Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach

Deep learning is the state-of-the-art artificial intelligence (AI) method for visual pattern detection and automated diagnosis. This paper describes the application of convolutional neural network (CNN), the deep learning model for visual recognition, to automatic detection of plasmodium parasitized red blood cells for malaria field screening and rapid diagnosis. The malaria thin blood smears are from Bangladesh and initially labeled by a specialist. 27,578 red blood cell images are segmented (raw set). The images are rotated clockwise three times to generate an augmented dataset with 110,312 red blood cell images. A 12-layer and an 18-layer CNN-based Malaria Net models are applied to classify both the raw data set and the augmented dataset. The performance is evaluated by ten-fold cross-validation and compared to a transfer learning model. In the ten-fold cross-validation test for Malaria Net, the average accuracy is 97.37% (18-layer) and 96.09% (12-layer) with the raw set, and is 97.93% and 96.75% with the augmented set, in comparison to 91.99% with the raw set and 94.26% with the augmented set in transfer learning. In addition, the two CNN models show superiority over transfer learning in all performance indicators such as sensitivity, specificity, precision, F1 score, and Matthews correlation coefficient. The Malaria Net can accurately detect malaria-infected red blood cells. A CNN model trained by domain-specific data shows superior performance over the transfer-learning method. Automatic image classification powered by deep learning offers not only an accurate method for the malaria field screening and rapid diagnosis but also a new solution for malaria control especially in resource-poor regions

Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation

Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%. In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing

On 3D simultaneous attack against manoeuvring target with communication delays

This article investigates the simultaneous attack problem of multiple missiles against a manoeuvring target with delayed information transmission in three-dimensional space. Based on the kinetic model of the missiles, the problem is divided into three demands: the velocity components normal to line-of-sight converge to zero in finite time, the component of motion states along line-of-sight should achieve consensus and converge to zero. The guidance law is designed for each demand and by theoretical proof, the upper bound of delay which can tolerate is presented and the consensus error of the relative distances can converge to a small neighbourhood of zero. And simulation example presented also demonstrates the validity of the theoretical result

Gene-associated Disease Discovery Powered by Large Language Models

The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases. The advent of advanced genome sequencing techniques has significantly improved the efficiency and cost-effectiveness of detecting these genetic markers, playing a crucial role in disease diagnosis and forming the basis for clinical decision-making and early risk assessment. To overcome the limitations of existing databases that record disease-gene associations from existing literature, which often lack real-time updates, we propose a novel framework employing Large Language Models (LLMs) for the discovery of diseases associated with specific genes. This framework aims to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases, thereby enhancing the efficiency of disease identification. Our approach involves using LLMs to conduct literature searches, summarize relevant findings, and pinpoint diseases related to specific genes. This paper details the development and application of our LLM-powered framework, demonstrating its potential in streamlining the complex process of literature retrieval and summarization to identify diseases associated with specific genetic variations.Comment: This is the official paper accepted by AAAI 2024 Workshop on Large Language Models for Biological Discoverie

Longitudinal control for person-following robots

Purpose: This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology. Design/methodology/approach: Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control. Findings: A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios. Originality/value: This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research

catena-Poly[[[tetra­quazinc(II)]-μ-2,5-dihydroxy­benzene-1,4-diacetato-κ2 O 1:O 4] dihydrate]

The title compound, {[Zn(C10H8O6)(H2O)4]·2H2O}n, is a one-dimensional coordination polymer with 2,5-dihydroxy­benzene-1,4-diacetate acting as bridging ligand. The zigzag chains, extending parallel to [011], are further packed into a three-dimensional network by hydrogen bonds

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pre-trained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.Comment: 40 pages, 8 tables, 7 figures, 3 supplementary figures and 4 supplementary table

Chinese international students in the United States: The interplay of students’ acculturative stress, academic standing, and quality of life

