Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening

Fully coherent Ge islands growth on Si nano-pillars by selective epitaxy

Our recent experimental results of Ge nanoheteroepitaxy (NHE) on Si nanopillars (NPs) are reviewed to confirm the possibility of relaxed Ge growth on Si without misfit dislocations (MDs) formation by elastic deformation. Selective Ge growth is performed by using reduced pressure chemical vapor deposition (CVD) on two types of Si NPs with thermal SiO2 or CVD SiO2 sidewalls and on Si nanoislands (NIs) on SiO2. By using thermal SiO2 sidewall, compressive strain is generated in the Si pillar and fixed by the thermal SiO2. This results in an incoherent Ge growth on Si NPs due to MD formation. By using CVD SiO2 sidewall, tensile strain formation due to thermal expansion during prebake for Ge epi process is observed. However, strain in Si due to Ge growth is not dominant. By introducing a Si0.5Ge0.5 buffer layer, no MD and stacking faults are observed by cross section TEM. The shape of Ge on Si NPs becomes more uniform due to improved crystal quality. On Si NIs on SiO2, a clear compliance effect is observed after Ge growth. Coherent growth of Ge on Si is also realized on Si NIs by using Si0.5Ge0.5 buffer

Systematic Generation of Anisotropic Coarse-Grained Lennard-Jones Potentials and Their Application to Ordered Soft Matter

We have developed an approach to coarse-grained (CG) modeling of the van der Waals (vdW) type of interactions among molecules by representing groups of atoms within those molecules in terms of ellipsoids (rather than spheres). Our approach systematically translates an arbitrary underlying all-atom (AA) representation of a molecular system to a multisite ellipsoidal potential within the family of Gay–Berne type potentials. As the method enables arbitrary levels of coarse-graining, or even multiple levels of coarse-graining within a single simulation, we describe the method as a Level of Detail (LoD) model. The LoD model, as integrated into our group’s Metropolis Monte Carlo computational package, is also capable of reducing the complexity of the molecular electrostatics by means of a multipole expansion of charges obtained from an AA force field (or directly from electronic structure calculations) of the charges within each ellipsoid. Electronic polarizability may additionally be included. The present CG representation does not include transformation of bonded interactions; ellipsoids are connected at the fully atomistic bond sites by freely rotating links that are constrained to maintain a constant distance. The accuracy of the method is demonstrated for three distinct types of self-assembling or self-organizing molecular systems: (1) the interaction between benzene and perfluorobenzene (dispersion interactions), (2) linear hydrocarbon chains (a system with large conformational flexibility), and (3) the self-organization of ethylene carbonate (a highly polar liquid). Lastly, the method is applied to the interaction of large (∼100 atom) molecules, which are typical of organic nonlinear optical chromophores, to demonstrate the effect of different CG models on molecular assembly

Nonlinearities of organic electro-optic materials in nanoscale slots and implications for the optimum modulator design

ISSN:1094-408

Submicrosecond Time Resolution Atomic Force Microscopy for Probing Nanoscale Dynamics

We propose, simulate, and experimentally validate a new mechanical detection method to analyze atomic force microscopy (AFM) cantilever motion that enables noncontact discrimination of transient events with ∼100 ns temporal resolution without the need for custom AFM probes, specialized instrumentation, or expensive add-on hardware. As an example application, we use the method to screen thermally annealed poly­(3-hexylthiophene):phenyl-C₆₁-butyric acid methyl ester photovoltaic devices under realistic testing conditions over a technologically relevant performance window. We show that variations in device efficiency and nanoscale transient charging behavior are correlated, thereby linking local dynamics with device behavior. We anticipate that this method will find application in scanning probe experiments of dynamic local mechanical, electronic, magnetic, and biophysical phenomena

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses

QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids.

QMCPACK is an open source quantum Monte Carlo package for ab initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wavefunctions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary-field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit and graphical processing unit systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://qmcpack.org