Yan Zhang

Electron tomography (ET) plays an important role in revealing biological structures, ranging from macromolecular to subcellular scale. Due to limited tilt angles, ET reconstruction always suffers from the 'missing wedge' artifacts, thus severely weakens the further biological interpretation. In this work, we developed an algorithm called Iterative Compressed-sensing Optimized Non-uniform fast Fourier transform reconstruction (ICON) based on the theory of compressed-sensing and the assumption of sparsity of biological specimens. ICON can significantly restore the missing information in comparison with other reconstruction algorithms. More importantly, we used the leave-one-out method to verify the validity of restored information for both simulated and experimental data. The significant improvement in sub-tomogram averaging by ICON indicates its great potential in the future application of high-resolution structural determination of macromolecules in situ.

Introduction: Insulin has an effect on neurodegenerative diseases. However, the role and mechanism of insulin in vascular dementia (VD) and its underlying mechanism are unknown. In this study, we aimed to investigate the effects and mechanism of insulin on VD. Methods: Experimental rats were randomly assigned to control (CK), Sham, VD, and insulin (INS) + VD groups. Insulin was administered by intranasal spray. Cognitive function was evaluated using the Morris's water maze. Nissl's staining and immunohistochemical staining were used to assess morphological alterations. Apoptosis was evaluated using TUNEL-staining. Transcriptome and metabolome analyses were performed to identify differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs), respectively. Results: Insulin significantly improved cognitive and memory functions in VD model rats ( p < 0.05). Compared with the VD group, the insulin + VD group exhibited significantly reduced the number of Nissl's bodies numbers, apoptosis level, GFAP-positive cell numbers, apoptosis rates, and p-tau and tau levels in the hippocampal CA1 region ( p < 0.05). Transcriptomic analysis found 1,257 and 938 DEGs in the VD vs. CK and insulin + VD vs. VD comparisons, respectively. The DEGs were mainly enriched in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse signaling pathways. In addition, metabolomic analysis identified 1 and 14 DEMs between groups in negative and positive modes, respectively. KEGG pathway analysis indicated that DEGs and DEMs were mostly enriched in metabolic pathway. Conclusion: Insulin could effectively improve cognitive function in VD model rats by downregulating tau and p-tau expression, inhibiting astrocyte inflammation and neuron apoptosis, and regulating genes involved in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse pathways, as well as metabolites involved in metabolic pathway.

Electron tomography (ET) combining subsequent sub-volume averaging has been becoming a unique way to study the in situ 3D structures of macromolecular complexes. However, information missing in electron tomography due to limited angular sampling is still the bottleneck in high-resolution electron tomography application. Here, based on the understanding of smooth nature of biological specimen, we present a new iterative image reconstruction algorithm, FIRT (filtered iterative reconstruction technique) for electron tomography by combining the algebra reconstruction technique (ART) and the nonlinear diffusion (ND) filter technique. Using both simulated and experimental data, in comparison to ART and weight back projection method, we proved that FIRT could generate a better reconstruction with reduced ray artifacts and significant improved correlation with the ground truth and partially restore the information at the non-sampled angular region, which was proved by investigating the 90° re-projection and by the cross-validation method. This new algorithm will be subsequently useful in the future for both cellular and molecular ET with better quality and improved structural details.

Abstract Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise which is asymptotic approximation of surprise. We analyzed the critical behaviors of asymptotic surprise in the phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale communities in networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we further demonstrated the critical behaviors of the asymptotic surprise, and the effectiveness of the improved Louvain algorithm; and then we validated the ability of our multi-resolution method to solve the first-type resolution limit and its strong tolerance against the second-type resolution limit; finally we confirmed its effectiveness in revealing multi-scale community structures in networks.

The result of forgetting some predicates in a first-order sentence may not exist in the sense that it might not be captured by any first-order sentences. This, indeed, severely restricts the usage of forgetting in applications. To address this issue, we propose a notion called k-forgetting, also called bounded forgetting in general, for any fixed number k. We present several equivalent characterizations of bounded forgetting and show that the result of bounded forgetting, on one hand, can always be captured by a single first-order sentence, and on the other hand, preserves the information that we are concerned with.