Designing a machine learning potential for molecular simulation of liquid alkanes

Molecular simulation is applied to understanding the behaviour of alkane liquids with the eventual goal of being able to predict the viscosity of an arbitrary alkane mixture from first principles. Such prediction would have numerous scientific and industrial applications, as alkanes are the largest component of fuels, lubricants, and waxes; furthermore, they form the backbones of a myriad of organic compounds. This dissertation details the creation of a potential, a model for how the atoms and molecules in the simulation interact, based on a systematic approximation of the quantum mechanical potential energy surface using machine learning. This approximation has the advantage of producing forces and energies of nearly quantum mechanical accuracy at a tiny fraction of the usual cost. It enables accurate simulation of the large systems and long timescales required for accurate prediction of properties such as the density and viscosity. The approach is developed and tested on methane, the simplest alkane, and investigations are made into potentials for longer, more complex alkanes. The results show that the approach is promising and should be pursued further to create an accurate machine learning potential for the alkanes. It could even be extended to more complex molecular liquids in the future.First-year training funded by the EPSRC as part of the centre for doctoral training in computational methods for materials science (CDT CMM) under grant number EP/L015552/1. PhD studentship funding by Shell Global Solutions International B.V. Computer time provided by ARCHER (http://archer.ac.uk) under the UKCP Consortium, EPSRC grant number EP/P022596/1

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials.

The predictive simulation of molecular liquids requires potential energy surface (PES) models that are not only accurate but also computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macroscopic properties. We present a new approach to the systematic approximation of the first-principles PES of molecular liquids using the GAP (Gaussian Approximation Potential) framework. The approach allows us to create potentials at several different levels of accuracy in reproducing the true PES and thus to determine the level of quantum chemistry that is necessary to accurately predict macroscopic properties. We test the approach by building a series of many-body potentials for liquid methane (CH4), which is difficult to model from first principles because its behavior is dominated by weak dispersion interactions with a significant many-body component. The increasing accuracy of the potentials in predicting the bulk density correlates with their fidelity to the true PES, whereas the trend with the empirical potentials tested is surprisingly the opposite. We conclude that an accurate, consistent prediction of its bulk density across wide ranges of temperature and pressure requires not only many-body dispersion but also quantum nuclear effects to be modeled accurately

Rosetta: Large scale system for text detection and recognition in images

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.Comment: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 2018, London, United Kingdo

A Proteomic Survey of Host and Virus Reveals Differential Dynamics

We studied the dynamics of the proteome of influenza virus A/PR/8/34 (H1N1) infected Madin-Darby canine kidney cells up to 12 hours post infection by mass spectrometry based quantitative proteomics using the approach of stable isotope labeling by amino acids in cell culture (SILAC). We identified 1311 cell proteins and, apart from the proton channel M2, all major virus proteins. Based on their abundance two groups of virus proteins could be distinguished being in line with the function of the proteins in genesis and formation of new virions. Further, the data indicate a correlation between the amount of proteins synthesized and their previously determined copy number inside the viral particle. We employed bioinformatic approaches such as functional clustering, gene ontology, and pathway (KEGG) enrichment tests to uncover co- regulated cellular protein sets, assigned the individual subsets to their biological function, and determined their interrelation within the progression of viral infection. For the first time we are able to describe dynamic changes of the cellular and, of note, the viral proteome in a time dependent manner simultaneously. Through cluster analysis, time dependent patterns of protein abundances revealed highly dynamic up- and/or down-regulation processes. Taken together our study provides strong evidence that virus infection has a major impact on the cell status at the protein level

Efficient implementation of atom-density representations

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection. They can all be expressed as a discretization of n-body correlation functions of the local atom density, suggesting the opportunity of standardizing and, more importantly, optimizing their evaluation. We present an implementation, named librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations. As an example, we discuss smooth overlap of atomic position (SOAP) features, perhaps the most widely used member of this family of representations, to show how the expansion of the local density can be optimized for any choice of radial basis sets. We discuss the representation in the context of a kernel ridge regression model, commonly used with SOAP features, and analyze how the computational effort scales for each of the individual steps of the calculation. By applying data reduction techniques in feature space, we show how to reduce the total computational cost by a factor of up to 4 without affecting the model’s symmetry properties and without significantly impacting its accuracy

FASTER and SCOTT&EVA trainings for adults with high-functioning autism spectrum disorder (ASD): study protocol for a randomized controlled trial

Background Autism spectrum disorder (ASD) is a chronic neurodevelopmental condition with a prevalence rate above 1%, characterized by deficits in social communication and interaction; restrictive, repetitive patterns of behavior, interests, or activities; and a preference for sameness and routines. The majority of adult ASD patients suffer from comorbid conditions such as depression and anxiety. Therapy options for adult ASD patients are lacking, with presently no available evidence-based interventions in Germany. Recently, two interventions to improve social responsiveness have been published. FASTER (“Freiburger Asperger-Spezifische Therapie für ERwachsene” = Freiburg Asperger-specific therapy for adults) is a manualized group psychotherapy program including three modules on psychoeducation, stress regulation management, and non-verbal and verbal social communication training with videotaped tasks. SCOTT&EVA (“Social Cognition Training Tool”, and its enhancement “Emotionen Verstehen und Ausdruecken” = understanding and expressing emotions) is a computer-based training program to enhance social cognition including video and audio material of emotional expressions and complex real-life social situations. Initial studies for both programs have shown good feasibility and efficacy. Methods Three hundred sixty adult participants with an autism spectrum disorder (ASD) will take part in a randomized controlled three-armed multi-center trial to prove the efficacy of manualized group psychotherapy and a manualized computer-based training program. Both interventions will be compared with a treatment as usual (TAU) group, aiming to establish evidence-based psychotherapy approaches for adult individuals with ASD. The primary outcome is evaluated by parents, spouses, or others who have sufficient insight into the respective participant’s social communication and interaction, and will be measured with the Social Responsiveness Scale. First, each of both interventions will be compared to TAU. If at least one of the differences is significant, both interventions will be compared against each other. The primary outcome will be measured at baseline (T0) and 4 months after baseline (T1). Discussion The trial is the first to validate psychiatric therapeutic and training interventions for adult ASD patients in Germany. A trial is needed because the prevalence of ASD in adulthood without intellectual disability is high, and no evidence-based intervention can be offered in Germany. Trial registration German Clinical Trial Register DRKS00017817. Registered on 20 April 2020.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Universitätsklinikum Freiburg (8975)Peer Reviewe

Carcinoma of Unknown Primary and the 8th Edition TNM Classification for Head and Neck Cancer

Objective: In the 8th Edition TNM Classification for Head and Neck Cancer, the classification for carcinoma of unknown primary (CUP) changed in addition to oropharyngeal carcinomas. The current classification considers extranodal extension (ENE), determination of p16 (surrogate marker for human papillomavirus), and detection of Epstein-Barr virus (EBV). The aim of this study was to investigate the influence of the new classification on the prognosis of p16-positive and p16-negative CUP and the impact of EBV proof. Methods: Clinical and pathological data from patients with CUP of the head and neck between 2009 and 2018 were evaluated. The 7th (UICC7) and 8th (UICC8) edition of the Union for International Cancer Control staging system were applied and compared. Results: There were 97 patients treated, 26.8% women and 73.2% men. The average age at initial diagnosis was 64.6 years. Of which, 58.8% had a documented history of smoking, 37.1% were positive for p16, 4.1% were positive for EBV, and 66% had ENE. Most of the patients were at stage III/IVa (78.4% according to UICC7). According to UICC8, p16+ patients were mainly at stage I (86.1%), and p16- at stage IVb (56.1%). P16 status (P = .002), ENE (P = .001), nodal category (TNM7, P < .001), UICC stage (TNM7, P < .001) and UICC stage (TNM8, P < .001) had a significant impact on survival in the univariate analysis. The 8th TNM classification resulted in a downstaging of p16-positive CUP syndromes and an upstaging of p16-negative syndromes. Conclusion: The 8th TNM classification shows the lower UICC stage in p16-positive CUP syndromes. The prognostic significance for survival has improved from the 7th to the 8th TNM classification. LEVEL OF EVIDENCE USING THE 2011 OCEBM: Level 3

The rationale of rationalization

Fiery Cushman argues that “[r]ationalization is designed not to accurately infer unconscious mental states, but to construct new ones; it is not a discovery, but a fiction”. While we agree in broad strokes with the characterization of rationalization as a ‘useful fiction’, we think that Cushman’s claim remains ambiguous in two crucial respects: (i) the reality of beliefs and desires, i.e. the fictional status of folk psychological entities, and (ii) the degree to which they should be understood as useful and representative. Our aim here is to clarify both points and illuminate how rationalization could be understood as a useful fiction. In doing so, we aim to explicate the Rationale of Rationalization

Myoglobin regulates fatty acid trafficking and lipid metabolism in mammary epithelial cells

Myoglobin (MB) is known to bind and deliver oxygen in striated muscles at high expression levels. MB is also expressed at much reduced levels in mammary epithelial cells, where the protein´s function is unclear. In this study, we aim to determine whether MB impacts fatty acid trafficking and facilitates aerobic fatty acid ß-oxidation in mammary epithelial cells. We utilized MB-wildtype versus MB-knockout mice and human breast cancer cells to examine the impact of MB and its oxygenation status on fatty acid metabolism in mouse milk and mammary epithelia. MB deficient cells were generated through CRISPR/Cas9 and TALEN approaches and exposed to various oxygen tensions. Fatty acid profiling of milk and cell extracts were performed along with cell labelling and immunocytochemistry. Our findings show that MB expression in mammary epithelial cells promoted fatty acid oxidation while reducing stearyl-CoA desaturase activity for lipogenesis. In cells and milk product, presence of oxygenated MB significantly elevated indices of limited fatty acid ß-oxidation, i.e., the organelle-bound removal of a C2 moiety from long-chain saturated or monounsaturated fatty acids, thus shifting the composition toward more saturated and shorter fatty acid species. Presence of the globin also increased cytoplasmic fatty acid solubility under normoxia and fatty acid deposition to lipid droplets under severe hypoxia. We conclude that MB can function in mammary epithelia as intracellular O$_{2}$-dependent shuttle of oxidizable fatty acid substrates. MB’s impact on limited oxidation of fatty acids could generate inflammatory mediator lipokines, such as 7-hexadecenoate. Thus, the novel functions of MB in breast epithelia described herein range from controlling fatty acid turnover and homeostasis to influencing inflammatory signalling cascade. Future work is needed to analyse to what extent these novel roles of MB also apply to myocytic cell physiology and malignant cell behaviour, respectively