Capillary Electrophoretic Characterization of Carbon Nanodots Prepared from Glutamic Acid in an Electric Furnace

Carbon nanodots (CNDs) prepared from glutamic acid or glutathione in an electric furnace were characterized by capillary electrophoresis. Two major peaks were detected in the electropherograms by capillary zone electrophoresis, corresponding to anionic and less-charged CNDs. The effective electrophoretic mobility of the anionic CND formed from glutamic acid was almost identical over neutral to weakly alkaline pH range, and the CND would not contain significant amount of amino group. On the other hand, the effective electrophoretic mobility tended to decrease with decreasing pH at weakly acidic pH conditions, suggesting the functional groups of carboxylate moiety on the anionic CNDs. Dodecyl sulfate ion was added in the separation buffer to give anionic charge to the less-charged CND by adsorption. However, the anionic charge induced was little, and the dodecyl sulfate ion was not likely adsorbed on the less-charged CND and the CND would be hydrophilic

Simultaneous Wide-field Imaging of Phase and Magnitude of AC Magnetic Signal Using Diamond Quantum Magnetometry

Spectroscopic analysis of AC magnetic signal using diamond quantum magnetometry is a promising technique for inductive imaging. Conventional dynamic decoupling like XY8 provides a high sensitivity of an oscillating magnetic signal with intricate dependence on magnitude and phase, complicating high throughput detection of each parameter. In this study, a simple measurement scheme for independent and simultaneous detection of magnitude and phase is demonstrated by a sequential measurement protocol. Wide-field imaging experiment was performed for an oscillating magnetic field with approximately 100$\mu$m-squared observation area. Single pixel phase precision was $2.1^\circ$ for 0.76$\mu$T AC magnetic signal. Our method enables potential applications including inductive inspection and impedance imaging.Comment: 9 pages, 4 figure

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Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy

With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease locations and superior classification performance. One drawback of these detection systems is dealing with unannotated healthy data with no real symptoms present. In practice, healthy plant data appear to be very similar to many disease data. Thus, those models often produce mis-detected boxes on healthy images. In addition, labeling new data for detection models is typically time-consuming. Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples. However, blindly selecting an arbitrary amount of hard-sample for re-training will result in the degradation of diagnostic performance for other diseases due to the high similarity between disease and healthy data. In this paper, we propose a simple but effective training strategy called hard-sample re-mining (HSReM), which is designed to enhance the diagnostic performance of healthy data and simultaneously improve the performance of disease data by strategically selecting hard-sample training images at an appropriate level. Experiments based on two practical in-field eight-class cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our HSReM training strategy leads to a substantial improvement in the overall diagnostic performance on large-scale unseen data. Specifically, the object detection model trained using the HSReM strategy not only achieved superior results as compared to the classification-based state-of-the-art EfficientNetV2-Large model and the original object detection model, but also outperformed the model using the HSM strategy

Staging the tumor and staging the host: A two centre, two country comparison of systemic inflammatory responses of patients undergoing resection of primary operable colorectal cancer

Background: How systemic inflammation-based prognostic scores such as the modified Glasgow Prognostic Score (mGPS) and neutrophil:lymphocyte ratio (NLR) differ across populations of patients with colorectal cancer (CRC) remains unknown. The present study examined the mGPS and NLR in patients from United Kingdom (UK) and Japan. Methods: Patients undergoing resection of TNM I-III CRC in two centres in the UK and Japan were included. Differences in clinicopathological characteristics and mGPS (0-CRP≤10 mg/L, 1-CRP>10 mg/L, 2-CRP>10 mg/L, albumin<35 g/L) and NLR (≤5/>5) were examined. Results: Patients from UK (n = 581) were more likely to be female, high ASA and BMI, present as an emergency (all P < 0.01) and have higher T stage compared to those from Japan (n = 559). After controlling for differences in tumor and host characteristics, patients from Japan were less likely to be systemically inflamed (OR: mGPS: 0.37, 95%CI 0.27–0.50, P < 0.001; NLR: 0.53, 95%CI 0.35–0.79, P = 0.002). Conclusion: Systemic inflammatory responses differ between populations with colorectal cancer. Given their prognostic value, reporting of systemic inflammation-based scores should be incorporated into future studies reporting patient outcomes. Summary: Although the systemic inflammatory response is recognised as a prognostic factor in patients with colorectal cancer, it is not clear how these may differ between distinct geographical populations. The present study examines differences in the prevalence of elevated systemic inflammatory responses (modified Glasgow Prognostic Score and neutrophil:lymphocyte ratio) between two populations undergoing resection of colorectal cancer in the United Kingdom and Japan