The Machine Learning Landscape of Top Taggers

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.Comment: Yet another tagger included

Quotient Complexity of Regular Languages

The past research on the state complexity of operations on regular languages is examined, and a new approach based on an old method (derivatives of regular expressions) is presented. Since state complexity is a property of a language, it is appropriate to define it in formal-language terms as the number of distinct quotients of the language, and to call it "quotient complexity". The problem of finding the quotient complexity of a language f(K,L) is considered, where K and L are regular languages and f is a regular operation, for example, union or concatenation. Since quotients can be represented by derivatives, one can find a formula for the typical quotient of f(K,L) in terms of the quotients of K and L. To obtain an upper bound on the number of quotients of f(K,L) all one has to do is count how many such quotients are possible, and this makes automaton constructions unnecessary. The advantages of this point of view are illustrated by many examples. Moreover, new general observations are presented to help in the estimation of the upper bounds on quotient complexity of regular operations

Fast analysis of antibody-derived therapeutics by automated multidimensional liquid chromatography - mass spectrometry

Characterization of post-translational modifications (PTMs) of therapeutic antibodies is commonly performed by bottom-up approaches, involving sample preparation and peptide analysis by liquid chromatography-mass spectrometry (LC-MS). Conventional sample preparation requires extensive hands-on time and can increase the risk of inducing artificial modifications as many off-line steps - denaturation, disulfide-reduction, alkylation and tryptic digestion - are performed. In this study, we developed an on-line multidimensional (mD)-LC-MS bottom-up approach for fast sample preparation and analysis of (formulated) monoclonal antibodies and antibody-derived therapeutics. This approach allows on-column reduction, tryptic digestion and subsequent peptide analysis by RP-MS. Optimization of the 1D -and 2D flow and temperature improved the trapping of small polar peptides during on-line peptide mapping analysis. These adaptations increased the sequence coverage (95-98% versus 86-94% for off-line approaches) and allowed identification of various PTMs (i.e. deamidation of asparagine, methionine oxidation and lysine glycation) within a single analysis. This workflow enables a fast (<2 h) characterization of antibody heterogeneities within a single run and a low amount of protein (10 mu g). Importantly, the new mD-LC-MS bottom-up method was able to detect the polar, fast-eluting peptides: Fc oxidation at Hc-Met-252 and the Fc N-glycosylation at Hc-Asn-297, which can be challenging using mD-LC-MS. Moreover, the method showed good comparability across the different measurements (RSD of retention time in the range of 0.2-1.8% for polar peptides). The LC system was controlled by only a standard commercial software package which makes implementation for fast characterization of quality attributes relatively easy. (C) 2021 The Author(s). Published by Elsevier B.V.Proteomic

Transcriptomes of <i>Trypanosoma brucei</i> rhodesiense from sleeping sickness patients, rodents and culture:Effects of strain, growth conditions and RNA preparation methods

All of our current knowledge of African trypanosome metabolism is based on results from trypanosomes grown in culture or in rodents. Drugs against sleeping sickness must however treat trypanosomes in humans. We here compare the transcriptomes of Trypanosoma brucei rhodesiense from the blood and cerebrospinal fluid of human patients with those of trypanosomes from culture and rodents. The data were aligned and analysed using new user-friendly applications designed for Kinetoplastid RNA-Seq data. The transcriptomes of trypanosomes from human blood and cerebrospinal fluid did not predict major metabolic differences that might affect drug susceptibility. Usefully, there were relatively few differences between the transcriptomes of trypanosomes from patients and those of similar trypanosomes grown in rats. Transcriptomes of monomorphic laboratory-adapted parasites grown in in vitro culture closely resembled those of the human parasites, but some differences were seen. In poly(A)-selected mRNA transcriptomes, mRNAs encoding some protein kinases and RNA-binding proteins were under-represented relative to mRNA that had not been poly(A) selected; further investigation revealed that the selection tends to result in loss of longer mRNAs

The Pesticide Risk Beliefs Inventory: A Quantitative Instrument for the Assessment of Beliefs about Pesticide Risks

Recent media attention has focused on the risks that agricultural pesticides pose to the environment and human health; thus, these topics provide focal areas for scientists and science educators to enhance public understanding of basic toxicology concepts. This study details the development of a quantitative inventory to gauge pesticide risk beliefs. The goal of the inventory was to characterize misconceptions and knowledge gaps, as well as expert-like beliefs, concerning pesticide risk. This study describes the development and field testing of the Pesticide Risk Beliefs Inventory with an important target audience: pesticide educators in a southeastern U.S. state. The 19-item, Likert-type inventory was found to be psychometrically sound with a Cronbach’s alpha of 0.780 and to be a valuable tool in capturing pesticide educators’ beliefs about pesticide risk, assessing beliefs in four key categories. The Pesticide Risk Beliefs Inventory could be useful in exploring beliefs about pesticide risks and in guiding efforts to address misconceptions held by a variety of formal and informal science learners, educators, practitioners, the agricultural labor force, and the general public

Pre-M Phase-promoting Factor Associates with Annulate Lamellae in Xenopus Oocytes and Egg Extracts

We have used complementary biochemical and in vivo approaches to study the compartmentalization of M phase-promoting factor (MPF) in prophase Xenopus eggs and oocytes. We first examined the distribution of MPF (Cdc2/CyclinB2) and membranous organelles in high-speed extracts of Xenopus eggs made during mitotic prophase. These extracts were found to lack mitochondria, Golgi membranes, and most endoplasmic reticulum (ER) but to contain the bulk of the pre-MPF pool. This pre-MPF could be pelleted by further centrifugation along with components necessary to activate it. On activation, Cdc2/CyclinB2 moved into the soluble fraction. Electron microscopy and Western blot analysis showed that the pre-MPF pellet contained a specific ER subdomain comprising "annulate lamellae" (AL): stacked ER membranes highly enriched in nuclear pores. Colocalization of pre-MPF with AL was demonstrated by anti-CyclinB2 immunofluorescence in prophase oocytes, in which AL are positioned close to the vegetal surface. Green fluorescent protein-CyclinB2 expressed in oocytes also localized at AL. These data suggest that inactive MPF associates with nuclear envelope components just before activation. This association may explain why nuclei and centrosomes stimulate MPF activation and provide a mechanism for targeting of MPF to some of its key substrates