Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

Short Paper: An Exploration of Code Diversity in the Cryptocurrency Landscape

Interest in cryptocurrencies has skyrocketed since their introduction a decade ago, with hundreds of billions of dollars now invested across a landscape of thousands of different cryptocurrencies. While there is significant diversity, there is also a significant number of scams as people seek to exploit the current popularity. In this paper, we seek to identify the extent of innovation in the cryptocurrency landscape using the open-source repositories associated with each one. Among other findings, we observe that while many cryptocurrencies are largely unchanged copies of Bitcoin, the use of Ethereum as a platform has enabled the deployment of cryptocurrencies with more diverse functionalities

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

Definition and characterization of localised meningitis epidemics in Burkina Faso: a longitudinal retrospective study

<p>Abstract</p> <p>Background</p> <p>The epidemiology of meningococcal meningitis in the African meningitis belt is characterised by seasonality, localised epidemics and epidemic waves. To facilitate research and surveillance, we aimed to develop a definition for localised epidemics to be used in real-time surveillance based on weekly case reports at the health centre level.</p> <p>Methods</p> <p>We used national routine surveillance data on suspected meningitis from January 2004 to December 2008 in six health districts in western and central Burkina Faso. We evaluated eight thresholds composed of weekly incidence rates at health centre level for their performance in predicting annual incidences of 0.4%and 0.8% in health centre areas. The eventually chosen definition was used to describe the spatiotemporal epidemiology and size of localised meningitis epidemics during the included district years.</p> <p>Results</p> <p>Among eight weekly thresholds evaluated, a weekly incidence rate of 75 cases per 100,000 inhabitants during at least two consecutive weeks with at least 5 cases per week had 100% sensitivity and 98% specificity for predicting an annual incidence of at least 0.8% in health centres. Using this definition, localised epidemics were identified in all but one years during 2004-2008, concerned less than 10% of the districts' population and often were geographically dispersed. Where sufficient laboratory data were available, localised epidemics were exclusively due to meningococci.</p> <p>Conclusions</p> <p>This definition of localised epidemics a the health centre level will be useful for risk factor and modelling studies to understand the meningitis belt phenomenon and help documenting vaccine impact against epidemic meningitis where no widespread laboratory surveillance exists for quantifying disease reduction after vaccination.</p

Antimicrobial Natural Products

Although the first antibiotic, penicillin, was discovered in 1928 from a microbial natural source (a mould, Penicillium notatum), there is earlier evidence of using natural materials including moulds and herbs for the treatment of infections. Following the serendipitous discovery of penicillin by Alexander Fleming, there have been hundreds of antibiotics (natural, semisynthetic and synthetic) discovered for clinical uses. However, the pathogenic organisms have developed resistances to existing antibiotics though various mechanisms. Such antibiotic resistance or antimicrobial resistance (AMR) is a critical problem of today’s healthcare system urging the development of new antibiotics. This chapter has primarily focused into antimicrobial compounds developed through natural routes that are currently available as antibiotics for clinical uses and/or are at various developmental stages within the drug development pipeline for potential treatment of minor and life threatening infections. The chapter also provides an overview on the catastrophic problem of antimicrobial resistance, its causes, how it spreads as well as modes of developing antimicrobial resistance (AMR)

A foundation model for atomistic materials chemistry

Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set problems in the physical sciences, including the properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry.Comment: 119 pages, 63 figures, 37MB PD

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Rational design and synthesis of modified teixobactin analogues: in vitro antibacterial activity against Staphylococcus aureus, Propionibacterium acnes and Pseudomonas aeruginosa

Teixobactin, a recently discovered depsipeptide that binds to bacterial lipid II and lipid III, provides a promising molecular scaffold for the design of new antimicrobials. Herein, we describe the synthesis and antimicrobial evaluation of systematically modified teixobactin analogues. The replacement of Ile11 residue with aliphatic isosteres, the modification of the guanidino group at residue 10 and the introduction of a rigidifying residue, dehydroamino acid into the macrocyclic ring generated useful structure‐activity information. Extensive antimicrobial susceptibility assessment against a panel of clinically relevant Staphylococcus aureus and Propionibacterium acnes led to the identification of a new lead compound, [Arg(Me)10,Nle11]teixobactin 63, with excellent bactericidal activity (MIC 2‐4 μg/mL). Significantly, the antimicrobial activity of several of the teixobactin analogues against the pathogenic Gram‐negative Pseudomonas aeruginosa was 'restored' when combined with sub‐MIC concentration of the outer membrane‐disruptive antibiotic, colistin. The antimicrobial effectiveness of [Tfn10,Nle11]teixobactin 66 (32 μg/mL)‐colistin (2 μg/mL; 0.5x MIC) combination against P. aeruginosa PAO1 reveal, for the first time, an alternative therapeutic option in the treatment of Gram‐negative infections

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab&nbsp;initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab&nbsp;initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab&nbsp;initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields