Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution.

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance.

Multi-level feature extraction and reconstruction for 3D MRI image super-resolution.

Magnetic resonance imaging (MRI) is an essential radiology technique in clinical diagnosis, but its spatial resolution may not suffice to meet the growing need for precise diagnosis due to hardware limitations and thicker slice thickness. Therefore, it is crucial to explore suitable methods to increase the resolution of MRI images. Recently, deep learning has yielded many impressive results in MRI image super-resolution (SR) reconstruction.

Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.

Purpose: To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification.

Methods: Two hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI).

MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images.

Aim: The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different infection sites, some researchers have improved the accuracy by adding more complexity. Also, they overlook the complexity of lesions, which hinder their ability to capture the relationship between segmentation sites and the background, as well as the edge contours and global context.

Coupled Gold Nanoparticles with Aptamers Colorimetry for Detection of Amoxicillin in Human Breast Milk Based on Image Preprocessing and BP-ANN.

Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk.

Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data.

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure.

3D dense convolutional neural network for fast and accurate single MR image super-resolution.

Super-resolution (SR) MR image reconstruction has shown to be a very promising direction to improve the spatial resolution of low-resolution (LR) MR images. In this paper, we presented a novel MR image SR method based on a dense convolutional neural network (DDSR), and its enhanced version called EDDSR. There are three major innovations: first, we re-designed dense modules to extract hierarchical features directly from LR images and propagate the extracted feature maps through dense connections.

Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network.

Aiming at the security issues in the storage and transmission of medical images in the medical information system, combined with the special requirements of medical images for the protection of lesion areas, this paper proposes a robust zero-watermarking algorithm for medical images' security based on VGG19. First, the pretrained VGG19 is used to extract deep feature maps of medical images, which are fused into the feature image. Second, the feature image is transformed by Fourier transform, and low-frequency coefficients of the Fourier transform are selected to construct the feature matrix of the medical image.

Impacts of land use at multiple buffer scales on seasonal water quality in a reticular river network area.

The assessment and prediction of regional water quality are fundamental inputs to environmental planning and watershed ecological management. This paper explored spatiotemporal changes in the correlation of water quality parameters (WQPs) and land-use types (LUTs) in a reticular river network area. Water samples of 44 sampling sites were collected every quarter from 2016 to 2018 and evaluated for dissolved oxygen (DO), total phosphorus (TP), ammonia nitrogen (NH3-N), and permanganate index (CODMn).

3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads.

The existing deep convolutional neural networks (DCNNs) based methods have achieved significant progress regarding automatic glioma segmentation in magnetic resonance imaging (MRI) data. However, there are two main problems affecting the performance of traditional DCNNs constructed by simply stacking convolutional layers, namely, exploding/vanishing gradients and limitations to the feature computations. To address these challenges, we propose a novel framework to automatically segment brain tumors.

Dissipation of naphthenic acids mixtures by lake biofilms.

The dissipation (combined sorption and biodegradation) of naphthenic acids (C(n)H(2n+z)O(2)) by lake biofilms with no previous adaptation to oil sands acids was investigated using rotating annular bioreactors. The dissipation by the biofilm was dependent on the chemical composition of the naphthenic acids mixture. There were 2 distinct groups of Fluka naphthenic acids which dissipated with pseudo first order kinetics: (a) t(1/2)= 7 days, r(2)= 0.

Mass spectrometry of the photolysis of sulfonylurea herbicides in Prairie waters.

This review of mass spectrometry of sulfonylurea herbicides includes a focus on studies relevant to Canadian Prairie waters. Emphasis is given to data gaps in the literature for the rates of photolysis of selected sulfonylurea herbicides in different water matrices. Specifically, results are evaluated for positive ion electrospray tandem mass spectrometry with liquid chromatography separation for the study of the photolysis of chlorsulfuron, tribenuron-methyl, thifensulfuron-methyl, metsulfuron-methyl, and ethametsulfuron-methyl.

Electrospray ionization mass spectrometry of the photodegradation of naphthenic acids mixtures irradiated with titanium dioxide.

Electrospray ionization mass spectrometry was used to study the photodegradation of an oil sands naphthenic acid (NA) mixture, a commercial Fluka NA mixture and a candidate NA, 4-Methyl-cyclohexaneaceticic acid (4-MCHAA) irradiated with TiO(2) (P25) suspension under both fluorescent and natural sunlight. Under natural sunlight irradiation over the TiO(2) suspension, approximately 75% of compounds in the NA mixtures and 100% of 4-MCHAA were degraded in 8 h. No degradation was observed under dark conditions, regardless of the presence or absence of TiO(2).

Evaluation of the apparent phytodegradation of pentachlorophenol by Chlorella pyrenoidosa.

Several factors influencing the apparent phytodegradation of pentachlorophenol (PCP) were investigated under controlled laboratory conditions including photolysis, biodegradation, and direct phytodegradation by the algae, Chlorella pyrenoidosa. PCP was observed to degrade over time in all instances. Degradation occurred both with and without the presence of algae.

Electron Spin Relaxation in Chromium-Nitrosyl Complexes.

A new method to prepare Cr(NO)(H(2)O)(5)(2+) from dichromate and NH(2)OH is reported. The chromium nitrosyls Cr(NO)(EHBA)(+) and Cr(NO)(EHBA)(2) (EHBA = 2-ethyl-2-hydoxybutyrate) were prepared by a literature reaction and characterized by continuous wave electron paramagnetic resonance and two-pulse electron spin echo spectroscopy at X-band. The g values are characteristic of a single unpaired electron in a predominantly d(xy)() orbital.