Browsing by Author "Wang, Wei"

Now showing 1 - 13 of 13
  • Thumbnail Image
    The Arabidopsis PHD-finger protein EDM2 has multiple roles in balancing NLR immune receptor gene expression
    Lai, Yan; Lu, Xueqing Maggie; Daron, Josquin; Pan, Songqin; Wang, Jianqiang; Wang, Wei; Tsuchiya, Tokuji; Holub, Eric; McDowell, John M.; Slotkin, R. Keith; Le Roch, Karine G.; Eulgem, Thomas (PLoS, 2020-09-01)
    Plant NLR-type receptors serve as sensitive triggers of host immunity. Their expression has to be well-balanced, due to their interference with various cellular processes and dose-dependency of their defense-inducing activity. A genetic “arms race” with fast-evolving pathogenic microbes requires plants to constantly innovate their NLR repertoires. We previously showed that insertion of the COPIA-R7 retrotransposon into RPP7 co-opted the epigenetic transposon silencing signal H3K9me2 to a new function promoting expression of this Arabidopsis thaliana NLR gene. Recruitment of the histone binding protein EDM2 to COPIA-R7-associated H3K9me2 is required for optimal expression of RPP7. By profiling of genome-wide effects of EDM2, we now uncovered additional examples illustrating effects of transposons on NLR gene expression, strongly suggesting that these mobile elements can play critical roles in the rapid evolution of plant NLR genes by providing the “raw material” for gene expression mechanisms. We further found EDM2 to have a global role in NLR expression control. Besides serving as a positive regulator of RPP7 and a small number of other NLR genes, EDM2 acts as a suppressor of a multitude of additional NLR genes. We speculate that the dual functionality of EDM2 in NLR expression control arose from the need to compensate for fitness penalties caused by high expression of some NLR genes by suppression of others. Moreover, we are providing new insights into functional relationships of EDM2 with its interaction partner, the RNA binding protein EDM3/AIPP1, and its target gene IBM1, encoding an H3K9-demethylase.
  • Thumbnail Image
    ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators
    Ramakrishnan, Naren; Butler, Patrick; Self, Nathan; Khandpur, Rupinder P.; Saraf, Parang; Wang, Wei; Cadena, Jose; Vullikanti, Anil Kumar S.; Korkmaz, Gizem; Kuhlman, Christopher J.; Marathe, Achla; Zhao, Liang; Ting, Hua; Huang, Bert; Srinivasan, Aravind; Trinh, Khoa; Getoor, Lise; Katz, Graham; Doyle, Andy; Ackermann, Chris; Zavorin, Ilya; Ford, Jim; Summers, Kristen; Fayed, Youssef; Arredondo, Jaime; Gupta, Dipak; Mares, David; Muthia, Sathappan; Chen, Feng; Lu, Chang-Tien (2014)
    We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
  • Thumbnail Image
    Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
    Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham A.; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, Donatien Chedom; Fourches, Denis; Friend, Stephen; Frigessi, Arnoldo; Gao, Feng; Gao, Xiaoting; Gerold, Jeffrey M.; Gestraud, Pierre; Ghosh, Samik; Gillberg, Jussi; Godoy-Lorite, Antonia; Godynyuk, Lizzy; Godzik, Adam; Goldenberg, Anna; Gomez-Cabrero, David; Gonen, Mehmet; de Graaf, Chris; Gray, Harry; Grechkin, Maxim; Guimera, Roger; Guney, Emre; Haibe-Kains, Benjamin; Han, Younghyun; Hase, Takeshi; He, Di; He, Liye; Heath, Lenwood S.; Hellton, Kristoffer