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1,353 results on '"Shu T"'

1. Retarding potential analyzer: Principles, designs, and space applications

Author

Shu T. Lai and Catherine Miller

Subjects
Physics, QC1-999
Abstract

The main emphasis of this paper is on the use of Retarding Potential Analyzers (RPAs) for measuring the electron and ion distribution functions, density, and temperature of space plasmas and the charging level of spacecraft surfaces interacting with the plasmas. Multiple grids of progressively negative potential are used to suppress the secondary electrons produced at the entrance of an RPA. We point out that it is impossible to achieve complete suppression. The secondary electrons collected by the receiver may produce a spurious lump in the distribution measured. Improved designs and space applications for spacecraft charging and ionic liquid ion beam diagnostics are discussed. Spacecraft charging will be very important for planetary explorations, whereas ionic liquid ion beams will be important for spacecraft propulsion in the future.

Published
2020
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2. Ritonavir-Mediated Induction of Apoptosis in Pancreatic Cancer Occurs via the RB/E2F-1 and AKT Pathways

Author

Ramesh B. Batchu, Oksana V. Gruzdyn, Christopher S. Bryant, Aamer M. Qazi, Sanjeev Kumar, Sreedhar Chamala, Shu T. Kung, Ramana S. Sanka, Udaya S. Puttagunta, Donald W. Weaver, and Scott A. Gruber

Subjects
ritonavir, pancreatic adenocarcinoma, AKT, retinoblastoma, 2F-1, Medicine, Pharmacy and materia medica, RS1-441
Abstract

Recent observations suggest a lower incidence of malignancies in patients infected with HIV during treatment with Highly Active Anti-Retroviral Therapy (HAART) utilizing protease inhibitors. We investigated the effects of ritonavir, a FDA approved HIV protease inhibitor, on proliferation of pancreatic ductal adeno-carcinoma (PDAC) cell lines. Human PDAC cell lines BxPC-3, MIA PaCa-2, and PANC-1 were propagated under standard conditions and treated with serial dilutions of ritonavir. Ritonavir inhibited cell growth in a dose-dependent manner as well as activated the intrinsic apoptotic pathway in human pancreatic ductal adenocarcinoma (PDAC) cell lines. We observed down-modulation of cell-cycle promoting and up-regulation of cell-cycle inhibitory genes; enhanced interaction of retinoblastoma protein (RB) with E2F-1 transcription factor; inhibition of phosphorylation of RB, resulting in sequestration of E2F-1 and subsequent down-regulation of S phase genes; decreased interaction of E2F-1 with its consensus binding sites; inhibition of cell motility and invasiveness; and inhibition of the AKT pathway. Our results demonstrate a potential use of ritonavir as part of combination chemotherapy for PDAC. Since ritonavir is FDA approved for HIV, drug repositioning for PDAC would limit the costs and reduce risks.

Published
2014
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18. Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

Author

Chu, NH, Van Huynh, N, Nguyen, DN, Hoang, DT, Gong, S, Shu, T, Dutkiewicz, E, Phan, KT, Chu, NH, Van Huynh, N, Nguyen, DN, Hoang, DT, Gong, S, Shu, T, Dutkiewicz, E, and Phan, KT

Abstract

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios.

