MDPI publications

Permanent URI for this collectionhttps://hdl.handle.net/10657/15459

This collection gathers materials published by University of Houston authors in MDPI journals

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Role of Gut Microbial Metabolites in Cardiovascular Diseases—Current Insights and the Road Ahead
(2024-09-23) Datta, Sayantap; Pasham, Sindhura; Inavolu, Sriram; Boini, Krishna M.; Koka, Saisudha
Cardiovascular diseases (CVDs) are the leading cause of premature morbidity and mortality globally. The identification of novel risk factors contributing to CVD onset and progression has enabled an improved understanding of CVD pathophysiology. In addition to the conventional risk factors like high blood pressure, diabetes, obesity and smoking, the role of gut microbiome and intestinal microbe-derived metabolites in maintaining cardiovascular health has gained recent attention in the field of CVD pathophysiology. The human gastrointestinal tract caters to a highly diverse spectrum of microbes recognized as the gut microbiota, which are central to several physiologically significant cascades such as metabolism, nutrient absorption, and energy balance. The manipulation of the gut microbial subtleties potentially contributes to CVD, inflammation, neurodegeneration, obesity, and diabetic onset. The existing paradigm of studies suggests that the disruption of the gut microbial dynamics contributes towards CVD incidence. However, the exact mechanistic understanding of such a correlation from a signaling perspective remains elusive. This review has focused upon an in-depth characterization of gut microbial metabolites and their role in varied pathophysiological conditions, and highlights the potential molecular and signaling mechanisms governing the gut microbial metabolites in CVDs. In addition, it summarizes the existing courses of therapy in modulating the gut microbiome and its metabolites, limitations and scientific gaps in our current understanding, as well as future directions of studies involving the modulation of the gut microbiome and its metabolites, which can be undertaken to develop CVD-associated treatment options. Clarity in the understanding of the molecular interaction(s) and associations governing the gut microbiome and CVD shall potentially enable the development of novel druggable targets to ameliorate CVD in the years to come.
The Evolution and Impact of Distilled Spirits Regulation in the United States: Considerations for Policymakers and Academia
(2024-09-23) Norris, Cortney L.; Taylor, Scott; Taylor, D. Christopher
This manuscript analyzes the issues presented in Executive Order 14036, which aimed to promote competition in the American economy, including specific directives for the alcohol industry. Specifically, this manuscript focuses on the issues regarding distilled spirits regulation and delves into the implications for the distilled spirits sector. The order addresses anti-competitive practices, encouraging regulatory bodies to review and revise existing policies that may hinder fair market practices. This paper explores these issues and provides a historical context of distilled spirits regulation in the United States, examining how past policies have shaped the current landscape. It analyzes the key provisions of Executive Order 14036, highlighting its potential to foster increased competition, innovation, and consumer choice within the distilled spirits market. Lastly, the article provides a call to action for policymakers, academia, and consumers which will aid distilled spirits producers in gaining parity with beer and wine producers.
Is Seeing Believing? A Practitioner’s Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies
(2024-09-16) Fan, Kun; Subedi, Srijana; Yang, Gongshun; Lu, Xi; Ren, Jie; Wu, Cen
Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the “unpenalized loss function + penalty term” formulation for regularization methods and the “likelihood function × shrinkage prior” framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.
A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms
(2024-09-16) Lertliangchai, Thitaree; Dindoruk, Birol; Lu, Ligang; Yang, Xi; Sinha, Utkarsh
Dew point pressure (DPP) emerges as a pivotal factor crucial for forecasting reservoir dynamics regarding condensate-to-gas ratio and addressing production/completion hurdles, alongside calibrating EOS models for integrated simulation. However, DPP presents challenges in terms of predictability. Acknowledging these complexities, we introduce a state-of-the-art approach for DPP estimation utilizing advanced machine learning (ML) techniques. Our methodology is juxtaposed against published empirical correlation-based methods on two datasets with limited sizes and diverse inputs. With superior performance over correlation-based estimators, our ML approach demonstrates adaptability and resilience even with restricted training datasets, spanning various fluid classifications. We acquired condensate PVT data from publicly available sources and GeoMark RFDBASE, encompassing dew point pressure (the target variable), as well as compositional data (mole percentages of each component), temperature, molecular weight (MW), and specific gravity (SG) of heptane plus, which served as input variables. Before initiating the study, thorough assessments of measurement quality and results using statistical methods were conducted leveraging domain expertise. Subsequently, advanced ML techniques were employed to train predictive models with cross-validation to mitigate overfitting to the limited datasets. Our models were juxtaposed against the foremost published DDP estimators utilizing empirical correlation-based methods, with correlation-based estimators also trained on the underlying datasets for equitable comparison. To improve outcomes, pseudo-critical properties and artificial proxy features were utilized, leveraging generalized input data.
Eco-Sustainable Cement: Natural Volcanic Tuffs’ Impact on Concrete Strength and Durability
(2024-09-14) Abutaqa, Ala; Mohsen, Mohamed O.; Aburumman, Mervat O.; Senouci, Ahmed; Taha, Ramzi; Maherzi, Walid; Qtiashat, Deya
This study underscores the potential of utilizing natural volcanic tuffs (NVTs) as supplementary cementitious materials (SCMs) in alignment with global sustainability efforts aimed at mitigating the cement industry’s negative impacts on both the economy and the environment. Experimental investigations were conducted on concrete mixtures containing 10%, 20%, 30%, 40%, and 50% NVT as partial cement replacements to assess their influence on concrete’s mechanical and microstructural properties. Based on the findings, concrete samples with 10% NVT replacements exhibited increased flexural and compressive strengths of 35.6% and 5.6%, respectively, compared with ordinary concrete after 28 days. The depth of water penetration in the concrete samples was significantly reduced by the inclusion of NVT, with a maximum reduction of 56.5%. Microstructural analysis using scanning electron microscopy (SEM) revealed enhanced densification of the concrete microstructures, attributed to the high pozzolanic activity of NVT use in cement-based composites. Analysis of variance (ANOVA) revealed statistically significant relationships between NVT content and both the compressive and flexural strengths of the concrete samples. In conclusion, substituting 10% cement with NVT not only enhances the mechanical properties of concrete but also decreases the energy demand for cement production and reduces carbon dioxide (CO₂) emissions, thus contributing to more sustainable construction practices.
Performance of a Modular Ton-Scale Pixel-Readout Liquid Argon Time Projection Chamber
(2024-09-11) Abed Abud, A.; Abi, B.; Acciarri, R.; Acero, M. A.; Adames, M. R.; Adamov, G.; Adamowski, M.; Adams, D.; Adinolfi, M.; Adriano, C.; Aduszkiewicz, A.; Aguilar, J.; Aimard, B.; Akbar, F.; Allison, K.; Monsalve, S. Alonso; Alrashed, M.; Alton, A.; Alvarez, R.; Alves, T.; Amar, H.; Amedo, P.; Anderson, J.; Andrade, D. A.; Andreopoulos, C.; Andreotti, M.; Andrews, M. P.; Andrianala, F.; Andringa, S.; Anfimov, N.; Ankowski, A.; Antoniassi, M.; Antonova, M.; Antoshkin, A.; Aranda-Fernandez, A.; Arellano, L.; Diaz, E. Arrieta; Arroyave, M. A.; Asaadi, J.; Ashkenazi, A.; Asner, D.; Asquith, L.; Atkin, E.; Auguste, D.; Aurisano, A.; Aushev, V.; Autiero, D.; Azfar, F.; Back, A.; Back, H.; Back, J. J.; Bagaturia, I.; Bagby, L.; Balashov, N.; Balasubramanian, S.; Baldi, P.; Baldini, W.; Baldonedo, J.; Baller, B.; Bambah, B.; Banerjee, R.; Barao, F.; Barenboim, G.; Alzás, P. B̃arham; Barker, G. J.; Barkhouse, W.; Barr, G.; Monarca, J. Barranco; Barros, A.; Barros, N.; Barrow, D.; Barrow, J. L.; Basharina-Freshville, A.; Bashyal, A.; Basque, V.; Batchelor, C.; Bathe-Peters, L.; Battat, J. B. R.; Battisti, F.; Bay, F.; Bazetto, M. C. Q.; Alba, J. L. L. Bazo; Beacom, J. F.; Bechetoille, E.; Behera, B.; Belchior, E.; Bell, G.; Bellantoni, L.; Bellettini, G.; Bellini, V.; Beltramello, O.; Benekos, N.; Montiel, C. Benitez; Benjamin, D.; Neves, F. Bento; Berger, J.; Berkman, S.; Bernal, J.; Bernardini, P.; Bersani, A.; Bertolucci, S.; Betancourt, M.; Rodríguez, A. Betancur; Bevan, A.; Bezawada, Y.; Bezerra, A. T.; Bezerra, T. J.; Bhat, A.; Bhatnagar, V.; Bhatt, J.; Bhattacharjee, M.; Bhattacharya, M.; Bhuller, S.; Bhuyan, B.; Biagi, S.; Bian, J.; Biery, K.; Bilki, B.; Bishai, M.; Bitadze, A.; Blake, A.; Blaszczyk, F. D.; Blazey, G. C.; Blucher, E.; Bogenschuetz, J.; Boissevain, J.; Bolognesi, S.; Bolton, T.; Bomben, L.; Bonesini, M.; Bonilla-Diaz, C.; Bonini, F.; Booth, A.; Boran, F.; Bordoni, S.; Merlo, R. Borges; Borkum, A.; Bostan, N.; Bracinik, J.; Braga, D.; Brahma, B.; Brailsford, D.; Bramati, F.; Branca, A.; Brandt, A.; Bremer, J.; Brew, C.; Brice, S. J.; Brio, V.; Brizzolari, C.; Bromberg, C.; Brooke, J.; Bross, A.; Brunetti, G.; Brunetti, M.; Buchanan, N.; Budd, H.; Buergi, J.; Burgardt, D.; Butchart, S.; V., G. Caceres; Cagnoli, I.; Cai, T.; Calabrese, R.; Calcutt, J.; Calin, M.; Calivers, L.; Calvo, E.; Caminata, A.; Camino, A. F.; Campanelli, W.; Campani, A.; Benitez, A. Campos; Canci, N.; Capó, J.; Caracas, I.; Caratelli, D.; Carber, D.; Carceller, J. M.; Carini, G.; Carlus, B.; Carneiro, M. F.; Carniti, P.; Terrazas, I. Caro; Carranza, H.; Carrara, N.; Carroll, L.; Carroll, T.; Carter, A.; Casarejos, E.; Casazza, D.; Forero, J. F. Castaño; Castaño, F. A.; Castillo, A.; Castromonte, C.; Catano-Mur, E.; Cattadori, C.; Cavalier, F.; Cavanna, F.; Centro, S.; Cerati, G.; Cerna, C.; Cervelli, A.; Villanueva, A. Cervera; Chakraborty, K.; Chakraborty, S.; Chalifour, M.; Chappell, A.; Charitonidis, N.; Chatterjee, A.; Chen, H.; Chen, M.; Chen, W. C.; Chen, Y.; Chen-Wishart, Z.; Cherdack, D.; Chi, C.; Chirco, R.; Chitirasreemadam, N.; Cho, K.; Choate, S.; Chokheli, D.; Chong, P. S.; Chowdhury, B.; Christian, D.; Chukanov, A.; Chung, M.; Church, E.; Cicala, M. F.; Cicerchia, M.; Cicero, V.; Ciolini, R.; Clarke, P.; Cline, G.; Coan, T. E.; Cocco, A. G.; Coelho, J. A. B.; Cohen, A.; Collazo, J.; Collot, J.; Conley, E.; Conrad, J. M.; Convery, M.; Copello, S.; Cova, P.; Cox, C.; Cremaldi, L.; Cremonesi, L.; Crespo-Anadón, J. I.; Crisler, M.; Cristaldo, E.; Crnkovic, J.; Crone, G.; Cross, R.; Cudd, A.; Cuesta, C.; Cui, Y.; Curciarello, F.; Cussans, D.; Dai, J.; Dalager, O.; Dallavalle, R.; Dallaway, W.; da Motta, H.; Dar, Z. A.; Darby, R.; Da Silva Peres, L.; David, Q.; Davies, G. S.; Davini, S.; Dawson, J.; De Aguiar, R.; De Almeida, P.; Debbins, P.; De Bonis, I.; Decowski, M. P.; de Gouvêa, A.; De Holanda, P. C.; De Icaza Astiz, I. L.; De Jong, P.; Del Amo Sanchez, P.; De la Torre, A.; De Lauretis, G.; Delbart, A.; Delepine, D.; Delgado, M.; Dell’Acqua, A.; Monache, G. Delle; Delmonte, N.; De Lurgio, P.; Demario, R.; De Matteis, G.; de Mello Neto, J. R. T.; DeMuth, D. M.; Dennis, S.; Densham, C.; Denton, P.; Deptuch, G. W.; De Roeck, A.; De Romeri, V.; Detje, J. P.; Devine, J.; Dharmapalan, R.; Dias, M.; Diaz, A.; Díaz, J. S.; Díaz, F.; Di Capua, F.; Di Domenico, A.; Di Domizio, S.; Di Falco, S.; Di Giulio, L.; Ding, P.; Di Noto, L.; Diociaiuti, E.; Distefano, C.; Diurba, R.; Diwan, M.; Djurcic, Z.; Doering, D.; Dolan, S.; Dolek, F.; Dolinski, M. J.; Domenici, D.; Domine, L.; Donati, S.; Donon, Y.; Doran, S.; Douglas, D.; Doyle, T. A.; Dragone, A.; Drielsma, F.; Duarte, L.; Duchesneau, D.; Duffy, K.; Dugas, K.; Dunne, P.; Dutta, B.; Duyang, H.; Dwyer, D. A.; Dyshkant, A. S.; Dytman, S.; Eads, M.; Earle, A.; Edayath, S.; Edmunds, D.; Eisch, J.; Englezos, P.; Ereditato, A.; Erjavec, T.; Escobar, C. O.; Evans, J. J.; Ewart, E.; Ezeribe, A. C.; Fahey, K.; Fajt, L.; Falcone, A.; Fani’, M.; Farnese, C.; Farrell, S.; Farzan, Y.; Fedoseev, D.; Felix, J.; Feng, Y.; Fernandez-Martinez, E.; Ferry, G.; Fields, L.; Filip, P.; Filkins, A.; Filthaut, F.; Fine, R.; Fiorillo, G.; Fiorini, M.; Fogarty, S.; Foreman, W.; Fowler, J.; Franc, J.; Francis, K.; Franco, D.; Franklin, J.; Freeman, J.; Fried, J.; Friedland, A.; Fuess, S.; Furic, I. K.; Furman, K.; Furmanski, A. P.; Gaba, R.; Gabrielli, A.; Gago, A. M.; Galizzi, F.; Gallagher, H.; Gallas, A.; Gallice, N.; Galymov, V.; Gamberini, E.; Gamble, T.; Ganacim, F.; Gandhi, R.; Ganguly, S.; Gao, F.; Gao, S.; Garcia-Gamez, D.; García-Peris, M. Á.; Gardim, F.; Gardiner, S.; Gastler, D.; Gauch, A.; Gauvreau, J.; Gauzzi, P.; Gazzana, S.; Ge, G.; Geffroy, N.; Gelli, B.; Gent, S.; Gerlach, L.; Ghorbani-Moghaddam, Z.; Giammaria, T.; Gibin, D.; Gil-Botella, I.; Gilligan, S.; Gioiosa, A.; Giovannella, S.; Girerd, C.; Giri, A. K.; Giugliano, C.; Giusti, V.; Gnani, D.; Gogota, O.; Gollapinni, S.; Gollwitzer, K.; Gomes, R. A.; Bermeo, L. V. Gomez; Fajardo, L. S. Gomez; Gonnella, F.; Gonzalez-Diaz, D.; Gonzalez-Lopez, M.; Goodman, M. C.; Goswami, S.; Gotti, C.; Goudeau, J.; Goudzovski, E.; Grace, C.; Gramellini, E.; Gran, R.; Granados, E.; Granger, P.; Grant, C.; Gratieri, D. R.; Grauso, G.; Green, P.; Greenberg, S.; Greer, J.; Griffith, W. C.; Groetschla, F. T.; Grzelak, K.; Gu, L.; Gu, W.; Guarino, V.; Guarise, M.; Guenette, R.; Guerard, E.; Guerzoni, M.; Guffanti, D.; Guglielmi, A.; Guo, B.; Guo, Y.; Gupta, A.; Gupta, V.; Gurung, G.; Gutierrez, D.; Guzowski, P.; Guzzo, M. M.; Gwon, S.; Habig, A.; Hadavand, H.; Haegel, L.; Haenni, R.; Hagaman, L.; Hahn, A.; Haiston, J.; Hakenmueller, J.; Hamernik, T.; Hamilton, P.; Hancock, J.; Happacher, F.; Harris, D. A.; Hartnell, J.; Hartnett, T.; Harton, J.; Hasegawa, T.; Hasnip, C.; Hatcher, R.; Hayrapetyan, K.; Hays, J.; Hazen, E.; He, M.; Heavey, A.; Heeger, K. M.; Heise, J.; Henry, S.; Morquecho, M. A. Hernandez; Herner, K.; Hewes, V.; Higuera, A.; Hilgenberg, C.; Hillier, S. J.; Himmel, A.; Hinkle, E.; Hirsch, L. R.; Ho, J.; Hoff, J.; Holin, A.; Holvey, T.; Hoppe, E.; Horiuchi, S.; Horton-Smith, G. A.; Hostert, M.; Houdy, T.; Howard, B.; Howell, R.; Hristova, I.; Hronek, M. S.; Huang, J.; Huang, R. G.; Hulcher, Z.; Ibrahim, M.; Iles, G.; Ilic, N.; Iliescu, A. M.; Illingworth, R.; Ingratta, G.; Ioannisian, A.; Irwin, B.; Isenhower, L.; Oliveira, M. Ismerio; Itay, R.; Jackson, C. M.; Jain, V.; James, E.; Jang, W.; Jargowsky, B.; Jena, D.; Jentz, I.; Ji, X.; Jiang, C.; Jiang, J.; Jiang, L.; Jipa, A.; Joaquim, F. R.; Johnson, W.; Jollet, C.; Jones, B.; Jones, R.; Fernández, D. José; Jovancevic, N.; Judah, M.; Jung, C. K.; Junk, T.; Jwa, Y.; Kabirnezhad, M.; Kaboth, A. C.; Kadenko, I.; Kakorin, I.; Kalitkina, A.; Kalra, D.; Kandemir, M.; Kaplan, D. M.; Karagiorgi, G.; Karaman, G.; Karcher, A.; Karyotakis, Y.; Kasai, S.; Kasetti, S. P.; Kashur, L.; Katsioulas, I.; Kauther, A.; Kazaryan, N.; Ke, L.; Kearns, E.; Keener, P. T.; Kelly, K. J.; Kemp, E.; Kemularia, O.; Kermaidic, Y.; Ketchum, W.; Kettell, S. H.; Khabibullin, M.; Khan, N.; Khvedelidze, A.; Kim, D.; Kim, J.; Kim, M.; King, B.; Kirby, B.; Kirby, M.; Kish, A.; Klein, J.; Kleykamp, J.; Klustova, A.; Kobilarcik, T.; Koch, L.; Koehler, K.; Koerner, L. W.; Koh, D. H.; Kolupaeva, L.; Korablev, D.; Kordosky, M.; Kosc, T.; Kose, U.; Kostelecký, V. A.; Kothekar, K.; Kotler, I.; Kovalcuk, M.; Kozhukalov, V.; Krah, W.; Kralik, R.; Kramer, M.; Kreczko, L.; Krennrich, F.; Kreslo, I.; Kroupova, T.; Kubota, S.; Kubu, M.; Kudenko, Y.; Kudryavtsev, V. A.; Kufatty, G.; Kuhlmann, S.; Kumar, J.; Kumar, P.; Kumaran, S.; Kunze, P.; Kunzmann, J.; Kuravi, R.; Kurita, N.; Kuruppu, C.; Kus, V.; Kutter, T.; Kvasnicka, J.; Labree, T.; Lackey, T.; Lambert, A.; Land, B. J.; Lane, C. E.; Lane, N.; Lang, K.; Langford, T.; Langstaff, M.; Lanni, F.; Lantwin, O.; Larkin, J.; Lasorak, P.; Last, D.; Laudrain, A.; Laundrie, A.; Laurenti, G.; Lavaut, E.; Lawrence, A.; Laycock, P.; Lazanu, I.; Lazzaroni, M.; Le, T.; Leardini, S.; Learned, J.; LeCompte, T.; Lee, C.; Legin, V.; Miotto, G. Lehmann; Lehnert, R.; de Oliveira, M. A. Leigui; Leitner, M.; Silverio, D. Leon; Lepin, L. M.; Li, J.-Y.; Li, S. W.; Li, Y.; Liao, H.; Lin, C. S.; Lindebaum, D.; Linden, S.; Lineros, R. A.; Ling, J.; Lister, A.; Littlejohn, B. R.; Liu, H.; Liu, J.; Liu, Y.; Lockwitz, S.; Lokajicek, M.; Lomidze, I.; Long, K.; Lopes, T. V.; Lopez, J.; de Rego, I. López; López-March, N.; Lord, T.; LoSecco, J. M.; Louis, W. C.; Sanchez, A. Lozano; Lu, X.-G.; Luk, K. B.; Lunday, B.; Luo, X.; Luppi, E.; Maalmi, J.; MacFarlane, D.; Machado, A. A.; Machado, P.; Macias, C. T.; Macier, J. R.; MacMahon, M.; Maddalena, A.; Madera, A.; Madigan, P.; Magill, S.; Magueur, C.; Mahn, K.; Maio, A.; Major, A.; Majumdar, K.; Man, M.; Mandujano, R. C.; Maneira, J.; Manly, S.; Mann, A.; Manolopoulos, K.; Plata, M. Manrique; Corchado, S. Manthey; Manyam, V. N.; Marchan, M.; Marchionni, A.; Marciano, W.; Marfatia, D.; Mariani, C.; Maricic, J.; Marinho, F.; Marino, A. D.; Markiewicz, T.; Das Chagas Marques, F.; Marquet, C.; Marsden, D.; Marshak, M.; Marshall, C. M.; Marshall, J.; Martina, L.; Martín-Albo, J.; Martinez, N.; Caicedo, D. A. Martinez; López, F. Martínez; Miravé, P. Martínez; Martynenko, S.; Mascagna, V.; Massari, C.; Mastbaum, A.; Matichard, F.; Matsuno, S.; Matteucci, G.; Matthews, J.; Mauger, C.; Mauri, N.; Mavrokoridis, K.; Mawby, I.; Mazza, R.; Mazzacane, A.; McAskill, T.; McConkey, N.; McFarland, K. S.; McGrew, C.; McNab, A.; Meazza, L.; Meddage, V. C. N.; Mehta, B.; Mehta, P.; Melas, P.; Mena, O.; Mendez, H.; Mendez, P.; Méndez, D. P.; Menegolli, A.; Meng, G.; Mercuri, A. C. E. A.; Meregaglia, A.; Messier, M. D.; Metallo, S.; Metcalf, J.; Metcalf, W.; Mewes, M.; Meyer, H.; Miao, T.; Miccoli, A.; Michna, G.; Mikola, V.; Milincic, R.; Miller, F.; Miller, G.; Miller, W.; Mineev, O.; Minotti, A.; Miralles, L.; Miranda, O. G.; Mironov, C.; Miryala, S.; Miscetti, S.; Mishra, C. S.; Mishra, S. R.; Mislivec, A.; Mitchell, M.; Mladenov, D.; Mocioiu, I.; Mogan, A.; Moggi, N.; Mohanta, R.; Mohayai, T. A.; Mokhov, N.; Molina, J.; Bueno, L. Molina; Montagna, E.; Montanari, A.; Montanari, C.; Montanari, D.; Montanino, D.; Zetina, L. M. Montaño; Mooney, M.; Moor, A. F.; Moore, Z.; Moreno, D.; Moreno-Palacios, O.; Morescalchi, L.; Moretti, D.; Moretti, R.; Morris, C.; Mossey, C.; Mote, M.; Moura, C. A.; Mouster, G.; Mu, W.; Mualem, L.; Mueller, J.; Muether, M.; Muheim, F.; Muir, A.; Mulhearn, M.; Munford, D.; Munteanu, L. J.; Muramatsu, H.; Muraz, J.; Murphy, M.; Murphy, T.; Muse, J.; Mytilinaki, A.; Nachtman, J.; Nagai, Y.; Nagu, S.; Nandakumar, R.; Naples, D.; Narita, S.; Nath, A.; Navrer-Agasson, A.; Nayak, N.; Nebot-Guinot, M.; Nehm, A.; Nelson, J. K.; Neogi, O.; Nesbit, J.; Nessi, M.; Newbold, D.; Newcomer, M.; Nichol, R.; Nicolas-Arnaldos, F.; Nikolica, A.; Nikolov, J.; Niner, E.; Nishimura, K.; Norman, A.; Norrick, A.; Novella, P.; Nowak, J. A.; Oberling, M.; Ochoa-Ricoux, J. P.; Oh, S.; Oh, S. B.; Olivier, A.; Olshevskiy, A.; Olson, T.; Onel, Y.; Onishchuk, Y.; Oranday, A.; Osbiston, M.; Vélez, J. A. Osorio; Ormachea, L. Otiniano; Ott, J.; Pagani, L.; Palacio, G.; Palamara, O.; Palestini, S.; Paley, J. M.; Pallavicini, M.; Palomares, C.; Pan, S.; Panda, P.; Vazquez, W. Panduro; Pantic, E.; Paolone, V.; Papadimitriou, V.; Papaleo, R.; Papanestis, A.; Papoulias, D.; Paramesvaran, S.; Paris, A.; Parke, S.; Parozzi, E.; Parsa, S.; Parsa, Z.; Parveen, S.; Parvu, M.; Pasciuto, D.; Pascoli, S.; Pasqualini, L.; Pasternak, J.; Patrick, C.; Patrizii, L.; Patterson, R. B.; Patzak, T.; Paudel, A.; Paulucci, L.; Pavlovic, Z.; Pawloski, G.; Payne, D.; Pec, V.; Pedreschi, E.; Peeters, S. J. M.; Pellico, W.; Perez, A. Pena; Pennacchio, E.; Penzo, A.; Peres, O. L. G.; Gonzalez, Y. F. Perez; Pérez-Molina, L.; Pernas, C.; Perry, J.; Pershey, D.; Pessina, G.; Petrillo, G.; Petta, C.; Petti, R.; Pfaff, M.; Pia, V.; Pickering, L.; Pietropaolo, F.; Pimentel, V. L.; Pinaroli, G.; Pinchault, J.; Pitts, K.; Plows, K.; Plunkett, R.; Pollack, C.; Pollman, T.; Polo-Toledo, D.; Pompa, F.; Pons, X.; Poonthottathil, N.; Popov, V.; Poppi, F.; Porter, J.; Potekhin, M.; Potenza, R.; Pozimski, J.; Pozzato, M.; Prakash, T.; Pratt, C.; Prest, M.; Psihas, F.; Pugnere, D.; Qian, X.; Raaf, J. L.; Radeka, V.; Rademacker, J.; Radics, B.; Rafique, A.; Raguzin, E.; Rai, M.; Rajagopalan, S.; Rajaoalisoa, M.; Rakhno, I.; Rakotondravohitra, L.; Ralte, L.; Delgado, M. A. Ramirez; Ramson, B.; Rappoldi, A.; Raselli, G.; Ratoff, P.; Ray, R.; Razafinime, H.; Rea, E. M.; Real, J. S.; Rebel, B.; Rechenmacher, R.; Reggiani-Guzzo, M.; Reichenbacher, J.; Reitzner, S. D.; Sfar, H. Rejeb; Renner, E.; Renshaw, A.; Rescia, S.; Resnati, F.; Restrepo, D.; Reynolds, C.; Ribas, M.; Riboldi, S.; Riccio, C.; Riccobene, G.; Ricol, J. S.; Rigan, M.; Rincón, E. V.; Ritchie-Yates, A.; Ritter, S.; Rivera, D.; Rivera, R.; Robert, A.; Rocha, J. L. Rocabado; Rochester, L.; Roda, M.; Rodrigues, P.; Alonso, M. J. Rodriguez; Rondon, J. Rodriguez; Rosauro-Alcaraz, S.; Rosier, P.; Ross, D.; Rossella, M.; Rossi, M.; Ross-Lonergan, M.; Roy, N.; Roy, P.; Rubbia, C.; Ruggeri, A.; Ferreira, G. Ruiz; Russell, B.; Ruterbories, D.; Rybnikov, A.; Saa-Hernandez, A.; Saakyan, R.; Sacerdoti, S.; Sahoo, S. K.; Sahu, N.; Sala, P.; Samios, N.; Samoylov, O.; Sanchez, M. C.; Bravo, A. Sánchez; Sanchez-Lucas, P.; Sandberg, V.; Sanders, D. A.; Sanfilippo, S.; Sankey, D.; Santoro, D.; Saoulidou, N.; Sapienza, P.; Sarasty, C.; Sarcevic, I.; Sarra, I.; Savage, G.; Savinov, V.; Scanavini, G.; Scaramelli, A.; Scarff, A.; Schefke, T.; Schellman, H.; Schifano, S.; Schlabach, P.; Schmitz, D.; Schneider, A. W.; Scholberg, K.; Schukraft, A.; Schuld, B.; Segade, A.; Segreto, E.; Selyunin, A.; Senise, C. R.; Sensenig, J.; Shaevitz, M. H.; Shanahan, P.; Sharma, P.; Kumar, R.; Shaw, K.; Shaw, T.; Shchablo, K.; Shen, J.; Shepherd-Themistocleous, C.; Sheshukov, A.; Shi, W.; Shin, S.; Shivakoti, S.; Shoemaker, I.; Shooltz, D.; Shrock, R.; Siddi, B.; Siden, M.; Silber, J.; Simard, L.; Sinclair, J.; Sinev, G.; Singh, Jaydip; Singh, J.; Singh, L.; Singh, P.; Singh, V.; Chauhan, S. Singh; Sipos, R.; Sironneau, C.; Sirri, G.; Siyeon, K.; Skarpaas, K.; Smedley, J.; Smith, E.; Smith, J.; Smith, P.; Smolik, J.; Smy, M.; Snape, M.; Snider, E. L.; Snopok, P.; Snowden-Ifft, D.; Nunes, M. Soares; Sobel, H.; Soderberg, M.; Sokolov, S.; Salinas, C. J. Solano; Söldner-Rembold, S.; Soleti, S. R.; Solomey, N.; Solovov, V.; Sondheim, W. E.; Sorel, M.; Sotnikov, A.; Soto-Oton, J.; Sousa, A.; Soustruznik, K.; Spinella, F.; Spitz, J.; Spooner, N. J. C.; Spurgeon, K.; Stalder, D.; Stancari, M.; Stanco, L.; Steenis, J.; Stein, R.; Steiner, H. M.; Lisbôa, A. F. Steklain; Stepanova, A.; Stewart, J.; Stillwell, B.; Stock, J.; Stocker, F.; Stokes, T.; Strait, M.; Strauss, T.; Strigari, L.; Stuart, A.; Suarez, J. G.; Subash, J.; Surdo, A.; Suter, L.; Sutera, C. M.; Sutton, K.; Suvorov, Y.; Svoboda, R.; Swain, S. K.; Szczerbinska, B.; Szelc, A. M.; Sztuc, A.; Taffara, A.; Talukdar, N.; Tamara, J.; Tanaka, H. A.; Tang, S.; Taniuchi, N.; Casanova, A. M. Tapia; Oregui, B. Tapia; Tapper, A.; Tariq, S.; Tarpara, E.; Tatar, E.; Tayloe, R.; Tedeschi, D.; Teklu, A. M.; Vidal, J. Tena; Tennessen, P.; Tenti, M.; Terao, K.; Terranova, F.; Testera, G.; Thakore, T.; Thea, A.; Thiebault, A.; Thomas, S.; Thompson, A.; Thorn, C.; Timm, S. C.; Tiras, E.; Tishchenko, V.; Todorović, N.; Tomassetti, L.; Tonazzo, A.; Torbunov, D.; Torti, M.; Tortola, M.; Tortorici, F.; Tosi, N.; Totani, D.; Toups, M.; Touramanis, C.; Tran, D.; Travaglini, R.; Trevor, J.; Triller, E.; Trilov, S.; Truchon, J.; Truncali, D.; Trzaska, W. H.; Tsai, Y.; Tsai, Y.-T.; Tsamalaidze, Z.; Tsang, K. V.; Tsverava, N.; Tu, S. Z.; Tufanli, S.; Tunnell, C.; Turner, J.; Tuzi, M.; Tyler, J.; Tyley, E.; Tzanov, M.; Uchida, M. A.; González, J. Ureña; Urheim, J.; Usher, T.; Utaegbulam, H.; Uzunyan, S.; Vagins, M. R.; Vahle, P.; Valder, S.; Valdiviesso, G. A.; Valencia, E.; Valentim, R.; Vallari, Z.; Vallazza, E.; Valle, J. W. F.; Van Berg, R.; Van de Water, R. G.; Forero, D. V.; Vannozzi, A.; Van Nuland-Troost, M.; Varanini, F.; Oliva, D. Vargas; Vasina, S.; Vaughan, N.; Vaziri, K.; Vázquez-Ramos, A.; Vega, J.; Ventura, S.; Verdugo, A.; Vergani, S.; Verzocchi, M.; Vetter, K.; Vicenzi, M.; de Souza, H. Vieira; Vignoli, C.; Vilela, C.; Villa, E.; Viola, S.; Viren, B.; Vizcaya-Hernandez, A.; Vrba, T.; Vuong, Q.; Waldron, A. V.; Wallbank, M.; Walsh, J.; Walton, T.; Wang, H.; Wang, J.; Wang, L.; Wang, M. H. L. S.; Wang, X.; Wang, Y.; Warburton, K.; Warner, D.; Warsame, L.; Wascko, M. O.; Waters, D.; Watson, A.; Wawrowska, K.; Weber, A.; Weber, C. M.; Weber, M.; Wei, H.; Weinstein, A.; Wenzel, H.; Westerdale, S.; Wetstein, M.; Whalen, K.; Whilhelmi, J.; White, A.; White, A.; Whitehead, L. H.; Whittington, D.; Wilking, M. J.; Wilkinson, A.; Wilkinson, C.; Wilson, F.; Wilson, R. J.; Winter, P.; Wisniewski, W.; Wolcott, J.; Wolfs, J.; Wongjirad, T.; Wood, A.; Wood, K.; Worcester, E.; Worcester, M.; Wospakrik, M.; Wresilo, K.; Wret, C.; Wu, S.; Wu, W.; Wu, W.; Wurm, M.; Wyenberg, J.; Xiao, Y.; Xiotidis, I.; Yaeggy, B.; Yahlali, N.; Yandel, E.; Yang, K.; Yang, T.; Yankelevich, A.; Yershov, N.; Yonehara, K.; Young, T.; Yu, B.; Yu, H.; Yu, J.; Yu, Y.; Yuan, W.; Zaki, R.; Zalesak, J.; Zambelli, L.; Zamorano, B.; Zani, A.; Zapata, O.; Zazueta, L.; Zeller, G. P.; Zennamo, J.; Zeug, K.; Zhang, C.; Zhang, S.; Zhao, M.; Zhivun, E.; Zimmerman, E. D.; Zucchelli, S.; Zuklin, J.; Zutshi, V.; Zwaska, R.; on behalf of the DUNE Collaboration,
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.
Deep Learning for Pavement Condition Evaluation Using Satellite Imagery
(2024-09-09) Lebaku, Prathyush Kumar Reddy; Gao, Lu; Lu, Pan; Sun, Jingran
Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future.
Net-Zero Greenhouse Gas Emission Electrified Aircraft Propulsion for Large Commercial Transport
(2024-09-08) Huang, Hao; Rajashekara, Kaushik
Until recently, electrified aircraft propulsion (EAP) technology development has been driven by the dual objectives of reducing greenhouse gas (GHG) emissions and addressing the depletion of fossil fuels. However, the increasing severity of climate change, posing a significant threat to all life forms, has resulted in the global consensus of achieving net-zero GHG emissions by 2050. This major shift has alerted the aviation electrification industry to consider the following: What is the clear path forward for EAP technology development to support the net-zero GHG goals for large commercial transport aviation? The purpose of this paper is to answer this question. After identifying four types of GHG emissions that should be used as metrics to measure the effectiveness of each technology for GHG reduction, the paper presents three significant categories of GHG reduction efforts regarding the engine, evaluates the potential of EAP technologies within each category as well as combinations of technologies among the different categories using the identified metrics, and thus determines the path forward to support the net-zero GHG objective. Specifically, the paper underscores the need for the aviation electrification industry to adapt, adjust, and integrate its EAP technology development into the emerging new engine classes. These innovations and collaborations are crucial to accelerate net-zero GHG efforts effectively.
On Extending the Applicability of Iterative Methods for Solving Systems of Nonlinear Equations
(2024-09-04) Bate, Indra; Murugan, Muniyasamy; George, Santhosh; Senapati, Kedarnath; Argyros, Ioannis K.; Regmi, Samundra
In this paper, we present a technique that improves the applicability of the result obtained by Cordero et al. in 2024 for solving nonlinear equations. Cordero et al. assumed the involved operator to be differentiable at least five times to extend a two-step p-order method to order p+3. We obtained the convergence order of Cordero et al.’s method by assuming only up to the third-order derivative of the operator. Our analysis is in a more general commutative Banach algebra setting and provides a radius of the convergence ball. Finally, we validate our theoretical findings with several numerical examples. Also, the concept of basin of attraction is discussed with examples.
The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists
(2024-08-27) Nardone, Valerio; Marmorino, Federica; Germani, Marco Maria; Cichowska-Cwalińska, Natalia; Menditti, Vittorio Salvatore; Gallo, Paolo; Studiale, Vittorio; Taravella, Ada; Landi, Matteo; Reginelli, Alfonso; Cappabianca, Salvatore; Girnyi, Sergii; Cwalinski, Tomasz; Boccardi, Virginia; Goyal, Aman; Skokowski, Jaroslaw; Oviedo, Rodolfo J.; Abou-Mrad, Adel; Marano, Luigi
The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients—surgeons, medical oncologists, and radiation oncologists—on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.
Atomristor Mott Theory of Sn Adatom Adlayer on a Si Surface
(2024-08-02) Craco, Luis; Chagas, Edson F.; Carara, Sabrina S.; Freelon, Byron
We use a combination of density functional theory (DFT) and dynamical mean field theory (DMFT) to unveil orbital field-induced electronic structure reconstruction of the atomic Sn layer deposited onto a Si(111) surface (Sn/Si(111)−3×3R30∘), also referred to as α-Sn. Our DFT + DMFT results indicate that α-Sn is an ideal testing ground to explore electric field-driven orbital selectivity and Mott memory behavior, all arising from the close proximity of α-Sn to metal insulator transitions. We discuss the relevance of orbital phase changes for α-Sn in the context of the current–voltage (I−V) characteristic for future silicon-based metal semiconductor atomristors.
Probiotics Alter the Microbial and Behavioral Consequences of Methamphetamine Exposure in a Sex-Selective Manner
(2024-07-01) Forouzan, Shadab; Hoffman, Kristi L.; Kosten, Therese A.
Methamphetamine use disorder (MuD) is a global health problem, with no FDA-approved medications. Our prior work demonstrated that repeated methamphetamine exposure alters the gut microbiota in male rats and results in depressive-like behaviors. In this study, we extend our findings to females and determine whether probiotics block these effects. Male and female rats were administered methamphetamine (2 mg/kg; SC) or saline twice daily with either a combination of two probiotics (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) or placebo solution for 14 days. Fecal samples were collected at baseline and other days after treatment cessation. Tests of anxiety- and depressive-like behaviors were conducted using open-field and forced-swim assays. Methamphetamine induced anxiety-like behavior in females and anxiety-like and depressive-like behaviors in males. Probiotics blocked the depressive-like effect in males but did not alter anxiety-like effects in either sex. Methamphetamine exposure decreased levels of alpha diversity in both sexes, but sex differences were seen in the ability of probiotics or methamphetamine to alter levels of various bacteria. These findings support the role of the gut–brain microbiome in the depressive effects of repeated methamphetamine exposure in males, suggesting that probiotics may be a viable treatment option for MuD.
Targeting Liver X Receptors in Cancer Drug Discovery
(2024-06-29) Premaratne, Asitha; Bagchi, Abhinav; Basu, Shinjini; Gustafsson, Jan-Åke; Lin, Chin-Yo
Liver X receptors (LXRs) are members of the nuclear receptor superfamily of ligand-dependent transcription factors. LXRα is predominantly expressed in metabolic tissues, whereas LXRβ is ubiquitously expressed. Upon ligand binding, they regulate the expression of target genes involved in lipid metabolism, cholesterol homeostasis, and immune responses, including those which function in pathways that are commonly reprogrammed during carcinogenesis. Known LXR ligands include oxysterols and natural and synthetic agonists which upregulate LXR transcriptional activity and target gene expression. Synthetic inverse agonists have also been identified that inhibit LXR activity. While both types of ligands have been shown to inhibit cancer cells and tumor growth either directly or indirectly by modulating the activities of stromal cells within the tumor microenvironment, they appear to target different aspects of cancer metabolism and other cancer hallmarks, including immune evasion. This review summarizes the characterization of LXRs and their ligands and their mechanisms of action in cancer models and discusses the future directions for translating these discoveries into novel cancer therapeutics.
Identifying Rare Earth Elements Using a Tripod and Drone-Mounted Hyperspectral Camera: A Case Study of the Mountain Pass Birthday Stock and Sulphide Queen Mine Pit, California
(2024-09-09) Qasim, Muhammad; Khan, Shuhab D.; Sisson, Virginia; Greer, Presley; Xia, Lin; Okyay, Unal; Franco, Nicole
As the 21st century advances, the demand for rare earth elements (REEs) is rising, necessitating more robust exploration methods. Our research group is using hyperspectral remote sensing as a tool for mapping REEs. Unique spectral features of bastnaesite mineral, has proven effective for detection of REE with both spaceborne and airborne data. In our study, we collected hyperspectral data using a Senop hyperspectral camera in field and a SPECIM hyperspectral camera in the laboratory settings. Data gathered from California’s Mountain Pass district revealed bastnaesite-rich zones and provided detailed insights into bastnaesite distribution within rocks. Further analysis identified specific bastnaesite-rich rock grains. Our results indicated higher concentrations of bastnaesite in carbonatite rocks compared to alkaline igneous rocks. Additionally, rocks from the Sulphide Queen mine showed richer bastnaesite concentrations than those from the Birthday shonkinite stock. Results were validated with thin-section studies and geochemical data, confirming the reliability across different hyperspectral data modalities. This study demonstrates the potential of drone-based hyperspectral technology in augmenting conventional mineral mapping methods and aiding the mining industry in making informed decisions about mining REEs efficiently and effectively.
Hydraulic Fracture Closure Detection Techniques: A Comprehensive Review
(2024-09-05) Gabry, Mohamed Adel; Eltaleb, Ibrahim; Ramadan, Amr; Rezaei, Ali; Soliman, Mohamed Y.
This study reviews methods for detecting fracture closure pressure in both unconventional and conventional reservoirs using mathematical models and fluid flow equations. It evaluates techniques such as the Nolte method, tangent method, and compliance method. The investigation relies on observing changes in fluid flow regimes from preclosure to postclosure using fluid flow equations to examine the postclosure flow regime effect on the G function. Reverse calculations model pressure decline across synthesized flow regimes, facilitating a detailed investigation of the closure process. The analysis reveals that the tangent method is sensitive to postclosure fluid flow, while the compliance method is less effective in reservoirs with significant tortuosity or natural fractures. This paper recommends assessing natural fractures’ characteristics and permeability to identify the source of leak-off before selecting a technique. It proposes integrating various methods to comprehensively understand subsurface formations, combining their strengths for accurate fracture closure identification and a better understanding of subsurface formations. The new proposed workflow employs the continuous wavelet transform (CWT) technique for fracture closure detection, avoiding physical model preassumptions or simplifications to confirm the results. This approach offers guidance on selecting appropriate methods by integrating different techniques.
Bioactive Compounds and the Antioxidant Activity of Selected Vegetable Microgreens: A Correlation Study
(2024-08-20) Stajčić, Slađana; Ćetković, Gordana; Tumbas Šaponjac, Vesna; Travičić, Vanja; Ilić, Petar; Brunet, Sara; Tomić, Ana
In this study, the content of bioactive compounds and antioxidant activity was determined in five selected vegetable microgreens (daikon, i.e., Japanese radish; Chinese red radish; pea; beetroot; and onion). Total phenolics and chlorophylls were analyzed spectrophotometrically in all investigated microgreens. In addition, the content of betalains was determined spectrophotometrically in beetroot microgreens. HPLC analysis was used to identify and quantify individual phenolic compounds. The antioxidant activity of microgreens was determined by DPPH, ABTS and reducing power assays. The highest content of total phenolics, chlorophyll a and chlorophyll b was found in beetroot microgreens (639.85 mg GAE/100 g DW, 202.17 mg/100 g DW and 79.53 mg/100 g DW, respectively). In beetroot microgreens, the content of total betalains, betacyanins and betaxanthins was determined to be 57.27 mg/100 g DW, 43.58 mg BE/100 g DW and 13.68 mg VE/100 g DW, respectively. Among the investigated microgreens, beetroot microgreens showed the highest antioxidant activity, while pea microgreens exhibited the lowest antioxidant activity in all applied assays. The highest correlation was observed for the content of total phenolics and phenolic acids, as determined by HPLC analysis with antioxidant activity using all applied assays, indicating that these compounds were most important contributors to the antioxidant activity of the investigated vegetable microgreens.
Myocarditis, Myositis, and Myasthenia Gravis Overlap Syndrome Associated with Immune Checkpoint Inhibitors: A Systematic Review
(2024-08-16) Lipe, Demis N.; Qdaisat, Aiham; Krishnamani, Pavitra P.; Nguyen, Trung D.; Chaftari, Patrick; El Messiri, Nour; Srinivasan, Aswin; Galvis-Carvajal, Elkin; Reyes-Gibby, Cielito C.; Wattana, Monica K.
Immune checkpoint inhibitors (ICIs) have significantly transformed cancer treatment, but their use is linked to immune-related adverse events (irAEs), including the rare ICI-associated myocarditis, myositis, and myasthenia gravis (MMM) overlap syndrome. This systematic review aims to highlight MMM’s clinical implications in emergency departments. PubMed and Embase were searched using a specific search strategy. Reports were eligible for inclusion if all three conditions were present and associated with the use of an ICI. Data were extracted by independent reviewers using the Rayyan web application for systematic reviews. Descriptive statistics and qualitative synthesis were used to summarize demographic, clinical, and treatment data for the reported cases. Among 50 cases, predominantly associated with melanoma, lung cancer, and renal cancer, the in-hospital mortality rate was 38.0%. The most commonly presenting symptoms were ptosis (58%), dyspnea (48%), diplopia (42%), or myalgia (36%). The median time from ICI initiation to MMM presentation was 21 days (interquartile range: 15–28 days). Corticosteroids were the primary treatment for the irAEs. MMM, a rare but potentially fatal complication of ICI therapy, requires prompt recognition in emergency settings. Corticosteroids should be initiated if suspected, without waiting for confirmation. Multidisciplinary collaboration is vital for diagnosis and treatment planning. Research on MMM’s link to specific cancers and ICIs is imperative for better risk assessment and interventions.
Lessons Learned from the Process of Water Injection Management in Impactful Onshore and Offshore Carbonate Reservoirs
(2024-08-09) Du, Xuejia; Thakur, Ganesh C.
This paper presents a comprehensive analysis of water injection management practices for complex and impactful onshore and offshore carbonate reservoirs. It delves into the fundamental aspects of waterflooding design, surveillance techniques, and monitoring methods tailored for the unique challenges posed by carbonate formations. Two case studies from the Permian Basin in Texas and two from Lula Field offshore Brazil and Agbami Field offshore Nigeria are examined considering scientific principles into practice to provide insights into best practices, lessons learned, and strategies to maximize the benefits derived from real noteworthy waterflood operations. The paper underscores the significance of rigorous reservoir characterization, including understanding reservoir architecture, heterogeneities, fracture networks, fluid communication pathways, and rock–fluid interactions. It emphasizes the crucial role of integrated multidisciplinary teams involving geologists, reservoir engineers, production engineers, and field operators in ensuring successful waterflood design, implementation, and optimization. Through the case studies, the paper highlights the importance of designing pattern configurations, well placements, and injection/production strategies to the specific reservoir characteristics, continually optimizing these elements based on surveillance data. It also stresses the necessity of comprehensive data acquisition, advanced analytics, numerical simulations, and frequent model updates for effective reservoir management and decision-making. The paper is impactful in terms of the lessons learned from the actual case studies, and how can these be implemented in actual field projects. Different case studies documented in the paper provide the challenges facing them and how different authors have addressed their problems in unique ways. The paper distills the information and important findings from a variety of case studies and provides succinct information that is of immense value as a reference. Important findings of these case studies are connected using creativity and are innovative as they introduce unique techniques and establish successful ideas to create new value in terms of maximizing oil recovery. Most importantly, this paper explores the application of innovative technologies, such as intelligent completions, 4D seismic monitoring, and water–alternating gas (WAG) injection, which can significantly improve waterflood performance in complex carbonate reservoirs. In summary, the paper provides a thorough understanding of the factors contributing to the success and failure of waterfloods in carbonate reservoirs through case studies based on factually and technically sound operations. It documents guidelines for optimizing waterflood performance and reducing or eliminating the potential for failures, reinforcing positive results in these challenging yet invaluable hydrocarbon resources.
A Monocentric Analysis of Implantable Ports in Cancer Treatment: Five-Year Efficacy and Safety Evaluation
(2024-08-09) Abou-Mrad, Adel; Marano, Luigi; Oviedo, Rodolfo J.
Background: Daily clinical practice requires repeated and prolonged venous access for delivering chemotherapy, antibiotics, antivirals, parenteral nutrition, or blood transfusions. This study aimed to investigate the performance and the safety of totally implantable vascular access devices (TIVADs) over a 5-year follow-up period through a standardized well-trained surgical technique and patient management under local anesthesia. Methods: In a retrospective, observational, and monocentric study, 70 patients receiving POLYSITE^® TIVADs for chemotherapy were included. The safety endpoints focused on the rate of perioperative, short-term, and long-term complications. The performance endpoints included vein identification for device insertion and procedural success rate. Results: The study demonstrated no perioperative or short-term complications related to the TIVADs. One (1.4%) complication related to device manipulation was identified as catheter flipping, which led to catheter adjustment 56 days post-placement. Moreover, one (1.4%) infection due to usage conditions was observed, leading to TIVAD removal 3 years and 4 months post-surgery. Catheter placement occurred in cephalic veins (71.4%), subclavian veins (20%), and internal jugular veins (8.6%). The procedural success rate was 100%. Overall, the implantable ports typically remained in place for an average of 22.4 months. Conclusions: This study confirmed the TIVADs’ performance and safety, underscored by low complication rates compared to published data, thereby emphasizing its potential and compelling significance for enhancing routine clinical practice using a standardized well-trained surgical technique and patient management.
Resilience in Action through Culture: Latinas Successfully Navigating STEM Spaces at an HSI
(2024-08-06) Perez, Emma Claudia; Gonzalez, Elsa Maria; Sanchez Hernandez, Isabella
Though research on the perspectives and assets of communities of color in higher education has grown, understanding how underrepresented groups in STEM use those assets to navigate and succeed in STEM fields is still in progress. In this study, Latina students majoring in STEM fields in a Hispanic-Serving Institution (HSI) were interviewed about their college experience and persistence. A Latine resilience model and an HSI servingness framework guided the analysis. Qualitative methodology via case study served to understand this research. Evidence gathered in this study demonstrates how social climate experiences and cultural background influence resilience and success strategies among diverse Latina STEM majors in an HSI. The STEM social climate or culture seemingly clashed with participants’ cultural backgrounds. Perhaps most pertinent to their cultural background and resilience as Latinas were the specific success strategies or assets that participants utilized to navigate the STEM experience. Participants gravitated to diverse spaces, desired more women and ethnic representation in their STEM departments, and practiced prosocial or communal motivations. Understanding STEM culture in conjunction with the assets and strategies that Latinas utilize as ethnic women is important for HSIs as they consider how they truly serve their constituents.

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