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1,873 results on '"Chen, Honglei"'

1. Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance

Author

Zheng, Ruxin, Sun, Shunqiao, Caesar, Holger, Chen, Honglei, and Li, Jian

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Millimeter-wave (mmWave) radars are indispensable for perception tasks of autonomous vehicles, thanks to their resilience in challenging weather conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar signal data. In response, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem by harnessing the radar signal processing domain knowledge, introducing innovative data normalization and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our tailored deep learning network for automotive radar imaging exhibits remarkable scalability, parameter efficiency and fast inference speed, alongside enhanced performance in terms of radar imaging quality and resolution. Extensive testing confirms that our SR-SPECNet sets a new benchmark in producing high-resolution radar range-azimuth images, outperforming existing methods across varied antenna configurations and dataset sizes. Source code and new radar dataset will be made publicly available online.

Published
2024

2. Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays

Author

Zheng, Ruxin, Sun, Shunqiao, Liu, Hongshan, Chen, Honglei, Soltanalian, Mojtaba, and Li, Jian

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments. Despite these advancements, existing research predominantly focuses on multi-snapshot scenarios, a limitation in the context of automotive radar systems which demand high angular resolution and often rely on limited snapshots, sometimes as scarce as a single snapshot. Furthermore, the increasing interest in sparse arrays for automotive radar, owing to their cost-effectiveness and reduced antenna element coupling, presents additional challenges including susceptibility to random sensor failures. This paper introduces a pioneering DL framework featuring a sparse signal augmentation layer, meticulously crafted to bolster single snapshot DOA estimation across diverse sparse array setups and amidst antenna failures. To our best knowledge, this is the first work to tackle this issue. Our approach improves the adaptability of deep learning techniques to overcome the unique difficulties posed by sparse arrays with single snapshot. We conduct thorough evaluations of our network's performance using simulated and real-world data, showcasing the efficacy and real-world viability of our proposed solution. The code and real-world dataset employed in this study are available at https://github.com/ruxinzh/Deep_RSA_DOA., Comment: Invited paper for IEEE Asilomar conference 2024

Published
2024

3. TriHelper: Zero-Shot Object Navigation with Dynamic Assistance

Author

Zhang, Lingfeng, Zhang, Qiang, Wang, Hao, Xiao, Erjia, Jiang, Zixuan, Chen, Honglei, and Xu, Renjing

Subjects
Computer Science - Robotics
Abstract

Navigating toward specific objects in unknown environments without additional training, known as Zero-Shot object navigation, poses a significant challenge in the field of robotics, which demands high levels of auxiliary information and strategic planning. Traditional works have focused on holistic solutions, overlooking the specific challenges agents encounter during navigation such as collision, low exploration efficiency, and misidentification of targets. To address these challenges, our work proposes TriHelper, a novel framework designed to assist agents dynamically through three primary navigation challenges: collision, exploration, and detection. Specifically, our framework consists of three innovative components: (i) Collision Helper, (ii) Exploration Helper, and (iii) Detection Helper. These components work collaboratively to solve these challenges throughout the navigation process. Experiments on the Habitat-Matterport 3D (HM3D) and Gibson datasets demonstrate that TriHelper significantly outperforms all existing baseline methods in Zero-Shot object navigation, showcasing superior success rates and exploration efficiency. Our ablation studies further underscore the effectiveness of each helper in addressing their respective challenges, notably enhancing the agent's navigation capabilities. By proposing TriHelper, we offer a fresh perspective on advancing the object navigation task, paving the way for future research in the domain of Embodied AI and visual-based navigation., Comment: 8 pages, 5 figures

Published
2024

4. Spiking Wavelet Transformer

Author

Fang, Yuetong, Wang, Ziqing, Zhang, Lingfeng, Cao, Jiahang, Chen, Honglei, and Xu, Renjing

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by emulating the event-driven processing manner of the brain. Incorporating Transformers with SNNs has shown promise for accuracy. However, they struggle to learn high-frequency patterns, such as moving edges and pixel-level brightness changes, because they rely on the global self-attention mechanism. Learning these high-frequency representations is challenging but essential for SNN-based event-driven vision. To address this issue, we propose the Spiking Wavelet Transformer (SWformer), an attention-free architecture that effectively learns comprehensive spatial-frequency features in a spike-driven manner by leveraging the sparse wavelet transform. The critical component is a Frequency-Aware Token Mixer (FATM) with three branches: 1) spiking wavelet learner for spatial-frequency domain learning, 2) convolution-based learner for spatial feature extraction, and 3) spiking pointwise convolution for cross-channel information aggregation - with negative spike dynamics incorporated in 1) to enhance frequency representation. The FATM enables the SWformer to outperform vanilla Spiking Transformers in capturing high-frequency visual components, as evidenced by our empirical results. Experiments on both static and neuromorphic datasets demonstrate SWformer's effectiveness in capturing spatial-frequency patterns in a multiplication-free and event-driven fashion, outperforming state-of-the-art SNNs. SWformer achieves a 22.03% reduction in parameter count, and a 2.52% performance improvement on the ImageNet dataset compared to vanilla Spiking Transformers. The code is available at: https://github.com/bic-L/Spiking-Wavelet-Transformer.

Published
2024

5. Fatigue behaviors and atomic-scale mechanisms in nanocrystalline gold thin film

Author

Chen, Honglei, Tan, Susheng, and Wang, Zhijie

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

The fatigue properties of 930 nm-thick Au films and 1 {\mu}m-thick Au film with a Ti interlayer are systematically investigated. The dominant damage behaviors of 930 nm-thick Au films under dynamic bending cyclic loading changed from extrusions to intergranular cracks with the decrease in strain ranges and the increase in cyclic cycles. The different fatigue behaviors are attributed to the process of edge dislocation annihilation and vacancy formation during cyclic deformation. Depositing 10 nm-thick Ti interlayers between the PI substrates and 1 {\mu}m-thick annealed Au films is effective to suppress strain localization and increase the rupture strain and the fatigue properties of thin Au films. This study shed lights on the fatigue mechanism and provide clues to design nanocomposites in the flexible displays in the practical application., Comment: 25 pages, 8 figures

Published
2023

6. Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation

Author

Zheng, Ruxin, Sun, Shunqiao, Liu, Hongshan, Chen, Honglei, and Li, Jian

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot. Classical subspace-based methods like MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single snapshot DOA estimation but face drawbacks in reduced array aperture and inapplicability to sparse arrays. Single-snapshot methods such as compressive sensing and iterative adaptation approach (IAA) encounter challenges with high computational costs and slow convergence, hampering real-time use. Recent deep learning DOA methods offer promising accuracy and speed. However, the practical deployment of deep networks is hindered by their black-box nature. To address this, we propose a deep-MPDR network translating minimum power distortionless response (MPDR)-type beamformer into deep learning, enhancing generalization and efficiency. Comprehensive experiments conducted using both simulated and real-world datasets substantiate its dominance in terms of inference time and accuracy in comparison to conventional methods. Moreover, it excels in terms of efficiency, generalizability, and interpretability when contrasted with other deep learning DOA estimation networks., Comment: 10 pages, 10 figures

Published
2023

7. Olfactory Dysfunction and Depression Trajectories in Community-Dwelling Older Adults.

Author

Kamath, Vidyulata, Jiang, Kening, Manning, Kevin, Mackin, R, Walker, Keenan, Powell, Danielle, Lin, Frank, Chen, Honglei, Brenowitz, Willa, Yaffe, Kristine, Simonsick, Eleanor, and Deal, Jennifer

Subjects
Affective, Alzheimer’s disease, Dementia, Mood, Olfaction, Smell, Humans, Female, Aged, Male, Smell, Depression, Independent Living, Olfaction Disorders, Risk Factors, Cognitive Dysfunction
Abstract

BACKGROUND: We examined the relationship between baseline olfactory performance and incident significant depressive symptoms and longitudinal depression trajectories in well-functioning older adults. Inflammation and cognitive status were examined as potential mediators. METHODS: Older adults (n = 2 125, 71-82 years, 51% female, 37% Black) completed an odor identification task at Year 3 (our study baseline) of the Health, Aging, and Body Composition study. Cognitive assessments, depressive symptoms, and inflammatory markers were ascertained across multiple visits over 8 years. Discrete-time complementary log-log models, group-based trajectory models, and multivariable-adjusted multinomial logistic regression were employed to assess the relationship between baseline olfaction and incident depression and longitudinal depression trajectories. Mediation analysis assessed the influence of cognitive status on these relationships. RESULTS: Individuals with lower olfaction had an increased risk of developing significant depressive symptoms at follow-up (hazard ratio = 1.04, 95% confidence interval [CI]: 1.00, 1.08). Of the 3 patterns of longitudinal depression scores identified (stable low, stable moderate, and stable high), poorer olfaction was associated with a 6% higher risk of membership in the stable moderate (relative risk ratio [RRR] = 1.06, 95% CI: 1.02, 1.10)/stable high (RRR = 1.06, 95% CI: 1.00, 1.12) groups, compared to the stable low group. Poor cognitive status, but not inflammation, partially mediated the relationship between olfactory performance and incident depression symptom severity. CONCLUSIONS: Suboptimal olfaction could serve as a prognostic indicator of vulnerability for the development of late-life depression. These findings underscore the need for a greater understanding of olfaction in late-life depression and the demographic, cognitive, and biological factors that influence these relationships over time.

Published
2024

10. Citizen scientists track a charismatic carnivore: Mapping the spread and impact of the South African Mantis (Miomantidae, Miomantis caffra) in Australia

Author

Connors, Matthew G., Chen, Honglei, Li, Haokun, Edmonds, Adam, Smith, Kimberley A, Gell, Colin, Clitheroe, Kelly, Miller, Ishbel Morag, Walker, Kenneth L., Nunn, Jack S., Nguyen, Linh, Quinane, Luke N., Andreoli, Chiara M., Galea, Jason A., Quan, Brendon, Sandiford, Katrina, Wallis, Brendan, Anderson, Matthew L., Canziani, Elizabeth Valeria, Craven, Jade, Hakim, Roi R. C., Lowther, Rod, Maneylaws, Cindy, Menz, Bastian A., Newman, John, Perkins, Harvey D., Smith, Alistair R., Webber, Vanessa H., Wishart, Dylan, and Pensoft Publishers

Subjects
Citizen science, Geographic distribution, iNaturalist, introduced species, Mantodea, ootheca, Pseudomantis
Published
2022

34. Identification of genetic risk loci and causal insights associated with Parkinson's disease in African and African admixed populations: a genome-wide association study

Author

Okubadejo, Njideka, Ojo, Oluwadamilola, Abiodun, Oladunni, Achoru, Charles, Agabi, Osigwe, Agulanna, Uchechi, Akinyemi, Rufus, Ali, Mohammed, Ani-Osheku, Ifeyinwa, Arigbodi, Ohwotemu, Bello, Abiodun, Erameh, Cyril, Farombi, Temitope, Fawale, Michael, Imarhiagbe, Frank, Iwuozo, Emmanuel, Komolafe, Morenikeji, Nwani, Paul, Nwazor, Ernest, Nyandaiti, Yakub, Obiabo, Yahaya, Odeniyi, Olanike, Odiase, Francis, Ojini, Francis, Onwuegbuzie, Gerald, Osaigbovo, Godwin, Osemwegie, Nosakhare, Oshinaike, Olajumoke, Otubogun, Folajimi, Oyakhire, Shyngle, Ozomma, Simon, Samuel, Sarah, Taiwo, Funmilola, Wahab, Kolawole, Zubair, Yusuf, Gams Massi, Daniel, Gueumekane Bila lamou, Eric, Njamnshi Nfor, Leonard, Magnerou, Mélanie Annick, Fogang Fogoum, Yannick, Shalash, Ali, El-FawaI, Hassan, Khedr, Eman, Fawi, Gharib, A. Eltantawi, Mohamed, Salama, Mohamed, El-Jaafary, Shaimaa, Hamed, Sharifa, Tafesse Mengesha, Abenet, Alemayehu Ayele, Biniyam, Melka Oda, Dereje, Zenebe Zewde, Yared, Debebe Gelan, Yohanesse, AkpaIu, Albert, Charway-Felli, Augustina, Stephen Sarfo, Fred, Adjei, Patrick, Obese, Vida, Bocoum, Abdoulaye, Koita, Abdou, Oumar Guinto, Cheick, Coulibaly, Toumany, Maiga, Youssoufa, Kone, Zaynab, Bell, Abiodun, Adebowale, Akintunde A., Akpekpe, John, lyagba, Alagoma, Wulgo, Ali Mohammed, Arabambi, Babawale, Agu, Christian, Dike, Franklin, Ishola, Ismail, Abiodun, Kehinde, Ekenze, Oluchi, Agabi Osigwe, Paul, Balarabe, Salisu, Abubakar, Sani, Williams, Uduak, Fall, Maouly, Mamadou Diop, Alassane, Hilaire Dominique, Ewodo Touna, Mochan, Andre, Modi, Girish, Dindayal, Saiesha, Ali Awadelkareem, Eman, Elsayed, Inas, Dahawi, Maha, Awadelkareem, Mosab Ali, Misbah, Sarah, Mushengez, Brighton, Kimambo, Henrika, Msango, Leonard, Adebayo, Philip, OKeng, Kigocha, Diekker, Marieke, URassa, Sarah, Gouider, Riadh, Ben