H.; Helmer-Citterich, Manuela; Hidalgo, Marta R.; Hidru, Daniel; Hill, Steven M.; Hochreiter, Sepp; Hong, Seungpyo; Hovig, Eivind; Hsueh, Ya-Chih; Hu, Zhiyuan; Huang, Justin K.; Huang, R. Stephanie; Hunyady, Laszlo; Hwang, Jinseub; Hwang, Tae Hyun; Hwang, Woochang; Hwang, Yongdeuk; Isayev, Olexandr; Walk, Oliver Bear Don't; Jack, John; Jahandideh, Samad; Ji, Jiadong; Jo, Yousang; Kamola, Piotr J.; Kanev, Georgi K.; Karacosta, Loukia; Karimi, Mostafa; Kaski, Samuel; Kazanov, Marat; Khamis, Abdullah M.; Khan, Suleiman Ali; Kiani, Narsis A.; Kim, Allen; Kim, Jinhan; Kim, Juntae; Kim, Kiseong; Kim, Kyung; Kim, Sunkyu; Kim, Yongsoo; Kim, Yunseong; Kirk, Paul D. W.; Kitano, Hiroaki; Klambauer, Gunter; Knowles, David; Ko, Melissa; Kohn-Luque, Alvaro; Kooistra, Albert J.; Kuenemann, Melaine A.; Kuiper, Martin; Kurz, Christoph; Kwon, Mijin; van Laarhoven, Twan; Laegreid, Astrid; Lederer, Simone; Lee, Heewon; Lee, Jeon; Lee, Yun Woo; Leppaho, Eemeli; Lewis, Richard; Li, Jing; Li, Lang; Liley, James; Lim, Weng Khong; Lin, Chieh; Liu, Yiyi; Lopez, Yosvany; Low, Joshua; Lysenko, Artem; Machado, Daniel; Madhukar, Neel; De Maeyer, Dries; Malpartida, Ana Belen; Mamitsuka, Hiroshi; Marabita, Francesco; Marchal, Kathleen; Marttinen, Pekka; Mason, Daniel; Mazaheri, Alireza; Mehmood, Arfa; Mehreen, Ali; Michaut, Magali; Miller, Ryan A.; Mitsopoulos, Costas; Modos, Dezso; Van Moerbeke, Marijke; Moo, Keagan; Motsinger-Reif, Alison; Movva, Rajiv; Muraru, Sebastian; Muratov, Eugene; Mushthofa, Mushthofa; Nagarajan, Niranjan; Nakken, Sigve; Nath, Aritro; Neuvial, Pierre; Newton, Richard; Ning, Zheng; De Niz, Carlos; Oliva, Baldo; Olsen, Catharina; Palmeri, Antonio; Panesar, Bhawan; Papadopoulos, Stavros; Park, Jaesub; Park, Seonyeong; Park, Sungjoon; Pawitan, Yudi; Peluso, Daniele; Pendyala, Sriram; Peng, Jian; Perfetto, Livia; Pirro, Stefano; Plevritis, Sylvia; Politi, Regina; Poon, Hoifung; Porta, Eduard; Prellner, Isak; Preuer, Kristina; Angel Pujana, Miguel; Ramnarine, Ricardo; Reid, John E.; Reyal, Fabien; Richardson, Sylvia; Ricketts, Camir; Rieswijk, Linda; Rocha, Miguel; Rodriguez-Gonzalvez, Carmen; Roell, Kyle; Rotroff, Daniel; de Ruiter, Julian R.; Rukawa, Ploy; Sadacca, Benjamin; Safikhani, Zhaleh; Safitri, Fita; Sales-Pardo, Marta; Sauer, Sebastian; Schlichting, Moritz; Seoane, Jose A.; Serra, Jordi; Shang, Ming-Mei; Sharma, Alok; Sharma, Hari; Shen, Yang; Shiga, Motoki; Shin, Moonshik; Shkedy, Ziv; Shopsowitz, Kevin; Sinai, Sam; Skola, Dylan; Smirnov, Petr; Soerensen, Izel Fourie; Soerensen, Peter; Song, Je-Hoon; Song, Sang Ok; Soufan, Othman; Spitzmueller, Andreas; Steipe, Boris; Suphavilai, Chayaporn; Tamayo, Sergio Pulido; Tamborero, David; Tang, Jing; Tanoli, Zia-ur-Rehman; Tarres-Deulofeu, Marc; Tegner, Jesper; Thommesen, Liv; Tonekaboni, Seyed Ali Madani; Tran, Hong T.; De Troyer, Ewoud; Truong, Amy; Tsunoda, Tatsuhiko; Turu, Gabor; Tzeng, Guang-Yo; Verbeke, Lieven; Videla, Santiago; Vis, Daniel; Voronkov, Andrey; Votis, Konstantinos; Wang, Ashley; Wang, Hong-Qiang Horace; Wang, Po-Wei; Wang, Sheng; Wang, Wei; Wang, Xiaochen; Wang, Xin; Wennerberg, Krister; Wernisch, Lorenz; Wessels, Lodewyk; van Westen, Gerard J. P.; Westerman, Bart A.; White, Simon Richard; Willighagen, Egon; Wurdinger, Tom; Xie, Lei; Xie, Shuilian; Xu, Hua; Yadav, Bhagwan; Yau, Christopher; Yeerna, Huwate; Yin, Jia Wei; Yu, Michael; Yu, MinHwan; Yun, So Jeong; Zakharov, Alexey; Zamichos, Alexandros; Zanin, Massimiliano; Zeng, Li; Zenil, Hector; Zhang, Frederick; Zhang, Pengyue; Zhang, Wei; Zhao, Hongyu; Zhao, Lan; Zheng, Wenjin; Zoufir, Azedine; Zucknick, Manuela (Springer Nature, 2019-06-17)
