Open Access Publications

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Open access publications by faculty, postdocs, and graduate students in the Department of Computer and Information Sciences.

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Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach
(BMC Genomics, 2019-06-17) Hubbard, Allen H.; Zhang, Xiaoke; Jastrebski, Sara; Singh, Abhyudai; Schmidt, Carl J.
Background We present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency. This study focuses on the liver because it is an important regulator of metabolism, controlling many of the physiological processes impacted by prolonged heat stress. Using statistical learning methods, we identify genes and metabolites that may regulate the heat stress response in the liver and adaptations required to acclimate to prolonged heat stress. Results We describe how disparate systems such as sugar, lipid and amino acid metabolism, are coordinated during the heat stress response. Conclusions Our findings provide more detailed context for genomic studies and generates hypotheses about dietary interventions that can mitigate the negative influence of heat stress on the poultry industry.
Transcriptome response to heat stress in a chicken hepatocellular carcinoma cell line
(Cell Stress and Chaperones, 2024-01-05) Sun, Liang; Lamont, Susan J.; Cooksey, Amanda M.; McCarthy, Fiona; Tudo, Catalina O.; Vijay-Shanker, K.; DeRita, Rachael M.; Rothschild, Max; Ashwell, Chris; Persia, Michael E.; Schmidt, Carl J.
Heat stress triggers an evolutionarily conserved set of responses in cells. The transcriptome responds to hyperthermia by altering expression of genes to adapt the cell or organism to survive the heat challenge. RNA-seq technology allows rapid identification of environmentally responsive genes on a large scale. In this study, we have used RNA-seq to identify heat stress responsive genes in the chicken male white leghorn hepatocellular (LMH) cell line. The transcripts of 812 genes were responsive to heat stress (p < 0.01) with 235 genes upregulated and 577 downregulated following 2.5 h of heat stress. Among the upregulated were genes whose products function as chaperones, along with genes affecting collagen synthesis and deposition, transcription factors, chromatin remodelers, and genes modulating the WNT and TGF-beta pathways. Predominant among the downregulated genes were ones that affect DNA replication and repair along with chromosomal segregation. Many of the genes identified in this study have not been previously implicated in the heat stress response. These data extend our understanding of the transcriptome response to heat stress with many of the identified biological processes and pathways likely to function in adapting cells and organisms to hyperthermic stress. Furthermore, this study should provide important insight to future efforts attempting to improve species abilities to withstand heat stress through genome-wide association studies and breeding.
MRI-based whole-brain elastography and volumetric measurements to predict brain age
(Biology Methods and Protocols, 2024-11-20) Claros-Olivares, Claudio Cesar; Clements, Rebecca G.; McIlvain, Grace; Johnson, Curtis L.; Brockmeier, Austin J.
Brain age, as a correlate of an individual’s chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain’s mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual’s chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlights important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.
Opportunities and Pitfalls with Large Language Models for Biomedical Annotation
(Biocomputing 2025, 2024-12) Arighi, Cecilia; Kim, Jin-Dong; Lu, Zhiyong; Rinaldi, Fabio
Large language models (LLMs) and biomedical annotations have a symbiotic relationship. LLMs rely on high-quality annotations for training and/or fine-tuning for specific biomedical tasks. These annotations are traditionally generated through expensive and time-consuming human curation. Meanwhile LLMs can also be used to accelerate the process of curation, thus simplifying the process, and potentially creating a virtuous feedback loop. However, their use also introduces new limitations and risks, which are as important to consider as the opportunities they offer. In this workshop, we will review the process that has led to the current rise of LLMs in several fields, and in particular in biomedicine, and discuss specifically the opportunities and pitfalls when they are applied to biomedical annotation and curation.
Formation of bijels stabilized by magnetic ellipsoidal particles in external magnetic fields
(Soft Matter, 2024-10-08) Karthikeyan, Nikhil; Schiller, Ulf D.
