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4,269 results on '"Rocke A"'

2. Machine Learning of the Prime Distribution

Author

Kolpakov, Alexander and Rocke, A. Alistair

Subjects
Computer Science - Information Theory, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Mathematics - Number Theory, 11N05
Abstract

In the present work we use maximum entropy methods to derive several theorems in probabilistic number theory, including a version of the Hardy-Ramanujan Theorem. We also provide a theoretical argument explaining the experimental observations of Yang-Hui He about the learnability of primes, and posit that the Erd\H{o}s-Kac law would very unlikely be discovered by current machine learning techniques. Numerical experiments that we perform corroborate our theoretical findings., Comment: 10 pages; parts of arXiv:2308.10817 reworked and amended; author's draft; accepted in PLOS ONE

Published
2024
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3. Human Keratinocyte Responses to Woodsmoke Chemicals.

Author

Karim, Noreen, Salemi, Michelle, Durbin-Johnson, Blythe, Rice, Robert, Rocke, David, Phinney, Brett, and Yang, Yatian

Subjects
Humans, Keratinocytes, Wood, Smoke, Furaldehyde, Cells, Cultured, Receptors, Aryl Hydrocarbon
Abstract

Air pollution consists of complex mixtures of chemicals with serious deleterious health effects from acute and chronic exposure. To help understand the mechanisms by which adverse effects occur, the present work examines the responses of cultured human epidermal keratinocytes to specific chemicals commonly found in woodsmoke. Our earlier findings with liquid smoke flavoring (aqueous extract of charred wood) revealed that such extracts stimulated the expression of genes associated with oxidative stress and proinflammatory response, activated the aryl hydrocarbon receptor, thereby inducing cytochrome P4501A1 activity, and induced cross-linked envelope formation, a lethal event ordinarily occurring during terminal differentiation. The present results showed that furfural produced transcriptional responses resembling those of liquid smoke, cyclohexanedione activated the aryl hydrocarbon receptor, and several chemicals induced envelope formation. Of these, syringol permeabilized the cells to the egress of lactate dehydrogenase at a concentration close to that yielding envelope formation, while furfural induced envelope formation without permeabilization detectable in this way. Furfural (but not syringol) stimulated the incorporation of amines into cell proteins in extracts in the absence of transglutaminase activity. Nevertheless, both chemicals substantially increased the amount of cellular protein incorporated into envelopes and greatly altered the envelope protein profile. Moreover, the proportion of keratin in the envelopes was dramatically increased. These findings are consistent with the chemically induced protein cross-linking in the cells. Elucidating mechanisms by which this phenomenon occurs may help understand how smoke chemicals interact with proteins to elicit cellular responses, interpret bioassays of complex pollutant mixtures, and suggest additional sensitive ways to monitor exposures.

Published
2024

4. The Information Geometry of UMAP

Author

Kolpakov, Alexander and Rocke, Aidan

Subjects
Computer Science - Computational Geometry, Computer Science - Discrete Mathematics, Computer Science - Information Theory, Mathematics - Geometric Topology, 53B12, 94A15
Abstract

In this note we highlight some connections of UMAP to the basic principles of Information Geometry. Originally, UMAP was derived from Category Theory observations. However, we posit that it also has a natural geometric interpretation., Comment: 12 pages, 2 figures, 3 tables ; Github repo (https://github.com/sashakolpakov/info-geometry-umap) ; to appear in Le Matematiche

Published
2023

5. Environmental pro-oxidants induce altered envelope protein profiles in human keratinocytes

Author

Lin, Lo-Wei, Durbin-Johnson, Blythe P, Rocke, David M, Salemi, Michelle, Phinney, Brett S, and Rice, Robert H

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Endocrine Disruptors, Agent Orange & Dioxin, 2.1 Biological and endogenous factors, Humans, Reactive Oxygen Species, Proteome, Lasalocid, Keratinocytes, Polychlorinated Dibenzodioxins, Receptors, Aryl Hydrocarbon, cornified envelope, covalent cross-link, oxidative stress, proteomics, Toxicology, Pharmacology and pharmaceutical sciences
Abstract

Cornified envelopes (CEs) of human epidermis ordinarily consist of transglutaminase-mediated cross-linked proteins and are essential for skin barrier function. However, in addition to enzyme-mediated isopeptide bonding, protein cross-linking could also arise from oxidative damage. Our group recently demonstrated abnormal incorporation of cellular proteins into CEs by pro-oxidants in woodsmoke. In this study, we focused on 2,3-dimethoxy-1,4-naphthoquinone (DMNQ), mesquite liquid smoke (MLS), and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), to further understand the mechanisms through which environmental pro-oxidants induce CE formation and alter the CE proteome. CEs induced by the ionophore X537A were used for comparison. Similar to X537A, DMNQ- and MLS-induced CE formation was associated with membrane permeabilization. However, since DMNQ is non-adduct forming, its CEs were similar in protein profile to those from X537A. By contrast, MLS, rich in reactive carbonyls that can form protein adducts, caused a dramatic change in the CE proteome. TCDD-CEs were found to contain many CE precursors, such as small proline-rich proteins and late cornified envelope proteins, encoded by the epidermal differentiation complex. Since expression of these proteins is mediated by the aryl hydrocarbon receptor (AhR), and its well-known downstream protein, CYP1A1, was exclusively present in the TCDD group, we suggest that TCDD alters the CE proteome through persistent AhR activation. This study demonstrates the potential of environmental pro-oxidants to alter the epidermal CE proteome and indicates that the cellular redox state has an important role in CE formation.

Published
2024

6. Optimized Real-Time Assembly in a RISC Simulator

Author

Shaban, Marwan and Rocke, Adam J.

Subjects
Computer Science - Hardware Architecture
Abstract

Simulators for the RISC-V instruction set architecture (ISA) are useful for teaching assembly language and modern CPU architecture concepts. The Assembly/Simulation Platform for Illustration of RISC-V in Education (ASPIRE) is an integrated RISC-V assembler and simulator used to illustrate these concepts and evaluate algorithms to generate machine language code. In this article, ASPIRE is introduced, selected features of the simulator that interactively explain the RISC-V ISA as teaching aides are presented, then two assembly algorithms are evaluated. Both assembly algorithms run in real time as code is being edited in the simulator. The optimized algorithm performs incremental assembly limited to only the portion of the program that is changed. Both algorithms are then evaluated based on overall run-time performance.

Published
2023

7. Near-Time Digital Mapping for Geoforensic Searches

Author

Benjamin Rocke and Alastair Ruffell

Subjects
geoforensic search, digital mapping, drones, forensic geology, bedrock/superficial mapping, remote sensing, Dynamic and structural geology, QE500-639.5
Abstract

Inadequate base maps with poor scale or low resolution demonstrate the need for contemporary topographic maps when conducting geological mapping. In neotectonic regimes and areas of dynamic geomorphology, archival or large-scale maps require time-consuming, on-site manual updating while mapping bedrock and superficial geology. In contrast, stable ground conditions may have suitable legacy maps in some locations but not in others, such as where surveying is absent, incomplete or subject to legal restrictions. The geologist tasked with mapping may have to do this on short notice at their first site visit with no time to search for or create digital or physical copies of background maps on a suitable scale. The field mapper may encounter any of the above scenarios, especially the Geoforensic specialist tasked with Search and Rescue, hazard assessment or preparation and desktop study for subsequent search teams or law enforcement. Drone-derived orthoimagery and digital surface modelling can be conducted on-site in near real time to provide high resolution georeferenced maps for direct input of geological information, thus bypassing either non-existent or unsuitable base maps.

Published
2024
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8. Racial and ethnic disparities in human papillomavirus (HPV) vaccine uptake among United States adults, aged 27–45 years

Author

Natalie L. Rincon, Kelsey Rae McDowell, Darien Weatherspoon, Tiarney D. Ritchwood, Daniel J. Rocke, Eric Adjei Boakye, and Nosayaba Osazuwa-Peters

Subjects
Human papillomavirus (HPV), HPV vaccine uptake, racial disparities, adults aged 27–45 years, National Health Interview Survey (NHIS), expanded HPV vaccine eligibility, Immunologic diseases. Allergy, RC581-607, Therapeutics. Pharmacology, RM1-950
Abstract

ABSTRACTIn 2018, the Food and Drug Administration expanded the age of eligibility for the human papillomavirus (HPV) vaccine to 27 to 45 years. However, it is unclear if there are racial/ethnic disparities in HPV vaccine uptake for this age-group following this expanded recommendation. We aimed to identify any disparities in HPV vaccine in 27 to 45 year-olds based on sociodemographic factors. We analyzed nationally representative, cross-sectional data from the 2019 National Health Interview Survey (n = 9440). Logistic regression models estimated the odds of vaccine uptake (receipt of ≥1 vaccine dose) based on sociodemographic factors. Participants were mostly Non-Hispanic Whites (60.7%) and females (50.9%). In adjusted models, females had over three times greater odds of vaccine uptake compared to males (aOR = 3.58; 95% CI 3.03, 4.23). Also, compared to Non-Hispanic Whites, Non-Hispanic Blacks were 36% more likely (aOR = 1.36; 95% CI 1.09, 1.70), and Hispanics were 27% less likely (aOR = 0.73; 95% CI 0.58, 0.92) to receive the vaccine. Additionally, individuals without a usual place of care had lower odds of vaccine uptake (aOR = 0.72; 95% CI 0.57, 0.93), as were those with lower educational levels (aORhigh school = 0.62; 95% CI 0.50, 0.78; aORsome college = 0.83; 95% CI 0.70, 0.98). There are disparities in HPV vaccine uptake among 27 to 45 year-olds, and adult Hispanics have lower odds of receiving the vaccine. Given the vaccine’s importance in cancer prevention, it is critical that these disparities are addressed and mitigated.

Published
2024
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10. Convection and extracellular matrix binding control interstitial transport of extracellular vesicles

Author

Sariano, Peter A, Mizenko, Rachel R, Shirure, Venktesh S, Brandt, Abigail K, Nguyen, Bryan B, Nesiri, Cem, Shergill, Bhupinder S, Brostoff, Terza, Rocke, David M, Borowsky, Alexander D, Carney, Randy P, and George, Steven C

Subjects
Biochemistry and Cell Biology, Biological Sciences, Bioengineering, Breast Cancer, Biotechnology, Cancer, Women's Health, Humans, Laminin, Convection, Integrin alpha6beta1, Extracellular Vesicles, Integrin alpha3beta1, Extracellular Matrix, diffusion, exosome, gradient, integrin binding, spatial concentration, Biochemistry and cell biology
Abstract

Extracellular vesicles (EVs) influence a host of normal and pathophysiological processes in vivo. Compared to soluble mediators, EVs can traffic a wide range of proteins on their surface including extracellular matrix (ECM) binding proteins, and their large size (∼30-150 nm) limits diffusion. We isolated EVs from the MCF10 series-a model human cell line of breast cancer progression-and demonstrated increasing presence of laminin-binding integrins α3β1 and α6β1 on the EVs as the malignant potential of the MCF10 cells increased. Transport of the EVs within a microfluidic device under controlled physiological interstitial flow (0.15-0.75 μm/s) demonstrated that convection was the dominant mechanism of transport. Binding of the EVs to the ECM enhanced the spatial concentration and gradient, which was mitigated by blocking integrins α3β1 and α6β1. Our studies demonstrate that convection and ECM binding are the dominant mechanisms controlling EV interstitial transport and should be leveraged in nanotherapeutic design.

Published
2023

11. Sex-biased infections scale to population impacts for an emerging wildlife disease

Author

Kailing, Macy J, Hoyt, Joseph R, White, J Paul, Kaarakka, Heather M, Redell, Jennifer A, Leon, Ariel E, Rocke, Tonie E, DePue, John E, Scullon, William H, Parise, Katy L, Foster, Jeffrey T, Kilpatrick, A Marm, and Langwig, Kate E

Subjects
Zoology, Ecology, Biological Sciences, Infectious Diseases, 2.2 Factors relating to the physical environment, 2.1 Biological and endogenous factors, Infection, Good Health and Well Being, Female, Male, Animals, Animals, Wild, Chiroptera, Mycoses, Fungi, Hibernation, sex-biased infection, emerging infectious disease, white-nose syndrome, bats, Allee effects, fungal disease, Agricultural and Veterinary Sciences, Medical and Health Sciences, Agricultural, veterinary and food sciences, Biological sciences, Environmental sciences
Abstract

Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behaviour. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects.

Published
2023

12. Deep Learning-Based Bioimpedance Spectroscopy Using Start of Frame Delimiter in Human Body Communications

Author

Roopnarine, Aaron, Rocke, Sean, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Mathew, Jimson, editor, Gopal, Lenin, editor, and Juwono, Filbert H., editor

Published
2024
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13. IRS-Aided Cyclostationary Spectrum Sensing in Dynamic Spectrum Access Networks

Author

Raghoonath, Sanjeev, Rocke, Sean, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Mathew, Jimson, editor, Gopal, Lenin, editor, and Juwono, Filbert H., editor

Published
2024
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14. Protein profiling of forehead epidermal corneocytes distinguishes frontal fibrosing from androgenetic alopecia.

Author

Karim, Noreen, Mirmirani, Paradi, Durbin-Johnson, Blythe P, Rocke, David M, Salemi, Michelle, Phinney, Brett S, and Rice, Robert H

Subjects
Forehead, Scalp, Epidermis, Skin, Humans, Alopecia, Lichen Planus, Fibrosis, Clinical Research, General Science & Technology
Abstract

Protein profiling offers an effective approach to characterizing how far epidermis departs from normal in disease states. The present pilot investigation tested the hypothesis that protein expression in epidermal corneocytes is perturbed in the forehead of subjects exhibiting frontal fibrosing alopecia. To this end, samples were collected by tape stripping from subjects diagnosed with this condition and compared to those from asymptomatic control subjects and from those exhibiting androgenetic alopecia. Unlike the latter, which exhibited only 3 proteins significantly different from controls in expression level, forehead samples from frontal fibrosing alopecia subjects displayed 72 proteins significantly different from controls, nearly two-thirds having lower expression. The results demonstrate frontal fibrosing alopecia exhibits altered corneocyte protein expression in epidermis beyond the scalp, indicative of a systemic condition. They also provide a basis for quantitative measures of departure from normal by assaying forehead epidermis, useful in monitoring response to treatment while avoiding invasive biopsy.

Published
2023

17. An information-theoretic proof of the Erd\H{o}s-Kac theorem

Author

Rocke, Aidan

Subjects
Computer Science - Information Theory, 11N05
Abstract

In this article we show that the Erd\H{o}s-Kac theorem, which informally states that the number of prime divisors of very large integers converges to a normal distribution, has an elegant proof via Algorithmic Information Theory., Comment: There is an error in this paper that is addressed in arXiv:2308.10817

Published
2022

18. All the information in the integers is in the primes

Author

Rocke, Aidan

Subjects
Mathematics - General Mathematics, 11N05
Abstract

Building upon the work of Chebyshev, Shannon and Kontoyiannis, it may be demonstrated that Chebyshev's asymptotic result: \begin{equation} \ln N \sim \sum_{p \leq N} \frac{1}{p} \cdot \ln p \end{equation} has a natural information-theoretic interpretation as the information-content of a typical integer $Z \sim U([1,N])$, sampled under the maximum entropy distribution, is asymptotically equivalent to the information content of all primes $p \leq N$ weighted by their typical frequency. This result is established by showing that the correct information-theoretic interpretation of the entropy $\ln p$ may be deduced from the author's recent observation that the prime numbers have a uniform source distribution. Moreover, using Chaitin's theorem that almost all integers are algorithmically random we may extend this asymptotic result to the setting of algorithmic information theory and derive an upper-bound on the algorithmic information of a typical integer., Comment: The correct proof of Chebyshev's theorem is in: arXiv:2308.10817

Published
2021

21. An information-theoretic upper bound on prime gaps

Author

Rocke, Aidan

Subjects
Mathematics - General Mathematics, 11N05
Abstract

Within the setting of rare event modelling, the method of level sets allows us to define an equivalence relation over rare events with distinct rates of entropy production. This method allows us to clarify the relation between the empirical density of primes and their source distribution which then allows us to address Cram\'er's conjecture, an open problem in probabilistic number theory. As a natural consequence, this analysis places strong epistemic limits on the application of machine learning to analyse the distribution of primes., Comment: The information-theoretic analysis used in this paper isn't strong enough to prove Cram\'er's Conjecture

Published
2021

22. Impact of 3D Printing Infill Patterns on the Effective Permittivity of 3D Printed Substrates

Author

Jeevan Persad and Sean Rocke

Subjects
Mixture models, 3D-printing, additive manufacturing, dielectrics, material characterization, Telecommunication, TK5101-6720, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

3D printing can potentially transform traditional electronics manufacturing by allowing for the accurate direct digital manufacture of complex electronic structures with a much smaller process footprint. However, there are challenges which restrict the use of 3D printing for electronic manufacturing. One significant challenge is the characterization of the electromagnetic properties of the 3D printed materials such as their resultant dielectric permittivity. This work reports on the investigation of existing mixture models to establish their suitability for predicting the dielectric permittivity of 3D printed binary materials for the test frequency range of 1 GHz to 10 GHz. The identified models included volume fraction mixture models which considered the material volume concentration of the binary material and shape factor mixture models which consider the geometry and distribution of the mixture constituents. The fused filament fabrication 3D printing process was used for this work. 3D printed samples were produced with varying percentage volume compositions and varying infill patterns. The dielectric permittivity of the samples was investigated using the two-layer stripline measurement method and the measured data compared to the mixture model estimates. The shape factor mixture models were found to not be in good agreement with the measured values of dielectric permittivity. This result was attributed to the relatively small size of the discontinuities within the 3D printed substrate being insufficient to present anisotropy relative to the wavelength of the applied test signals. The volume fraction models were found to be in close agreement for samples with select infill patterns.

Published
2024
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24. A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts

Author

Bravo, Laura, Simões, Joana FF, Cardoso, Victor R, Adisa, Adewale, Aguilera, Maria L, Arnaud, Alexis, Biccard, Bruce, Calvache, Jose, Chernbumroong, Saisakul, Elhadi, Muhammed, Ghosh, Dhruv, Gujjuri, Rohan, Harrison, Ewen, Ho, Michael WS, Kasivisvanathan, Veerappan, Kouli, Omar, Lederhuber, Hans, Li, Elizabeth, Löffler, Markus W, Isik, Arda, Marcus, Hani, Martin, Janet, McLean, Kenneth A, Minaya-Bravo, Ana, Modolo, María Marta, Nepogodiev, Dmitri, Pellino, Gianluca, Picciochi, Maria, Pockney, Peter, van Ramshorst, Gabriëlle, Riad, Aya, Sayyed, Raza, Sund, Malin, Gkoutos, Georgios, Bhangu, Aneel A, Glasbey, James C, Bravo, L, Cardoso, V, Glasbey, J, Chernbumroong, S, Nepogodiev, D, Simoes, JFF, Mclean, KA, Bhangu, AA, Gkoutos, G, Kadir, B, Omar, O, Revell, E, Bahrami-Hessari, M, Ahmed, Waheed-Ul-Rahman, Argus, Leah, Ball, Alasdair, Bhangu, Aneel, Bywater, Edward P, Blanco-Colino, Ruth, Brar, Amanpreet, Chaudhry, Daoud, Dawson, Brett E, Duran, Irani, Gujjuri, Rohan R, Jones, Conor S, Harrison, Ewen M, Kamarajah, Sivesh K, Keatley, James M, Lawday, Samuel, Mann, Harvinder, Marson, Ella J, Mclean, Kenneth A, Norman, Lisa, Ots, Riinu, Outani, Oumaima, Santos, Irène, Shaw, Catherine, Taylor, Elliott H, Trout, Isobel M, Varghese, Chris, Venn, Mary L, Xu, William, Dajti, Irida, Gjata, Arben, Kacimi, Salah Eddine Oussama, Boccalatte, Luis, Cox, Daniel, Aigner, Felix, Kronberger, Irmgard Elisabeth, Samadov, Elgun, Alderazi, Amer, Padmore, Greg, van Ramshorst, Gabrielle, Lawani, Ismaïl, Cerovac, Anis, Delibegovic, Samir, Baiocchi, Glauco, Gomes, Gustavo Mendonça Ataíde, Lima Buarque, Igor, Gohar, Muhammad, Slavchev, Mihail, Nwegbu, Chukwuemeka, Agarwal, Arnav, Ng-Kamstra, Joshua, Olivos, Maricarmen, Lou, Wenhui, Ren, Dong-Lin, Calvache, Jose Andres, Perez Rivera, Carlos J, Danic Hadzibegovic, Ana, Kopjar, Tomislav, Mihanovic, Jakov, Avilés Jiménez, Pablo Mijahil, Gouvas, Nikolaos, Klat, Jaroslav, Novysedlák, René, Amisi, Nicolas, Christensen, Peter, El-Hussuna, Alaa, Batista, Sylvia, Lincango-Naranjo, Eddy, Emile, Sameh, Arévalo Sandoval, Danilo Alfonso, Dhufera, Hailu, Hailu, Samuel, Mengesha, Mengistu G, Kauppila, Joonas H, Arnaud, Alexis P, Demetrashvili, Zaza, Albertsmeier, Markus, Löffler, Markus W., Kwesi Acquah, Daniel, Ofori, Bernard, Tabiri, Stephen, Metallidis, Symeon, Tsoulfas, Georgios, Aguilera-Arevalo, Maria-Lorena, Recinos, Gustavo, Mersich, Tamás, Wettstein, Dániel, Ghosh, Dhruva, Kembuan, Gabriele, Brouki Milan, Peiman, Khosravi, Mohammad Hossein, Mozafari, Masoud, Hilmi, Ahmed, Mohan, Helen, Zmora, Oded, Gallo, Gaetano, Pata, Francesco, Fujimoto, Yuki, Kuroda, Naoto, Satoi, Sohei, Abou Chaar, Mohamad K., Ayasra, Faris, Fakhradiyev, Ildar, Hamdun, Intisar Hisham Said, Jin-Young, Jang, Jamal, Mohammad, Karout, Lina, Gulla, Aiste, Rasoaherinomenjanahary, Fanjandrainy, Samison, Luc Hervé, Roslani, April Camilla, Durán Sánchez, Iran Irani, Gonzalez, Diana Samantha, Martinez, Laura, Martínez, María José, Nayen, Alejandra, Ramos-De la Medina, Antonio, Nunez, Jade, Nashidengo, Pueya Rashid, Shrestha, Ashish Lal, Jonker, Pascal, Kruijff, Schelto, Noltes, Milou, Steinkamp, Pieter, Wright, Deborah, Abdur-Rahman, Lukman, Ademuyiwa, Adesoji, Osinaike, Babatunde, Seyi-Olajide, Justina, Williams, Omolara, Williams, Emmanuel, Pejkova, Sofija, Al Balushi, Zainab, Qureshi, Ahmad Uzair, Abo Mohsen, Mustafa, Abukhalaf, Sadi A., Cukier, Moises, Gomez-Fernandez, Hugo, Shu Yip, Sebastian, Vasquez Ojeda, Ximena Paola, Sacdalan, Marie Dione, Major, Piotr, Azevedo, José, Cunha, Miguel F., Zarour, Ahmad, Bonci, Eduard-Alexandru, Negoi, Ionut, Efetov, Sergey, Kochetkov, Viktor, Litvin, Andrey, Allen Ingabire, Jc, Bucyibaruta, Georges, Faustin, Ntirenganya, Habumuremyi, Sosthene, Imanishimwe, Alphonsine, Jean de Dieu, Haragirimana, Munyaneza, Emmanuel, Ncogoza, Isaie, Alameer, Ehab, Ndong, Abdourahmane, Radenkovic, Dejan, Chew, Min Hoe, Koh, Frederick, Ngu, James, Panyko, Arpád, Bele, Uros, Košir, Jurij Aleš, Daoud, Hassan, Minaya Bravo, Ana Maria, Jayarajah, Umesh, Wickramasinghe, Dakshitha, Adam Essa Adam, Mohammed Elmujtba, Rutegård, Martin, Adamina, Michel, Gialamas, Eleftherios, Horisberger, Karoline, Alshaar, Muhammad, Lohsiriwat, Varut, Charles, Shane, Leventoğlu, Sezai, Lekuya, Hervé Monka, Lule, Herman, Kopetskyi, Slava, Alsaadi, Hayder, Alshryda, Sattar, Alser, Osaid, Bankhead-Kendall, Brittany, Breen, Kerry, Kaafarani, Haytham, Mashbari, Hassan, Bonilla Cal, Fernando, Al-Naggar, Hamza, Maimbo, Mayaba, Mazingi, Dennis, Abbott, Tom, Akhbari, Melika, Bhanderi, Shivam, Chakrabortee, Sohini, Costas-Chavarri, Ainhoa, Demetriades, Andreas K, Desai, Anant, Di Saverio, Salomone, Drake, Thomas, Edwards, John, Evans, Jonathan, Fiore, Marco, Ford, Samuel, Fotopoulou, Christina, Fowler, Alexander, Futaba, Kaori, Ganly, Ian, Grace James, Harelimana, Griffiths, Ewen, Hutchinson, Peter, Hyman, Gabriella Yael, Incorvia, Joseph, Jain, Ritu, Jenkinson, Michael, Khan, Tabassum, Knight, Stephen Richard, Kolias, Angelos, Kudsk-Iversen, Søren, Kwan, Tsun Yu, Leung, Elaine, Mayol, Julio, McKay, Siobhan, Meara, John G., Mills, Emily, Moug, Susan, Patel, Akshay, Perinotti, Roberto, Rice, Henry E., Roberts, Keith, Schache, Andrew, Shaw, Richard, Smart, Neil, Stephens, Matthew, Stewart, Grant D., Teasdale, Ella, Vidya, Raghavan, Wright, Naomi, Wuraola, Funmilola, Agastra, Ervis, Thereska, Dariel, Lucchini, Sergio Martin, Laudani, Veronica, Chwat, Carina, Pedraza Salazar, Ivana Ines, Pantoja Pachajoa, Diana Alejandra, Duro, Agustin, Calderón Arancibia, José Alfredo, Bright, Tim, Hollington, Paul, Zhou, Xuanyu, Kroon, Hidde M, Farfus, Anthony, Barker, John, Watson, Eleanor, Stevens, Sean, Latif, Haider, Dawson, Amanda Caroline, Chuan, Alwin, Muralidharan, Vijayaragavan, Wong, Enoch, Ackermann, Travis, Pacilli, Maurizio, Hodgson, Russell, Heriot, Alexander, Choong, Peter, Brown, Wendy, Lidder, Surjit, Yeung, Justin, Traeger, Luke, Regalo, Guillermo, Gourlay, Ralph, Badiani, Sarit, Koh, Cherry, Putnis, Soni, 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Derilo, Habtamu, Degefa, Eyueal, Tadesse, Anteneh, Nidaw, Melkamu, Sarjanoja, Elise, Testelin, Sylvie, Boucher, Sophie, Jouffret, Lionel, Lakkis, Zaher, Zarzavadjian Le Bian, Alban, Harper, Luke, Danguy des Déserts, Marc, André, Benoît, Slim, Karem, Verhaeghe, Romain, Police, Andrea, Girard, Edouard, Chebaro, Alexandre, Subayi Nkembi, Armande, Arnalsteen, Laurent, Ballouhey, Quentin, Mege, Diane, Jeandel, Clement, Duchalais, Emilie, Bouche, Pierre-Alban, Manceau, Gilles, Crétolle, Célia, Hervieux, Erik, Girard, Noémie, Seguin-Givelet, Agathe, Gaujoux, Sebastien, De Simone, Belinda, Boisson, Matthieu, Bergeat, Damien, Fredon, Fabien, Nappi, Francesco, Kassir, Radwan, Scalabre, Aurélien, Migliorelli, Federico, Ezanno, Anne-Cecile, Seeliger, Barbara, Vaysse, Charlotte, Charbonneau, Helene, Misrai, Vincent, Abbo, Olivier, Angeles, Martina Aida, Brunaud, Laurent, Modabber, Ali, Wolf, Sebastian, Kamphues, Carsten, Höhn, Philipp, Glowka, Tim R., Rokohl, Alexander Christopher, Bork, Ulrich, 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R, Nissi, Kannaiyan, Lavanya, Matai, Dr. Anil, Misra, Sanjeev, Bhende, Vishal, Muthu, Sathish, Ghosh, Indranil, Sharma, Abhishek, Bajaj, Ankur, Rajan, Shiv, Agarwal, Gaurav, Pawar, Pranay, Alexander, Philip, Vijayakumar, M, Hameed, BM Zeeshan, L, Badareesh, Chaudhry, Navneet Kumar, Baliarsing, Lipika, Dharap, Satish, Kulkarni, Amruta, Thyavihally, Yuvaraja, Pramesh, C S, Soni, Rajesh, Dube, Surya Kumar, Sharma, Shilpa, Singh, Harvinder, Bains, Lovenish, Ghodke, Rahul, Kumar, Ashwani, Sodhai, Vivek, Maji, Suvendu, Basu, Somprakas, Mahakalkar, Chandrashekhar, Kannan, Ravi, Mehraj, Asif, N, Ranganath, Phadnis, Ashish, Yadev, I, Kavalakat, Alfie, Mittal, Rohin, Vallam, Karthik Chandra, Akhavizadegan, Hamed, Rezghi Maleki, Esmaeil, Yousefzadeh Kandevani, Naser, Ikele, Hilary, McNestry, Catherine, Fleming, Christina, O’Brien, Stephen, Abd Elwahab, Sami, Davis, Niall, Javadpour, Mohsen, McDonnell, Brendan, O Connor, Clare, Bolger, Jarlath, Clancy, Cillian, Croghan, Stefanie M, Donlon, Noel, 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Sareyah, Sultan, Rizwan, Altaf, Humera Naz, Bhatti, Abu Bakar Hafeez, Waqar, Shahzad Hussain, Aziz, Aliya, Kerawala, Asad Ali, Rai, Lajpat, Anwer, Mariyah, Tariq, Aiman, Ayub, Bushra, Niazi, Sami ullah, Naseem, Muhammad Yasir, Sarwar, Muhammad Zeeshan, Khokhar, Muhammad Imran, Zahid, Imdad Ahmad, Majid, Haroon Javaid, Talat, Nabila, Asif, Muhammad, Chaudhary, Muhammad Hamid, Farooq, Umer, Ahmad, Siddique, Mabood, Waleed, Bukhari, Syed Imran, Tariq, Muhammad, Yaqoob, Eesha, Javed, Saad, Usman Malik, Muhammad, Yaqoob, Hassan Nawaz, Falcon Pacheco, Glenda Marina, Mas Melendez, Robinson, Paucar Urbina, Arazzelly Del Pilar, Rios Chiuyari, Jose, Otiniano Alvarado, Carlos Eduardo, Fuentes Rivera Lau, Lorena, Borda-Luque, Giuliano, Niquen-Jimenez, Milagros, Arias, Claudia, Zegarra, Sergio, Betalleluz Pallardel, Jenner, Ugarte Oscco, Regina Amparo, Mendiola, Gian, Carpio Colmenares, Yahaira Tatiana, Shiraishi Zapata, Carlos, Ortiz, Maria Rosa, Castro Borges, Filipe, Viveiros, Octavio, 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Paterson, Amanda, Perez Cerdeira, Marisol, Ting, Caleb, Willoughby, Ellisha, Bailey, Ashley, Narayanan, Viswanathan, Wilkes, Anna, Zhao, Jie, D’Souza, Carl, Forbes, Clara, Haddleton, Monique, Mohammed Abdul, Naseer, Archer, Leigh, Dilevska, Tina, Hans, Tasvinder, Kumar, Mohit, Pham, Terence, Picazo Pineda, Fernando, Suk-Udom, Supisara, Jeyamanoharan, Nivedan, Sala, Michael, Dravid, Madhulika, McCartney, Conor, Sam, Apostolou, Christos, Berney, Christophe, Lam, Andrew, Ma, Vanessa, Cope, Jennifer, Llorando, Alyssa, Mahin, Humaira Haider, Syed, Faisal, Ireland, Patrick, Zhu, Bonnie, Zitt, Matthias, Königsrainer, Ingmar, Allmer, Caterina, Mitteregger, Martin, Seitinger, Gerald, Duller, David, Mathew, Erwin, Skias, Christoph, Brinskelle, Petra, Holzmeister, Clemens, Kahn, Judith, Kresic, Josip, Lindenmann, Joerg, Lumenta, David, Puchwein, Paul, Singer, Georg, Wolf, Axel, Arco, Daniel, Kilic, Nura, Widschwendter, Peter, Freyschlag, Christian, Graber, Michael, Hirsch, Jakob, Kuen, Marlene, Liebensteiner, Michael, Messner, Franka, Messner, Alex, Naegele, Felix, Osl, Antonia, Ower, Cornelia, Rauchegger, Teresa, Reimer, Daniel, Sokolovski, Filipp, Süss, Markus, Thaler, Martin, Zehetner, Claus, Allerstorfer, Jakob, Haslhofer, David, Pisecky, Lorenz, Poier, Nikolaus, Schmolmüller, Christoph, Zwittag, Paul, Windischbauer, Amadeus, Fischer, Ines, Kirchweger, Patrick, Saini, Thomas, Berger, Julian, Fraz, Wolfgang, Nia, Arastoo, Kenez, Mihaly, Nichita, Margit, Noe, Chiara, Borhanian, Kurosch, Emmanuel, Klaus, Gantschnigg, Antonia, Grechenig, Michael, Gruber, Ricarda, Manzenreiter, Lisa, Schredl, Philipp, Weitzendorfer, Michael, Gruber, Michaela, Grünbart, Martin, Kemmetinger, Vanessa, Mosshammer, Victoria, Schaetz, Tobias, Lanner, Maximilian, Binder, Alf-Dorian, Bernardi, Martin H., Dawoud, Christopher, Foessleitner, Philipp, Grimm, Christoph, Pesta, Maximilian, Pillerstorff, Robert, Riss, Stefan, Scheriau, Georg, Fisus, Andreea, Ruiss, Manuel, Meindl, Nikolaus, Primavesi, Florian, Steiner, Florian, Falkensammer, Eva, Knotzer, Hans, Trivik-Barrientos, Felipe, Alam, Mahmood, Mulla, Husain, Qader, Kawthar, Shirazi, Ahmed, Juma, Isam, Waheed Akbar Mohamad Akbar Nawab Deen, Fatema, AbdulQadir, Seemal, Jabr, Abdulla, Almoosa, Noora, Alam, Mohammed Shadrul, Basher, AKM Khairul, Islam, S.M. 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Claudio Samuel, Figueroa, Jatnna, Garcia-Dubus Rodriguez, U, Guzman, Lillian, Lopez, Ariela, Maletta Francisco, Leeany, Munoz, Herisardy, Perez Fernandez, Maricely Ambar, Rodríguez, Ada, Soto, Joel, Ubinas, Raul, Villegas, Aaron, Acra-Tolari, Ricardo, Beltran, Larissa, Betances, Luis Fernand, Díaz Vásquez, Pedro Pablo, Fernandez, Damaris, Figueroa Germosen, Jiomar Manuel, Mejia De la Cruz, Dolores, Ortiz De La Cruz, Fabio, Pelletier, Gabriela, Próspero Enrique, Rodriguez Pumarol, Suero Almanzar, Irina, Negrete Ocampo, José Ricardo, Sallam, Ibrahim, Sherief, Mohamed, Abdou, Mostafa, Abo Shanab, Ahmed, Abodeeb, Aya, Aboelkhel, Roger, Abosamak, Nour Eldin, Abouelnagah, Yossof, Adel, Dina, Ahmed, Sara, Alberkamy, Abdelrahman, Ali, Ahmed, Ashraf, Olfat, Darwish, Sara, El kharashy, Rasha, Elbadawy, Seifeldin, Elsaka, Alromisaa, Ewedah, Moataz, Ezz, Yara, Gadelrab, Ziyad, Hafez, Nour, Hafez, Youssef, Hassanin, Mohamed, Hussein Aly Salama Aly, Hamza, Ibrahim, Abdelrahman, Mahmoud Abo shabana, Alaa, Mourad, Mohamed, Mubarek, Kamilia, Omar, Samaa, Ragal, Marawan, Romany, John, Salem, Alaa, Samir Abdelaal, Ahmed, Shehata, Abdelrahman, Shenit, Karim, Yehia, Nermin, Khaled Mohamed Eid, Omar, Ibrahim Elsayed, Omar, Shehata, Mostafa Ahmed, Abd El-Ghani, Wael, Abd Elazeem, Hossam Aldein S., Abdalla, Shimaa, Abdel-Aleem, Mahmoud, Abdelfattah, Mahmoud, Abdelhafez, Mohammed, Abdelkarem, Mohamed M., Abubeih, Hossam, Ahmed, Nagm Eldin Abu Elnga, Ahmed, Ahmed, Ahmed Saad, Sarah, Ali, Abdelrahman Ahmed Abdelrahman, Elmaghraby, Khaled, Elsdfy, Shady, Eltayeb, Almoutaz, Essam, Esraa, Gadelkareem, Rabea, Ghoneim, Ahmed, Hamada Takrouney, Mohammed, Hasan, Ahmad, Hassanin, Mohamed Abdelghafor, Mahran, Wesam, Mahrous, Doha, Ragab, Abdelrahman, Sabry, Aya, Safwat, Hadeer, Saleh, Ahmed, Samir, Ahmed, Sayed, Esraa, Taher, Mohamed Gamal, Yousof, Ebrahim Ahmed, Youssef, Ahmed, Zein Elabedeen, Omar, Ali, Samar, Elbehairy, Gehad, Eleisawy, Mahmoud, Elgendy, Abdelrahman, Elhawary, Rewan, Hamdy, 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Shalaby, Ghada, Wahbah, Mahmoud, Zakaria Abdelbary, Eman, Zayan, Ahmad Helmy, Afify, Abdelrahman, Mohamed Ahmed, Mostafa, Abdeldayem, Hesham, Abdelkader Salama, Ibrahim, Ammar, Khaled, Ayoub, Islam, Gad, Emad Hamdy, Hammad, Essam, Macshut, Mahmoud, Shoreem, Hany, Soliman, Hossam Eldeen, Eldaly, Abdullah, Mashaly, Sarah, Abd-Elsalam, Sherief, Abdel-Elsalam, Wafaa, Morsy, Mohamed Sherif, Nasreddin, Mohammed, Sarsik, Sameh, Kams, Liisa, Rätsep, Tõnu, Riips, Karolin, Abebe, Kirubel, Asfaw, Fitsum, Eshete, Mahder, Gedefaw, Yetsedaw, Menjeta, Abeje, Muleta, Mahteme Bekele, Sefera, Sena, Teklu, Daniel, Tirfe, Leake, Worku, Bereket, Eado, Yegeremu, Woldemariam, Mersha Abebe, Alemu, Megersa, Azmach, Dawit, Mamo, Dr.Mickyas, Menkir, Abel, Woldemariam, Dereje, Wubishet, Estifanos, Supha, Melka, Abebe, Nebyou, Chanie, Abera, Gebre, Hiwot, Laeke, Tsegazeab, Mammo, Tihitena Negussie, Sahlu, Abat, Tiruneh, Abraham Genetu, Zewdneh, Betelhem, Fentaw, Jibril, Tabore, Ketema, Addisalem, Abreha, Tassew, Abdissa, Desalegn, Tesfaw, Mequannet, Asmamaw, Dawit, Bedane, Dereje, Bekele, Ephrem, Getie, Aderaw, Girma, Adissu, Shimelash, Dagim, Ayalew, Tewabe, Kassahun, Berhanu, Mulugeta, Esubalew, Ibrahim, Adem, Mekete, Alem, Mohammed, Adnan Abdulkadir, Abdullahi, Yasir Younis, Adem, Ephrem, Siyoum, Eneyew Getachew, Zeleke, Ewunetu, Degefe, Derje Worku, Eliyas, Nebiyu, Deressa, Yadani, Melese, Lemi, Muleta, Lemesa, Teshome, Abraham, Tesso, Birhanu, Gebrekirstos, Dr.Gebreagziabher, Pitkänen, Joel, Qian, Cheng, Sinikumpu, Jaakko, Dakpé, Stéphanie, Devauchelle, Bernard, Lavagen, Nolwenn, Bastard, François, Bin, Kim, Moukoko, Didier, Alshawared, Fadi, Beyrne, Carlos Daniel, Lugans, Laurene, Doussot, Alexandre, Onorati, Ilaria, Radu, Dana, Marion, Herjean, Trilling, Bertrand, Boleslawski, Emmanuel, Noiret, Barbara, Aubry, Estelle, Christou, Niki, Usseglio, Julie, Denis, Waast, Regenet, Nicolas, Rigaud, Jerome, Anract, Philippe, Dohan, Anthony, Cazelles, Antoine, Chamouni, Alexandre, Karoui, Mehdi, Mejean, Arnaud, Thouement, Clermidi, Pauline, Langlais, Tristan, Leonelli, Lorenzo, Thomin, Anne, Athiel, Yoann, Cathala, Nathalie, Levy-Zauberman, Yael, Macek, Petr, Mombet, Annick, Ollat, Didier, Fabrice, Chartier-Kastler, Emmanuel, Chereau, Nathalie, Melot, Charlotte, Turco, Célia, Chouillard, Elie, Frasca, Denis, Livin, Marie, Mahmoud, Fedy, Sulpice, Laurent, Cazemajou, Clément Thierry, Nyangoh Timoh, Krystel, Renault, Annaëlle, Cherkaoui, Zineb, Vix, Michel, Gornes, Hugo, Berthoumieu, Pierre, Pierre, Trocard, Del, Mathilde, Vancon, Antoine, Machemedze, Solomon, Muhemi, Roger, Ndizeye, Olivier, O’Connor, Jennifer, Tchoba, Simplice, Schäfer, Benedikt, Wallqvist, Julia, Ziemann, Sebastian, Anthuber, Matthias, Broecheler, Tobias, Goßlau, Yvonne, Maksymiw, Florian, Sommer, Florian, Sommer, Björn, Loch, Florian N, Aghalarov, Ilgar, Braumann, Chris, Horn, Julian, Egger, Eva, Enderes, Jana, Strieth, Sebastian, Treede, Hendrik, Wittmann, Maria, Marche, Benedikt, Schulz, Sissy-Amelie, Fuchs, Hans, Meisel, Cornelia, Petzold, Andrea, Knoefel, Wolfram Trudo, Solodarenko, Oleksandra, Grützmann, Robert, Hackner, Danilo, Faqar-Uz-Zaman, Sara Fatima, Fung, Christian, Leiber, Christian, Neidert, Nicolas, Sandkamp, Richard, Strähle, Jakob, Becker, Johannes, Gonschor, Bernhard, Herbolzheimer, Marit, Hecker, Matthias, Reichert, Martin, Sander, Michael, Schmidt, Götz, Schneck, Emmanuel, Kleeff, Jorg, Lorenz, Kerstin, Thomssen, Christoph, Dumpies, Christian W., Fischer, Isabel, Gessner, Michael, Klauke, Friederike, Schmidt, Birte, Vinz, Franziska, Sander, Johannes, Betz, Christian Stephan, Bewarder, Julian, Burg, Simon, Klatte, Till Orla, König, Daniela, Köpke, Leon-Gordian, Stangenberg, Martin, Roux, Frederic, Hakami, Ibrahim Abdullah, Mall, Julian W, Graeb, Christian, Huber-Strößner, Kristin, Pempe, Christina, Kelly, Kathrin, Siebert, Julia, Agrawal, Rachit, Qureischie, Homeira, Meyer, Frank, Turial, Salmai, Al-Nawas, Bilal, Battista, Marco Johannes, Heider, Julia, Mueller, Lena Katharina, Jentschura, Sina- Louisa Patrizia, Kowalewski, Karl-Friedrich, Reissfelder, Christoph, Rotter, Nicole, Scherl, Claudia, Seyfried, Steffen, Fichter, Andreas, Schäffer, Christoph, Wegmann, Helmut, Aghamaliyev, Ughur, Böcker, Wolfgang, von Ehrlich-Treuenstätt, Viktor H., Ladurner, Roland, Schlager, Justin Gabriel, Smolka, Wenko, Glowalla, Claudio, Schneidmueller, Dorien, Henkel, Karl Wilhelm, Hölz, Katharina, Prantl, Lukas, Kröger, Marie, Niemeier, Andreas, Sras, Yara, Steidle, Christoph, Dayan, Davut, Greve, Jens, Löbig, Niklas, Schochter, Fabienne, Wezel, Felix, Schmidbauer, Stefan, Boenicke, Lars, Degener, Stephan, Seiberth, Rose, Smit, Marieke, von Rundstedt, Friedrich-Carl, Germer, Christoph-Thomas, Lock, Johan, Lotz, Christopher, Schlesinger, Tobias, Schmid, Benedikt, Treutlein, Agnes, Agyeman-Duah, Nana Kwaku, Appiah, Enoch, Gakpetor, Delali, Minlah Allah, Stephen, Adu-Aryee, Nii Armah, Agboadoh, Nelson, Bediako Bowan, Antoinette, Brown, George Darko, Clegg-Lamptey, Joe-Nat, Fenu, Benjamin Sena, Kumassah, Philemon, Hussey, Romeo, Agbeno, Evans Kofi, Amoako-Boateng, Mabel, Debrah, Samuel, Doku, Dr Kingsley, Mensah, Philip, Nortey, Michael, Opandoh, Isabella Naa Morkor, Yigah, Makafui, Jiagge, Nuna, Acquaye, Jane, Agyemang-Prempeh, Akosua, Amo-Antwi, Kwabena, Amoah, Michael, Amoah, George, Aning, Daniel Gyawu, Annor Mintah, Dominic, Arthur, Joshua, Darko- Asante, Regina, Gaveh, Valerie, Gyamfi, Frank Enoch, Jonathan, Boakye - Yiadom, Konney, Thomas Okpoti, Kyei, Ishmael, Nimako, Boateng, Ofosu-Barko, Ben Blay, Osabutey, Anita, Sagoe, Robert, Yifieyeh, Abiboye, Ansong, George, Abdul-Mumin, Alhassan, Amadu, Munira, Azahares Leal, German, Buunaaim, Alexis, Cheyuo, Ernest, Daboo Salifu, Latif, Issaka, Adamu, Yakubu, Musah, Grypiotis, Ioannis, Papadopoulou, Triada, Korkolis, Dimitrios, Thomakos, Nikolaos, Dellaportas, Dionysios, Dimitra, Papalouka, Hadjizacharias, Theodoros, Konstadoulakis, Manousos, Massaras, Dimitrios, Vezakis, Antonios, Avgerinos, 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Манкевич, Николай, Ten, Oleg, Zakharenko, Aleksandr, Kim, Gleb, Shkarupa, Dmitry, Shulgin, Andrei, Swetlana, Nepomnyaschaya, Trofimow, Alexey, Zinkevich, Denis, Zolotoukho, Anna, Байбуз, Дмитрий, Drozdov, Evgeniy, Chavkin, Petr, Shamsutdinov, Saydash, Abdullaev, Abakar, Gachabayov, Mahir, Кудрявцева, Анна, Umutesi, Sandrine, Eugene, Ngoga, Joseph, Lule, Liliane, Nzabamwita, Runanira, Daniel MUHIRE, Shumbusho, Jean Paul, Shyaka, Ian, Sibomana, Alphonse Marie, Bizimana, Achille, Bunogerane Juru, Gisele, Gilbert, Gasengayire, Irakoze, Magnifique, Muneza, Severien, Nshuti, Steven, Nyirahabimana, Jeannette, Mutimamwiza, Immaculee, Niyongombwa, Irénée, Alghamdi, Abdulaziz, Almutawa, Abdulrahman, Rayzah, Musaed, Abd ElGhany, Reda, Bawa, Dauda, Elzain, Mohammed, Wakill, Fadi, Fahiem-ul-Hassan, Mir, Al Duhileb, Mohammed, Alazzeh, Ghaleb, Alghazal, Thabet, Alkhatib, Abdulrahman, Almatar, Bikheet, Alnaimi, Manal, Alsuwaimel, Munir, Balhareth, Ameera, Abutaleb, Malak, Ageeli, Mohammed, Alhazmi, 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Perez, Raquel, Marco Garrido, Alfonso, Medina, Esther, Onate, Julian, Peña Ros, Emilio, Sanchez Rodriguez, Carlos, Valero Soriano, María, Yepes García, María Teresa, Conesa, Ana, Delegido García, Ana, Gómez Valles, Paula, Martínez, Francisco, Gómez Viana, Leticia, García Flórez, Luis Joaquín, Suárez Sánchez, Aida, Concepcion, Aguirre, Asier, Alomar Bofill, Marta, Baena, Santiago, Castro Suárez, Marta, Cifuentes Rodenas, José Andrés, Mata, Javier, Morell, Andreu, Pagès, Naila, Rodriguez Vaquero, Leticia, Santos Cidon, Pilar, Soler Pedrola, Maria, Tasende, Marta, Segura-Sampedro, Juan José, Tello Galindo, Isabel, Cámara Cabrera, Jaume, de la Rosa-Estadella, Marta, Molina-Corbacho, Matilde, Santos Espí, Marta, Diego-Alonso, Elisa Angela, Aguinagalde, Borja, Andres Imaz, Ainhoa, Armendariz, Mikel, Arrabal, Lorena, Augusto, Iñigo, Cuevas, Ainhoa, Elúa, Alejandro, Gallego, Lander, Garmendia, Gregorio, Grañén, Carmen, Iraola, Maria, Lizarbe, Jon Ander, Lopes Moreira, Claudia Cristina, Pastor, 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Raja, M, Reyhani, H, Shahmiri, A, Small, O, Soni, U, Aguirrezabala