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Search Results (9)

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Authors = J. A. Gonzalez-Cuevas

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10 pages, 670 KiB  
Article
The Screening and Correlation of Trace Elements in the Blood and Urine of School-Aged Children (5–12 Years): A Pilot Biomonitoring Study
by Arlette A. Camacho-delaCruz, Oliver Mendoza-Cano, Xóchitl Trujillo, Miguel Huerta, Mónica Ríos-Silva, Irma Elizabeth Gonzalez-Curiel, Agustin Lugo-Radillo, María Fernanda Romo-García, Herguin Benjamin Cuevas-Arellano, Ángel Gabriel Hilerio-López, Ramón Solano-Barajas, Jaime Alberto Bricio-Barrios, Juan Manuel Uribe-Ramos, J. Francisco Ventura-Ramírez, Alma Alejandra Solano-Mendoza, Fernando Sánchez-Cárdenas, Verónica Benites-Godínez, Eder Fernando Ríos-Bracamontes, Jesús Venegas-Ramírez and Efrén Murillo-Zamora
Toxics 2025, 13(6), 431; https://doi.org/10.3390/toxics13060431 - 25 May 2025
Viewed by 1020
Abstract
Children constitute a population at risk from environmental exposure to trace elements. This study aimed to evaluate correlations between urinary and blood levels of multiple elements in school-aged children (5–12 years), assessing whether urine, a less invasive matrix, could complement or replace blood [...] Read more.
Children constitute a population at risk from environmental exposure to trace elements. This study aimed to evaluate correlations between urinary and blood levels of multiple elements in school-aged children (5–12 years), assessing whether urine, a less invasive matrix, could complement or replace blood sampling. A pilot biomonitoring study was conducted, and 91 children provided urine and venous blood samples in which the levels of 17 contaminants (Al, As, Ba, Cs, Co, Cu, I, Pb, Li, Mn, Mo, Ni, Se, Sr, Te, Ti, and Zn) were assessed. Spearman correlation coefficients (rho) and 95% confidence intervals (CI) were computed. Urinary and blood levels of arsenic (rho = 0.23, 95% CI 0.01–0.44), lead (rho = 0.43, 95% CI 0.24–0.61), and strontium (rho = 0.22, 95% CI 0.03–0.40) showed significant correlations. These findings suggest that urine sampling could serve as a practical alternative to blood collection for monitoring specific trace elements like lead in pediatric populations, particularly in large-scale studies where participant compliance is critical. However, modest correlations for other elements highlight the need for element-specific validation before adopting urine as a universal biomonitoring matrix. Future research should explore the pharmacokinetic and exposure-related factors driving these relationships to optimize non-invasive surveillance strategies for children’s environmental health. Full article
(This article belongs to the Section Exposome Analysis and Risk Assessment)
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13 pages, 2844 KiB  
Article
Hemodynamic Response to Lipopolysaccharide Infusion and Effect of Meloxicam Administration on Cardiac Function in Donkeys
by Francisco J. Mendoza, Antonio Buzon-Cuevas, Raul Aguilera-Aguilera, Carlos A. Gonzalez-De Cara, Adelaida De Las Heras and Alejandro Perez-Ecija
Animals 2024, 14(24), 3660; https://doi.org/10.3390/ani14243660 - 18 Dec 2024
Cited by 2 | Viewed by 918
Abstract
Systemic inflammatory response syndrome (SIRS) in donkeys is observed to be secondary to colic, diarrhea or pleuropneumonia, among other disorders. Horses with SIRS develop secondary disturbances such as hyperlipemia, laminitis, disseminated intravascular coagulopathy, and hemodynamic and cardiac derangements, which impair their prognosis and [...] Read more.
Systemic inflammatory response syndrome (SIRS) in donkeys is observed to be secondary to colic, diarrhea or pleuropneumonia, among other disorders. Horses with SIRS develop secondary disturbances such as hyperlipemia, laminitis, disseminated intravascular coagulopathy, and hemodynamic and cardiac derangements, which impair their prognosis and increase the mortality rate. In donkeys, no information is available on the effect of experimentally induced endotoxemia in the cardiovascular system. Acute experimental endotoxemia was induced by lipopolysaccharide (LPS) infusion in six healthy adult non-pregnant jennies. Physical signs, arterial (systolic, diastolic and mean) and central venous pressure were monitored during 360 min. Cardiac troponin I (cTnI) concentrations were measured in blood samples, and echocardiography was performed. LPS infusion caused an increase in cTnI, hypotension and diminution of central venous pressure, cardiac dysfunction, with a decrease in stroke volume (SV), cardiac output (CO) and cardiac index, and impairment of ultrasonographic ventricular function parameters. Intravenous meloxicam administration prevented the cTnI increase, hypotension, diminution of SV and CO, and changes in ultrasonographic parameters related to ventricular dysfunction. Thus, meloxicam could be proposed as an effective therapeutical option to control the hemodynamic and cardiac derangements observed in donkeys with SIRS. Full article
(This article belongs to the Special Issue Current Research on Donkeys and Mules)
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18 pages, 4058 KiB  
Article
Surface Roughness Analysis of Microchannels Featuring Microfluidic Devices Fabricated by Three Different Materials and Methods
by José M. Acosta-Cuevas, Mario A. García-Ramírez, Gabriela Hinojosa-Ventura, Álvaro J. Martínez-Gómez, Víctor H. Pérez-Luna and Orfil González-Reynoso
Coatings 2023, 13(10), 1676; https://doi.org/10.3390/coatings13101676 - 25 Sep 2023
Cited by 10 | Viewed by 3332
Abstract
In recent years, the utilization of microfluidic devices for precise manipulation of small flows has significantly increased. The effective management of microfluidics is closely associated with microchannel fabrication. The fabrication method employed for microfluidic devices directly impacts the roughness of the microchannels, consequently [...] Read more.
In recent years, the utilization of microfluidic devices for precise manipulation of small flows has significantly increased. The effective management of microfluidics is closely associated with microchannel fabrication. The fabrication method employed for microfluidic devices directly impacts the roughness of the microchannels, consequently influencing the flows within them. In this study, the surface roughness of microchannels was investigated through three different fabrication processes: PDMS lithography, PLA printing, and UV resin printing. This research compared and analyzed the surface roughness of the microchannels fabricated using these methods. Furthermore, supported by a dynamic fluid simulator, the impact of surface roughness on flow behavior was shown. Results reveal varying degrees of roughness prominence in curved regions. Comparing microfluidic device fabrication techniques is crucial to optimize the process, control roughness, analyze flow rates, and select a proper material to be used in the development of microfluidic devices. Full article
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15 pages, 1099 KiB  
Article
Methionine Supplementation during Pregnancy of Goats Improves Kids’ Birth Weight, Body Mass Index, and Postnatal Growth Pattern
by Diego Castillo-Gutierrez, Luisa E. S. Hernández-Arteaga, Manuel J. Flores-Najera, Venancio Cuevas-Reyes, Juan M. Vázquez-García, Catarina Loredo-Osti, Sergio Beltrán-López, Gilberto Ballesteros-Rodea, Antonio Gonzalez-Bulnes, Cesar A. Meza-Herrera and Cesar A. Rosales-Nieto
Biology 2022, 11(7), 1065; https://doi.org/10.3390/biology11071065 - 18 Jul 2022
Cited by 6 | Viewed by 3153
Abstract
The last third of gestation is a period of high energy and protein demand for the dam to support fetal growth and the following onset of lactation. Methionine is an essential amino acid that contributes to protein formation, fetal development, and milk synthesis; [...] Read more.
The last third of gestation is a period of high energy and protein demand for the dam to support fetal growth and the following onset of lactation. Methionine is an essential amino acid that contributes to protein formation, fetal development, and milk synthesis; thus, is likely to have positive effects on the weight and size of the newborn and, afterward, milk yield and milk composition, which may improve growth patterns of the progeny. To test these hypotheses, we used 60 pregnant multiparous Alpine goats with similar live weights and gestational ages (~Day 100 of pregnancy; Mean ± SD; 1410 ± 14 days old and 50.4 ± 6.6 kg) and were separated into two groups: control and supplemented with the delivery. Treatments were T-MET (n = 30; received 1% herbal methionine Optimethione® dry matter based on from Day 100 of the pregnancy to delivery) or T-CTL (n = 30; served as the control and did not receive methionine). The methionine powder provided individual supplementation and was adjusted every week as the live weight and dry matter intake changed. At birth, the weight, body mass index (BMI), birth type, and sex of the kids were determined. Subsequently, the progeny was weighed weekly up to weaning. Two weeks after parturition, the milk composition was recorded weekly, and the milk yield was recorded monthly. The maternal live weight at the start (Mean ± SEM; T-CTL: 50.5 ± 1.1 vs. T-MET: 50.3 ± 1.3 kg) and end (T-CTL: 54.2 ± 1.3 vs. T-MET: 52.8 ± 1.4 kg) of the experiment did not differ statistically among treatments (p > 0.05); however, daily live weight changes tended to differ between groups (T-CTL: 73 ± 10 vs. T-MET: 51 ± 7 g day−1; p = 0.06). The birth weight (T-CTL: 3.1 ± 0.1 vs. T-MET: 3.5 ± 0.1 kg; p < 0.001), daily live weight change (T-CTL: 121 ± 6 vs. T-MET: 141 ± 6 g day−1; p < 0.01), and weaning weight (T-CTL: 8.3 ± 0.2 vs. T-MET: 9.3 ± 0.3 kg; p < 0.01) differed between treatments. The BMI at birth (T-CTL: 0.28 ± 0.01 vs. T-MET: 0.3 ± 0.01 units kg m−2; p < 0.01) and at weaning (T-CTL: 0.85 ± 0.1 kg vs. T-MET: 1.00 ± 0.06 units kg m−2; p < 0.05) differed between treatments. Milk components (protein, fat, lactose, and solids non-fat) and milk yield were similar between treatments (p > 0.05). It is concluded that the inclusion of methionine in the maternal goat diet during the last third of gestation increases the birth and growth variables of the progeny but without significant influence on the milk yield and composition. Full article
(This article belongs to the Section Physiology)
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26 pages, 2105 KiB  
Article
Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking
by Carlos Soubervielle-Montalvo, Oscar E. Perez-Cham, Cesar Puente, Emilio J. Gonzalez-Galvan, Gustavo Olague, Carlos A. Aguirre-Salado, Juan C. Cuevas-Tello and Luis J. Ontanon-Garcia
Sensors 2022, 22(3), 1280; https://doi.org/10.3390/s22031280 - 8 Feb 2022
Cited by 7 | Viewed by 5210
Abstract
Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number [...] Read more.
Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands. This work presents the design, implementation and assessment of a low-power embedded system based on an SoC-FPGA platform and the honeybee search algorithm (HSA) for real-time video tracking. HSA is a meta-heuristic that combines evolutionary computing and swarm intelligence techniques. Our findings demonstrated that the combination of SoC-FPGA and HSA reduced the consumption of computational resources, allowing real-time multiprocessing without a reduction in precision, and with the advantage of lower power consumption, which enabled portability. A starker difference was observed when measuring the power consumption. The proposed SoC-FPGA system consumed about 5 Watts, whereas the CPU-GPU system required more than 200 Watts. A general recommendation obtained from this research is to use SoC-FPGA over CPU-GPU to work with meta-heuristics in computer vision applications when an embedded solution is required. Full article
(This article belongs to the Topic Complex Systems and Artificial Intelligence)
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250 pages, 84542 KiB  
Article
Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report
by A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, G. Adamov, D. Adams, M. Adinolfi, A. Aduszkiewicz, Z. Ahmad, J. Ahmed, T. Alion, S. Alonso Monsalve, M. Alrashed, C. Alt, A. Alton, P. Amedo, J. Anderson, C. Andreopoulos, M. P. Andrews, F. Andrianala, S. Andringa, N. Anfimov, A. Ankowski, M. Antonova, S. Antusch, A. Aranda-Fernandez, A. Ariga, L. O. Arnold, M. A. Arroyave, J. Asaadi, A. Aurisano, V. Aushev, D. Autiero, M. Ayala-Torres, F. Azfar, A. Back, H. Back, J. J. Back, C. Backhouse, P. Baesso, I. Bagaturia, L. Bagby, S. Balasubramanian, P. Baldi, B. Baller, B. Bambah, F. Barao, G. Barenboim, G. J. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, N. Barros, J. L. Barrow, A. Basharina-Freshville, A. Bashyal, V. Basque, E. Belchior, J. B. R. Battat, F. Battisti, F. Bay, J. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, D. Belver, N. Benekos, F. Bento Neves, S. Berkman, P. Bernardini, R. M. Berner, H. Berns, S. Bertolucci, M. Betancourt, A. Betancur Rodríguez, M. Bhattacharjee, S. Bhuller, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, F. D. M. Blaszczyk, G. C. Blazey, E. Blucher, J. Boissevain, S. Bolognesi, T. Bolton, L. Bomben, M. Bonesini, M. Bongrand, F. Bonini, A. Booth, C. Booth, S. Bordoni, A. Borkum, T. Boschi, N. Bostan, P. Bour, C. Bourgeois, S. B. Boyd, D. Boyden, J. Bracinik, D. Braga, D. Brailsford, A. Brandt, J. Bremer, C. Brew, E. Brianne, S. J. Brice, C. Brizzolari, C. Bromberg, G. Brooijmans, J. Brooke, A. Bross, G. Brunetti, M. Brunetti, N. Buchanan, H. Budd, I. Cagnoli, D. Caiulo, P. Calafiura, J. Calcutt, M. Calin, S. Calvez, E. Calvo, A. Caminata, M. Campanelli, K. Cankocak, D. Caratelli, G. Carini, B. Carlus, P. Carniti, I. Caro Terrazas, H. Carranza, T. Carroll, J. F. Castaño Forero, A. Castillo, C. Castromonte, E. Catano-Mur, C. Cattadori, F. Cavalier, F. Cavanna, S. Centro, G. Cerati, A. Cervelli, A. Cervera