Search

Using first-principles calculations in the framework of density functional theory, we studied the electronic structure and optical performance of TiN. It was found that the calculated structure parameter and optical performance are in better agreement with the latest relevant experimental data, and our theoretical studies showed that TiN is a perfect near infrared absorber with high visible light transmittance and could serve as references for future experimental study and its applications as solar radiation shielding material for windows. [ABSTRACT FROM AUTHOR]

In semi-supervised learning (SSL) for medical image classification, model performance is often hindered by the scarcity of labeled data and the complexity of unlabeled data. This paper proposes an enhanced SSL approach to address these challenges by effectively utilizing unlabeled data through a combination of pseudo-labeling and contrastive learning. The key contribution of our method is the introduction of a Dynamic Sample Reweighting strategy to select reliable unlabeled samples, thereby improving the model's utilization of unlabeled data. Additionally, we incorporate multiple data augmentation strategies based on the Mean Teacher (MT) model to ensure consistent outputs across different perturbations. To better capture and integrate multi-scale features, we propose a novel feature fusion network, the Medical Multi-scale Feature Fusion Network (MedFuseNet), which enhances the model's ability to classify complex medical images. Finally, we introduce a pseudo-label guided contrastive learning (PGC) loss function that improves intra-class compactness and inter-class separability of the model's feature representations. Extensive experiments on three public medical image datasets demonstrate that our method outperforms existing SSL approaches, achieving 93.16% accuracy on the ISIC2018 dataset using only 20% labeled data, highlighting the potential of our approach to advance medical image classification under limited supervision. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of medical image classification models necessitates a substantial number of labeled images for model training. In real-world scenarios, sample sizes are typically limited and labeled samples often constitute only a small portion of the dataset. This paper aims to investigate a collaborative similarity learning strategy that optimizes pseudo-labels to enhance model accuracy and expedite its convergence, known as the joint similarity learning framework. By integrating semantic similarity and instance similarity, the pseudo-labels are mutually refined to ensure their quality during initial training. Furthermore, the similarity score is utilized as a weight to guide samples away from misclassification predictions during the classification process. To enhance the model's generalization ability, an adaptive consistency constraint is introduced into the loss function to improve performance on untrained datasets. The model achieved a satisfactory accuracy of 93.65% at 80% labeling ratio, comparable to supervised learning methods' performance. Even with very low labeling ratio (e.g., 5%), the model still attained an accuracy of 74.28%. Comparison with other techniques such as Mean Teacher and FixMatch revealed that our approach significantly outperforms them in medical image classification tasks through improving accuracy by approximately 2%, demonstrating this framework's leadership in medical image classification. [ABSTRACT FROM AUTHOR]

With the evaluation of Artificial Intelligence (AI), there has been a resurgence of interest in how to use AI algorithms on low-power embedded systems to broaden potential use cases of the Internet of Things (IoT). To mimic multimodal human perception, multimodal deep neural networks (M-DNN) have recently become very popular with the classification task due to their impressive performance for computer vision and audio processing tasks. This article presents TinyM2Net-V2—a compact low-power software hardware architecture for multimodal deep neural networks for resource-constrained tiny devices. To compress the models to implement on tiny devices, cyclicly sparsification and hybrid quantization (4-bits weights and 8-bits activations) methods are used. Although model compression techniques are an active research area, we are the first to demonstrate their efficacy for multimodal deep neural networks, using cyclicly sparsification and hybrid quantization of weights/activations. TinyM2Net-V2 shows that even a tiny multimodal deep neural network model can improve the classification accuracy more than that of any unimodal counterparts. Parameterized M-DNN model architecture was designed to be evaluated in two different case-studies: vehicle detection from multimodal images and audios and COVID-19 detection from multimodal audio recordings. The most compressed TinyM2Net-V2 achieves 92.5% COVID-19 detection accuracy (6.8% improvement from the unimodal full precision model) and 90.6% vehicle classification accuracy (7.7% improvement from the unimodal full precision model). A parameterized and flexible FPGA hardware accelerator was designed as well for TinyM2Net-V2 models. To the best of our knowledge, this is the first work accelerating multimodal deep neural network models on low-power Artix-7 FPGA hardware. We achieved energy efficiency of 9.04 GOP/s/W and 15.38 GOP/s/W for case-study 1 and case-study 2, respectively, which is comparable to the state-of-the-art results. Finally, we compared our tiny FPGA hardware implementation results with off-the-shelf resource-constrained devices and showed our implementation is faster and consumed less power compared to the off-the-shelf resource-constrained devices. [ABSTRACT FROM AUTHOR]

