Distributed Signal and Image Processing: Particle Filters, Context Grammars, and Dynamic Games

A novel distributed video and image processing framework is presented in our work. Our work involves a serious of new algorithms in video processing and dynamical games. In the first part of our work, we present a distributed graph-based sequential particle filtering framework for visual tracking from single and multiple collaborative cameras in lossy networks. Many practical visual processing applications require a robust and efficient algorithm to handle occlusions for visual tracking from degraded visual data in camera networks that utilizes limited computational resources. Firstly, distributed graph-based particle filtering for visual tracking from one view is introduced. Specifically, two new distributed approaches: the graph-based sequential particle filtering framework and its hierarchical counterpart are proposed from one camera. We subsequently derive a distributed visual tracking solution from multiple cameras to handle object occlusions in the presence of frame loss by using collaborative particle filters. The proposed approach relies on Markov Properties and partial-order relations to derive a close-form sequential updating scheme on general graphs in lossy networks. The resulting distributed visual tracking technique is therefore robust to occlusion and sensor errors from specific camera views. Furthermore, the computational complexity of the proposed distributed approach from multiple cameras grows linearly with the number of cameras and objects in each camera. The resulting experiments further demonstrate the superiority of our approach to deal with severe occlusions in the presence of frame loss compared with existing methods. In the second part of our work, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that the SCSGs model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential computing framework. A series of statistical algorithms are expected to learn SCFGs subsequently. Specific to our case, a hybrid of general Forward-Backward Algorithm, Inside Algorithm and Expectation- Maximization Technique will be used to estimate the parameters for SCFGs. The SCSGs can be then used to represent multiple-trajectory. Experimental results demonstrate the improved performance of our method compared with existing methods for multiple-trajectory classifica- tion. In the third part of our work, we propose a Compressed-Sensing Game Theory (CSGT) framework to solve the Nash equilibria. We demonstrate that the proposed CSGT framework provides a polynomial complexity solution to the Nash Equilibria, thus allowing more general pay-off functions for certain classes of two-player dynamic games. We also provide numerical examples that demonstrate the efficiency of proposed CSGT framework in solving the Nash equilibria for two-player games in comparison to existing algorithms

Toward a leading-agency coordinated collaboration model? lessons learned from interagency collaboration in Chinese environmental protection

In this study, we are interested primarily in how a structural factor – social capital – relates to Chinese national government agencies’ partner selection in environmental protection. Our study finds that their partner selection is associated positively with activity closure and popularity closure while being negatively influenced by cyclicity closure. Moreover, their partner selection is characterized predominantly by a leading-agency coordinated collaboration model, which favors the engagement of a shared third agency and emphasizes the similarity of interests. This study expands the theoretical connotations of social capital and provides new insights into the mechanisms underlying actors’ partner selection in interagency collaboration.</p

DP/MM: A Hybrid Model for Zinc–Protein Interactions in Molecular Dynamics

Zinc-containing proteins are vital for many biological processes, yet accurately modeling them using classical force fields is hindered by complicated polarization and charge transfer effects. This study introduces DP/MM, a hybrid force field scheme that utilizes a deep potential model to correct the atomic forces of zinc ions and their coordinated atoms, elevating them from MM to QM levels of accuracy. Trained on the difference between MM and QM atomic forces across diverse zinc coordination groups, the DP/MM model faithfully reproduces structural characteristics of zinc coordination during simulations, such as the tetrahedral coordination of Cys4 and Cys3His1 groups. Furthermore, DP/MM allows water exchange in the zinc coordination environment. With its unique blend of accuracy, efficiency, flexibility, and transferability, DP/MM serves as a valuable tool for studying structures and dynamics of zinc-containing proteins and also represents a pioneering approach in the evolving landscape of machine learning potentials for molecular modeling

EViS: An Enhanced Virtual Screening Approach Based on Pocket–Ligand Similarity

Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein–ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket–ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein–ligand structure database

Image_1_CD8+T Cell-Related Gene Biomarkers in Macular Edema of Diabetic Retinopathy.tif

