Original speech and its echo are segregated and separately processed in the human brain.

Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.

PathNet: Path-Selective Point Cloud Denoising.

Current point cloud denoising (PCD) models optimize single networks, trying to make their parameters adaptive to each point in a large pool of point clouds. Such a denoising network paradigm neglects that different points are often corrupted by different levels of noise and they may convey different geometric structures. Thus, the intricacy of both noise and geometry poses side effects including remnant noise, wrongly-smoothed edges, and distorted shape after denoising. We propose PathNet, a path-selective PCD paradigm based on reinforcement learning (RL). Unlike existing efforts, PathNet enables dynamic selection of the most appropriate denoising path for each point, best moving it onto its underlying surface. We have two more contributions besides the proposed framework of path-selective PCD for the first time. First, to leverage geometry expertise and benefit from training data, we propose a noise- and geometry-aware reward function to train the routing agent in RL. Second, the routing agent and the denoising network are trained jointly to avoid under- and over-smoothing. Extensive experiments show promising improvements of PathNet over its competitors, in terms of the effectiveness for removing different levels of noise and preserving multi-scale surface geometries. Furthermore, PathNet generalizes itself more smoothly to real scans than cutting-edge models.

Apelin receptor inhibition in ischemia-reperfused mouse hearts protected by endogenous n-3 polyunsaturated fatty acids.

Background: While the protective effects of n-3 polyunsaturated fatty acids (PUFAs) on cardiac ischemia-reperfusion (IR) injury have been previously reported, limited data are available regarding how these fatty acids affect membrane receptors and their downstream signaling following IR injury. We aimed to identify potential receptors activated by n-3 PUFAs in IR hearts to understand the regulatory mechanisms of these receptors. Methods: We used fat-1 mice, which naturally have elevated levels of n-3 PUFAs, and C57BL/6J mice as a control group to create a myocardial IR injury model through Langendorff perfusion. We assessed the impact of endogenous n-3 PUFAs on left ventricular function, myocardial infarct size, myocardial apoptosis, and ATP production. RNA sequencing (RNA-seq) and bioinformatics analysis were conducted to identify molecular targets affected by n-3 PUFAs. Based on these analyses we then treated IR hearts of WT and fat-1 mice with an antagonist (ML221) or an agonist (apelin-13) for the predicted receptor to assess cardiac contractile function and intracellular signaling pathways. An in vitro hypoxia-reoxygenation (HR) model was also used to confirm the effects of n-3 PUFAs on the examined intracellular signaling pathways. Results: Endogenous n-3 PUFAs protected cardiac structure and function in post-IR hearts, and modulated phosphorylation patterns in the PI3K-AKT-mTOR signaling pathways. RNA-seq analysis revealed that n-3 PUFAs affected multiple biological processes as well as levels of the apelin receptor (APLNR). Consistent with a role for the PLNNR, ML221 synchronized the activation of the PI3K-AKT-mTOR signaling axis, suppressed the expression of PKCδ and phosphorylated p38α, upregulated PKCε expression, upregulated or restored the phosphorylation of myofilaments, and prevented myocardial injury and contractile dysfunction in WT IR hearts. By contrast, apelin-13 disrupted the PI3K-AKT-mTOR signaling axis in post-IR fat-1 hearts. The phosphorylation signaling targeted by APLNR inhibition in post-IR fat-1 hearts was also observed after treating HR cells with eicosatetraenoic acid (EPA). Conclusion: Endogenous n-3 PUFAs protect against post-IR injury and preserve cardiac contractile function possibly through APLNR inhibition. This inhibition synchronizes the PI3K-AKT-mTOR axis, suppresses detrimental phosphorylation signaling, and restores or increases myofilament phosphorylation in post-IR hearts. The beneficial effects observed in fat-1 transgenic mouse hearts can be attributed, at least in part, to elevated EPA levels. This study is the first to demonstrate that n-3 PUFAs protect hearts against IR injury through APLNR inhibition.

