Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures.

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes.

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

LTBP2 regulates cisplatin resistance in GC cells via activation of the NF-κB2/BCL3 pathway.

Gastric cancer (GC) often develops resistance to cisplatin treatment, but while latent transforming growth factor ß-binding protein (LTBP2) is recognized as a potential regulator in GC, its specific role in cisplatin resistance is not fully understood. This study investigated LTBP2's impact on cisplatin resistance in GC. LTBP2 expression was assessed in various GC cell lines, and its correlation with cisplatin sensitivity was determined through cell viability assays. Lentivirus-mediated LTBP2 silencing in HGC-27 cells demonstrated enhanced cisplatin sensitivity, reduced cell proliferation, and inhibition of the NF-κB2/Bcl-3/cyclin D1 pathway. Additionally, transient transfection overexpressed the NFκB2 gene in LTBP2-silenced HGC-27/DDPR cells, restoring cisplatin sensitivity and upregulating p52/Bcl-3/cyclin D1. In conclusion, silencing LTBP2 could effectively inhibit cell proliferation and mitigate cisplatin resistance via the NFKB noncanonical pathway NFKB2 p52/Bcl-3/cyclin D1. These findings propose LTBP2 as a potential therapeutic target for overcoming cisplatin resistance in GC patients.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

Bridging atomistic simulations and thermodynamic hydration models of aqueous electrolyte solutions.

Chemical thermodynamic models of solvent and solute activities predict the equilibrium behavior of aqueous solutions. However, these models are semi-empirical. They represent micro-scale ion and solvent behaviors controlling the macroscopic properties using small numbers of parameters whose values are obtained by fitting to activities and other partial derivatives of the Gibbs energy measured for the bulk solutions. We have conducted atomistic simulations of aqueous electrolyte solutions (MgCl2 and CaCl2) to determine the parameters of thermodynamic hydration models. We have implemented a cooperative hydration model to categorize the water molecules in electrolyte solutions into different subpopulations. The value of the electrolyte-specific parameter, k, was determined from the ion-affected subpopulation with the lowest absolute value of the free energy of removing the water molecule. The other equilibrium constant parameter, K1, associated with the first degree of hydration, was computed from the free energy of hydration of hydrated clusters. The hydration number, h, was determined from a reorientation dynamic analysis of the water subpopulations compared to bulk-like behavior. The reparameterized models [R. H. Stokes and R. H. Robinson, J. Solution Chem. 2, 173 (1973) and Balomenos et al., Fluid Phase Equilib. 243, 29 (2006)] using the computed values of the parameters lead to the osmotic coefficients of MgCl2 solutions that are consistent with measurements. Such an approach removes the dependence on the availability of experimental data and could lead to aqueous thermodynamic models capable of estimating the values of solute and solvent activities as well as thermal and volumetric properties for a wide range of compositions and concentrations.

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering.

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.

Hydrogen-Bond Structure and Low-Frequency Dynamics of Electrolyte Solutions: Hydration Numbers from ab Initio Water Reorientation Dynamics and Dielectric Relaxation Spectroscopy.

We present an atomistic simulation scheme for the determination of the hydration number (h) of aqueous electrolyte solutions based on the calculation of the water dipole reorientation dynamics. In this methodology, the time evolution of an aqueous electrolyte solution generated from ab initio molecular dynamics simulations is used to compute the reorientation time of different water subpopulations. The value of h is determined by considering whether the reorientation time of the water subpopulations is retarded with respect to bulk-like behavior. The application of this computational protocol to magnesium chloride (MgCl2 ) solutions at different concentrations (0.6-2.8 mol kg-1 ) gives h values in excellent agreement with experimental hydration numbers obtained using GHz-to-THz dielectric relaxation spectroscopy. This methodology is attractive because it is based on a well-defined criterion for the definition of hydration number and provides a link with the molecular-level processes responsible for affecting bulk solution behavior. Analysis of the ab initio molecular dynamics trajectories using radial distribution functions, hydrogen bonding statistics, vibrational density of states, water-water hydrogen bonding lifetimes, and water dipole reorientation reveals that MgCl2 has a considerable influence on the hydrogen bond network compared with bulk water. These effects have been assigned to the specific strong Mg-water interaction rather than the Cl-water interaction.

Density functional theory based molecular dynamics study of solution composition effects on the solvation shell of metal ions.

We present an ab initio molecular dynamics study of the alkali metal ions Li+, Na+, K+ and Cs+, and of the alkaline earth metal ions Mg2+ and Ca2+ in both pure water and electrolyte solutions containing the counterions Cl- and SO42-. Simulations were conducted using different density functional theory methods (PBE, BLYP and revPBE), with and without the inclusion of dispersion interactions (-D3). Analysis of the ion-water structure and interaction strength, water exchange between the first and second hydration shell, and hydrogen bond network and low-frequency reorientation dynamics around the metal ions have been used to characterise the influence of solution composition on the ionic solvation shell. Counterions affect the properties of the hydration shell not only when they are directly coordinated to the metal ion, but also when they are at the second coordination shell. Chloride ions reduce the sodium hydration shell and expand the calcium hydration shell by stabilizing under-coordinated hydrated Na(H2O)5+ complexes and over-coordinated Ca(H2O)72+. The same behaviour is observed in CaSO4(aq), where Ca2+ and SO42- form almost exclusively solvent-shared ion pairs. Water exchange between the first and second hydration shell around Ca2+ in CaSO4(aq) is drastically decelerated compared with the simulations of the hydrated metal ion (single Ca2+, no counterions). Velocity autocorrelation function analysis, used to probe the strength of the local ion-water interaction, shows a smoother decay of Mg2+ in MgCl2(aq), which is a clear indication of a looser inter-hexahedral vibration in the presence of chloride ions located in the second coordination shell of Mg2+. The hydrogen bond statistics and orientational dynamics in the ionic solvation shell show that the influence on the water-water network cannot only be ascribed to the specific cation-water interaction, but also to the subtle interplay between the level of hydration of the ions, and the interactions between ions, especially those of opposite charge. As many reactive processes involving solvated metal ions occur in environments that are far from pure water but rich in ions, this computational study shows how the solution composition can result in significant differences in behaviour and function of the ionic solvation shell.

Longer rewarming time in finger cooling test in association with HbA1c level in diabetics.

OBJECTIVE: To assess if rewarming time in finger cooling test (FCT) as an indicator of microvascular dysfunction is abnormal in patients with type 2 diabetes mellitus (T2DM). METHODS: Forty-three T2DM patients and 48 healthy controls with similarly distributed baseline demographic, clinical and laboratory parameters were subjected to FCT involving 60-second index finger immersion into water at 4°C. Finger temperature was measured before FCT (baseline-T), immediately after cooling stimulus (T0), and at one-minute intervals until baseline-T recovery. Temperature decline amplitude was calculated as the difference between T0 and baseline-T, and rewarming time as time elapsed from T0 to baseline-T recovery. RESULTS: T2DM patients compared with healthy controls had statistically similar baseline-T, significantly larger temperature decline amplitude, significantly lower T0, and significantly longer rewarming time. In T2DM patients, rewarming time positively correlated with T2DM duration (r=0.513, p<0.001) and glycated hemoglobin (HbA1c) level (r=0.446, p=0.003), which also were its independent predictors in multivariate regression analysis. CONCLUSIONS: Patients with T2DM display abnormal FCT results suggestive of microvascular dysfunction, with T2DM duration and HbA1c level independently predicting rewarming time.

Field monitoring of a LID-BMP treatment train system in China.

