Lasso

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

Lasso peptides form a growing family of fascinating ribosomally-synthesized and post-translationally modified peptides produced by bacteria. They contain 15 to 24 residues and share a unique interlocked topology that involves an N-terminal 7 to 9-residue macrolactam ring where the C-terminal tail is threaded and irreversibly trapped. The ring results from the condensation of the N-terminal amino group with a side-chain carboxylate of a glutamate at position 8 or 9, or an aspartate at position 7, 8 or 9. The trapping of the tail involves bulky amino acids located in the tail below and above the ring and/or disulfide bridges connecting the ring and the tail. Lasso peptides are subdivided into three subtypes depending on the absence (class II) or presence of one (class III) or two (class I) disulfide bridges. The lasso topology results in highly compact structures that give to lasso peptides an extraordinary stability towards both protease degradation and denaturing conditions. Lasso peptides are generally receptor antagonists, enzyme inhibitors and/or antibacterial or antiviral (anti-HIV) agents. The lasso scaffold and the associated biological activities shown by lasso peptides on different key targets make them promising molecules with high therapeutic potential. Their application in drug design has been exemplified by the development of an integrin antagonist based on a lasso peptide scaffold. The biosynthesis machinery of lasso peptides is therefore of high biotechnological interest, especially since such highly compact and stable structures have to date revealed inaccessible by peptide synthesis. Lasso peptides are produced from a linear precursor LasA, which undergoes a maturation process involving several steps, in particular cleavage of the leader peptide and cyclization. The post-translational modifications are ensured by a dedicated enzymatic machinery, which is composed of an ATP-dependent cysteine protease (LasB) and a lactam synthetase (LasC) that form an enzymatic complex called lasso synthetase. Microcin J25, produced by Escherichia coli AY25, is the archetype of lasso peptides and the most extensively studied. To date only around forty lasso peptides have been isolated, but genome mining approaches have revealed that they are widely distributed among Proteobacteria and Actinobacteria, particularly in Streptomyces, making available a rich resource of novel lasso peptides and enzyme machineries towards lasso topologies.

"Ted Lasso" is an American comedy TV series that premiered on August 14, 2020, on Apple TV+. The show is created by Bill Lawrence, Jason Sudeikis, Joe Kelly, and Brendan Hunt, and stars Jason Sudeikis as the titular character, Ted Lasso. Ted is a cheerful and optimistic American football coach who is hired by a struggling English Premier League team, AFC Richmond, to manage their soccer team, despite having no prior experience in soccer. The series follows the misadventures of Ted Lasso, his assistant coach, Coach Beard, and the rest of the team, as they try to turn Richmond's fortunes around. The show has received widespread critical acclaim for its engaging storyline, witty writing, and brilliant acting. Critics have praised Jason Sudeikis' performance as Ted Lasso, with many calling it one of the best performances of his career. The show has been praised for its uplifting and positive message, with many viewers finding it to be a much-needed escape from the stresses of daily life. "Ted Lasso" has also been lauded for its diverse cast of characters and big-hearted approach to storytelling, making it one of the most beloved TV shows of recent times.

This is the first book to be released on Lasso that provides a step by step how to guide to creating Web solutions. The focus is on teaching essential elements of Lasso for dynamic Web content.

Welcome to The Adventures of Lasso Locks By: Caprice St. Pierre Showing the horrendous side of sexual abuse that victims every day must go through, Welcome to The Adventures of Lasso Locks shares the ugly truth. Showing the point of view of not only the victim but the predator as well, Lasso shows how a victim can heal and stop the cycle of sexual abuse.

An accessible and engaging journey through the philosophical themes and concepts of Ted Lasso Ted Lasso and Philosophy explores the hidden depths beneath the vibrant veneer of AppleTV’s breakout, award-winning sitcom. Blending philosophical sophistication with winsome appreciation of this feel-good comedy, the collection features 20 original essays canvassing the breadth of the series and carefully considering the ideas it presents, including the goal of competition, the role of mental health, sportsmanship, revenge versus justice, the importance of friendship, the imperative of respect for persons, humility, leadership, identity, character growth, courage, journalistic ethics, belief, forgiveness, what love looks like, and just how evil tea is. In a nod to the show’s many literary allusions, the compilation concludes with a whimsical appendix that catalogs the books most significant to Ted Lasso’s themes and characters. If football is life, as Dani Rojas fondly repeats, then this book’s a fitting primer. Covers the full breadth of the original Ted Lasso series, including the third season Explores every major character and all of the show's significant subplots and elements Written in the spirit of the show, with in-jokes that will appeal to Ted Lasso fans Features an introduction that guides readers through the book’s materials Includes Beard's Bookshelf, a bibliography of the most significant books shown or alluded to in the series Ted Lasso and Philosophy is for the curious, not judgmental. Sport is quite the metaphor, and we can’t wait to unpack it with you.

Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.