Alzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its ...progression, no effective cures are available for AD. Accurate early-stage diagnosis of AD is vital for the prevention and intervention of the disease progression. Hippocampus is one of the first affected brain regions in AD. To help AD diagnosis, the shape and volume of the hippocampus are often measured using structural magnetic resonance imaging (MRI). However, these features encode limited information and may suffer from segmentation errors. Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data. Firstly, a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification. Then, we construct a 3D Densely Connected Convolutional Networks (3D DenseNet) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task. Finally, the learned features from the multi-task CNN and DenseNet models are combined to classify disease status. Our method is evaluated on the baseline T1-weighted structural MRI data collected from 97 AD, 233 MCI, 119 Normal Control (NC) subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves a dice similarity coefficient of 87.0% for hippocampal segmentation. In addition, the proposed method achieves an accuracy of 88.9% and an AUC (area under the ROC curve) of 92.5% for classifying AD vs. NC subjects, and an accuracy of 76.2% and an AUC of 77.5% for classifying MCI vs. NC subjects. Our empirical study also demonstrates that the proposed multi-model method outperforms the single-model methods and several other competing methods.
Extractive distillation is an effective method for separating azeotropic or close boiling point mixtures by adding a third component. Various technologies for performing the extractive distillation ...process have been explored to protect the environment and save resources. This paper focuses on the improvement of these advanced technologies in recent years. Extractive distillation is retrieved and analyzed from the view of phase equilibrium, selection of solvent in extractive distillation, process design, energy conservation, and dynamic control. The quantitative structure–property relationship used in extractive distillation is discussed, and the future development of extractive distillation is proposed to determine how the solvent affects the relative volatility of the separated mixture. In the steady state design, the relationship between the curvature of the residue curve and parameters of the optimal steady state is also highlighted as another field worthy of further study to simplify the distillation process.
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The bioaccumulation and biomagnification of perfluoroalkyl acids (PFAAs) in temperate urban lacustrine ecosystems is poorly understood. We investigated the occurrence and trophic transfer of and ...probabilistic health risk from 15 PFAAs in the food web of Luoma Lake, a temperate urban lake in East China. The target PFAAs were widely distributed in the water (∑PFAA: 77.09 ± 9.07 ng/L), suspended particulate matter (SPM) (∑PFAA: 284.07 ± 118.05 ng/g dw), and sediment samples (∑PFAA: 67.77 ± 17.96 ng/g dw) and occurred in all biotic samples (∑PFAA: 443.27 ± 124.89 ng/g dw for aquatic plants; 294.99 ± 90.82 for aquatic animals). PFBA was predominant in water and SPM, with 40.11% and 21.35% of the total PFAAs, respectively, while PFOS was the most abundant in sediments (14.11% of the total PFAAs) and organisms (14.33% of the total PFAAs). Sediment exposure may be the major route of biological uptake of PFAAs. The PFAA accumulation capacity was the highest in submerged plants, followed by emergent plants > bivalves > crustaceans > fish > floating plants. Long-chain PFAAs were biomagnified, and short-chain PFAAs were biodiluted across the entire lacustrine food web. PFOS exhibited the greatest bioaccumulation and biomagnification potential among the target PFAAs. However, biomagnification of short-chain PFAAs was also observed within the low trophic-level part of the food web. Human health risk assessment indicated that perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA) posed health risks to all age groups, while the other PFAAs were unlikely to cause immediate harm to consumers in the region. This study fills a gap in the knowledge of the transfer of PFAAs in the food webs of temperate urban lakes.
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•We studied the biotransfer of PFAAs in the food web of a temperate urban lake.•Sediment exposure was the major source of ingested PFAAs for organisms.•Long-chain PFAAs were biomagnified; short-chain PFAAs were diluted in the food web.•PFOS exhibited the greatest bioaccumulation and biomagnification potential.•PFOS and PFOA in aquatic products posed health risks to 2–6-year-old children.
Abstract
Background
Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and ...eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification.
Methods
A neuropathological diagnostic platform is designed comprising a slide scanner and deep convolutional neural networks (CNNs) to classify 5 major histological subtypes of glioma to assist pathologists. The CNNs were trained and verified on over 79 990 histological patch images from 267 patients. A logical algorithm is used when molecular profiles are available.
Results
A new model of the squeeze-and-excitation block DenseNet with weighted cross-entropy (named SD-Net_WCE) is developed for the glioma classification task, which learns the recognizable features of glioma histology CNN-based independent diagnostic testing on data from 56 patients with 17 262 histological patch images demonstrated patch level accuracy of 86.5% and patient level accuracy of 87.5%. Histopathological classifications could be further amplified to integrated neuropathological diagnosis by 2 molecular markers (isocitrate dehydrogenase and 1p/19q).
Conclusion
The model is capable of solving multiple classification tasks and can satisfactorily classify glioma subtypes. The system provides a novel aid for the integrated neuropathological diagnostic workflow of glioma.
