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What Makes a Paper Be Highly Cited? 60 Years of the Journal of Chemical Information and Modeling

Abstract

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Authors: Ariane Nunes-Alves, Angelica Mazzolari, Kenneth M. Merz

Date Published: 28th Dec 2020

Publication Type: Journal

Ultrafast dynamics of 2-thiouracil investigated by time-resolved Auger spectroscopy

Abstract (Expand)

We present time-resolved ultraviolet-pump x-ray probe Auger spectra of 2-thiouracil. An ultraviolet induced shift towards higher kinetic energies is observed in the sulfur 2p Auger decay. The difference … Auger spectra of pumped and unpumped molecules exhibit ultrafast dynamics in the shift amplitude, in which three phases can be recognized. In the first 100 fs, a shift towards higher kinetic energies is observed, followed by a 400 fs shift back to lower kinetic energies and a 1 ps shift again to higher kinetic energies. We use a simple Coulomb-model, aided by quantum chemical calculations of potential energy states, to deduce a C–S bond expansion within the first 100 fs. The bond elongation triggers internal conversion from the photoexcited S2 to the S1 state. Based on timescales, the subsequent dynamics can be interpreted in terms of S1 nuclear relaxation and S1-triplet internal conversion.

Authors: F Lever, D Mayer, D Picconi, J Metje, S Alisauskas, F Calegari, S Düsterer, C Ehlert, R Feifel, M Niebuhr, B Manschwetus, M Kuhlmann, T Mazza, M S Robinson, R J Squibb, A Trabattoni, M Wallner, P Saalfrank, T J A Wolf, M Gühr

Date Published: 17th Dec 2020

Publication Type: Journal

Phylogenetic analysis of SARS-CoV-2 data is difficult

Abstract

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Authors: Benoit Morel, Pierre Barbera, Lucas Czech, Ben Bettisworth, Lukas Hübner, Sarah Lutteropp, Dora Serdari, Evangelia-Georgia Kostaki, Ioannis Mamais, Alexey M Kozlov, Pavlos Pavlidis, Dimitrios Paraskevis, Alexandros Stamatakis

Date Published: 15th Dec 2020

Publication Type: Journal

Eleven grand challenges in single-cell data science

Abstract (Expand)

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed … in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Authors: David Lähnemann, Johannes Köster, Ewa Szczurek, Davis J. McCarthy, Stephanie C. Hicks, Mark D. Robinson, Catalina A. Vallejos, Kieran R. Campbell, Niko Beerenwinkel, Ahmed Mahfouz, Luca Pinello, Pavel Skums, Alexandros Stamatakis, Camille Stephan-Otto Attolini, Samuel Aparicio, Jasmijn Baaijens, Marleen Balvert, Buys de Barbanson, Antonio Cappuccio, Giacomo Corleone, Bas E. Dutilh, Maria Florescu, Victor Guryev, Rens Holmer, Katharina Jahn, Thamar Jessurun Lobo, Emma M. Keizer, Indu Khatri, Szymon M. Kielbasa, Jan O. Korbel, Alexey M. Kozlov, Tzu-Hao Kuo, Boudewijn P.F. Lelieveldt, Ion I. Mandoiu, John C. Marioni, Tobias Marschall, Felix Mölder, Amir Niknejad, Lukasz Raczkowski, Marcel Reinders, Jeroen de Ridder, Antoine-Emmanuel Saliba, Antonios Somarakis, Oliver Stegle, Fabian J. Theis, Huan Yang, Alex Zelikovsky, Alice C. McHardy, Benjamin J. Raphael, Sohrab P. Shah, Alexander Schönhuth

Date Published: 1st Dec 2020

Publication Type: Journal

The Effect of Force-Field Parameters on Cytochrome P450-Membrane Interactions: Structure and Dynamics

Abstract

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Authors: Ghulam Mustafa, Prajwal P. Nandekar, Goutam Mukherjee, Neil J. Bruce, Rebecca C. Wade

Date Published: 1st Dec 2020

Publication Type: Journal

Asteroseismic inference of subgiant evolutionary parameters with deep learning

Abstract (Expand)

With the observations of an unprecedented number of oscillating subgiant stars expected from NASA's TESS mission, the asteroseismic characterization of subgiant stars will be a vital task for stellar … population studies and for testing our theories of stellar evolution. To determine the fundamental properties of a large sample of subgiant stars efficiently, we developed a deep learning method that estimates distributions of fundamental parameters like age and mass over a wide range of input physics by learning from a grid of stellar models varied in eight physical parameters. We applied our method to four Kepler subgiant stars and compare our results with previously determined estimates. Our results show good agreement with previous estimates for three of them (KIC 11026764, KIC 10920273, KIC 11395018). With the ability to explore a vast range of stellar parameters, we determine that the remaining star, KIC 10005473, is likely to have an age 1 Gyr younger than its previously determined estimate. Our method also estimates the efficiency of overshooting, undershooting, and microscopic diffusion processes, from which we determined that the parameters governing such processes are generally poorly constrained in subgiant models. We further demonstrate our method's utility for ensemble asteroseismology by characterizing a sample of 30 Kepler subgiant stars, where we find a majority of our age, mass, and radius estimates agree within uncertainties from more computationally expensive grid-based modelling techniques.

Authors: Marc Hon, Earl P Bellinger, Saskia Hekker, Dennis Stello, James S Kuszlewicz

Date Published: 1st Dec 2020

Publication Type: Journal

Incremental Neural Lexical Coherence Modeling

Abstract (Expand)

Pretrained language models, neural models pretrained on massive amounts of data, have established the state of the art in a range of NLP tasks. They are based on a modern machine-learning technique, … the Transformer which relates all items simultaneously to capture semantic relations in sequences. However, it differs from what humans do. Humans read sentences one-by-one, incrementally. Can neural models benefit by interpreting texts incrementally as humans do? We investigate this question in coherence modeling. We propose a coherence model which interprets sentences incrementally to capture lexical relations between them. We compare the state of the art in each task, simple neural models relying on a pretrained language model, and our model in two downstream tasks. Our findings suggest that interpreting texts incrementally as humans could be useful to design more advanced models.

Authors: Sungho Jeon, Michael Strube

Date Published: 1st Dec 2020

Publication Type: InProceedings

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