    The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
  • Thumbnail Image
    A comprehensive investigation of Bronze Age human dietary strategies from different altitudinal environments in the Inner Asian Mountain Corridor
    Wang, Wei; Liu, Yi; Duan, Futao; Zhang, Jie; Liu, Xinyi; Reid, Rachel E. B.; Zhang, Man; Dong, Weimiao; Wang, Yongqiang; Ruan, Qiurong; Li, Wenying; An, Cheng-Bang (2020-09)
    The early presence of crops from East Asia and Southwest Asia in the Inner Asian Mountain Corridor (IAMC) has drawn attention to the Bronze Age mountain archaeology of Central Asia. Namely, the Bronze Age diffusion and utilization of grains in this region remains unknown as contrasts and extremes characterize the territory in environmental terms, especially elevation. Researchers continue to reflect on how, during the second millennium BC, Bronze Age populations used new crops and local animal resources to adapt to different elevation environments of the IAMC. In this study, we analyzed the 41 latest stable carbon and nitrogen isotopic results from human and faunal bones from six Bronze Age sites in the IAMC, 261 previously published stable isotopic datasets, and 12 archaeobotanical and four zooarchaeological results to investigate the dietary strategies of populations from different elevation environments in the Bronze Age IAMC. The results show an altitudinal gradient in dietary choices among Bronze Age populations in the IAMC, with mixed C-4 and C-3 consumption at the low-mid elevations and notable C-3 consumption at the high elevations. Archaeobotanical and faunal remains also support these isotopic results. Our study further highlights that the differentiated dietary strategies adopted by the Bronze Age population in IAMC may have been the product of adaptation to local geographic environments. Social interaction may have also played a role in certain types of special dietary consumption.
  • Thumbnail Image
    Endogenous Regulation and Pharmacological Modulation of Sepsis-Induced HMGB1 Release and Action: An Updated Review
    Zhu, Cassie Shu; Wang, Wei; Qiang, Xiaoling; Chen, Weiqiang; Lan, Xiqian; Li, Jianhua; Wang, Haichao (MDPI, 2021-08-27)
    Sepsis remains a common cause of death in intensive care units, accounting for approximately 20% of total deaths worldwide. Its pathogenesis is partly attributable to dysregulated inflammatory responses to bacterial endotoxins (such as lipopolysaccharide, LPS), which stimulate innate immune cells to sequentially release early cytokines (such as tumor necrosis factor (TNF) and interferons (IFNs)) and late mediators (such as high-mobility group box 1, HMGB1). Despite difficulties in translating mechanistic insights into effective therapies, an improved understanding of the complex mechanisms underlying the pathogenesis of sepsis is still urgently needed. Here, we review recent progress in elucidating the intricate mechanisms underlying the regulation of HMGB1 release and action, and propose a few potential therapeutic candidates for future clinical investigations.
  • Thumbnail Image
    Event Detection and Extraction from News Articles
    Wang, Wei (Virginia Tech, 2018-02-21)
    Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly.
  • Thumbnail Image
    Highly porous gold supraparticles as surface-enhanced Raman spectroscopy (SERS) substrates for sensitive detection of environmental contaminants
    Kang, Seju; Wang, Wei; Rahman, Asifur; Nam, Wonil; Zhou, Wei; Vikesland, Peter J. (Royal Society of Chemistry, 2022-11-15)
    Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles (i.e., ∼100 μm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 μM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications.
  • Thumbnail Image
    The Impact of Volitional Feedback on Learners' Self-Efficacy and Course Satisfaction in a College Assignment System
    Wang, Wei (Virginia Tech, 2011-08-30)
    In contemporary Chinese higher education, classroom lectures combined with a web-based learning support system are broadly applied. This study investigated what kind of feedback strategy could be effective in improving students' self-efficacy and course satisfaction in a blended learning context. Standard volitional messages were constructed and—along with traditional feedback content (knowledge of results and knowledge of correct response)—distributed to a large undergraduate class in China. Sixty-seven freshmen participated in this pure experimental study. Results indicated that students' learning self-efficacy and course satisfaction were significantly correlated. In addition, participants who received the knowledge of correct response plus volitional messages (KCR+V) showed greater course satisfaction than those who received other types of feedback messages. No significant difference emerged in self-efficacy. Future research directions are discussed.
  • Thumbnail Image
    Inputs for staple crop production in China drive burden shifting of water and carbon footprints transgressing part of provincial planetary boundaries
    Feng, Bianbian; Zhuo, La; Mekonnen, Mesfin M.; Marston, Landon T.; Yang, Xi; Xu, Zenghui; Liu, Yilin; Wang, Wei; Li, Zhibin; Li, Meng; Ji, Xiangxiang; Wu, Pute (Pergamon-Elsevier, 2022-08-01)
    Crop production is the biggest water user and key contributor to anthropogenic greenhouse gas emissions. Increasing crop yields to ensure adequate food supply under water and land scarcity is excessively dependents on intensive agricultural inputs (such as fertilizers, pesticides, agri-films, or energy), resulting in unintended environmental consequences. Supply chains bringing environmental-intensive inputs from their place of production to the croplands. However, most food-related environmental assessments ignore the environmental burden of agricultural input production, trade, and consumption. Here, we estimate spatially-detailed water (WF) and carbon footprints (CF) of wheat, maize, and rice production in China with extended system boundary from upstream raw material mining to the field. The agricultural inputs account for up to 24% and 89% of a crop's WF and CF, respectively, at the provincial level. The total local generated WF in Chinese northern provinces and CF in Shanxi and Inner Mongolia provinces for producing crops and agricultural inputs transgresses the corresponding downscaled blue water and carbon planetary boundaries. The study broadens the scope of traditional environmental impact assessments in agricultural production and sheds light on the significances to manage the linkages between the crop production and the agricultural inputs' upstream supply chains towards more efficient water use and less greenhouse gas emissions in food system.