Bicontinuous interfacially-jammed emulsion gels (bijels) are increasingly used as emulsion templates for the fabrication of functional porous materials including membranes, electrodes, and biomaterials. Control over the domain size and structure is highly desirable in these applications. For bijels stabilized by spherical particles, particle size and volume fraction are the main parameters that determine the emulsion structure. Here, we investigate the use of ellipsoidal magnetic particles and study the effect of external magnetic fields on the formation of bijels. Using hybrid Lattice Boltzmann-molecular dynamics simulations, we analyze the effect of the magnetic field on emulsion dynamics and the structural properties of the resulting bijel. We find that the formation of bijels remains robust in the presence of magnetic fields, and that the domain size and tortuosity become anisotropic when ellipsoidal particles are used. We show that the magnetic fields lead to orientational ordering of the particles which in turn leads to alignment of the interfaces. The orientational order facilitates enhanced packing of particles in the interface which leads to different jamming times in the directions parallel and perpendicular to the field. Our results highlight the potential of magnetic particles for fabrication and processing of emulsion systems with tunable properties.
Soybean Bradyrhizobium spp. Spontaneously Produce Abundant and Diverse Temperate Phages in Culture
(Viruses, 2024-11-07) Richards, Vanessa A.; Ferrell, Barbra D.; Polson, Shawn W.; Wommack, K. Eric; Fuhrmann, Jeffry J.
Soybean bradyrhizobia (Bradyrhizobium spp.) are symbiotic root-nodulating bacteria that fix atmospheric nitrogen for the host plant. The University of Delaware Bradyrhizobium Culture Collection (UDBCC; 353 accessions) was created to study the diversity and ecology of soybean bradyrhizobia. Some UDBCC accessions produce temperate (lysogenic) bacteriophages spontaneously under routine culture conditions without chemical or other apparent inducing agents. Spontaneous phage production may promote horizontal gene transfer and shape bacterial genomes and associated phenotypes. A diverse subset (n = 98) of the UDBCC was examined for spontaneously produced virus-like particles (VLPs) using epifluorescent microscopy, with a majority (69%) producing detectable VLPs (>1 × 107 mL−1) in laboratory culture. Phages from the higher-producing accessions (>2.0 × 108 VLP mL−1; n = 44) were examined using transmission electron microscopy. Diverse morphologies were observed, including various tail types and lengths, capsid sizes and shapes, and the presence of collars or baseplates. In many instances, putative extracellular vesicles of a size similar to virions were also observed. Three of the four species examined (B. japonicum, B. elkanii, and B. diazoefficiens) produced apparently tailless phages. All species except B. ottawaense also produced siphovirus-like phages, while all but B. diazoefficiens additionally produced podovirus-like phages. Myovirus-like phages were restricted to B. japonicum and B. elkanii. At least three strains were polylysogens, producing up to three distinct morphotypes. These observations suggest spontaneously produced phages may play a significant role in the ecology and evolution of soybean bradyrhizobia.
A long-term high-fat diet induces differential gene expression changes in spatially distinct adipose tissue of male mice
(Physiological Genomics, 2024-11-11) Alradi, Malak; Askari, Hassan; Shaw, Mark; Bhavsar, Jaysheel D.; Kingham, Brewster F.; Polson, Shawn W.; Fancher, Ibra S.