Published
2024

19. Spoofing Detection for LiDAR in Autonomous Vehicles: A Physical-Layer Approach

Author

Hu, X, Liu, T, Shu, T, Nguyen, D, Hu, X, Liu, T, Shu, T, and Nguyen, D

Abstract

Recent years have witnessed the ever-growing interest and adoption of autonomous vehicles (AVs), thanks to the latest advancement in sensing and artificial intelligence (AI) technologies. The LiDAR sensor is adopted by most AV manufacturers for its high precision and high reliability. Unfortunately, LiDARs are susceptible to malicious spoofing attacks, which can lead to severe safety consequences for AVs. Most current work focuses on protecting LiDAR against spoofing attacks by using perception model-level defense methods, whose effectiveness unfortunately depends on the correctness of the LiDAR's sensing outcome. A spoofer thus can elude from these methods as long as it fabricates points that maintain the right contextual relationship held by the legitimate points. In this article, we propose to use the signal's Doppler frequency shift to verify the sender of the signal and detect potential spoofing attacks. To this end, we first thoroughly analyze the working principle of LiDAR and conduct real-world experiments to deeply understand and reveal the vulnerability of LiDAR sensors. We then prove that the Doppler frequency shifts of legitimate and spoofing signals present different characteristics, which can be used to fundamentally protect the LiDAR sensing outcome. For better demonstration purposes, we consider three attack models, including static attacker, moving attacker, and moving attacker with control of both velocity and signal frequency. For each of the models, we first show how the spoofing attack is performed and then present our countermeasures. We then propose a statistical spoofing detection framework to jointly consider the impact of short-term uncertainty in vehicle velocity, which can provide more accurate spoofing detection results in realistic environments. Extensive numerical results are provided in a wide range of settings and road conditions.

Published
2024

20. Spacecraft charging: incoming and outgoing electrons

Author

Lai, Shu T.

Subjects
Physics - Space Physics, Physics - Accelerator Physics, Physics - Plasma Physics
Abstract

This paper presents an overview of the roles played by incoming and outgoing electrons in spacecraft surface and stresses the importance of surface conditions for spacecraft charging. The balance between the incoming electron current from the ambient plasma and the outgoing currents of secondary electrons, backscattered electrons, and photoelectrons from the surfaces determines the surface potential. Since surface conditions significantly affect the outgoing currents, the critical temperature and the surface potential are also significantly affected. As a corollary, high level differential charging of adjacent surfaces with very different surface conditions is a space hazard., Comment: 4 pages, contribution to the Joint INFN-CERN-EuCARD-AccNet Workshop on Electron-Cloud Effects: ECLOUD'12; 5-9 Jun 2012, La Biodola, Isola d'Elba, Italy

Published
2013
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25. MetaSlicing: A Novel Resource Allocation Framework for Metaverse

Author

Chu, NH, Hoang, DT, Nguyen, DN, Phan, KT, Dutkiewicz, E, Niyato, D, Shu, T, Chu, NH, Hoang, DT, Nguyen, DN, Phan, KT, Dutkiewicz, E, Niyato, D, and Shu, T

Abstract

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

Published
2023

26. Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

Author

Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D, Shu, T, Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D, and Shu, T

Abstract

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

Published
2023

27. TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression

Author

Chen, S, Shu, T, Zhao, H, Zhong, G, Chen, X, Chen, S, Shu, T, Zhao, H, Zhong, G, and Chen, X

Abstract

Meteorological radar reflectivity data (i.e., radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex numerical weather prediction (NWP) models. In comparison to conventional models, deep-learning (DL)-based radar echo extrapolation algorithms exhibit higher effectiveness and efficiency. Nevertheless, the development of a reliable and generalized echo extrapolation algorithm is impeded by three primary challenges: cumulative error spreading, imprecise representation of sparsely distributed echoes, and inaccurate description of nonstationary motion processes. To tackle these challenges, this article proposes a novel radar echo extrapolation algorithm called temporal-spatial parallel transformer, referred to as TempEE. TempEE avoids using autoregression and instead employs a one-step forward strategy to prevent the cumulative error from spreading during the extrapolation process. Additionally, we propose the incorporation of a multilevel temporal-spatial attention mechanism to improve the algorithm's capability of capturing both global and local information while emphasizing task-related regions, including sparse echo representations, in an efficient manner. Furthermore, the algorithm extracts spatio-temporal representations from continuous echo images using a parallel encoder to model the nonstationary motion process for echo extrapolation. The superiority of our TempEE has been demonstrated in the context of the classic radar echo extrapolation task, utilizing a real-world dataset. Extensive experiments have further validated the efficacy and indispensability of various components within TempEE.

Published
2023

28. An X-ray activatable gold nanorod encapsulated liposome delivery system for mitochondria-targeted photodynamic therapy (PDT).

Author

Gu, X, Shu, T, Deng, W, Shen, C, Wu, Y, Gu, X, Shu, T, Deng, W, Shen, C, and Wu, Y

Abstract

In this work, we developed a mitochondria-targeted nanomaterial for neoadjuvant X-ray-triggered photodynamic therapy of rectal cancer. Herein, we designed a biodegradable liposome incorporating a photosensitizer, verteporfin, to generate X-ray-induced reactive oxygen species, gold nanorods as radiation enhancers, and triphenylphosphonium as the mitochondrial targeting moiety. The average size of the nanocarrier was about 150 nm. Due to the synergetic effect between X-ray and a combination of verteporfin and gold nanorods, as well as precise site-targeted TPP-modified liposomal nanocarriers, our nanoconjugates generated sufficient cytotoxic singlet oxygen within the mitochondria under X-ray irradiation, triggering the loss of membrane potential and mitochondria-related apoptosis of cancer cells.

Published
2023

29. MASK-CNN-Transformer for real-time multi-label weather recognition

Author

Chen, S, Shu, T, Zhao, H, Tang, YY, Chen, S, Shu, T, Zhao, H, and Tang, YY

Abstract

Weather recognition is an essential support for many practical life applications, including traffic safety, environment, and meteorology. However, many existing related works cannot comprehensively describe weather conditions due to their complex co-occurrence dependencies. This paper proposes a novel multi-label weather recognition model considering these dependencies. The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism. The model employs multiple convolutional layers to extract features from weather images and a Transformer encoder to calculate the probability of each weather condition based on the extracted features. To improve the generalization ability of MASK-CT, a MASK mechanism is used during the training phase. The effect of the MASK mechanism is explored and discussed. The Mask mechanism randomly withholds some information from one-pair training instances (one image and its corresponding label). There are two types of MASK methods. Specifically, MASK-I is designed and deployed on the image before feeding it into the weather feature extractor and MASK-II is applied to the image label. The Transformer encoder is then utilized on the randomly masked image features and labels. The experimental results from various real-world weather recognition datasets demonstrate that the proposed MASK-CT model outperforms state-of-the-art methods. Furthermore, the high-speed dynamic real-time weather recognition capability of the MASK-CT is evaluated.

Published
2023

31. An Integrated Anti-Fouling and Anti-Corrosion Coating Enabled by rGO/AgNPs and Amphiphilic Networks

Author

Shu Tian, Jinli Zhang, Shuan Liu, Jingyu Li, Jibin Pu, Yugang Hao, Guobing Ying, Qunji Xue, and Guangming Lu

Subjects
Anti-fouling, Anti-corrosion, rGO/AgNPs, Amphiphilic coating, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Marine corrosion and biofouling are challenges that affect marine industrial equipment, and protecting equipment with functional coatings is a simple and effective approach. However, it is extremely difficult to combine anti-corrosion and anti-fouling properties in a single coating. In this work, we combine reduced graphene oxide (rGO)/silver nanoparticles (AgNPs) with a hydrophilic polymer in a bio-based silicone–epoxy resin to create a coating with both anti-fouling and anti-corrosion properties. The excellent anti-fouling performance of the coating results from a ternary synergistic mechanism involving fouling release, contact inhibition, and a hydration effect, while the outstanding anti-corrosion performance is provided by a ternary synergistic anti-corrosion mechanism that includes a dense interpenetrating network (IPN) structure, a barrier effect, and passivation. The results show that the obtained coating possesses superior anti-fouling activity against protein, bacteria, algae, and other marine organisms, as well as excellent anti-corrosion and certain self-healing properties due to its dynamic cross-linked network of rGO/AgNPs and the hydrophilic polymer. This work provides an anti-corrosion and anti-fouling integrated coating for marine industrial equipment.

Published
2024
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32. Unclean but affordable solid fuels effectively sustained household energy equity

Author

Ke Jiang, Ran Xing, Zhihan Luo, Yaojie Li, Jinghang Wang, Wenxiao Zhang, Yaqi Zhu, Yatai Men, Guofeng Shen, and Shu Tao

Subjects
Science
Abstract

Abstract Extensive use of traditional solid fuels necessitates a clean transition to modern energy, yet rising costs hinder equitable progress, presenting a challenge that remains underexplored. Here we quantify household energy inequities in China and evaluate shifts during the cooking and heating transition by compiling data from nationwide questionnaire surveys and statistic datasets. We find that by meeting 42.6% of household energy needs at low costs, solid fuels sustain equitable energy consumption across different income groups, being measured by the Concentration Index (CI). However, energy burden inequity remains substantially with the CI value increases by up to 43% during the transition, particularly when moving away from biomass for cooking. Switching to electric heating with natural gas cooking would limit such increases by only 15.5%. The study underscores the negative equity impacts of solid fuel cessation, advocating for phased transitions and targeted subsidies to ensure a just clean energy transition.