The article discusses the growing use of artificial intelligence (AI) in healthcare and its potential to revolutionize the field. It highlights various applications of AI, such as disease screening and diagnosis, COVID-19 patient management, and public health research. The article also acknowledges the challenges in implementing AI in healthcare, including issues with generalization and patient safety. Overall, the article provides a comprehensive overview of the advancements, benefits, and obstacles associated with AI in healthcare. [Extracted from the article]

Background: Previous in vitro and animal experiments have shown that copper plays an important role in cardiovascular health. Dietary copper is the main source of copper in the human body and the association between dietary copper and cardiovascular disease remains unclear. Our study aimed to investigate the associations of dietary copper intake with the risk of major cardiovascular disease incidence, cardiovascular disease mortality, and all-cause mortality in Chinese adults. Methods: Our study is based on Prospective Urban Rural Epidemiology China (PURE-China), a large prospective cohort study of 47 931 individuals aged 35–70 years from 12 provinces in China. Dietary intake was recorded using a validated semi-quantitative food frequency questionnaire designed specifically for the Chinese population. The daily intake of copper was obtained by multiplying the daily food intake with the nutrient content provided in the Chinese Food Composition Table (2002). Cox frailty proportional hazards models were developed to evaluate the association between dietary copper intake with mortality, major cardiovascular disease events, and their composite. Results: A total of 45 101 participants (mean age: 51.1 ± 9.7 years old) with complete information were included in the current study. The mean dietary copper intake was 2.6 ± 1.1 mg/d. During the 482 833 person-years of follow-up, 2 644(5.9%) participants died, 4 012(8.9%) developed new cardiovascular diseases, and 5 608(12.4%) participants experienced the composite endpoint. Compared with those in the first and second quartile of dietary copper intake, individuals in the third and fourth quantile had higher risk of composite outcomes, all-cause death, cardiovascular disease death, major cardiovascular disease and stroke occurrences. The associations remained similar in the subgroup and sensitivity analyses. Conclusions: Our findings demonstrated that excessive dietary copper intake was associated with higher risks of death and cardiovascular diseases in Chinese adults. Further studies in populations with different dietary characteristics are needed to obtain dose–response relationships and to refine global dietary recommendations. [ABSTRACT FROM AUTHOR]

Aim: Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA‐seq) of ALS patient peripheral blood. Methods: Whole blood RNA‐seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS–control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co‐expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected. Results: Two hundred and forty‐five differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress‐related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS patients from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects. Conclusions: Our findings suggest that peripheral blood RNA‐seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation. [ABSTRACT FROM AUTHOR]

DNA comprises molecular information stored in genetic and epigenetic bases, both of which are vital to our understanding of biology. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases fail to capture common C-to-T mutations or distinguish 5-methylcytosine from 5-hydroxymethylcytosine. We present a single base-resolution sequencing methodology that sequences complete genetics and the two most common cytosine modifications in a single workflow. DNA is copied and bases are enzymatically converted. Coupled decoding of bases across the original and copy strand provides a phased digital readout. Methods are demonstrated on human genomic DNA and cell-free DNA from a blood sample of a patient with cancer. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. Simultaneous, phased reading of genetic and epigenetic bases provides a more complete picture of the information stored in genomes and has applications throughout biomedicine. A six-letter sequencing workflow can simultaneously detect genetic and epigenetic bases. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiomics involves the extraction of quantitative features from medical images, which can provide more detailed and objective information about the features of a tumor compared to visual inspection alone. By analyzing the extensive range of features obtained through radiomics, machine-learning techniques can enhance tumor diagnosis, assess treatment response, and predict patient prognosis. This review highlights the mutual impact between the tumor and its microenvironment (habitat), in which tumor cells can modify the microenvironment to promote their growth and survival. At the same time, the habitat can also influence the behavior of tumor cells. Encouragingly, radiomics and machine learning have shown promising potential in diagnosing brain tumors and predicting prognosis. However, several limitations still need to be improved for their practical application in clinical settings. Further research is required to optimize radiomic feature extraction, standardize imaging protocols, validate models on larger datasets, and integrate diverse data to facilitate a more comprehensive analysis. Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other—tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats. [ABSTRACT FROM AUTHOR]