Background: Acculturation could cause grave health consequences in international students. However, there is a shortage of research into how acculturative stress might affect international students’ quality of life in light of their academic standing and experience. The lack of research is particularly pronounced among Chinese international students, representing the largest body of international students studying in the United States (U.S.). Thus, to bridge the research gap, this study aims to examine the interplay between international students’ acculturative stress, academic standing, and quality of life among a nationally representative sample of Chinese international students studying in the United States. Methods: An online survey that gauges Chinese international students’ levels of acculturative stress, academic standing, and quality of life was developed. Over 350 higher education institutions across the United States were approached, including public universities, private universities, and community colleges, among which approximately 220 institutions responded positively and supported survey distribution. A total of 751 students completed the survey. Multiple regression analyses were carried out to examine the associations between students’ acculturative stress, academic standing, and quality of life. Results: Findings reveal that acculturative stress negatively affects all four domains of Chinese international students’ quality of life, irrespective of their academic standing. Data analyses also show that compared to master’s and doctoral students, undergraduates experience the highest levels of acculturative stress. Furthermore, a significant difference emerged among undergraduate and doctoral international students’ acculturative stress levels, but not among undergraduate and master’s students, or master’s and doctoral students. Conclusion: Our study found that, compared to master’s and doctoral students, undergraduates had more significant acculturative stress associated with lower levels of quality of life. This finding highlights the potentially positive role of academic experience – while acculturative stress deteriorates international students’ quality of life, students’ academic standing and experience could be the protective factor in the equation. Future research could further examine how universities and colleges can capitalize on their academic apparatuses and resources to improve international students’ academic performance and students’ acculturation experience and quality of life

A Novel Role of Protein Tyrosine Kinase2 in Mediating Chloride Secretion in Human Airway Epithelial Cells

Ca2+ activated Cl− channels (CaCC) are up-regulated in cystic fibrosis (CF) airway surface epithelia. The presence and functional properties of CaCC make it a possible therapeutic target to compensate for the deficiency of Cl− secretion in CF epithelia. CaCC is activated by an increase in cytosolic Ca2+, which not only activates epithelial CaCCs, but also inhibits epithelial Na+ hyperabsorption, which may also be beneficial in CF. Our previous study has shown that spiperone, a known antipsychotic drug, activates CaCCs and stimulates Cl− secretion in polarized human non-CF and CF airway epithelial cell monolayers in vitro, and in Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) knockout mice in vivo. Spiperone activates CaCC not by acting in its well-known role as an antagonist of either 5-HT2 or D2 receptors, but through a protein tyrosine kinase-coupled phospholipase C-dependent pathway. Moreover, spiperone independently activates CFTR through a novel mechanism. Herein, we performed a mass spectrometry analysis and identified the signaling molecule that mediates the spiperone effect in activating chloride secretion through CaCC and CFTR. Proline-rich tyrosine kinase 2 (PYK2) is a non-receptor protein tyrosine kinase, which belongs to the focal adhesion kinase family. The inhibition of PYK2 notably reduced the ability of spiperone to increase intracellular Ca2+ and Cl− secretion. In conclusion, we have identified the tyrosine kinase, PYK2, as the modulator, which plays a crucial role in the activation of CaCC and CFTR by spiperone. The identification of this novel role of PYK2 reveals a new signaling pathway in human airway epithelial cells

Bi-scale Car-following Model Calibration for Corridor Based on Trajectory

The precise estimation of macroscopic traffic parameters, such as travel time and fuel consumption, is essential for the optimization of traffic management systems. Despite its importance, the comprehensive acquisition of vehicle trajectory data for the calculation of these macroscopic measures presents a challenge. To bridge this gap, this study aims to calibrate car-following models capable of predicting both microscopic measures and macroscopic measures. We conduct a numerical analysis to trace the cumulative process of model prediction errors across various measurements, and our findings indicate that macroscopic measures encapsulate the accumulation of model errors. By incorporating macroscopic measures into vehicle model calibration, we can mitigate the impact of noise on microscopic data measurements. We compare three car-following model calibration methods: MiC (using microscopic measurements), MaC (using macroscopic measurements), and BiC (using both microscopic and macroscopic measurements): utilizing real-world trajectory data. The BiC method emerges as the most successful in reconstructing vehicle trajectories and accurately estimating travel time and fuel consumption, whereas the MiC method leads to overfitting and inaccurate macro-measurement predictions. This study underscores the importance of bi-scale calibration for precise traffic and energy consumption predictions, laying the groundwork for future research aimed at enhancing traffic management strategies