Chromosome Evolution in New World Monkeys (Platyrrhini)

During the last decades, New World monkey (NWM, Platyrrhini, Anthropoideae) comparative cytogenetics has shed light on many fundamental aspects of genome organisation and evolution in this fascinating, but also highly endangered group of neotropical primates. In this review, we first provide an overview about the evolutionary origin of the inferred ancestral NWM karyotype of 2n = 54 chromosomes and about the lineage-specific chromosome rearrangements resulting in the highly divergent karyotypes of extant NWM species, ranging from 2n = 16 in a titi monkey to 2n = 62 in a woolly monkey. Next, we discuss the available data on the chromosome phylogeny of NWM in the context of recent molecular phylogenetic analyses. In the last part, we highlight some recent research on the molecular mechanisms responsible for the large-scale evolutionary genomic changes in platyrrhine monkeys. Copyright (C) 2012 S. Karger AG, Base

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., J. Phys. Condens. Matter 29, 273002 (2017)) and i-PI (Kapil et al., Comput. Phys. Commun. 236, 214-223 (2019)) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations

Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018); Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the "gold standard" CCSD(T) method. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and $n\to\pi^*$ interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy

Degradation of Chloroaromatics: Purification and Characterization of a Novel Type of Chlorocatechol 2,3-Dioxygenase of Pseudomonas putida GJ31

A purification procedure for a new kind of extradiol dioxygenase, termed chlorocatechol 2,3-dioxygenase, that converts 3-chlorocatechol productively was developed. Structural and kinetic properties of the enzyme, which is part of the degradative pathway used for growth of Pseudomonas putida GJ31 with chlorobenzene, were investigated. The enzyme has a subunit molecular mass of 33.4 kDa by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Estimation of the native Mr value under nondenaturating conditions by gel filtration gave a molecular mass of 135 ± 10 kDa, indicating a homotetrameric enzyme structure (4 × 33.4 kDa). The pI of the enzyme was estimated to be 7.1 ± 0.1. The N-terminal amino acid sequence (43 residues) of the enzyme was determined and exhibits 70 to 42% identity with other extradiol dioxygenases. Fe(II) seems to be a cofactor of the enzyme, as it is for other catechol 2,3-dioxygenases. In contrast to other extradiol dioxygenases, the enzyme exhibited great sensitivity to temperatures above 40°C. The reactivity of this enzyme toward various substituted catechols, especially 3-chlorocatechol, was different from that observed for other catechol 2,3-dioxygenases. Stoichiometric displacement of chloride occurred from 3-chlorocatechol, leading to the production of 2-hydroxymuconate.

Fish in the city

Aquaculture is the most recent addition to animal husbandry and it is the fastest growing food production industry. Its contribution to world food security in the 21st century is already significant and it is bound to continue to grow because demand for fish for human consumption is rapidly increasing whereas fish supplies from ocean fisheries are likely to decline. The rapid evolution of aquaculture involved a host of innovations of which many were based on R&D activities by public and private research organizations. Applied R&D tends to be the more effective the better focused it is on specific research problems or opportunities. Among the many possible aquaculture production systems on which aquaculture R&D might focus are recirculation aquaculture systems and in this paper we explore crucial aspects of the potential of urban recirculation aquaculture. Our exploration begins with a vision of recirculation aquaculture production plants located at the fringes of cities of converging economies. Such production systems are distinctly different from conventional urban aquaculture systems based on urban sewage. We scrutinize our vision from four perspectives: (i) the expected demand for aquaculture fish from urban consumers; (ii) cost competitiveness of fish produced at the fringes of cities as compared to fish produced in the rural hinterland; (iii) the potential for integration of urban recirculation aquaculture production into the modern food supply chains that are now emerging in converging economies, and (iv) the ecological footprint of aquaculture production compared to that of chicken production. Based on trends in the growth of urban populations world-wide and trends in demand for fish for food we estimate a total urban demand for aquaculture finfish between 11 and 51 million tons in 2025. We use von Thünen's location theory to provide support for the vision to locate recirculation aquaculture plants not within cities and not in their rural hinterland but on the fringes of cities. Moreover, we argue that tightly controlled recirculation aquaculture production would seem to be particularly well suited for being integrated into modern food supply chains. Finally, we compare the ecological footprint of recirculation aquaculture fish with that of industrially produced chicken and we find that the ecological balance depends on the source of energy used. We conclude our exploratory study with some thoughts on the implication for aquaculture R&D of the potential for recirculation aquaculture located on the fringes of cities in emerging economy countries. --

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work

Chromosome Evolution in New World Monkeys (Platyrrhini)

During the last decades, New World monkey (NWM, Platyrrhini, Anthropoideae) comparative cytogenetics has shed light on many fundamental aspects of genome organisation and evolution in this fascinating, but also highly endangered group of neotropical primates. In this review, we first provide an overview about the evolutionary origin of the inferred ancestral NWM karyotype of 2n = 54 chromosomes and about the lineage-specific chromosome rearrangements resulting in the highly divergent karyotypes of extant NWM species, ranging from 2n = 16 in a titi monkey to 2n = 62 in a woolly monkey. Next, we discuss the available data on the chromosome phylogeny of NWM in the context of recent molecular phylogenetic analyses. In the last part, we highlight some recent research on the molecular mechanisms responsible for the large-scale evolutionary genomic changes in platyrrhine monkeys. Copyright (C) 2012 S. Karger AG, Base

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations