Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture.

PURPOSE: The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times. METHODS: Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution. RESULTS: SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels. CONCLUSION: Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Efficient Adsorption of Tebuconazole in Aqueous Solution by Calcium Modified Water Hyacinth-Based Biochar: Adsorption Kinetics, Mechanism, and Feasibility.

The application of fungicides (such as tebuconazole) can impose harmful impacts on the ecosystem and humans. In this study, a new calcium modified water hyacinth-based biochar (WHCBC) was prepared and its effectiveness for removing tebuconazole (TE) via adsorption from water was tested. The results showed that Ca was loaded chemically (CaC2O4) onto the surface of WHCBC. The adsorption capacity of the modified biochar increased by 2.5 times in comparison to that of the unmodified water hyacinth biochar. The enhanced adsorption was attributed to the improved chemical adsorption capacity of the biochar through calcium modification. The adsorption data were better fitted to the pseudo-second-order kinetics and the Langmuir isotherm model, indicating that the adsorption process was dominated by monolayer adsorption. It was found that liquid film diffusion was the main rate-limiting step in the adsorption process. The maximum adsorption capacity of WHCBC was 40.5 mg/g for TE. The results indicate that the absorption mechanisms involved surface complexation, hydrogen bonding, and π-π interactions. The inhibitory rate of Cu2+ and Ca2+ on the adsorption of TE by WHCBC were at 4.05-22.8%. In contrast, the presence of other coexisting cations (Cr6+, K+, Mg2+, Pb2+), as well as natural organic matter (humic acid), could promote the adsorption of TE by 4.45-20.9%. In addition, the regeneration rate of WHCBC was able to reach up to 83.3% after five regeneration cycles by desorption stirring with 0.2 mol/L HCl (t = 360 min). The results suggest that WHCBC has a potential in application for removing TE from water.

A Stable Fe-Zn Modified Sludge-Derived Biochar for Diuron Removal: Kinetics, Isotherms, Mechanism, and Practical Research.

To remove typical herbicide diuron effectively, a novel sludge-derived modified biochar (SDMBC600) was prepared using sludge-derived biochar (SDBC600) as raw material and Fe-Zn as an activator and modifier in this study. The physico-chemical properties of SDMBC600 and the adsorption behavior of diuron on the SDMBC600 were studied systematically. The adsorption mechanisms as well as practical applications of SDMBC600 were also investigated and examined. The results showed that the SDMBC600 was chemically loaded with Fe-Zn and SDMBC600 had a larger specific surface area (204 m2/g) and pore volume (0.0985 cm3/g). The adsorption of diuron on SDMBC600 followed pseudo-second-order kinetics and the Langmuir isotherm model, with a maximum diuron adsorption capacity of 17.7 mg/g. The biochar could maintain a good adsorption performance (8.88-12.9 mg/g) under wide water quality conditions, in the pH of 2-10 and with the presence of humic acid and six typical metallic ions of 0-20 mg/L. The adsorption mechanisms of SDMBC600 for diuron were found to include surface complexation, π-π binding, hydrogen bonding, as well as pore filling. Additionally, the SDMBC600 was tested to be very stable with very low Fe and Zn leaching concentration ≤0.203 mg/L in the wide pH range. In addition, the SDMBC600 could maintain a high adsorption capacity (99.6%) after four times of regeneration and therefore, SDMBC600 could have a promising application for diuron removal in water treatment.

Redefining ß-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis.

BACKGROUND: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of ß-blocker efficacy in patients with sinus rhythm and atrial fibrillation. METHODS: Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of ß blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012). FINDINGS: 15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56-72) and LVEF 27% (IQR 21-33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from ß blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67-1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of ß blockers versus placebo (OR 0·92, 0·77-1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with ß blockers (OR 0·57, 0·35-0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials. INTERPRETATION: An artificial intelligence-based clustering approach was able to distinguish prognostic response from ß blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where ß blockers did reduce mortality. FUNDING: Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.

Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination.