Proteomics offers vast potential for studying the molecular regulation of the human brain. Formalin fixation is a common method for preserving human tissue; however, it presents challenges for proteomic analysis. In this study, we compared the efficiency of two different protein-extraction buffers on three post-mortem, formalin-fixed human brains. Equal amounts of extracted proteins were subjected to in-gel tryptic digestion and LC-MS/MS. Protein, peptide sequence, and peptide group identifications; protein abundance; and gene ontology pathways were analyzed. Protein extraction was superior using lysis buffer containing tris(hydroxymethyl)aminomethane hydrochloride, sodium dodecyl sulfate, sodium deoxycholate, and Triton X-100 (TrisHCl, SDS, SDC, Triton X-100), which was then used for inter-regional analysis. Pre-frontal, motor, temporal, and occipital cortex tissues were analyzed by label free quantification (LFQ) proteomics, Ingenuity Pathway Analysis and PANTHERdb. Inter-regional analysis revealed differential enrichment of proteins. We found similarly activated cellular signaling pathways in different brain regions, suggesting commonalities in the molecular regulation of neuroanatomically-linked brain functions. Overall, we developed an optimized, robust, and efficient method for protein extraction from formalin-fixed human brain tissue for in-depth LFQ proteomics. We also demonstrate herein that this method is suitable for rapid and routine analysis to uncover molecular signaling pathways in the human brain. [ABSTRACT FROM AUTHOR]

Deep learning has substantially improved the state-of-the-art in object detection and image classification. Deep learning usually requires large-scale labelled datasets to train the models; however, due to the restrictions in medical data sharing and accessibility and the expensive labelling cost, the application of deep learning in medical image classification has been dramatically hindered. In this study, we propose a novel method that leverages semi-supervised adversarial learning and pseudo-labelling to incorporate the unlabelled images in model learning. We validate the proposed method on two public databases, including ChestX-ray14 for lung disease classification and BreakHis for breast cancer histopathological image diagnosis. The results show that our method achieved highly effective performance with an accuracy of 93.15% while using only 30% of the labelled samples, which is comparable to the state-of-the-art accuracy for chest X-ray classification; it also outperformed the current methods in multi-class breast cancer histopathological image classification with a high accuracy of 96.87%. [ABSTRACT FROM AUTHOR]

This study aimed to identify the interaction of depression and diets on cardiovascular diseases (CVD) incident and death in China and key subpopulations. We included 40,925 participants from the Prospective Urban Rural Epidemiology (PURE)-China cohort which recruited participants aged 35–70 years from 45 urban and 70 rural communities. Depression was measured by the adapted Short-Form (CIDI-SF). The unhealthy diet was considered when the score of Alternative Healthy Eating Index was below the lowest tertile. The primary outcome was a composite outcome of incident CVD and all-cause mortality. Cox frailty models were used to examine the associations. During a median follow-up of 11.9 years (IQR: 9.6–12.6 years), depression significantly increased the risk of the composite outcome (HR = 2.00; 95% CI, 1.16–3.27), major CVD (HR = 1.82; 95% CI, 1.48–2.23), and all-cause mortality (HR = 2.21; 95% CI, 1.51–3.24) for the unhealthy diet group, but not for the healthy diet group. The interaction between depression and diet for the composite outcome was statistically significant (RERI = 1.19; 95% CI, 0.66–1.72; AP = 0.42, 95% CI, 0.27–0.61; SI = 3.30, 95% CI, 1.42–7.66; multiplicative-scale = 1.74 95% CI, 1.27–2.39), even in the subgroup and sensitivity analyses. In addition, the intake of vegetable and polyunsaturated fatty acids contributed most to the interaction of diets and depression. Depressive participants should focus on healthy diets, especially vegetables and polyunsaturated fatty acids, to avoid premature death and CVD. [ABSTRACT FROM AUTHOR]

Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching "sequences of fixations over time" are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction. [ABSTRACT FROM AUTHOR]

Histopathological analysis requires a lot of clinical experience and time for pathologists. Artificial intelligence (AI) may have an important role in assisting pathologists and leading to more efficient and effective histopathological diagnoses. To address the challenge of requiring a large number of labelled images to train deep learning models in breast cancer histopathological image classification, a self‐training semi‐supervised learning method consisting three components is proposed: Firstly, a pre‐trained ResNet‐18 was used to extract features and generate pseudo‐labels for unlabelled data; secondly, a relational weight network based on the squeeze‐and‐excitation network (SENet) was trained to calculate the non‐linear distance metrices between labelled and unlabelled samples, in order to improve the accuracy of pseudo‐labelling; lastly, a consistency loss—maximum mean difference (MMD)—was added into the model to minimize the divergence between distributions of unlabelled and labelled samples. Extensive experiments were conducted on the open access BreakHis dataset. The proposed method outperformed the state‐of‐the‐art semi‐supervised methods at all tested annotated percentages (10–70%), and also achieved comparable performance with supervised methods at higher annotated percentages (50%, 70%). [ABSTRACT FROM AUTHOR]