In order to assess the urban runoff control effectiveness of a low-impact development best management practice (LID-BMP) treatment train system, a field test of selected LID-BMPs was conducted in China. The LID-BMPs selected include three grassed swales, a buffer strip, a bioretention cell, two infiltration pits, and a constructed wetland. The test site is in a campus in southern China. The LID-BMPs, connected in a series, received stormwater runoff from four tennis courts with an area of 2808 m(2) and eight basketball courts with an area of 4864 m(2). Construction of the LID-BMPs was completed in early spring of 2012, and the sampling was conducted during May of 2012 to September of 2013. During the sampling effort, besides the performance evaluations of grassed swales and the bioretention cell in controlling runoff quantity as well as quality, the emphasis was also on determining the performance of the LID-BMP treatment train system. A total of 19 storm events were monitored, with nine producing no runoff and ten producing runoff. Data collected from the ten storm events were analyzed for estimating runoff quantity (peak flow rate and total runoff volume) and quality reduction by the LID-BMPs. The sum of loads (SOL) method was used for calculating the water quality performance of LID-BMPs. Results indicated that, for peak flow rate, a bioretention cell reduction of 50-84 % was obtained, and grassed swale reduction was 17-79 %, with a runoff volume reduction of 47-80 and 9-74 %, respectively. For water quality, the bioretention cell in general showed good removal for zinc (nearly 100 %), copper (69 %), NH3-N (ammonia nitrogen) (51 %), and total nitrogen (TN) (49 %); fair removal for chemical oxygen demand (COD) (18 %); and poor removal for total suspended solids (TSS) (-11 %) and total phosphorus (TP) (-21 %). And its performance effectiveness for pollutant removal increased in the second year after 1 year of stabilizing. When considering the aggregated effect of the LID-BMP treatment train system, it showed excellent removal for NH3-N (73 %), TN (74 %), and TP (95 %) and fair removal for COD (19 %) and TSS (35 %). The assessment results of the LID-BMP treatment train system provide valuable information on how to link the different types of LID-BMP facilities and maximize the integrated effectiveness on urban runoff control.

STDP-based adaptive graph convolutional networks for automatic sleep staging.

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.

Metabolic reprogramming, autophagy, and ferroptosis: Novel arsenals to overcome immunotherapy resistance in gastrointestinal cancer.

BACKGROUND: Gastrointestinal cancer poses a serious health threat owing to its high morbidity and mortality. Although immune checkpoint blockade (ICB) therapies have achieved meaningful success in most solid tumors, the improvement in survival in gastrointestinal cancers is modest, owing to sparse immune response and widespread resistance. Metabolic reprogramming, autophagy, and ferroptosis are key regulators of tumor progression. METHODS: A literature review was conducted to investigate the role of the metabolic reprogramming, autophagy, and ferroptosis in immunotherapy resistance of gastrointestinal cancer. RESULTS: Metabolic reprogramming, autophagy, and ferroptosis play pivotal roles in regulating the survival, differentiation, and function of immune cells within the tumor microenvironment. These processes redefine the nutrient allocation blueprint between cancer cells and immune cells, facilitating tumor immune evasion, which critically impacts the therapeutic efficacy of immunotherapy for gastrointestinal cancers. Additionally, there exists profound crosstalk among metabolic reprogramming, autophagy, and ferroptosis. These interactions are paramount in anti-tumor immunity, further promoting the formation of an immunosuppressive microenvironment and resistance to immunotherapy. CONCLUSIONS: Consequently, it is imperative to conduct comprehensive research on the roles of metabolic reprogramming, autophagy, and ferroptosis in the resistance of gastrointestinal tumor immunotherapy. This understanding will illuminate the clinical potential of targeting these pathways and their regulatory mechanisms to overcome immunotherapy resistance in gastrointestinal cancers.

Crowding Effect-Induced Zinc-Enriched/Water-Lean Polymer Interfacial Layer Toward Practical Zn-Iodine Batteries.

Although the meticulous design of functional diversity within the polymer interfacial layer holds paramount significance in mitigating the challenges associated with hydrogen evolution reactions and dendrite growth in zinc anodes, this pursuit remains a formidable task. Here, a large-scale producible zinc-enriched/water-lean polymer interfacial layer, derived from carboxymethyl chitosan (CCS), is constructed on zinc anodes by integration of electrodeposition and a targeted complexation strategy for highly reversible Zn plating/stripping chemistry. Zinc ions-induced crowding effect between CCS skeleton creates a strong hydrogen bonding environment and squeezes the moving space for water/anion counterparts, therefore greatly reducing the number of active water molecules and alleviating cathodic I3- attack. Moreover, the as-constructed Zn2+-enriched layer substantially facilitate rapid Zn2+ migration through the NH2-Zn2+-NH2 binding/dissociation mode of CCS molecule chain. Consequently, the large-format Zn symmetry cell (9 cm2) with a Zn-CCS electrode demonstrates excellent cycling stability over 1100 h without bulging. When coupled with an I2 cathode, the assembled Zn-I2 multilayer pouch cell displays an exceptionally high capacity of 140 mAh and superior long-term cycle performance of 400 cycles. This work provides a universal strategy to prepare large-scale production and high-performance polymer crowding layer for metal anode-based battery, analogous outcomes were veritably observed on other metals (Al, Cu, Sn).

