Response of Juvenile Saccharina japonica to the Combined Stressors of Elevated pCO2 and Excess Copper.

Coastal macroalgae may be subjected to global and local environmental stressors, such as ocean acidification and heavy-metal pollution. We investigated the growth, photosynthetic characteristics, and biochemical compositions of juvenile sporophytes of Saccharina japonica cultivated at two pCO2 levels (400 and 1000 ppmv) and four copper concentrations (natural seawater, control; 0.2 µM, low level; 0.5 µM, medium level; and 1 µM, high level) to better understand how macroalgae respond to ongoing environmental changes. The results showed that the responses of juvenile S. japonica to copper concentrations depended on the pCO2 level. Under the 400 ppmv condition, medium and high copper concentrations significantly decreased the relative growth rate (RGR) and non-photochemical quenching (NPQ) but increased the relative electron transfer rate (rETR) and chlorophyll a (Chl a), chlorophyll c (Chl c), carotenoid (Car), and soluble carbohydrate contents. At 1000 ppmv, however, none of the parameters had significant differences between the different copper concentrations. Our data suggest that excess copper may inhibit the growth of juvenile sporophytes of S. japonica, but this negative effect could be alleviated by CO2-induced ocean acidification.

Learning Smooth Representation for Unsupervised Domain Adaptation.

Typical adversarial-training-based unsupervised domain adaptation (UDA) methods are vulnerable when the source and target datasets are highly complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to UDA and usually perform poorly on large-scale datasets. In this article, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of UDA. A connection between them is built, and an illustration of how Lipschitzness reduces the error bound is presented. A local smooth discrepancy is defined to measure the Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of UDA, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension, and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension, and batchsize of samples, indeed, greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.

Decreased Salinity Offsets the Stimulation of Elevated pCO2 on Photosynthesis and Synergistically Inhibits the Growth of Juvenile Sporophyte of Saccharina japonica (Laminariaceae, Phaeophyta).

The combined effect of elevated pCO2 (Partial Pressure of Carbon Dioxide) and decreased salinity, which is mainly caused by freshwater input, on the growth and physiological traits of algae has been poorly assessed. In order to investigate their individual and interactive effects on the development of commercially farmed algae, the juvenile sporophytes of Saccharina japonica were cultivated under different levels of salinity (30, 25 and 20 psu) and pCO2 (400 and 1000 µatm). Individually, decreased salinity significantly reduced the growth rate and pigments of S. japonica, indicating that the alga was low-salinity stressed. The maximum quantum yield, Fv/Fm, declined at low salinities independent of pCO2, suggesting that the hyposalinity showed the main effect. Unexpectedly, the higher pCO2 enhanced the maximum relative electron transport rate (rETRmax) but decreased the growth rate, pigments and soluble carbohydrates contents. This implies a decoupling between the photosynthesis and growth of this alga, which may be linked to an energetic reallocation among the different metabolic processes. Interactively and previously untested, the decreased salinity offset the improvement of rETRmax and aggravated the declines of growth rate and pigment content caused by the elevated pCO2. These behaviors could be associated with the additionally decreased pH that was induced by the low salinity. Our data, therefore, unveils that the decreased salinity may increase the risks of future CO2-induced ocean acidification on the production of S. japonica.

Gabor Feature Based LogDemons with Inertial Constraint for Nonrigid Image Registration.

Nonrigid image registration plays an important role in the field of computer vision and medical application. The methods based on Demons algorithm for image registration usually use intensity difference as similarity criteria. However, intensity based methods can not preserve image texture details well and are limited by local minima. In order to solve these problems, we propose a Gabor feature based LogDemons registration method in this paper, called GFDemons. We extract Gabor features of the registered images to construct feature similarity metric since Gabor filters are suitable to extract image texture information. Furthermore, because of the weak gradients in some image regions, the update fields are too small to transform the moving image to the fixed image correctly. In order to compensate this deficiency, we propose an inertial constraint strategy based on GFDemons, named IGFDemons, using the previous update fields to provide guided information for the current update field. The inertial constraint strategy can further improve the performance of the proposed method in terms of accuracy and convergence. We conduct experiments on three different types of images and the results demonstrate that the proposed methods achieve better performance than some popular methods.