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9–88.3 and 90.5–91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40–60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert's ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases. [ABSTRACT FROM AUTHOR]

Purpose: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making. Methods: A retrospective MRI dataset of patients diagnosed with CM1 and healthy individuals with normal brain MRIs from the period January 2010 to May 2020 was used to train ResNet50 and VGG19 CNN models to automatically classify images as CM1 or normal. A total of 101 patients diagnosed with CM1 requiring surgery and 111 patients with normal brain MRIs were included (median age 30 with an interquartile range of 23–43; 81 women with CM1). Isotropic volume transformation, image cropping, skull stripping, and data augmentation were employed to optimize model accuracy. K-fold cross validation was used to calculate sensitivity, specificity, and the area under receiver operating characteristic curve (AUC) for model evaluation. Results: The VGG19 model with data augmentation achieved a sensitivity of 97.1% and a specificity of 97.4% with an AUC of 0.99. The ResNet50 model achieved a sensitivity of 94.0% and a specificity of 94.4% with an AUC of 0.98. Conclusions: VGG19 and ResNet50 CNN models can be trained to automatically detect clinically significant CM1 on MRI with a high sensitivity and specificity. These models have the potential to be developed into clinical support tools in diagnosing CM1. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results in brain tumor segmentation tasks; however, DCNN models fail the task when applied to volumes that are different from the training dataset. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models' accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas' segmentation on Tumor Core and Enhancing Tumor classes, achieving a further improvement in accuracy by merging the two models together. The proposed model is not resolution-dependent, thus improving segmentation accuracy and theoretically solving some transfer learning problems related to the domain shifting, at least in terms of image resolution in the datasets. [ABSTRACT FROM AUTHOR]

Purpose: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. Methods: We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model's accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. Results: The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. Conclusion: The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is an irreversible neurogenerative disorder that undergoes progressive decline in memory and cognitive function and is characterized by structural brain Magnetic Resonance Images (sMRI). In recent years, sMRI data has played a vital role in the evaluation of brain anatomical changes, leading to early detection of AD through deep networks. The existing AD problems such as preprocessing complexity and unreliability are major concerns at present. To overcome these, a model (FEESCTL) has been proposed with an entropy slicing for feature extraction and Transfer Learning for classification. In the present study, the entropy image slicing method is attempted for selecting the most informative MRI slices during training stages. The ADNI dataset is trained on Transfer Learning adopted by VGG-16 network for classifying the AD with normal individuals. The experimental results reveal that the proposed model has achieved an accuracy level of 93.05%, 86.39%, 92.00% for binary classifications (AD/MCI, MCI/CN, AD/CN) and 93.12% for ternary classification (AD/MCI/CN), respectively, and henceforth the efficiency in diagnosing AD is proved through comparative analysis. [ABSTRACT FROM AUTHOR]

Our response to the COVID-19 pandemic The I MJA i is proud to have played a role in the national discussion and debate about the COVID-19 pandemic, with the very rapid publication of evidence-based guidelines, perspectives, and expert commentaries. Med J Aust 2020; 213: 276 - 279. https://www.mja.com.au/journal/2020/213/6/pandemic-printing-novel-3d-printed-swab-detecting-sars-cov-2 7 Blakely T, Thompson J, Carvalho N, et al. Med J Aust 2020; 213: 251 - 252.e1. https://www.mja.com.au/journal/2020/213/6/current-covid-19-guidelines-respiratory-protection-health-care-workers-are 11 Hyde Z. COVID-19, children, and schools: overlooked and at risk. Med J Aust 2020; 213: 293 - 295.e1. https://www.mja.com.au/journal/2020/213/7/fit-testing-n95-or-p2-masks-protect-health-care-workers 13 Kault D. Superspreaders, asymptomatics and COVID-19 elimination. [Extracted from the article]

