A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level ...differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k+l) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k+l, and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.
A novel light field super-resolution algorithm to improve the spatial and angular resolutions of light field images is proposed in this work. We develop spatial and angular super-resolution (SR) ...networks, which can faithfully interpolate images in the spatial and angular domains regardless of the angular coordinates. For each input image, we feed adjacent images into the SR networks to extract multi-view features using a trainable disparity estimator. We concatenate the multi-view features and remix them through the proposed adaptive feature remixing (AFR) module, which performs channel-wise pooling. Finally, the remixed feature is used to augment the spatial or angular resolution. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms on various light field datasets. The source codes and pre-trained models are available at https://github.com/keunsoo-ko/ LFSR-AFR
The large‐scale production of metal–air batteries, an appealing solution for next‐generation energy storage, requires low‐cost, earth‐abundant, and efficient oxygen electrode materials, yet insights ...into active catalyst structures and synergistic reactivity remain largely unknown. Here, a new bifunctional oxygen electrode based on nitrogen‐doped carbon nanotubes decorated by spinel CuCo2O4 quantum dots (CuCo2O4/N‐CNTs) is reported, outperforming the benchmark of state‐of‐the‐art noble metal catalysts. Combining spectroscopic characterization and electrochemical studies, a prominent synergetic effect between CuCo2O4 and N‐doped carbon nanotubes is uncovered: the high conductivity, large active surface area, and increase in the number of catalytic sites induced by Cu doping (i.e., Cu2+ and CuN) can be beneficial to the overall electrocatalytic activities. Remarkably, the native flexibility of CuCo2O4/N‐CNTs allows its direct use as reversible oxygen electrodes in Zn–air batteries either with liquid alkaline electrolyte or in the all‐solid‐state configuration. The prepared devices demonstrate excellent discharging/charging performance, large energy density (83.83 mW cm−2 in liquid state, 1.86 W g−1 in all‐solid‐state), and long lifetime (48 h in liquid state, 9 h in all‐solid‐state), holding great promise in the practical application of rechargeable metal–air batteries and other fuel cells.
Advanced Cu Co bimetallic oxide quantum dots are decorated on nitrogen‐doped carbon nanotubes to serve as the bifunctional oxygen catalyst. A strong synergetic coupling in CuCo2O4/N‐CNTs is proposed, which provides advantaged local chemical environment and enriched catalytic sites. Benefiting from these features, CuCo2O4/N‐CNTs with reversible oxygen catalytic activity is capable of operating the new‐generation rechargeable zinc–air batteries.
Kidney transplantation is the optimal therapy for end‐stage renal disease, prolonging survival and reducing spending. Prior economic analyses of kidney transplantation, using Markov models, have ...generally assumed compatible, low‐risk donors. The economic implications of transplantation with high Kidney Donor Profile Index (KDPI) deceased donors, ABO incompatible living donors, and HLA incompatible living donors have not been assessed. The costs of transplantation and dialysis were compared with the use of discrete event simulation over a 10‐year period, with data from the United States Renal Data System, University HealthSystem Consortium, and literature review. Graft failure rates and expenditures were adjusted for donor characteristics. All transplantation options were associated with improved survival compared with dialysis (transplantation: 5.20‐6.34 quality‐adjusted life‐years QALYs vs dialysis: 4.03 QALYs). Living donor and low‐KDPI deceased donor transplantations were cost‐saving compared with dialysis, while transplantations using high‐KDPI deceased donor, ABO‐incompatible or HLA‐incompatible living donors were cost‐effective (<$100 000 per QALY). Predicted costs per QALY range from $39 939 for HLA‐compatible living donor transplantation to $80 486 for HLA‐incompatible donors compared with $72 476 for dialysis. In conclusion, kidney transplantation is cost‐effective across all donor types despite higher costs for marginal organs and innovative living donor practices.
Kidney transplant in contemporary practice remains cost‐effective despite a higher cost of transplant associated with donor quality and immunologic complexity. See Jay and Abecassis's editorial on page 1044.
Abstract
Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have ...been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.
Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist ...mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.
A novel algorithm to remove rain or snow streaks from a video sequence using temporal correlation and low-rank matrix completion is proposed in this paper. Based on the observation that rain streaks ...are too small and move too fast to affect the optical flow estimation between consecutive frames, we obtain an initial rain map by subtracting temporally warped frames from a current frame. Then, we decompose the initial rain map into basis vectors based on the sparse representation, and classify those basis vectors into rain streak ones and outliers with a support vector machine. We then refine the rain map by excluding the outliers. Finally, we remove the detected rain streaks by employing a low-rank matrix completion technique. Furthermore, we extend the proposed algorithm to stereo video deraining. Experimental results demonstrate that the proposed algorithm detects and removes rain or snow streaks efficiently, outperforming conventional algorithms.
The case of dual manipulators with shared workspace, asynchronous manufacturing tasks, and independent objects is named a dual manipulator cooperative manufacturing system, which requires ...collision-free path planning as a vital issue in terms of safety and efficiency. This paper combines the mathematical modeling method with the time sampling method in the classification of robot path-planning algorithms. Through this attempt we can achieve an optimal local search path during each sampling period interval. Our strategy is to build the corresponding non-linear optimization functions set based on the motion characteristics of the dual manipulator system. In this way, the path-planning problem can be turned into a purely mathematical problem of solving the non-linear optimization programming equations set. The spatial geometric analysis is used to linearize the predicted dual-manipulator minimum distance equation, thus linearizing the non-linear optimization equations set. Finally, this system of linear optimization equations will be mapped directly into a virtual Euclidean space and then solved intuitively using the spatial geometry theory. By simulation and comparing with the previous strategies, we find that the planning results of the newly proposed planning strategy are smoother and have shorter deviations as well as a higher algorithmic efficiency in terms of spatial geometric properties.
As emerging contaminants, antibiotic resistance genes (ARGs) have become a public concern. This study aimed to investigate the occurrence and diversity of ARGs, and variation in the composition of ...bacterial communities in source water, drinking water treatment plants, and tap water in the Pearl River Delta region, South China. Various ARGs were present in the different types of water. Among the 27 target ARGs, floR and sul1 dominated in source water from three large rivers in the region. Pearson correlation analysis suggested that sul1, sul2, floR, and cmlA could be potential indicators for ARGs in water samples. The total abundance of the detected ARGs in tap water was much lower than that in source water. Sand filtration and sedimentation in drinking water treatment plants could effectively remove ARGs; in contrast, granular activated carbon filtration increased the abundance of ARGs. It was found that Pseudomonas may be involved in the proliferation and dissemination of ARGs in the studied drinking water treatment system. Bacteria and ARGs were still present in tap water after treatment, though they were significantly reduced. More research is needed to optimize the water treatment process for ARG removal.
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•Diverse ARGs present in different types of waters.•Sul1, sul2, floR, cmlA could be potential indicators for ARGs in the water samples.•Sand filter and sedimentation were effective in removing ARGs.•Granular activated carbon filtration increased the ARGs abundance.•ARGs still existed in tap water after treatment despite significantly reduced.
Abstract
Background
The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in ...population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China.
Methods
We conducted a cross-sectional analysis of 4923 adults aged ≥55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI.
Results
The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374,
p
< 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 (
p
< 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall (
p
< 0.0001). Increasing age (MMSE: OR = 2.073 for ≥75 years; MoCA: OR = 1.869 for≥75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all
p
< 0.05).
Conclusions
MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.
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