The MHC class I antigen processing pathway plays a critical role in the adaptive immune system by presenting peptides for recognition by CD8+ T cells. While most prediction tools focus on MHC binding, accurately identifying immunogenic T-cell epitopes requires accounting for additional factors such as antigen processing and peptide-MHC binding thermostability. We developed a bioinformatics tool that integrates MHC binding predictions with thermostability assessment and antigen processing steps to enhance T cell epitope identification accuracy for C57BL/6 mice. Our machine learning models, trained on a comprehensive dataset of eluted H2-Kband H2-Dbligands and thermostability data across a range of physiologically relevant temperatures (37°C, 50°C, 70°C), were rigorously validated. These models showed improved overall accuracy on an external validation dataset compared to the widely used NetMHCPan-4.1. We consolidated the models into a user-friendly web-based application named PREDBL6 to facilitate accurate predictions of immunogenic peptides that stably bind H2b molecules and stimulate immune responses in C57BL/6 mice. PREDBL6 is accessible at http://met-hilab.org:3001/.

As the cloud-based file sharing becomes increasingly popular, it is crucial to protect the outsourced data against unauthorized access. In this paper, we propose EnShare, a secure and practical file sharing system that leverages cooperation of server-side and client-side enclaves to enforce access control, with the former responsible for registration, authentication and access control enforcement and the latter performing file decryption. Such design significantly reduces the computation workload of server-side enclaves, thus capable of handling concurrent requests. Meanwhile, it also supports immediate permission revocation, since the file decryption keys inside the client-side enclaves are destroyed immediately after use. We implement a prototype of EnShare and the evaluation demonstrates it enforces access control securely with high throughput and low latency.

The number of single-cell transcriptomic (SCT) studies is rapidly increasing. More than 15000 single cell gene expression data sets are available in public repositories. More than 2400 of these sets involve Peripheral Blood Mononuclear Cells (PBMC) data sets. Main cell types of PBMC are B cells, dendritic cells, monocytes, natural killer cells, and T cells. Labels of individual PBMC are usually provided in metadata accompanying the data sets or are implicit as data set partitions for sorted cells. We analyzed the correctness of labels assigned to individual cells from PBMC in primary reports. The correctness of primary labels was assessed by using Artificial Neural Network (ANN) classifier and Confident Learning (CL) approach. We assessed that the number of mislabels on average in our data sets is about2%. The label accuracy varied broadly between data sets, particularly among those generated by experimental cell sorting followed by SCT.

We investigate the characteristics of medical expenditures associated with mental illness hospitalizations using the Truven Health MarketScan Database. We focus on the inpatient admissions due to mental illness of adults aged 1S to 64 between 2006 to 2019. We aim to answer the following questions: (1) Did the financial crisis of 2008 impact mental health in the U.S.?(2) What are the other macro-level (socioeconomic and regulartory) and micro-level (individualpatient related) factors that affect the cost of inpatient care due to mental illness; (3) Did mental illness affect men and women differently? (4) How were different regions within the U.S. affected by mental illness?

Single-cell-derived-class (SCDC) profiles capture characteristic gene expression from single cells representing types and subtypes and their conditions. SCDC profiles show high reproducibility across similar single-cell types processed under the same conditions. We have demonstrated two applications of SCDC profiles-classification of single cells from PBMC into six main classes (B cells, cDC, pDC, monocytes, NK cells, and T cells) and into three super-classes (BC+pDC,MC+cDC, and TC+NK). The minimum number of individual cells required for building an effective reference SCDC profile has been assessed to be between 160 and 640 cells. The variability of SCDC gradually decreases as the number of cells used to derive the profile increases from 10-cells to 640-cells. The classification accuracy of PBMC extracted by PBMC separation by SCDC profiles was 85-100% and 95-100% for supertypes depending on the cell type or supertype. Classification accuracy for PBMC cell types is lower for samples that are processed by cell sorting, or other sample processing steps.

Blockchain is a technology to enable decentralized collaboration among un-trusted entities. Academia and industry are rushing to uncover its potential for their field of interest. Due to the novelty of the technology and its diverse applications, there are some ambiguities in approaches and trends. In this paper, we analyze the scientific publications and patents from the past five years to identify the trends for blockchain integration with healthcare. For this purpose, we have adopted a quantitative (clustering) and qualitative (theme extraction) approach to discover themes and temporal dynamics in academia and industry. Our results shed light on the potential challenges and vision for future works.

We previously developed TANTIGEN, a comprehensive online database cataloging more than 1000 T cell epitopes and HLA ligands from 292 tumor antigens. In TANTIGEN 2.0, we significantly expanded coverage in both immune response targets (T cell epitopes and HLA ligands) and tumor antigens. It catalogs 4,296 antigen variants from 403 unique tumor antigens and more than 1500 T cell epitopes and HLA ligands. We also included neoantigens, a class of tumor antigens generated through mutations resulting in new amino acid sequences in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes and tumor antigen gene/mRNA/protein expression information in major human cancers extracted by Human Pathology Atlas. TANTIGEN 2.0 is a rich data resource for tumor antigens and their associated epitopes and neoepitopes. It hosts a set of tailored data analytics tools tightly integrated with the data to form meaningful analysis workflows. It is freely available at http://projects.met-hilab.org/tadb .

We implemented an automated system for single-cell classification using artificial neural networks (ANN). Our system takes single-cell gene expression sparse matrices and trains ANN to classify cell types and subtypes. The assemblies of ANNs predict cell classes by voting. We tested the system in a case study where we trained ANNs with a dataset containing approximately 120,000 single cells and tested the resulting model using an independent data set of 13,000 single cells. The overall accuracy of the 5-class classification was 95%. We trained and tested a total of 100 ANNs in 10 cycles. The prediction system demonstrated excellent reproducibility. The analysis of misclassifications indicated that 2% were likely classification errors, while the remaining 3% were likely due to mislabeled types and subtypes in the test set.

Telemedicine plays an increasingly important role in global healthcare. In this study, we summarized the latest developments related to telemedicine and discussed the obstacles and challenges to its wide adoption with a focus on the impact of COVID-19.

We developed a method for building gene expression profiles from single-cell gene expression matrices. We named these profiles the “single-cell-derived-class” or SCDC profiles. They represent characteristic patterns of gene expressions of the types and subtypes of cells derived from single-cell transcriptome experiments. We deployed this method on classes and subclasses of peripheral blood mononuclear cells (PBMC). We used 47 human single-cell transcriptomics (SCT) data sets representing various classes, subclasses, and sample processing conditions. From comparisons of these profiles we found that they are highly reproducible, even when derived from unrelated studies as long as the processing steps are identical. The most similar profiles are those that are minimally processed. Cell sorting using FACS, cell enrichment, or fixing in methanol make profiles distinct from those derived from normal healthy samples. Our results suggest that approximately 50-200 cells are sufficient for building a useful SCDC profile.

Single cell transcriptomics enables a high-resolution concurrent measurement of gene expression from tens of thousands of cells. We developed a method for determining standardized profiles from SCT data. We defined 48 data sets from 13 different studies and developed single-cellderived-class” (SCDC) profiles representing multiple classes and subclasses of peripheral blood mononuclear cells (PBMC). We applied pattern recognition analysis by calculating the distance from each query cell to the SCDC profiles (excluding the profiles of the query cells). Classification of cells by pattern recognition showed excellent performance for PBMC that were isolated, but not further processed by cell sorting.

Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.

We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of ~120,000 single cell instances we showed the accuracy of classification of PBMC call of ~ 90%. Optimization techniques and the addition of new high-quality data sets for model training are expected to improve PBMC subtype classification accuracy.

We previously developed TANTIGEN, a comprehensive web-based database cataloging more than 1,000 T cell epitopes and HLA ligands from 292 tumor antigens. TANTIGEN 2.0 is significantly expanded the number and coverage of immune response targets (T cell epitopes and HLA ligands) of previously cataloged tumor antigens. We expanded the number of cataloged tumor antigens to more than 4,000 and have added their reported targets of immune responses. We also included neoantigens, a new class of tumor antigens generated through mutations that results in a new amino acid sequence in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes. Gene expression information was extracted from tumor antigen gene/mRNA/protein expression information in major human cancers provided by Human Pathology Atlas. TANTIGEN 2.0 provides a rich data resource for tumor-associated epitope and neoepitope discovery studies. It is freely available at http://projects.met-hilab.org/tadb.

