Showing papers by "Georgia State University published in 2022"

Worldwide Survey of Fitness Trends for 2022

Abstract: In Brief Apply It! From this article, the reader should understand the following concepts: • Explain the differences between a fad and a trend • Use the worldwide fitness trends in the commercial, corporate, clinical (including medical fitness), and community health fitness industry to further promote physical activity • Study expert opinions about identified fitness trends for 2021 The annual ACSM’s Health & Fitness Journal® worldwide survey to determine industry trends by health fitness professionals is now in its 16th consecutive year. The COVID-19 pandemic certainly made an impact on the 2021 survey and continues to influence survey results for 2022. The new no. 1 trend is wearable technology. Wearable technology (no. 2 for 2021) was the no. 1 trend in 2019 and again in 2020 after dropping to no. 3 in 2018. The no. 1 trend for 2021 was online training, which was no. 26 for 2020, and now has fallen to no. 9 for 2022. Home exercise gyms debuted at no. 2 this year, and group training (more than five participants), which was recently rated no. 2 (2018, 2019) and no. 3 (2020), fell dramatically to no. 17 in 2021 and even further to no. 20 for 2022. It appears that COVID-19 recommendations to limit social gatherings (including exercise classes) to very small groups had a dramatic impact on the rankings of group training. High-intensity interval training has dropped from a high of no. 2 in 2020 to no. 5 for 2021 and now no. 7 for 2022. Outcome measurements (no. 20 in 2021) was the only trend that dropped out of the top 20 rankings from 2021 to 2022.

Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT

Abstract: Under the needs of processing huge amounts of data, providing high-quality service, and protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has been treated as a promising technique to facilitate distributed learning with privacy protection. Although the importance of developing privacy-preserving FL has attracted a lot of attentions, the existing research only focuses on FL with independent identically distributed (i.i.d.) data and lacks study of non-i.i.d. scenario. What is worse, the assumption of i.i.d. data is impractical, reducing the performance of privacy protection in real applications. In this article, we carry out an innovative exploration of privacy protection in FL with non-i.i.d. data. First, a thorough analysis on privacy leakage in FL is conducted with proving the performance upper bound of privacy inference attack. Based on our analysis, a novel algorithm, 2DP-FL, is designed to achieve differential privacy by adding noise during training local models and when distributing global model. Especially, our 2DP-FL algorithm has a flexibility of noise addition to meet various needs and has a convergence upper bound. Finally, the real-data experiments can validate the results of our the oretical analysis and the advantages of 2DP-FL in privacy protection, learning convergence, and model accuracy.

Tumor‐Associated Immune‐Cell‐Mediated Tumor‐Targeting Mechanism with NIR‐II Fluorescence Imaging

Abstract: The strategy of structure-inherent tumor targeting (SITT) with cyanine-based fluorophores is receiving more attention because no chemical conjugation of targeting moieties is required. However, the targeting mechanism behind SITT has not yet been well explained. Here, it is demonstrated that heptamethine-cyanine-based fluorophores possess not only targetability of tumor microenvironments without the need for additional targeting ligands but also second near-infrared spectral window (NIR-II) imaging capabilities, i.e., minimum scattering and ultralow autofluorescence. The new SITT mechanism suggests that bone-marrow-derived and/or tissue-resident/tumor-associated immune cells can be a principal target for cancer detection due to their abundance in tumoral tissues. Among the tested, SH1 provides ubiquitous tumor targetability and a high tumor-to-background ratio (TBR) ranging from 9.5 to 47 in pancreatic, breast, and lung cancer mouse models upon a single bolus intravenous injection. Furthermore, SH1 can be used to detect small cancerous tissues smaller than 2 mm in diameter in orthotopic lung cancer models. Thus, SH1 could be a promising cancer-targeting agent and have a bright future for intraoperative optical imaging and image-guided cancer surgery.

A survey on blockchain systems: Attacks, defenses, and privacy preservation

Abstract: Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.

Recent advances in nanotechnology-based COVID-19 vaccines and therapeutic antibodies

Abstract: COVID-19 has caused a global pandemic and millions of deaths. It is imperative to develop effective countermeasures against the causative viral agent, SARS-CoV-2 and its many variants. Vaccines and therapeutic antibodies are the most effective approaches for preventing and treating COVID-19, respectively. SARS-CoV-2 enters host cells through the activities of the virus-surface spike (S) protein. Accordingly, the S protein is a prime target for vaccines and therapeutic antibodies. Dealing with particles with dimensions on the scale of nanometers, nanotechnology has emerged as a critical tool for rapidly designing and developing safe, effective, and urgently needed vaccines and therapeutics to control the COVID-19 pandemic. For example, nanotechnology was key to the fast-track approval of two mRNA vaccines for their wide use in human populations. In this review article, we first explore the roles of nanotechnology in battling COVID-19, including protein nanoparticles (for presentation of protein vaccines), lipid nanoparticles (for formulation with mRNAs), and nanobodies (as unique therapeutic antibodies). We then summarize the currently available COVID-19 vaccines and therapeutics based on nanotechnology.

