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Finding a treatment for cancer is a major challenge of our time. In the ongoing research, combination therapies (the use of several drugs together) are of high interest. In comparison with the use of a single drug, combinations of synergistic drugs (i.e. drugs that are more effective together than alone) can be as effective while allowing to overcome the drug resistance, to reduce the doses at which the drugs are used, and consequently decrease their toxic effect and multiply their targets. However, the space of all potentially effective combinations is too large to experimentally test all of them and assess their effectiveness, this is known as the combinatorial explosion problem. To overcome that, the identification of interesting combinations requires the help of computational tools. In recent years, machine learning models have been successfully used in biomedical applications. They are typically used in order to determine which combinations would be interesting to be experimentally tested. Since some models aiming at predicting the responses of pairwise combinations already exist, there are only a few machine learning models able to predict responses of higher order drug combinations (the order of a drug combination is defined as the number of drugs in the combination). In addition to the response of a drug combination (typically expressed as a growth percentage), the synergy score of this combination is of high interest. The synergy score allow to answer the question: how much are those drugs more effective together than individually? This work is a step towards the use of machine learning to predict the effect of higher order (order larger than two) cancer drug combinations. It has been made in collaboration with Aalto University (Finland), where a machine learning tool called ComboFM has been developed. ComboFM is able to efficiently predict pairwise responses of cancer drugs. The goal of this work is to extend the use of ComboFM to the predictions of higher order drug combinations. To that end, we propose to combine ComboFM with another model, called the Dose model. The Dose model computes the responses of any order drug combinations, based on all the pairwise responses existing in the combination. This work investigates how those two models can be combined together in order to predict responses of higher order drug combinations while decreasing the amount of required experimental data (pairwise responses). This combination of models gives rise to several issues that are tackled and investigated. The experiments made in this thesis showed that ComboFM and the Dose model can efficiently be combined, as long as the parameters of both models are optimized specifically for this application.

Combination therapy --- Cancer treatment --- Machine learning --- Higher order drug combination --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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Native mass spectrometry (nMS) coupled to ion mobility (IM-MS) is an analytical strategy for studying 
biological systems, particularly proteins and peptides, after transferring them to the gas phase. 
However, can the absence of certain interactions in the gas phase compared to those in solution result 
in new conformations compared to their native states? Or are ionic conformations kinetically trapped? 
The aim of this work is to provide some answers to these questions by studying peptides obtained 
following trypsin digestion of a well-known protein, bovine serum albumin. The peptides derived from 
this digestion were studied using two separation methods, one in the gas phase (ion mobility) coupled 
with another in solution (capillary electrophoresis), which also provided information on the structural 
parameters of the systems studied. Both methods were tested under different conditions (pH, 
interface between the two methods, ion activation regime) to determine the influence of experimental 
parameters. After analysis and interpretation of the results, it was found that despite similar 
conformational distributions between the two phases for the majority of peptides. Some peptides 
undergo conformational changes after desolvation.

Capillary electrophoresis, Ion mobility, mass spectrometry, peptides, Higher order structures --- Physique, chimie, mathématiques & sciences de la terre > Chimie

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Psychology --- hand preference --- cerebral dominance --- brain functioning --- sensorimotor control --- higher-order processing --- skilled actions --- praxis --- laterality --- spatial discrimination --- tool affordances

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An accessible, streamlined, and user-friendly approach to calculusCalculus is a beautiful subject that most of us learn from professors, textbooks, or supplementary texts. Each of these resources has strengths but also weaknesses. In Calculus Simplified, Oscar Fernandez combines the strengths and omits the weaknesses, resulting in a "Goldilocks approach" to learning calculus: just the right level of detail, the right depth of insights, and the flexibility to customize your calculus adventure.Fernandez begins by offering an intuitive introduction to the three key ideas in calculus-limits, derivatives, and integrals. The mathematical details of each of these pillars of calculus are then covered in subsequent chapters, which are organized into mini-lessons on topics found in a college-level calculus course. Each mini-lesson focuses first on developing the intuition behind calculus and then on conceptual and computational mastery. Nearly 200 solved examples and more than 300 exercises allow for ample opportunities to practice calculus. And additional resources-including video tutorials and interactive graphs-are available on the book's website.Calculus Simplified also gives you the option of personalizing your calculus journey. For example, you can learn all of calculus with zero knowledge of exponential, logarithmic, and trigonometric functions-these are discussed at the end of each mini-lesson. You can also opt for a more in-depth understanding of topics-chapter appendices provide additional insights and detail. Finally, an additional appendix explores more in-depth real-world applications of calculus.Learning calculus should be an exciting voyage, not a daunting task. Calculus Simplified gives you the freedom to choose your calculus experience, and the right support to help you conquer the subject with confidence.· An accessible, intuitive introduction to first-semester calculus· Nearly 200 solved problems and more than 300 exercises (all with answers)· No prior knowledge of exponential, logarithmic, or trigonometric functions required· Additional online resources-video tutorials and supplementary exercises-provided

Calculus --- Infinitesimal change. --- Leibniz’s notation for the integral. --- antiderivatives. --- at a point. --- continuity. --- derivative at a point. --- differentiability. --- differentiation shortcuts. --- differentiation. --- higher-order derivatives. --- indefinite integrals. --- instantaneous rate of change interpretation of the derivative. --- instantaneous speed problem. --- limit laws. --- limits approaching infinity. --- limits yielding infinity. --- linearization. --- on an interval. --- one-sided limits. --- optimization theory. --- tangent line problem. --- two-sided limits.

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In 1922, Harald Bohr and Johannes Mollerup established a remarkable characterization of the Euler gamma function using its log-convexity property. A decade later, Emil Artin investigated this result and used it to derive the basic properties of the gamma function using elementary methods of the calculus. Bohr-Mollerup's theorem was then adopted by Nicolas Bourbaki as the starting point for his exposition of the gamma function. This open access book develops a far-reaching generalization of Bohr-Mollerup's theorem to higher order convex functions, along lines initiated by Wolfgang Krull, Roger Webster, and some others but going considerably further than past work. In particular, this generalization shows using elementary techniques that a very rich spectrum of functions satisfy analogues of several classical properties of the gamma function, including Bohr-Mollerup's theorem itself, Euler's reflection formula, Gauss' multiplication theorem, Stirling's formula, and Weierstrass' canonical factorization. The scope of the theory developed in this work is illustrated through various examples, ranging from the gamma function itself and its variants and generalizations (q-gamma, polygamma, multiple gamma functions) to important special functions such as the Hurwitz zeta function and the generalized Stieltjes constants. This volume is also an opportunity to honor the 100th anniversary of Bohr-Mollerup's theorem and to spark the interest of a large number of researchers in this beautiful theory.

Convex functions. --- Gamma functions. --- Functions, Convex --- Functions of real variables --- Functions, Gamma --- Transcendental functions --- Difference Equation --- Higher Order Convexity --- Bohr-Mollerup's Theorem --- Principal Indefinite Sums --- Gauss' Limit --- Euler Product Form --- Raabe's Formula --- Binet's Function --- Stirling's Formula --- Euler's Infinite Product --- Euler's Reflection Formula --- Weierstrass' Infinite Product --- Gauss Multiplication Formula --- Euler's Constant --- Gamma Function --- Polygamma Functions --- Hurwitz Zeta Function --- Generalized Stieltjes Constants