PURPOSE: To systematically investigate the influence of various data consistency layers and regularization networks with respect to variations in the training and test data domain, for sensitivity-encoded accelerated parallel MR image reconstruction. THEORY AND METHODS: Magnetic resonance (MR) image reconstruction is formulated as a learned unrolled optimization scheme with a down-up network as regularization and varying data consistency layers. The proposed networks are compared to other state-of-the-art approaches on the publicly available fastMRI knee and neuro dataset and tested for stability across different training configurations regarding anatomy and number of training samples. RESULTS: Data consistency layers and expressive regularization networks, such as the proposed down-up networks, form the cornerstone for robust MR image reconstruction. Physics-based reconstruction networks outperform post-processing methods substantially for R = 4 in all cases and for R = 8 when the training and test data are aligned. At R = 8, aligning training and test data is more important than architectural choices. CONCLUSION: In this work, we study how dataset sizes affect single-anatomy and cross-anatomy training of neural networks for MRI reconstruction. The study provides insights into the robustness, properties, and acceleration limits of state-of-the-art networks, and our proposed down-up networks. These key insights provide essential aspects to successfully translate learning-based MRI reconstruction to clinical practice, where we are confronted with limited datasets and various imaged anatomies.

Complementary time-frequency domain networks for dynamic parallel MR image reconstruction.

PURPOSE: To introduce a novel deep learning-based approach for fast and high-quality dynamic multicoil MR reconstruction by learning a complementary time-frequency domain network that exploits spatiotemporal correlations simultaneously from complementary domains. THEORY AND METHODS: Dynamic parallel MR image reconstruction is formulated as a multivariable minimization problem, where the data are regularized in combined temporal Fourier and spatial (x-f) domain as well as in spatiotemporal image (x-t) domain. An iterative algorithm based on variable splitting technique is derived, which alternates among signal de-aliasing steps in x-f and x-t spaces, a closed-form point-wise data consistency step and a weighted coupling step. The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatiotemporal redundancies in complementary domains. RESULTS: Experiments were performed on two datasets of highly undersampled multicoil short-axis cardiac cine MRI scans. Results demonstrate that our proposed method outperforms the current state-of-the-art approaches both quantitatively and qualitatively. The proposed model can also generalize well to data acquired from a different scanner and data with pathologies that were not seen in the training set. CONCLUSION: The work shows the benefit of reconstructing dynamic parallel MRI in complementary time-frequency domains with deep neural networks. The method can effectively and robustly reconstruct high-quality images from highly undersampled dynamic multicoil data ( 16× and 24× yielding 15 s and 10 s scan times respectively) with fast reconstruction speed (2.8 seconds). This could potentially facilitate achieving fast single-breath-hold clinical 2D cardiac cine imaging.

Efficient Propulsion and Hovering of Bubble-Driven Hollow Micromotors underneath an Air-Liquid Interface.

Bubble-driven micromotors have attracted substantial interest due to their remarkable self-motile and cargo-delivering abilities in biomedical or environmental applications. Here, we developed a hollow micromotor that experiences fast self-propulsion underneath an air-liquid interface by periodic bubble growth and collapse. The collapsing of a single microbubble induces a ∼1 m·s-1 impulsive jetting flow that instantaneously pushes the micromotor forward. Unlike previously reported micromotors propelled by the recoiling of bubbles, cavitation-induced jetting further utilizes the energy stored in the bubble to propel the micromotor and thus enhances the energy conversion efficiency by 3 orders of magnitude. Four different modes of propulsion are, for the first time, identified by quantifying the dependence of propulsion strength on microbubble size. Meanwhile, the vertical component of the jetting flow counteracts the buoyancy of the micromotor-bubble dimer and facilitates counterintuitive hovering underneath the air-liquid interface. This work not only enriches the understanding of the propulsion mechanism of bubble-driven micromotors but also gives insight into the physical aspects of cavitation bubble dynamics near the air-liquid interface on the microscale.

Heavy metal pollution and health risk assessment in the Wei River in China.

