Single particle tomography (SPT), also known as subtomogram averaging, is a powerful technique uniquely poised to address questions in structural biology that are not amenable to more traditional approaches like X-ray crystallography, nuclear magnetic resonance, and conventional cryoEM single particle analysis. Owing to its potential for in situ structural biology at subnanometer resolution, SPT has been gaining enormous momentum in the last five years and is becoming a prominent, widely used technique. This method can be applied to unambiguously determine the structures of macromolecular complexes that exhibit compositional and conformational heterogeneity, both in vitro and in situ. Here we review the development of SPT, highlighting its applications and identifying areas of ongoing development.

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The advent of structural biology in situ by single particle cryo-electron tomography.

BACKGROUND The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. OBJECTIVE There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. METHODS In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. RESULTS The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. CONCLUSION The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image.

Simultaneous algebraic reconstruction Technique (SART) based iterative method play a significant role in the quality of images reconstructed by Computed Tomography (CT) The basic limitations associated with this method include slow convergence, poor visual quality and ill-posedness To address these drawbacks, SART iterative method is modified in two steps The first step removes the issue of slow convergence and poor visual quality by incorporating one-step-late (OSL) technique in SART referred as modified SART (SART+OSL) The result of first step undergoes in Anisotropic Diffusion (AD) as regularization prior to deal with illposedness issue To evaluate the proposed method, both qualitative and quantitative studies were conducted and results were compared with existing methods using two different simulated test phantoms Experimental results show that the proposed model yields significant gain in terms of visual reconstructed image quality and noise suppression

Regularization Based Modified SART Iterative Method for CT Image Reconstruction

Acute myeloid leukaemia (AML) is a heterogeneous disease with one of the worst survival rates of all cancers. The bone marrow microenvironment is increasingly being recognised as an important mediator of AML chemoresistance and relapse, supporting leukaemia stem cell survival through interactions among stromal, haematopoietic progenitor and leukaemic cells. Traditional therapies targeting leukaemic cells have failed to improve long term survival rates, and as such, the bone marrow niche has become a promising new source of potential therapeutic targets, particularly for relapsed and refractory AML. This review briefly discusses the role of the bone marrow microenvironment in AML development and progression, and as a source of novel therapeutic targets for AML. The main focus of this review is on drugs that modulate/target this bone marrow microenvironment and have been examined in in vivo models or clinically.

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Bone Marrow Microenvironment as a Source of New Drug Targets for the Treatment of Acute Myeloid Leukaemia

FOXM1: A small fox that makes more tracks for cancer progression and metastasis.

FOXM1 is a potent oncogenic transcription factor essential for cancer initiation, progression, and drug resistance. FOXM1 regulatory network is a major predictor of adverse outcomes in various human cancers. Inhibition of FOXM1 transcription factor function is a potential strategy in cancer treatment. In this study, we performed structure-based in silico screening to discover small molecules targeting the FOXM1 DNA-binding domain (DBD). Compound XST-20 was identified to effectively suppress FOXM1 transcriptional activities and inhibit ovarian cancer cell proliferation. XST-20 directly interacts with the FOXM1 DNA-binding domain determined by SPR assay. Furthermore, XST-20 was found to significantly reduce the colony-forming efficiency and induce cell cycle arrest and apoptosis. Our study provides a lead compound of FOXM1 inhibitor which may serve as a potential targeted therapy agent for ovarian cancer. 

https://www.nature.com/articles/s41420-022-01070-w.pdf

Small molecule targeting FOXM1 DNA binding domain exhibits anti-tumor activity in ovarian cancer

Electron tomography (ET) is an essential imaging technique for studying structures of large biological specimens. These structures are reconstructed from a set of projections obtained at different sample orientations by tilting the specimen. However, most of existing reconstruction methods are not appropriate when the data are extremely noisy and incomplete. A new iterative method has been proposed: adaptive simultaneous algebraic reconstruction with inter-iteration adaptive non-linear anisotropic diffusion (NAD) filter (FASART). We also adopted an adaptive parameter and discussed the step for the filter in this reconstruction method. Experimental results show that FASART can restrain the noise generated in the process of iterative reconstruction and still preserve the more details of the structure edges.

