Improved Acquisition Methods for Hyperpolarized Carbon-13 Magnetic Resonance Imaging

Magnetic resonance imaging with hyperpolarized 13C-labeled compounds via dynamic nuclear polarization (DNP) has been used to non-invasively study metabolic processes in vivo. This method provides a transient signal enhancement of more than 10,000 fold compared to imaging 13C compounds at thermal equilibrium. However, as soon as the pre-polarized 13C-labeled compound leaves the polarizer, its hyperpolarized state would irreversibly decay to the thermal equilibrium with a decay constant characterized by T1, which is typically less than one minute. The rapid loss of nonrenewable polarization brings challenges in hyperpolarized 13C magnetic resonance imaging. This dissertation presents improved acquisition methods for hyperpolarized 13C imaging with the injection of hyperpolarized [1-13C]pyruvate, which is the most widely studied substrate to date. The improved acquisition methods include a regional bolus tracking sequence for automatic acquisition timing, real-time calibration of frequency and RF power for more robust acquisitions, metabolite specific balanced steady state free precession (bSSFP) sequence and metabolite specific fast spin echo sequence for efficient use of polarization in hyperpolarized [1-13C] imaging. The proposed acquisition methods have been demonstrated in various clinical applications on a MR 3T scanner. Bolus tracking and real-time acquisition methods have been used in imaging human brain, heart, kidney and prostate. Metabolite specific bSSFP sequence has been applied in imaging human kidney. Metabolite specific fast spin echo sequence has been demonstrated in imaging human brain

Chloroplot : An Online Program for the Versatile Plotting of Organelle Genomes

Understanding the complexity of genomic structures and their unique architecture is linked with the power of visualization tools used to represent these features. Such tools should be able to provide a realistic and scalable version of genomic content. Here, we present an online organelle plotting tool focused on chloroplasts, which were developed to visualize the exclusive structure of these genomes. The distinguished unique features of this program include its ability to represent the Single Short Copy (SSC) regions in reverse complement, which allows the depiction of the codon usage bias index for each gene, along with the possibility of the minor mismatches between inverted repeat (IR) regions and user-specified plotting layers. The versatile color schemes and diverse functionalities of the program are specifically designed to reflect the accurate scalable representation of the plastid genomes. We introduce a Shiny app website for easy use of the program; a more advanced application of the tool is possible by further development and modification of the downloadable source codes provided online. The software and its libraries are completely coded in R, available at https://irscope.shinyapps.io/chloroplot/.Peer reviewe

A Low-cost, High-impact Node Injection Approach for Attacking Social Network Alignment

Social network alignment (SNA) holds significant importance for various downstream applications, prompting numerous professionals to develop and share SNA tools. Unfortunately, these tools can be exploited by malicious actors to integrate sensitive user information, posing cybersecurity risks. While many researchers have explored attacking SNA (ASNA) through a network modification attack way, practical feasibility remains a challenge. This paper introduces a novel approach, the node injection attack. To overcome the problem of modeling and solving within a limited time and balancing costs and benefits, we propose a low-cost, high-impact node injection attack via dynamic programming (DPNIA) framework. DPNIA models ASNA as a problem of maximizing the number of confirmed incorrect correspondent node pairs who have a greater similarity scores than the pairs between existing nodes, making ASNA solvable. Meanwhile, it employs a cross-network evaluation method to identify node vulnerability, facilitating a progressive attack from easy to difficult. Additionally, it utilizes an optimal injection strategy searching method, based on dynamic programming, to determine which links should be added between injected nodes and existing nodes, thereby achieving a high impact for attack effectiveness at a low cost. Experiments on four real-world datasets consistently demonstrate that DPNIA consistently and significantly outperforms various attack baselines

RouteKG: A knowledge graph-based framework for route prediction on road networks

Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling a plethora of intelligent transportation applications such as dynamic traffic control or personalized route recommendation. Despite recent advances in this area, existing methods focus primarily on learning sequential transition patterns, neglecting the inherent spatial structural relations in road networks that can affect human routing decisions. To fill this gap, this paper introduces RouteKG, a novel Knowledge Graph-based framework for route prediction. Specifically, we construct a Knowledge Graph on the road network, thereby learning and leveraging spatial relations, especially moving directions, which are crucial for human navigation. Moreover, an n-ary tree-based algorithm is introduced to efficiently generate top-K routes in a batch mode, enhancing scalability and computational efficiency. To further optimize the prediction performance, a rank refinement module is incorporated to fine-tune the candidate route rankings. The model performance is evaluated using two real-world vehicle trajectory datasets from two Chinese cities, Chengdu and Shanghai, under various practical scenarios. The results demonstrate a significant improvement in accuracy over baseline methods.We further validate our model through a case study that utilizes the pre-trained model as a simulator for real-time traffic flow estimation at the link level. The proposed RouteKG promises wide-ranging applications in vehicle navigation, traffic management, and other intelligent transportation tasks

Anticancer drug synergy prediction in understudied tissues using transfer learning

ocaa212Objective: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. Materials and Methods: We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. Results: We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. Conclusions: Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.Peer reviewe

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

gkab438Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.Peer reviewe

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.Peer reviewe

Induction of apoptosis and inhibition of cell growth by tbx5 knockdown contribute to dysmorphogenesis in Zebrafish embryos

<p>Abstract</p> <p>Background</p> <p>The tbx5 mutation in human causes Holt-Oram syndrome, an autosomal dominant condition characterized by a familial history of congenital heart defects and preaxial radial upper-limb defects. We report aberrant apoptosis and dormant cell growth over head, heart, trunk, fin, and tail of zebrafish embryos with tbx5 deficiency correspond to the dysmorphogenesis of tbx5 morphants.</p> <p>Methods</p> <p>Wild-type zebrafish embryos at the 1-cell stage were injected with 4.3 nl of 19.4 ng of tbx5 morpholino or mismatch-tbx5-MO respectively in tbx5 morphants and mismatched control group. Semi-quantitative RT-PCR was used to for expression analysis of apoptosis and cell cycle-related genes. TUNEL and immunohistochemical assay showed the apoptosis spots within the local tissues. Ultra-structure of cardiac myocardium was examined by transmission electron microscope.</p> <p>Results</p> <p>Apoptosis-related genes (bad, bax, and bcl2), and cell cycle-related genes (cdk2, pcna, p27, and p57) showed remarkable increases in transcriptional level by RT-PCR. Using a TUNEL and immnuohistochemical assay, apoptosis was observed in the organs including the head, heart, pectoral fins, trunk, and tail of tbx5 knockdown embryos. Under transmission electron microscopic examination, mitochondria in cardiomyocytes became swollen and the myocardium was largely disorganized with a disarrayed appearance, compatible with reduced enhancement of myosin in the cardiac wall. The ATP level was reduced, and the ADP/ATP ratio as an apoptotic index significantly increased in the tbx5 deficient embryos.</p> <p>Conclusion</p> <p>Our study highlighted that tbx5 deficiency evoked apoptosis, distributed on multiple organs corresponding to dysmorphogenesis with the shortage of promising maturation, in tbx5 knockdown zebrafish embryos. We hypothesized that mesenchymal cell apoptosis associated with altered TBX5 level may subsequently interfered with organogenesis and contributed to dysmorphogenesis in tbx5 deficiency zebrafish embryos.</p

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.Peer reviewe