  • Thumbnail Image
    Microporous Multiresonant Plasmonic Meshes by Hierarchical Micro-Nanoimprinting for Bio-Interfaced SERS Imaging and Nonlinear Nano-Optics
    Garg, Aditya; Mejia, Elieser; Nam, Wonil; Nie, Meitong; Wang, Wei; Vikesland, Peter J.; Zhou, Wei (Wiley-V C H Verlag, 2022-04)
    Microporous mesh plasmonic devices have the potential to combine the biocompatibility of microporous polymeric meshes with the capabilities of plasmonic nanostructures to enhance nanoscale light-matter interactions for bio-interfaced optical sensing and actuation. However, scalable integration of dense and uniformly structured plasmonic hotspot arrays with microporous polymeric meshes remains challenging due to the processing incompatibility of conventional nanofabrication methods with flexible microporous substrates. Here, scalable nanofabrication of microporous multiresonant plasmonic meshes (MMPMs) is achieved via a hierarchical micro-/nanoimprint lithography approach using dissolvable polymeric templates. It is demonstrated that MMPMs can serve as broadband nonlinear nanoplasmonic devices to generate second-harmonic generation, third-harmonic generation, and upconversion photoluminescence signals with multiresonant plasmonic enhancement under fs pulse excitation. Moreover, MMPMs are employed and explored as bio-interfaced surface-enhanced Raman spectroscopy mesh sensors to enable in situ spatiotemporal molecular profiling of bacterial biofilm activity. Microporous mesh plasmonic devices open exciting avenues for bio-interfaced optical sensing and actuation applications, such as inflammation-free epidermal sensors in conformal contact with skin, combined tissue-engineering and biosensing scaffolds for in vitro 3D cell culture models, and minimally invasive implantable probes for long-term disease diagnostics and therapeutics.
  • Thumbnail Image
    Power Module with Series-connected MOSFETs in Flip-chip Configuration
    Wang, Wei (Virginia Tech, 2010-08-23)
    Power module design is needed for high system performance and reliability, especially in terms of high efficiency and high power density. Low parasitic impedance and thermal management is desired for the lower power loss and device stress. For power module with high efficiency and improved breakdown voltage, this thesis proposes a novel series-connected power MOSFETs module. Three IRF7832 MOSFETs (30 V breakdown voltage) in series are simulated in a chopper circuit. The drain-source voltage sharing in switching off-mode shows that the devices can share voltage within their breakdown ranges. The switching characteristics are studied, and the switching energy losses without parasitic inductance and with 5 nH parasitic inductances are 203.38 µJ and 316.49 µJ, respectively. The critical parasitic inductance is the one connecting the source of the upper MOSFET and the drain of the middle MOSFET. The switching energy loss due to critical parasitic inductance is about 44.4% of the total switching energy loss. The layout is designed for the double-substrates direct-bond module and wire-bonded module using direct-bond-copper (DBC) substrate. Based on layout dimensions and packaging materials, the packaging module's parasitic parameters are obtained using Ansoft® Q3D extractor. Using parasitic inductance values from simulation, the switching energy losses of direct-bond module and wire-bonded module are 296.18 µJ and 238.99 µJ, respectively. Thermal management is then studied using Ansoft® ePhysics. The MOSFET junction-to-air thermal resistances of the double-substrate direct-bond module and the single-substrate wire-bonded module are 33oC/W and 82oC/W, respectively. Hence, by comparing the direct-bond module with a wire-bonded power module, direct-bond module shows lower parasitic impedances and better thermal management. To test the breakdown voltage of series-connected power MOSFETs module, three TI DualCoolTM N-channel NexFET Power MOSFETs (25 V breakdown voltage) in series are assembled using flip-chip direct-bond technology. Three samples are assembled and the breakdown voltages are measured by using high-power curve tracer as 76 V, 82 V, and 72 V. The more accurate method for testing breakdown voltages by digital voltmeter obtains 77.51 V, 82.31 V, and 73.06 V. The series-connected power MOSFETs module shows compact volume, low parasitic impedances, thermal resistances and improved breakdown voltage. This power module has strong potential for use in applications that require minimized packaging size and parasitic inductance for high voltage, high switching frequency, and high efficiency.