Armbruster, B, Hakim, MA, Hawkins-Hooker, L, Howell, KA, Hullait, R, Jaskowska, A, Ottewell, L, Thomas-Jones, I, Vasudev, A, Clements, B, Fenton, J, Gill, M, Haider, S, Lim, AJM, Maguire, H, McMullan, J, Nicoletti, J, Samuel, S, Unais, MA, White, N, Yao, PC, Yow, L, Brady, R, Cheekoty, P, Cheong, J, Chew, SJHL, Chow, R, Ganewatta Kankanamge, D, Mamer, L, Mohammed, B, Ng Chieng Hin, J, Renji Chungath, R, Royston, A, Sharrad, E, Sinclair, R, Treherne, K, Wyatt, F, Maniarasu, VS, Appanna, T, Bucknall, T, Hussain, F, Owen, A, Parry, R, Sagua, N, Spofforth, K, Yuen, ECT, Bosley, N, Hardie, W, Moore, T, Regas, C, Abdel-Khaleq, S, Ali, N, Bashiti, H, Buxton-Hopley, R, Constantinides, M, D’Afflitto, M, Deshpande, A, Duque Golding, J, Frisira, E, Germani Batacchi, M, Gomaa, A, Hay, D, Iakovou, A, Iakovou, D, Ismail, E, Jefferson, S, Khouli, Y, Mason, J, McCaughan, R, Moffatt, J, Morawala, A, Nadir, H, Neyroud, F, Nikookam, Y, Parmar, A, Pinto, L, Richards, E, Trainer, C, Valetopoulou, A, Vassiliou, A, Wantman, A, Wilde, S, Dickinson, M, Rockall, T, Senn, D, Wcislo, K, Zalmay, P, Adelekan, K, Allen, K, Bajaj, M, Gatumbu, P, Hang, S, Hashmi, Y, Kaur, T, Kawesha, A, Woodmass, M, Adelowo, T, Ahari, D, Alhwaishel, K, Clayton, B, Cockroft, A, Curtis Lopez, C, Hilton, M, Ismail, N, Kouadria, M, Lee, L, MacConnachie, A, Monks, F, Mungroo, S, Nikoletopoulou, C, Sara, X, Shahid, A, Suresh, G, Wilcha, R, Atiyah, A, Dermanis, A, Gibbons, H, Hyde, A, Lawson, A, Lee, C, Leung-Tack, M, Li Saw Hee, J, Mostafa, O, Pattani, N, Plumbley-Jones, J, Pufal, K, Sanghera, J, Saram, S, Scadding, S, See, S, Stringer, H, Torrance, A, Vardon, H, Wyn-Griffiths, F, Brew, A, Kaur, G, Soni, D, Tickle, A, Akbar, Z, Appleyard, T, Figg, K, Jayawardena, P, Johnson, A, Kamran Siddiqui, Z, Lacy-Colson, J, Oatham, R, Rowlands, B, Sludden, E, Turnbull, C, Allin, D, Ansar, Z, Azeez, Z, Dale, VH, Garg, J, Horner, A, Knight, S, McGregor, C, McKenna, J, McLelland, T, Packham-Smith, A, Rowsell, K, Spector-Hill, I, Adeniken, E, Baker, J, Bartlett, M, Chikomba, L, Connell, B, Deekonda, P, Dhar, M, Elmansouri, A, Gamage, K, Goodhew, R, Hanna, P, Knight, J, Luca, A, Maasoumi, N, Mahamoud, F, Manji, S, Marwaha, PK, Mason, F, Oluboyede, A, Pigott, L, Razaq, AM, Richardson, M, Saddaoui, I, Wijeyendram, P, Yau, S, Atkins, W, Liang, K, Miles, N, Praveen, B, Ashai, S, Braganza, J, Common, J, Cundy, A, Davies, R, Guthrie, J, Handa, I, Iqbal, M, Ismail, R, Jones, I, Lee, KS, Levene, A, Okocha, M, Smith, A, Subramaniam, E, Tandle, S, Wang, A, Watson, A, Wilson, C, Chan, XHF, Khoo, E, Montgomery, C, Norris, M, Pugalenthi, PP, Common, T, Cook, E, Mistry, H, Shinmar, HS, Agarwal, G, Bandyopadhyay, S, Brazier, B, Carroll, L, Goede, A, Harbourne, A, Lakhani, A, Larwood, J, Merchant, J, Pattenden, S, Pradhan, A, Raafat, N, Rothwell, E, Shammoon, Y, Sudarshan, R, Vickers, E, Wingfield, L, Ashworth, I, Azizi, S, Bhate, R, Chowdhury, T, Christou, A, Davies, L, Dwaraknath, M, Farah, Y, Garner, J, Gureviciute, E, Hart, E, Javid, S, Kankam, HK, Kaur Toor, P, Kaz, R, Kermali, M, Khan, I, Mattson, A, McManus, A, Murphy, M, Nair, K, Ngemoh, D, Norton, E, Olabiran, A, Parry, L, Payne, T, Pillai, K, Punjabi, K, Raghunathan, A, Raza, M, Ritehnia, J, Simpson, G, Smith, W, Sodeinde, S, Studd, L, Subramaniam, M, Thomas, J, Towey, S, Tsang, E, Tuteja, D, Vasani, J, Vio, M, Adams, J, Anthony Wilkinson, J, Asvandi, S, Austin, T, Bald, A, Bix, E, Carrick, M, Chander, B, Cooper Drake, B, Crosbie, S, D Portela, S, Francis, D, Gallagher, C, Gillespie, R, Gravett, H, Gupta, P, Ilyas, C, Johny, J, Jones, A, Kinder, F, MacLeod, C, Macrow, C, Maqsood-Shah, A, Mather, J, McCann, L, McMahon, R, Mitham, E, Mohamed, M, Munton, E, Nightingale, K, O‚ÄôNeill, K, Onyemuchara, I, Senior, R, Shanahan, A, Sherlock, J, Spyridoulias, A, Stavrou, C, Stokes, D, Tamang, R, Taylor, E, Trafford, C, Uden, C, Waddington, C, Yassin, D, Zaman, M, Bangi, S, Cheng, T, Hussain, N, Imani-Masouleh, S, Mahasivam, G, McKnight, G, Ng, HL, Ota, HC, Pasha, T, Ravindran, W, Vishnu K, S, Zaman, S, Carr, W, Cope, S, Eagles, EJ, Howarth-Maddison, M, Li, CY, Reed, J, Ridge, A, Stubbs, T, Teasdaled, D, Umar, R, Worthington, J, Dhebri, A, Kalenderov, R, Alattas, A, Arain, Z, Bhudia, R, Chia, D, Daniel, S, Dar, T, Garland, H, Girish, M, Hampson, A, Lehovsky, K, Mullins, W, Omorphos, N, Vasdev, N, Venkatesh, A, Waldock, W, Bhandari, A, Brown, G, Choa, G, Eichenauer, CE, Ezennia, K, Kidwai, Z, Lloyd-Thomas, A, Macaskill Stewart, A, Massardi, C, Sinclair, E, Skajaa, N, Tan, I, Afsheen, N, Anuar, A, Azam, Z, Bhatia, P, Davies-kelly, N, Dickinson, S, Elkawafi, M, Ganapathy, M, Khoury, EG, Licudi, D, Mehta, V, Neequaye, S, Nita, G, Tay, VL, Zhao, S, Botsa, E, Cuthbert, H, Elliott, J, Furlepa, M, Lehmann, J, Mangtani, A, Narayan, A, Shah, D, Shaw, C, Zhao, Z, Beck, C, Caldwell, S, Clements, JM, French, B, Kenny, R, Kirk, S, McClung, A, McLaughlin, N, Watson, S, Whiteside, E, Alyacoubi, S, Arumugam, V, Beg, R, Dawas, K, Garg, S, Lloyd, ER, Mahfouz, Y, Manobharath, N, Moonesinghe, R, Morka, N, Prashar, J, Yip, S, Adeeko, ES, Ajekigbe, F, Bhat, A, Farrugia, A, Gurung, C, Long, T, Malik, B, Manirajan, S, Newport, D, Rayer, J, Ridha, A, Saran, T, Sinker, A, Waruingi, D, Allen, R, Al Sadek, Y, Alves do Canto Brum, H, Asharaf, H, Ashman, M, Balakumar, V, Barrington, J, Baskaran, R, Berry, A, Bhachoo, H, Bilal, A, Boaden, L, Chia, WL, Covell, G, Crook, D, Dadnam, F, Davis, L, De Berker, H, Doyle, C, Fox, C, Gruffydd-Davies, M, Hafouda, Y, Hill, A, Hubbard, E, Hunter, A, Inpadhas, V, Jamshaid, M, Jandu, G, Jeyanthi, M, Kantor, C, Kwak, SY, Malik, N, Matt, R, McNulty, P, Miles, C, Mohomed, A, Myat, P, Niharika, J, Nixon, A, O‚ÄôReilly, D, Parmar, K, Pengelly, S, Price, L, Ramsden, M, Turnor, R, Wales, E, Waring, H, Wu, M, Yang, T, Ye, TTS, Zander, A, Zeicu, C, Bellam, S, Francombe, J, Kawamoto, N, Rahman, MR, Sathyanarayana, A, Tang, HT, Hollingshead, J, Page, V, Sugarman, J, Chiong, J, Fung, E, Kan, SY, Kiang, J, Kok, J, Krahelski, O, Liew, MY, Lyell, B, Sharif, Z, Speake, D, Alim, L, Amakye, NY, Chandrasekaran, J, Chandratreya, N, Drake, J, Owoso, T, Thu, YM, Abou El Ela Bourquin, B, Alberts, J, Chapman, D, Rehnnuma, N, Ainsworth, K, Carpenter, H, Emmanuel, T, Fisher, T, Gabrel, M, Guan, Z, Hollows, S, Hotouras, A, Ip Fung Chun, N, Jaffer, S, Kallikas, G, Lewinsohn, B, Liu, FY, Mohammed, S, Rutherfurd, A, Situ, T, Stammer, A, Taylor, F, Thin, N, Urgesi, E, Zhang, N, Ahmad, MA, Bishop, A, Bowes, A, Dixit, A, Glasson, R, Hatta, S, Hatt, K, Larcombe, S, Preece, J, Riordan, E, Fegredo, D, Haq, MZ, Li, C, McCann, G, Stewart, D, Baraza, W, Bhullar, D, Burt, G, Coyle, J, Deans, J, Devine, A, Hird, R, Ikotun, O, Manchip, G, Ross, C, Storey, L, Tan, WWL, Tse, C, Warner, C, Whitehead, M, Wu, F, Court, EL, Crisp, E, Huttman, M, Mayes, F, Robertson, H, Rosen, H, Sandberg, C, Al Bakry, M, Ashwell, W, Bajaj, S, Bandyopadhyay, D, Browlee, O, Burway, S, Chand, CP, Elsayeh, K, Elsharkawi, A, Evans, E, Ferrin, S, Fort-Schaale, A, Iacob, M, I, K, Impelliziere Licastro, G, Mankoo, AS, Olaniyan, T, Otun, J, Reddy, R, Saeed, D, Simmonds, O, Singhal, G, Tron, K, Wickstone, C, Williams, R, Bradshaw, E, De Kock Jewell, V, Knight, C, Metezai, H, Mirza-Davies, A, Spink, D, and Wischhusen, S