Demonstration of fast multi-slice quasi-steady-state chemical exchange saturation transfer (QUASS CEST) human brain imaging at 3T.

Abstract: PURPOSE To combine multi-slice chemical exchange saturation transfer (CEST) imaging with quasi-steady-state (QUASS) processing and demonstrate the feasibility of fast QUASS CEST MRI at 3T. METHODS Fast multi-slice echo planar imaging (EPI) CEST imaging was developed with concatenated slice acquisition after single radiofrequency irradiation. The multi-slice CEST signal evolution was described by the spin-lock relaxation during saturation duration (Ts ) and longitudinal relaxation during the relaxation delay time (Td ) and post-label delay (PLD), from which the QUASS CEST was generalized to fast multi-slice acquisition. In addition, numerical simulations, phantom, and normal human subjects scans were performed to compare the conventional apparent and QUASS CEST measurements with different Ts , Td, and PLD. RESULTS The numerical simulation showed that the apparent CEST effect strongly depends on Ts , Td , and PLD, while the QUASS CEST algorithm minimizes such dependences. In the L-carnosine gel phantom, the proposed QUASS CEST effects (2.68 ± 0.12% [mean ± SD]) were higher than the apparent CEST effects (1.85 ± 0.26%, p < 5e-4). In the human brain imaging, Bland-Altman analysis bias of the proposed QUASS CEST effects was much smaller than the PLD-corrected apparent CEST effects (0.03% vs. -0.54%), indicating the proposed fast multi-slice CEST imaging is robust and accurate. CONCLUSIONS The QUASS processing enables fast multi-slice CEST imaging with minimal loss in the measurement of the CEST effect.

Cationic Effects on the Net Hydrogen Atom Bond Dissociation Free Energy of High-Valent Manganese Imido Complexes

Abstract: Local electric fields can alter energy landscapes to impart enhanced reactivity in enzymes and at surfaces. Similar fields can be generated in molecular systems using charged functionalities. Manganese(V) salen nitrido complexes (salen = N,N'-ethylenebis(salicylideneaminato)) appended with a crown ether unit containing Na+ (1-Na), K+, (1-K), Ba2+ (1-Ba), Sr2+ (1-Sr), La3+ (1-La), or Eu3+ (1-Eu) cation were investigated to determine the effect of charge on pKa, E1/2, and the net bond dissociation free energy (BDFE) of N-H bonds. The series, which includes the manganese(V) salen nitrido without an appended crown, spans 4 units of charge. Bounds for the pKa values of the transient imido complexes were used with the Mn(VI/V) reduction potentials to calculate the N-H BDFEs of the imidos in acetonitrile. Despite a span of >700 mV and >9 pKa units across the series, the hydrogen atom BDFE only spans ∼6 kcal/mol (between 73 and 79 kcal/mol). These results suggest that the incorporation of cationic functionalities is an effective strategy for accessing wide ranges of reduction potentials and pKa values while minimally affecting the BDFE, which is essential to modulating electron, proton, or hydrogen atom transfer pathways.

Taxonomy of stressors and traumas: An update of the development-based trauma framework (DBTF): A life-course perspective on stress and trauma.

Abstract: COVID-19 challenged the current paradigms of traumatic stress Although there are diagnostic taxonomies of mental disorders such as Diagnostic and Statistical Manual of Mental Disorders and International Statistical Classification of Diseases and Related Health Problems, the taxonomy of stressors and traumas that contribute significantly to such disorders is lacking The current article tried to fill parts of this gap by proposing an update and refinement of the development-based taxonomy of stressors and traumas from a life-course perspective We discussed the different trends in defining trauma and their limitations considering the recent empirical data that provided evidence for the limited predictive validity of the current posttraumatic stress disorder model and when confronting serious real-life events such as the COVID-19 pandemic The updated taxonomy presented in this paper included preidentity (complicated birth, attachment disruptions, early childhood adversities), identity traumas (physical, personal, and social), interdependence (primary, secondary, and tertiary), and aging stressors and traumas, with the severity of stressors, is categorized on a scale from I to III We identified 4 primary sources and pathways of these development-based stressors: intrapersonal, interpersonal, systemic, and environmental The systemic sources are further divided into systemic A, including traumas perpetrated by groups, institutions, or governments, and systemic B, traumas such as recessions and global warming The environmental sources and pathways are further divided into environmental A (physical), traumas such as earthquakes and hurricanes, and environmental B (biological/pathogenic), traumas such as pandemics The macrodynamics of accumulation and proliferation and the interaction among preidentity, identity, and postidentity stressors and traumas determine their total mental health impact from a life-course perspective (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Novel Methods for Elucidating Modality Importance in Multimodal Electrophysiology Classifiers