  • Thumbnail Image
    Surface-Enhanced Raman Spectroscopy Enabled Microbial Sensing
    Wang, Wei (Virginia Tech, 2024-03-04)
    Pathogenic microbial contamination of the environment poses a significant threat to human health. Accordingly, microbial surveillance is needed to ensure safe drinking water and air quality. Current analytical methods for microbes are generally either culture-based, gene amplification-based, or sequencing-based. However, these approaches require centralized facilities, well-trained personnel, and specialized instruments that result in high costs and long turnaround times. Surface-enhanced Raman spectroscopy (SERS)-based techniques have been proposed to overcome these limitations. In this dissertation, we discuss work conducted to develop novel SERS-based methods to enable both sensitive microbial quantification and analysis of the interactions of pathogens, their hosts, and the surrounding environment. We first developed a labeled SERS-based lateral flow test for virus quantification. Optimization of the lateral flow design and digital signal analysis enabled high sensitivity towards SARS-CoV-2. To elicit a comprehensive understanding of pathogen infection, label-free living-cell SERS sensors were engineered by incubating host cells with nanoparticles. SERS spectral changes in host cellular components and metabolites during infection were used for viral quantification and offered inherent insights into the temporal and spatial molecular-level mechanisms of infection. These biosensors were validated using bacteriophage Phi6 and then developed for infectious H1N1 influenza. To understand microbial survival in the environment, living-cell SERS methods were applied under various conditions. Results showed cell inactivation and antibiotic treatment induced significant cellular and metabolic responses in the living whole-cell sensors, implying their potential applicability to various environmental conditions. Our research achieves rapid and on-site pathogen quantification and infection mechanism identification.
  • Thumbnail Image
    Understanding the Impact of Plant Nutrition on Plant-Oomycete Interactions
    Wang, Wei (Virginia Tech, 2022-02-25)
    Plants are surrounded by various threats from the environment such as pathogens, abiotic stresses, and animal attacks. Nutrient content and distribution are essential for plant growth and development as well as plant immunity. Pathogens extract nutrients from host plants to benefit their own growth and reproduction. Sulfate, amino acids, and phosphate are indispensable elements for plant growth, plant nutrition, and plant resistance/susceptibility to disease. However, the role of these nutrients in plant-oomycete interactions is an unexplored area. We developed a hydroponic system to precisely control the nutrients applied to plants. We used Arabidopsis thaliana and Nicotiana benthamiana (N. b) as model plants. Hyaloperonospora arabidopsidis as well as two Phytophthora species, Phytophothora capsici (P. cap) and Phytophothora nicotianae (P. nic) were used as model oomycete pathogens. Hpa is an obligate biotrophic pathogen that obtains nutrients directly from the host plant without causing cell death, while P. cap and P. nic are hemibiotrophic pathogens that display a biotrophic phase followed by a necrotrophic phase where they feed on dead cells. Genomic evidence suggests that these pathogens might obtain nutrients including sulfur in different forms from the host (organic and inorganic respectively). We have optimized the hydroponic system and used Taqman PCR assays and sporangiophore counts to assay the influence of sulfur nutrients on Hpa and P. cap infections. We found that (1) sulfur transporter and metabolism genes play essential roles in plant-oomycete interactions; (2) sulfur is critical components for HR responses against Hpa; (3) low sulfur induces pathogenesis related genes as a systemic acquired response. RNA-seq analysis on Phytophthora-infected Arabidopsis suggested that sulfur transport, assimilation, and metabolism play an important role in plant-oomycete interactions. A second project used RNA-seq analysis on P. nic infected N. b, to identify potential nutrition-related-plant genes that are necessary for full pathogen virulence. RNAi knockdowns of N. b AAP6 (amino acid permease 6) and PHT4 (phosphate transporter 4) genes showed an inhibition of oomycete colonization. These experiments together advance the study on the interplay between nutrient assimilation/metabolism in host plants and oomycete infection which will provide insight into the mechanisms how pathogens intercept nutrients from host. In the long-term, this research could reveal new traits applicable for disease resistance to promote crop and food production.