High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation

Quantum chemical calculations on atomistic systems have evolved into a standard approach to study molecular matter. These calculations often involve a significant amount of manual input and expertise although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput lectronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject hem to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all omponents of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow at the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.Comment: 29 pages, 11 figure

Hyperacute Directional Hearing and Phonotactic Steering in the Cricket (Gryllus bimaculatus deGeer)

Background: Auditory mate or prey localisation is central to the lifestyle of many animals and requires precise directional hearing. However, when the incident angle of sound approaches 0u azimuth, interaural time and intensity differences gradually vanish. This poses a demanding challenge to animals especially when interaural distances are small. To cope with these limitations imposed by the laws of acoustics, crickets employ a frequency tuned peripheral hearing system. Although this enhances auditory directionality the actual precision of directional hearing and phonotactic steering has never been studied in the behaviourally important frontal range. Principal Findings: Here we analysed the directionality of phonotaxis in female crickets (Gryllus bimaculatus) walking on an open-loop trackball system by measuring their steering accuracy towards male calling song presented at frontal angles of incidence. Within the range of 630u, females reliably discriminated the side of acoustic stimulation, even when the sound source deviated by only 1u from the animal’s length axis. Moreover, for angles of sound incidence between 1u and 6u the females precisely walked towards the sound source. Measuring the tympanic membrane oscillations of the front leg ears with a laser vibrometer revealed between 0u and 30u a linear increasing function of interaural amplitude differences with a slope of 0.4 dB/u. Auditory nerve recordings closely reflected these bilateral differences in afferent response latency and intensity that provide the physiological basis for precise auditory steering

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

Grand Challenges in global eye health: a global prioritisation process using Delphi method

Background We undertook a Grand Challenges in Global Eye Health prioritisation exercise to identify the key issues that must be addressed to improve eye health in the context of an ageing population, to eliminate persistent inequities in health-care access, and to mitigate widespread resource limitations. Methods Drawing on methods used in previous Grand Challenges studies, we used a multi-step recruitment strategy to assemble a diverse panel of individuals from a range of disciplines relevant to global eye health from all regions globally to participate in a three-round, online, Delphi-like, prioritisation process to nominate and rank challenges in global eye health. Through this process, we developed both global and regional priority lists. Findings Between Sept 1 and Dec 12, 2019, 470 individuals complete round 1 of the process, of whom 336 completed all three rounds (round 2 between Feb 26 and March 18, 2020, and round 3 between April 2 and April 25, 2020) 156 (46%) of 336 were women, 180 (54%) were men. The proportion of participants who worked in each region ranged from 104 (31%) in sub-Saharan Africa to 21 (6%) in central Europe, eastern Europe, and in central Asia. Of 85 unique challenges identified after round 1, 16 challenges were prioritised at the global level; six focused on detection and treatment of conditions (cataract, refractive error, glaucoma, diabetic retinopathy, services for children and screening for early detection), two focused on addressing shortages in human resource capacity, five on other health service and policy factors (including strengthening policies, integration, health information systems, and budget allocation), and three on improving access to care and promoting equity. Interpretation This list of Grand Challenges serves as a starting point for immediate action by funders to guide investment in research and innovation in eye health. It challenges researchers, clinicians, and policy makers to build collaborations to address specific challenge

Data_Mörchen et al. 2023

Mörchen et al. Manuscript, in Review, iSciencePreprint: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4603950"Orangutan males make increased use of social learning opportunities, when resource availability is high"THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Zips : mining compressing sequential patterns in streams

We propose a streaming algorithm, based on the minimal description length (MDL) principle, for extracting non-redundant sequential patterns. For static databases, the MDL-based approach that selects patterns based on their capacity to compress data rather than their frequency, was shown to be remarkably effective for extracting meaningful patterns and solving the redundancy issue in frequent itemset and sequence mining. The existing MDL-based algorithms, however, either start from a seed set of frequent patterns, or require multiple passes through the data. As such, the existing approaches scale poorly and are unsuitable for large datasets. Therefore, our main contribution is the proposal of a new, streaming algorithm, called Zips, that does not require a seed set of patterns and requires only one scan over the data. For Zips, we extended the Lempel-Ziv (LZ) compression algorithm in three ways: first, whereas LZ assigns codes uniformly as it builds up its dictionary while scanning the input, Zips assigns codewords according to the usage of the dictionary words; more heaviliy used words get shorter code-lengths. Secondly, Zips exploits also non-consecutive occurences of dictionary words for compression. And, third, the well-known space-saving algorithm is used to evict unpromising words from the dictionary. Experiments on one synthetic and two real-world large-scale datasets show that our approach extracts meaningful compressing patterns with similar quality to the state-of-the-art multi-pass algorithms proposed for static databases of sequences. Moreover, our approach scales linearly with the size of data streams while all the existing algorithms do not