Transcriptome analysis reveals molecular strategies in gills and heart of large yellow croaker (Larimichthys crocea) under hypoxia stress.

The gills and heart are two major targets of hypoxia in fish. However, the molecular responses in fish gills and heart to hypoxia challenge remain unclear. Here, RNA-Seq technology was used to study the gene expression profiles in gills and heart of large yellow croaker (Larimichthys crocea) at 6, 24, and 48 h after hypoxia stress. A total of 1,546 and 2,746 differentially expressed genes (DEGs) were identified in gills and heart, respectively. Expression changes of nine genes in each tissue were further validated by the qPCR. Based on KEGG and Gene ontology enrichments, we found that various innate immunity-related genes, such as complement components (C1qs, C2, C3, C6, and C7), chemokines (CCL3, CCL17, CCL19, CCL25, and CXCL8_L3), chemokine receptors (CCR9, CXCR1, and CXCR3), and nitric oxide synthase (NOS), were significantly down-regulated in gills and/or heart, suggesting that innate immune processes mediated by these genes may be inhibited by hypoxia. The genes involved in both glycolysis pathway (LDHA) and tricarboxylic acid cycle (IDH2 and OGDH) were up-regulated in gills and heart of hypoxic large yellow croakers, possibly because gill and heart tissues need enough energy to accelerate gas exchange and blood circulation. Hypoxia also affected the ion transport in gills of large yellow croaker, through down-regulating the expression levels of numerous classical ion transporters, including HVCN1, SLC20A2, SLC4A4, RHBG, RHCG, and SCN4A, suggesting an energy conservation strategy to hypoxia stress. All these results indicate that the immune processes, glycolytic pathways, and ion transport were significantly altered in gills and/or heart of large yellow croaker under hypoxia, possibly contributing to maintain cellular energy balance during hypoxia. Our data, therefore, afford new information to understand the tissue-specific molecular responses of bony fish to hypoxia stress.

Engineering antigen as photosensitiser nanocarrier to facilitate ROS triggered immune cascade for photodynamic immunotherapy.

Despite of the documented immunogenic cell death (ICD) and antigen cross-presentation (AC) in photodynamic therapy (PDT), the overall immune efficacy is rather limited. This study aims to expand the immune potential of PDT by spatially packaging antigen as photosensitiser nanocarrier to trigger efficient immune cascade for photodynamic immunotherapy. The package of ovalbumin antigen (OVA) into sub-100 nm nano-assembly is realized by driving intermolecular disulfide network between OVA molecules. OVA nanoparticles loading photosensitiser Ce6 (ON) are subsequently coated with B16-OVA cancer cell membrane, resulting in membrane cloaked ON (MON). Importantly, laser irradiation generated ROS significantly potentiates OVA antigen cross-presentation efficiency. Whilst, MON is endowed with homophilic targeting towards tumor due to cancer cell membrane coating. In treating B16-OVA tumor-bearing mice, MON effectively triggers the immune cascade, completely eliminates the tumor under laser irradiation and provokes a long-term antitumor immune memory effect. Conversely, a marginal effect is found if substituting OVA for bovine serum protein (BSA) in nanoparticle design or using MON to treat non-OVA expressing tumor. The antigen nanocarrier design promises to complement conventional PDT by boosting immune cascade, thereby leading to unique photodynamic immunotherapy.

Unsupervised Domain Adaptation With Adversarial Residual Transform Networks.

