Robust visual SLAM algorithm based on target detection and clustering in dynamic scenarios.

We propose a visual Simultaneous Localization and Mapping (SLAM) algorithm that integrates target detection and clustering techniques in dynamic scenarios to address the vulnerability of traditional SLAM algorithms to moving targets. The proposed algorithm integrates the target detection module into the front end of the SLAM and identifies dynamic objects within the visual range by improving the YOLOv5. Feature points associated with the dynamic objects are disregarded, and only those that correspond to static targets are utilized for frame-to-frame matching. This approach effectively addresses the camera pose estimation in dynamic environments, enhances system positioning accuracy, and optimizes the visual SLAM performance. Experiments on the TUM public dataset and comparison with the traditional ORB-SLAM3 algorithm and DS-SLAM algorithm validate that the proposed visual SLAM algorithm demonstrates an average improvement of 85.70 and 30.92% in positioning accuracy in highly dynamic scenarios. In comparison to the DynaSLAM system using MASK-RCNN, our system exhibits superior real-time performance while maintaining a comparable ATE index. These results highlight that our pro-posed SLAM algorithm effectively reduces pose estimation errors, enhances positioning accuracy, and showcases enhanced robustness compared to conventional visual SLAM algorithms.

Establishment of a corneal ulcer prognostic model based on machine learning.

Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model's performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63-1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63-0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92-1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94-1.00) for visual impairment.

The Function of Probiotics and Prebiotics on Canine Intestinal Health and Their Evaluation Criteria.

Maintaining homeostasis within the intestinal microbiota is imperative for assessing the health status of hosts, and dysbiosis within the intestinal microbiota is closely associated with canine intestinal diseases. In recent decades, the modulation of canine intestinal health through probiotics and prebiotics has emerged as a prominent area of investigation. Evidence indicates that probiotics and prebiotics play pivotal roles in regulating intestinal health by modulating the intestinal microbiota, fortifying the epithelial barrier, and enhancing intestinal immunity. This review consolidates literature on using probiotics and prebiotics for regulating microbiota homeostasis in canines, thereby furnishing references for prospective studies and formulating evaluation criteria.

Streamlined whole-genome genotyping through NGS-enhanced thermal asymmetric interlaced (TAIL)-PCR.

Whole-genome genotyping (WGG) stands as a pivotal element in genomic-assisted plant breeding. Nevertheless, sequencing-based approaches for WGG continue to be costly, primarily owing to the high expenses associated with library preparation and the laborious protocol. During prior development of foreground and background integrated genotyping by sequencing (FBI-seq), we discovered that any sequence-specific primer (SP) inherently possesses the capability to amplify a massive array of stable and reproducible non-specific PCR products across the genome. Here, we further improved FBI-seq by replacing the adapter ligated by Tn5 transposase with an arbitrary degenerate (AD) primer. The protocol for the enhanced FBI-seq unexpectedly mirrors a simplified thermal asymmetric interlaced (TAIL)-PCR, a technique that is widely used for isolation of flanking sequences. However, the improved TAIL-PCR maximizes the primer-template mismatched annealing capabilities of both SP and AD primers. In addition, leveraging of next-generation sequencing enhances the ability of this technique to assay tens of thousands of genome-wide loci for any species. This cost-effective, user-friendly, and powerful WGG tool, which we have named TAIL-PCR by sequencing (TAIL-peq), holds great potential for widespread application in breeding programs, thereby facilitating genome-assisted crop improvement.

The impact of chitooligosaccharides with a certain degree of polymerization on diabetic nephropathic mice and high glucose-damaged HK-2 cells.

Diabetic nephropathy (DN) is a primary diabetic complication ascribed to the pathological changes in renal microvessels. This study investigated the nuclear factor erythroid 2-related factor 2 (Nrf2)/Kelch ECH associating protein (Keap1)/antioxidant response element (ARE) signaling pathway impact of chitooligosaccharides (COS) with a certain degree of polymerization (DP) on DN mouse models and high glucose-damaged human kidney 2 (HK-2) cells. The findings indicated that COS effectively reduced the renal function indexes (uric acid [UA], urinary albumin excretion rate [UAER], urine albumin-to-creatinine ratio [UACR], blood urea nitrogen [BUN], and creatinine [Cre]) of DN mice. It increased (p < .05) the superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) antioxidant enzyme activity in the serum and kidneys, and decreased (p < .05) the malondialdehyde (MDA) content. The mechanistic investigation showed that COS significantly increased (p < .05) Nrf2 and downstream target gene (GCLM, GCLC, HO-1, and NQO-1) expression, and substantially decreased (p < .05) Keap1 expression. The protein level was consistent with the messenger RNA (mRNA) level in in vitro and in vivo models. The docking data indicated that COS and Keap1 protein binding included six hydrogen bond formation processes (Gly364, Arg415, Arg483, His436, Ser431, and Arg380). The COS intervention mechanism may be related to the Nrf2/Keap1/ARE antioxidant pathway. Therefore, it provides a scientific basis for COS application in developing special medical food for DN patients.

