Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data.

BACKGROUND: Real-valued mutual information (MI) has been used in spatial functional network connectivity (FNC) to measure high-order and nonlinear dependence between spatial maps extracted from magnitude-only functional magnetic resonance imaging (fMRI). However, real-valued MI cannot fully capture the group differences in spatial FNC from complex-valued fMRI data with magnitude and phase dependence. METHODS: We propose a complete complex-valued MI method according to the chain rule of MI. We fully exploit the dependence among magnitudes and phases of two complex-valued signals using second and fourth-order joint entropies, and propose to use a Gaussian copula transformation with a lower bound property to avoid inaccurate estimation of joint probability density function when computing the joint entropies. RESULTS: The proposed method achieves more accurate MI estimates than the two histogram-based (normal and symbolic approaches) and kernel density estimation methods for simulated signals, and enhances group differences in spatial functional network connectivity for experimental complex-valued fMRI data. COMPARISON WITH EXISTING METHODS: Compared with the simplified complex-valued MI and real-valued MI, the proposed method yields higher MI estimation accuracy, leading to 17.4 % and 145.5 % wider MI ranges, and more significant connectivity differences between healthy controls and schizophrenia patients. A unique connection between executive control network (EC) and right frontal parietal areas, and three additional connections mainly related to EC are detected than the simplified complex-valued MI. CONCLUSIONS: With capability in quantifying MI fully and accurately, the proposed complex-valued MI is promising in providing qualified FNC biomarkers for identifying mental disorders such as schizophrenia.

Synergistic Construction of Sub-Nanometer Channel Membranes through MOF-Polymer Composites: Strategies and Nanofiltration Applications.

The selective separation of small molecules at the sub-nanometer scale has broad application prospects in the field, such as energy, catalysis, and separation. Conventional polymeric membrane materials (e.g., nanofiltration membranes) for sub-nanometer scale separations face challenges, such as inhomogeneous channel sizes and unstable pore structures. Combining polymers with metal-organic frameworks (MOFs), which possess uniform and intrinsic pore structures, may overcome this limitation. This combination has resulted in three distinct types of membranes: MOF polycrystalline membranes, mixed-matrix membranes (MMMs), and thin-film nanocomposite (TFN) membranes. However, their effectiveness is hindered by the limited regulation of the surface properties and growth of MOFs and their poor interfacial compatibility. The main issues in preparing MOF polycrystalline membranes are the uncontrollable growth of MOFs and the poor adhesion between MOFs and the substrate. Here, polymers could serve as a simple and precise tool for regulating the growth and surface functionalities of MOFs while enhancing their adhesion to the substrate. For MOF mixed-matrix membranes, the primary challenge is the poor interfacial compatibility between polymers and MOFs. Strategies for the mutual modification of MOFs and polymers to enhance their interfacial compatibility are introduced. For TFN membranes, the challenges include the difficulty in controlling the growth of the polymer selective layer and the performance limitations caused by the "trade-off" effect. MOFs can modulate the formation process of the polymer selective layer and establish transport channels within the polymer matrix to overcome the "trade-off" effect limitations. This review focuses on the mechanisms of synergistic construction of polymer-MOF membranes and their structure-nanofiltration performance relationships, which have not been sufficiently addressed in the past.

Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia.

BACKGROUND: Dynamic spatial functional network connectivity (dsFNC) has shown advantages in detecting functional alterations impacted by mental disorders using magnitude-only fMRI data. However, complete fMRI data are complex-valued with unique and useful phase information. METHODS: We propose dsFNC of spatial source phase (SSP) maps, derived from complex-valued fMRI data (named SSP-dsFNC), to capture the dynamics elicited by the phase. We compute mutual information for connectivity quantification, employ statistical analysis and Markov chains to assess dynamics, ultimately classifying schizophrenia patients (SZs) and healthy controls (HCs) based on connectivity variance and Markov chain state transitions across windows. RESULTS: SSP-dsFNC yielded greater dynamics and more significant HC-SZ differences, due to the use of complete brain information from complex-valued fMRI data. COMPARISON WITH EXISTING METHODS: Compared with magnitude-dsFNC, SSP-dsFNC detected additional and meaningful connections across windows (e.g., for right frontal parietal) and achieved 14.6% higher accuracy for classifying HCs and SZs. CONCLUSIONS: This work provides new evidence about how SSP-dsFNC could be impacted by schizophrenia, and this information could be used to identify potential imaging biomarkers for psychotic diagnosis.

Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data.

Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex-valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude-only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex-valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex-valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex-valued fMRI, this framework is generalized to work with magnitude-only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex-valued data from University of New Mexico and magnitude-only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude-only fMRI data in terms of retaining more BOLD-related activity and fewer unwanted voxels, compared with amplitude-based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.

