Mechanical signatures of microbial biofilms in micropillar-embedded growth chambers

Biofilms are surface-attached communities of microorganisms embedded in an extracellular matrix and are essential for the cycling of organic matter in natural and engineered environments. They are also the leading cause of many infections, for example, those associated with chronic wounds and implanted medical devices. The extracellular matrix is a key biofilm component that determines its architecture and defines its physical properties. Herein, we used growth chambers embedded with micropillars to study the net mechanical forces (differential pressure) exerted during biofilm formation in situ. Pressure from the biofilm is transferred to the micropillars via the extracellular matrix, and reduction of major matrix components decreases the magnitude of micropillar deflections. The spatial arrangement of micropillar deflections caused by pressure differences in the different biofilm strains may potentially be used as mechanical signatures for biofilm characterization. Hence, we submit that micropillar-embedded growth chambers provide insights into the mechanical properties and dynamics of the biofilm and its matrix.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology (SMART)

Fuzzy Students’ Knowledge Modelling System through Revised Bloom’s Taxonomy

The conveniences of web-based educational systems have attracted a large heterogeneous group of learners with various knowledge levels, learning goals, and others learning characteristics, to study online. To enhance the effectiveness of the web-based educational system in delivery knowledge, a system should be capable to identify the learners’ learning characteristics, and adapt the instructional process accordingly. Hence, this paper presented a students’ knowledge modelling system that is capable of infer and updating the students’ knowledge level in accordance to the cognitive processes dimension in the Revised Bloom’s Taxonomy. However, the students’ knowledge modeling process consists of tasks and factors that are vague and unmeasured, thus Fuzzy Logic is integrated into the students’ knowledge modeling system to deal with such uncertainties. The proposed fuzzy students’ knowledge modeling system uses fuzzy sets to represent students’ knowledge level and other influencing factors, and uses Mamdani type inference technique to determine and update knowledge levels

Decay and coherence of two-photon excited yellow ortho-excitons in Cu2O

Photoluminescence excitation spectroscopy has revealed a novel, highly efficient two-photon excitation method to produce a cold, uniformly distributed high density excitonic gas in bulk cuprous oxide. A study of the time evolution of the density, temperature and chemical potential of the exciton gas shows that the so called quantum saturation effect that prevents Bose-Einstein condensation of the ortho-exciton gas originates from an unfavorable ratio between the cooling and recombination rates. Oscillations observed in the temporal decay of the ortho-excitonic luminescence intensity are discussed in terms of polaritonic beating. We present the semiclassical description of polaritonic oscillations in linear and non-linear optical processes.Comment: 14 pages, 12 figure

Protocol for a case-control diagnostic accuracy study to develop diagnostic criteria for psoriasis in children (DIPSOC study): a multicentre study recruiting in UK paediatric dermatology clinics

Introduction Diagnosing psoriasis in children can be challenging. Early and accurate diagnosis is important to ensure patients receive psoriasis specific treatment and monitoring. It is recognised that the physical, psychological, quality of life, financial and comorbid burden of psoriasis are significant. The aim of this study is to develop clinical examination and history-based diagnostic criteria for psoriasis in children to help differentiate psoriasis from other scaly inflammatory rashes. The criteria tested in this study were developed through a consensus study with a group of international psoriasis experts (International Psoriasis Council). Methods and analysis Children and young people (<18 years) with psoriasis (cases) and other scaly inflammatory skin diseases (controls) diagnosed by a dermatologist are eligible for recruitment. All participants complete a single research visit including a diagnostic criteria assessment by a trained investigator blinded to the participant’s diagnosis. The reference standard of a dermatologist’s diagnosis is extracted from the medical record. Sensitivity and specificity of the consensus derived diagnostic criteria will be calculated and the best predictive criteria developed using multivariate logistic regression. Ethics and dissemination Health Regulatory Authority and National Health Service Research Ethics Committee approvals were granted in February 2017 (REC Ref: 17/ EM/0035). Dissemination will be guided by stakeholders; patients, children and

Modelling ambitious climate mitigation pathways for Australia's built environment