Application of AI-assisted MRI for the identification of surgical target areas in pediatric hip and periarticular infections.

OBJECTIVE: To develop an AI-assisted MRI model to identify surgical target areas in pediatric hip and periarticular infections. METHODS: A retrospective study was conducted on the pediatric patients with hip and periarticular infections who underwent Magnetic Resonance Imaging(MRI)examinations from January 2010 to January 2023 in three hospitals in China. A total of 7970 axial Short Tau Inversion Recovery (STIR) images were selected, and the corresponding regions of osteomyelitis (label 1) and abscess (label 2) were labeled using the Labelme software. The images were randomly divided into training group, validation group, and test group at a ratio of 7:2:1. A Mask R-CNN model was constructed and optimized, and the performance of identifying label 1 and label 2 was evaluated using receiver operating characteristic (ROC) curves. Calculation of the average time it took for the model and specialists to process an image in the test group. Comparison of the accuracy of the model in the interpretation of MRI images with four orthopaedic surgeons, with statistical significance set at P < 0.05. RESULTS: A total of 275 patients were enrolled, comprising 197 males and 78 females, with an average age of 7.10 ± 3.59 years, ranging from 0.00 to 14.00 years. The area under curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score for the model to identify label 1 were 0.810, 0.976, 0.995, 0.969, 0.922, and 0.957, respectively. The AUC, accuracy, sensitivity, specificity, precision, and F1 score for the model to identify label 2 were 0.890, 0.957, 0.969, 0.915, 0.976, and 0.972, respectively. The model demonstrated a significant speed advantage, taking only 0.2 s to process an image compared to average 10 s required by the specialists. The model identified osteomyelitis with an accuracy of 0.976 and abscess with an accuracy of 0.957, both statistically better than the four orthopaedic surgeons, P < 0.05. CONCLUSION: The Mask R-CNN model is reliable for identifying surgical target areas in pediatric hip and periarticular infections, offering a more convenient and rapid option. It can assist unexperienced physicians in pre-treatment assessments, reducing the risk of missed and misdiagnosis.

Revealing potential Rab proteins participate in regulation of secretory autophagy machinery.

Autophagy can be classified as degradative and secretory based on distinct functions. The small GTPase proteins Rab8a and Rab37 are responsible for secretory autophagy-mediated exocytosis of IL-1ß, insulin, and TIMP1 (tissue inhibitor of 54 metalloproteinase 1). Other Rab family members participating in secretory autophagy are poorly understood. Herein, we identified 26 overlapped Rab proteins in purified autophagosomes of mouse pancreatic ß-cell "Min-6" and human lung cancer cell "CL1-5-Q89L" with high secretory autophagy tendency by LC-MS/MS proteomics analysis. Six Rab proteins (Rab8a, Rab11b, Rab27a, Rab35, Rab37, and Rab7a) were detected in autophagosomes of four cell lines, associating them with autophagy-related vesicle trafficking. We used CL1-5-Q89L cell line model to evaluate the levels of Rab proteins colocalization with autophagy LC3 proteins and presence in purified autophagosomes. We found five Rab proteins (Rab8a, Rab11b, Rab27a, Rab35, and Rab37) are highly expressed in the autophagosome compared to the normal control by immunoblotting under active secretion conditions. However, only Rab8a, Rab35, and Rab37 showing high colocalization with LC3 protein by cofocal microscopy. Despite the discrepancy between the image and immunoblotting analysis, our data sustains the speculation that Rab8a, Rab11b, Rab27a, Rab35, and Rab37 are possibly associated with the secretory autophagy machinery. In contrast, Rab7a shows low colocalization with LC3 puncta and low level in the autophagosome, suggesting it regulates different vesicle trafficking machineries. Our findings open a new direction toward exploring the role of Rab proteins in secretory autophagy-related cargo exocytosis and identifying the cargoes and effectors regulated by specific Rab proteins.

Risk prediction models of depression in older adults with chronic diseases.

