An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study the effect of secondary structure content and data quality on the performance of DeepSSETracer, a deep learning method that segments regions of protein secondary structures from cryo-EM map components. Results show that various content levels in the secondary structure and data quality influence the performance of segmentation for DeepSSETracer

Travel Mode Recognition from GPS Data Based on LSTM

A large amount of GPS data contains valuable hidden information. With GPS trajectory data, a Long Short-Term Memory model (LSTM) is used to identify passengers' travel modes, i.e., walking, riding buses, or driving cars. Moreover, the Quantum Genetic Algorithm (QGA) is used to optimize the LSTM model parameters, and the optimized model is used to identify the travel mode. Compared with the state-of-the-art studies, the contributions are: 1. We designed a method of data processing. We process the GPS data by pixelating, get grayscale images, and import them into the LSTM model. Finally, we use the QGA to optimize four parameters of the model, including the number of neurons and the number of hidden layers, the learning rate, and the number of iterations. LSTM is used as the classification method where QGA is adopted to optimize the parameters of the model. 2. Experimental results show that the proposed approach has higher accuracy than BP Neural Network, Random Forest and Convolutional Neural Networks (CNN), and the QGA parameter optimization method can further improve the recognition accuracy

Improved Performance of LiFe0.25Mn0.75PO4 by using Graphene and Fluorine- Doped Carbon Coating

Lithium transition metal phosphatesusually need modification of morphology and electron conductivity for improving the electrochemical performance [1,2]. In this work, the electron conductivity of LiFe0.25Mn0.75PO4 is increased by adopting graphene and F-doped carbon.The reductive graphene oxide and F-doped carbon coating LiFe0.25Mn0.75PO4 (LFMP/C-F/rGO) is synthesized by a simple ball milling method

A Tool for Segmentation of Secondary Structures in 3D Cryo-EM Density Map Components Using Deep Convolutional Neural Networks

Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structures when the resolution of cryo-EM density maps is in the medium resolution range, such as 5–10 Å. Detection of protein secondary structures, such as helices and β-sheets, from cryo-EM density maps provides constraints for deriving atomic structures from such maps. As more deep learning methodologies are being developed for solving various molecular problems, effective tools are needed for users to access them. We have developed an effective software bundle, DeepSSETracer, for the detection of protein secondary structure from cryo-EM component maps in medium resolution. The bundle contains the network architecture and a U-Net model trained with a curriculum and gradient of episodic memory (GEM). The bundle integrates the deep neural network with the visualization capacity provided in ChimeraX. Using a Linux server that is remotely accessed by Windows users, it takes about 6 s on one CPU and one GPU for the trained deep neural network to detect secondary structures in a cryo-EM component map containing 446 amino acids. A test using 28 chain components of cryo-EM maps shows overall residue-level F1 scores of 0.72 and 0.65 to detect helices and β-sheets, respectively. Although deep learning applications are built on software frameworks, such as PyTorch and Tensorflow, our pioneer work here shows that integration of deep learning applications with ChimeraX is a promising and effective approach. Our experiments show that the F1 score measured at the residue level is an effective evaluation of secondary structure detection for individual classes. The test using 28 cryo-EM component maps shows that DeepSSETracer detects β-sheets more accurately than Emap2sec+, with a weighted average residue-level F1 score of 0.65 and 0.42, respectively. It also shows that Emap2sec+ detects helices more accurately than DeepSSETracer with a weighted average residue-level F1 score of 0.77 and 0.72 respectively

Failure of PCR to Detect Treponema pallidum ssp. pertenue DNA in Blood in Latent Yaws.

Yaws, caused by Treponema pallidum ssp. pertenue, is a neglected tropical disease closely related to venereal syphilis and is targeted for eradication by 2020. Latent yaws represents a diagnostic challenge, and current tools cannot adequately distinguish between individuals with true latent infection and individuals who are serofast following successful treatment. PCR on blood has previously been shown to detect T. pallidum DNA in patients with syphilis, suggesting that this approach may be of value in yaws. We performed real-time PCR for Treponema pallidum ssp. pertenue on blood samples from 140 children with positive T. pallidum Particle Agglutination (TPPA) and Rapid Plasma Reagin (RPR) tests and 7 controls (negative serology), all collected as part of a prospective study of yaws in the Solomon Islands. All samples were also tested by a nested PCR for T. pallidum. 12 patients had clinical evidence of active yaws whilst 128 were considered to have latent yaws. 43 children had high titre rapid plasma reagins (RPRs) of ≥1:32. PCR testing with both assays gave negative results in all cases. It is possible that the failure to detect T. pallidum ssp. pertenue in blood reflects lower loads of organism in latent yaws compared to those in latent infection with T. pallidum ssp. pertenue, and/or a lower propensity for haematogenous dissemination in yaws than in syphilis. As the goal of the yaws control programme is eradication, a tool that can differentiate true latent infection from individuals who are serofast would be of value; however, PCR of blood is not that tool