Djebara, Mouna, Gargouri, Amina, Kacem, Imen, Nasri, Amina, Mrabet, Saloua, Sghaier, Ikram, Mkada, Imen, Atadzhanov, Masharip, Chishimba, Lorraine, Rizig, Mie, Jama, Fatumah, Okunoye, Olaitan, Hardy, John, Houlden, Henry, Singleton, Andrew, Nalls, Mike, Shamim, Ejaz, Jonas, Cabell, Williamson, Jared, Hall, Deborah A., Rosenbaum, Marc, Davis, Staci, Dean, Marissa, Cromer, Candace, Smith, Jenna, Ruffrage, Lauren, Richardson, Joseph, Sipma, Rebeka, Xie, Tao, Padmanaban, Manesh, Warren, Natalie, Mercado, Tomas, Disbrow, Elizabeth, Chauppeta, Brian, Thomas-Dean, Fermine, Toms, Jamie, Lofton, Katelyn, Rawls, Ashley, Rizer, Kyle, Black, Nieci, Solle, Justin, O'Grady, Alyssa, Sherer, Todd, Fiske, Brian, Başak, A. Nazlı, Tan, Ai Huey, Noyce, Alastair, Akpalu, Albert, Espay, Alberto, Martínez-Carrasco, Alejandro, Medina, Alex, Zimprich, Alexander, Brice, Alexis, Karimova, Altynay, Hernandez, Alvaro, Illarionova, Anastasia, Quattrone, Andrea, Singleton, Andrew B, Sobering, Andrew K., Vinuela, Angel, Sanyaolu, Arinola, Schumacher-Schuh, Artur F., Kishore, Asha, Ahmad-Annuar, Azlina, Al Mubarak, Bashayer, Tang, Beisha, Pizarro Galleguillos, Benjamin, Jeon, Beomseok, Siddiqi, Bernadette, Casey, Bradford, Mollenhauer, Brit, Carroll, Camille, Rieder, Carlos, Pantazis, Caroline B., Comart, Charisse, Lin, Chin-Hsien, Klein, Christine, Bale, Claire, Shepherd, Claire E, Wegel, Claire, Blauwendraat, Cornelis, Vitale, Dan, Martinez-Ramirez, Daniel, Hall, Deborah, Hernandez, Dena, KP, Divya, Nguyen, Duan, Fon, Edward A., Dadiotis, Efthymios, Riley, Ekemini, Iakovenko, Elena, Stafford, Ellie, Gatto, Emilia M, Valente, Enza Maria, Vollstedt, Eva-Juliane, Faghri, Faraz, Genc, Gencer, Xiromerisiou, Georgia, Hadjigorgiou, Georgios, Hiu-Fai Chan, Germaine, Arboleda, Gonzalo, Kaishibayeva, Gulnaz, Höglinger, Günter, Leonard, Hampton, Madoev, Harutyun, Iwaki, Hirotaka, Chen, Honglei, Wu, Hsiu-Chuan, Shang, Huifang, Morris, Huw R, F. Mata, Ignacio, Keller Sarmiento, Ignacio Juan, Dagklis, Ioannis, Tarnanas, Ioannis, Aasly, Jan O., Hoenicka, Janet, Corvol, Jean-Christophe, Foo, Jia Nee, Guo, Jifeng, Junker, Johanna, Carr, Jonathan, Kim, Jonggeol Jeff, Orozco, Jorge, Jankovic, Joseph, Shulman, Joshua, Hunter, Julie, Solle, Justin C., Murphy, Kaileigh, Nuytemans, Karen, Kieburtz, Karl, Lohmann, Katja, Marek, Kenneth, Mok, Kin Ying, Kumar, Kishore, Levine, Kristin, Chahine, Lana M., Lange, Lara M., Pihlstrøm, Lasse, Screven, Laurel, Stefanis, Leonidas, Shulman, Lisa, Marsili, Luca, Parnetti, Lucilla, Kuhl, Maggie, Funayama, Manabu, Sharma, Manu, Tan, Manuela, Kauffman, Marcelo, Miranda, Marcelo, Bustamante, Maria Leonor, Stamelou, Maria, Periñan Tocino, Maria Teresa, Cornejo-Olivas, Mario, Jimenez