It has long been debated whether tree leaves from shady environments exhibit higher photosynthetic induction efficiency than those from sunny environments and how the shade tolerance of tree species ...and the light environment of leaves contribute to the dynamics of photosynthesis. To address these questions, we investigated leaf photosynthetic responses to simulated changes of light intensity in seedlings of six tree species with differential shade tolerance. The seedlings were growing under different light environments in a lowland tropical forest. We proposed an index of relative shade tolerance (RST) to assess species-specific capacity to tolerate shade, and we quantified the light environment of individual leaves by the index of daily light integral (DLI), the averaged daily total light intensity. We obtained the following results. Photosynthetic induction efficiency (IE), which is the ratio of the achieved carbon gain to the expected carbon gain, was significantly higher for species with a higher RST than for that with a lower RST. The impacts of light environment on the IE of individual leaves within the same species varied largely among different species. In the three species with relatively low RST, the IE of individual leaves decreased at higher DLIs when DLI < 10 mol m-2 d-1. Seedlings with high initial stomatal conductance before induction (gs50) possessed a higher IE than those with low gs50 from the same species. A trade-off existed between IE and steady-state photosynthetic rates. These results suggest a complex interaction between the shade tolerance of species and the light environments of individual leaves for photosynthetic induction, and provide new insights into the adaptation strategy for understory seedlings under sunfleck environments.
Atherosclerosis remains the most common cause of deaths worldwide. Endothelial cell apoptosis is an important process in the progress of atherosclerosis, as it can cause the endothelium to lose their ...capability in regulating the lipid homeostasis, inflammation, and immunity. Endothelial cell injury can disrupt the integrity and barrier function of an endothelium and facilitate lipid deposition, leading to atherogenesis. Chinese medicine techniques for preventing and treating atherosclerosis are gaining attention, especially natural products. In this study, we demonstrated that gypenoside could decrease the levels of serum lipid, alleviate the formation of atherosclerotic plaque, and lessen aortic intima thickening. Gypenoside potentially activates the PI3K/Akt/Bad signal pathway to modulate the apoptosis-related protein expression in the aorta. Moreover, gypenoside downregulated mitochondrial fission and fusion proteins, mitochondrial energy-related proteins in the mouse aorta. In conclusion, this study demonstrated a new function of gypenoside in endothelial apoptosis and suggested a therapeutic potential of gypenoside in atherosclerosis associated with apoptosis by modulating mitochondrial function through the PI3K/Akt/Bad pathway.
•Fractional anisotropy (FA) and radiality index (RI) sampled along cortical columns.•FA local max/min and RI max at intermediate cortical depths in most regions.•Results consistent between repeated ...scans and across different healthy subjects.•FA and RI peaks more notable at the banks than the crown of gyri or fundus of sulci.•FA and RI peaks more notable as the cortical thickness increases.
High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.
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This wok proposed the extraction distillation coupled pervaporation (ED+PV) technology process using two different solvents to separate isopropanol (IPA) and diisopropyl ether (DIPE) ...from DIPE/IPA/H2O ternary heterogeneous azeotropes in industrial wastewater from the synthesis of isopropanol in this study. Based on strict design specifications, simulation and sequential iteration methods are used for process design and optimization. Compared to the ethylene glycol (EG)-EG+H2O process and the 1,3-propanediol (PDO)-IPA+H2O process, the total annual cost (TAC) of the EG-IPA+H2O process decreased by 20.76% and 7.86% (PDO). Compared to the EG-EG+H2O process, the TAC of the PDO-IPA+H2O process reduced 14%, but the global warming potential (GWP) and human toxicity of the PDO-IPA+H2O process increased 11.3% and 4.07% respectively. Compared to the PDO-IPA+H2O process, the EG-IPA+H2O process saves 7.86% (TAC), 9.78% (GWP) and 9.85% (human toxicity). The ED+PV process with EG is superior to PDO in factors of TAC, energy consumption, human toxicity and environment. The EG-IPA+H2O process changed the separation order of the products of the multi-azeotropic system, reduced the cost and energy conservation of the system, and enhanced the environmental protection evaluation of the process, is the best process through life cycle assessment for analyzing the economy, energy conservation, environmental assessment and human toxicity, designing cleaner products, controlling waste discharge, and promoting the chemical purification industry. This work provides a new process design and optimized separation ideas, will have a good guiding significance for the research and application separation of multi-azeotropic mixture with mixed solvents in organic wastewater from the cleaner chemical production, has been up to standard wastewater discharge process, and realized the development goal of carbon peak and carbon neutrality in the sustainable development of chemical clean industry.
The advantages of noninvasive, fast speed, low-cost, and nonionizing radiation hazard have made electrical impedance tomography (EIT) an attractive imaging technology in geological, medical, and ...industrial applications. However, the EIT image has low spatial resolution due to the limited number of independent measurements and soft-field property. Focused on the fact that, in some applications, the conductivity variation exists only in part of the sensing field, this paper proposes a new strategy to enhance the image quality by restricting the image reconstruction within a region of interest (RROI). Compared with the conventional image reconstruction over the entire sensing field, the proposed strategy of RROI can improve regional image resolution effectively without increasing the number of electrodes and the complexity of the data acquisition system. The implementation of a conventional sensitivity-theorem-based conjugate gradient algorithm with the proposed RROI strategy is presented, and the improvement of image spatial resolution in region of interest is demonstrated by both simulations and phantom experiments.
Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is ...used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.
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