Objective: Previous prospective studies on the association of white rice intake with incident diabetes have shown contradictory results but were conducted in single countries and predominantly in Asia. We report on the association of white rice with risk of diabetes in the multinational Prospective Urban Rural Epidemiology (PURE) study.Research Design and Methods: Data on 132,373 individuals aged 35-70 years from 21 countries were analyzed. White rice consumption (cooked) was categorized as <150, ≥150 to <300, ≥300 to <450, and ≥450 g/day, based on one cup of cooked rice = 150 g. The primary outcome was incident diabetes. Hazard ratios (HRs) were calculated using a multivariable Cox frailty model.Results: During a mean follow-up period of 9.5 years, 6,129 individuals without baseline diabetes developed incident diabetes. In the overall cohort, higher intake of white rice (≥450 g/day compared with <150 g/day) was associated with increased risk of diabetes (HR 1.20; 95% CI 1.02-1.40; P for trend = 0.003). However, the highest risk was seen in South Asia (HR 1.61; 95% CI 1.13-2.30; P for trend = 0.02), followed by other regions of the world (which included South East Asia, Middle East, South America, North America, Europe, and Africa) (HR 1.41; 95% CI 1.08-1.86; P for trend = 0.01), while in China there was no significant association (HR 1.04; 95% CI 0.77-1.40; P for trend = 0.38).Conclusions: Higher consumption of white rice is associated with an increased risk of incident diabetes with the strongest association being observed in South Asia, while in other regions, a modest, nonsignificant association was seen. [ABSTRACT FROM AUTHOR]

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient's treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients' age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas' IDH status prediction. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed. The proposed network has a multi-scale architecture and consists of three cascading sub-networks, each with a VGG19 network for image feature extraction and an attention proposal network for locating feature area. A scale-dependent pooling algorithm is designed to select an appropriate convolution in the VGG19 network for classification, and a multi-feature mechanism is introduced in attention proposal network to describe the feature regions. The VGG19 and attention proposal network are cross-trained to accelerate convergence and to improve detection accuracy. The proposed method is trained and validated on a self-built ship database and effectively improve the detection accuracy to 86.7% outperforming the baseline VGG19 and recurrent attention convolutional neural network methods. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. Themachine learning techniques are surveyed under threemain categories: support vectormachine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions. [ABSTRACT FROM AUTHOR]

Background: The differentiation of large vessel occlusion caused by intracranial atherosclerotic stenosis (ICAS) or intracranial embolism significantly impacts the course of treatment (i.e., intravenous thrombolysis versus mechanical thrombectomy) for acute cerebral infarction. Currently, there is no objective evidence to indicate ICAS-related middle cerebral artery M1 segment occlusion before treatment. In cases of ICAS, it is often observed that the infarct core caused by ICAS-related M1 segment middle cerebral artery occlusion (MCAO) is located in deeper parts of the brain (basal ganglia or semiovoid region). Objective: To evaluate whether the location of the infarct core, identified using diffusion-weighted imaging (DWI), can be used to differentiate ICAS from intracranial embolism. Methods: Thirty-one consecutive patients diagnosed with acute cerebral infarction caused by middle cerebral artery M1 segment occlusion were retrospectively included based on angiographic findings to distinguish ICAS from embolic occlusion. Patients were divided into two groups based on the location of the infarct core on DWI: in the deep part of the brain (basal ganglia or semiovoid region) or more superficially (i.e., cortex). Results: In 16 patients, the infarct core was mainly in the deep part of the brain on DWI [14 of 16 patients in the ICAS group and only 2 in the non-ICAS group (93.3 vs. 6.7%, respectively; P < 0.001)]. The diagnostic sensitivity of DWI for ICAS was 93.3%, with a specificity of 87.5%, a Positive predictive value (PPV) of 87.5%, and an Negative predictive value (NPV) of 93.3%, the accuracy was 88.5%. Conclusion: Intracranial atherosclerotic disease-related acute MCAO can be predicted using DWI. [ABSTRACT FROM AUTHOR]