del Rio, Marlene, Makarious, Mary B, Koretsky, Mathew, Rodriguez-Violante, Mayela, Ellis, Melina, Avenali, Micol, Rentería, Miguel E., Inca-Martines, Miguel z, Nalls, Mike A., Ibrahim Norlinah, Mohamed, Umair, Muhammad, Ip, Nancy, Louie, Naomi, Cheung, Nelson Yuk-Fai, Mencacci, Niccolò Emanuele, Wood, Nicholas, Williams, Nigel, Hattori, Nobutaka, Abdul Murad, Nor Azian, Ibrahim, Norlinah Mohamed, Monchi, Oury, Öztop Çakmak, Özgür, Öztop Çakmak, Patricio Öztop Çakmak, Lewis, Patrick Alfryn, Pastor, Pau, Reyes-Pérez, Paula, Saffie Awad, Paula, Chana, Pedro, Chan, Phillip, Kung, Pin-Jui, Chan, Piu, Pal, Pramod, Lingappa Kukkle, Prashanth, Ojha, Rajeev, Kaiyrzhanov, Rauan, Krüger, Rejko, Amouri, Rim, Weil, Rimona, Rajan, Roopa, Alcalay, Roy, Wu, Ruey-Meei, Borgohain, Rupam, Sassi, Samia Ben, Khachatryan, Samson, Bandres-Ciga, Sara, El-Sadig, Sarah, Wu, Serena, Groppa, Sergio, Azmin, Shahrul, Lim, Shen-Yang, Ur-Rehman, Shoaib, Ertan, Sibel, Stott, Simon, Jasaitye, Simona, Chowdhury, Sohini, Dumanis, Sonya, Bardien, Soraya, Lubbe, Steven, Koks, Sulev, Dey, Sumit, Foroud, Tatiana, Fon, Ted, Beach, Thomas, Gasser, Thomas, Anderson, Tim, Nguyen, Toan, Schirinzi, Tommaso, Shiraishi, Tomotaka, Pitcher, Toni, Tumas, Vitor, Mohamed, Wael, Kamel, Walaa A., Luo, Wei, Zhou, Xiaopu, Zewde, Yared Z., Song, Yeajin, Wen, Yi, Wu, Yihru, Joong Kim, Yun, Tavadyan, Zaruhi, Fang, Zih-Hua, Ojo, Oluwadamilola Omolara, Crea, Peter Wild, Abiodun, Oladunni Victoria, Levine, Kristin S, Abubakar, Sani Atta, Achoru, Charles Obiora, Adeniji, Olaleye Akinmola, Agabi, Osigwe Paul, Koretsky, Mathew J, Hall, Deborah A, Akinyemi, Rufus Olusola, Ali, Mohammed Wulgo, Shamim, Ejaz A, Padmanaban, Mahesh, Arigbodi, Ohwotemu Michael, Standaert, David G, Bello, Abiodun Hamzat, Dean, Marissa N, Erameh, Cyril Oshomah, Farombi, Temitope Hannah, Fawale, Michael Bimbola, Billingsley, Kimberley J, Imarhiagbe, Frank Aiwansoba, Jerez, Pilar Alvarez, Iwuozo, Emmanuel Uzodinma, Baker, Breeana, Komolafe, Morenikeji Adeyoyin, Malik, Laksh, Nwani, Paul Osemeke, Daida, Kensuke, Nwazor, Ernest Okwundu, Miano-Burkhardt, Abigail, Nyandaiti, Yakub Wilberforce, Obiabo, Yahaya Olugbo, Kluss, Jillian H, Odeniyi, Olanike Adedoyin, Hernandez, Dena G, Odiase, Francis Ehidiamen, Tayebi, Nahid, Ojini, Francis Ibe, Sidranksy, Ellen, Onwuegbuzie, Gerald Awele, D'Souza, Andrea M, Osaigbovo, Godwin Osawaru, Berhe, Bahafta, Reed, Xylena, Oshinaike, Olajumoke Olufemi, Leonard, Hampton L, Otubogun, Folajimi Morenikeji, Alvarado, Chelsea X, Oyakhire, Shyngle Imiewan, Ozomma, Simon Izuchukwu, Samuel, Sarah Chabiri, Taiwo, Funmilola Tolulope, Wahab, Kolawole Wasiu, Zubair, Yusuf Agboola, Kim, Jonggeol Jeffrey, Nalls, Mike A, Heilbron, Karl, Norcliffe-Kaufmann, Lucy, and Okubadejo, Njideka Ulunma