Nitrogen-doped carbon decorated 3D NiCoSe2 micro-flowers as high-performance anode materials for lithium-ion batteries.

Compared with monometallic selenides, bimetallic selenides have better synergistic effects and more abundant active sites for electrochemical reactions. As an important member of the transition metal oxide family, NiCoSe2 has been widely used in energy storage devices and has shown excellent electrochemical performance. So in this paper, nitrogen-doped carbon decorated NiCoSe2 composites (NiCoSe2/NC-700, NiCoSe2/NC-800, and NiCoSe2/NC-900) with a microflower structure were synthesized by calcining nickel-cobalt bimetallic organic skeleton materials at different temperatures, and were used as anode materials for rechargeable lithium-ion batteries. Because the MOF precursor has many advantages such as structural controllability, and a bimetal synergistic effect, the test results showed that the prepared NiCoSe2/NC composites have a special morphology, outstanding electrical conductivity, excellent lithium storage performance and electrochemical cycling performance in the process of being used as anode materials for lithium-ion batteries. The NiCoSe2/NC-800 materials displayed a high initial capacity (2099.8/1084.3 mA h g-1), and still maintained a high capacity (1041.2/989.9 mA h g-1) after 100 cycles at a current density of 0.1 A g-1 and in the voltage range of 0.01-3.0 V. In addition, at high current densities of 0.5 A g-1 and 1.0 A g-1, the increased capacity of NiCoSe2/NC composites may be due to the activation of electrodes and the pseudocapacitance during cycling. Through ex situ XRD experiments, the lithium storage mechanism of the NiCoSe2/NC-800 electrode material during cycling was further studied, and NiCoSe2/NC-800 was continuously converted into Ni, Co, and Li2Se during cycling.

CSDN: Cross-Modal Shape-Transfer Dual-Refinement Network for Point Cloud Completion.

How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, and then refine its local details. We are motivated to imitate the physical repair procedure to address point cloud completion. To this end, we propose a cross-modal shape-transfer dual-refinement network (termed CSDN), a coarse-to-fine paradigm with images of full-cycle participation, for quality point cloud completion. CSDN mainly consists of "shape fusion" and "dual-refinement" modules to tackle the cross-modal challenge. The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion. The second module refines the coarse output by adjusting the positions of the generated points, where the local refinement unit exploits the geometric relation between the novel and the input points by graph convolution, and the global constraint unit utilizes the input image to fine-tune the generated offset. Different from most existing approaches, CSDN not only explores the complementary information from images but also effectively exploits cross-modal data in the whole coarse-to-fine completion procedure. Experimental results indicate that CSDN performs favorably against twelve competitors on the cross-modal benchmark.

Clinical ultra-high resolution CT scans enabled by using a generative adversarial network.

BACKGROUND: Ultra-high resolution computed tomography (UHRCT) has shown great potential for the detection of pulmonary diseases. However, UHRCT scanning generally induces increases in scanning time and radiation exposure. Super resolution is a gradually prosperous application in CT imaging despite higher radiation dose. Recent works have proved that the convolution neural network especially the generative adversarial network (GAN) based model could generate high-resolution CT using phantom images or simulated low resolution data without extra dose. Research that used clinical CT particularly lung images are rare due to the difficulty in collecting paired dataset. PURPOSE: To generate clinical UHRCT in lung from low resolution computed tomography (LRCT) using a GAN model. METHODS: 43 clinical scans with LRCT and UHRCT were collected in this study. Paired patches were selected using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) threshold. A relativistic GAN with gradient guidance was trained to learn the mapping from LRCT to UHRCT. The performance of the proposed method was evaluated using PSNR and SSIM. A reader study with five-point Likert score (five for the worst and one for the best) is also applied to assess the proposed method in terms of general quality, diagnostic confidence, sharpness and denoise level. RESULTS: Experimental results show that our method got PSNR 32.60 ± 2.92 and SSIM 0.881 ± 0.057 on our clinical CT dataset, outperforming other state-of-the-art methods based on the simulated scenarios. Moreover, reader study shows that our method reached the good clinical performance in terms of general quality (1.14 ± 0.36), diagnostic confidence (1.36 ± 0.49), sharpness (1.07 ± 0.27) and high denoise level (1.29 ± 0.61) compared to other SR methods. CONCLUSION: This study demonstrated the feasibility of generating UHRCT images from LRCT without longer scanning time or increased radiation dose.

GeoDualCNN: Geometry-Supporting Dual Convolutional Neural Network for Noisy Point Clouds.

We propose a geometry-supporting dual convolutional neural network (GeoDualCNN) for both point cloud normal estimation and denoising. GeoDualCNN fuses the geometry domain knowledge that the underlying surface of a noisy point cloud is piecewisely smooth with the fact that a point normal is properly defined only when local surface smoothness is guaranteed. Centered around this insight, we define the homogeneous neighborhood (HoNe) which stays clear of surface discontinuities, and associate each HoNe with a point whose geometry and normal orientation is mostly consistent with that of HoNe. Thus, we not only obtain initial estimates of the point normals by performing PCA on HoNes, but also for the first time optimize these initial point normals by learning the mapping from two proposed geometric descriptors to the ground-truth point normals. GeoDualCNN consists of two parallel branches that remove noise using the first geometric descriptor (a homogeneous height map, which encodes the point-position information), while preserving surface features using the second geometric descriptor (a homogeneous normal map, which encodes the point-normal information). Such geometry-supporting network architectures enable our model to leverage previous geometry expertise and to benefit from training data. Experiments with noisy point clouds show that GeoDualCNN outperforms the state-of-the-art methods in terms of both noise-robustness and feature preservation.

Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds.

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate normals for noisy point clouds. Traditional normal estimation wisdom heavily depends on priors such as surface shapes or noise distributions, while learning-based solutions settle for single types of hand-crafted features. Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations. To this end, several feature modules are developed and incorporated into Refine-Net by a novel connection module. Besides the overall network architecture of Refine-Net, we propose a new multi-scale fitting patch selection scheme for the initial normal estimation, by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal estimation framework: 1) point normals obtained from other methods can be further refined, and 2) any feature module related to the surface geometric structures can be potentially integrated into the framework. Qualitative and quantitative evaluations demonstrate the clear superiority of Refine-Net over the state-of-the-arts on both synthetic and real-scanned datasets.

Does Supplementary Information Add Value to Functional Food? Evidence from a Choice Experiment in China.

Establishing an effective choice architecture system enables people to improve their ability to make better food choices and encourage transformation of the food system into one that is more efficient, healthy, and sustainable. However, affecting consumer preferences by improving information supply is still a crucial issue that has not been comprehensively explored in China and many developing countries. This study aimed to identify the most effective information treatment method for increasing the likelihood of purchase and willingness to pay (WTP) for nutritionally enhanced eggs. A survey with five information treatments and a choice experiment was completed by a random sample of 2379 Chinese consumers, and the mixed logit model was subsequently applied to interpret the results. It was found that when nutritional information (NI), health benefit information (HBI), and/or market status quo information (MSQ) was presented to consumers, their utility increased. Different schemes had different effects on participants' WTP. The HBI from scientific research institution, provided in the form of leaflets, has the most significant effect on improving WTP, increasing the WTP of consumers by 31.65%. WTP for functional eggs increased similarly in response to NI and MSQ information. However, adding NI to HBI did not significantly increase the value of functional eggs, especially when the information was presented to the interviewees in the form of short videos. This research broadens the present knowledge and application of an information communication strategy by suggesting that the combination of information content, carriers, source influence consumer preference and WTP for nutritionally enhanced eggs. The results have implications for the communication practices of food enterprises to optimize their marketing strategies and improve product innovation to add more value to the functional food.

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network.

Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.

Consumer Preference for Nutritionally Fortified Eggs and Impact of Health Benefit Information.