Mobility management is a challenging problem in current networks, typically requiring dedicated, specialised protocols that manage the lifetime of a series of tunnels that follow mobile hosts as they roam through the network. The fundamental issue that complicates the mobility management problem is the lack of a complete naming and addressing schema in the current Internet architecture. This paper analyses what properties such schema needs to have, and discusses how Internet mobility solutions are missing parts of it. Then it looks at RINA, a network architecture with a complete naming scheme. Theoretical analysis backed up by experimental validation of the main properties for mobility support shows that managing mobility in RINA networks not only is simpler and easier to scale compared to the Internet situation, but also that no special protocols or mechanisms need to be added to RINA in order to support mobility.

Merkel Cell Polyomavirus (MCV) is associated with more than 80% of Merkel cell carcinoma (MCC), a rare but highly lethal form of skin cancer. We made use of the immunological data on MCV available through publications and databases and constructed MCV T cell Antigen Database (MCVdb). MCVdb contains 734 curated antigen entries of MCV antigenic proteins and 30 experimentally verified T cell epitopes. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). A set of computational tools for in-depth analysis, such as sequence comparison using BLAST search, multiple alignments of antigens, and T cell epitope conservation analysis have been integrated within the MCVdb. Predicted Class I and Class II HLA-binding peptides for 15 common HLA alleles are included in this database as putative targets. MCVdb is a unique data source providing a comprehensive list of MCV antigens and peptides. MCVdb is publicly available at http://projects.met-hilab.org/mcv/.

Current Internet security is complex, expensive and ineffective. The usual argument is that the TCP/IP protocol suite was not designed having security in mind and security mechanisms have been added as add-ons or separate protocols. We argue that fundamental limitations in the Internet architecture are a major factor contributing to the insecurity of the Net. In this paper we explore the security properties of the Recursive InterNetwork Architecture, analyzing the principles that make RINA networks inherently more secure than TCP/IP-based ones. We perform the specification, implementation and experimental evaluation of the first authentication and SDU protection policies for RINA networks. RINA's approach to securing layers instead of protocols increases the security of networks, while reducing the complexity and cost of providing security.

Android as an open platform dominates the booming mobile market. However its permission mechanism is inflexible and often results in over-privileged applications. This in turn creates severe security issues. Aiming to support the Principle of Least Privilege, we propose and implement a Dynamic Role Based Access Control for Android (DR BACA) model to enhance Android security, particularly in corporate environment. Our system offers multi-user management on Android mobile devices comparable to traditional workstations, and provides fine-grained Role Based Access Control (RBAC) to enhance Android security at both the application and permission level. Moreover, by leveraging context-aware capabilities of mobile devices and Near Field communication (NFC) technology, our solution supports dynamic RBAC to provide more flexible access control while still being able to mitigate some of the most serious security risks on mobile devices. The DR BACA system can easily be managed, even in large business environments with many mobile devices. We show that our DR BACA system can be deployed and used with ease. With a proper security policy, our evaluation shows that DR BACA can effectively mitigate the security risks posed by both malicious and vulnerable non-malicious applications while incurring only a small overall system overhead.

The TCP/IP architecture was originally designed without taking security measures into consideration. Over the years, it has been subjected to many attacks, which has led to many patches to counter them. Our investigations into the fundamental principles of networking have shown that carefully following an abstract model of Inter-Process Communication (IPC) addresses many problems [1]. Guided by this IPC principle, we designed a clean-slate Recursive InterNetwork Architecture (RINA) [2]. In this paper, we show how, without the aid of cryptographic techniques, the bare-bones architecture of RINA can resist most of the security attacks faced by TCP/IP, and of course, is only more secure if cryptographic techniques are employed. Specifically, the RINA model decouples different concerns that makes it more resistant to transport-level attacks: (1) RINA decouples authentication from connection management, thus transport-level attacks are limited to “insider” attacks, and (2) RINA decouples transport port allocation and access control from data synchronization and transfer, thus making transport-level attacks much harder to mount. Using typical field lengths in packet headers, we analyze how hard it is for an intruder to compromise RINA.