The accumulation of visceral adipose tissue (VAT) is strongly associated with cardiovascular disease and diabetes. In contrast, individuals with increased subcutaneous adipose tissue (SAT) without corresponding increases in VAT are associated with a metabolic healthy obese phenotype. These observations implicate dysfunctional VAT as a driver of disease processes, warranting investigation into obesity-induced alterations of distinct adipose depots. To determine the effects of obesity on adipose gene expression, male mice (n = 4) were fed a high-fat diet to induce obesity or a normal laboratory diet (lean controls) for 12–14 mo. Mesenteric VAT and inguinal SAT were isolated for bulk RNA sequencing. AT from lean controls served as a reference to obesity-induced changes. The long-term high-fat diet induced the expression of 169 and 814 unique genes in SAT and VAT, respectively. SAT from obese mice exhibited 308 differentially expressed genes (164 upregulated and 144 downregulated). VAT from obese mice exhibited 690 differentially expressed genes (262 genes upregulated and 428 downregulated). KEGG pathway and GO analyses revealed that metabolic pathways were upregulated in SAT versus downregulated in VAT while inflammatory signaling was upregulated in VAT. We next determined common genes that were differentially regulated between SAT and VAT in response to obesity and identified four genes that exhibited this profile: elovl6 and kcnj15 were upregulated in SAT/downregulated in VAT while trdn and hspb7 were downregulated in SAT/upregulated in VAT. We propose that these genes in particular should be further pursued to determine their roles in SAT versus VAT with respect to obesity. NEW & NOTEWORTHY A long-term high-fat diet induced the expression of more than 980 unique genes across subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). The high-fat diet also induced the differential expression of nearly 1,000 AT genes. We identified four genes that were oppositely expressed in SAT versus VAT in response to the high-fat diet and propose that these genes in particular may serve as promising targets aimed at resolving VAT dysfunction in obesity.
Heterogeneous Task Oriented Data Scheduling in Vehicular Edge Computing via Deep Reinforcement Learning
(IEEE Transactions on Vehicular Technology, 2024-08-16) Luo, Quyuan; Luan, Tom H.; Shi, Weisong; Fan, Pingzhi
In vehicular edge computing environment, massive computation-intensive tasks would be produced from diverse vehicular applications. Data scheduling among vehicles and roadside units(RSUs) is a fundamental issue in timely processing those tasks. However, the task heterogeneity with different computation resource requirements and delay constraints, the distinct capacities of vehicles and RSUs, and the stochastic task arrival, pose significant challenges in realizing efficient data scheduling. The existing literature ignores the multi-core feature of both vehicles and RSUs in data scheduling, which may lead to an inefficient resource usage. To cope with these challenges, in this paper, we first construct a multi-queue multi-block model for heterogeneous task oriented data caching on both vehicle and RSU sides. By fully utilizing the multi-core features of both vehicles and RSUs, a fine-grained offloading model is then developed, involving the association between data blocks and computing cores, and the allocation of computation and communication resources. After that, a long-term loss minimization problem is formulated to facilitate data processing. We leverage the Markov decision process (MDP) to model the optimization problem, which is then solved by our proposed deep deterministic policy gradient (DDPG) based association mapping and resource allocation algorithm (D-AMRA). In D-AMRA, an action transformation method is proposed to map the outputs of DDPG to the form of optimization variables. Eventually, extensive simulations with comparative benchmarks are conducted to evaluate the effectiveness of our proposed D-AMRA.
Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset
(Scientific Reports, 2024-09-30) Agraz, Melih; Deng, Yixiang; Karniadakis, George Em; Mantzoros, Christos Socrates
Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the elderly, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its multifaced nature, contributed from factors such as medications, lifestyle choices, and metabolic measurements. In this study, we propose a systematic approach to improve the robustness and accuracy of SH predictions using machine learning models, guided by clinical feature selection. Our focus is on developing long-term SH prediction models using both semi-supervised learning and supervised learning algorithms. Using the action to control cardiovascular risk in diabetes trial, which includes electronic health records for over 10,000 individuals, we focus on studying adults with T2DM. Our results indicate that the application of a multi-view co-training method, incorporating the random forest algorithm, improves the specificity of SH prediction, while the same setup with Naive Bayes replacing random forest demonstrates better sensitivity. Our framework also provides interpretability of machine learning models by identifying key predictors for hypoglycemia, including fasting plasma glucose, hemoglobin A1c, general diabetes education, and NPH or L insulins. The integration of data routinely available in electronic health records significantly enhances our model’s capability to predict SH events, showcasing its potential to transform clinical practice by facilitating early interventions and optimizing patient management. By enhancing prediction accuracy and identifying crucial predictive features, our study contributes to advancing the understanding and management of hypoglycemia in this population.