Risk factors for difficult mask ventilation and difficult intubation among patients undergoing pharyngeal and laryngeal surgery.

Background: The prediction of difficult mask ventilation (DMV) and difficult intubation (DI) are key questions in anesthesia fields. DMV or DI related to pharyngeal and laryngeal diseases are a special kind of difficult airways. However, there is a lack of risk factors for prediction. Methods: This study retrospectively collected data from patients who were admitted to the Eye & ENT Hospital of Fudan University from May 2014 to May 2018 and underwent laryngopharyngeal surgery under general anesthesia. Results: A total of 126 patients were included. Twenty patients suffered from DMV. Preoperative laryngeal obstruction classification (OR = 7.46, 95% CI: 2.56-21.76, P < 0.001) and airway patency after sevoflurane inhalation (OR = 10.96, 95% CI: 2.70-44.43, p = 0.001) were independently associated with DMV. Seventy-six patients could be intubated at the first attempt. Preoperative laryngeal obstruction grade (OR = 0.28, 95% CI: 0.13-0.62, P = 0.002), neoplasm size (OR = 0.43, 95% CI: 0.22-0.82, P = 0.011), and airway patency after sevoflurane inhalation (OR = 0.14, 95% CI: 0.05-0.36, P < 0.001) were independently associated with first-attempt successful intubation. Conclusion: Among patients with pharyngeal and laryngeal diseases, the degree of laryngeal obstruction before the operation and the degree of airway obstruction after inhaling sevoflurane are the risk factors of DMV. The degree of laryngeal obstruction before the operation, airway obstruction after inhaling sevoflurane, and the neoplasm size are the risk factors of DI.

Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts.

BACKGROUND: Spatial sparsity has been found to be in line with the intrinsic characteristic of brain activation. However, identifying a sparse representation of complex-valued fMRI data is challenging due to high noise within the phase data. NEW METHODS: We propose to reduce the noise by combining real and imaginary parts of complex-valued fMRI data along spatial and temporal dimensions to form a real-valued spatiotemporal concatenation model. This model not only enables flexible usage of existing real-valued sparse representation algorithms but also allows for the reconstruction of complex-valued spatial and temporal components from their real and imaginary estimates. We propose to select components from both real and imaginary estimates to reconstruct the complex-valued component, using phase denoising to recover weak brain activity from high-amplitude noise. RESULTS: The K-SVD algorithm was used to obtain a sparse representation within the spatiotemporal concatenation model. The results from simulated and experimental complex-valued fMRI datasets validated the efficacy of our method. COMPARISON WITH EXISTING METHODS: Compared to a magnitude-only approach, the proposed method detected additional voxels manifest within several specific regions expected to be involved but likely missing from the magnitude-only data, e.g., in the anterior cingulate cortex region. Simulation results showed that the additional voxels were accurate and unique information from the phase data. Compared to a complex-valued dictionary learning algorithm, our method exhibited lower noise for both magnitude and phase maps. CONCLUSIONS: The proposed method is robust to noise and effective for identifying a sparse representation of the natively complex-valued fMRI data.

Heat-shock protein 70 (HSP70) polymorphisms affect the risk of coke-oven emission-induced neurobehavioral damage.

OBJECTIVES: Epidemiology studies indicated that coke-oven workers with long-term exposure to polycyclic aromatic hydrocarbons (PAHs) often have some neurobehavioral abnormalities especially impairment for cognitive function, while the underlying mechanisms are not fully understood. Numerous studies have indicated the antioxidant and anti-apoptosis roles of heat shock protein 70 (Hsp70). The genetic polymorphisms in HSP70 genes are associated with multiple diseases including neurotoxicity. However, it is unclear whether HSP70 polymorphisms are related to the neurotoxicity of PAH. We, therefore, investigate the possible association between HSP70 polymorphisms and neurobehavioral abnormalities. METHODS: 188 coke-oven workers and 137 control workers were recruited in this study. Emotional and cognitive function was assessed using the WHO/NCTB. HSP70 polymorphisms (HSP70-1 G190C, HSP70-2 G1267 A and HSP70-hom T2437C) were checked by PCR-RFLP. RESULTS: The results indicated that HSP70-1 CC genotypes in coke-oven workers were associated with poor neurobehavioral performance such as the attention /response speed and visual perception/memory, while the HSP70-2 AA genotypes were associated with lower short-term auditory memory. CONCLUSIONS: HSP70-1 CC and HSP70-2 AA genotypes in coke-oven workers may increase the risk for neurobehavioral damage, especially attention, learning and memory.

Erratum: Publisher Correction: Cytological, physiological, and transcriptomic analyses of golden leaf coloration in Ginkgo biloba L.

[This corrects the article DOI: 10.1038/s41438-018-0015-4.].