Published
2024
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33. Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study

Author

Ryo Hidaka, Kenta Matsuda, Takashi Igari, Shu Takeuchi, Yuichi Imoto, Satoshi Yagi, and Hirotaka Kawano

Subjects
Hip, Osteoarthritis, Artificial intelligence, Kellgren–Lawrence classification, Convolutional neural network, Deep learning, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Background Predicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain radiographs and patient data and to determine its accuracy. Methods This retrospective study utilized anteroposterior pelvic radiographs of consecutive patients with hip OA who underwent primary unilateral total hip arthroplasty. Radiographs diagnosed with Kellgren–Lawrence (KL) grade 0–2 were extracted from 361 patients and 1697 images. This AI model was developed to predict whether OA would progress from KL grade 0–2 to KL grade ≥ 3 within n years (n = 3, 4, 5). A gradient-boosting decision tree approach was utilized according to feature extractions obtained by a convolutional neural network from radiographs and patient data (height, body weight, sex, age, and KL grade given by an orthopedic surgeon) with five-fold cross-validation. The model performance was assessed using accuracy, specificity, sensitivity, and the area under the receiver operating characteristic curve (AUC). Results The mean accuracy, specificity, sensitivity, and AUC of our prediction model were, respectively, 81.8%, 88.0%, 66.7%, and 0.836 for 3 years; 79.8%, 85.0%, 71.6%, and 0.836 for 4 years; and 78.5%, 80.4%, 76.9%, and 0.846 for 5 years. Conclusions The proposed AI model performed adequately in predicting hip OA progression and may be clinically applicable with additional datasets and validation.

Published
2024
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34. Contributions of ecological restoration policies to China’s land carbon balance

Author

Chao Yue, Mengyang Xu, Philippe Ciais, Shu Tao, Huizhong Shen, Jinfeng Chang, Wei Li, Lei Deng, Junhao He, Yi Leng, Yu Li, Jiaming Wang, Can Xu, Han Zhang, Pengyi Zhang, Liankai Zhang, Jie Zhao, Lei Zhu, and Shilong Piao

Subjects
Science
Abstract

Abstract Unleashing the land sector’s potential for climate mitigation requires purpose-driven changes in land management. However, contributions of past management changes to the current global and regional carbon cycles remain unclear. Here, we use vegetation modelling to reveal how a portfolio of ecological restoration policies has impacted China’s terrestrial carbon balance through developing counterfactual ‘no-policy’ scenarios. Pursuing conventional policies and assuming no changes in climate or atmospheric carbon dioxide (CO2) since 1980 would have led China’s land sector to be a carbon source of 0.11 Pg C yr−1 for 2001–2020, in stark contrast to a sink of 175.9 Tg C yr−1 in reality. About 72.7% of this difference can be attributed to land management changes, including afforestation and reforestation (49.0%), reduced wood extraction (21.8%), fire prevention and suppression (1.6%) and grassland grazing exclusion (0.3%). The remaining 27.3% come from changes in atmospheric CO2 (42.2%) and climate (−14.9%). Our results underscore the potential of active land management in achieving ‘carbon-neutrality’ in China.

Published
2024
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42. Exploration on the surface water leakage mechanism of travertine pools in Salix cupularis shrub area of Huanglong Scenic Spot, Sichuan Province

Author

Xinze LIU, Wenhao GAO, Ronglin XU, Dong SUN, Liping JIANG, Shu TANG, Qingzhou REN, Mengyu XU, and Shengqiang ZHAI

Subjects
huanglong scenic spot, salix cupularis shrub, travertine pool, leakage, active porosity, permeability coefficient, Geology, QE1-996.5
Abstract