BACKGROUND: Detecting potential depression and identifying the critical predictors of depression among older adults with chronic diseases are essential for timely intervention and management of depression. Therefore, risk prediction models (RPMs) of depression in elderly people should be further explored. METHODS: A total of 3959 respondents aged 60 years or over from the wave four survey of the China Health and Retired Longitudinal Study (CHARLS) were included in this study. We used five machine learning (ML) algorithms and three data balancing techniques to construct RPMs of depression and calculated feature importance scores to determine which features are essential to depression. RESULTS: The prevalence of depression was 19.2 % among older Chinese adults with chronic diseases in the wave four survey. The random forest (RF) model was more accurate than the other models after balancing the data using the Synthetic Minority Oversampling Technique (SMOTE) algorithm, with an area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) of 0.957 and 0.920, respectively, a balanced accuracy of 0.891 and a sensitivity of 0.875. Furthermore, we further identified several important predictors between male and female patients via constructed sex-stratified models. LIMITATIONS: Further research on the clinical impact studies of our models and external validation are needed. CONCLUSIONS: After several techniques were used to address class imbalance issues, most RPMs achieved satisfactory accuracy in predicting depression among elderly people with chronic diseases. RPMs may thus become valuable screening tools for both older individuals and healthcare practitioners to assess the risk of depression.

Protective effect of chicken yolk antibody Y against Campylobacter jejuni induced diarrhea in cats.

Campylobacter jejuni (C. jejuni) is a common pathogen that often causes diarrhea, loss of appetite, and even enteritis in domestic cats, affecting their growth and development, especially in kittens under 6 months of age. Oral passive immunization with chicken yolk antibody Y has been proved effective for the treatment of gastrointestinal pathogen infections due to its high specificity. In this study, C. jejuni was isolated from diarrheal cat feces, and the specific egg yolk antibody Y against C. jejuni was demonstrated to effectively inhibit its proliferation in vitro experiments. To evaluate the effect of anti-C. jejuni IgY, the mouse C. jejuni infection model was established and it was found that IgY could alleviate C. jejuni-induced clinical symptoms. Consistent with these results, the reduction of pro-inflammatory factors and intestinal colonization by C. jejuni in the IgY-treated groups, especially in the high dose group. We then evaluated the protective effect of IgY on young Ragdoll cats infected with C. jejuni. This specific antibody reduced the rate of feline diarrhea, protected the growth of young cats, inhibited systemic inflammatory hyperactivation, and increased fecal short-chain fatty acid concentrations. Notably, IgY may have a protective role by changing intestinal amino acid metabolism and affecting C. jejuni chemotaxis. Collectively, specific IgY is a promising therapeutic strategy for C. jejuni-induced cat diarrhea.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs.

BACKGROUND: Enhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking. RESULTS: Here, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model. CONCLUSIONS: Our work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

Excess Salt Intake Activates IL-21-Dominant Autoimmune Diabetogenesis via a Salt-Regulated Ste20-Related Proline/Alanine-Rich Kinase in CD4 T Cells.

The fundamental mechanisms by which a diet affects susceptibility to or modifies autoimmune diseases are poorly understood. Excess dietary salt intake acts as a risk factor for autoimmune diseases; however, little information exists on the impact of salt intake on type 1 diabetes. To elucidate the potential effect of high salt intake on autoimmune diabetes, nonobese diabetic (NOD) mice were fed a high-salt diet (HSD) or a normal-salt diet (NSD) from 6 to 12 weeks of age and monitored for diabetes development. Our results revealed that the HSD accelerated diabetes progression with more severe insulitis in NOD mice in a CD4+ T-cell-autonomous manner when compared with the NSD group. Moreover, expression of IL-21 and SPAK in splenic CD4+ T cells from HSD-fed mice was significantly upregulated. Accordingly, we generated T-cell-specific SPAK knockout (CKO) NOD mice and demonstrated that SPAK deficiency in T cells significantly attenuated diabetes development in NOD mice by downregulating IL-21 expression in CD4+ T cells. Furthermore, HSD-triggered diabetes acceleration was abolished in HSD-fed SPAK CKO mice when compared with HSD-fed NOD mice, suggesting an essential role of SPAK in salt-exacerbated T-cell pathogenicity. Finally, pharmacological inhibition of SPAK activity using a specific SPAK inhibitor (closantel) in NOD mice ameliorated diabetogenesis, further illuminating the potential of a SPAK-targeting immunotherapeutic approach for autoimmune diabetes. Here, we illustrate that a substantial association between salt sensitivity and the functional impact of SPAK on T-cell pathogenicity is a central player linking high-salt-intake influences to immunopathophysiology of diabetogenesis in NOD mice.