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Using single-particle electron cryo-microscopy (cryo-EM), it is possible to obtain multiple reconstructions showing the 3D structures of proteins imaged as a mixture. Here, it is shown that automatic map interpretation based on such reconstructions can be used to create atomic models of proteins as well as to match the proteins to the correct sequences and thereby to identify them. This procedure was tested using two proteins previously identified from a mixture at resolutions of 3.2 Å, as well as using 91 deposited maps with resolutions between 2 and 4.5 Å. The approach is found to be highly effective for maps obtained at resolutions of 3.5 Å and better, and to have some utility at resolutions as low as 4 Å.

The RhopH complex is implicated in malaria parasites' ability to invade and create new permeability pathways in host erythrocytes, but its mechanisms remain poorly understood. Here, we enrich the endogenous RhopH complex in a native soluble form, comprising RhopH2, CLAG3.1, and RhopH3, directly from parasite cell lysates and determine its atomic structure using cryo-electron microscopy (cryo-EM), mass spectrometry, and the cryoID program. CLAG3.1 is positioned between RhopH2 and RhopH3, which both share substantial binding interfaces with CLAG3.1 but make minimal contacts with each other. The forces stabilizing individual subunits include 13 intramolecular disulfide bonds. Notably, CLAG3.1 residues 1210 to 1223, previously predicted to constitute a transmembrane helix, are embedded within a helical bundle formed by residues 979 to 1289 near the C terminus of CLAG3.1. Buried in the core of the RhopH complex and largely shielded from solvent, insertion of this putative transmembrane helix into the erythrocyte membrane would likely require a large conformational rearrangement. Given the unusually high disulfide content of the complex, it is possible that such a rearrangement could be initiated by the breakage of allosteric disulfide bonds, potentially triggered by interactions at the erythrocyte membrane. This first direct observation of an exported Plasmodium falciparum transmembrane protein-in a soluble, trafficking state and with atomic details of buried putative membrane-insertion helices-offers insights into the assembly and trafficking of RhopH and other parasite-derived complexes to the erythrocyte membrane. Our study demonstrates the potential the endogenous structural proteomics approach holds for elucidating the molecular mechanisms of hard-to-isolate complexes in their native, functional forms.

Using single-particle electron cryo-microscopy (cryo-EM), it is possible to obtain multiple reconstructions showing the 3D structures of proteins imaged as a mixture. Here, it is shown that automatic map interpretation based on such reconstructions can be used to create atomic models of proteins as well as to match the proteins to the correct sequences and thereby to identify them. This procedure was tested using two proteins previously identified from a mixture at resolutions of 3.2 Å, as well as using 91 deposited maps with resolutions between 2 and 4.5 Å. The approach is found to be highly effective for maps obtained at resolutions of 3.5 Å and better, and to have some utility at resolutions as low as 4 Å.

The RhopH complex is implicated in malaria parasites' ability to invade and create new permeability pathways in host erythrocytes, but its mechanisms remain poorly understood. Here, we enrich the endogenous RhopH complex in a native soluble form, comprising RhopH2, CLAG3.1, and RhopH3, directly from parasite cell lysates and determine its atomic structure using cryo-electron microscopy (cryo-EM), mass spectrometry, and the cryoID program. CLAG3.1 is positioned between RhopH2 and RhopH3, which both share substantial binding interfaces with CLAG3.1 but make minimal contacts with each other. The forces stabilizing individual subunits include 13 intramolecular disulfide bonds. Notably, CLAG3.1 residues 1210 to 1223, previously predicted to constitute a transmembrane helix, are embedded within a helical bundle formed by residues 979 to 1289 near the C terminus of CLAG3.1. Buried in the core of the RhopH complex and largely shielded from solvent, insertion of this putative transmembrane helix into the erythrocyte membrane would likely require a large conformational rearrangement. Given the unusually high disulfide content of the complex, it is possible that such a rearrangement could be initiated by the breakage of allosteric disulfide bonds, potentially triggered by interactions at the erythrocyte membrane. This first direct observation of an exported Plasmodium falciparum transmembrane protein-in a soluble, trafficking state and with atomic details of buried putative membrane-insertion helices-offers insights into the assembly and trafficking of RhopH and other parasite-derived complexes to the erythrocyte membrane. Our study demonstrates the potential the endogenous structural proteomics approach holds for elucidating the molecular mechanisms of hard-to-isolate complexes in their native, functional forms.