Domain adaptation (DA) is widely used in learning problems lacking labels. Recent studies show that deep adversarial DA models can make markable improvements in performance, which include symmetric and asymmetric architectures. However, the former has poor generalization ability, whereas the latter is very hard to train. In this article, we propose a novel adversarial DA method named adversarial residual transform networks (ARTNs) to improve the generalization ability, which directly transforms the source features into the space of target features. In this model, residual connections are used to share features and adversarial loss is reconstructed, thus making the model more generalized and easier to train. Moreover, a special regularization term is added to the loss function to alleviate a vanishing gradient problem, which enables its training process stable. A series of experiments based on Amazon review data set, digits data sets, and Office-31 image data sets are conducted to show that the proposed ARTN can be comparable with the methods of the state of the art.

Remodeling of Cellular Surfaces via Fast Disulfide-Thiol Exchange To Regulate Cell Behaviors.

Remodeling of cellular surfaces is shown highly effective in the manipulation and control of cell behaviors via nonbiological means. By 5-thio-2-nitrobenzoate-mediated, fast, and reversible disulfide-thiol exchange, a sequential layer by layer assembly process was developed to grow albumin protein shells on cellular surfaces fixed by a disulfide-linked network, in a cytocompatible manner. The artificial shells, accomplished by a double-assembly process, were sustainable up to >1 day, and thereafter gradually bioabsorbed with unaffected cell viability. The surface engineering process enabled dynamic remodeling of cellular surfaces that effectively controlled cell behaviors including regulated cell proliferation, enhanced uptake efficiency of dextran-fluorescein isothiocyanate that is known for cell-impermeability, and targeted imaging. This unique approach was well-validated on tumor cells (B16), immune cells (DC2.4), and neutrophils, showing its potential universality for most of the cells that are rich in thiols. The new strategy will show promise in cell manipulation and targeted imaging.

Incorporation of Structural Tensor and Driving Force Into Log-Demons for Large-Deformation Image Registration.

Large-deformation image registration is important in theory and application in computer vision, but is a difficult task for non-rigid registration methods. In this paper, we propose a structural Tensor and Driving force-based Log-Demons algorithm for it, named TDLog-Demons for short. The structural tensor of an image is proposed to obtain a highly accurate deformation field. The driving force is proposed to solve the registration issue of large-deformation that often causes Log-Demons to trap into local minima. It is defined as a point correspondence obtained via multisupport-region-order-based gradient histogram descriptor matching on image's boundary points. It is integrated into an exponentially decreasing form with the velocity field of Log-Demons to move the points accurately and to speed up a registration process. Consequently, the driving force-based Log-Demons can well deal with large-deformation image registration. Extensive experiments demonstrate that the TDLog-Demons not only captures large deformations at a high accuracy but also yields a smooth deformation.

Fever-Inspired Immunotherapy Based on Photothermal CpG Nanotherapeutics: The Critical Role of Mild Heat in Regulating Tumor Microenvironment.

Although there have been more than 100 clinical trials, CpG-based immunotherapy has been seriously hindered by complications in the immunosuppressive microenvironment of established tumors. Inspired by the decisive role of fever upon systemic immunity, a photothermal CpG nanotherapeutics (PCN) method with the capability to induce an immunofavorable tumor microenvironment by casting a fever-relevant heat (43 °C) in the tumor region is developed. High-throughput gene profile analysis identifies nine differentially expressed genes that are closely immune-related upon mild heat, accompanied by IL-6 upregulation, a pyrogenic cytokine usually found during fever. When treated with intratumor PCN injection enabling mild heating in the tumor region, the 4T1 tumor-bearing mice exhibit significantly improved antitumor immune effects compared with the control group. Superb efficacy is evident from pronounced apoptotic cell death, activated innate immune cells, enhanced tumor perfusion, and intensified innate and adaptive immune responses. This work highlights the crucial role of mild heat in modulating the microenvironment in optimum for improved immunotherapy, by converting the tumor into an in situ vaccine.

"Minimalist" Nanovaccine Constituted from Near Whole Antigen for Cancer Immunotherapy.

