Antioxidant insights: investigating the protective role of oxidative balance in inflammatory bowel disease

BackgroundLimited studies have investigated the relationship between systemic oxidative stress and inflammatory bowel disease (IBD). The purpose of this study was to explore the relationship between oxidative balance score (OBS) and IBD.MethodsWe included 175,808 participants from the UK Biobank database from 2006 to 2010. OBS scores were calculated based on 22 lifestyle and dietary factors. Multiple variable Cox proportional regression models, as well as gender stratification and subgroup analysis, were utilized to investigate the relationship between OBS and IBD.ResultsThere is a significant negative correlation between OBS and the occurrence of IBD, ulcerative colitis (UC), and Crohn’s disease (CD). Additionally, OBS is significantly negatively correlated with intestinal obstruction in CD patients. Gender stratified analysis suggest a significant correlation between OBS and CD in female patients, particularly pronounced in those under 60 years old. Sensitivity analysis indicates a significant negative correlation between lifestyle-related OBS and diet-related OBS with the occurrence of CD in females, diet-related OBS is negatively correlated with CD in males.ConclusionOBS showed a significant negative correlation with IBD, especially in female CD patients. This study underscores the importance of antioxidant diet and lifestyle, which may provide a greater advantage for female CD patients

Electromagnetic properties of aluminum-based bilayers for kinetic inductance detectors

The complex conductivity of a superconducting thin film is related to the quasiparticle density, which depends on the physical temperature and can also be modified by external pair breaking with photons and phonons. This relationship forms the underlying operating principle of Kinetic Inductance Detectors (KIDs), where the detection threshold is governed by the superconducting energy gap. We investigate the electromagnetic properties of thin-film aluminum that is proximitized with either a normal metal layer of copper or a superconducting layer with a lower TC , such as iridium, in order to extend the operating range of KIDs. Using the Usadel equations along with the Nam expressions for complex conductivity, we calculate the density of states and the complex conductivity of the resulting bilayers to understand the dependence of the pair breaking threshold, surface impedance, and intrinsic quality factor of superconducting bilayers on the relative film thicknesses. The calculations and analyses provide theoretical insights in designing aluminum-based bilayer kinetic inductance detectors for detection of microwave photons and athermal phonons at the frequencies well below the pair breaking threshold of a pure aluminum film

Design and characterization of the SPT-3G receiver

The SPT-3G receiver was commissioned in early 2017 on the 10-meter South Pole Telescope (SPT) to map anisotropies in the cosmic microwave background (CMB). New optics, detector, and readout technologies have yielded a multichroic, high-resolution, low-noise camera with impressive throughput and sensitivity, offering the potential to improve our understanding of inflationary physics, astroparticle physics, and growth of structure. We highlight several key features and design principles of the new receiver, and summarize its performance to date

Impact of electrical contacts design and materials on the stability of Ti superconducting transition shape

The South Pole Telescope SPT-3G camera utilizes Ti/Au transition edge sensors (TESs). A key requirement for these sensors is reproducibility and long-term stability of the superconducting (SC) transitions. Here, we discuss the impact of electrical contacts design and materials on the shape of the SC transitions. Using scanning electron microscope, atomic force microscope, and optical differential interference contrast microscopy, we observed the presence of unexpected defects of morphological nature on the titanium surface and their evolution in time in proximity to Nb contacts. We found direct correlation between the variations of the morphology and the SC transition shape. Experiments with different diffusion barriers between TES and Nb leads were performed to clarify the origin of this problem. We have demonstrated that the reproducibility of superconducting transitions can be significantly improved by preventing diffusion processes in the TES–leads contact areas

Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model

The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope

Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit