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93,898 results on '"Morrow AS"'

1. An automated framework for exploring and learning potential-energy surfaces

Author

Liu, Yuanbin, Morrow, Joe D., Ertural, Christina, Fragapane, Natascia L., Gardner, John L. A., Naik, Aakash A., Zhou, Yuxing, George, Janine, and Deringer, Volker L.

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

Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science.

Published
2024

2. The Inverse Laplacian: Traces in Infinite Dimensions

Author

Morrow, Bryce

Subjects
Mathematics - Spectral Theory, Mathematics - Analysis of PDEs, Mathematics - Differential Geometry, 35P05 (Primary) 46N20 (Secondary)
Abstract

In this paper, we present a concise development of the well-studied theory of trace class operators on infinite dimensional (separable) Hilbert spaces suitable for an advanced undergraduate, as well as a construction of the inverse Laplacian on closed manifolds. With these developments acting as prerequisite, we present original trace computations involving the inverse Laplacian on the (flat) torus, generalizing computations done by R. Grady and O. Gwilliam within the context of topological quantum field theory., Comment: 31 pages

Published
2024

3. Early Formula Supplementation Differs by Maternal Body Mass Index but Does Not Explain Breastfeeding Outcomes in Mothers Who Intend to Exclusively Breastfeed.

Author

Conrey, Shannon, Nommsen-Rivers, Laurie, Burrell, Allison, Staat, Mary, and Morrow, Ardythe

Subjects
Baby Friendly Hospital, PREVAIL cohort, breastfeeding, breastfeeding intention, exclusive breastfeeding, infant formula, obesity
Abstract

BACKGROUND: Early formula supplementation (EFS, formula on birthdate or day after) is associated with maternal obesity and reduced breastfeeding, but the effect of prenatal breastfeeding intention on these relationships is understudied. OBJECTIVES: We evaluated how EFS affected breastfeeding outcomes after controlling for obesity, sociodemographic and health factors. METHODS: Multivariable regression modeling, stratified by prenatal breastfeeding intention. CONCLUSIONS: Our findings suggest that EFS may be less disruptive to breastfeeding in mothers with strong intention to meet breastfeeding recommendations, regardless of maternal BMI.

Published
2024

4. Measurement of elliptic flow of J$/\psi$ in $\sqrt{s_{_{NN}}}=200$ GeV Au$+$Au collisions at forward rapidity

Author

PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Boer, M., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Corliss, R., Csanád, M., Csörgő, T., Liu, L. D., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Elder, T., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, M. H., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., Masuda, H., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Mignerey, A. C., Mihalik, D. E., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novotny, R., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Syed, S., Sziklai, J., Takeda, A., Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vazquez-Carson, S., Velkovska, J., Virius, M., Vrba, V., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yanovich, A., Yin, P., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.

Subjects
Nuclear Experiment
Abstract

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Published
2024

5. Measurements at forward rapidity of elliptic flow of charged hadrons and open-heavy-flavor muons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author

PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Boer, M., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Corliss, R., Csanád, M., Csörgő, T., Liu, L. D., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Elder, T., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, M. H., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., Masuda, H., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Mignerey, A. C., Mihalik, D. E., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novotny, R., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Syed, S., Sziklai, J., Takeda, A., Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vazquez-Carson, S., Velkovska, J., Virius, M., Vrba, V., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yanovich, A., Yin, P., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.

Subjects
Nuclear Experiment
Abstract

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

Published
2024

6. Glyph-Based Uncertainty Visualization and Analysis of Time-Varying Vector Fields

Author

Ouermi, Timbwaoga A. J., Li, Jixian, Morrow, Zachary, Waanders, Bart van Bloemen, and Johnson, Chris R.

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance the understanding and interpretability of vector field data. For instance, in the context of severe weather events such as hurricanes and wildfires, effective uncertainty visualization can provide crucial insights about fire spread or hurricane behavior and aid in resource management and risk mitigation. Glyphs are commonly used for representing vector uncertainty but are often limited to 2D. In this work, we present a glyph-based technique for accurately representing 3D vector uncertainty and a comprehensive framework for visualization, exploration, and analysis using our new glyphs. We employ hurricane and wildfire examples to demonstrate the efficacy of our glyph design and visualization tool in conveying vector field uncertainty.

Published
2024

7. Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

Author

Lowery, Matthew, Turnage, John, Morrow, Zachary, Jakeman, John D., Narayan, Akil, Zhe, Shandian, and Shankar, Varun

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract

This paper introduces the Kernel Neural Operator (KNO), a novel operator learning technique that uses deep kernel-based integral operators in conjunction with quadrature for function-space approximation of operators (maps from functions to functions). KNOs use parameterized, closed-form, finitely-smooth, and compactly-supported kernels with trainable sparsity parameters within the integral operators to significantly reduce the number of parameters that must be learned relative to existing neural operators. Moreover, the use of quadrature for numerical integration endows the KNO with geometric flexibility that enables operator learning on irregular geometries. Numerical results demonstrate that on existing benchmarks the training and test accuracy of KNOs is higher than popular operator learning techniques while using at least an order of magnitude fewer trainable parameters. KNOs thus represent a new paradigm of low-memory, geometrically-flexible, deep operator learning, while retaining the implementation simplicity and transparency of traditional kernel methods from both scientific computing and machine learning., Comment: 10 pages + 5 page appendix, 8 figures

Published
2024

10. Myocardial ischaemia following COVID-19: a cardiovascular magnetic resonance study

Author

Arnold, J. Ranjit, Yeo, Jian L., Budgeon, Charley A., Shergill, Simran, England, Rachel, Shiwani, Hunain, Artico, Jessica, Moon, James C., Gorecka, Miroslawa, Roditi, Giles, Morrow, Andrew, Mangion, Kenneth, Shanmuganathan, Mayooran, Miller, Christopher A., Chiribiri, Amedeo, Alzahir, Mohammed, Ramirez, Sara, Lin, Andrew, Swoboda, Peter P., McDiarmid, Adam K., Sykes, Robert, Singh, Trisha, Bucciarelli-Ducci, Chiara, Dawson, Dana, Fontana, Marianna, Manisty, Charlotte, Treibel, Thomas A., Levelt, Eylem, Young, Robin, McConnachie, Alex, Neubauer, Stefan, Piechnik, Stefan K., Davies, Rhodri H., Ferreira, Vanessa M., Dweck, Marc R., Berry, Colin, McCann, Gerry P., and Greenwood, John P.

Published
2024
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15. Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances

Author

Bryant, Alex K, Zamora‐Resendiz, Rafael, Dai, Xin, Morrow, Destinee, Lin, Yuewei, Jungles, Kassidy M, Rae, James M, Tate, Akshay, Pearson, Ashley N, Jiang, Ralph, Fritsche, Lars, Lawrence, Theodore S, Zou, Weiping, Schipper, Matthew, Ramnath, Nithya, Yoo, Shinjae, Crivelli, Silvia, and Green, Michael D

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Clinical Research, Cancer, Bioengineering, Humans, Artificial Intelligence, Medical Oncology, Neoplasms, Cancer Outcomes Research, Large language models, Observational Data, prognostic factor, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract

PurposeReal world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.MethodsWe performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.ResultsArtificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.ConclusionsDespite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

Published
2024

16. A View from the CT Foxhole: General Bryan Fenton, Commander, U.S. Special Operations Command

Author

Morrow, Sean and Rassler, Don

Subjects
United States. Department of Defense -- International economic relations, United States. Special Operations Command -- International economic relations, Special forces (Military science) -- Military aspects, Infantry -- Military aspects, Military and naval science
Abstract

General Bryan P. Fenton is a career Special Forces (Green Beret) Officer. He currently serves as the 13th Commander of U.S. Special Operations Command (USSOCOM) where he oversees all Special Operations for the U.S. Department of Defense. Before assuming command of USSOCOM, General Fenton served as the Commander of Joint Special Operations Command (JSOC). Prior to that, he was the Senior Military Assistant for two U.S. Secretaries of Defense. General Fenton's other general officer assignments include: Deputy Commander of U.S. Indo-Pacific Command; Commander of U.S. Special Operations - Pacific; and Deputy Commanding General of the U.S. Army's 25th Infantry Division in Hawaii., CTC: U.S. CT has been going through a more intense evolution over the past five years and the 'Global War on Terrorism' is a thing of the past. What are [...]

Published
2024

17. Convergence and the CT Return on Investment: A Framework

Author

Rassler, Don and Morrow, Sean

Subjects
Islamic Revolutionary Guards Corps, National security, Rate of return, Antiterrorism measures, Return on investment, Military and naval science
Abstract

Since 2018, the United States has been trying to figure out what counterterrorism looks like during an era of strategic competition, and how it can maximize and optimize returns from its counterterrorism investments. There are important differences between these two national security priorities--strategic competition and counterterrorism--but if the United States wants to gain resource efficiencies, it should look across the gray space at how and where these two priorities interplay and converge. This is because a key part of the pathway to CT optimization lies in realizing how counterterrorism has evolved as a form of influence. This article introduces a conceptual framework to help the counterterrorism community situate the returns from CT investments, especially deployed CT force activity. It recommends that those returns be understood through two lenses: 1) those that are direct and oriented around threat mitigation and 2) those that are intersectional and oriented around influence. Interviews with three experts provide context to elements of the framework and highlight the interplay between counterterrorism and strategic competition in different regional areas., The day after al-Qa'ida's surprise attack on 9/H was the beginning of a new era for the United States. It usually takes time for the U.S. national security apparatus to [...]