Contralateral intermittent theta burst stimulation (iTBS) can potentially improve swallowing disorders with unilateral lesion of the swallowing cortex. However, the after-effects of iTBS on brain excitability remain largely unknown. Here, we investigated the alterations of temporal dynamics of inter-regional connectivity induced by iTBS following continuous TBS (cTBS) in the contralateral suprahyoid muscle cortex. A total of 20 right-handed healthy subjects underwent cTBS over the left suprahyoid muscle motor cortex and then immediately afterward, iTBS was applied to the contralateral homologous area. All of the subjects underwent resting-state functional magnetic resonance imaging (Rs-fMRI) pre- and post-TBS implemented on a different day. We compared the static and dynamic functional connectivity (FC) between the post-TBS and the baseline. The whole-cortical time series and a sliding-window correlation approach were used to quantify the dynamic characteristics of FC. Compared with the baseline, for static FC measurement, increased FC was found in the precuneus (BA 19), left fusiform gyrus (BA 37), and right pre/post-central gyrus (BA 4/3), and decreased FC was observed in the posterior cingulate gyrus (PCC) (BA 29) and left inferior parietal lobule (BA 39). However, in the dynamic FC analysis, post-TBS showed reduced FC in the left angular and PCC in the early windows, and in the following windows, increased FC in multiple cortical areas including bilateral pre- and postcentral gyri and paracentral lobule and non-sensorimotor areas including the prefrontal, temporal and occipital gyrus, and brain stem. Our results indicate that iTBS reverses the aftereffects induced by cTBS on the contralateral suprahyoid muscle cortex. Dynamic FC analysis displayed a different pattern of alteration compared with the static FC approach in brain excitability induced by TBS. Our results provide novel evidence for us in understanding the topographical and temporal aftereffects linked to brain excitability induced by different TBS protocols and might be valuable information for their application in the rehabilitation of deglutition. [ABSTRACT FROM AUTHOR]

The spread of neurodegeneration through the human brain network is reported as underlying the progression of neurodegenerative disorders. However, the exact mechanisms remain unknown. The human visual pathway is characterized by its unique hierarchical architecture and, therefore, represents an ideal model to study trans-synaptic degeneration, in contrast to the complexity in neural connectivity of the whole brain. Here we show in two specifically selected patient cohorts, including (i) glaucoma patients with symmetrical bilateral hemifield defects respecting the horizontal meridian (n = 25, 14 females, 64.8 ± 10.1 years; versus 13 normal controls with similar age/sex distributions); and (ii) multiple sclerosis patients without optic radiation lesions (to avoid potential effects of lesions on diffusivity measures) (n = 30, 25 females, 37.9 ± 10.8 years; versus 20 controls), that there are measurable topographic changes in the posterior visual pathways corresponding to the primary optic nerve defects. A significant anisotropic increase of water diffusion was detected in both patient cohorts in the optic radiations, characterized by changes in perpendicular (radial) diffusivity (a measure of myelin integrity) that extended more posteriorly than those observed in parallel (axial) diffusivity (reflecting axonal integrity). In glaucoma, which is not considered a demyelinating disease, the observed increase in radial diffusivity within the optic radiations was validated by topographically linked delay of visual evoked potential latency, a functional measure of demyelination. Radial diffusivity change in the optic radiations was also associated with an asymmetrical reduction in the thickness of the calcarine cortex in glaucoma. In addition, 3 years longitudinal observation of the multiple sclerosis patient cohort revealed an anterograde increase of radial diffusivity in the anterior part of optic radiations which again was retinotopically associated with the primary damage caused by optic neuritis. Finally, in an animal model of optic nerve injury, we observed early glial activation and demyelination in the posterior visual projections, evidenced by the presence of myelin-laden macrophages. This occurred prior to the appearance of amyloid precursor protein accumulation, an indicator of disrupted fast axonal transport. This study demonstrated strong topographical spread of neurodegeneration along recognized neural projections and showed that myelin and glial pathology precedes axonal loss in the process, suggesting that the mechanism of trans-synaptic damage may be at least partially mediated by glial components at the cellular level. The findings may have broad biological and therapeutic implications for other neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Filtering is an essential step in the process of obtaining rock data. To the best of our knowledge, there are no special algorithms for use in the point clouds of rock masses. Existing filtering methods remove noisy points by fitting the surface of the ground and deleting the points above the surface around a range of values. This type of methods has certain limitations in rock engineering owing the uniqueness of the particular rockmass being studied. In this paper, a method for filtering the rock points is proposed based on a backpropagation (BP) neural network and principal component analysis (PCA). In the proposed method, a PCA is applied for feature extraction, and for obtaining the dimensional information, which can be used to effectively distinguish the rock and other points at different scales. A BP neural network, which has a strong nonlinear processing capability, is then used to obtain the exact points of rock with the above characteristics. In the present paper, the efficiency of the proposed technique is illustrated by classifying steep rocky slopes as rock and vegetation. A comparison with existing methods indicates the superiority of the proposed method in terms of the point cloud filtering of rock masses. [ABSTRACT FROM AUTHOR]