Abstract: Abstract Multimodal classification is increasingly common in biomedical informatics studies. Many such studies use deep learning classifiers with raw data, which makes explainability difficult. As such, only a few studies have applied explainability methods, and new methods are needed. In this study, we propose sleep stage classification as a testbed for method development and train a convolutional neural network with electroencephalogram (EEG), electrooculogram, and electromyogram data. We then present a global approach that is uniquely adapted for electrophysiology analysis. We further present two local approaches that can identify subject-level differences in explanations that would be obscured by global methods and that can provide insight into the effects of clinical and demographic variables upon the patterns learned by the classifier. We find that EEG is globally the most important modality for all sleep stages, except non-rapid eye movement stage 1 and that local subject-level differences in importance arise. We further show that sex, followed by medication and age had significant effects upon the patterns learned by the classifier. Our novel methods enhance explainability for the growing field of multimodal classification, provide avenues for the advancement of personalized medicine, and yield novel insights into the effects of demographic and clinical variables upon classifiers.

CircR2Disease v2.0: An Updated Web Server for Experimentally Validated circRNA–disease Associations and Its Application

Abstract: With accumulating dysregulated circular RNAs (circRNAs) in pathological processes, the regulatory functions of circRNAs, especially circRNAs as microRNA (miRNA) sponges and their interactions with RNA-binding proteins (RBPs), have been widely validated. However, the collected information on experimentally validated circRNA-disease associations is only preliminary. Therefore, an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs and diseases in diverse species is necessary. Here, we present an updated CircR2Disease v2.0 with the increased number of circRNA-disease associations and novel characteristics. CircR2Disease v2.0 provides more than 5-fold experimentally validated circRNA-disease associations compared to its previous version. This version includes 4201 entries between 3077 circRNAs and 312 disease subtypes. Secondly, the information of circRNA-miRNA, circRNA-miRNA-target, and circRNA-RBP interactions has been manually collected for various diseases. Thirdly, the gene symbols of circRNAs and disease name IDs can be linked with various nomenclature databases. Detailed descriptions such as samples and journals have also been integrated into the updated version. Thus, CircR2Disease v2.0 can serve as a platform for users to systematically investigate the roles of dysregulated circRNAs in various diseases and further explore the posttranscriptional regulatory function in diseases. Finally, we propose a computational method named circDis based on the graph convolutional network (GCN) and gradient boosting decision tree (GBDT) to illustrate the applications of the CircR2Disease v2.0 database. CircR2Disease v2.0 is available at http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0 and https://github.com/bioinforlab/CircR2Disease-v2.0.

Hierarchical BERT with an adaptive fine-tuning strategy for document classification

Abstract: Pretrained language models (PLMs) have achieved impressive results and have become vital tools for various natural language processing (NLP) tasks. However, there is a limitation that applying these PLMs to document classification when the document length exceeds the maximum acceptable length of the PLM since the excess portion is truncated in these models. If the keywords are in the truncated part, then the performance of the model declines. To address this problem, this paper proposes a hierarchical BERT with an adaptive fine-tuning strategy (HAdaBERT). It consists of a BERT-based model as the local encoder and an attention-based gated memory network as the global encoder. In contrast to existing PLMs that directly truncate documents, the proposed model uses a part of the document as a region, dividing input document into several containers. This allows the useful information in each container to be extracted by a local encoder and composed by a global encoder according to its contribution to the classification. To further improve the performance of the model, this paper proposes an adaptive fine-tuning strategy, which dynamically decides the layers of BERT to be fine-tuned instead of fine-tuning all layers for each input text. Experimental results on different corpora indicated that this method outperformed existing neural networks for document classification. • The hierarchical BERT model consist of local encoder and global encoder. • An adaptive fine-tuning strategy improve the performance of the PLMs. • An attention-based gated memory network model global information.

The impact of the COVID-19 recession on Mexican households: evidence from employment and time use for men, women, and children

Abstract: Abstract This study examines changes in labor supply, income, and time allocation during the COVID-19 pandemic in Mexico. Using an event-study design, we show that the COVID-19 recession had severe negative consequences for Mexican households. In the first month of the pandemic, employment declined by 17 percentage points. Men recovered their employment faster than women, where men’s employment approaches original levels by 2021Q2. Women, on the other hand, experienced persistent employment losses. Within-household, men also increased their time spent on household chores while neither gender (persistently) increased their time caring for others. Instead, children reduced their time spent on schoolwork by 25%.

Fitting Censored and Truncated Regression Data Using the Mixture of Experts Models

Abstract: The logit-weighted reduced mixture of experts model (LRMoE) is a flexible yet analytically tractable non-linear regression model. Though it has shown usefulness in modeling insurance loss frequencies and severities, model calibration becomes challenging when censored and truncated data are involved, which is common in actuarial practice. In this article, we present an extended expectation–conditional maximization (ECM) algorithm that efficiently fits the LRMoE to random censored and random truncated regression data. The effectiveness of the proposed algorithm is empirically examined through a simulation study. Using real automobile insurance data sets, the usefulness and importance of the proposed algorithm are demonstrated through two actuarial applications: individual claim reserving and deductible ratemaking.