From data collected monthly at 26 monitoring cross sections in the Wei River in the Shaanxi Region of China during the period 2008-2012, the temporal pollution characteristics of heavy metals (Hg, Cd, Cr(VI), Pb, and As) were analyzed based on a heavy metal pollution index (HPI). The monthly HPI values of the five heavy metals in the river fluctuated greatly in 2008 and then declined gradually with time. This general trend of reduction in HPI appears not to have a seasonal variation and most likely resulted from the continued improvement in heavy metal pollution control strategies implemented by local environmental agencies combined with a significant improvement in wastewater treatment capacities. Among the five heavy metals, Cd and Pb were below 0.1 and 3 µg L(-1), respectively, at all the sampling points in the studied areas in the year 2012. The detection rates of As, Hg, and Cr(VI) were in the order of Hg > Cr(VI) > As. Hg, Cr(VI), and As exceeded, in a month of the dry season in 2012, the standard limits for category III surface waters according to the China Environment Quality Standards for Surface Water (CEQSSW). Based on the assessment using the HPI method, the pollution status of these heavy metals in water of the Wei River in the Shaanxi Region was generally at an acceptable level, but exhibited distinctive characteristics between the main stream river and tributaries. Most of the tributaries were more seriously polluted than the main river. A health risk assessment was conducted based on the Human Health Risk Assessment (HHRA) method recommended by the United States Environmental Protection Agency (USEPA). Apart from As, the health risk for the five heavy metals in the region were at acceptable levels for drinking water sources (hazard quotient (HQ) < 1, carcinogenic risk (CR) ranged from 10(-4)-10(-6)) according to the Risk Assessment Guidance for Superfund (RAGS), USEPA. Arsenic was identified as the most important pollutant of concern among the five heavy metals; both its values of the HQ and CR indicated potentially adverse health risks for the local population.

Insights from arsenate adsorption on rutile (110): grazing-incidence X-ray absorption fine structure spectroscopy and DFT+U study.

Insights into the bonding of As(V) at the metal oxide/aqueous interface can further our understanding of its fate and transport in the environment. The motivation of this work is to explore the interfacial configuration of As(V) on single crystal rutile (110) using grazing-incidence X-ray absorption fine structure spectroscopy (GI-XAFS) and planewave density functional calculations with on-site repulsion (DFT+U). In contrast to the commonly considered corner-sharing bidentate binuclear structure, tetrahedral As(V) binds as an edge/corner-sharing tridentate binuclear complex on rutile (110), as evidenced by observation of three As-Ti distances at 2.83, 3.36, and 4.05 Å. In agreement with the GI-XAFS analysis, our DFT+U calculations for this configuration resulted in the lowest adsorption energy among five possible alternatives. In addition, the electron density difference further demonstrated the transfer of charge between surface Ti atoms and O atoms in AsO4. This charge transfer consequently induced the formation of a chemical bond, which is also confirmed by the partial density of states analysis. Our results may shed new light on coupling the GI-XAFS and DFT approaches to explore molecular-scale adsorption mechanisms on single crystal surfaces.

Removal of arsenate with hydrous ferric oxide coprecipitation: effect of humic acid.

Insights from the adverse effect of humic acid (HA) on arsenate removal with hydrous ferric oxide (HFO) coprecipitation can further our understanding of the fate of As(V) in water treatment process. The motivation of our study is to explore the competitive adsorption mechanisms of humic acid and As(V) on HFO on the molecular scale. Multiple complementary techniques were used including macroscopic adsorption experiments, surface enhanced Raman scattering (SERS), extended X-ray absorption fine structure (EXAFS) spectroscopy, flow-cell attenuated total reflectance Fourier transform infrared (ATR-FTIR) measurement, and charge distribution multisite complexation (CD-MUSIC) modeling. The As(V) removal efficiency was reduced from over 95% to about 10% with the increasing HA concentration to 25 times of As(V) mass concentration. The SERS analysis excluded the HA-As(V) complex formation. The EXAFS results indicate that As(V) formed bidentate binuclear surface complexes in the presence of HA as evidenced by an As-Fe distance of 3.26-3.31 angstroms. The in situ ATR-FTIR measurements show that As(V) replaces surface hydroxyl groups and forms innersphere complex. High concentrations of HA may physically block the surface sites and inhibit the As(V) access. The adsorption of As(V) and HA decreased the point of zero charge of HFO from 7.8 to 5.8 and 6.3, respectively. The CD-MUSIC model described the zeta potential curves and adsorption edges of As(V) and HA reasonably well.

Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks.

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.

The effectiveness and feasibility of ball-milled powdered activated carbon (BPAC) for removal of organic pesticides in conventional drinking water treatment process.

In the conventional drinking water treatment process (CDWTP), powdered activated carbon (PAC) is commonly used for removing organic pesticides, or other organic contaminants. However, the hydraulic retention time (HRT) in CDWTP is insufficient for fulfilling PAC adsorption equilibrium to realize its full capacity. This study examined the adsorption kinetics, adsorption thermal dynamics, and removal efficiency for six organic pesticides using the ball-milled PAC (BPAC) with varying particle sizes in CDWTP. Based on the experiments with the pesticides of atrazine, diazinon, dimethoate, fenitrothion, isoproturon and thiometon, the results indicated that as the particle size reduced from around 38 µm for the commercial PAC to 1 µm for the BPAC, the adsorption rates for hydrophobic pesticides increased up to twentyfold. Diffusional adsorption from the bulk solution to the external PAC surface is the most likely predominant mechanism. This could allow a sufficient pesticides' adsorption within the limited HRT and to achieve a great depth removal of these toxic compounds. However, the addition of BPAC with a diameter of 1 µm was observed to significantly increase residual particles in treated water after the conventional treatment process. With a further systematic evaluation of both adsorption rate and particle penetration, a particle size of around 6 µm BPAC was considered a practical compromise between the adsorption rate and particle penetration for real application. Results from five surface waters of different water quality indicated that, compared to commercial PAC, application of 6 µm BPAC could achieve up to a 75% reduction in adsorbent dosage while maintaining around the same pesticide removal efficiencies. Additionally, thermodynamic analyses suggest that adsorption of these pesticides could be enthalpically or entropically driven depending on the degree of pesticide hydrophobicity.

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment.

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model's superior performance on segmentation and clinical function assessment.

Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.

Simultaneous removal of NH4(+) and PO4(3-) at low concentrations from aqueous solution by modified converter slag.

In this study, modified converter slag (CS) was characterized in relation to its physicochemical structure, and used for the simultaneous removal of NH4(+) and PO4(3-) at low concentrations from aqueous solutions. The effects of contact time, pH, adsorbent dosage, and temperature on the adsorption process were studied in batch experiments. The results showed that the adsorption capacity of modified converter slag was found to sharply increase as a result of modification. The optimum pH is 5-8. The adsorption process was able to reach equilibrium in 90 minutes. Kinetic data were best described by the pseudo-second-order model. The sorption isotherms were a good fit with the Langmuir model. The maximum adsorption capacities of modified converter slag for NH4(+) and PO4(3-) were 2.59 mg/g and 1.185 mg/g, respectively. Thermodynamic studies indicated that the adsorption was a spontaneous and endothermic process. The calculated values of enthalpy change indicated that ligand exchange, chemical reactions, and precipitation are dominating mechanisms of PO4(3-) removal, while physisorption and ion-exchange are major mechanisms of NH4(+) removal.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Effects of Ca(OH)2 assisted aluminum sulfate coagulation on the removal of humic acid and the formation potentials of tri-halomethanes and haloacetic acids in chlorination.