Published
2024
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26. Witch Nails (Krt90whnl): A spontaneous mouse mutation affecting nail growth and development.

Author

Sundberg, John P, Galantino-Homer, Hannah, Fairfield, Heather, Ward-Bailey, Patricia F, Harris, Belinda S, Berry, Melissa, Pratt, C Herbert, Gott, Nicholas E, Bechtold, Lesley S, Kaplan, Pauline R, Durbin-Johnson, Blythe P, Rocke, David M, and Rice, Robert H

Subjects
Veterinary Sciences, Agricultural, Veterinary and Food Sciences, Biological Sciences, Genetics, 2.1 Biological and endogenous factors, Animals, Mice, Growth and Development, Horses, Mutation, Nail Diseases, Nails, Nails, Malformed, General Science & Technology
Abstract

Numerous single gene mutations identified in humans and mice result in nail deformities with many similarities between the species. A spontaneous, autosomal, recessive mutation called witch nails (whnl) is described here where the distal nail matrix and nail bed undergo degenerative changes resulting in formation of an abnormal nail plate causing mice to develop long, curved nails. This mutation arose spontaneously in a colony of MRL/MpJ-Faslpr/J at The Jackson Laboratory. Homozygous mutant mice are recognizable by 8 weeks of age by their long, curved nails. The whnl mutation, mapped on Chromosome 15, is due to a 7-bp insertion identified in the 3' region of exon 9 in the Krt90 gene (formerly Riken cDNA 4732456N10Rik), and is predicted to result in a frameshift that changes serine 476 to arginine and subsequently introduces 36 novel amino acids into the protein before a premature stop codon (p. Ser476ArgfsTer36). By immunohistochemistry the normal KRT90 protein is expressed in the nail matrix and nail bed in control mice where lesions are located in mutant mice. Immunoreactivity toward equine KRT124, the ortholog of mouse KRT90, is restricted to the hoof lamellae (equine hoof wall and lamellae are homologous to the mouse nail plate and nail bed) and the mouse nail bed. Equine laminitis lesions are similar to those observed in this mutant mouse suggesting that the latter may be a useful model for hoof and nail diseases. This first spontaneous mouse mutation affecting the novel Krt90 gene provides new insight into the normal regulation of the molecular pathways of nail development.

Published
2022

29. Water filtration by endobenthic sandprawns enhances resilience against eutrophication under experimental global change conditions

Author

C. M. Thomas, C. de Cerff, G. A. V. Maniel, A. E. Oyatoye, E. Rocke, H. G. Marco, and D. Pillay

Subjects
Medicine, Science
Abstract

Abstract Identifying processes that confer resilience against global change is a scientific challenge but is central to managing ecosystem functionality in future. Detecting resilience-enhancing mechanisms is especially relevant in coastal ecosystems, where multi-stressor interactions can drive degradation over time. Here, we quantify the resilience-conferring potential of endobenthic sandprawns against eutrophication, including under high temperatures. We show using a global change mesocosm experiment that sandprawn presence was associated with declines in phytoplankton biomass, particularly under eutrophic conditions, where sandprawns reduced phytoplankton biomass by approximately 74% and prevented a shift to extreme eutrophy. Eutrophic waters were nanophytoplankton-dominated, but sandprawn presence countered this, resulting in even contributions of pico- and nanophytoplankton. Our findings highlight the potential for sandprawns to increase resilience against eutrophication by limiting phytoplankton blooms, preventing extreme eutrophy and counteracting nanophytoplankton dominance. Incorporating endobenthic crustaceans into resilience-based management practices can assist in arresting future water quality declines in coastal ecosystems.

Published
2023
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30. GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification

Author

Halloran, John T. and Rocke, David M.

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

One of the most efficient methods to solve L2-regularized primal problems, such as logistic regression and linear support vector machine (SVM) classification, is the widely used trust region Newton algorithm, TRON. While TRON has recently been shown to enjoy substantial speedups on shared-memory multi-core systems, exploiting graphical processing units (GPUs) to speed up the method is significantly more difficult, owing to the highly complex and heavily sequential nature of the algorithm. In this work, we show that using judicious GPU-optimization principles, TRON training time for different losses and feature representations may be drastically reduced. For sparse feature sets, we show that using GPUs to train logistic regression classifiers in LIBLINEAR is up to an order-of-magnitude faster than solely using multithreading. For dense feature sets--which impose far more stringent memory constraints--we show that GPUs substantially reduce the lengthy SVM learning times required for state-of-the-art proteomics analysis, leading to dramatic improvements over recently proposed speedups. Furthermore, we show how GPU speedups may be mixed with multithreading to enable such speedups when the dataset is too large for GPU memory requirements; on a massive dense proteomics dataset of nearly a quarter-billion data instances, these mixed-architecture speedups reduce SVM analysis time from over half a week to less than a single day while using limited GPU memory., Comment: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada

Published
2020

31. Elucidation of familial relationships using hair shaft proteomics

Author

Karim, Noreen, Plott, Tempest J, Durbin-Johnson, Blythe P, Rocke, David M, Salemi, Michelle, Phinney, Brett S, Goecker, Zachary C, Pieterse, Marc JM, Parker, Glendon J, and Rice, Robert H

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, 1.1 Normal biological development and functioning, Hair, Humans, Mass Spectrometry, Peptides, Polymorphism, Single Nucleotide, Proteomics, Proteomic profiling, Genetically variant peptides, Human hair, Forensic investigation, Relationship testing, Mathematical Sciences, Law and Legal Studies, Legal & Forensic Medicine, Biological sciences, Law and legal studies, Mathematical sciences
Abstract

This study examines the potential of hair shaft proteomic analysis to delineate genetic relatedness. Proteomic profiling and amino acid sequence analysis provide information for quantitative and statistically-based analysis of individualization and sample similarity. Protein expression levels are a function of cell-specific transcriptional and translational programs. These programs are greatly influenced by an individual's genetic background, and are therefore influenced by familial relatedness as well as ancestry and genetic disease. Proteomic profiles should therefore be more similar among related individuals than unrelated individuals. Likewise, profiles of genetically variant peptides that contain single amino acid polymorphisms, the result of non-synonymous SNP alleles, should behave similarly. The proteomically-inferred SNP alleles should also provide a basis for calculation of combined paternity and sibship indices. We test these hypotheses using matching proteomic and genetic datasets from a family of two adults and four siblings, one of which has a genetic condition that perturbs hair structure and properties. We demonstrate that related individuals, compared to those who are unrelated, have more similar proteomic profiles, profiles of genetically variant peptides and higher combined paternity indices and combined sibship indices. This study builds on previous analyses of hair shaft protein profiling and genetically variant peptide profiles in different real-world scenarios including different human hair shaft body locations and pigmentation status. It also validates the inclusion of proteomic information with other biomolecular substrates in forensic hair shaft analysis, including mitochondrial and nuclear DNA.

Published
2021

36. READ-ing antimicrobial stewardship in the Caribbean: a tri-nation document review

Author

Tamarie Rocke, Aniebiot-abasi Udofia, Nathalie El Omeiri, and Pilar Ramon-Pardo

Subjects
review, antimicrobial stewardship, drug resistance, microbial, caribbean region, Medicine, Arctic medicine. Tropical medicine, RC955-962, Public aspects of medicine, RA1-1270
Abstract

Objective. To explore the antimicrobial stewardship policy landscape in three English-speaking Caribbean countries (Barbados, Guyana, and Saint Lucia) and examine the key enablers and challenges to the design and implementation of formal antimicrobial stewardship programs. Methods. A document analysis that searched for existing policy, communications, and contributions on antimicrobial stewardship from these three countries, adapting the READ (Ready materials; Extract data; Analyze data; Distill findings) approach, a systematic procedure for health policy document review. Results. The search strategy identified 726 initial records. Of those, 15 (2%) met the inclusion criteria. The analysis included official policy documents (n = 3), scholarly works/reviews (n = 3), advocacy documents (n = 2), news articles (n = 4), and confidential reports (n = 3) from the three countries. Conclusions. Critical matters such as cross-programmatic coordination, the significance of individual action, and the need for bidirectional knowledge discourse are prominent in optimizing antimicrobial stewardship adaptation in these countries. CARICOM regional coordination has positively impacted the integration of infection prevention and control with antimicrobial stewardship across this knowledge network.

Published
2023
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43. Kommentar

Author

Boeck, Gisela, Rocke, Alan J., Breidbach, Olaf, Founding Editor, Jost, Jürgen, Founding Editor, Stock, Armin, Series Editor, Boeck, Gisela, and Rocke, Alan J.

Published
2022
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47. Commentary

Author

Boeck, Gisela, Rocke, Alan J., Jost, Jürgen, Series Editor, Stock, Armin, Series Editor, Boeck, Gisela, and Rocke, Alan J.

Published
2022
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49. Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra

Author

Halloran, John T. and Rocke, David M.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the original sample. In this work, we greatly expand the parameter learning capabilities of a dynamic Bayesian network (DBN) peptide-scoring algorithm, Didea, by deriving emission distributions for which its conditional log-likelihood scoring function remains concave. We show that this class of emission distributions, called Convex Virtual Emissions (CVEs), naturally generalizes the log-sum-exp function while rendering both maximum likelihood estimation and conditional maximum likelihood estimation concave for a wide range of Bayesian networks. Utilizing CVEs in Didea allows efficient learning of a large number of parameters while ensuring global convergence, in stark contrast to Didea's previous parameter learning framework (which could only learn a single parameter using a costly grid search) and other trainable models (which only ensure convergence to local optima). The newly trained scoring function substantially outperforms the state-of-the-art in both scoring function accuracy and downstream Fisher kernel analysis. Furthermore, we significantly improve Didea's runtime performance through successive optimizations to its message passing schedule and derive explicit connections between Didea's new concave score and related MS/MS scoring functions., Comment: 16 pages. A partitioned version of this appeared in NeurIPS 2018

Published
2019

50. Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra

Author

Halloran, John T. and Rocke, David M.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) present in the original complex sample. In this work, we leverage the log-likelihood gradients of generative models to improve the identification of such spectra. In particular, we show that the gradient of a recently proposed dynamic Bayesian network (DBN) may be naturally employed by a kernel-based discriminative classifier. The resulting Fisher kernel substantially improves upon recent attempts to combine generative and discriminative models for post-processing analysis, outperforming all other methods on the evaluated datasets. We extend the improved accuracy offered by the Fisher kernel framework to other search algorithms by introducing Theseus, a DBN representing a large number of widely used MS/MS scoring functions. Furthermore, with gradient ascent and max-product inference at hand, we use Theseus to learn model parameters without any supervision., Comment: 13 pages. A partitioned version of this appeared in NIPS 2017

Published
2019

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