One of the major challenges in vaccine design has been the over dependence on incorporation of abundant adjuvants, which in fact is in violation of the "minimalist" principle. In the present study, a compact nanovaccine derived from a near whole antigen (up to 97 wt %) was developed. The nanovaccine structure was stabilized by free cysteines within each antigen (ovalbumin, OVA), which were tempospatially exposed and heat-driven to form an extensive intermolecular disulfide network. This process enables the engineering of a nanovaccine upon integration of the danger signal (CpG-SH) into the network during the synthetic process. The 50 nm-sized nanovaccine was developed comprising approximately 500 antigen molecules per nanoparticle. The nanovaccine prophylactically protected 70% of mice from tumorigenesis (0% for the control group) in murine B16-OVA melanoma. Significant tumor inhibition was achieved by strongly nanovaccine-induced cytotoxic T lymphocytes. This strategy can be adapted for the future design of vaccine for a minimalist composition in clinical settings.

The characterization of RHEB gene and its responses to hypoxia and thermal stresses in the small abalone Haliotis diversicolor.

RHEB (Ras Homolog Enriched in Brain) is a GTP-binding protein that is ubiquitously expressed in humans and other mammals. The protein is largely involved in the mechanistic target of rapamycin (mTOR) pathway, and regulates the cell cycle progression and growth. The goal of this study was to characterize the RHEB gene in the small abalone Haliotis diversicolor, and identify the responses of RHEB gene to stresses of hypoxia or/and thermal. The objectives were to: 1) clone the full-length cDNA RHEB gene in the H. diversicolor (HdRHEB); 2) quantify the expression of HdRHEB gene in tissues of haemocytes, mantle, kidney, gill, digestive tract, colleterial gland, and hepatopancreas by using RT-PCR, and 3) evaluate the responses of HdRHEB in gill and haemocyte to stresses of hypoxia (0.2mg/l00ml), thermal (31°C), and combination of hypoxia (0.4mg/l00ml) and thermal (30°C) at exposure time of 0, 4, 24, 96, and 192h. The full length cDNA of HdRHEB was 1044bp encoding a peptide of 182 amino acid residues. Expression of HdRHEB gene was detected in all of the 7 tissues and showed the highest in mantle (P<0.05). Under hypoxia, expression of HdRHEB in gill increased significantly at 4h, 24h and 96h (P<0.05), and that in haemocyte increased significantly at 24h, 96h and 192h (P<0.05). Under thermal stress, expression of HdRHEB gene in gill decreased significantly at 4h and 24h, while expression in haemocyte decreased significantly all the time. Under thermal and hypoxia stresses, expression of HdRHEB gene in gill and haemocyte was up-regulated significantly at 24h and 96h (P<0.05). The results in this study demonstrated for the first time that RHEB gene in abalones is able to response to stress stimuli of hypoxia or/and thermal.

A highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images.

Spatial normalization plays a key role in voxel-based analyses of brain images. We propose a highly accurate algorithm for high-dimensional spatial normalization of brain images based on the technique of symmetric optical flow. We first construct a three dimension optical model with the consistency assumption of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. Then, an efficient inverse consistency optical flow is proposed with aims of higher registration accuracy, where the flow is naturally symmetric. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering brain images data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is not only better than that of those traditional optical flow algorithms, but also comparable to other registration methods used extensively in the medical imaging community. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention.

Measuring temporal dynamics of resting-state fMRI data.

Resting state functional MRI (rs-fMRI), which is used to measure blood oxygen level-dependent (BOLD) from resting brains, is a relatively new and powerful method for evaluating regional interactions that occur when a participant is not performing an explicit task. Because of the sensitiveness to the phase shift and length of time courses of the BOLD recordings, region of interest based conventional correlation and coherence methods are no longer suitable for rs-fMRI analyses. In this paper, we propose a more robust and consistent method, dominant frequency mapping, to analyze rs-fMRI data. We found a dominant frequency of BOLD recordings, 0.0137 Hz, in resting human brains that is consistent across participants and brain regions. This frequency is detected mainly in Gyrus Rectus, Frontal Medial Orbital, Frontal Superior Orbital and Olfactory Sulcus, which control the human social behavior, emotion, and decision making. In the meantime, we found that BOLD frequencies are most inconsistent in the brain regions of PrecentralGyrus, Superior Frontal gyrus, Insula, Caudate nucleus, Putamen, and part of the cerebellum, whose functions are about motor.

