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Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias. [ABSTRACT FROM AUTHOR]

Measuring the dynamics of vibrating cables in situ can be challenging using traditional contact-based sensors (e.g., accelerometer). This work proposes a novel computer vision–based technique using a portable unmanned aerial system (UAS) (also referred to as drones) sensing platform to enable the measurement of the dynamic displacements of a cable in a unique blast-loading experiment where other types of sensing may be impractical. The proposed technique utilizes one optical camera equipped on a UAS to record the dynamic displacement of the blast-loaded cable and measure the drift of the UAS as it hovers. Artificial targets attached to the cable are not required because the technique proves effective when tracking natural features inherent in the cable. This makes the proposed technique versatile and convenient for field implementation. This technique also allows simultaneous displacement measurement at multiple points along the cable across the entire field of view of the camera. The natural frequency, damping, and mode shape are successfully identified from the measured cable displacement time histories. Measuring the vibration of a cable is challenging with traditional techniques. A novel technique is proposed to use only one camera attached to a drone to measure the vibration of a cable directly. However, because the drone hovers in the air, there is inherent drift of the drone. The drone's drift is measured with respect to a stationary target using the camera and compensated for to measure the true motion of the cable. To test the proposed technique, a blast was detonated close to the cable to produce vibration in the cable, and the drone hovered above the cable to record its movements. Because the actual movement of the cable is unknown, finite-element simulations were performed to provide a basis for validating the proposed technique. The natural frequency of the finite-element model is compared with that obtained from the measured vibration of the cable. A good agreement was achieved with a 2.1% difference between the simulated and measured natural frequencies. [ABSTRACT FROM AUTHOR]

Lysosomal dysfunction can drive carcinogenesis. Lysosomal‐associated membrane protein 3 (LAMP3), is a member of the Lysosome Associated Membrane Proteins and is involved in the malignant phenotype such as tumour metastasis and drug resistance, while the mechanisms that regulate the malignant progression of tumour remain vague. Our study aims to provide a more systematic and comprehensive understanding of the role of LAMP3 in the progression of various cancers by various databases.We explored the role of LAMP3 in pan‐cancer using The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) database. Multiple online web platforms and software were used for data analysis, including HPA, TIMER, TISIDB, GEPIA, UALCAN, Kaplan–Meier plotter, DAVID and TIGER. The immunohistochemistry was used to quantify the LAMP3 and PD‐L1 expression levels in cancer.High LAMP3 expression was found in most cancers and differentially expressed across molecular and immune subtypes. The expression of LAMP3 was involved in the immune‐associated processes of Antigen processing and presentation, Th17 cell differentiation, Th1 and Th2 cell differentiation, and the immune‐associated pathways of T cell receptor and B cell receptor signalling pathways in most cancers. It also correlated with genetic markers of immunomodulators in various cancers. LAMP3 and PD‐L1 expression in BRCA and HNSC tissues was higher than that in corresponding adjacent normal tissues by immunohistochemistry. There is a significant correlation between the expression of LAMP3 and PD‐L1.Our study elucidates that LAMP3 has different expression patterns and genetic alteration patterns in different tumours. It is a potential biomarker for immune‐related cancer diagnosis, prognosis and efficacy prediction. [ABSTRACT FROM AUTHOR]