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FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter

Fu et al. develop a new tool for real-time monitoring of ribosomal RNA synthesis in living cells. This technology can be used to quantitatively measure RNA polymerase I (Pol I) transcriptional activity under various conditions, which facilitates large-scale screening of Pol I regulators.

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Real-time imaging of RNA polymerase I activity in living human cells

There is an urgent need to develop new effective therapies for HCC. Our previous study identified ULK1 as the potential target for HCC therapy and screened the compound XST-14 as a specific inhibitor of ULK1 to suppress HCC progression. However, the poor manufacturability of XST-14 impeded the process of its clinical translation. In this study, we first generated pharmacophore models of ULK1 based on the X-ray structure of UKL1 in complex with ligands. We then screened the Specs chemical library for potential UKL1 inhibitors. By molecular docking, we screened out the 19 compounds through structure-based virtual screening. Through CCK8 activity screening on HCC cells, we found that ZZY-19 displayed obvious cell killing effects on HCC cells. SPR assay indicated that ZZY-19 had a higher binding affinity for ULK1 than XST-14. Moreover, ZZY-19 induced the effects of anti-proliferation, anti-invasion and anti-migration in HCC cells. Mechanistically, ZZY-19 induces autophagy inhibition by reducing the expression of ULK1 on HCC cells. Especially, the combination of ZZY-19 with sorafenib synergistically suppresses the progression of HCC in vivo. Taken together, ZZY-19 was a potential candidate compound that targeted ULK1 and possessed promising anti-HCC activities by inhibiting autophagy.

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Structure-Based Virtual Screening towards the Discovery of Novel ULK1 Inhibitors with Anti-HCC Activities

Climate change and rapid urbanization are two global processes that have significantly aggravated natural disasters, such as drought and flooding. Urbanization without resilient and sustainable planning and execution could lead to undesirable changes in landscapes and stormwater regulation capacity. These changes have exacerbated the effects of extreme climatic events with disastrous consequences in many cities worldwide. Unfortunately, the major storm in Zhengzhou, China on 20 July 2021 was one of these examples. This event provided a rare opportunity to study the key roles of green infrastructures (GI) in mitigating flooding risks in a major urban center after a devasting flood event. Using the data from high-resolution images collected via two satellites, a comprehensive study of the Jialu System in Greater Zhengzhou was conducted to systematically compare how far the river water had reached before and after the 20 July 2021 major storm in order to identify the main weak links in the city’s GI and stormwater management system. A flood inundation intensity index (FI) in the Upper (UJLR), Middle (MJLR), and Lower (LJLR) Regions of the Jialu River System was generated. Bivariate Moran’s I, a correlation coefficient between FI and landscape characteristics, was calculated and used to identify problem areas for future improvements. Our results showed that the MJLR had the severest flooding impacts. LJLR had the biggest change in how far the river water reached after flooding, ranging from 4.59 m to 706.28 m. In UJLR, the percentages of mine, crop land, and green space had the highest global bivariate Moran’s I correlation coefficients. In MJLR, the percentages of vacant land, impervious surfaces, and water body had the highest global bivariate Moran’s I correlation coefficients. In LJLR, the percentages of vacant land, water body, and crop land had the highest global bivariate Moran’s I correlation coefficients. The total percentages of both high landscape characteristics indices-high flood inundation intensity indices and low landscape characteristics indices-high flood inundation intensity indices areas are 12.96%, 13.47%, and 13.80% in UJLR, MJLR, and LJLR, respectively. These land cover composition types identified for each region can be treated as areas of primary focus. However, GeoDector Model (GDM) analyses showed that our eight variables of landscape characteristics were not independent. Hence, a more comprehensive approach integrating all eight variables is still necessary in future flood mitigation efforts.

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Ziying Zhou

Papers

Small molecule targeting FOXM1 DNA binding domain exhibits anti-tumor activity in ovarian cancer

FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter

Real-time imaging of RNA polymerase I activity in living human cells

Structure-Based Virtual Screening towards the Discovery of Novel ULK1 Inhibitors with Anti-HCC Activities

“The 20 July 2021 Major Flood Event” in Greater Zhengzhou, China: A Case Study of Flooding Severity and Landscape Characteristics