Villanueva, M. Chalifour, A. Chappell, E. Chardonnet, N. Charitonidis, A. Chatterjee, S. Chattopadhyay, H. Chen, M. Chen, Y. Chen, Z. Chen, D. Cherdack, C. Chi, S. Childress, A. Chiriacescu, G. Chisnall, K. Cho, S. Choate, D. Chokheli, S. Choubey, A. Christensen, D. Christian, G. Christodoulou, A. Chukanov, E. Church, V. Cicero, P. Clarke, T. E. Coan, A. G. Cocco, J. A. B. Coelho, E. Conley, R. Conley, J. M. Conrad, M. Convery, S. Copello, L. Corwin, L. Cremaldi, L. Cremonesi, J. I. Crespo-Anadón, E. Cristaldo, R. Cross, A. Cudd, C. Cuesta, Y. Cui, D. Cussans, M. Dabrowski, O. Dalager, H. da Motta, L. Da Silva Peres, C. David, Q. David, G. S. Davies, S. Davini, J. Dawson, K. De, R. M. De Almeida, P. Debbins, I. De Bonis, M. P. Decowski, A. de Gouvêa, P. C. De Holanda, I. L. De Icaza Astiz, A. Deisting, P. De Jong, A. Delbart, D. Delepine, M. Delgado, A. Dell’Acqua, P. De Lurgio, J. R. T. de Mello Neto, D. M. DeMuth, S. Dennis, C. Densham, G. W. Deptuch, A. De Roeck, V. De Romeri, G. De Souza, R. Dharmapalan, F. Diaz, J. S. Díaz, S. Di Domizio, L. Di Giulio, P. Ding, L. Di Noto, C. Distefano, R. Diurba, M. Diwan, Z. Djurcic, N. Dokania, S. Dolan, M. J. Dolinski, L. Domine, D. Douglas, D. Douillet, G. Drake, F. Drielsma, D. Duchesneau, K. Duffy, P. Dunne, T. Durkin, H. Duyang, O. Dvornikov, D. A. Dwyer, A. S. Dyshkant, M. Eads, A. Earle, D. Edmunds, J. Eisch, L. Emberger, S. Emery, A. Ereditato, C. O. Escobar, G. Eurin, J. J. Evans, E. Ewart, A. C. Ezeribe, K. Fahey, A. Falcone, C. Farnese, Y. Farzan, J. Felix, M. Fernandes Carneiro da Silva, E. Fernandez-Martinez, P. Fernandez Menendez, F. Ferraro, L. Fields, F. Filthaut, A. Fiorentini, R. S. Fitzpatrick, W. Flanagan, B. Fleming, R. Flight, D. V. Forero, J. Fowler, W. Fox, J. Franc, K. Francis, D. Franco, J. Freeman, J. Freestone, J. Fried, A. Friedland, S. Fuess, I. Furic, A. P. Furmanski, A. Gabrielli, A. Gago, H. Gallagher, A. Gallas, A. Gallego-Ros, N. Gallice, V. Galymov, E. Gamberini, T. Gamble, R. Gandhi, R. Gandrajula, F. Gao, S. Gao, D. Garcia-Gamez, M. Á. García-Peris, S. Gardiner, D. Gastler, G. Ge, B. Gelli, A. Gendotti, S. Gent, Z. Ghorbani-Moghaddam, D. Gibin, I. Gil-Botella, S. Gilligan, C. Girerd, A. K. Giri, D. Gnani, O. Gogota, M. Gold, S. Gollapinni, K. Gollwitzer, R. A. Gomes, L. V. Gomez Bermeo, L. S. Gomez Fajardo, F. Gonnella, J. A. Gonzalez-Cuevas, D. Gonzalez-Diaz, M. Gonzalez-Lopez, M. C. Goodman, O. Goodwin, S. Goswami, C. Gotti, E. Goudzovski, C. Grace, M. Graham, R. Gran, E. Granados, P. Granger, A. Grant, C. Grant, D. Gratieri, P. Green, L. Greenler, J. Greer, W. C. Griffith, M. Groh, J. Grudzinski, K. Grzelak, W. Gu, V. Guarino, R. Guenette, E. Guerard, M. Guerzoni, A. Guglielmi, B. Guo, K. K. Guthikonda, R. Gutierrez, P. Guzowski, M. M. Guzzo, S. Gwon, A. Habig, H. Hadavand, R. Haenni, A. Hahn, J. Haiston, P. Hamacher-Baumann, T. Hamernik, P. Hamilton, J. Han, D. A. Harris, J. Hartnell, J. Harton, T. Hasegawa, C. Hasnip, R. Hatcher, K. W. Hatfield, A. Hatzikoutelis, C. Hayes, E. Hazen, A. Heavey, K. M. Heeger, J. Heise, K. Hennessy, S. Henry, M. A. Hernandez Morquecho, K. Herner, L. Hertel, J. Hewes, A. Higuera, T. Hill, S. J. Hillier, A. Himmel, J. Hoff, C. Hohl, A. Holin, E. Hoppe, G. A. Horton-Smith, M. Hostert, A. Hourlier, B. Howard, R. Howell, J. Huang, J. Huang, J. Hugon, G. Iles, N. Ilic, A. M. Iliescu, R. Illingworth, G. Ingratta, A. Ioannisian, L. Isenhower, R. Itay, A. Izmaylov, S. Jackson, V. Jain, E. James, B. Jargowsky, F. Jediny, D. Jena, Y. S. Jeong, C. Jesús-Valls, X. Ji, L. Jiang, S. Jiménez, A. Jipa, R. Johnson, N. Johnston, B. Jones, S. B. Jones, M. Judah, C. K. Jung, T. Junk, Y. Jwa, M. Kabirnezhad, A. Kaboth, I. Kadenko, I. Kakorin, F. Kamiya, N. Kaneshige, G. Karagiorgi, G. Karaman, A. Karcher, M. Karolak, Y. Karyotakis, S. Kasai, S. P. Kasetti, L. Kashur, N. Kazaryan, E. Kearns, P. Keener, K. J. Kelly, E. Kemp, O. Kemularia, W. Ketchum, S. H. Kettell, M. Khabibullin, A. Khotjantsev, A. Khvedelidze, D. Kim, B. King, B. Kirby, M. Kirby, J. Klein, K. Koehler, L. W. Koerner, S. Kohn, P. P. Koller, L. Kolupaeva, M. Kordosky, T. Kosc, U. Kose, V. A. Kostelecký, K. Kothekar, F. Krennrich, I. Kreslo, Y. Kudenko, V. A. Kudryavtsev, S. Kulagin, J. Kumar, P. Kumar, R. Kumar, P. Kunze, N. Kurita, C. Kuruppu, V. Kus, T. Kutter, A. Lambert, B. Land, K. Lande, C. E. Lane, K. Lang, T. Langford, J. Larkin, P. Lasorak, D. Last, C. Lastoria, A. Laundrie, G. Laurenti, A. Lawrence, I. Lazanu, R. LaZur, T. Le, S. Leardini, J. Learned, P. LeBrun, T. LeCompte, G. Lehmann Miotto, R. Lehnert, M. A. Leigui de Oliveira, M. Leitner, L. Li, S. W. Li, T. Li, Y. Li, H. Liao, C. S. Lin, Q. Lin, S. Lin, A. Lister, B. R. Littlejohn, J. Liu, S. Lockwitz, T. Loew, M. Lokajicek, I. Lomidze, K. Long, K. Loo, D. Lorca, T. Lord, J. M. LoSecco, W. C. Louis, X. G. Lu, K. B. Luk, X. Luo, N. Lurkin, T. Lux, V. P. Luzio, D. MacFarlane, A. A. Machado, P. Machado, C. T. Macias, J. R. Macier, A. Maddalena, A. Madera, P. Madigan, S. Magill, K. Mahn, A. Maio, A. Major, J. A. Maloney, G. Mandrioli, R. C. Mandujano, J. Maneira, L. Manenti, S. Manly, A. Mann, K. Manolopoulos, M. Manrique Plata, V. N. Manyam, L. Manzanillas, M. Marchan, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani, J. Maricic, R. Marie, F. Marinho, A. D. Marino, D. Marsden, M. Marshak, C. M. Marshall, J. Marshall, J. Marteau, J. Martin-Albo, N. Martinez, D. A. Martinez Caicedo, S. Martynenko, K. Mason, A. Mastbaum, M. Masud, S. Matsuno, J. Matthews, C. Mauger, N. Mauri, K. Mavrokoridis, I. Mawby, R. Mazza, A. Mazzacane, E. Mazzucato, T. McAskill, E. McCluskey, N. McConkey, K. S. McFarland, C. McGrew, A. McNab, A. Mefodiev, P. Mehta, P. Melas, O. Mena, S. Menary, H. Mendez, D. P. Méndez, A. Menegolli, G. Meng, M. D. Messier, W. Metcalf, T. Mettler, M. Mewes, H. Meyer, T. Miao, G. Michna, T. Miedema, J. Migenda, V. Mikola, R. Milincic, W. Miller, J. Mills, C. Milne, O. Mineev, O. G. Miranda, S. Miryala, C. S. Mishra, S. R. Mishra, A. Mislivec, D. Mladenov, I. Mocioiu, K. Moffat, N. Moggi, R. Mohanta, T. A. Mohayai, N. Mokhov, J. Molina, L. Molina Bueno, A. Montanari, C. Montanari, D. Montanari, E. Montagna, L. M. Montano Zetina, J. Moon, M. Mooney, A. F. Moor, D. Moreno, C. Morris, C. Mossey, E. Motuk, C. A. Moura, J. Mousseau, W. Mu, L. Mualem, J. Mueller, M. Muether, S. Mufson, F. Muheim, A. Muir, M. Mulhearn, D. Munford, H. Muramatsu, S. Murphy, J. Musser, J. Nachtman, S. Nagu, M. Nalbandyan, R. Nandakumar, D. Naples, S. Narita, D. Navas-Nicolás, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, K. Negishi, J. K. Nelson, J. Nesbit, M. Nessi, D. Newbold, M. Newcomer, D. Newhart, H. Newton, M. Niccolo, R. Nichol, F. Nicolas-Arnaldos, M. Nicoletta, E. Niner, K. Nishimura, A. Norman, A. Norrick, R. Northrop, P. Novella, J. A. Nowak, M. Oberling, J. P. Ochoa-Ricoux, A. Olivares Del Campo, A. Olivier, A. Olshevskiy, Y. Onel, Y. Onishchuk, J. Ott, L. Pagani, S. Pakvasa, G. Palacio, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, C. Palomares, J. L. Palomino-Gallo, E. Pantic, V. Paolone, V. Papadimitriou, R. Papaleo, A. Papanestis, S. Paramesvaran, S. Parke, Z. Parsa, M. Parvu, S. Pascoli, L. Pasqualini, J. Pasternak, J. Pater, C. Patrick, L. Patrizii, R. B. Patterson, S. J. Patton, T. Patzak, A. Paudel, B. Paulos, L. Paulucci, Z. Pavlovic, G. Pawloski, D. Payne, V. Pec, S. J. M. Peeters, E. Pennacchio, A. Penzo, O. L. G. Peres, J. Perry, D. Pershey, G. Pessina, G. Petrillo, C. Petta, R. Petti, F. Piastra, L. Pickering, F. Pietropaolo, R. Plunkett, R. Poling, X. Pons, N. Poonthottathil, F. Poppi, S. Pordes, J. Porter, M. Potekhin, R. Potenza, B. V. K. S. Potukuchi, J. Pozimski, M. Pozzato, S. Prakash, T. Prakash, S. Prince, D. Pugnere, X. Qian, M. C. Queiroga Bazetto, J. L. Raaf, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai, M. Rajaoalisoa, I. Rakhno, A. Rakotonandrasana, L. Rakotondravohitra, Y. A. Ramachers, R. Rameika, M. A. Ramirez Delgado, B. Ramson, A. Rappoldi, G. Raselli, P. Ratoff, S. Raut, R. F. Razakamiandra, J. S. Real, B. Rebel, M. Reggiani-Guzzo, T. Rehak, J. Reichenbacher, S. D. Reitzner, H. Rejeb Sfar, A. Renshaw, S. Rescia, F. Resnati, A. Reynolds, C. Riccio, G. Riccobene, L. C. J. Rice, J. Ricol, A. Rigamonti, Y. Rigaut, D. Rivera, L. Rochester, M. Roda, P. Rodrigues, M. J. Rodriguez Alonso, E. Rodriguez Bonilla, J. Rodriguez Rondon, S. Rosauro-Alcaraz, M. Rosenberg, P. Rosier, B. Roskovec, M. Rossella, J. Rout, P. Roy, S. Roy, A. Rubbia, C. Rubbia, F. C. Rubio, B. Russell, D. Ruterbories, R. Saakyan, S. Sacerdoti, T. Safford, R. Sahay, N. Sahu, P. Sala, N. Samios, O. Samoylov, M. C. Sanchez, D. A. Sanders, D. Sankey, S. Santana, M. Santos-Maldonado, N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, G. Savage, V. Savinov, A. Scaramelli, A. Scarff, A. Scarpelli, T. Schaffer, H. Schellman, P. Schlabach, D. Schmitz, K. Scholberg, A. Schukraft, E. Segreto, J. Sensenig, I. Seong, A. Sergi, D. Sgalaberna, M. H. Shaevitz, S. Shafaq, M. Shamma, R. Sharankova, H. R. Sharma, R. Sharma, T. Shaw, C. Shepherd-Themistocleous, S. Shin, D. Shooltz, R. Shrock, L. Simard, F. Simon, N. Simos, J. Sinclair, G. Sinev, J. Singh, J. Singh, V. Singh, R. Sipos, F. W. Sippach, G. Sirri, A. Sitraka, K. Siyeon, K. Skarpaas VIII, A. Smith, E. Smith, P. Smith, J. Smolik, M. Smy, E. L. Snider, P. Snopok, M. Soares Nunes, H. Sobel, M. Soderberg, C. J. Solano Salinas, S. Söldner-Rembold, S. Soleti, N. Solomey, V. Solovov, W. E. Sondheim, M. Sorel, J. Soto-Oton, A. Sousa, K. Soustruznik, F. Spagliardi, M. Spanu, J. Spitz, N. J. C. Spooner, K. Spurgeon, R. Staley, M. Stancari, L. Stanco, R. Stanley, R. Stein, H. M. Steiner, J. Stewart, B. Stillwell, J. Stock, F. Stocker, T. Stokes, M. Strait, T. Strauss, S. Striganov, A. Stuart, J. G. Suarez, H. Sullivan, D. Summers, A. Surdo, V. Susic, L. Suter, C. M. Sutera, R. Svoboda, B. Szczerbinska, A. M. Szelc, R. Talaga, H. A. Tanaka, B. Tapia Oregui, A. Tapper, S. Tariq, E. Tatar, R. Tayloe, A. M. Teklu, M. Tenti, K. Terao, C. A. Ternes, F. Terranova, G. Testera, A. Thea, J. L. Thompson, C. Thorn, S. C. Timm, J. Todd, A. Tonazzo, D. Torbunov, M. Torti, M. Tortola, F. Tortorici, D. Totani, M. Toups, C. Touramanis, N. Tosi, R. Travaglini, J. Trevor, S. Trilov, W. H. Trzaska, Y. T. Tsai, Z. Tsamalaidze, K. V. Tsang, N. Tsverava, S. Tufanli, C. Tull, E. Tyley, M. Tzanov, M. A. Uchida, J. Urheim, T. Usher, S. Uzunyan, M. R. Vagins, P. Vahle, G. A. Valdiviesso, E. Valencia, P. Valerio, Z. Vallari, J. W. F. Valle, S. Vallecorsa, R. Van Berg, R. G. Van de Water, F. Varanini, D. Vargas, G. Varner, J. Vasel, S. Vasina, G. Vasseur, N. Vaughan, K. Vaziri, S. Ventura, A. Verdugo, S. Vergani, M. A. Vermeulen, M. Verzocchi, M. Vicenzi, H. Vieira de Souza, C. Vignoli, C. Vilela, B. Viren, T. Vrba, T. Wachala, A. V. Waldron, M. Wallbank, H. Wang, J. Wang, L. Wang, M. H. L. S. Wang, Y. Wang, Y. Wang, K. Warburton, D. Warner, M. Wascko, D. Waters, A. Watson, P. Weatherly, A. Weber, M. Weber, H. Wei, A. Weinstein, D. Wenman, M. Wetstein, A. White, L. H. Whitehead, D. Whittington, M. J. Wilking, C. Wilkinson, Z. Williams, F. Wilson, R. J. Wilson, J. Wolcott, T. Wongjirad, A. Wood, K. Wood, E. Worcester, M. Worcester, C. Wret, W. Wu, W. Wu, Y. Xiao, E. Yandel, G. Yang, K. Yang, S. Yang, T. Yang, A. Yankelevich, N. Yershov, K. Yonehara, T. Young, B. Yu, H. Yu, J. Yu, W. Yuan, R. Zaki, J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, L. Zazueta, G. Zeit, G. P. Zeller, J. Zennamo, K. Zeug, C. Zhang, M. Zhao, E. Zhivun, G. Zhu, P. Zilberman, E. D. Zimmerman, M. Zito, 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 2021, 5(4), 31; https://doi.org/10.3390/instruments5040031 - 29 Sep 2021
Cited by 131 | Viewed by 18093
Abstract
The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in [...] Read more.
The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents. Full article
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18 pages, 3718 KiB  
Article
Similar Features, Different Behaviors: A Comparative In Vitro Study of the Adipogenic Potential of Stem Cells from Human Follicle, Dental Pulp, and Periodontal Ligament
by Melissa D. Mercado-Rubio, Erick Pérez-Argueta, Alejandro Zepeda-Pedreguera, Fernando J. Aguilar-Ayala, Ricardo Peñaloza-Cuevas, Angela Kú-González, Rafael A. Rojas-Herrera, Beatriz A. Rodas-Junco and Geovanny I. Nic-Can
J. Pers. Med. 2021, 11(8), 738; https://doi.org/10.3390/jpm11080738 - 28 Jul 2021
Cited by 16 | Viewed by 4719
Abstract
Dental tissue-derived mesenchymal stem cells (DT-MSCs) are a promising resource for tissue regeneration due to their multilineage potential. Despite accumulating data regarding the biology and differentiation potential of DT-MSCs, few studies have investigated their adipogenic capacity. In this study, we have investigated the [...] Read more.
Dental tissue-derived mesenchymal stem cells (DT-MSCs) are a promising resource for tissue regeneration due to their multilineage potential. Despite accumulating data regarding the biology and differentiation potential of DT-MSCs, few studies have investigated their adipogenic capacity. In this study, we have investigated the mesenchymal features of dental pulp stem cells (DPSCs), as well as the in vitro effects of different adipogenic media on these cells, and compared them to those of periodontal ligament stem cells (PLSCs) and dental follicle stem cells (DFSCs). DFSC, PLSCs, and DPSCs exhibit similar morphology and proliferation capacity, but they differ in their self-renewal ability and expression of stemness markers (e.g OCT4 and c-MYC). Interestingly, DFSCs and PLSCs exhibited more lipid accumulation than DPSCs when induced to adipogenic differentiation. In addition, the mRNA levels of adipogenic markers (PPAR, LPL, and ADIPOQ) were significantly higher in DFSCs and PLSCs than in DPSCs, which could be related to the differences in the adipogenic commitment in those cells. These findings reveal that the adipogenic capacity differ among DT-MSCs, features that might be advantageous to increasing our understanding about the developmental origins and regulation of adipogenic commitment. Full article
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13 pages, 591 KiB  
Article
Resistance Training Diminishes the Expression of Exosome CD63 Protein without Modification of Plasma miR-146a-5p and cfDNA in the Elderly
by Brisamar Estébanez, Nishant P. Visavadiya, José A. de Paz, Michael Whitehurst, María J. Cuevas, Javier González-Gallego and Chun-Jung Huang
Nutrients 2021, 13(2), 665; https://doi.org/10.3390/nu13020665 - 19 Feb 2021
Cited by 25 | Viewed by 5371
Abstract
Aging-associated inflammation is characterized by senescent cell-mediated secretion of high levels of inflammatory mediators, such as microRNA (miR)-146a. Moreover, a rise of circulating cell-free DNA (cfDNA) is also related to systemic inflammation and frailty in the elderly. Exosome-mediated cell-to-cell communication is fundamental in [...] Read more.
Aging-associated inflammation is characterized by senescent cell-mediated secretion of high levels of inflammatory mediators, such as microRNA (miR)-146a. Moreover, a rise of circulating cell-free DNA (cfDNA) is also related to systemic inflammation and frailty in the elderly. Exosome-mediated cell-to-cell communication is fundamental in cellular senescence and aging. The plasma changes in exercise-promoted miR-146a-5p, cfDNA, and exosome release could be the key to facilitate intercellular communication and systemic adaptations to exercise in aging. Thirty-eight elderly subjects (28 trained and 10 controls) volunteered in an 8-week resistance training protocol. The levels of plasma miR-146a-5p, cfDNA, and exosome markers (CD9, CD14, CD63, CD81, Flotillin [Flot]-1, and VDAC1) were measured prior to and following training. Results showed no changes in plasma miR-146a-5p and cfDNA levels with training. The levels of exosome markers (Flot-1, CD9, and CD81) as well as exosome-carried proteins (CD14 and VDAC1) remained unchanged, whereas an attenuated CD63 response was found in the trained group compared to the controls. These findings might partially support the anti-inflammatory effect of resistance training in the elderly as evidenced by the diminishment of exosome CD63 protein expression, without modification of plasma miR-146a-5p and cfDNA. Full article
(This article belongs to the Section Nutritional Epidemiology)
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15 pages, 1142 KiB  
Article
Modified Serum ALP Values and Timing of Apparition of Knee Epiphyseal Ossification Centers in Preterm Infants with Cholestasis and Risk of Concomitant Metabolic Bone Disease of Prematurity
by Sandra Llorente-Pelayo, Pablo Docio, Bernardo A. Lavín-Gómez, María T. García-Unzueta, Isabel de las Cuevas, Luis de la Rubia, María J. Cabero-Pérez and Domingo González-Lamuño
Nutrients 2020, 12(12), 3854; https://doi.org/10.3390/nu12123854 - 17 Dec 2020
Cited by 15 | Viewed by 3101
Abstract
The usefulness of serum alkaline phosphatase (ALP) and phosphorous in screening and monitoring of metabolic bone disease of prematurity (MBDP) still has some limitations, especially in preterm infants with concomitant conditions such as cholestasis. We aimed to assess a modification of serum ALP [...] Read more.
The usefulness of serum alkaline phosphatase (ALP) and phosphorous in screening and monitoring of metabolic bone disease of prematurity (MBDP) still has some limitations, especially in preterm infants with concomitant conditions such as cholestasis. We aimed to assess a modification of serum ALP (M-ALP) as a biomarker for MBDP in preterm infants, and the use of ultrasound monitoring for the apparition of knee ossification centers as marker of bone mineralization. Biochemical and clinical registers were taken from 94 preterm newborns <32 weeks. A significant correlation existed between serum ALP and direct bilirubin (DB), expressed by the regression equation: M-ALP (IU/L) = 302.1 + 96.9 (DB (mg/dL)). The ratio ALP/M-ALP > 1 was demonstrated to be more specific (87.5%) in the diagnosis of MBDP than the cut-off value of serum ALP > 500 IU/L (62.5%). ALP/M-ALP > 1 showed 100% sensitivity and specificity for the diagnosis of MBDP, and a good correlation with specific bone ALP (B-ALP). Patients with the knee nucleus by post-menstrual week 37 had lower B-ALP compared to patients with no nucleus, and no patients with MBDP presented the nucleus by the 40th week. In the absence of reliable specific B-ALP, reinterpreting serum ALP values by M-ALP plus monitoring of knee ossification centers contribute to better management of MBDP in preterm infants with cholestasis. Full article
(This article belongs to the Special Issue Bone Mineralization and Calcium Phosphorus Metabolism)
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