Multimodal Learning for Non-small Cell Lung Cancer Prognosis

This paper focuses on the task of survival time analysis for lung cancer. Although much progress has been made in this problem in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep learning-based survival time analyses for lung cancer are mostly based on textual clinical information such as staging, age, histology, etc. Unlike existing methods that predicting on the single modality, we observe that a human clinician usually takes multimodal data such as text clinical data and visual scans to estimate survival time. Motivated by this, in this work, we contribute a smart cross-modality network for survival analysis network named Lite-ProSENet that simulates a human's manner of decision making. Extensive experiments were conducted using data from 422 NSCLC patients from The Cancer Imaging Archive (TCIA). The results show that our Lite-ProSENet outperforms favorably again all comparison methods and achieves the new state of the art with the 89.3% on concordance. The code will be made publicly available.Comment: 11 pages, 6 figures, Multimodal learning, NSCLC, Survival analysis, Transforme

Validating the Efficacy of an Established Micropropagation Protocol for Commercial Propagation of Neolamarckia cadamba

Background and Objective: Micropropagation is an efficient technique for mass-producing superior clones used in establishing planted forests. However, there is a lack of comprehensive reports on the effectiveness and reliability of the established micropropagation protocol for Neolamarckia cadamba. The aim of this study was to demonstrate the effectiveness and reliability of the established micropropagation protocol for mass propagating true-to-type N. cadamba clones. Materials and Methods: Two selected candidates plus trees of N. cadamba were cultured in B5 media supplemented with 0.8 mg L–1 BAP for shoot multiplication and in ½ B5 media supplemented with 0.1 mg L–1 PBZ for root regeneration. The growth performance, the presence of phytopathogens and morphological differences were investigated. The collected data were subjected to a two-tailed t-test (p<0.05). Results: The results showed no significant variation (p<0.05) in the number of shoots regenerated from each explant compared to the reference clone N5 (B39 = 4.6, B42 = 4.3 and N5 = 4.8). Moreover, the rooting patterns of the investigated clones (B39 = 14.5 and B42 = 9.4) significantly outperformed clone N5 (6.9), with over 90% successful root regeneration. Phytopathogen analysis using ERIC-PCR assay confirmed that the in vitro regenerants were free of any phytopathogens. Additionally, histological examination revealed no significant differences between the stock plants and in vitro regenerants. Conclusion: This study successfully ascertained the effectiveness and reliability of the established micropropagation protocol for mass propagating true-to-type N. cadamba clones

Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data

Parkinson’s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject’s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section

Entomologic and molecular investigation into Plasmodium vivax transmission in Singapore, 2009

<p>Abstract</p> <p>Background</p> <p>Singapore has been certified malaria free since November 1982 by the World Health Organization and despite occasional local transmission, the country has maintained the standing. In 2009, three clusters of malaria cases were reported in Singapore.</p> <p>Methods</p> <p>Epidemiological, entomological and molecular studies were carried out to investigate the three clusters, namely Mandai-Sungei Kadut, Jurong Island and Sembawang.</p> <p>Results</p> <p>A total of 29 malaria patients, with no recent travel history, were reported in the three clusters. Molecular analysis based on the <it>msp3α </it>and <it>msp1 </it>genes showed two independent local transmissions: one in Mandai-Sungei Kadut and another in Sembawang. Almost all cases within each cluster were epidemiologically linked. In Jurong Island cluster, epidemiological link remains uncertain, as almost all cases had a unique genetic profile. Only two cases shared a common profile and were found to be linked to the Mandai-Sungei Kadut cluster. Entomological investigation found <it>Anopheles sinensis </it>to be the predominant Anopheline in the two areas where local transmission of <it>P. vivax </it>was confirmed. <it>Anopheles sinensis </it>was found to be attracted to human bait and bites as early as 19:45 hrs. However, all <it>Anopheles </it>mosquitoes caught were negative for sporozoites and oocysts by dissection.</p> <p>Conclusion</p> <p>Investigation of <it>P. vivax </it>cases from the three cluster areas confirmed the occurrence of local transmission in two areas. Although <it>An. sinensis </it>was the predominant Anopheline found in areas with confirmed transmission, the vector/s responsible for the outbreaks still remains cryptic.</p