HAI-178 antibody-conjugated fluorescent magnetic nanoparticles for targeted imaging and simultaneous therapy of gastric cancer

The successful development of safe and highly effective nanoprobes for targeted imaging and simultaneous therapy of in vivo gastric cancer is a great challenge. Herein we reported for the first time that anti-α-subunit of ATP synthase antibody, HAI-178 monoclonal antibody-conjugated fluorescent magnetic nanoparticles, was successfully used for targeted imaging and simultaneous therapy of in vivo gastric cancer. A total of 172 specimens of gastric cancer tissues were collected, and the expression of α-subunit of ATP synthase in gastric cancer tissues was investigated by immunohistochemistry method. Fluorescent magnetic nanoparticles were prepared and conjugated with HAI-178 monoclonal antibody, and the resultant HAI-178 antibody-conjugated fluorescent magnetic nanoparticles (HAI-178-FMNPs) were co-incubated with gastric cancer MGC803 cells and gastric mucous GES-1 cells. Gastric cancer-bearing nude mice models were established, were injected with prepared HAI-178-FMNPs via tail vein, and were imaged by magnetic resonance imaging and small animal fluorescent imaging system. The results showed that the α-subunit of ATP synthase exhibited high expression in 94.7% of the gastric cancer tissues. The prepared HAI-178-FMNPs could target actively MGC803 cells, realized fluorescent imaging and magnetic resonance imaging of in vivo gastric cancer, and actively inhibited growth of gastric cancer cells. In conclusion, HAI-178 antibody-conjugated fluorescent magnetic nanoparticles have a great potential in applications such as targeted imaging and simultaneous therapy of in vivo early gastric cancer cells in the near future

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

BackgroundThis study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls.MethodsA total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set.ResultsThe final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients.ConclusionThe radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool

Notice of Violation of IEEE Publication Principles: Single-Phase Common-Ground-Type Transformerless PV Grid-Connected Inverters

This paper presents a family of novel common-ground-type transformerless photovoltaic (PV) grid-connected inverters, which requires only five power switches, one capacitor, and one filter. A simple dual-closed led-loop control is used to improve control stabilization and accuracy. The main advantages of proposed inverters are: 1) the leakage current is completely eliminated (unlike traditional topologies, which can only suppress leakage current); 2) the devices used are a few and the cost is low; 3) low loss and high efficiency; 4) the ability of realizing reactive power; and 5) there is no need for high DC input voltage compared with half-bridge-type topologies. The operating principle, modulation mode, and control strategy are introduced in detail. The performance of the proposed topology is compared with that of several traditional topologies. The leakage current suppression ability and efficiency of the proposed topology are superior to those of the traditional topologies. The model predictive control (MPC) is applied in the proposed topology, which is easy to realize and can accelerate the dynamic response. Finally, the simulation and experimental results of a 1-kVA prototype are given, which proves the validity of the proposed topology in PV grid-connected system

Exploring the Spatial Heterogeneity and Driving Factors of UAV Logistics Network: Case Study of Hangzhou, China

Urban logistics is an important research topic in human and economic geography; unmanned aerial vehicles (UAVs) are an emerging technology that has huge potential in the field of logistics with the release of control restrictions on low-altitude airspace. The scientific identification of the spatial pattern and impact factors of UAV logistics networks is greatly significant in regards to UAV logistics planning and scheduling. This study considered the urban logistics network of Hangzhou in 2020 as the research topic and used kernel density estimation, a geodetector, and geographic information system (GIS) spatial analysis technology to systematically analyze the spatial patterns and influencing factors at the city and district scales. The study found that a significant spatial pattern was revealed in the UAV logistics network in Hangzhou, and the logistics nodes showed an obvious “core-edge” structure. The urban population, market scale and logistics infrastructure jointly shaped the structure and function of the UAV logistics network, and logistics nodes had a strong coupling relationship with the urban spatial structure. Through interaction detectors, the technical route of urban UAV logistics network construction was analyzed and summarized, and results can provide a scientific basis and case reference for other cities to build and plan UAV logistics networks

SkyroadAR: An Augmented Reality System for UAVs Low-Altitude Public Air Route Visualization

Augmented Reality (AR) technology visualizes virtual objects in the real environment, offering users an immersive experience that enhances their spatial perception of virtual objects. This makes AR an important tool for visualization in engineering, education, and gaming. The Unmanned Aerial Vehicles’ (UAVs’) low-altitude public air route (Skyroad) is a forward-looking virtual transportation infrastructure flying over complex terrain, presenting challenges for user perception due to its invisibility. In order to achieve a 3D and intuitive visualization of Skyroad, this paper proposes an AR visualization framework based on a physical sandbox. The framework consists of four processes: reconstructing and 3D-printing a sandbox model, producing virtual scenes for UAVs Skyroad, implementing a markerless registration and tracking method, and displaying Skyroad scenes on the sandbox with GPU-based occlusion handling. With the support of the framework, a mobile application called SkyroadAR was developed. System performance tests and user questionnaires were conducted on SkyroadAR; the results showed that our approachs to tracking and occlusion provided an efficient and stable AR effect for Skyroad. This intuitive visualization is recognized by both professional and non-professional users

Structured and natural responses co-generation for conversational search

Ministry of Education, Singapore under its Academic Research Funding Tier 1; Sea-NExT Joint La

An Efficient, Amine-Specific, and Cost-Effective Method for TMT 6/11-plex Labeling Improves the Proteome Coverage, Quantitative Accuracy and Precision

Tandem mass tags (TMT) are widely used in proteomics to simultaneously quantify multiple samples in a single experiment. The tags can be easily added to the primary amines of peptides/proteins through chemical reactions. In addition to amines, TMT reagents also partially react with the hydroxyl groups of serine, threonine, and tyrosine residues under alkaline conditions, which significantly compromises the analytical sensitivity and precision. Under alkaline conditions, reducing the TMT molar excess can partially mitigate overlabeling of histidine-free peptides, but has a limited effect on peptides containing histidine and hydroxyl groups. Here, we present a method under acidic conditions to suppress overlabeling while efficiently labeling amines, using only one-fifth of the TMT amount recommended by the manufacturer. In a deep-scale analysis of a yeast/human two-proteome sample, we systematically evaluated our method against the manufacturer’s method and a previously reported TMT-reduced method. Our method reduced overlabeled peptides by 9-fold and 6-fold, respectively, resulting in the substantial enhancement in peptide/protein identification rates. More importantly, the quantitative accuracy and precision were improved as overlabeling was reduced, endowing our method with greater statistical power to detect 42% and 12% more statistically significant yeast proteins compared to the standard and TMT-reduced methods, respectively. Mass spectrometric data have been deposited in the ProteomeXchange Consortium via the iProX partner repository with the data set identifier PXD047052