In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to the powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden in the latent space are not discovered by exploiting the reconstruction error because the probability distribution of anomalies is not explicitly modeled. To address the issue, we propose a novel probability distribution representation detector (PDRD) that explores the intrinsic distribution of both the background and the anomalies in original data for hyperspectral anomaly detection in this paper. First, we represent the hyperspectral data with multivariate Gaussian distributions from a probabilistic perspective. Then, we combine the local statistics with the obtained distributions to leverage the spatial information. Finally, the difference between the corresponding distributions of the test pixel and the average expectation of the pixels in the Chebyshev neighborhood is measured by computing the modified Wasserstein distance to acquire the detection map. We conduct the experiments on four real data sets to evaluate the performance of our proposed method. Experimental results demonstrate the accuracy and efficiency of our proposed method compared to the state-of-the-art detection methods.

X-ray crystallography often requires non-native constructs involving mutations or truncations, and is challenged by membrane proteins and large multicomponent complexes. We present here a bottom-up endogenous structural proteomics approach whereby near-atomic-resolution cryo electron microscopy (cryoEM) maps are reconstructed ab initio from unidentified protein complexes enriched directly from the endogenous cellular milieu, followed by identification and atomic modeling of the proteins. The proteins in each complex are identified using cryoID, a program we developed to identify proteins in ab initio cryoEM maps. As a proof of principle, we applied this approach to the malaria-causing parasite Plasmodium falciparum, an organism that has resisted conventional structural-biology approaches, to obtain atomic models of multiple protein complexes implicated in intraerythrocytic survival of the parasite. Our approach is broadly applicable for determining structures of undiscovered protein complexes enriched directly from endogenous sources.

X-ray crystallography often requires non-native constructs involving mutations or truncations, and is challenged by membrane proteins and large multicomponent complexes. We present here a bottom-up endogenous structural proteomics approach whereby near-atomic-resolution cryo electron microscopy (cryoEM) maps are reconstructed ab initio from unidentified protein complexes enriched directly from the endogenous cellular milieu, followed by identification and atomic modeling of the proteins. The proteins in each complex are identified using cryoID, a program we developed to identify proteins in ab initio cryoEM maps. As a proof of principle, we applied this approach to the malaria-causing parasite Plasmodium falciparum, an organism that has resisted conventional structural-biology approaches, to obtain atomic models of multiple protein complexes implicated in intraerythrocytic survival of the parasite. Our approach is broadly applicable for determining structures of undiscovered protein complexes enriched directly from endogenous sources.

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is very important for infrared search and tracking systems. In this paper, we establish a detection method derived from maximal entropy random walk (MERW) to robustly detect multiple small targets. Initially, we introduce the primal MERW and analyze the feasibility of applying it to small target detection. However, the original weight matrix of the MERW is sensitive to interferences. Therefore, a specific weight matrix is designed for the MERW in principle of enhancing characteristics of small targets and suppressing strong clutters. Moreover, the primal MERW has a critical limitation of strong bias to the most salient small target. To achieve multiple small targets detection, we develop a hierarchical version of the MERW method. Based on the hierarchical MERW (HMERW), we propose a small target detection method as follows. First, filtering technique is used to smooth the infrared image. Second, an output map is obtained by importing the filtered image into the HMERW. Then, a coefficient map is constructed to fuse the stationary dirtribution map of the HMERW. Finally, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical data sets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression and multiple small targets detection.

Unsupervised learning methods for feature extraction are becoming more and more popular. We combine the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. Experiments have proved that our two proposed autoencoder networks have good feature learning capabilities by themselves, and the contrastive learning network we designed can better combine the features of the two to learn more representative features. As a result, our method surpasses other comparison methods in the hyperspectral classification experiments, including some supervised methods. Moreover, our method maintains a fast feature extraction speed than baseline methods. In addition, our method reduces the requirements for huge computing resources, separates feature extraction and contrastive learning, and allows more researchers to conduct research and experiments on unsupervised contrastive learning.