A Simple Mobile Plausibly Deniable System Using Image Steganography and Secure Hardware
(Proceedings of the 2024 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems, 2024-06-19) Xia, Lichen; Liao, Jinghui; Chen, Niusen; Chen, Bo; Shi, Weisong
Traditional encryption methods cannot defend against coercive attacks in which the adversary captures both the user and the possessed computing device, and forces the user to disclose the decryption keys. Plausibly deniable encryption (PDE) has been designed to defend against this strong coercive attacker. At its core, PDE allows the victim to plausibly deny the very existence of hidden sensitive data and the corresponding decryption keys upon being coerced. Designing an efficient PDE system for a mobile platform, however, is challenging due to various design constraints bound to the mobile systems. Leveraging image steganography and the built-in hardware security feature of mobile devices, namely TrustZone, we have designed a Simple Mobile Plausibly Deniable Encryption (SMPDE) system which can combat coercive adversaries and, meanwhile, is able to overcome unique design constraints. In our design, the encoding/decoding process of image steganography is bounded together with Arm TrustZone. In this manner, the coercive adversary will be given a decoy key, which can only activate a DUMMY trusted application that will instead sanitize the sensitive information stored hidden in the stego-image upon decoding. On the contrary, the actual user can be given the true key, which can activate the PDE trusted application that can really extract the sensitive information from the stego-image upon decoding. Security analysis and experimental evaluation justify both the security and the efficiency of our design.
Quantum computing for finance
(Nature Reviews Physics, 2023-07-11) Herman, Dylan; Googin, Cody; Liu, Xiaoyuan; Sun, Yue; Galda, Alexey; Safro, Ilya; Pistoia, Marco; Alexeev, Yuri
Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modelling, optimization and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle. Key points - Quantum algorithms for stochastic modelling, optimization and machine learning are applicable to various financial problems. - Quantum Monte Carlo integration and gradient estimation can provide quadratic speedup over classical methods, but more work is required to reduce the amount of quantum resources for early fault-tolerant feasibility and achieving an actual speedup. - Financial optimization problems can be continuous (convex or non-convex), discrete or mixed, and thus quantum algorithms for these problems can be applied. - The advantages and challenges of quantum machine learning for classical problems are also apparent in finance.
Improvements in viral gene annotation using large language models and soft alignments
(BMC Bioinformatics, 2024-04-25) Harrigan, William L.; Ferrell, Barbra D.; Wommack, K. Eric; Polson, Shawn W.; Schreiber, Zachary D.; Belcaid, Mahdi
Background The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings. Results Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect. Conclusion The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.
Integrative data analysis to identify persistent post-concussion deficits and subsequent musculoskeletal injury risk: project structure and methods
(BMJ Open Sport & Exercise Medicine, 2024-01-19) Anderson, Melissa; Claros, Claudio Cesar; Qian, Wei; Brockmeier, Austin; Buckley, Thomas A
Concussions are a serious public health problem, with significant healthcare costs and risks. One of the most serious complications of concussions is an increased risk of subsequent musculoskeletal injuries (MSKI). However, there is currently no reliable way to identify which individuals are at highest risk for post-concussion MSKIs. This study proposes a novel data analysis strategy for developing a clinically feasible risk score for post-concussion MSKIs in student-athletes. The data set consists of one-time tests (eg, mental health questionnaires), relevant information on demographics, health history (including details regarding the concussion such as day of the year and time lost) and athletic participation (current sport and contact level) that were collected at a single time point as well as multiple time points (baseline and follow-up time points after the concussion) of the clinical assessments (ie, cognitive, postural stability, reaction time and vestibular and ocular motor testing). The follow-up time point measurements were treated as individual variables and as differences from the baseline. Our approach used a weight-of-evidence (WoE) transformation to handle missing data and variable heterogeneity and machine learning methods for variable selection and model fitting. We applied a training-testing sample splitting scheme and performed variable preprocessing with the WoE transformation. Then, machine learning methods were applied to predict the MSKI indicator prediction, thereby constructing a composite risk score for the training-testing sample. This methodology demonstrates the potential of using machine learning methods to improve the accuracy and interpretability of risk scores for MSKI.
Targeting of plasmodesmal proteins requires unconventional signals
(The Plant Cell, 2023-08-02) Luna, Gabriel Robles; Li, Jiefu; Wang, Xu; Liao, Li; Lee, Jung-Youn
Effective cellular signaling relies on precise spatial localization and dynamic interactions among proteins in specific subcellular compartments or niches, such as cell-to-cell contact sites and junctions. In plants, endogenous and pathogenic proteins gained the ability to target plasmodesmata, membrane-lined cytoplasmic connections, through evolution to regulate or exploit cellular signaling across cell wall boundaries. For example, the receptor-like membrane protein PLASMODESMATA-LOCATED PROTEIN 5 (PDLP5), a potent regulator of plasmodesmal permeability, generates feed-forward or feed-back signals important for plant immunity and root development. However, the molecular features that determine the plasmodesmal association of PDLP5 or other proteins remain largely unknown, and no protein motifs have been identified as plasmodesmal targeting signals. Here, we developed an approach combining custom-built machine-learning algorithms and targeted mutagenesis to examine PDLP5 in Arabidopsis thaliana and Nicotiana benthamiana. We report that PDLP5 and its closely related proteins carry unconventional targeting signals consisting of short stretches of amino acids. PDLP5 contains 2 divergent, tandemly arranged signals, either of which is sufficient for localization and biological function in regulating viral movement through plasmodesmata. Notably, plasmodesmal targeting signals exhibit little sequence conservation but are located similarly proximal to the membrane. These features appear to be a common theme in plasmodesmal targeting.
Towards C-V2X Enabled Collaborative Autonomous Driving
(IEEE Transactions on Vehicular Technology, 2023-08-14) He, Yuankai; Wu, Baofu; Dong, Zheng; Wan, Jian; Shi, Weisong
Intelligent vehicles, including autonomous vehicles and vehicles equipped with ADAS systems, are single-agent systems that navigate solely on the information collected by themselves. However, despite rapid advancements in hardware and algorithms, many accidents still occur due to the limited sensing coverage from a single-agent perception angle. These tragedies raise a critical question of whether single-agent autonomous driving is safe. Preliminary investigations on this safety issue led us to create a C-V2X-enabled collaborative autonomous driving framework (CCAD) to observe the driving circumstance from multiple perception angles. Our framework uses C-V2X technology to connect infrastructure with vehicles and vehicles with vehicles to transmit safety-critical information and to add safety redundancies. By enabling these communication channels, we connect previously independent single-agent vehicles and existing infrastructure. This paper presents a prototype of our CCAD framework with RSU and OBU as communication devices and an edge-computing device for data processing. We also present a case study of successfully implementing an infrastructure-based collaborative lane-keeping with the CCAD framework. Our case study evaluations demonstrate that the CCAD framework can transmit, in real-time, personalized lane-keeping guidance information when the vehicle cannot find the lanes. The evaluations also indicate that the CCAD framework can drastically improve the safety of single-agent intelligent vehicles and open the doors to many more collaborative autonomous driving applications.
E3-UAV: An Edge-Based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles
(IEEE Internet of Things Journal, 2023-08-03) Suo, Jiashun; Zhang, Xingzhou; Shi, Weisong; Zhou, Wei
Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.
A comprehensive analysis of the integration of team research between sport psychology and management
(Psychology of Sport and Exercise, 2020-06-13) Emich, Kyle J.; Norder, Kurt; Lu, Li; Sawhney, Aman
Both sports and organizations rely on teams. As such, the sport psychology and management literatures have contributed greatly to our understanding of team functioning. Despite this, previous reviews based on subsets of articles in these literatures indicate a lack of communication between them. In this article, we assess the state of integration between the entirety of the sport psychology and management literatures on teams by considering the full set of interconnected team articles in the SCOPUS database (6974 articles over 69 years). We use this data to conduct a combination of citation network analysis and content analysis via topic modeling to evaluate conceptual integration. The data show that interdisciplinary discussion between these two fields is lacking, particularly regarding the integration of sport psychology into management research. Whereas 7% of references to team articles in sport psychology come from management journals, only 0.6% of team references in management journals come from sport psychology. Despite this, longitudinal analysis indicates that in the last 10 years the rate of integration between these fields is increasing. We identify specific topics that have accounted for this integration and suggest topics ripe for future integration.
