Active Open-Vocabulary Recognition: Let Intelligent Moving Mitigate CLIP Limitations

Active recognition, which allows intelligent agents to explore observations for better recognition performance, serves as a prerequisite for various embodied AI tasks, such as grasping, navigation and room arrangements. Given the evolving environment and the multitude of object classes, it is impractical to include all possible classes during the training stage. In this paper, we aim at advancing active open-vocabulary recognition, empowering embodied agents to actively perceive and classify arbitrary objects. However, directly adopting recent open-vocabulary classification models, like Contrastive Language Image Pretraining (CLIP), poses its unique challenges. Specifically, we observe that CLIP's performance is heavily affected by the viewpoint and occlusions, compromising its reliability in unconstrained embodied perception scenarios. Further, the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues, we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features, without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset, the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition, without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method

TNFα induces Ca2+ influx to accelerate extrinsic apoptosis in hepatocellular carcinoma cells

BACKGROUND: Tumor necrosis factor-α has been proven an effective anticancer agent in preclinical studies. However, the translation of TNFα from research to clinic has been blocked by significant systemic toxicity and limited efficacy at maximal tolerated dose, which need urgently to be solved. METHODS: The level of cytosolic Ca RESULTS: Here, we demonstrated that TNFα induced extracellular Ca CONCLUSIONS: Our study provides the evidence supporting a novel mechanism by which TNFα induces extracellular C

Predictive value of alpha-fetoprotein in the long-term risk of developing hepatocellular carcinoma in patients with hepatitis B virus infection--results from a clinic-based longitudinal cohort.

BACKGROUND: Although serum level of alpha-fetoprotein (AFP) has long been used to complement imaging tests in the screening and diagnosis of hepatocellular carcinoma (HCC), whether it can be used as a predictive marker of long-term risk for developing HCC in patients with hepatitis B virus (HBV) has not been extensively evaluated and thus remains controversial. METHODS: We retrospectively conducted a clinic-based longitudinal cohort study including 617 Korean American patients with HBV who had been followed for up to 22 years (median follow-up time, 6.2 years) to evaluate the association between baseline serum AFP level and the long-term risk of HCC. RESULTS: The median baseline AFP value of these patients was 3.8 ng/ml. Compared to patients with lower-than-median AFP value, those with higher-than-median baseline serum AFP had a significantly increased risk of developing HCC with a hazard ratio (HR) of 2.73 (95% confidence interval [CI] 1.25-5.99), independent of other major HCC risk factors. In addition, we calculated the cumulative incidence of HCC during different years of follow-up time by baseline serum AFP, and found that the cumulative incidence of HCC was significantly higher in HBV patients with high baseline serum AFP compared to those with low baseline serum AFP in each of the five follow-up time periods examined. CONCLUSIONS: Our results indicated that AFP was a strong independent prospective predictor of long-term HCC risk in high-risk HBV patients. More targeted prevention and early detection of HCC may be considered for these patients

Neuroglobin-overexpression reduces traumatic brain lesion size in mice

Background: Accumulating evidence has demonstrated that over-expression of Neuroglobin (Ngb) is neuroprotective against hypoxic/ischemic brain injuries. In this study we tested the neuroprotective effects of Ngb over-expression against traumatic brain injury (TBI) in mice. Results: Both Ngb over-expression transgenic (Ngb-Tg) and wild-type (WT) control mice were subjected to TBI induced by a controlled cortical impact (CCI) device. TBI significantly increased Ngb expression in the brains of both WT and Ngb-Tg mice, but Ngb-Tg mice had significantly higher Ngb protein levels at the pre-injury baseline and post-TBI. Production of oxidative tissue damage biomarker 3NT in the brain was significantly reduced in Ngb-Tg mice compared to WT controls at 6 hours after TBI. The traumatic brain lesion volume was significantly reduced in Ngb Tg mice compared to WT mice at 3 weeks after TBI; however, there were no significant differences in the recovery of sensorimotor and spatial memory functional deficits between Ngb-Tg and WT control mice for up to 3 weeks after TBI. Conclusion: Ngb over-expression reduced traumatic lesion volume, which might partially be achieved by decreasing oxidative stress

Characteristics of primary rat microglia isolated from mixed cultures using two different methods