BackgroundCD8+T lymphocytes have a strong pro-inflammatory effect in all parts of the tissue, and some studies have demonstrated that its concentration in the vitreous increased significantly, suggesting that CD8+T cells play a pivotal role in the inflammatory response of diabetic retinopathy (DR). However, the infiltration of CD8+T cells in the DR retina, especially in diabetic macular edema (DME), and its related genes are still unclear.MethodsDownload the GSE16036 dataset from the Gene Expression Omnibus (GEO) database. The ImmuCellAI program was performed to evaluate the abundance of 24 immune cells including CD8+T cells. The CD8+T cell-related genes (DECD8+TRGs) between non-proliferative diabetic retinopathy (NPDR) and DME were detected via difference analysis and correlation analysis. Enrichment analysis and protein-protein interaction (PPI) network mapping were implemented to explore the potential function of DECD8+TRGs. Lasso regression, support vector machine recursive feature elimination (SVM-RFE), CytoHubba plug-in and MCODE plug-in in Cytoscape software, and Weighted Gene Co-Expression Network Analysis (WGCNA) were performed to comprehensively analyze and obtain Hub DECD8+TRGs. Hub DECD8+TRGs expression patterns were further validated in other two DR-related independent datasets. The CD8+TRG score was defined as the genetic characterization of Hub DECD8+TRGs using the GSVA sample scoring method, which can be administered to distinguish early and advanced diabetic nephropathy (DN) as well as normal and DN. Finally, the transcription level of DECD8+TRGs in DR model mouse were verified by quantitative real-time PCR (qPCR).ResultsA total of 371 DECD8+TRGs were identified, of which 294 genes were positively correlated and only 77 genes were negatively correlated. Eight genes (IKZF1, PTPRC, ITGB2, ITGAX, TLR7, LYN, CD74, SPI1) were recognized as Hub DECD8+TRGs. DR and DN, which have strong clinical correlation, have been proved to be associated with CD8+T cell-related hub genes by multiple independent data sets. Hub DECD8+TRGs can not only distinguish PDR from normal and DN from normal, but also play a role in the early and progressive stages of the two diseases (NPDR vs DME, Early DN vs Advanced DN). The qPCR transcription level and trend of Hub DECD8+TRGs in DR mouse model was basically the same as that in human transcriptome.ConclusionThis study not only increases our understanding of the molecular mechanism of CD8+T cells in the progression of DME, but also expands people’s cognitive vision of the molecular mechanism of crosstalk of CD8+T cells in the eyes and kidneys of patients with diabetes.</p

Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods

Data_Sheet_1_A comparative study of frequency effect on acquisition of grammar and meaning of words between Chinese and foreign learners of English language.docx

Frequency effect on vocabulary acquisition has been widely investigated in second language acquisition (SLA) research, whereas comparative studies of vocabulary acquisition of learners from different language types, such as hieroglyphic writing and alphabetic writing, are still rarely found. This type of studies could be of great significance in exploring some unique characteristics of how second language learners of native languages of different writing perceive and acquire second language. Using artificial words of alphabetic writing and low-frequency English words as experimental materials, this study aims to compare the effect of frequency on the acquisition of grammar and meaning of alphabetic words between Chinese learners of the hieroglyphic native language and foreign learners of alphabetic native languages. Specifically, the study intends to find out whether frequency effect plays the key role in language acquisition; to what extent frequency effect affects language acquisition; and whether there are any differences between learners of different language types for vocabulary acquisition in terms of frequency effect. The results show that Chinese and foreign learners of English language have no significant differences as a whole in terms of type of languages affecting the acquisition of grammar and meaning of artificial words and English words, indicating the difference in the type of mother tongue might not be the factor causing differences on grammar and meaning acquisition of vocabulary. Learner types, language types, frequency and part of speech of a word have interaction effect toward the acquisition of grammar and meaning of a word. However, exposure frequency of vocabulary plays the determining role in the acquisition of grammar and meaning of words.</p

Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods

Explicit Hydrogen-Bond Potentials and Their Application to NMR Scalar Couplings in Proteins

Hydrogen bonds (H bonds) are fundamental for the stability, structure, and dynamics of chemically and biologically relevant systems. One of the direct means to detect H bonds in proteins is NMR spectroscopy. As H bonds are dynamic in nature, atomistic simulations offer a meaningful way to characterize and analyze properties of hydrogen bonds, provided a sufficiently accurate interaction potential is available. Here, we use explicit H-bond potentials to investigate scalar coupling constants h3JNC′ and characterize the conformational ensemble for increasingly accurate intermolecular potentials. By considering a range of proteins with different overall topology a general procedure to improve the hydrogen-bonding potential (“morphing potentials”) based on experimental information is derived. The robustness of this approach is established through explicit simulations in full solvation and comparison with experimental results. The H-bond potentials used here lead to more directional H bonds than conventional electrostatic representations employed in molecular mechanics potentials. It is found that the optimized potentials lead to H-bond geometries in remarkable agreement with previous ab initio and knowledge-based approaches to H bonds in model systems and in proteins. This suggests that, by combining theory, computation, and experimental data, H-bonding potentials can be improved and are potentially useful to better study coupling, energy transfer, and allosteric communication in proteins

Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?

Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods