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328 results on '"A. Guzman"'

1. Strain-balanced type-II superlattices for efficient multi-junction solar cells

Author

A. Gonzalo, A. D. Utrilla, D. F. Reyes, V. Braza, J. M. Llorens, D. Fuertes Marrón, B. Alén, T. Ben, D. González, A. Guzman, A. Hierro, and J. M. Ulloa

Subjects
Medicine, Science
Abstract

Abstract Multi-junction solar cells made by assembling semiconductor materials with different bandgap energies have hold the record conversion efficiencies for many years and are currently approaching 50%. Theoretical efficiency limits make use of optimum designs with the right lattice constant-bandgap energy combination, which requires a 1.0–1.15 eV material lattice-matched to GaAs/Ge. Nevertheless, the lack of suitable semiconductor materials is hindering the achievement of the predicted efficiencies, since the only candidates were up to now complex quaternary and quinary alloys with inherent epitaxial growth problems that degrade carrier dynamics. Here we show how the use of strain-balanced GaAsSb/GaAsN superlattices might solve this problem. We demonstrate that the spatial separation of Sb and N atoms avoids the ubiquitous growth problems and improves crystal quality. Moreover, these new structures allow for additional control of the effective bandgap through the period thickness and provide a type-II band alignment with long carrier lifetimes. All this leads to a strong enhancement of the external quantum efficiency under photovoltaic conditions with respect to bulk layers of equivalent thickness. Our results show that GaAsSb/GaAsN superlattices with short periods are the ideal (pseudo)material to be integrated in new GaAs/Ge-based multi-junction solar cells that could approach the theoretical efficiency limit.

Published
2017
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5. Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network

Author

Vassantachart, April, Cao, Yufeng, Gribble, Michael, Guzman, Samuel, Ye, Jason C, Hurth, Kyle, Mathew, Anna, Zada, Gabriel, Fan, Zhaoyang, Chang, Eric L, and Yang, Wensha

Subjects
Rare Diseases, Brain Disorders, Clinical Research, Brain Cancer, Cancer, Bioengineering, Child, Humans, Magnetic Resonance Imaging, Meningeal Neoplasms, Meningioma, Neural Networks, Computer, Retrospective Studies
Abstract

The Grade of meningioma has significant implications for selecting treatment regimens ranging from observation to surgical resection with adjuvant radiation. For most patients, meningiomas are diagnosed radiologically, and Grade is not determined unless a surgical procedure is performed. The goal of this study is to train a novel auto-classification network to determine Grade I and II meningiomas using T1-contrast enhancing (T1-CE) and T2-Fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. Ninety-six consecutive treatment naïve patients with pre-operative T1-CE and T2-FLAIR MR images and subsequent pathologically diagnosed intracranial meningiomas were evaluated. Delineation of meningiomas was completed on both MR images. A novel asymmetric 3D convolutional neural network (CNN) architecture was constructed with two encoding paths based on T1-CE and T2-FLAIR. Each path used the same 3 × 3 × 3 kernel with different filters to weigh the spatial features of each sequence separately. Final model performance was assessed by tenfold cross-validation. Of the 96 patients, 55 (57%) were pathologically classified as Grade I and 41 (43%) as Grade II meningiomas. Optimization of our model led to a filter weighting of 18:2 between the T1-CE and T2-FLAIR MR image paths. 86 (90%) patients were classified correctly, and 10 (10%) were misclassified based on their pre-operative MRs with a model sensitivity of 0.85 and specificity of 0.93. Among the misclassified, 4 were Grade I, and 6 were Grade II. The model is robust to tumor locations and sizes. A novel asymmetric CNN with two differently weighted encoding paths was developed for successful automated meningioma grade classification. Our model outperforms CNN using a single path for single or multimodal MR-based classification.

Published
2022

6. The impact of non-structural carbohydrates (NSC) concentration on yield in Prunus dulcis, Pistacia vera, and Juglans regia

Author

Zwieniecki, Maciej A, Davidson, Anna M, Orozco, Jessica, Cooper, Katelyn B, and Guzman-Delgado, Paula

Subjects
Agricultural, Veterinary and Food Sciences, Crop and Pasture Production, Carbohydrates, Hexoses, Juglans, Pistacia, Prunus dulcis, Seasons
Abstract

Successful yield in orchards is the culmination of a series of events that start with plants entering dormancy with adequate energy reserves (non-structural carbohydrates; NSC). These NSC are responsible for the maintenance of activities during dormancy and extending onto the period of activeness. Using multi-year yield information and monthly NSC content in twigs, we show that high levels of carbohydrate in Prunus dulcis, Pistachio vera, and Juglans regia during the winter months are indeed associated with high yield, while high levels of the NSC in late summer often correlate with low yield. An evaluation of monthly NSC level importance on yield revealed that for P. dulcis high levels in February were a good predictor of yield and that low levels throughout summer were associated with high yield. In P. vera, high levels of NSC in December were best predictors of yield. J. regia exhibited peculiar patterns; while high pre-budbreak reserves were associated with high yields they only played a minor role in explaining crop, the most important months for predicting yields were June and July. Results suggest that NSC levels can serve as good predictors of orchard yield potential and should be monitored to inform orchard management.

Published
2022

14. Circulating proteins to predict COVID-19 severity

Author

Su, Chen-Yang, Zhou, Sirui, Gonzalez-Kozlova, Edgar, Butler-Laporte, Guillaume, Brunet-Ratnasingham, Elsa, Nakanishi, Tomoko, Jeon, Wonseok, Morrison, David R., Laurent, Laetitia, Afilalo, Jonathan, Afilalo, Marc, Henry, Danielle, Chen, Yiheng, Carrasco-Zanini, Julia, Farjoun, Yossi, Pietzner, Maik, Kimchi, Nofar, Afrasiabi, Zaman, Rezk, Nardin, Bouab, Meriem, Petitjean, Louis, Guzman, Charlotte, Xue, Xiaoqing, Tselios, Chris, Vulesevic, Branka, Adeleye, Olumide, Abdullah, Tala, Almamlouk, Noor, Moussa, Yara, DeLuca, Chantal, Duggan, Naomi, Schurr, Erwin, Brassard, Nathalie, Durand, Madeleine, Del Valle, Diane Marie, Thompson, Ryan, Cedillo, Mario A., Schadt, Eric, Nie, Kai, Simons, Nicole W., Mouskas, Konstantinos, Zaki, Nicolas, Patel, Manishkumar, Xie, Hui, Harris, Jocelyn, Marvin, Robert, Cheng, Esther, Tuballes, Kevin, Argueta, Kimberly, Scott, Ieisha, Greenwood, Celia M. T., Paterson, Clare, Hinterberg, Michael A., Langenberg, Claudia, Forgetta, Vincenzo, Pineau, Joelle, Mooser, Vincent, Marron, Thomas, Beckmann, Noam D., Kim-schulze, Seunghee, Charney, Alexander W., Gnjatic, Sacha, Kaufmann, Daniel E., Merad, Miriam, and Richards, J. Brent

Published
2023
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15. Extracellular detection of neuronal coupling

Author

Guzman, Elmer, Cheng, Zhuowei, Hansma, Paul K, Tovar, Kenneth R, Petzold, Linda R, and Kosik, Kenneth S

Subjects
Biomedical and Clinical Sciences, Engineering, Biomedical Engineering, Information and Computing Sciences, Neurosciences, Machine Learning, Bioengineering, Neurological, Action Potentials, Algorithms, Animals, Animals, Newborn, Cells, Cultured, Electrophysiology, Extracellular Space, Male, Mice, Mice, Inbred C57BL, Microelectrodes, Neurons, Synapses, Synaptic Potentials
Abstract

We developed a method to non-invasively detect synaptic relationships among neurons from in vitro networks. Our method uses microelectrode arrays on which neurons are cultured and from which propagation of extracellular action potentials (eAPs) in single axons are recorded at multiple electrodes. Detecting eAP propagation bypasses ambiguity introduced by spike sorting. Our methods identify short latency spiking relationships between neurons with properties expected of synaptically coupled neurons, namely they were recapitulated by direct stimulation and were sensitive to changing the number of active synaptic sites. Our methods enabled us to assemble a functional subset of neuronal connectivity in our cultures.

Published
2021

16. Regional patterns and climatic predictors of viruses in honey bee (Apis mellifera) colonies over time.

Author

McAfee, Alison, Alavi-Shoushtari, Niloofar, Labuschagne, Renata, Tran, Lan, Common, Julia, Higo, Heather, Pernal, Stephen F., Giovenazzo, Pierre, Hoover, Shelley E., Guzman-Novoa, Ernesto, Currie, Robert W., Veiga, Patricia Wolf, French, Sarah K., Conflitti, Ida M., Pepinelli, Mateus, Borges, Daniel, Walsh, Elizabeth M., Bishop, Christine A., Zayed, Amro, and Duffe, Jason

Subjects
MEDICAL sciences, MEDICAL microbiology, WIND speed, BEES, QUEENS (Insects), HONEYBEES
Abstract

Honey bee viruses are serious pathogens that can cause poor colony health and productivity. We analyzed a multi-year longitudinal dataset of abundances of nine honey bee viruses (deformed wing virus A, deformed wing virus B, black queen cell virus, sacbrood virus, Lake Sinai virus, Kashmir bee virus, acute bee paralysis virus, chronic bee paralysis virus, and Israeli acute paralysis virus) in colonies located across Canada to describe broad trends in virus intensity and occurrence among regions and years. We also tested climatic variables (temperature, wind speed, and precipitation) as predictors in an effort to understand possible drivers underlying seasonal patterns in viral prevalence. Temperature was a significant positive predictor of the total number of viruses per sample, which was highest in British Columbia (mean = 5.0). Lake Sinai virus (LSV) was the most prevalent overall (at 89%) and had the highest infection intensity, at an average of 3.9 × 108 copies per bee. Acute bee paralysis virus was the least prevalent virus (at 4.7%) and had the lowest infection intensity (1.9 × 105 copies per bee). Surprisingly, including Varroa abundance as a covariate did not significantly improve model fit for any virus. All viruses, except Kashmir bee virus, varied by region, and one or more climatic variables were significant predictors for six of the nine viruses. Although climatic effects were often inconsistent among individual viruses, we show that climatic variables can be better predictors of virus intensity and occurrence than Varroa mite abundance, at least when infestation rates are low. [ABSTRACT FROM AUTHOR]

Published
2025
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40. A pioneer calf foetus microbiome

Author

Guzman, Cesar E., Wood, Jennifer L., Egidi, Eleonora, White-Monsant, Alison C., Semenec, Lucie, Grommen, Sylvia V. H., Hill-Yardin, Elisa L., De Groef, Bert, and Franks, Ashley E.

Published
2020
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