Recently, some travertine landscape groups in Huanglong Scenic Spot have been degraded to varying degrees. Surface water leakage, blackening and desertification have occurred on a large scale in the dense distribution area of Salix cupularis shrub, which has adversely affected the core value of the Huanglong World Natural Heritage Site. The relationship between shrub expansion and leakage enhancement has not been fully understood. In this study, travertine pool group of typical shrub area was selected in the north of Yingyue Pool in the upper reaches of Huanglong, and 4 shrub cover levels were set: no shrub (coverage 60%). In-situ infiltration test was conducted to determine the leakage intensity and to evaluate the changes of travertine pore structure and permeability parameters in shrub area. With the comparison of the leakage intensities in different shrub cover level areas, the mechanism of surface water leakage of travertine under the influence of Salix cupularis scrub is summarized. The results show that (1) the leakage intensity increases with the increase of coverage. When the coverage of Salix cupularis shrub exceeds 60%, the leakage intensity changes significantly, which should be related to the rapid diversion of large pores. (2) Under the influence of algae activities and decomposition of plant residues, new travertine with more pores and holes is formed in the scrub area, resulting in higher natural leakage intensity than that in the non-scrub area. (3) The root activities of Salix cupularis shrub changes the primary travertine structure, leading to loose primary travertine and increases of pore diameter, effective porosity, and permeability coefficient. The enhanced groundwater seepage resulted in enhanced overall surface water leakage in the travertine pool. (4) The transformation of travertine pore structure by Salix cupularis roots is depended on the root cleavage. The dissolution of travertine cement by root solution leads travertine to uniform loose accumulation. The results show that the root and algal activities in Salix cupularis scrub area mainly control the process of surface water leakage enhancement. This study have basic value for the evolution analysis and conservation of Huanglong travertine natural heritage landscape.

Published
2024
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43. Association between real-time polymerase chain reaction cycle threshold value and clinical presentation in children with Bordetella pertussis

Author

Wen Li, Huaping Wang, Shu Teng, Yalin Sun, Qi Jia, Zhenghong Qi, Lingbo Wang, Zhangnv Yang, and Shiyong Zhao

Subjects
Pertussis, Cycle threshold, Polymerase chain reaction, Pediatrics, RJ1-570
Abstract

Abstract Background The cycle threshold (Ct) value is inversely proportional to the number of copies of the target region in a sample, suggesting that a low Ct value indicates a high pathogen load. The relationship between Ct value and clinical presentation in children with pertussis is not well-defined. Methods We investigated the relationships between the Ct value of nasopharyngeal samples positive for Bordetella pertussis deoxyribonucleic acid via real-time polymerase chain reaction (PCR), collected from children on admission and their adult family members between May 2022 and March 2024 at Hangzhou Children’s Hospital, China. The study focused on the correlation between Ct value and clinical presentation in children with pertussis. Results The Ct value was positively correlated with age (r = 0.362, P = 0.001). The mean Ct value for children with pertussis was 28.0 (range: 22.0–32.0), which was lower than the 32.0 (range: 30.0–34.0) observed in adults. Ct value was inversely correlated with length of stay, an indicator of disease severity (r = -0.356, P = 0.001). Logistic regression analyses revealed that both Ct value (OR: 0.891, 95% CI: 0.799–0.993, P = 0.036) and white blood cell count (OR: 1.127, 95% CI: 1.005–1.263, P = 0.040) were independently associated with severity of pertussis. Conclusions Real-time PCR Ct values at initial diagnosis for pertussis may potentially predict severe disease outcomes in children.

Published
2024
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44. Three-dimensional cell culture using CD9-positive cells isolated from marginal cell layer of intermediate lobe of rats sustains in vivo-like primary niche environment

Author

Kotaro HORIGUCHI, Takehiro TSUKADA, Saishu YOSHIDA, Ken FUJIWARA, Takashi NAKAKURA, Morio AZUMA, Ayano SHINDO, Rumi HASEGAWA, and Shu TAKIGAMI

Subjects
cd9, hanging drop three-dimensional cell culture, intermediate lobe, pituitary gland, stem cells, Reproduction, QH471-489, Internal medicine, RC31-1245
Abstract

The adenohypophysis is composed of the anterior and intermediate lobes (AL and IL, respectively), and secretes hormones that play an important role in reproduction. CD9- and SOX2-double (CD9/SOX2) positive cells located in the marginal cell layer (MCL) facing the Rathke’s cleft in the AL and IL form the primary stem cell niche in the adult adenohypophysis of rats. In this study, we successfully obtained 3-dimensional (3D) cell aggregates that closely resembled the primary niche of MCL in vivo. After incubation in a Matrigel containing several growth factors, approximately 20% of the cells in the CD9/SOX2-positive cell aggregates were differentiated into hormone-producing cells. The cell aggregates generated in this study may provide insight into the regulation of the pituitary stem/progenitor cell niche and the turnover of hormone-producing cells.