Published
2024

20. Monoallelic loss-of-function variants in GSK3B lead to autism and developmental delay

Author

Tan, Senwei, Zhang, Qiumeng, Zhan, Rui, Luo, Si, Han, Yaoling, Yu, Bin, Muss, Candace, Pingault, Veronique, Marlin, Sandrine, Delahaye, Andrée, Peters, Sophia, Perne, Claudia, Kreiß, Martina, Spataro, Nino, Trujillo-Quintero, Juan Pablo, Racine, Caroline, Tran-Mau-Them, Frederic, Phornphutkul, Chanika, Besterman, Aaron D., Martinez, Julian, Wang, Xiuxia, Tian, Xiaoyu, Srivastava, Siddharth, Urion, David K., Madden, Jill A., Saif, Hind Al, Morrow, Michelle M., Begtrup, Amber, Li, Xing, Jurgensmeyer, Sarah, Leahy, Peter, Zhou, Shimin, Li, Faxiang, Hu, Zhengmao, Tan, Jieqiong, Xia, Kun, and Guo, Hui

Published
2024
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27. The first genetically confirmed cohort of Facioscapulohumeral Muscular Dystrophy from Northern India

Author

Vishnu, Venugopalan Y., Lemmers, Richard J. L. F., Reyaz, Alisha, Mishra, Rinkle, Ahmad, Tanveer, van der Vliet, Patrick J., Kretkiewicz, Marcelina M., Macken, William L., Efthymiou, Stephanie, Dominik, Natalia, Morrow, Jasper M., Bhatia, Rohit, Wilson, Lindsay A., Houlden, Henry, Hanna, Michael G., Bugiardini, Enrico, van der Maarel, Silvère M., and Srivastava, M. V. Padma

Published
2024
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31. De-escalation of Disease-Modifying Therapy for People with Multiple Sclerosis Due to Safety Considerations: Characterizing 1-Year Outcomes in 25 People Who Switched from Ocrelizumab to Diroximel Fumarate

Author

Gudesblatt, Mark, Bumstead, Barbara, Buhse, Marijean, Zarif, Myassar, Morrow, Sarah A., Nicholas, Jacqueline A., Hancock, Laura M., Wilken, Jeffrey, Weller, Joanna, Scott, Nicole, Gocke, Anne, Lewin, James B., Kaczmarek, Olivia, Mendoza, Jason P., and Golan, Daniel

Published
2024
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33. Connections between K-stability and Vojta's conjecture

Author

Morrow, Jackson S. and Wu, Yueqiao

Subjects
Mathematics - Algebraic Geometry, Mathematics - Number Theory
Abstract

In this note, we use recent advances concerning the K-stability of $\mathbb{Q}$-Fano varieties to provide settings for which Vojta's conjecture holds., Comment: 11 pages, comments welcome!

Published
2024

35. Computational deconvolution of cell type-specific gene expression in COPD and IPF lungs reveals disease severity associations

Author

Ryu, Min Hyung, Yun, Jeong H., Kim, Kangjin, Gentili, Michele, Ghosh, Auyon, Sciurba, Frank, Barwick, Lucas, Limper, Andrew, Criner, Gerard, Brown, Kevin K., Wise, Robert, Martinez, Fernando J., Flaherty, Kevin R., Cho, Michael H., Castaldi, Peter J., DeMeo, Dawn L., Silverman, Edwin K., Hersh, Craig P., and Morrow, Jarrett D.

Published
2024
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47. Effectiveness of smoking cessation interventions among adults: an overview of systematic reviews

Author

Hersi, Mona, Beck, Andrew, Hamel, Candyce, Esmaeilisaraji, Leila, Pussegoda, Kusala, Austin, Bradley, Ahmadzai, Nadera, Pratt, Misty, Thuku, Micere, Yazdi, Fatemeh, Bennett, Alexandria, Shaver, Nicole, Vyas, Niyati, Skidmore, Becky, Hutton, Brian, Manuel, Douglas, Morrow, Matt, Pakhale, Smita, Presseau, Justin, Shea, Beverley J., Little, Julian, Moher, David, and Stevens, Adrienne

Published
2024
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49. Metabolism-driven in vitro/in vivo disconnect of an oral ERɑ VHL-PROTAC

Author

Hayhow, Thomas G., Williamson, Beth, Lawson, Mandy, Cureton, Natalie, Braybrooke, Erin L., Campbell, Andrew, Carbajo, Rodrigo J., Cheraghchi-Bashi, Azadeh, Chiarparin, Elisabetta, Diène, Coura R., Fallan, Charlene, Fisher, David I., Goldberg, Frederick W., Hopcroft, Lorna, Hopcroft, Philip, Jackson, Anne, Kettle, Jason G., Klinowska, Teresa, Künzel, Ulrike, Lamont, Gillian, Lewis, Hilary J., Maglennon, Gareth, Martin, Scott, Gutierrez, Pablo Morentin, Morrow, Christopher J., Nikolaou, Myria, Nissink, J. Willem M., O’Shea, Patrick, Polanski, Radoslaw, Schade, Markus, Scott, James S., Smith, Aaron, Weber, Judith, Wilson, Joanne, Yang, Bin, and Crafter, Claire

Published
2024
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