrane illustrated the role of Cl(-) secretion in the SW-type ionocytes of the brackish medaka. A combination of different observations in this study indicated that the opercular membrane could be a useful surrogate model for histological and functional studies on the epithelial ionocytes of fish gills.

[Pathology, imaging and treatment of rare types of intracranial aneurysms].

The formation mechanisms of rare intracranial aneurysms are various, which lead to various kinds of treatment methods. The present article summarized the pathogenesis, pathologic changes in vascular walls and imaging features of rare intracranial aneurysms including segmental ectasia, aneurysms with dissection, aneurysms with intramural hemorrhage, mycotic aneurysms, aneurysms related to HIV, neoplastic aneurysms and traumatic aneurysms through literature review.

Open-ICL: Open-Set Modulation Classification via Incremental Contrastive Learning.

Open-set modulation classification (OMC) of signals is a challenging task for handling "unknown" modulation types that are not included in the training dataset. This article proposes an incremental contrastive learning method for OMC, called Open-ICL, to accurately identify unknown modulation types of signals. First, a dual-path 1-D network (DONet) with a classification path (CLP) and a contrast path (COP) is designed to learn discriminative signal features cooperatively. In the COP, the deep features of the input signal are compared with the semantic feature centers (SFCs) of known classes calculated from the network, to infer its signal novelty. An unknown signal bank (USB) is defined to store unknown signals, and a novel moving intersection algorithm (MIA) is proposed to dynamically select reliable unknown signals for the USB. The "unknown" instances, together with SFCs, are continuously optimized and updated, facilitating the process of incremental learning. Furthermore, a dynamic adaptive threshold (DAT) strategy is proposed to enable Open-ICL to adaptively learn changing signal distributions. Extensive experiments are performed on two benchmark datasets, and the results demonstrate the effectiveness of Open-ICL for OMC.

Sacrificial hydrogen bonds enhance the performance of covalently crosslinked composite films derived from soy protein isolate and dialdehyde starch.

Soy protein films have the advantage of being eco-friendly and renewable, but their practical applications are hindered by the mechanical properties. The exceptional tensile strength and fracture toughness of natural silk stem from sacrificial hydrogen bonds it contains that effectively dissipates energy. In this study, we draw inspiration from silk's structural principles to create biodegradable films based on soy protein isolate (SPI). Notably, composite films containing sodium lignosulfonate (LS) demonstrate exceptional strain at break (up to 153%) due to the augmentation of reversible hydrogen bonding, contrasted to films with the addition of solely dialdehyde starch (DAS). The enhancement of tensile strength is realized through a combination of Schiff base cross-linking and sacrificial hydrogen bonding. Furthermore, the incorporation of LS markedly improves the films' ultraviolet (UV) blocking capabilities and hydrophobicity. This innovative design strategy holds great promise for advancing the production of eco-friendly SPI-based films that combine strength and toughness.

Learning Cross-Domain Features With Dual-Path Signal Transformer.

The past decade has witnessed the rapid development of deep neural networks (DNNs) for automatic modulation classification (AMC). However, most of the available works learn signal features from only a single domain via DNNs, which is not reliable enough to work in uncertain and complex electromagnetic environments. In this brief, a new cross-domain signal transformer (CDSiT) is proposed for AMC, to explore the latent association between different domains of signals. By constructing a signal fusion bottleneck (SFB), CDSiT can implicitly fuse and classify signal features with complementary structures in different domains. Extensive experiments are performed on RadioML2016.10A and RadioML2018.01A, and the results show that CDSiT outperforms its counterparts, particularly for some modulation modes that are difficult to classify before. Through ablation experiences, we also verify the effectiveness of each module in CDSiT.

A Spatial-Spectral Relation-Guided Fusion Network for Multisource Optical RS Image Classification.

Multisource optical remote sensing (RS) image classification has obtained extensive research interest with demonstrated superiority. Existing approaches mainly improve classification performance by exploiting complementary information from multisource data. However, these approaches are insufficient in effectively extracting data features and utilizing correlations of multisource optical RS images. For this purpose, this article proposes a generalized spatial-spectral relation-guided fusion network ( S2 RGF-Net) for multisource optical RS image classification. First, we elaborate on spatial-and spectral-domain-specific feature encoders based on data characteristics to explore the rich feature information of optical RS data deeply. Subsequently, two relation-guided fusion strategies are proposed at the dual-level (intradomain and interdomain) to integrate multisource image information effectively. In the intradomain feature fusion, an adaptive de-redundancy fusion module (ADRF) is introduced to eliminate redundancy so that the spatial and spectral features are complete and compact, respectively. In interdomain feature fusion, we construct a spatial-spectral joint attention module (SSJA) based on interdomain relationships to sufficiently enhance the complementary features, so as to facilitate later fusion. Experiments on various multisource optical RS datasets demonstrate that S2 RGF-Net outperforms other state-of-the-art (SOTA) methods.

CoT: Contourlet Transformer for Hierarchical Semantic Segmentation.

The Transformer-convolutional neural network (CNN) hybrid learning approach is gaining traction for balancing deep and shallow image features for hierarchical semantic segmentation. However, they are still confronted with a contradiction between comprehensive semantic understanding and meticulous detail extraction. To solve this problem, this article proposes a novel Transformer-CNN hybrid hierarchical network, dubbed contourlet transformer (CoT). In the CoT framework, the semantic representation process of the Transformer is unavoidably peppered with sparsely distributed points that, while not desired, demand finer detail. Therefore, we design a deep detail representation (DDR) structure to investigate their fine-grained features. First, through contourlet transform (CT), we distill the high-frequency directional components from the raw image, yielding localized features that accommodate the inductive bias of CNN. Second, a CNN deep sparse learning (DSL) module takes them as input to represent the underlying detailed features. This memory-and energy-efficient learning method can keep the same sparse pattern between input and output. Finally, the decoder hierarchically fuses the detailed features with the semantic features via an image reconstruction-like fashion. Experiments demonstrate that CoT achieves competitive performance on three benchmark datasets: PASCAL Context 57.21% mean intersection over union (mIoU), ADE20K (54.16% mIoU), and Cityscapes (84.23% mIoU). Furthermore, we conducted robustness studies to validate its resistance against various sorts of corruption. Our code is available at: https://github.com/yilinshao/CoT-Contourlet-Transformer.

Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey.

Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation. Third, the theory of multiscale deep learning is presented, which mainly discusses the multiscale modeling in convolutional neural networks (CNNs) and Vision Transformers (ViTs). Fourth, we compare the performance of multiple multiscale methods on different tasks, illustrating the effectiveness of different multiscale structural designs. Finally, based on the in-depth understanding of the existing methods, we point out several open issues and future directions for multiscale deep learning.