A Multicenter Cohort Study on Children Suffering from Acute Lymphoblastic Leukemia: Effects of Obesity on Mortality.

Background: Overweight and obesity have been reported in specific patients and disease survivors compared to other types of childhood cancer. This study is aimed at determining the effect of children's obesity on the mortality of acute lymphoblastic leukemia. Method: Children admitted to Inner Mongolia International Mongolian Hospital from 1 January 2017 to 31 December 2020 participated in this study. 1070 children were analyzed. A multi-middle-class poll was conducted. All children under the age of 15 were followed up within 24 months of diagnosis. Overweight and obesity are identified according to the World Health Organization and the Centers for Disease Control and Prevention. Premature death and reoccurrence of emergencies are the main consequences. Results: The initial ethical rate for the first 24 months of testing was 19.9% (NS 213). The lowest cancer survival rate (DFS) was childhood obesity (73%) (24 months), compared with average weight (81%). Diagnosis of overweight/obesity is a predictor of early death (WHO: HR = 1.4, 95% CI: 1.0-2.0; CDC: HR = 1.6, 95% CI: 1.1-2.3). However, there was no association between overweight and obesity (WHO: HR = 1.5, 95% effective interval: 0.9-2.5; CDC: human resources = 1.0, 95% effective interval: 0.6-1.6) and obesity (WHO: HR = 1.5, 95% effective interval: 0.7-3.2; CDC: HR = 1.4, 95% effective interval: 0.9-2.3). Early recurrence was observed. Conclusion: Overweight and obese people belong to the subclass with a high risk of death in the treatment of leukemia.

Two Different Transplant Preconditioning Regimens Combined with Irradiation and Chemotherapy in the Treatment of Childhood Leukemia: Systematic Review and Meta-Analysis.

Objective: To observe the therapeutic effect and the incidence of adverse reactions of total body irradiation plus cyclophosphamide (TBI/CY) and busulfan plus cyclophosphamide (BU/CY) in the treatment of pediatric hematopoietic stem cell transplantation. Methods: By searching the Cochrane Library, PubMed, Web of Knowledge, Embase, Chinese Biomedical Literature Database (CBM), and screening randomized controlled trials (RCTs), quality evaluation and data extraction were performed for the included literature, and meta-analysis was performed for RCTs included at using Review Manager 5.2 software. Results: A total of 10160 patients were enrolled in 15 RCTs, including 5211 patients in the TBI/CY group and 4949 patients in the BU/CY group. Meta-analysis showed that there was a statistical difference in transplant failure rate (OR = 1.56, 95% CI (1.23, 1.97), P = 0.0002, I 2 = 56%, Z = 3.69), transplant mortality (OR = 1.45, 95% CI (1.24, 1.68), P < 0.00001, I 2 = 76%, Z = 4.80), transplantation long-term disease-free survival rate (OR = 1.52, 95% CI (1.09, 2.12), P = 0.01, I 2 = 0%, Z = 2.50), and transplantation adverse reactions (OR = 1.28, 95% CI (1.08, 1.52), P = 0.004, I 2 = 0%, Z = 2.85). Conclusion: Meta-analysis showed that TBI/CY combined pretreatment regimen was more effective than BU/CY regimen alone in the treatment of pediatric hematologic transplantation, with a lower incidence of adverse reactions and significant long-term survival efficacy.

Secolignans with antiangiogenic activities from Peperomia dindygulensis.

Two new secolignans, peperomins G and H (1 and 2, resp.), were isolated from the whole plant of Peperomia dindygulensis, together with five known secolignans, peperomin A (3), peperomin E (4), peperomin B (5), 2,3-trans-2-methyl-3-{(3-hydroxy-4,5-dimethoxyphenyl)[5-methoxy-3,4-(methylenedioxy)phenyl]methyl}butyrolactone (6), 2,3-cis-2-(hydroxymethyl)-3-{bis[5-methoxy-3,4-(methylenedioxy)phenyl]methyl}butyrolactone (7). Their structures and configurations were elucidated by spectroscopic methods including 2D-NMR techniques. Antiangiogenic effects of all compounds were evaluated using human umbilical vein endothelial cells (HUVEC) proliferation and tube-formation tests, with compounds 4 and 5 being active in the bioassay. Compounds 4 and 5 induced obvious cell toxicity to HUVEC with IC(50) values of 1.64±0.19 and 8.44±0.4 µM, respectively. Compounds 4 and 5 also exhibited significant HUVEC tube formation-inhibiting activity with IC(50) values of 3.13±0.09 and 6.24±0.12 µM, respectively.

Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms.

Bcl2-associated athanogene 4 promotes the invasion and metastasis of gastric cancer cells by activating the PI3K/AKT/NF-κB/ZEB1 axis.

Bcl2-associated athanogene 4 (BAG4) has been found to be aberrantly expressed in several types of human cancers. However, little is known about its expression, role, and clinical significance in gastric cancer (GC). In this study, we aimed to address these issues and to explore the underlying mechanisms. The expression level of BAG4, measured by immunohistochemistry, was significantly higher in GC tissues than in paired normal tissues. Elevated BAG4 expression was positively correlated with T stage, lymph node metastasis, and tumor size of GC and was associated with unfavorable outcomes of the patients. The overexpression of BAG4 promoted the in vitro invasion and in vivo metastasis of GC cells, and opposite results were observed after silencing of BAG4. Silencing of BAG4 significantly reduced the phosphorylation of PI3K, AKT, and p65, whereas overexpression of BAG4 markedly enhanced the phosphorylation of these molecules. At the same time, manipulating BAG4 expression resulted in the corresponding changes in p65 nuclear translocation and ZEB1 expression. Luciferase reporter and chromatin immunoprecipitation assays verified that p65 binds to the promoter of ZEB1 to upregulate its transcription. Our results demonstrate that BAG4 plays an oncogenic role in the invasion and metastasis of GC cells by activating the PI3K/AKT/NF-κB/ZEB1 axis to induce epithelial-mesenchymal transition.

Supervised learning in spiking neural networks: A review of algorithms and evaluations.

As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural networks are composed of biologically plausible spiking neurons, which have become suitable tools for processing complex temporal or spatiotemporal information. However, because of their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, and has become an important problem in this research field. This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively. First, a comparison between spiking neural networks and traditional artificial neural networks is provided. The general framework and some related theories of supervised learning for spiking neural networks are then introduced. Furthermore, the state-of-the-art supervised learning algorithms in recent years are reviewed from the perspectives of applicability to spiking neural network architecture and the inherent mechanisms of supervised learning algorithms. A performance comparison of spike train learning of some representative algorithms is also made. In addition, we provide five qualitative performance evaluation criteria for supervised learning algorithms for spiking neural networks and further present a new taxonomy for supervised learning algorithms depending on these five performance evaluation criteria. Finally, some future research directions in this research field are outlined.

Rate Distortion Optimization: A Joint Framework and Algorithms for Random Access Hierarchical Video Coding.

This paper revisits the problem of rate distortion optimization (RDO) with focus on inter-picture dependence. A joint RDO framework which incorporates the Lagrange multiplier as one of parameters to be optimized is proposed. Simplification strategies are demonstrated for practical applications. To make the problem tractable, we consider an approach where prediction residuals of pictures in a video sequence are assumed to be emitted from a finite set of sources. Consequently the RDO problem is formulated as finding optimal coding parameters for a finite number of sources, regardless of the length of the video sequence. Specifically, in cases where a hierarchical prediction structure is used, prediction residuals of pictures at the same prediction layer are assumed to be emitted from a common source. Following this approach, we propose an iterative algorithm to alternatively optimize the selections of quantization parameters (QPs) and the corresponding Lagrange multipliers. Based on the results of the iterative algorithm, we further propose two practical algorithms to compute QPs and the Lagrange multipliers for the RA(random access) hierarchical video coding: the first practical algorithm uses a fixed formula to compute QPs and the Lagrange multipliers, and the second practical algorithm adaptively adjusts both QPs and the Lagrange multipliers. Experimental results show that these three algorithms, integrated into the HM 16.20 reference software of HEVC, can achieve considerable RD improvements over the standard HM 16.20 encoder, in the common RA test configuration.