The potential contribution of nutritionally fortified foods to the improvement of public health has been recognized internationally; however, the extent of people's preferences for functional foods and the influence of information intervention on consumers' acceptance and selection of nutritious foods have not been comprehensively studied in China. The main purposes of this study are to assess Chinese consumers' perceptions towards nutritionally fortified eggs and to explore the ways in which information about the health benefits and the international market status quo of functional eggs impacts Chinese consumers' preferences and their willingness to pay (WTP) for nutritional fortification. Discrete choice experiments were used to elicit the preferences of 740 egg consumers from four cities in China, and a mixed logit model subsequently utilized to interpret the results. It was found that the provision of comprehensive information regarding the health benefits of trace elements and unsaturated fatty acids, as well as insight into the current market status quo, significantly improved participants' preferences and their WTP for functional eggs. Furthermore, the heterogeneous effects of demographic and sociocultural factors on consumers' treatment of this information were explored. It was found that the study participants with children and those with prior purchase experience exhibited a relatively stronger response to the information, while those who had expressed trust in the human health benefits of the nutritional content of functional eggs were not as sensitive as expected to the additional information. Therefore, if the government and enterprises design appropriate information treatment and nudging methods according to the current consumption characteristics of nutritionally fortified eggs, this will help to improve consumers' purchase confidence in the health efficacy of functional food and play a positive role in promoting people's healthy food consumption.

Developmental trajectory of magnetic susceptibility in the healthy rhesus macaque brain.

Quantitative susceptibility mapping (QSM) is used to quantify iron deposition in non-human primates in our study. Although QSM has many applications in detecting iron deposits in the human brain, including the distribution of iron deposits in specific brain regions, the change of iron deposition with aging, and the comparison of iron deposits between diseased groups and healthy controls, few studies have applied QSM to non-human primates, while most animal brain experiments focus on biochemical and anatomical results instead of non-invasive experiments. Additionally, brain imaging in children's research is difficult, but can be substituted using young rhesus monkeys, which are very similar to humans, as research animals. Therefore, understanding the relationship between iron deposition and age in rhesus macaques' brains can offer insights into both the developmental trajectory of magnetic susceptibility in the animal model and the correlated evidence in children's research. Twenty-three healthy rhesus macaque monkeys (23 ± 7.85 years, range 2-29 years) were included in this research. Seven regions of interest (ROIs-globus pallidus, substantia nigra, dentate nucleus, caudate nucleus, putamen, thalamus, red nucleus) have been analyzed in terms of QSM and R2 * (apparent relaxation rate). Susceptibility in most ROIs correlated significantly with the growth of age, similarly to the results for R2 *, but showed different trends in the thalamus and red nucleus, which may be caused by the different sensitivities of myelination and iron deposition in R2 * and QSM analysis. By assessing the correlation between iron content and age in healthy rhesus macaques' brains using QSM, we provide a piece of pilot information on normality for advanced animal disease models. Meanwhile, this study also could serve as the normative basis for further clinical studies using QSM for iron content quantification. Due to the comparison of the susceptibility on the same experimental objects, this research can also provide practical support for future research on characteristics for QSM and R2 *.

A Surgical Model of Heart Failure with Preserved Ejection Fraction in Tibetan Minipigs.

More than half of heart failure (HF) cases are classified as heart failure with preserved ejection fraction (HFpEF) worldwide. Large animal models are limited for investigating the fundamental mechanisms of HFpEF and identifying potential therapeutic targets. This work provides a detailed description of the surgical procedure of descending aortic constriction (DAC) in Tibetan minipigs to establish a large animal model of HFpEF. This model used a precisely controlled constriction of the descending aorta to induce chronic pressure overload in the left ventricle. Echocardiography was used to evaluate the morphological and functional changes in the heart. After 12 weeks of DAC stress, the ventricular septum was hypertrophic, but the thickness of the posterior wall was significantly reduced, accompanied by dilation of the left ventricle. However, the LV ejection fraction of the model hearts was maintained at >50% during the 12-week period. Furthermore, the DAC model displayed cardiac damage, including fibrosis, inflammation, and cardiomyocyte hypertrophy. Heart failure marker levels were significantly elevated in the DAC group. This DAC-induced HFpEF in minipigs is a powerful tool for investigating molecular mechanisms of this disease and for preclinical testing.