The effects of addition of calcium hydroxide on aluminum sulphate (or alum) coagulation for removal of natural organic matter (NOM) and its subsequent effect on the formation potentials of two major types of regulated disinfection byproducts (DBPs), haloacetic acids (HAAs) and trihalomethanes (THMs), have been examined. The results revealed several noteworthy phenomena. At the optimal coagulation pH (i.e. 6), the coagulation behavior of NOM water solutions versus alum dose, showed large variation and a consequent great change in the formation potentials of the DBPs at certain coagulant doses. However, with addition of a relatively small amount of Ca(OH)2, although the zeta potential of coagulated flocs remained almost the same, NOM removal became more consistent with alum dose. Importantly, also the detrimental effect of charge reversal on NOM removal at the low coagulant dose disappeared. This resulted in a steady decrease in the formation potentials of DBPs as a function of the coagulant dose. Moreover, the addition of Ca(OH)2 broadened the pH range of alum coagulation and promoted further reduction of the formation potentials of the DBPs. The enhancement effects of Ca(OH)2 assisted alum coagulation are especially pronounced at pH 7 and 8. Finally, synchronous fluorescence spectra showed that the reduction in DBPs formation potential by Ca(OH)2-assisted alum coagulation was connected to an enhanced removal of small hydrophobic and hydrophilic HA molecules. Ca(OH)2-assistance of alum coagulation appeared to increase substantially the removal of the hydrophilic HA fraction responsible for HAAs formation, prompting further reduction of HAA formation potentials.

DVS-Net: Dual-domain Variable Splitting Network for Accelerated Parallel MRI Data.

Parallel imaging is an important method to accel-erate the acquisition of magnetic resonance imaging data, which can shorten the breath-hold times and reduce motion artifacts. In this paper, we propose a joint frequency domain and image domain (dual-domain) reconstruction method by introducing the full sampling condition for the undersampled multi-coil MR data. The motivation is that the dual domain method can provide more information for accurate image reconstruction. An efficient iterative algorithm is developed based on the variable splitting technique and alternating direction method of multipliers, which is unrolled into an end-to-end trainable deep neural network. We evaluate the proposed network on complex valued multi-coil knee images for both 6-fold and 8-fold acceleration factors, and compare with both variational and deep learning based reconstruction algorithms. The numerical results demonstrate that our method provides better reconstruction accuracy and perceptual quality by making using of the dual domain information. Clinical relevance: This improves the reconstruction quality for accelerated parallel MRI data both visually and quantitatively.

A sensitive approach for screening acetylcholinesterase inhibition of water samples using ultra-performance liquid chromatography-tandem mass spectrometry.

A sensitive assay was developed to evaluate inhibitory effects of aqueous solution on acetylcholinesterase (AChE) activity via measuring hydrolysis rates of acetylcholine (ACh) based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Upon having identified precursor ions and product ions of the ACh and its hydrolysis products choline (Ch), the separation chromatogram for these two analytes has been established using a 50 mm reverse-phase BEH Shield RP18 column. The total chromatographic separation time is 7 min; limits of detection (LODs) for ACh and Ch are 0.14 µg L-1 and 0.12 µg L-1, respectively. A simple method for inactivation of AChE and optimization of operational parameters were then sequentially performed. It was found that adjusting solution pH to 2.5 not only can terminate the enzymatic reaction but also solve band shifting and broadening caused by aqueous matrices in chromatographic separation during UPLC-MS/MS detection. Under conditions of 0.00075 U mL-1 AChE, initial concentration of ACh at 100 µg L-1 and 20 min observation time, IC50 values of the proposed assay for chlorpyrifos-oxon, diazoxon, malaoxon, methidathion oxon, omethoate and paraoxon were 3.5 nM, 16.8 nM, 2.4 nM, 6.8 nM, 270 nM and 36.9 nM, respectively. They are 4.5-51.9 times smaller than those reported in a LC-MS based method, and >120 times lower than those obtained by the traditional Ellman method. The results suggested that, the proposed assay significantly increases the sensitivity of commercial AChE. In addition, inhibition efficiencies of three surface waters, a groundwater and four commercial brands of bottled drinking water samples on AChE activity were firstly measured using this UPLC-MS/MS based method. These water samples were proved to have different inhibitory effects on AChE activity, and the inhibition efficiencies dependent on concentrations of dissolved organic carbon (DOC) but are independent of UV absorbance at 254 nm (UV254) values. These results indicate that the proposed method has advantages of high sensitivity over all other conventional methods. It may become a promising AChE inhibition assay for assessing toxicity of aqueous solution containing neurotoxicity contaminants such as organophosphorus pesticides (OPPs) at low levels, or used to evaluate potential inhibition effects of natural waters on AChE activity.