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

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

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

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

A 2DRF pulse sequence for bolus tracking in hyperpolarized 13C imaging

PurposeA novel application of two-dimensional (2D) spatially selective radiofrequency (2DRF) excitation pulses in hyperpolarized 13C imaging is proposed for monitoring the bolus injection with highly efficient sampling of the initially polarized substrate, thus leaving more polarization available for detection of the subsequently generated metabolic products.MethodsA 2DRF pulse was designed with a spiral trajectory and conventional clinical gradient performance. To demonstrate the ability of our 2DRF bolus tracking pulse sequence, hyperpolarized [1-(13)ruvate in vivo imaging experiments were performed in normal rats, with a comparison to 1DRF excitation pulses.ResultsOur designed 2DRF pulse was able to rapidly and efficiently monitor the injected bolus dynamics in vivo, with an 8-fold enhanced time resolution in comparison with 1DRF in our experimental settings. When applied at the pyruvate frequency for bolus tracking, our 2DRF pulse demonstrated reduced saturation of the hyperpolarization for the substrate and metabolic products compared to a 1DRF pulse, while being immune to ±0.5 ppm magnetic field inhomogeneity at 3T.Conclusion2DRF pulses in hyperpolarized 13C imaging can be used to efficiently monitor the bolus injection with reduced hyperpolarization saturation compared to 1DRF pulses. The parameters of our design are based on clinical scanner limits, which allows for rapid translation to human studies

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

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

Simultaneous Segmentation and Relaxometry for MRI through Multitask Learning

Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multi-contrast brain images. The deep neural network contained 3 residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on 3 healthy volunteers as well as animal (N=6) and prostate patient (N=1) experiments. Results: In animal validation experiment, the differences/errors (mean difference $\pm$ standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 $\pm$ 486 and 154 $\pm$ 512 ms for T1, and 5 $\pm$ 33 and 7 $\pm$ 41 ms for T2, respectively. In healthy volunteer experiments (N=3), whole brain segmentation and relaxometry were finished within ~5 seconds. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1 and T2 weighted images. Conclusion: The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1 and T2 weighted images, in conjunction with segmentation of gray matter, white matter and CSF