Categorizing Flight Paths using Data Visualization and Clustering Methodologies

This work leverages the U.S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8, a recently developed tool for highly interactive visualization of air traffic data, to develop clustering algorithms for categorizing air traffic by their varying flight paths. Two clustering methodologies, a spatial-based geographic distance model, and a vector-based cosine similarity model, are demonstrated and compared for their clustering effectiveness. Examples of their applications reveal successful, realistic clustering based on automated clustering result determination and human-in-the-loop processes, with geographic distance algorithms performing better for enroute portions of flight paths and cosine similarity algorithms performing better for near-terminal operations, such as arrival paths. A point extraction technique is applied to improve computation efficiency.Comment: Published in the 9th International Conference on Research in Air Transportation (ICRAT'20): https://www.icrat.org/previous-conferences/9th-international-conference/papers

I Still See You: Why Existing IoT Traffic Reshaping Fails

The Internet traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and device manufacturers, and often shared with their third parties to maintain and enhance user services. Unfortunately, on-path adversaries could infer and fingerprint users' sensitive privacy information such as occupancy and user activities by analyzing these network traffic traces. While there's a growing body of literature on defending against this side-channel attack-malicious IoT traffic analytics (TA), there's currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework-IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. In particular, we also design a novel image-based attack capable of inferring sensitive user information, even when users employ the most robust preventative measures in their smart homes. Researchers could leverage our new image-based attack to systematize and understand the existing literature on IoT traffic analysis attacks and preventing studies. Our results show that current defending approaches are not sufficient to protect IoT device user privacy. IoT devices are significantly vulnerable to our new image-based user privacy inference attacks, posing a grave threat to IoT device user privacy. We also highlight potential future improvements to enhance the defending approaches. ITEMTK's flexibility allows other researchers for easy expansion by integrating new TA attack models and prevention methods to benchmark their future work.Comment: EWSN'24 paper accepted, to appea

Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space

In recent years, the task of weakly supervised audio-visual violence detection has gained considerable attention. The goal of this task is to identify violent segments within multimodal data based on video-level labels. Despite advances in this field, traditional Euclidean neural networks, which have been used in prior research, encounter difficulties in capturing highly discriminative representations due to limitations of the feature space. To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination. Our framework comprises a detour fusion module for multimodal fusion, effectively alleviating modality inconsistency between audio and visual signals. Additionally, we contribute two branches of fully hyperbolic graph convolutional networks that excavate feature similarities and temporal relationships among snippets in hyperbolic space. By learning snippet representations in this space, the framework effectively learns semantic discrepancies between violent and normal events. Extensive experiments on the XD-Violence benchmark demonstrate that our method outperforms state-of-the-art methods by a sizable margin.Comment: 8 pages, 5 figure

Values of lymphocyte-related ratios in predicting the clinical outcome of acute ischemic stroke patients receiving intravenous thrombolysis based on different etiologies

BackgroundWhile neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) have been associated with acute ischemic stroke (AIS) outcomes, their differential predictive value across etiological subtypes (TOAST classification) in thrombolysis-treated patients remains underexplored.MethodsIn this retrospective cohort study, we analyzed 381 AIS patients receiving intravenous thrombolysis. Hematological indices were calculated from pre-thrombolysis. Using multivariable logistic regression adjusted for age, NIHSS, and comorbidities, we assessed associations between baseline ratios and 90-day unfavorable outcomes (mRS 3–6). Receiver operating characteristic (ROC) analysis was used to determine optimal cutoffs stratified by TOAST subtypes.ResultsA total of 381 patients were included in the study. NLR showed superior predictive performance: large-artery atherosclerosis: AUC = 0.702 (aOR = 1.35, 95%CI = 1.14–1.61, p = 0.001), small-artery occlusion: AUC = 0.750 (aOR = 1.51, 95%CI = 1.08–2.10, p = 0.015), cardioembolic stroke: AUC = 0.679 (aOR = 1.82, 95%CI = 1.07–3.10, p = 0.028). LMR showed predictive value only in large-artery atherosclerosis (AUC = 0.632, p = 0.004). Optimal NLR cutoffs: 3.19 (large-artery), 3.94 (small-artery), 3.17 (cardioembolic stroke).ConclusionNLR emerged as a robust, subtype-specific predictor of post-thrombolysis outcomes, particularly in atherosclerotic stroke variants. These findings supported NLR’s clinical utility for risk stratification in thrombolysis-eligible AIS patients

Novel Pan-ERR Agonists Ameliorate Heart Failure Through Enhancing Cardiac Fatty Acid Metabolism and Mitochondrial Function

BACKGROUND: Cardiac metabolic dysfunction is a hallmark of heart failure (HF). Estrogen-related receptors ERRα and ERRγ are essential regulators of cardiac metabolism. Therefore, activation of ERR could be a potential therapeutic intervention for HF. However, in vivo studies demonstrating the potential usefulness of ERR agonist for HF treatment are lacking, because compounds with pharmacokinetics appropriate for in vivo use have not been available. METHODS: Using a structure-based design approach, we designed and synthesized 2 structurally distinct pan-ERR agonists, SLU-PP-332 and SLU-PP-915. We investigated the effect of ERR agonist on cardiac function in a pressure overload-induced HF model in vivo. We conducted comprehensive functional, multi-omics (RNA sequencing and metabolomics studies), and genetic dependency studies both in vivo and in vitro to dissect the molecular mechanism, ERR isoform dependency, and target specificity. RESULTS: Both SLU-PP-332 and SLU-PP-915 significantly improved ejection fraction, ameliorated fibrosis, and increased survival associated with pressure overload-induced HF without affecting cardiac hypertrophy. A broad spectrum of metabolic genes was transcriptionally activated by ERR agonists, particularly genes involved in fatty acid metabolism and mitochondrial function. Metabolomics analysis showed substantial normalization of metabolic profiles in fatty acid/lipid and tricarboxylic acid/oxidative phosphorylation metabolites in the mouse heart with 6-week pressure overload. ERR agonists increase mitochondria oxidative capacity and fatty acid use in vitro and in vivo. Using both in vitro and in vivo genetic dependency experiments, we show that ERRγ is the main mediator of ERR agonism-induced transcriptional regulation and cardioprotection and definitively demonstrated target specificity. ERR agonism also led to downregulation of cell cycle and development pathways, which was partially mediated by E2F1 in cardiomyocytes. CONCLUSIONS: ERR agonists maintain oxidative metabolism, which confers cardiac protection against pressure overload-induced HF in vivo. Our results provide direct pharmacologic evidence supporting the further development of ERR agonists as novel HF therapeutics

Playbook workflow builder: Interactive construction of bioinformatics workflows

The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of scientific language , whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

Safeguarding user privacy in the IoT era

Includes bibliographical references.2024 Spring.The Internet of Things (IoT) has been erupting the world widely over the decade. Smart home and smart building owners are increasingly deploying IoT devices to monitor and control their environments due to the rapid decline in the price of IoT devices. However, serious privacy threat was revealed by recent research and media reports. First, the Internet traffic data generated by IoT devices are collected by Internet Service Providers (ISPs) and IoT device manufacturers, and often shared with third-parties to maintain and enhance user services. Extensive recent research has shown that on-path adversaries can infer and fingerprint user's sensitive privacy information such as occupancy and user in-home activities by analyzing IoT network traffic rates alone. Most recent approaches that aim at defending against these malicious IoT traffic analytics can not sufficiently protect user privacy with reasonable Internet traffic and hardware resource overhead. In particular, many approaches did not consider practical limitations, e.g., network bandwidth, maximum package injection rate or actual user in-home behavior in their design. Second, such privacy threats also shows in some specific types of IoT devices, like smart cameras. Significant recent research has uncovered potential user privacy threats associated with popular commercial camera systems. The manufacture design of these commercial camera systems usually requires smart camera users to relinquish their control of camera recorded data. For instance, these cameras often upload camera recordings to their cloud servers or data centers to enable advanced data analysis for camera app services. To facilitate enhanced camera services, the data may also be shared with on-path vendors, third parties of manufacturers, and cloud providers, potentially allowing them to access video footage or image captures without users' awareness or obtaining meaningful consent. To address these problems, my research aims at building and implementing computer systems in different scales of implementation, to allow Cyber-Physical System (CPS) and IoT users to regain the comprehensive control over their privacy, and the following contributions were made: \underline{\textit{SmartAttack}} and \underline{\textit{TrafficSpy}} aim at disaggregating individual IoT devices from both raw and VPN-encrypted IoT network traffic data. I designed and implemented two Machine Learning (ML) and Deep Learning (DL)-based adversarial attack model frameworks to mimic the malicious external adversaries carrying on user activity inference attacks. In addition to proving the severeness of smart home user privacy threat, these two works can also be leveraged by researchers and industrial users from IoT security community, to better evaluate their privacy preserving works. \underline{\textit{PrivacyGuard}}, as the first prototype, successfully provided an open-sourced, low-cost, user-tunable defense system, that enable users to significantly reduce the private information leaked through IoT device network traffic data, while still permitting sophisticated data analytics or control that is necessary in smart home management. I evaluated PrivacyGuard using IoT network traffic traces of 31 IoT devices from 5 smart homes and deploying a Raspberry Pi 4-based prototype. And the result shows that PrivacyGuard can effectively prevent a wide range of state-of-the-art machine learning-based occupancy and other 9 user-in-home activity detection attacks. \underline{\textit{PAROS}} - Privacy As a Router Operating system Service, made significant improvement from PrivacyGuard, to lift the requirement for installation of additional hub hardware, and still maintain comparable privacy preserving performance and system overhead. By leveraging Hidden Markov Model (HMM)-based artificial traffic signature injection, and Support Vector Machines (SVM)-based memory replacement scheme, the performance of PAROS was significantly optimized. \underline{\textit{SecCam}}, designed to solve the second half of the privacy threat, has provided a new open-sourced, adaptive and distributed privacy-preserving camera system. By harnessing the technique of on-device learning, SecCam provided several tiny intelligent camera services that offer the same features found in the commercially available cameras. SecCam enables user to regain the control of their data while still retaining access to regular camera services. The SecCam was evaluated using multiple camera video footage traces and on multiple real camera prototypes. In the future, I plan to dive deeper on safeguarding the IoT user privacy by improving my current systems, developing new attacking approaches, and doing ethical related work in the field of CPS and IoT