Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (85)

Search Parameters:
Authors = C. Qian

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
13 pages, 391 KiB  
Article
The Use of RE-AIM to Evaluate a Pharmacist-Led Transitions of Care Service for Multivisit Patients at a Regional Hospital
by Courtney E. Gamston, Salisa C. Westrick, Mafe Zmajevac, Jingjing Qian, Greg Peden, Dillon Hagan and Kimberly Braxton Lloyd
Pharmacy 2025, 13(4), 99; https://doi.org/10.3390/pharmacy13040099 - 23 Jul 2025
Viewed by 213
Abstract
Pharmacist-led transitions of care (TOC) services decrease preventable hospital readmission. TOC service implementation assessment can inform translation to real-world settings. The purpose of this study was to evaluate the implementation of a TOC service for patients with multiple admissions at a regional hospital [...] Read more.
Pharmacist-led transitions of care (TOC) services decrease preventable hospital readmission. TOC service implementation assessment can inform translation to real-world settings. The purpose of this study was to evaluate the implementation of a TOC service for patients with multiple admissions at a regional hospital using the RE-AIM framework. In this quasi-experimental, non-randomized study, individuals with ≥2 recent hospitalizations received pharmacist-led discharge medication reconciliation and counseling, management of drug-related problems, post-discharge telephonic visits, and social support. The reach, effectiveness, implementation, and maintenance RE-AIM dimensions were assessed using patient and service records. Outcomes included 30-day readmission rates for individuals completing ≥1 outpatient pharmacist visit (intervention) versus those unreachable in the outpatient setting (comparison), completed interventions, implementation features, and service adaptations. Chi-square and Fisher’s exact tests were used for comparison of categorical variables and the t-test was used for continuous variables. From February 2022 to August 2023, 72.7% of the 66 service participants participated in the intervention (reach). Additionally, 30-day readmission was 22.9% (intervention) versus 55.6% (comparison; p = 0.01). In total, 2279 interventions were documented (effectiveness). The service was adapted (implementation) and expanded to include additional populations (maintenance) to enhance sustainability. Based on RE-AIM evaluation, the pharmacist-led TOC intervention appears to be a sustainable solution for addressing readmission in multivisit patients. Full article
(This article belongs to the Section Pharmacy Practice and Practice-Based Research)
Show Figures

Figure 1

20 pages, 2843 KiB  
Review
Neural Mechanisms and Alterations of Sweet Sensing: Insights from Functional Magnetic Resonance Imaging Studies
by Tobias Long, Colette C. Milbourn, Alison Smith, Kyaw Linn Su Khin, Amanda J. Page, Iskandar Idris, Qian Yang, Richard L. Young and Sally Eldeghaidy
Life 2025, 15(7), 1075; https://doi.org/10.3390/life15071075 - 5 Jul 2025
Viewed by 718
Abstract
Sweet sensing is a fundamental sensory experience that plays a critical role not only in food preference, reward and dietary behaviour but also in glucose metabolism. Sweet taste receptors (STRs), composed of a heterodimer of taste receptor type 1 member 2 (T1R2) and [...] Read more.
Sweet sensing is a fundamental sensory experience that plays a critical role not only in food preference, reward and dietary behaviour but also in glucose metabolism. Sweet taste receptors (STRs), composed of a heterodimer of taste receptor type 1 member 2 (T1R2) and member 3 (T1R3), are now recognised as being widely distributed throughout the body, including the gastrointestinal tract. Preclinical studies suggest these receptors are central to nutrient and glucose sensing, detecting energy availability and triggering metabolic and behavioural responses to maintain energy balance. Both internal and external factors tightly regulate their signalling pathways, and dysfunction within these systems may contribute to the development of metabolic disorders such as obesity and type 2 diabetes (T2D). Functional magnetic resonance imaging (fMRI) has provided valuable insights into the neural mechanisms underlying sweet sensing by mapping brain responses to both lingual/oral and gastrointestinal sweet stimuli. This review highlights key findings from fMRI studies and explores how these neural responses are modulated by metabolic state and individual characteristics such as body mass index, habitual intake and metabolic health. By integrating current evidence, this review advances our understanding of the complex interplay between sweet sensing, brain responses, and health and identifies key gaps and directions for future research in nutritional neuroscience. Full article
(This article belongs to the Special Issue New Advances in Neuroimaging and Brain Functions: 2nd Edition)
Show Figures

Figure 1

14 pages, 4339 KiB  
Article
Discrimination of Smoke-Exposed Pinot Noir Wines by Volatile Phenols and Volatile Phenol-Glycosides
by Armando Alcazar-Magana, Ruiwen Yang, Michael C. Qian and Yanping L. Qian
Molecules 2025, 30(13), 2719; https://doi.org/10.3390/molecules30132719 - 24 Jun 2025
Viewed by 335
Abstract
This study investigated the correlation between five primary volatile phenols (VPs) and their glycosides in smoke-exposed and non-smoke-exposed Pinot noir wines to assess and identify potential markers for smoke taint. The results showed that all putative VP-glycosides in smoke-exposed wines were higher than [...] Read more.
This study investigated the correlation between five primary volatile phenols (VPs) and their glycosides in smoke-exposed and non-smoke-exposed Pinot noir wines to assess and identify potential markers for smoke taint. The results showed that all putative VP-glycosides in smoke-exposed wines were higher than in non-smoke-exposed wines, with a fold change ranging from 2.11 to 31.88 for the top fifteen differentiations. VP-glycosides showed strong positive correlations among themselves, with correlation coefficients of 0.94 for hexose-guaiacol vs. pentose (P)-hexose (H)-cresol and 0.92 for syringyl-β-D-glucopyranoside vs. H-P-4-methylguaiacol. VP-glycosides also showed relatively high correlations with free and strong acid-hydrolyzed VPs. The correlation coefficient between H-P-guaiacol and free-form guaiacol is 0.71, and between H-P-guaiacol and total guaiacol is 0.78. The strong correlation suggests that these compounds are interconnected and regulated by the severity of smoke exposure. Multivariate analysis effectively differentiated smoke-exposed wines from non-smoke-exposed ones. However, more research is needed to fill the gaps in understanding smoke-derived compounds. Full article
Show Figures

Figure 1

24 pages, 5289 KiB  
Article
Immunosuppressive Tumor Microenvironment of Osteosarcoma
by Aaron Michael Taylor, Jianting Sheng, Patrick Kwok Shing Ng, Jeffrey M. Harder, Parveen Kumar, Ju Young Ahn, Yuliang Cao, Alissa M. Dzis, Nathaniel L. Jillette, Andrew Goodspeed, Avery Bodlak, Qian Wu, Michael S. Isakoff, Joshy George, Jessica D. S. Grassmann, Diane Luo, William F. Flynn, Elise T. Courtois, Paul Robson, Masanori Hayashi, Alini Trujillo Paolillo, Antonio Sergio Petrilli, Silvia Regina Caminada de Toledo, Fabiola Sara Balarezo, Adam D. Lindsay, Bang Hoang, Stephen T. C. Wong and Ching C. Lauadd Show full author list remove Hide full author list
Cancers 2025, 17(13), 2117; https://doi.org/10.3390/cancers17132117 - 24 Jun 2025
Viewed by 1162
Abstract
Background/Objectives: Osteosarcoma is the most common malignant bone tumor in children, characterized by a high degree of genomic instability, resulting in copy number alterations and genomic rearrangements without disease-defining recurrent mutations. Clinical trials based on molecular characterization have failed to find new effective [...] Read more.
Background/Objectives: Osteosarcoma is the most common malignant bone tumor in children, characterized by a high degree of genomic instability, resulting in copy number alterations and genomic rearrangements without disease-defining recurrent mutations. Clinical trials based on molecular characterization have failed to find new effective therapies or improve outcomes over the last 40 years. Methods: To better understand the immune microenvironment of osteosarcoma, we performed single-cell RNA sequencing on six tumor biopsy samples, combined with a previously published cohort of six samples. Additional osteosarcoma samples were profiled using spatial transcriptomics for the validation of discovered subtypes and to add spatial context. Results: Analysis revealed immunosuppressive cells, including myeloid-derived suppressor cells (MDSCs), regulatory and exhausted T cells, and LAMP3+ dendritic cells. Conclusions: Using cell–cell communication modeling, we identified robust interactions between MDSCs and other cells, leading to NF-κB upregulation and an immunosuppressive microenvironment, as well as interactions involving regulatory T cells and osteosarcoma cells that promoted tumor progression and a proangiogenic niche. Full article
(This article belongs to the Special Issue Feature Papers in Section "Tumor Microenvironment")
Show Figures

Figure 1

20 pages, 4805 KiB  
Article
A Novel Primary Cell Line Model of Localized Prostate Cancer and Radioresistance—A Role for Nicotinamide N-Methyltransferase
by Jessica A. Wright, Stephanie D. White, Gavin Frame, Ana Bosiljkov, Shahbaz Khan, Roni Haas, Qian Yang, Minzhi Sheng, Xiaoyong Huang, Geoff S. Higgins, Ian Mills, Michelle R. Downes, Danny Vesprini, Hans T. Chung, Robert A. Screaton, Hon S. Leong, Paul C. Boutros, Thomas Kislinger and Stanley K. Liu
Cells 2025, 14(11), 819; https://doi.org/10.3390/cells14110819 - 31 May 2025
Cited by 1 | Viewed by 1125
Abstract
Prostate cancer cell lines are particularly clinically homogenous, mostly representing metastatic states rather than localized disease. While there has been significant work in the development of additional models, few have been created without oncogenic transformation. We derived a primary prostate cancer cell line [...] Read more.
Prostate cancer cell lines are particularly clinically homogenous, mostly representing metastatic states rather than localized disease. While there has been significant work in the development of additional models, few have been created without oncogenic transformation. We derived a primary prostate cancer cell line from a patient with localized Gleason 7 prostate cancer—designated CaB34—which spontaneously immortalized. We leveraged CaB34 to generate a paired radioresistant subline, CaB34-CF, using a clinically relevant fractionated radiotherapy schedule. These two paired cell lines were investigated extensively to determine their molecular characteristics and therapy responses. Both CaB34 and CaB34-CF express prostate-specific markers, including KRT18, NKX3.1, and AMACR. Multi-omic analyses using RNAseq and shotgun proteomics identified NNMT as the most significantly dysregulated component in CaB34-CF. A bioinformatic analysis determined that NNMT was more abundant within prostate tumors compared to benign prostate, suggesting a role in tumor progression. Knockdown studies of NNMT demonstrated significant radiosensitization of CaB34-CF cells, which was largely a result of increased radiation-induced cellular senescence. Growth as 3D organoids was significantly higher in the CaB34-CF line, and demonstrated a less structured pattern of expression of cytokeratin markers. Radiosensitization with NNMT siRNA was recapitulated in a 3D organoid clonogenic assay in CaB34-CF cells. Our research provides a paired primary model of treatment-naïve and radioresistant disease to address mechanisms of therapy resistance, while expanding the repertoire of localized prostate cancer cell lines for the research community. In addition, our findings present NNMT as a potential therapeutic target for sensitization of radioresistant disease. Full article
(This article belongs to the Special Issue Pathogenesis and Novel Therapies in Epithelial Cancers)
Show Figures

Figure 1

15 pages, 1035 KiB  
Article
Genomic Landscapes of Early-Onset Versus Average-Onset Colorectal Cancer Populations
by Michael H. Storandt, Qian Shi, Cathy Eng, Christopher Lieu, Thomas George, Melissa C. Stoppler, Elizabeth Mauer, Binyam Yilma, Stamatina Fragkogianni, Emily A. Teslow, Amit Mahipal and Zhaohui Jin
Cancers 2025, 17(5), 836; https://doi.org/10.3390/cancers17050836 - 28 Feb 2025
Viewed by 1164
Abstract
Background: Rates of early-onset colorectal cancer (eoCRC), defined as disease diagnosed at <50 years of age, are increasing. The incidence and spectrum of somatic and pathogenic germline variants (PGV) in this population are not well understood. Methods: This cross-sectional study leveraged Tempus’ clinicogenomic [...] Read more.
Background: Rates of early-onset colorectal cancer (eoCRC), defined as disease diagnosed at <50 years of age, are increasing. The incidence and spectrum of somatic and pathogenic germline variants (PGV) in this population are not well understood. Methods: This cross-sectional study leveraged Tempus’ clinicogenomic database, including de-identified records of patients diagnosed with CRC between 2000–2022, to analyze and compare eoCRC and average-onset colorectal cancer (aoCRC, disease diagnosed ≥50 years of age) patients. The frequency and spectrum of somatic mutations and PGVs in patients with eoCRC and aoCRC were evaluated and compared. Results: Among 11,006 participants in this study, 57% were male, 76% were white, and 80% had stage 4 disease. Within the total cohort, 2379 had eoCRC and 8627 had aoCRC. Among patients with eoCRC, 4.2% had a tumor with high microsatellite instability and/or deficient mismatch repair (MSI-H/dMMR) and 6.8% with aoCRC had an MSI-H/dMMR tumor (p < 0.001). The most frequent somatic mutations involved TP53, APC, and KRAS, with the most significant difference in BRAF, which was more frequently mutated in aoCRC (9.8% vs. 4.7%, p < 0.0001). In total, 1413 (59.4%) eoCRC and 4898 (56.8%) aoCRC patients had matched normal specimen (blood or saliva) sequencing and a PGV was identified in 6.9% of eoCRC and 5.0% of aoCRC patients. Conclusions: Somatic and germline mutation profiles were similar for eoCRC and aoCRC patients and may not adequately explain differences in tumor behavior and age of disease onset. Full article
(This article belongs to the Section Molecular Cancer Biology)
Show Figures

Figure 1

22 pages, 16205 KiB  
Article
Hyper Spectral Camera ANalyzer (HyperSCAN)
by Wen-Qian Chang, Hsun-Ya Hou, Pei-Yuan Li, Michael W. Shen, Cheng-Ling Kuo, Tang-Huang Lin, Loren C. Chang, Chi-Kuang Chao and Jann-Yenq Liu
Remote Sens. 2025, 17(5), 842; https://doi.org/10.3390/rs17050842 - 27 Feb 2025
Viewed by 1248
Abstract
HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular [...] Read more.
HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. Full article
Show Figures

Figure 1

14 pages, 1433 KiB  
Article
Impact of Systemic and Radiation Therapy on Survival of Primary Central Nervous System Lymphoma
by James Robert Janopaul-Naylor, Jimmy S. Patel, Manali Rupji, Kimberly Bojanowski Hoang, Neal Sean McCall, David C. Qian, Madison Lee Shoaf, Shawn Kothari, Jeffrey J. Olson, Hui-Kuo G. Shu, Alfredo Voloschin, Jim Zhong, Stewart G. Neill and Bree Eaton
Cancers 2025, 17(4), 618; https://doi.org/10.3390/cancers17040618 - 12 Feb 2025
Cited by 1 | Viewed by 1068
Abstract
Introduction: Treatment for primary central nervous system lymphoma (PCNSL) includes high-dose methotrexate (HD-MTX)-based systemic therapy. Multiple regimens exist with no clear standard of care. We evaluated the impact of different therapies on PCNSL outcomes at a single institution. Materials and Methods: [...] Read more.
Introduction: Treatment for primary central nervous system lymphoma (PCNSL) includes high-dose methotrexate (HD-MTX)-based systemic therapy. Multiple regimens exist with no clear standard of care. We evaluated the impact of different therapies on PCNSL outcomes at a single institution. Materials and Methods: A total of 95 consecutive patients with PCNSL from 2002 to 2021 were retrospectively reviewed. The overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan–Meier method. The log-rank test and univariate and multivariable Cox regression analysis were used to evaluate the relationship between clinicopathologic and treatment variables with outcomes. Results: Among the 62 patients treated with definitive systemic therapy, the median age was 58; 71% had a Karnofsky performance status > 70, 49% had a single lesion, 31% received HD-MTX alone, and 61% had HD-MTX + rituximab. The two-year OS and PFS were 64% (95% CI: 49.8–75.0%) and 49% (95% CI: 35.0–60.9%), respectively. On multivariable analysis, the completion of > six cycles of HD-MTX (HR 0.40; 95% CI: 0.21–0.76; p = 0.01) was associated with superior OS, while the use of rituximab was associated with inferior OS (HR 2.82; 95% CI: 1.37–5.83; p = 0.01). There were no significant associations between the OS and PFS with temozolomide, the extent of surgical resection, radiation, or the size or number of initial lesions (all p > 0.05). Discussion: Innovation is needed to improve the outcomes for patients with PCNSL. Full article
(This article belongs to the Section Cancer Therapy)
Show Figures

Figure 1

18 pages, 2550 KiB  
Article
A Strategy for Simultaneous Engineering of Interspecies Cross-Reactivity, Thermostability, and Expression of a Bispecific 5T4 x CD3 DART® Molecule for Treatment of Solid Tumors
by Renhua R. Huang, Michael Spliedt, Tom Kaufman, Sergey Gorlatov, Bhaswati Barat, Kalpana Shah, Jeffrey Gill, Kurt Stahl, Jennifer DiChiara, Qian Wang, Jonathan C. Li, Ralph Alderson, Paul A. Moore, Jennifer G. Brown, James Tamura, Xiaoyu Zhang, Ezio Bonvini and Gundo Diedrich
Antibodies 2025, 14(1), 7; https://doi.org/10.3390/antib14010007 - 17 Jan 2025
Viewed by 2051
Abstract
Background: Bispecific antibodies represent a promising class of biologics for cancer treatment. However, their dual specificity and complex structure pose challenges in the engineering process, often resulting in molecules with good functional but poor physicochemical properties. Method: To overcome limitations in the properties [...] Read more.
Background: Bispecific antibodies represent a promising class of biologics for cancer treatment. However, their dual specificity and complex structure pose challenges in the engineering process, often resulting in molecules with good functional but poor physicochemical properties. Method: To overcome limitations in the properties of an anti-5T4 x anti-CD3 (α5T4 x αCD3) DART molecule, a phage-display method was developed, which succeeded in simultaneously engineering cross-reactivity to the cynomolgus 5T4 ortholog, improving thermostability and the elevating expression level. Results: This approach generated multiple DART molecules that exhibited significant improvements in all three properties. The lead DART molecule demonstrated potent in vitro and in vivo anti-tumor activity. Although its clearance in human FcRn-transgenic mice was comparable to that of the parental molecule, faster clearance was observed in cynomolgus monkeys. The lead α5T4 x αCD3 DART molecule displayed no evidence of off-target binding or polyspecificity, suggesting that the increased affinity for the target may account for its accelerated clearance in cynomolgus monkeys. Conclusions: This may reflect target-mediated drug disposition (TMDD), a potential limitation of targeting 5T4, despite its limited expression in healthy tissues. Full article
(This article belongs to the Section Antibody Discovery and Engineering)
Show Figures

Figure 1

16 pages, 6966 KiB  
Article
An Immunocytochemistry Method to Investigate the Translationally Active HIV Reservoir
by Guoxin Wu, Samuel H. Keller, Ryan T. Walters, Yuan Li, Jan Kristoff, Brian C. Magliaro, Paul Zuck, Tracy L. Diamond, Jill W. Maxwell, Carol Cheney, Qian Huang, Carl J. Balibar, Thomas Rush, Bonnie J. Howell and Luca Sardo
Int. J. Mol. Sci. 2025, 26(2), 682; https://doi.org/10.3390/ijms26020682 - 15 Jan 2025
Viewed by 1729
Abstract
Despite the success of combination antiretroviral therapy (cART) to suppress HIV replication, HIV persists in a long-lived reservoir that can give rise to rebounding viremia upon cART cessation. The translationally active reservoir consists of HIV-infected cells that continue to produce viral proteins even [...] Read more.
Despite the success of combination antiretroviral therapy (cART) to suppress HIV replication, HIV persists in a long-lived reservoir that can give rise to rebounding viremia upon cART cessation. The translationally active reservoir consists of HIV-infected cells that continue to produce viral proteins even in the presence of cART. These active reservoir cells are implicated in the resultant viremia upon cART cessation and likely contribute to chronic immune activation in people living with HIV (PLWH) on cART. Methodologies to quantify the active reservoir are needed. Here, an automated immunocytochemistry (ICC) assay coupled with computational image analysis to detect and quantify intracellular Gag capsid protein (CA) is described (CA-ICC). For this purpose, fixed cells were deposited on microscopy slides by the cytospin technique and stained with antibodies against CA by an automated stainer, followed by slide digitization. Nuclear staining was used to count the number of cells in the specimen, and the chromogenic signal was quantified to determine the percentage of CA-positive cells. In comparative analyses, digital ELISA, qPCR, and flow cytometry were used to validate CA-ICC. The specificity and sensitivity of CA-ICC were assessed by staining a cell line that expresses CA (MOLT IIIB) alongside a control cell line (Jurkat) devoid of this marker, as well as peripheral blood mononuclear cells (PBMCs) from HIV seronegative donors before or after ex vivo infection with an HIV laboratory strain. The sensitivity of CA-ICC was further assayed by spiking MOLT IIIB cells into uninfected Jurkat cells in limiting dilutions. In those analyses, CA-ICC could detect down to 10 CA-positive cells per million with a sensitivity superior to flow cytometry. To demonstrate the application of CA-ICC in pre-clinical research, bulk PBMCs obtained from mouse and non-human primate animal models were stained to detect HIV CA and SIV p27, respectively. The level of intracellular CA quantified by CA-ICC in PBMCs obtained from animal models was associated with plasma viral loads and cell-associated CA measured by qPCR and ELISA, respectively. The application of CA-ICC to evaluate the activity of small-molecule targeted activator of cell-kill (TACK) in clinical specimens is presented. Overall, CA-ICC offers a simple imaging method for specific and sensitive detection of CA-positive cells in bulk cell preparations. Full article
Show Figures

Figure 1

17 pages, 2343 KiB  
Review
Artificial Intelligence Transforming Post-Translational Modification Research
by Doo Nam Kim, Tianzhixi Yin, Tong Zhang, Alexandria K. Im, John R. Cort, Jordan C. Rozum, David Pollock, Wei-Jun Qian and Song Feng
Bioengineering 2025, 12(1), 26; https://doi.org/10.3390/bioengineering12010026 - 31 Dec 2024
Cited by 3 | Viewed by 3532
Abstract
Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In [...] Read more.
Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering. Full article
Show Figures

Figure 1

11 pages, 681 KiB  
Systematic Review
Genetics and Genomics of Gastroschisis, Elucidating a Potential Genetic Etiology for the Most Common Abdominal Defect: A Systematic Review
by John P. Marquart, Qian Nie, Tessa Gonzalez, Angie C. Jelin, Ulrich Broeckel, Amy J. Wagner and Honey V. Reddi
J. Dev. Biol. 2024, 12(4), 34; https://doi.org/10.3390/jdb12040034 - 19 Dec 2024
Cited by 1 | Viewed by 2193
Abstract
(1) Background: The exact etiology for gastroschisis, the most common abdominal defect, is yet to be known, despite the rising prevalence of this condition. The leading theory suggests an increased familial risk, indicating a possible genetic component possibly in the context of environmental [...] Read more.
(1) Background: The exact etiology for gastroschisis, the most common abdominal defect, is yet to be known, despite the rising prevalence of this condition. The leading theory suggests an increased familial risk, indicating a possible genetic component possibly in the context of environmental risk factors. This systematic review aims to summarize the studies focused on the identification of a potential genetic etiology for gastroschisis to elucidate the status of the field. (2) Methods: Following the PRISMA-ScR method, Pubmed and Google Scholar were searched, and eligible publications were mined for key data fields such as study aims, cohort demographics, technologies used, and outcomes in terms of genes identified. Data from 14 human studies, with varied cohort sizes from 40 to 1966 individuals for patient vs. healthy controls, respectively, were mined to delineate the technologies evaluated. (3) Results: Our results continue the theory that gastroschisis is likely caused by gene–environment interactions. The 14 studies utilized traditional methodologies that may not be adequate to identify genetic involvement in gastroschisis. (4) Conclusions: The etiology of gastroschisis continues to remain elusive. A combination of omics and epigenetic evaluation studies would help delineate a possible genetic etiology for gastroschisis. Full article
Show Figures

Figure 1

12 pages, 1648 KiB  
Article
How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?
by Aniket A. Saoji, Bilal A. Sheikh, Natasha J. Bertsch, Kayla R. Goulson, Madison K. Graham, Elizabeth A. McDonald, Abigail E. Bross, Jonathan M. Vaisberg, Volker Kühnel, Solveig C. Voss, Jinyu Qian, Cynthia H. Hogan and Melissa D. DeJong
Audiol. Res. 2024, 14(6), 1114-1125; https://doi.org/10.3390/audiolres14060092 - 13 Dec 2024
Cited by 1 | Viewed by 2506
Abstract
Background/Objectives: Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep [...] Read more.
Background/Objectives: Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation. Methods: Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker. Sentence recognition scores were measured for 10 hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility. Results: The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. Thus, DNN-based noise reduction can improve speech perception in noisy environments, potentially reducing the need for cochlear implants in some cases. The phase inversion method showed a 4–5 dB SNR improvement for the DNN-based noise reduction program compared to the other two programs. HASPI v2 predicted slightly better speech intelligibility than was measured in this study. Conclusions: DNN-based noise reduction might make it difficult for some patients with significant residual hearing to qualify for cochlear implantation, potentially delaying its adoption or eliminating the need for it entirely. Full article
Show Figures

Figure 1

16 pages, 4203 KiB  
Article
HC-HA/PTX3 from Human Amniotic Membrane Induced Differential Gene Expressions in DRG Neurons: Insights into the Modulation of Pain
by Shao-Qiu He, Chi Zhang, Xue-Wei Wang, Qian Huang, Jing Liu, Qing Lin, Hua He, Da-Zhi Yang, Scheffer C. Tseng and Yun Guan
Cells 2024, 13(22), 1887; https://doi.org/10.3390/cells13221887 - 15 Nov 2024
Viewed by 1510
Abstract
Background: The biologics derived from human amniotic membranes (AMs) demonstrate potential pain-inhibitory effects in clinical settings. However, the molecular basis underlying this therapeutic effect remains elusive. HC-HA/PTX3 is a unique water-soluble regenerative matrix that is purified from human AMs. We examined whether HC-HA/PTX3 [...] Read more.
Background: The biologics derived from human amniotic membranes (AMs) demonstrate potential pain-inhibitory effects in clinical settings. However, the molecular basis underlying this therapeutic effect remains elusive. HC-HA/PTX3 is a unique water-soluble regenerative matrix that is purified from human AMs. We examined whether HC-HA/PTX3 can modulate the gene networks and transcriptional signatures in the dorsal root ganglia (DRG) neurons transmitting peripheral sensory inputs to the spinal cord. Methods: We conducted bulk RNA-sequencing (RNA-seq) of mouse DRG neurons after treating them with HC-HA/PTX3 (15 µg/mL) for 10 min and 24 h in culture. Differential gene expression analysis was performed using the limma package, and Gene Ontology (GO) and protein–protein interaction (PPI) analyses were conducted to identify the networks of pain-related genes. Western blotting and in vitro calcium imaging were used to examine the protein levels and signaling of pro-opiomelanocortin (POMC) in DRG neurons. Results: Compared to the vehicle-treated group, 24 h treatment with HC-HA/PTX3 induced 2047 differentially expressed genes (DEGs), which were centered on the ATPase activity, receptor–ligand activity, and extracellular matrix pathways. Importantly, PPI analysis revealed that over 50 of these DEGs are closely related to pain and analgesia. Notably, HC-HA/PTX3 increased the expression and signaling pathway of POMC, which may affect opioid analgesia. Conclusions: HC-HA/PTX3 induced profound changes in the gene expression in DRG neurons, centered around various neurochemical mechanisms associated with pain modulation. Our findings suggest that HC-HA/PTX3 may be an important biological active component in human AMs that partly underlies its pain inhibitory effect, presenting a new strategy for pain treatment. Full article
(This article belongs to the Section Cells of the Nervous System)
Show Figures

Figure 1

45 pages, 30346 KiB  
Article
Performance of a Modular Ton-Scale Pixel-Readout Liquid Argon Time Projection Chamber
by A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, B. Aimard, F. Akbar, K. Allison, S. Alonso Monsalve, M. Alrashed, A. Alton, R. Alvarez, T. Alves, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos, M. Andreotti, M. P. Andrews, F. Andrianala, S. Andringa, N. Anfimov, A. Ankowski, M. Antoniassi, M. Antonova, A. Antoshkin, A. Aranda-Fernandez, L. Arellano, E. Arrieta Diaz, M. A. Arroyave, J. Asaadi, A. Ashkenazi, D. Asner, L. Asquith, E. Atkin, D. Auguste, A. Aurisano, V. Aushev, D. Autiero, F. Azfar, A. Back, H. Back, J. J. Back, I. Bagaturia, L. Bagby, N. Balashov, S. Balasubramanian, P. Baldi, W. Baldini, J. Baldonedo, B. Baller, B. Bambah, R. Banerjee, F. Barao, G. Barenboim, P. B̃arham Alzás, G. J. Barker, W. Barkhouse, G. Barr, J. Barranco Monarca, A. Barros, N. Barros, D. Barrow, J. L. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, C. Batchelor, L. Bathe-Peters, J. B. R. Battat, F. Battisti, F. Bay, M. C. Q. Bazetto, J. L. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, E. Belchior, G. Bell, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, N. Benekos, C. Benitez Montiel, D. Benjamin, F. Bento Neves, J. Berger, S. Berkman, J. Bernal, P. Bernardini, A. Bersani, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, A. Bevan, Y. Bezawada, A. T. Bezerra, T. J. Bezerra, A. Bhat, V. Bhatnagar, J. Bhatt, M. Bhattacharjee, M. Bhattacharya, S. Bhuller, B. Bhuyan, S. Biagi, J. Bian, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. D. Blaszczyk, G. C. Blazey, E. Blucher, J. Bogenschuetz, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, C. Bonilla-Diaz, F. Bonini, A. Booth, F. Boran, S. Bordoni, R. Borges Merlo, A. Borkum, N. Bostan, J. Bracinik, D. Braga, B. Brahma, D. Brailsford, F. Bramati, A. Branca, A. Brandt, J. Bremer, C. Brew, S. J. Brice, V. Brio, C. Brizzolari, C. Bromberg, J. Brooke, A. Bross, G. Brunetti, M. Brunetti, N. Buchanan, H. Budd, J. Buergi, D. Burgardt, S. Butchart, G. Caceres V., I. Cagnoli, T. Cai, R. Calabrese, J. Calcutt, M. Calin, L. Calivers, E. Calvo, A. Caminata, A. F. Camino, W. Campanelli, A. Campani, A. Campos Benitez, N. Canci, J. Capó, I. Caracas, D. Caratelli, D. Carber, J. M. Carceller, G. Carini, B. Carlus, M. F. Carneiro, P. Carniti, I. Caro Terrazas, H. Carranza, N. Carrara, L. Carroll, T. Carroll, A. Carter, E. Casarejos, D. Casazza, J. F. Castaño Forero, F. A. Castaño, A. Castillo, C. Castromonte, E. Catano-Mur, C. Cattadori, F. Cavalier, F. Cavanna, S. Centro, G. Cerati, C. Cerna, A. Cervelli, A. Cervera Villanueva, K. Chakraborty, S. Chakraborty, M. Chalifour, A. Chappell, N. Charitonidis, A. Chatterjee, H. Chen, M. Chen, W. C. Chen, Y. Chen, Z. Chen-Wishart, D. Cherdack, C. Chi, R. Chirco, N. Chitirasreemadam, K. Cho, S. Choate, D. Chokheli, P. S. Chong, B. Chowdhury, D. Christian, A. Chukanov, M. Chung, E. Church, M. F. Cicala, M. Cicerchia, V. Cicero, R. Ciolini, P. Clarke, G. Cline, T. E. Coan, A. G. Cocco, J. A. B. Coelho, A. Cohen, J. Collazo, J. Collot, E. Conley, J. M. Conrad, M. Convery, S. Copello, P. Cova, C. Cox, L. Cremaldi, L. Cremonesi, J. I. Crespo-Anadón, M. Crisler, E. Cristaldo, J. Crnkovic, G. Crone, R. Cross, A. Cudd, C. Cuesta, Y. Cui, F. Curciarello, D. Cussans, J. Dai, O. Dalager, R. Dallavalle, W. Dallaway, H. da Motta, Z. A. Dar, R. Darby, L. Da Silva Peres, Q. David, G. S. Davies, S. Davini, J. Dawson, R. De Aguiar, P. De Almeida, P. Debbins, I. De Bonis, M. P. Decowski, A. de Gouvêa, P. C. De Holanda, I. L. De Icaza Astiz, P. De Jong, P. Del Amo Sanchez, A. De la Torre, G. De Lauretis, A. Delbart, D. Delepine, M. Delgado, A. Dell’Acqua, G. Delle Monache, N. Delmonte, P. De Lurgio, R. Demario, G. De Matteis, J. R. T. de Mello Neto, D. M. DeMuth, S. Dennis, C. Densham, P. Denton, G. W. Deptuch, A. De Roeck, V. De Romeri, J. P. Detje, J. Devine, R. Dharmapalan, M. Dias, A. Diaz, J. S. Díaz, F. Díaz, F. Di Capua, A. Di Domenico, S. Di Domizio, S. Di Falco, L. Di Giulio, P. Ding, L. Di Noto, E. Diociaiuti, C. Distefano, R. Diurba, M. Diwan, Z. Djurcic, D. Doering, S. Dolan, F. Dolek, M. J. Dolinski, D. Domenici, L. Domine, S. Donati, Y. Donon, S. Doran, D. Douglas, T. A. Doyle, A. Dragone, F. Drielsma, L. Duarte, D. Duchesneau, K. Duffy, K. Dugas, P. Dunne, B. Dutta, H. Duyang, D. A. Dwyer, A. S. Dyshkant, S. Dytman, M. Eads, A. Earle, S. Edayath, D. Edmunds, J. Eisch, P. Englezos, A. Ereditato, T. Erjavec, C. O. Escobar, J. J. Evans, E. Ewart, A. C. Ezeribe, K. Fahey, L. Fajt, A. Falcone, M. Fani’, C. Farnese, S. Farrell, Y. Farzan, D. Fedoseev, J. Felix, Y. Feng, E. Fernandez-Martinez, G. Ferry, L. Fields, P. Filip, A. Filkins, F. Filthaut, R. Fine, G. Fiorillo, M. Fiorini, S. Fogarty, W. Foreman, J. Fowler, J. Franc, K. Francis, D. Franco, J. Franklin, J. Freeman, J. Fried, A. Friedland, S. Fuess, I. K. Furic, K. Furman, A. P. Furmanski, R. Gaba, A. Gabrielli, A. M. Gago, F. Galizzi, H. Gallagher, A. Gallas, N. Gallice, V. Galymov, E. Gamberini, T. Gamble, F. Ganacim, R. Gandhi, S. Ganguly, F. Gao, S. Gao, D. Garcia-Gamez, M. Á. García-Peris, F. Gardim, S. Gardiner, D. Gastler, A. Gauch, J. Gauvreau, P. Gauzzi, S. Gazzana, G. Ge, N. Geffroy, B. Gelli, S. Gent, L. Gerlach, Z. Ghorbani-Moghaddam, T. Giammaria, D. Gibin, I. Gil-Botella, S. Gilligan, A. Gioiosa, S. Giovannella, C. Girerd, A. K. Giri, C. Giugliano, V. Giusti, D. Gnani, O. Gogota, S. Gollapinni, K. Gollwitzer, R. A. Gomes, L. V. Gomez Bermeo, L. S. Gomez Fajardo, F. Gonnella, D. Gonzalez-Diaz, M. Gonzalez-Lopez, M. C. Goodman, S. Goswami, C. Gotti, J. Goudeau, E. Goudzovski, C. Grace, E. Gramellini, R. Gran, E. Granados, P. Granger, C. Grant, D. R. Gratieri, G. Grauso, P. Green, S. Greenberg, J. Greer, W. C. Griffith, F. T. Groetschla, K. Grzelak, L. Gu, W. Gu, V. Guarino, M. Guarise, R. Guenette, E. Guerard, M. Guerzoni, D. Guffanti, A. Guglielmi, B. Guo, Y. Guo, A. Gupta, V. Gupta, G. Gurung, D. Gutierrez, P. Guzowski, M. M. Guzzo, S. Gwon, A. Habig, H. Hadavand, L. Haegel, R. Haenni, L. Hagaman, A. Hahn, J. Haiston, J. Hakenmueller, T. Hamernik, P. Hamilton, J. Hancock, F. Happacher, D. A. Harris, J. Hartnell, T. Hartnett, J. Harton, T. Hasegawa, C. Hasnip, R. Hatcher, K. Hayrapetyan, J. Hays, E. Hazen, M. He, A. Heavey, K. M. Heeger, J. Heise, S. Henry, M. A. Hernandez Morquecho, K. Herner, V. Hewes, A. Higuera, C. Hilgenberg, S. J. Hillier, A. Himmel, E. Hinkle, L. R. Hirsch, J. Ho, J. Hoff, A. Holin, T. Holvey, E. Hoppe, S. Horiuchi, G. A. Horton-Smith, M. Hostert, T. Houdy, B. Howard, R. Howell, I. Hristova, M. S. Hronek, J. Huang, R. G. Huang, Z. Hulcher, M. Ibrahim, G. Iles, N. Ilic, A. M. Iliescu, R. Illingworth, G. Ingratta, A. Ioannisian, B. Irwin, L. Isenhower, M. Ismerio Oliveira, R. Itay, C. M. Jackson, V. Jain, E. James, W. Jang, B. Jargowsky, D. Jena, I. Jentz, X. Ji, C. Jiang, J. Jiang, L. Jiang, A. Jipa, F. R. Joaquim, W. Johnson, C. Jollet, B. Jones, R. Jones, D. José Fernández, N. Jovancevic, M. Judah, C. K. Jung, T. Junk, Y. Jwa, M. Kabirnezhad, A. C. Kaboth, I. Kadenko, I. Kakorin, A. Kalitkina, D. Kalra, M. Kandemir, D. M. Kaplan, G. Karagiorgi, G. Karaman, A. Karcher, Y. Karyotakis, S. Kasai, S. P. Kasetti, L. Kashur, I. Katsioulas, A. Kauther, N. Kazaryan, L. Ke, E. Kearns, P. T. Keener, K. J. Kelly, E. Kemp, O. Kemularia, Y. Kermaidic, W. Ketchum, S. H. Kettell, M. Khabibullin, N. Khan, A. Khvedelidze, D. Kim, J. Kim, M. Kim, B. King, B. Kirby, M. Kirby, A. Kish, J. Klein, J. Kleykamp, A. Klustova, T. Kobilarcik, L. Koch, K. Koehler, L. W. Koerner, D. H. Koh, L. Kolupaeva, D. Korablev, M. Kordosky, T. Kosc, U. Kose, V. A. Kostelecký, K. Kothekar, I. Kotler, M. Kovalcuk, V. Kozhukalov, W. Krah, R. Kralik, M. Kramer, L. Kreczko, F. Krennrich, I. Kreslo, T. Kroupova, S. Kubota, M. Kubu, Y. Kudenko, V. A. Kudryavtsev, G. Kufatty, S. Kuhlmann, J. Kumar, P. Kumar, S. Kumaran, P. Kunze, J. Kunzmann, R. Kuravi, N. Kurita, C. Kuruppu, V. Kus, T. Kutter, J. Kvasnicka, T. Labree, T. Lackey, A. Lambert, B. J. Land, C. E. Lane, N. Lane, K. Lang, T. Langford, M. Langstaff, F. Lanni, O. Lantwin, J. Larkin, P. Lasorak, D. Last, A. Laudrain, A. Laundrie, G. Laurenti, E. Lavaut, A. Lawrence, P. Laycock, I. Lazanu, M. Lazzaroni, T. Le, S. Leardini, J. Learned, T. LeCompte, C. Lee, V. Legin, G. Lehmann Miotto, R. Lehnert, M. A. Leigui de Oliveira, M. Leitner, D. Leon Silverio, L. M. Lepin, J.-Y. Li, S. W. Li, Y. Li, H. Liao, C. S. Lin, D. Lindebaum, S. Linden, R. A. Lineros, J. Ling, A. Lister, B. R. Littlejohn, H. Liu, J. Liu, Y. Liu, S. Lockwitz, M. Lokajicek, I. Lomidze, K. Long, T. V. Lopes, J. Lopez, I. López de Rego, N. López-March, T. Lord, J. M. LoSecco, W. C. Louis, A. Lozano Sanchez, X.-G. Lu, K. B. Luk, B. Lunday, X. Luo, E. Luppi, J. Maalmi, D. MacFarlane, A. A. Machado, P. Machado, C. T. Macias, J. R. Macier, M. MacMahon, A. Maddalena, A. Madera, P. Madigan, S. Magill, C. Magueur, K. Mahn, A. Maio, A. Major, K. Majumdar, M. Man, R. C. Mandujano, J. Maneira, S. Manly, A. Mann, K. Manolopoulos, M. Manrique Plata, S. Manthey Corchado, V. N. Manyam, M. Marchan, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani, J. Maricic, F. Marinho, A. D. Marino, T. Markiewicz, F. Das Chagas Marques, C. Marquet, D. Marsden, M. Marshak, C. M. Marshall, J. Marshall, L. Martina, J. Martín-Albo, N. Martinez, D. A. Martinez Caicedo, F. Martínez López, P. Martínez Miravé, S. Martynenko, V. Mascagna, C. Massari, A. Mastbaum, F. Matichard, S. Matsuno, G. Matteucci, J. Matthews, C. Mauger, N. Mauri, K. Mavrokoridis, I. Mawby, R. Mazza, A. Mazzacane, T. McAskill, N. McConkey, K. S. McFarland, C. McGrew, A. McNab, L. Meazza, V. C. N. Meddage, B. Mehta, P. Mehta, P. Melas, O. Mena, H. Mendez, P. Mendez, D. P. Méndez, A. Menegolli, G. Meng, A. C. E. A. Mercuri, A. Meregaglia, M. D. Messier, S. Metallo, J. Metcalf, W. Metcalf, M. Mewes, H. Meyer, T. Miao, A. Miccoli, G. Michna, V. Mikola, R. Milincic, F. Miller, G. Miller, W. Miller, O. Mineev, A. Minotti, L. Miralles, O. G. Miranda, C. Mironov, S. Miryala, S. Miscetti, C. S. Mishra, S. R. Mishra, A. Mislivec, M. Mitchell, D. Mladenov, I. Mocioiu, A. Mogan, N. Moggi, R. Mohanta, T. A. Mohayai, N. Mokhov, J. Molina, L. Molina Bueno, E. Montagna, A. Montanari, C. Montanari, D. Montanari, D. Montanino, L. M. Montaño Zetina, M. Mooney, A. F. Moor, Z. Moore, D. Moreno, O. Moreno-Palacios, L. Morescalchi, D. Moretti, R. Moretti, C. Morris, C. Mossey, M. Mote, C. A. Moura, G. Mouster, W. Mu, L. Mualem, J. Mueller, M. Muether, F. Muheim, A. Muir, M. Mulhearn, D. Munford, L. J. Munteanu, H. Muramatsu, J. Muraz, M. Murphy, T. Murphy, J. Muse, A. Mytilinaki, J. Nachtman, Y. Nagai, S. Nagu, R. Nandakumar, D. Naples, S. Narita, A. Nath, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, A. Nehm, J. K. Nelson, O. Neogi, J. Nesbit, M. Nessi, D. Newbold, M. Newcomer, R. Nichol, F. Nicolas-Arnaldos, A. Nikolica, J. Nikolov, E. Niner, K. Nishimura, A. Norman, A. Norrick, P. Novella, J. A. Nowak, M. Oberling, J. P. Ochoa-Ricoux, S. Oh, S. B. Oh, A. Olivier, A. Olshevskiy, T. Olson, Y. Onel, Y. Onishchuk, A. Oranday, M. Osbiston, J. A. Osorio Vélez, L. Otiniano Ormachea, J. Ott, L. Pagani, G. Palacio, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, C. Palomares, S. Pan, P. Panda, W. Panduro Vazquez, E. Pantic, V. Paolone, V. Papadimitriou, R. Papaleo, A. Papanestis, D. Papoulias, S. Paramesvaran, A. Paris, S. Parke, E. Parozzi, S. Parsa, Z. Parsa, S. Parveen, M. Parvu, D. Pasciuto, S. Pascoli, L. Pasqualini, J. Pasternak, C. Patrick, L. Patrizii, R. B. Patterson, T. Patzak, A. Paudel, L. Paulucci, Z. Pavlovic, G. Pawloski, D. Payne, V. Pec, E. Pedreschi, S. J. M. Peeters, W. Pellico, A. Pena Perez, E. Pennacchio, A. Penzo, O. L. G. Peres, Y. F. Perez Gonzalez, L. Pérez-Molina, C. Pernas, J. Perry, D. Pershey, G. Pessina, G. Petrillo, C. Petta, R. Petti, M. Pfaff, V. Pia, L. Pickering, F. Pietropaolo, V. L. Pimentel, G. Pinaroli, J. Pinchault, K. Pitts, K. Plows, R. Plunkett, C. Pollack, T. Pollman, D. Polo-Toledo, F. Pompa, X. Pons, N. Poonthottathil, V. Popov, F. Poppi, J. Porter, M. Potekhin, R. Potenza, J. Pozimski, M. Pozzato, T. Prakash, C. Pratt, M. Prest, F. Psihas, D. Pugnere, X. Qian, J. L. Raaf, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai, S. Rajagopalan, M. Rajaoalisoa, I. Rakhno, L. Rakotondravohitra, L. Ralte, M. A. Ramirez Delgado, B. Ramson, A. Rappoldi, G. Raselli, P. Ratoff, R. Ray, H. Razafinime, E. M. Rea, J. S. Real, B. Rebel, R. Rechenmacher, M. Reggiani-Guzzo, J. Reichenbacher, S. D. Reitzner, H. Rejeb Sfar, E. Renner, A. Renshaw, S. Rescia, F. Resnati, D. Restrepo, C. Reynolds, M. Ribas, S. Riboldi, C. Riccio, G. Riccobene, J. S. Ricol, M. Rigan, E. V. Rincón, A. Ritchie-Yates, S. Ritter, D. Rivera, R. Rivera, A. Robert, J. L. Rocabado Rocha, L. Rochester, M. Roda, P. Rodrigues, M. J. Rodriguez Alonso, J. Rodriguez Rondon, S. Rosauro-Alcaraz, P. Rosier, D. Ross, M. Rossella, M. Rossi, M. Ross-Lonergan, N. Roy, P. Roy, C. Rubbia, A. Ruggeri, G. Ruiz Ferreira, B. Russell, D. Ruterbories, A. Rybnikov, A. Saa-Hernandez, R. Saakyan, S. Sacerdoti, S. K. Sahoo, N. Sahu, P. Sala, N. Samios, O. Samoylov, M. C. Sanchez, A. Sánchez Bravo, P. Sanchez-Lucas, V. Sandberg, D. A. Sanders, S. Sanfilippo, D. Sankey, D. Santoro, N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, I. Sarra, G. Savage, V. Savinov, G. Scanavini, A. Scaramelli, A. Scarff, T. Schefke, H. Schellman, S. Schifano, P. Schlabach, D. Schmitz, A. W. Schneider, K. Scholberg, A. Schukraft, B. Schuld, A. Segade, E. Segreto, A. Selyunin, C. R. Senise, J. Sensenig, M. H. Shaevitz, P. Shanahan, P. Sharma, R. Kumar, K. Shaw, T. Shaw, K. Shchablo, J. Shen, C. Shepherd-Themistocleous, A. Sheshukov, W. Shi, S. Shin, S. Shivakoti, I. Shoemaker, D. Shooltz, R. Shrock, B. Siddi, M. Siden, J. Silber, L. Simard, J. Sinclair, G. Sinev, Jaydip Singh, J. Singh, L. Singh, P. Singh, V. Singh, S. Singh Chauhan, R. Sipos, C. Sironneau, G. Sirri, K. Siyeon, K. Skarpaas, J. Smedley, E. Smith, J. Smith, P. Smith, J. Smolik, M. Smy, M. Snape, E. L. Snider, P. Snopok, D. Snowden-Ifft, M. Soares Nunes, H. Sobel, M. Soderberg, S. Sokolov, C. J. Solano Salinas, S. Söldner-Rembold, S. R. Soleti, N. Solomey, V. Solovov, W. E. Sondheim, M. Sorel, A. Sotnikov, J. Soto-Oton, A. Sousa, K. Soustruznik, F. Spinella, J. Spitz, N. J. C. Spooner, K. Spurgeon, D. Stalder, M. Stancari, L. Stanco, J. Steenis, R. Stein, H. M. Steiner, A. F. Steklain Lisbôa, A. Stepanova, J. Stewart, B. Stillwell, J. Stock, F. Stocker, T. Stokes, M. Strait, T. Strauss, L. Strigari, A. Stuart, J. G. Suarez, J. Subash, A. Surdo, L. Suter, C. M. Sutera, K. Sutton, Y. Suvorov, R. Svoboda, S. K. Swain, B. Szczerbinska, A. M. Szelc, A. Sztuc, A. Taffara, N. Talukdar, J. Tamara, H. A. Tanaka, S. Tang, N. Taniuchi, A. M. Tapia Casanova, B. Tapia Oregui, A. Tapper, S. Tariq, E. Tarpara, E. Tatar, R. Tayloe, D. Tedeschi, A. M. Teklu, J. Tena Vidal, P. Tennessen, M. Tenti, K. Terao, F. Terranova, G. Testera, T. Thakore, A. Thea, A. Thiebault, S. Thomas, A. Thompson, C. Thorn, S. C. Timm, E. Tiras, V. Tishchenko, N. Todorović, L. Tomassetti, A. Tonazzo, D. Torbunov, M. Torti, M. Tortola, F. Tortorici, N. Tosi, D. Totani, M. Toups, C. Touramanis, D. Tran, R. Travaglini, J. Trevor, E. Triller, S. Trilov, J. Truchon, D. Truncali, W. H. Trzaska, Y. Tsai, Y.-T. Tsai, Z. Tsamalaidze, K. V. Tsang, N. Tsverava, S. Z. Tu, S. Tufanli, C. Tunnell, J. Turner, M. Tuzi, J. Tyler, E. Tyley, M. Tzanov, M. A. Uchida, J. Ureña González, J. Urheim, T. Usher, H. Utaegbulam, S. Uzunyan, M. R. Vagins, P. Vahle, S. Valder, G. A. Valdiviesso, E. Valencia, R. Valentim, Z. Vallari, E. Vallazza, J. W. F. Valle, R. Van Berg, R. G. Van de Water, D. V. Forero, A. Vannozzi, M. Van Nuland-Troost, F. Varanini, D. Vargas Oliva, S. Vasina, N. Vaughan, K. Vaziri, A. Vázquez-Ramos, J. Vega, S. Ventura, A. Verdugo, S. Vergani, M. Verzocchi, K. Vetter, M. Vicenzi, H. Vieira de Souza, C. Vignoli, C. Vilela, E. Villa, S. Viola, B. Viren, A. Vizcaya-Hernandez, T. Vrba, Q. Vuong, A. V. Waldron, M. Wallbank, J. Walsh, T. Walton, H. Wang, J. Wang, L. Wang, M. H. L. S. Wang, X. Wang, Y. Wang, K. Warburton, D. Warner, L. Warsame, M. O. Wascko, D. Waters, A. Watson, K. Wawrowska, A. Weber, C. M. Weber, M. Weber, H. Wei, A. Weinstein, H. Wenzel, S. Westerdale, M. Wetstein, K. Whalen, J. Whilhelmi, A. White, A. White, L. H. Whitehead, D. Whittington, M. J. Wilking, A. Wilkinson, C. Wilkinson, F. Wilson, R. J. Wilson, P. Winter, W. Wisniewski, J. Wolcott, J. Wolfs, T. Wongjirad, A. Wood, K. Wood, E. Worcester, M. Worcester, M. Wospakrik, K. Wresilo, C. Wret, S. Wu, W. Wu, W. Wu, M. Wurm, J. Wyenberg, Y. Xiao, I. Xiotidis, B. Yaeggy, N. Yahlali, E. Yandel, K. Yang, T. Yang, A. Yankelevich, N. Yershov, K. Yonehara, T. Young, B. Yu, H. Yu, J. Yu, Y. Yu, W. Yuan, R. Zaki, J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, O. Zapata, L. Zazueta, G. P. Zeller, J. Zennamo, K. Zeug, C. Zhang, S. Zhang, M. Zhao, E. Zhivun, E. D. Zimmerman, S. Zucchelli, J. Zuklin, V. Zutshi, R. Zwaska and on behalf of the DUNE Collaborationadd Show full author list remove Hide full author list
Instruments 2024, 8(3), 41; https://doi.org/10.3390/instruments8030041 - 11 Sep 2024
Cited by 4 | Viewed by 3777
Abstract
The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection [...] Read more.
The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations. Full article
Show Figures

Figure 1

Back to TopTop