A statistical approach to segmentation of diffusion tensor imaging.

The aim of this study is to design a statistical segmentation technique to allow extraction of grey matter, white matter and cerebral spinal fluid volumes from diffusion tensor imaging. Four channel maps of the DTI are used as the input features, which provide more information for brain tissue segmentation compared with single channel map. An Improved Bayesian decision in the subspace spanned by the eigenvectors which are associated with the smaller eigenvalues in each class is adopted as the brain tissue segmentation criterion. Our method performed well, giving an average segmentation accuracy of about 0.88, 0.85 and 0.76 for white matter, gray matter and cerebrospinal fluid respectively in terms of volume overlap.

Brain tissue classification based on DTI using an improved fuzzy C-means algorithm with spatial constraints.

We present an effective method for brain tissue classification based on diffusion tensor imaging (DTI) data. The method accounts for two main DTI segmentation obstacles: random noise and magnetic field inhomogeneities. In the proposed method, DTI parametric maps were used to resolve intensity inhomogeneities of brain tissue segmentation because they could provide complementary information for tissues and define accurate tissue maps. An improved fuzzy c-means with spatial constraints proposal was used to enhance the noise and artifact robustness of DTI segmentation. Fuzzy c-means clustering with spatial constraints (FCM_S) could effectively segment images corrupted by noise, outliers, and other imaging artifacts. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to the exploitation of spatial contextual information. We proposed an improved FCM_S applied on DTI parametric maps, which explores the mean and covariance of the feature spatial information for automated segmentation of DTI. The experiments on synthetic images and real-world datasets showed that our proposed algorithms, especially with new spatial constraints, were more effective.

Complex-number representation of informed basis functions in general linear modeling of Functional Magnetic Resonance Imaging.

Functional Magnetic Resonance Imaging (fMRI), measuring Blood Oxygen Level-Dependent (BOLD), is a widely used tool to reveal spatiotemporal pattern of neural activity in human brain. Standard analysis of fMRI data relies on a general linear model and the model is constructed by convolving the task stimuli with a hypothesized hemodynamic response function (HRF). To capture possible phase shifts in the observed BOLD response, the informed basis functions including canonical HRF and its temporal derivative, have been proposed to extend the hypothesized hemodynamic response in order to obtain a good fitting model. Different t contrasts are constructed from the estimated model parameters for detecting the neural activity between different task conditions. However, the estimated model parameters corresponding to the orthogonal basis functions have different physical meanings. It remains unclear how to combine the neural features detected by the two basis functions and construct t contrasts for further analyses. In this paper, we have proposed a novel method for representing multiple basis functions in complex domain to model the task-driven fMRI data. Using this method, we can treat each pair of model parameters, corresponding respectively to canonical HRF and its temporal derivative, as one complex number for each task condition. Using the specific rule we have defined, we can conveniently perform arithmetical operations on the estimated model parameters and generate different t contrasts. We validate this method using the fMRI data acquired from twenty-two healthy participants who underwent an auditory stimulation task.

An Enhanced LBP Feature Based on Facial Expression Recognition.

Because of excellent capability of description of local texture, Local Binary Patterns (LBP) have been applied in many areas. In this paper, we enhance the classical LBP method from three aspects for facial expression recognition: image data, extracting features and the way of combining all these features. At first, we adopt wavelet to decomposed images into four kinds of frequency images from which the features are extracted to increase original data. Then we extract LBP features with a new local and holistic way to make features more robust. At last, in order to use the extracted features more logical, we combine all data in an adaptive weight mechanism. All experiments are also proved that the proposed improvements in this paper have promoted the performance of facial expression recognition greatly.