Optically Mapping Multiple Bacterial Genomes Simultaneously in a Single Run

Optical mapping of bacterial chromosomes provides an unambiguous low-resolution sequence scaffold of the entire chromosome. In comparison to some techniques, such as pulse field gel electrophoresis, cost and throughput limit the application of this technique outside of genome finishing. We have demonstrated the production of multiple bacterial maps using a single set of consumables; this significantly reduces the time and expense of map production

Investigation of thermal influence on weld microstructure and mechanical properties in wire and arc additive manufacturing of steels

Alloy steels are commonly used in many industrial and consumer products to take advantage of their strength, ductility, and toughness properties. In addition, their machinability and weldability performance make alloy steels suitable for a range of manufacturing operations. The advent of additive manufacturing technologies, such as wire and arc additive manufacturing (WAAM), has enabled welding of alloy steels into complex and customized near net-shape products. However, the functional reliability of as-built WAAM products is often uncertain due to a lack of understanding of the effects of process parameters on the material microstructure and mechanical properties that develop during welding, primarily driven by thermal phenomena. This study investigated the influence of thermal phenomena in WAAM on the microstructure and mechanical properties of two alloy steels (G4Si1, a mild steel, and AM70, a high-strength, low-alloy steel). The interrelationships between process parameters, heating and cooling cycles of the welded part, and the resultant microstructure and mechanical properties were characterized. The welded part experienced multiple reheating cycles, a consequence of the layer-by-layer manufacturing approach. Thus, high temperature gradients at the start of the weld formed fine grain structure, while coarser grains were formed as the height of the part increases and the temperature gradient decreased. Microstructural analysis identified the presence of acicular ferrite and equiaxed ferrite structures in G4Si1 welds, as well as a small volume fraction of pearlite along the ferrite grain boundaries. Analysis of AM70 welds found acicular ferrite, martensite, and bainite structures. Mechanical testing for both materials found that the hardness of the material decreased with the increase in the height of the welded part as a result of the decrease in the temperature gradient and cooling rate. In addition, higher hardness and yield strength, and lower elongation at failure was observed for parts printed using process parameters with lower energy input. The findings from this work can support automated process parameter tuning to control thermal phenomena during welding and, in turn, control the microstructure and mechanical properties of printed parts.publishedVersionPeer reviewe

Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract: [Figure not available: see fulltext.].publishedVersionPeer reviewe

Graph models for engineering design : Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-The-Art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.publishedVersionPeer reviewe

Resting natural killer cell homeostasis relies on tryptophan/NAD+^{+} metabolism and HIF-1α

Natural killer (NK) cells are forced to cope with different oxygen environments even under resting conditions. The adaptation to low oxygen is regulated by oxygen-sensitive transcription factors, the hypoxia-inducible factors (HIFs). The function of HIFs for NK cell activation and metabolic rewiring remains controversial. Activated NK cells are predominantly glycolytic, but the metabolic programs that ensure the maintenance of resting NK cells are enigmatic. By combining in situ metabolomic and transcriptomic analyses in resting murine NK cells, our study defines HIF-1α as a regulator of tryptophan metabolism and cellular nicotinamide adenine dinucleotide (NAD$^{+}$ ) levels. The HIF-1α/NAD$^{+}$ axis prevents ROS production during oxidative phosphorylation (OxPhos) and thereby blocks DNA damage and NK cell apoptosis under steady-state conditions. In contrast, in activated NK cells under hypoxia, HIF-1α is required for glycolysis, and forced HIF-1α expression boosts glycolysis and NK cell performance in vitro and in vivo. Our data highlight two distinct pathways by which HIF-1α interferes with NK cell metabolism. While HIF-1α-driven glycolysis is essential for NK cell activation, resting NK cell homeostasis relies on HIF-1α-dependent tryptophan/NAD$^{+}$ metabolism