Cytological, physiological, and transcriptomic analyses of golden leaf coloration in Ginkgo biloba L.

Ginkgo biloba is grown worldwide as an ornamental plant for its golden leaf color. However, the regulatory mechanism of leaf coloration in G. biloba remains unclear. Here, we compared G. biloba gold-colored mutant leaves and normal green leaves in cytological, physiological and transcriptomic terms. We found that chloroplasts of the mutant were fewer and smaller, and exhibited ruptured thylakoid membranes, indistinct stromal lamellae and irregularly arranged vesicles. Physiological experiments also showed that the mutant had a lower chlorophyll, lower flavonoid and higher carotenoid contents (especially lutein). We further used transcriptomic sequencing to identify 116 differentially expressed genes (DEGs) and 46 transcription factors (TFs) involved in chloroplast development, chlorophyll metabolism, pigment biosynthesis and photosynthesis. Among these, the chlorophyll biosynthesis-related PPO showed down-regulation, while chlorophyll degradation-related NYC/NOL had up-regulated expression in mutant leaves. Z-ISO, ZDS, and LCYE, which are involved in carotenoid biosynthesis were up-regulated. Quantitative real-time PCR (RT-qPCR) further confirmed the altered expression levels of these genes at three stages. The alteration of PPO and NYC/NOL gene expression might affect chlorophyll biosynthesis and promote degradation of chlorophyll b to chlorophyll a, while the up-regulated genes Z-ISO, ZDS and LCYE enhanced carotenoid accumulation. Consequently, changes in the ratio of carotenoids to chlorophylls were the main factors driving the golden leaf coloration in the mutant G. biloba.

Opposite effects on tumor growth depending on dose of Achyranthes bidentata polysaccharides in C57BL/6 mice.

We report here the investigation on the effects of Achyranthes bidentata polysaccharides (ABPS) against Lewis lung cancer (LLC) in C57BL/6 mice. Depending on its doses administered in vivo, ABPS was shown to have inhibitory as well as stimulative effects on tumor growth in LLC-bearing C57BL/6 mice. ABPS at low dose could significantly inhibit LLC growth, while high dose treatment of ABPS stimulated, rather than inhibited, LLC growth in C57BL/6 mice. Tumor cell cycle analysis revealed that more tumor cells arrested at G2/M phase after daily low dose intraperitoneal injection of ABPS for consecutive 15 days. The spleen weight increased markedly in LLC-bearing C57BL/6 mice treated with high dose of ABPS. However, the spleen cytotoxicity activity was significantly despaired in mice of high dose treatment of ABPS. Furthermore, we demonstrated that the expressions of IL-6 mRNA and TNF-alpha mRNA were markedly up-regulated in spleens from mice treated with a high dose of ABPS by RT-PCR reactions, suggesting that the low dose of ABPS inhibits tumor growth via its effect on tumor cell cycle distribution, rather than activation of NK activity as previously suggested. We postulate that the stimulation of tumor growth by high dose of ABPS is associated with dysfunction of NK cell and up-regulation of IL-6 mRNA and TNF-alpha mRNA expression in murine spleen.

[Effect of gastric mucosa cell turnover on the adaptive cytoprotection in chronic alcohol drinking rat].

AIM: To investigate the correlation between the gastric adaptive cytoprotection and the low concentration alcohol intake in a chronic drinking rat model and the effect of chronic ethanol exposures on the cell turnover of the gastric mucosa and its possible role in adaptive cytoprotection. METHODS: Sprague-Dawley rats received the drinking water containing 6% (v/v) ethanol as their only water intake for 28 days. In the different stages of the 28 days (1st, 3rd, 7th, 14th and 28th days), the stomachs of the rats were cannulated and perfused with pure ethanol, and the severity of mucosal lesions was measured in 2 hours at the end of perfusion respectively. The cell proliferation and apoptosis in gastric mucosa of rats in different groups were analyzed by flow cytometer, immunohistochemistry and computer image analysis. RESULTS: Pure ethanol caused ulcer and haemorrhagic damage in the corpus and antral mucosa of the control rats. These lesions were prevented by pretreatment of the animals with ethanol exposure in the 3 rd to 14 th days. The damage index was decreased by 80%, as compared with those in control rats. There was no significant difference in the rats exposed to the ethanol in the 1st and 28th days. Compared with control, the cell apoptosis in gastric mucosa of the rats was enhanced during they exposure to the ethanol in the 3rd to 28th days. Otherwise the cell proliferation was increased in the 3rd to 28th days, and decreased in the 28th days, respectively. CONCLUSION: Chronic adequate alcohol intake may enhance the cell turnover of gastric mucosa and lead to an adaptive cytoprotection. Long-term stimulus with the low concentration ethanol may cause the atrophy of gastric mucosa and reduce the gastric mucosal cytoprotective effect.