Multimodal multiobjective optimization with structural network control principles to optimize personalized drug targets for drug discovery of individual patients.

Structural network control principles provided novel and efficient clues for the optimization of personalized drug targets (PDTs) related to state transitions of individual patients. However, most existing methods focus on one subnetwork or module as drug targets through the identification of the minimal set of driver nodes and ignore the state transition capabilities of other modules with different configurations of drug targets [i.e.

Constrained Multi-objective Optimization-Based Temporal Network Observability for Biomarker Identification of Individual Patients.

Identifying the biomarkers from the personalized gene interaction network of individual patients is important for disease diagnosis. However, existing methods not only ignore the prior biomarkers for practical use but also ignore the observability of the entire system. Therefore, this paper proposes a new constrained multi-objective optimization-based temporal network observability model (CMTNO) to identify biomarkers, which not only requires minimizing the number of selected nodes including ordinary nodes and prior nodes (the first optimization objective) but also maximizing the number of selected prior nodes (the second optimization objective) on the premise of ensuring network observability (the constraint condition).

Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.

Efficient genome editing in cotton using the virus-mediated CRISPR/Cas9 and grafting system.

The extensive application of CRISPR in cotton was limited due to the labor-intensive transformation process. Thus, we here established a convenient method of CRISPR in cotton by CLCrV-mediated sgRNA delivery.

Fully automated pixel-wise quantitative CMR-myocardial perfusion with CMR-coronary angiography to detect hemodynamically significant coronary artery disease.

Objectives: We applied a fully automated pixel-wise post-processing framework to evaluate fully quantitative cardiovascular magnetic resonance myocardial perfusion imaging (CMR-MPI). In addition, we aimed to evaluate the additive value of coronary magnetic resonance angiography (CMRA) to the diagnostic performance of fully automated pixel-wise quantitative CMR-MPI for detecting hemodynamically significant coronary artery disease (CAD).

Methods: A total of 109 patients with suspected CAD were prospectively enrolled and underwent stress and rest CMR-MPI, CMRA, invasive coronary angiography (ICA), and fractional flow reserve (FFR).

Alterations of the gut microbiota in patients with immunoglobulin light chain amyloidosis.

Background: Emerging evidence revealed that gut microbial dysbiosis is implicated in the development of plasma cell dyscrasias and amyloid deposition diseases, but no data are available on the relationship between gut microbiota and immunoglobulin light chain (AL) amyloidosis.

Methods: To characterize the gut microbiota in patients with AL amyloidosis, we collected fecal samples from patients with AL amyloidosis (n=27) and age-, gender-, and BMI-matched healthy controls (n=27), and conducted 16S rRNA MiSeq sequencing and amplicon sequence variants (ASV)-based analysis.

Results: There were significant differences in gut microbial communities between the two groups.

Clinical characteristics and prognostic value of renal immune complex deposition in patients with light chain amyloidosis.

Although patients with light chain amyloidosis (AL) may present with co-deposition of amyloid and immune complexes (ICs) in renal biopsies, data on clinical characteristics and prognostic value of renal IC deposition are limited. A total of 73 patients with AL amyloidosis who were newly diagnosed by renal biopsy in Xijing Hospital (Xi'an, China) were divided into two groups (IC and non-IC groups). As a result, renal IC deposition was found in 26% of patients.

Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.

Background: Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e.

Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.

Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size.

KIF4A Regulates the Progression of Pancreatic Ductal Adenocarcinoma through Proliferation and Invasion.

Background: Pancreatic cancer is a malignant tumor of the digestive tract, which is difficult to diagnose and treat due to bad early diagnosis. We aimed to explore the role of kinesin superfamily 4A (KIF4A) in pancreatic ductal adenocarcinoma (PDAC).

Methods: We first used the bioinformatic website to screen the data of pancreatic cancer in TCGA, and KIF4A protein was detected among the 86 specimens of patients in our hospital combined with clinic-pathological characteristics and survival analysis.

qPTMplants: an integrative database of quantitative post-translational modifications in plants.

As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed.

KIF20B Promotes Cell Proliferation and May Be a Potential Therapeutic Target in Pancreatic Cancer.

KIFs have been reported to play a critical role in a variety of tumors, and KIF20B is a protein in KFIs. In this research, KIF20B was highly expressed in the GEO database and our hospital's data, and high expression of KIF20B suggested poor prognosis. We detect the expression of KIF20B in pancreatic cancer and adjacent normal tissues using immunohistochemistry.

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods.

Weighted minimum feedback vertex sets and implementation in human cancer genes detection.

Background: Recently, many computational methods have been proposed to predict cancer genes. One typical kind of method is to find the differentially expressed genes between tumour and normal samples. However, there are also some genes, for example, 'dark' genes, that play important roles at the network level but are difficult to find by traditional differential gene expression analysis.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective.

Diagnostic value of bronchoalveolar lavage fluid cryptococcal antigen-lateral flow immunochromatographic assay for pulmonary cryptococcosis in non-HIV patients.

Background: The aim of this study was to investigate the diagnostic value of cryptococcal antigen-lateral flow immunochromatographic assay (CrAg-LFA) in bronchoalveolar lavage fluid (BALF) of patients with pulmonary cryptococcosis (PC).

Methods: A total of 308 patients were divided into the PC group (n = 72) and the non-PC group (n = 236). The clinical data, pathogen detection, radiological imaging, and the detection of the cryptococcal antigen in blood and BALF samples were analyzed.

Network control principles for identifying personalized driver genes in cancer.

To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level.

Aberrant default mode network in amnestic mild cognitive impairment: a meta-analysis of independent component analysis studies.

Independent component analysis (ICA) is one of the most popular and valid methods to investigate the default mode network (DMN), an intrinsic network which attracts particular attention in amnestic mild cognitive impairment (aMCI). However, previous studies present inconsistent results regarding the topographical organization of the DMN in aMCI. Therefore, we conducted a quantitative, voxel-wise meta-analysis of resting-state ICA studies using Seed-based d Mapping to establish the most consistent pattern of DMN functional connectivity alterations in aMCI.

A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

Background: The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g.

Discovering personalized driver mutation profiles of single samples in cancer by network control strategy.

Motivation: It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.

Catalytic Asymmetric [4+2] Cycloaddition of in Situ Generated o-Quinone Methide Imines with o-Hydroxystyrenes: Diastereo- and Enantioselective Construction of Tetrahydroquinoline Frameworks.

A catalytic asymmetric [4+2] cycloaddition of ortho-quinone methide imines in situ generated from o-aminobenzyl alcohols with o-hydroxystyrenes has been established under the catalysis of chiral phosphoramide, which afforded chiral tetrahydroquinolines in moderate to good yields, good enantioselectivities, and excellent diastereoselectivities (up to 82% yield, 93:7 er, all >95:5 dr). In this catalytic asymmetric [4+2] cycloaddition, the hydrogen-bonding interaction between chiral phosphoramide and two substrates was proposed to play a crucial role in controlling the enantioselectivity. This reaction not only provides a useful approach for constructing chiral tetrahydroquinoline frameworks, but also demonstrates the great practicability of ortho-quinone methide imines in catalytic asymmetric cycloadditions.