Learning with Weak Supervision for Email Intent Detection

Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend appropriate actions. The problem has been mostly posed as a supervised learning problem where models of different complexities were proposed to classify an email message into a predefined taxonomy of intents or classes. The need for labeled data has always been one of the largest bottlenecks in training supervised models. This is especially the case for many real-world tasks, such as email intent classification, where large scale annotated examples are either hard to acquire or unavailable due to privacy or data access constraints. Email users often take actions in response to intents expressed in an email (e.g., setting up a meeting in response to an email with a scheduling request). Such actions can be inferred from user interaction logs. In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails. We develop an end-to-end robust deep neural network model for email intent identification that leverages both clean annotated data and noisy weak supervision along with a self-paced learning mechanism. Extensive experiments on three different intent detection tasks show that our approach can effectively leverage the weakly supervised data to improve intent detection in emails.Comment: 10 pages, 3 figure

Net ultrafiltration prescription and practice among critically ill patients receiving renal replacement therapy : a multinational survey of critical care practitioners

Objectives: To assess the attitudes of practitioners with respect to net ultrafiltration prescription and practice among critically ill patients with acute kidney injury treated with renal replacement therapy. Design: Multinational internet-assisted survey. Setting: Critical care practitioners involved with 14 societies in 80 countries. Subjects: Intensivists, nephrologists, advanced practice providers, ICU and dialysis nurses. Intervention: A cross-sectional survey. Measurement and Main Results: Of 2,567 practitioners who initiated the survey, 1,569 (61.1%) completed the survey. Most practitioners were intensivists (72.7%) with a median duration of 13.2 years of practice (interquartile range, 7.2-22.0 yr). Two third of practitioners (71.0%; regional range, 55.0-95.5%) reported using continuous renal replacement therapy with a net ultrafiltration rate prescription of median 80.0 mL/hr (interquartile range, 49.0-111.0 mL/hr) for hemodynamically unstable and a maximal rate of 299.0 mL/hr (interquartile range, 200.0-365.0 mL/hr) for hemodynamically stable patients, with regional variation. Only a third of practitioners (31.5%; range, 13.7-47.8%) assessed hourly net fluid balance during continuous renal replacement therapy. Hemodynamic instability was reported in 20% (range, 20-38%) of patients and practitioners decreased the rate of fluid removal (70.3%); started or increased the dose of a vasopressor (51.5%); completely stopped fluid removal (35.8%); and administered a fluid bolus (31.6%), with significant regional variation. Compared with physicians, nurses were most likely to report patient intolerance to net ultrafiltration (73.4% vs 81.3%; p = 0.002), frequent interruptions (40.4% vs 54.5%; p < 0.001), and unavailability of trained staff (11.9% vs 15.6%; p = 0.04), whereas physicians reported unavailability of dialysis machines (14.3% vs 6.1%; p < 0.001) and costs associated with treatment as barriers (12.1% vs 3.0%; p < 0.001) with significant regional variation. Conclusions: Our study provides new knowledge about the presence and extent of international practice variation in net ultrafiltration. We also identified barriers and specific targets for quality improvement initiatives. Our data reflect the need for evidence-based practice guidelines for net ultrafiltration

Recent advances in research on botrytis gray mold of chickpea: summary proceedings of the third working Group Meeting to Discuss Collaborative Research on Botrytis Gray Mold of Chickpea, 15-17 Apr 1996, Pantnagar, Uttar Pradesh, India

The Third Working Group Meet ing on Botrytis Gray Mold of Chickpea reviewed research progress during the last 3 years, in Bangladesh, India, and Nepal. Summaries of these research findings are presented here. The preliminary report on the occurrence of botryt is gray mold of chickpea in Myanmar is noteworthy. Field trials conducted in Bangladesh, India, and Nepal indicate that an integrated disease management, if practised, can reduce disease intensity in chickpea fields, and increase chickpea product ion in disease-prone areas. Recommendations were made for future research priorities

Botulinum Neurotoxin Detection Methods for Public Health Response and Surveillance

Botulism outbreak due to consumption of food contaminated with botulinum neurotoxins (BoNTs) is a public health emergency. The threat of bioterrorism through deliberate distribution in food sources and/or aerosolization of BoNTs raises global public health and security concerns due to the potential for high mortality and morbidity. Rapid and reliable detection methods are necessary to support clinical diagnosis and surveillance for identifying the source of contamination, performing epidemiological analysis of the outbreak, preventing and responding to botulism outbreaks. This review considers the applicability of various BoNT detection methods and examines their fitness-for-purpose in safeguarding the public health and security goals