Published
2024
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45. Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort

Author

Miyuki Sakurai, Ikuko N. Motoike, Eiji Hishinuma, Yuichi Aoki, Shu Tadaka, Mana Kogure, Masatsugu Orui, Mami Ishikuro, Taku Obara, Naoki Nakaya, Kazuki Kumada, Atsushi Hozawa, Shinichi Kuriyama, Masayuki Yamamoto, Seizo Koshiba, and Kengo Kinoshita

Subjects
Medicine, Science
Abstract

Abstract Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55–59 to 60–64 years being a period of significant metabolic change, particularly in men, and from 45–49 to 50–54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

Published
2024
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46. Substantial differences in source contributions to carbon emissions and health damage necessitate balanced synergistic control plans in China

Author

Yilin Chen, Huizhong Shen, Guofeng Shen, Jianmin Ma, Yafang Cheng, Armistead G. Russell, Shunliu Zhao, Amir Hakami, and Shu Tao

Subjects
Science
Abstract

Abstract China’s strategy to concurrently address climate change and air pollution mitigation is hindered by a lack of comprehensive information on source contributions to health damage and carbon emissions. Here we show notable discrepancies between source contributions to CO2 emissions and fine particulate matter (PM2.5)-related mortality by using adjoint emission sensitivity modeling to attribute premature mortality in 2017 to 53 sector and fuel/process combinations with high spatial resolution. Our findings reveal that monetized PM2.5 health damage exceeds climate impacts in over half of the analyzed subsectors. In addition to coal-fired energy generators and industrial boilers, the combined health and climate costs from energy-intensive processes, diesel-powered vehicles, domestic coal combustion, and agricultural activities exceed 100 billion US dollars, with health-related costs predominating. This research highlights the critical need to integrate the social costs of health damage with climate impacts to develop more balanced mitigation strategies toward these dual goals, particularly during fuel transition and industrial structure upgrading.

Published
2024
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47. Enhancing teaching and learning of evidence-based practice via game-based learning

Author

Rosy Swee Cheng Tay, Debby Regina Wegner, Li Siong Lim, Joshua Ting, and Shu Ting Ong

Subjects
evidence-based teaching, evidence-based practice (ebp), nursing education, game-based learning (gbl), teaching and learning ebp, nursing students, gamification, Education (General), L7-991, Medicine (General), R5-920
Abstract

Introduction: The Singapore Institute of Technology-University of Glasgow (SIT-UofG) Nursing Programme has traditionally taken a didactic teaching approach in the delivery of the Evidence-Based Practice (EBP) module. A hybrid approach was introduced using Game-Based Learning (GBL) to encourage active learning through gameplay. Methods: A Randomised Controlled Trial (RCT) was undertaken encompassing a cohort of 100 Nursing students taking the EBP module in their first year at the Singapore Institute of Technology (SIT) in the 2021/22 academic year. The experimental group (n=27) worked through the online GBL intervention and the EBP module, while the control group (n=27) took the EBP module alone. The GBL included five Learning Quests and three case studies. Results: High levels of satisfaction were reported by both the experimental group (n=22) and the control group (n=15) on the traditional content and delivery of the EBP module. High levels of engagement were reported by the experimental group on the GBL intervention; a one-sample statistics analysis confirming a significant level of engagement (p

Published
2024
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48. Accreditation and Educational Quality: Are Students in Accredited Programs More Academically Engaged?

Author

Cole, James S. and Cole, Shu T.

Abstract

There has been a great deal of debate regarding the value of program accreditation. Two research questions guided this study: 1) are students enrolled in accredited parks, recreation, and leisure programs more academically engaged than students enrolled in non-accredited programs, and 2) do students enrolled in accredited parks, recreation, and leisure programs report higher gains regarding academic learning, practical competence, and satisfaction than students in non-accredited programs? This study found students in accredited programs were significantly more engaged in educationally relevant behaviors than students in non-accredited programs. In addition, results indicated students in accredited programs reported higher gains in practical competence and satisfaction. NRPA's new direction for the accreditation process is to focus on student learning outcomes, which may lead to greater differences in the quality of education provided by accredited versus non-accredited programs.

Published
2008

49. Topical 5% Imiquimod as Monotherapy for Primary Cutaneous and Cutaneous Metastatic Melanoma: Systematic Review of the Literature

Author

Keegan O’Hern, Shu T. Liang, Meagan Chambers, Michael S. Chapman, and Dylan J. Badin

Subjects
medicine.medical_specialty, Metastatic melanoma, business.industry, Melanoma, medicine.medical_treatment, Imiquimod, Dermatology, Immunotherapy, medicine.disease, 030207 dermatology & venereal diseases, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Medicine, business, medicine.drug
Abstract

Background: Surgery is the gold standard treatment for primary cutaneous melanoma but may not be suitable given some comorbiditiies, lesion size or location, or anticipated functional impairment. Imiquimod (IMQ) is a topical immunotherapy infrequently used for melanoma, often in combination with other treatments. Objective: The present work aims to review the available literature on the safety and efficacy of imiquimod in the treatment of cutaneous primary and metastatic melanoma. Methods: We systematically reviewed the literature on topical imiquimod as monotherapy for melanoma, excluding in situ disease. MEDLINE, EMBASE, and CINAHL searches were conducted using terms related to imiquimod and melanoma, results summarized according to the PRISMA Guidelines and quality of evidence assessed using the GRADE tool. Results: Of 559 citations identified, 14 case reports and series with 38 patients with 95 lesions met inclusion criteria. There was heterogeneity in treatment regimens, including the number of applications and treatment length. Complete clearance was observed in 39% of cases, while the stable or progressive disease was seen in 42% of cases; treatment efficacy was limited in cases with prior metastatic disease. Conclusion: The current literature for the use of imiquimod in cutaneous primary and metastatic melanoma remains scarce, with most evidence derived from case reports and series likely to be influenced by selection bias for positive treatment results. Nevertheless, imiquimod remains a relatively well-tolerated treatment for cutaneous primary and metastatic melanoma that may be used in selected cases.

Published
2021
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50. High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation

Author

Liu, T, Hu, X, Xu, H, Shu, T, Nguyen, DN, Liu, T, Hu, X, Xu, H, Shu, T, and Nguyen, DN

Abstract

With the rapid development of the Internet of Things (IoT), federated learning (FL) has been widely used to obtain insights from collected data while preserving data privacy. Differential privacy (DP) is an additive noise scheme that has been widely studied as a privacy-preserving approach on FL. However, privacy protection under DP usually comes at the cost of model accuracy for the underlying FL process. In this paper, we propose a novel low-cost (for both communication and computational overhead) adaptive noise perturbation/masking scheme to protect FL clients’ privacy without degrading the global model accuracy. In particular, we set the magnitude of the additive noise to adaptively change with the magnitude of the local model updates. Then, a direction-based filtering scheme is used to accelerate the convergence of the FL model. A maximum tolerable noise bound for local clients is derived using the central limit theorem (CLT). The designed noise maximizes privacy protection for clients while preserving the accuracy and convergence rate of the FL model, as a result of the noise cancelling out and forming a more concentrated distribution after the aggregation operation on the server. We theoretically prove that FL with the proposed noise perturbation scheme retains the same accuracy and convergence rate (O(1/T) for convex loss functions and O(1/T) for non-convex loss functions) as that of non-private FL with SGD. We also evaluate the performance of the proposed scheme in terms of convergence behavior, computational efficiency, and privacy protection against state-of-the-art privacy inference attacks on real-world datasets. Experimental results show that FL with our proposed perturbation scheme outperforms DP in the accuracy and convergence rate of the FL model in both client dropout and non-client dropout scenarios. Compared with DP, our proposed scheme does not incur additional computational and communication overhead. Our approach provides DP-comparable or bett

Published
2022

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