MicroRNA-148a Regulates Low-Density Lipoprotein Metabolism by Repressing the (Pro)renin Receptor

High plasma LDL cholesterol (LDL-c) concentration is a major risk factor for atherosclerosis. Hepatic LDL receptor (LDLR) regulates LDL metabolism, and thereby plasma LDL-c concentration. Recently, we have identified the (pro)renin receptor [(P)RR] as a novel regulator of LDL metabolism, which regulates LDLR degradation and hence its protein abundance and activity. In silico analysis suggests that the (P)RR is a target of miR-148a. In this study we determined whether miR-148a could regulate LDL metabolism by regulating (P)RR expression in HepG2 and Huh7 cells. We found that miR-148a suppressed (P)RR expression by binding to the 3’-untranslated regions (3’-UTR) of the (P)RR mRNA. Mutating the binding sites for miR-148a in the 3’-UTR of (P)RR mRNA completely abolished the inhibitory effects of miR-148a on (P)RR expression. In line with our recent findings, reduced (P)RR expression resulted in decreased cellular LDL uptake, likely as a consequence of decreased LDLR protein abundance. Overexpressing the (P)RR prevented miR-148a-induced reduction in LDLR abundance and cellular LDL uptake. Our study supports a new concept that miR-148a is a regulator of (P)RR expression. By reducing (P)RR abundance, miR-148a decreases LDLR protein abundance and consequently cellular LDL uptake

Functional integration of 3D-printed cerebral cortical tissue into a brain lesion

Engineering human tissue with diverse cell types and desired cellular architectures and functions is a considerable challenge. The cerebral cortex, which has a layered cellular architecture composed of layer-specific neurons organised into vertical columns, delivers higher cognition through intricately wired neural circuits. However, current tissue engineering approaches cannot produce such structures. Here, we use a droplet printing technique to fabricate tissues comprising simplified cerebral cortical columns. Human induced pluripotent stem cells (hiPSCs) were differentiated into upper- and deep-layer neural progenitors, which were then printed to form cerebral cortical tissues with a two-layer organization. The tissues showed layer-specific biomarker expression and developed an integrated network of processes. Implantation of the printed cortical tissues into mouse brain explants resulted in substantial implant-host integration across the tissue boundaries as demonstrated by the projection of processes, the migration of neurons and the appearance of correlated Ca2+ signals. The approach we have developed might be used for the evaluation of drugs and nutrients that promote tissue integration. Importantly, our approach might be applied in personalised implantation treatments that restore the cellular structure and function of a damaged brain by using 3D tissues derived from a patient’s own iPSCs

Integration of 3D-printed cerebral cortical tissue into an ex vivo lesioned brain slice

Engineering human tissue with diverse cell types and architectures remains challenging. The cerebral cortex, which has a layered cellular architecture composed of layer-specific neurons organised into vertical columns, delivers higher cognition through intricately wired neural circuits. However, current tissue engineering approaches cannot produce such structures. Here, we use a droplet printing technique to fabricate tissues comprising simplified cerebral cortical columns. Human induced pluripotent stem cells are differentiated into upper- and deep-layer neural progenitors, which are then printed to form cerebral cortical tissues with a two-layer organization. The tissues show layer-specific biomarker expression and develop a structurally integrated network of processes. Implantation of the printed cortical tissues into ex vivo mouse brain explants results in substantial structural implant-host integration across the tissue boundaries as demonstrated by the projection of processes and the migration of neurons, and leads to the appearance of correlated Ca2+ oscillations across the interface. The presented approach might be used for the evaluation of drugs and nutrients that promote tissue integration. Importantly, our methodology offers a technical reservoir for future personalized implantation treatments that use 3D tissues derived from a patient’s own induced pluripotent stem cells