Differential mRNA Expression and Circular RNA-Based Competitive Endogenous RNA Networks in the Three Stages of Heart Failure in Transverse Aortic Constriction Mice.

Background: The murine transverse aortic constriction (TAC) model is frequently used to investigate molecular mechanisms underlying heart failure. However, limited data is available regarding the expression of mRNAs and circRNAs in murine heart failure progression induced by pressure overload. Methods: Transverse aortic constriction was used to induce pressure overload for 2, 4, and 8 weeks in mice. Echocardiographic measurements in B-mode and M-mode, as well as blood flow Doppler data were collected in mice without (sham) and with (2W-, 4W-, and 8W-post-TAC) pressure load. Hearts were excised and morphology, cardiomyocyte size, and fibrosis were determined. RNA sequencing, circRNA microarray, functional mRNA enrichment analysis, hub gene identification, target miRNA interaction, and competitive endogenous RNA (ceRNA) network construction were conducted. Results: Heart weight, cardiomyocyte hypertrophy, and fibrosis gradually increased over time in the hearts with pressure overload. The 2W-post-TAC hearts displayed concentric hypertrophy, thickened left ventricular walls, and increased EF and FS. The 4W-post-TAC hearts were characterized by preserved EF and FS, dilated atria, and increased left ventricle (LV) systolic volume. The 8W-post-TAC hearts presented with ventricular and atrial dilation, increased LV systolic and diastolic volume, reduced EF and FS, and increased ejection time (MV ET). mRNA expression analysis suggested that cardiac remodeling, immune response dysregulation, and metabolic disorder were the key cellular events in heart failure progression. Depression in chemotaxis and mitochondrial function were predicted in 4W- and 8W-post-TAC myocardia, respectively. A ceRNA network analysis demonstrated that the circRNAs targeted the expression of genes enriched in metabolism dysregulation in the 2W-post-TAC hypertrophic hearts, while they targeted genes enriched in cardiac remodeling in the 4W-post-TAC EF-preserved hearts and in the suppression of oxidative phosphorylation and cardiac contraction in the 8W-post-TAC EF-reduced hearts. Conclusion: Our work empirically demonstrates that distinctive features of heart failure, including ventricular hypertrophy, heart failure with preserved EF (HFpEF), and heart failure with reduced EF (HFrEF) are present in the murine pressure overload models. The three stages of heart failure vary in terms of mRNA and circRNA expression, as well as ceRNA regulation in a manner consistent with their structural, functional, and pathological differences.

A Porcine Model of Heart Failure With Preserved Ejection Fraction Induced by Chronic Pressure Overload Characterized by Cardiac Fibrosis and Remodeling.

Heart failure is induced by multiple pathological mechanisms, and current therapies are ineffective against heart failure with preserved ejection fraction (HFpEF). As there are limited animal models of HFpEF, its underlying mechanisms have not yet been elucidated. Here, we employed the descending aortic constriction (DAC) technique to induce chronic pressure overload in the left ventricles of Tibetan minipigs for 12 weeks. Cardiac function, pathological and cellular changes, fibrotic signaling activation, and gene expression profiles were explored. The left ventricles developed concentric hypertrophy from weeks 4 to 6 and transition to dilation starting in week 10. Notably, the left ventricular ejection fraction was maintained at >50% in the DAC group during the 12-week period. Pathological examination, biochemical analyses, and gene profile analysis revealed evidence of inflammation, fibrosis, cell death, and myofilament dephosphorylation in the myocardium of HFpEF model animals, together with gene expression shifts promoting cardiac remodeling and downregulating metabolic pathways. Furthermore, we noted the activation of several signaling proteins that impact cardiac fibrosis and remodeling, including transforming growth factor-ß/SMAD family members 2/3, type I/III/V collagens, phosphatidylinositol 3-kinase, extracellular signal-regulated kinase, matrix metalloproteinases 2 and 9, tissue inhibitor of metalloproteinases 1 and 2, interleukins 6 and 1ß, and inhibitor of κBα/nuclear factor-κB. Our findings demonstrate that this chronic pressure overload-induced porcine HFpEF model is a powerful tool to elucidate the mechanisms of this disease and translate preclinical findings.

Effective Analysis of Inpatient Satisfaction: The Random Forest Algorithm.

PURPOSE: To identify the factors influencing inpatient satisfaction by fitting the optimal discriminant model. PATIENTS AND METHODS: A cross-sectional survey of inpatient satisfaction was conducted with 3888 patients in 16 large public hospitals in Zhejiang Province. Independent variables were screened by single-factor analysis, and the importance of all variables was comprehensively evaluated. The relationship between patients' overall satisfaction and influencing factors was established, the relative risk was evaluated by marginal benefit, and the optimal model was fitted using the receiver operating characteristic curve. RESULTS: Patients' overall satisfaction was 79.73%. The five most influential factors on inpatient satisfaction, in this order, were: patients' right to know, timely nursing response, satisfaction with medical staff service, integrity of medical staff, and accuracy of diagnosis. The prediction accuracy of the random forest model was higher than that of the multiple logistic regression and naive Bayesian models. CONCLUSION: Inpatient satisfaction is related to healthcare quality, diagnosis, and treatment process. Rapid identification and active improvement of the factors affecting patient satisfaction can reduce public hospital operating costs and improve patient experiences and the efficiency of health resource allocation. Public hospitals should strengthen the exchange of medical information between doctors and patients, shorten waiting time, and improve the level of medical technology, service attitude, and transparency of information disclosure.

Cardioprotective Effects of n-3 Polyunsaturated Fatty Acids: Orchestration of mRNA Expression, Protein Phosphorylation, and Lipid Metabolism in Pressure Overload Hearts.

Background: Pressure overload can result in dilated cardiomyopathy. The beneficial effects of n-3 polyunsaturated fatty acids (n-3 PUFAs) on heart disorders have been widely recognized. However, the molecular mechanisms underlying their protective effects against cardiomyopathy remain unclear. Methods: Pressure overload in mice induced by 8 weeks of transverse aortic constriction was used to induce dilated cardiomyopathy. A transgenic fat-1 mouse model carrying the n-3 fatty acid desaturase gene fat-1 gene from Caenorhabditis elegans was used to evaluate the mechanism of n-3 PUFAs in this disease. Echocardiography, transmission electron microscopy, and histopathological analyses were used to evaluate the structural integrity and function in pressure overloaded fat-1 hearts. mRNA sequencing, label-free phosphoprotein quantification, lipidomics, Western blotting, RT-qPCR, and ATP detection were performed to examine the effects of n-3 PUFAs in the heart. Results: Compared with wild-type hearts, left ventricular ejection fraction was significantly improved (C57BL/6J [32%] vs. fat-1 [53%]), while the internal diameters of the left ventricle at systole and diastole were reduced in the fat-1 pressure overload hearts. mRNA expression, protein phosphorylation and lipid metabolism were remodeled by pressure overload in wild-type and fat-1 hearts. Specifically, elevation of endogenous n-3 PUFAs maintained the phosphorylation states of proteins in the subcellular compartments of sarcomeres, cytoplasm, membranes, sarcoplasmic reticulum, and mitochondria. Moreover, transcriptomic analysis predicted that endogenous n-3 PUFAs restored mitochondrial respiratory chain function that was lost in the dilated hearts, and this was supported by reductions in detrimental oxylipins and protection of mitochondrial structure, oxidative phosphorylation, and ATP production. Conclusions: Endogenous n-3 PUFAs prevents dilated cardiomyopathy via orchestrating gene expression, protein phosphorylation, and lipid metabolism. This is the first study provides mechanistic insights into the cardioprotective effects of n-3 PUFAs in dilated cardiomyopathy through integrated multi-omics data analysis.

Selective Guidance Normal Filter for Geometric Texture Removal.

There is typically a trade-off between removing the detailed appearance (i.e., geometric textures) and preserving the intrinsic properties (i.e., geometric structures) of 3D surfaces. The conventional use of mesh vertex/facet-centered patches in many filters leads to side-effects including remnant textures, improperly filtered structures, and distorted shapes. We propose a selective guidance normal filter (SGNF) which adapts the Relative Total Variation (RTV) to a maximal/minimal scheme (mmRTV). The mmRTV measures the geometric flatness of surface patches, which helps in finding adaptive patches whose boundaries are aligned with the facet being processed. The adaptive patches provide selective guidance normals, which are subsequently used for normal filtering. The filtering smooths out the geometric textures by using guidance normals estimated from patches with maximal RTV (the least flatness), and preserves the geometric structures by using normals estimated from patches with minimal RTV (the most flatness). This simple yet effective modification of the RTV makes our SGNF specialized rather than trade off between texture removal and structure preservation, which is distinct from existing mesh filters. Experiments show that our approach is visually and numerically comparable to the state-of-the-art mesh filters, in most cases. In addition, the mmRTV is generally applicable to bas-relief modeling and image texture removal.

Real-Time Plane Detection with Consistency from Point Cloud Sequences.

Real-time consistent plane detection (RCPD) from structured point cloud sequences facilitates various high-level computer vision and robotic tasks. However, it remains a challenge. Existing techniques for plane detection suffer from a long running time or the problem that the plane detection result is not precise. Meanwhile, labels of planes are not consistent over the whole image sequence due to plane loss in the detection stage. In order to resolve these issues, we propose a novel superpixel-based real-time plane detection approach, while keeping their consistencies over frames simultaneously. In summary, our method has the following key contributions: (i) a real-time plane detection algorithm to extract planes from raw structured three-dimensional (3D) point clouds collected by depth sensors; (ii) a superpixel-based segmentation method to make the detected plane exactly match its actual boundary; and, (iii) a robust strategy to recover the missing planes by utilizing the contextual correspondences information in adjacent frames. Extensive visual and numerical experiments demonstrate that our method outperforms state-of-the-art methods in terms of efficiency and accuracy.

Bacillus amyloliquefaciens Strain PMB05 Intensifies Plant Immune Responses to Confer Resistance Against Bacterial Wilt of Tomato.

Tomato is an economic crop worldwide. Many limiting factors reduce the production of tomato, with bacterial wilt caused by Ralstonia solanacearum being the most destructive disease. Our previous study showed that the disease resistance to bacterial soft rot is enhanced by Bacillus amyloliquefaciens strain PMB05. This enhanced resistance is associated with the intensification of pathogen-associated molecular patterns (PAMP)-triggered immunity (PTI). To determine whether the PTI-intensifying Bacillus spp. strains are able to confer disease resistance to bacterial wilt, their effects on PTI signals triggered by PAMP from R. solanacearum and on the occurrence of bacterial wilt were assayed. Before assay, a gene that encodes harpin from R. solanacearum, PopW, was applied as a PAMP. Results revealed that the B. amyloliquefaciens strain PMB05 was the one strain among 9 Bacillus rhizobacterial strains which could significantly intensify the PopW-induced hypersensitive response (HR) on Arabidopsis leaves. Moreover, we observed that the signals of PopW-induced reactive oxygen species generation and callose deposition were increased, confirming that the PTI was intensified by PMB05. The intensification of the PopW-triggered HR by PMB05 in Arabidopsis was reduced upon treatment with inhibitors in PTI pathways. Furthermore, the application of Bacillus spp. strains on tomato plants showed that only the use of PMB05 resulted in significantly increased resistance to bacterial wilt. Moreover, the PTI signals were also intensified in the tomato leaves. Taken together, we demonstrated that PMB05 is a PTI-intensifying bacterium that confers resistance to tomato bacterial wilt. Screening of plant immunity intensifying rhizobacteria is a possible strategy to control tomato bacterial wilt.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.