Improving Inter-Helix Contact Prediction With Local 2D Topological Information
(IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023-05-08) Li, Jiefu; Sawhney, Aman; Lee, Jung-Youn; Liao, Li
Inter-helix contact prediction is to identify residue contact across different helices in α-helical integral membrane proteins. Despite the progress made by various computational methods, contact prediction remains as a challenging task, and there is no method to our knowledge that directly tap into the contact map in an alignment free manner. We build 2D contact models from an independent dataset to capture the topological patterns in the neighborhood of a residue pair depending it is a contact or not, and apply the models to the state-of-art method's predictions to extract the features reflecting 2D inter-helix contact patterns. A secondary classifier is trained on such features. Realizing that the achievable improvement is intrinsically hinged on the quality of original predictions, we devise a mechanism to deal with the issue by introducing, 1) partial discretization of original prediction scores to more effectively leverage useful information 2) fuzzy score to assess the quality of the original prediction to help with selecting the residue pairs where improvement is more achievable. The cross-validation results show that the prediction from our method outperforms other methods including the state-of-the-art method (DeepHelicon) by a notable degree even without using the refinement selection scheme. By applying the refinement selection scheme, our method outperforms the state-of-the-art method significantly in these selected sequences.
WiLDAR: WiFi Signal-Based Lightweight Deep Learning Model for Human Activity Recognition
(IEEE Internet of Things Journal, 2023-07-11) Deng, Fuxiang; Jovanov, Emil; Song, Houbing; Shi, Weisong; Zhang, Yuan; Xu, Wenyao
In recent years, the WiFi channel state information (CSI) has been increasingly used for human activity recognition (HAR) during activities of daily living, because of non-intrusiveness and privacy preserving properties. However, most previous works require complex processing of CSI signals, and the large number of classification network parameters significantly increases the recognition time and deployment costs. Accordingly, a WiFi signal based lightweight deep learning (WiLDAR) network is developed in this study to ensure systematic operation on edge computing devices. We combine the random convolution kernel with deep separable convolution and residual structure, so that WiLDAR can easily extract CSI signal features without filtering and denoising. The parameter number and training time of WiLDAR are thus much less than those of previous neural networks. In addition, a tiny HAR system using only Raspberry Pi and router is implemented. Experiments verify that WiLDAR can achieve real-time HAR on IoT devices, which makes HAR deployment more convenient. We test WiLDAR on three different fine-grained action datasets to achieve 99%, 93.5% and 97.5% recognition accuracy, respectively. The demonstrated learning capability of WiLDAR makes it an excellent option for the remote HAR.
Towards Resilient Network Slicing for Satellite-Terrestrial Edge Computing IoT
(IEEE Internet of Things Journal, 2023-05-18) Esmat, Haitham H.; Lorenzo, Beatriz; Shi, Weisong
Satellite-Terrestrial Edge Computing Networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs such as Software-Defined Networking (SDN), satellite edge computing, and Network Function Virtualization (NFV) are key to realizing this vision. In this paper, we survey and analyze network slicing solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite edge computing, mmWave/THz, and AI solutions to make network slicing adaptive. In addition, we identify challenges and open issues to slice STECNs. In particular, resilient network slicing is crucial for essential and critical services. Network failures are unavoidable in large networks and can cause significant disruptions in network slicing, compromising many services. To this end, we present a resilient network slicing design to cope with failures and guarantee service continuity which is agnostic to the integration architecture and inherently multi-domain. Further, we present strategies to achieve resilient networking and slicing in STECNs including planning and provisioning of redundant network resources, design rules for service level agreement decomposition, and cross-domain solutions to detect and mitigate failures. Finally, promising future research directions are highlighted. This paper provides valuable guidelines for slicing STECNs and will benefit key sectors, such as smart healthcare, e-commerce, industrial IoT, and education, among others.

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