Background: Microglial cultures comprise a critically important model system for investigating inflammatory mechanisms in almost all CNS disorders. Mild trypsinization and shaking are the two most commonly used methods to isolate primary microglia from mixed glial cultures. In this study, we characterized and compared microglia obtained using these two methods. Methods: Primary rat microglia cultures were prepared from cerebral cortices of 1–2-day-old neonatal Sprague-Dawley rats. After achieving confluency at about 14 days in vitro, microglia were isolated from mixed glial cultures via either mild trypsinization or shaking. The purity of microglia was estimated by flow cytometry. Quantitative real-time PCR was used to measure mRNA expression. TNFα, IL-1β, IL-10, and IGF-1 in cell culture supernatant were measured using ELISA kits. Phagocytic function was assessed using fluorescein-labeled Escherichia coli K-12 BioParticles. Results: Mild trypsinization generated a higher yield and purity than shaking. Microglia isolated by mild trypsinization appeared to be in a quiescent state with ramified morphology. Microglia isolated by shaking showed a more heterogenous morphology, including cells with rounded shapes suggestive of activation. Compared with shaking, microglia isolated by trypsinization also had lower baseline phenotype markers (iNOS, CD86, CD206, and arginase 1) and lower levels of cytokines (TNFα, IL-1β, IL-10, and IGF-1) as well as reduced phagocytic capability. Both methods yielded microglia that were responsive to various stimuli such as IL-4, lipopolysaccharide (LPS), or interferon-γ (IFNγ). Although stimulated patterns of gene expression and cytokine release were generally similar, there were also significant differences in terms of absolute response. LPS treatment induced significantly higher levels of TNFα and IL-10 in microglia isolated by mild trypsinization versus shaking. IFNγ induced a lower response in TNFα in microglia obtained by mild trypsinization versus shaking. Conclusions: Our results suggest that isolating microglia with the shaking method may induce slight activation even at baseline, and this may affect stimulus responses in subsequent experiments. Caution and attention should be warranted when choosing isolation protocols for primary microglia cultures

Relative telomere length: a novel non-invasive biomarker for the risk of non-cirrhotic hepatocellular carcinoma in patients with chronic hepatitis B infection.

BACKGROUND AND AIMS: Telomere length has emerged as a promising risk predictor of various cancers including hepatocellular carcinoma (HCC). However, the majority of studies in this area measured telomere length in hepatocytes and one in lymphocytes with conflicting results. Moreover, no studies have been reported on using circulating DNA telomere length as a non-invasive HCC biomarker. METHODS: We conducted a nested case-control study to determine the relative telomere length (RTL) in serum DNA from 140 hepatitis B virus (HBV)-related HCC cases and 280 frequency-matched cancer-free HBV controls. RESULTS: Cases had a significantly longer RTL (median, 0.31; range, 0.02-2.31) than controls (median, 0.20; range, 0.01-1.60) (P = 0.003). Consistently, longer RTLs conferred a significantly increased HCC risk compared to short RTLs in a univariate logistic regression analysis (odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.02-2.33, P = 0.038). This association attenuated after multivariate adjustment (OR = 1.40, 95% CI = 0.90-2.19, P = 0.132). In a quartile analysis, a significant dose-response relationship was noted in univariate analysis (P(trend) = 0.017) which was again attenuated in multivariate analysis (P(trend) = 0.079). Further analyses revealed that the significant association between serum RTL and HCC risk was evident in non-cirrhotic (OR = 3.54, 95% CI 1.58-7.93 P = 0.002), but not cirrhotic (OR = 0.95, 95% CI 0.55-1.64, P = 0.860) HBV patients. Moreover, the significantly increased HCC risk conferred by cirrhosis was modulated by RTL with a significant interaction effect (P(interaction) = 0.013). CONCLUSIONS: RTL in circulating cell-free serum DNA could potentially be used as a novel non-invasive biomarker for non-cirrhotic HCC. Prospective cohort studies are warranted to validate this finding and assess its clinical significance in HCC prevention

Logistic regression analysis of clinical and computed tomography features of pulmonary abscesses and risk factors for pulmonary abscess-related empyema

OBJECTIVES: This study was conducted to investigate the risk factors for pulmonary abscess-related empyema by investigating the clinical characteristics and chest computed tomography imaging features of patients with pulmonary abscesses. METHODS: We retrospectively analyzed the chest computed tomography findings and clinical features of 101 cases of pulmonary abscess, including 25 cases with empyema (the experimental group) and 76 cases with no empyema (the control group). The potential risk factors for pulmonary abscess-related empyema were compared between the groups by using univariate and multivariate logistic regression analyses. RESULTS: The incidence of pulmonary abscess-related empyema was 24.8% (25/101). Univariate analysis showed that male gender, diabetes, pleuritic symptoms, white blood cells 410 109 /L, albumin level o25 g/L, and positive sputum cultures were potential clinical-related risk factors and that an abscess 45 cm in diameter and transpulmonary fissure abscesses were potential computed tomography imaging-related risk factors for pulmonary abscess-related empyema. Multivariate logistic regression analysis showed that transpulmonary fissure abscesses (odds ratio=9.102, p=0.003), diabetes (odds ratio=9.066, p=0.003), an abscess 45 cm in diameter (odds ratio=8.998, p=0.002), and pleuritic symptoms (odds ratio=5.395, p=0.015) were independent risk factors for pulmonary abscess-related empyema. CONCLUSIONS: Transpulmonary fissure abscesses, diabetes, giant pulmonary abscesses, and pleuritic symptoms increased the risk of empyema among patients with pulmonary abscesses

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features