Published
2023
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35. Air Pollutants and Risk of Parkinson's Disease among Women in the Sister Study

Author

Cao, Zichun, Yuan, Yaqun, White, Alexandra J., Li, Chenxi, Luo, Zhehui, D'Aloisio, Aimee A., Huang, Xuemei, Kaufman, Joel D., Sandler, Dale P., and Chen, Honglei

Subjects
Exercise, Nitrogen dioxide, Pollutants, Medical research, Medicine, Experimental, Caffeine, Air pollution, Environmental issues, Health, World Health Organization
Abstract

Background: Air pollutants may contribute to the development of Parkinson's disease (PD), but empirical evidence is limited and inconsistent. Objectives: This study aimed to prospectively investigate the associations of PD with ambient exposures to fine particulate matter with aerodynamic diameter [less than or equal to]2.5 [micro]m (P[M.sub.2.5]) and nitrogen dioxide (N[O.sub.2]). Methods: We analyzed data from 47,108 US women from the Sister Study, enrolled from 2003-2009 (35-80 years of age) and followed through 2018. Exposures of interest included address-level ambient P[M.sub.2.5] and N[O.sub.2] in 2009 and their cumulative averages from 2009 to PD diagnosis with varying lag-years. The primary outcome was PD diagnosis between 2009 and 2018 (n = 163). We used multivariable Cox proportional hazards and time-varying Cox models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: N[O.sub.2] exposure in 2009 was associated with PD risk in a dose- response manner. The HR and 95% CI were 1.22 (95% CI: 1.03, 1.46) for one interquartile [4.8 parts per billion (ppb)] increment in N[O.sub.2], adjusting for age, race and ethnicity, education, smoking status, alcohol drinking, caffeine intake, body mass index, physical activity, census region, residential area type, area deprivation index (ADI), and self-reported health status. The association was confirmed in secondary analyses with time-varying averaged cumulative exposures. For example, the multivariable adjusted HR for PD per 4.8 ppb increment in N[O.sub.2] was 1.25 (95% CI: 1.05, 1.50) in the 2-year lag analysis using cumulative average exposure. Post hoc subgroup analyses overall confirmed the association. However, statistical interaction analyses found that the positive association of N[O.sub.2] with PD risk was limited to women in urban, rural, and small town areas and women with [greater than or equal to]50th percentile ADI but not among women from suburban areas or areas with Discussion: In this nationwide cohort of US women, higher level exposure to ambient N[O.sub.2] is associated with a greater risk of PD. This finding needs to be independently confirmed and the underlying mechanisms warrant further investigation. https://doi.org/10.1289/EHP13009, Introduction Idiopathic Parkinson's disease (PD) is the second-most prevalent neurodegenerative disease in the US, primarily affecting adults 65 years of age or older. (1) Despite an increasing disease burden due [...]

Published
2024
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39. Citizen scientists track a charismatic carnivore : Mapping the spread and impact of the South African Mantis (Miomantidae, Miomantis caffra ) in Australia

Author

Connors, Matthew G., Chen, Honglei, Li, Haokun, Edmonds, Adam, Smith, Kimberley A., Gell, Colin, Clitheroe, Kelly, Miller, Ishbel Morag, Walker, Kenneth L., Nunn, Jack S., Nguyen, Linh, Quinane, Luke N., Andreoli, Chiara M., Galea, Jason A., Quan, Brendon, Sandiford, Katrina, Wallis, Brendan, Anderson, Matthew L., Canziani, Elizabeth Valeria, Craven, Jade, Hakim, Roi R. C., Lowther, Rod, Maneylaws, Cindy, Menz, Bastian A., Newman, John, Perkins, Harvey D., Smith, Alistair R., Webber, Vanessa H., and Wishart, Dylan

Published
2022

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