Achieving net zero operational and embodied greenhouse gas (GHG) emissions in the built environment is recognised in Australia and globally as a key strategy to address climate change and achieve the United Nations Sustainable Development Goals (SDGs). However, gaps in knowledge remain regarding potential national pathways to achieve this outcome in Australia. This study further extends and applies a national-scale integrated macroeconomic simulation model to explore coherent pathways to net zero emissions in the built environment sector by 2050. The scope of the study includes residential and commercial buildings and both operational and embodied emissions. It applies scenario analysis incorporating different levels of climate ambition, including a shift to renewable energy, electrifying buildings, improving energy efficiency and replacing carbon-intensive materials. We find that a high ambition scenario (Scenario 2) delivers a 94% reduction in GHG emissions by 2050 when compared against business-as-usual, placing a net-zero target within reach. Improvements on Australia's SDGs performance are also attained. Through subsequent pathways analysis we find that achieving net zero or even net negative operational and embodied emissions is feasible with more ambitious action in key areas, including increasing the share of mass-timber buildings and reducing end-of-life losses in sequestered carbon

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

Targeting 1.5 degrees with the global carbon footprint of the Australian Capital Territory

In 2019 the Australian Capital Territory (ACT) government stated an ambition to prioritise reduction of Scope 3 greenhouse gas emissions, the size of which had not been fully quantified previously. This study calculated the total carbon footprint of the ACT in 2018, including Scope 1, 2 and 3 emissions and modelled scenarios to reduce all emissions in line with a 1.5 °C target approach. This is the first time a multi-scale analysis of local, sub-national and international supply chains has been undertaken for a city, using a nested and trade-adjusted global multi-region input-output model. This allowed for the quantification of global origins and destinations of emissions, which showed that the 2018 carbon footprint for the ACT was approximately 34.7 t CO2-eq/cap, with 83% attributed to Scope 3. Main contributions came from transport, electricity, manufacturing and public administration and safety, with emissions generated primarily in Australian States and Territories. Modelling in accordance with a 1.5 °C warming scenario showed a plausible reduction to 5.2 t CO2-eq/cap by 2045 (excluding offsets or carbon dioxide removal technologies), with remaining emissions predominantly embodied in international supply chains. This study demonstrates the radical changes required by a wealthy Australian city to achieve 1.5 °C compliance and identifies sectors and supply chains for prioritising policies to best achieve this outcome

Development of efficient, integrated cellulosic biorefineries : LDRD final report.

Cellulosic ethanol, generated from lignocellulosic biomass sources such as grasses and trees, is a promising alternative to conventional starch- and sugar-based ethanol production in terms of potential production quantities, CO{sub 2} impact, and economic competitiveness. In addition, cellulosic ethanol can be generated (at least in principle) without competing with food production. However, approximately 1/3 of the lignocellulosic biomass material (including all of the lignin) cannot be converted to ethanol through biochemical means and must be extracted at some point in the biochemical process. In this project we gathered basic information on the prospects for utilizing this lignin residue material in thermochemical conversion processes to improve the overall energy efficiency or liquid fuel production capacity of cellulosic biorefineries. Two existing pretreatment approaches, soaking in aqueous ammonia (SAA) and the Arkenol (strong sulfuric acid) process, were implemented at Sandia and used to generated suitable quantities of residue material from corn stover and eucalyptus feedstocks for subsequent thermochemical research. A third, novel technique, using ionic liquids (IL) was investigated by Sandia researchers at the Joint Bioenergy Institute (JBEI), but was not successful in isolating sufficient lignin residue. Additional residue material for thermochemical research was supplied from the dilute-acid simultaneous saccharification/fermentation (SSF) pilot-scale process at the National Renewable Energy Laboratory (NREL). The high-temperature volatiles yields of the different residues were measured, as were the char combustion reactivities. The residue chars showed slightly lower reactivity than raw biomass char, except for the SSF residue, which had substantially lower reactivity. Exergy analysis was applied to the NREL standard process design model for thermochemical ethanol production and from a prototypical dedicated biochemical process, with process data supplied by a recent report from the National Research Council (NRC). The thermochemical system analysis revealed that most of the system inefficiency is associated with the gasification process and subsequent tar reforming step. For the biochemical process, the steam generation from residue combustion, providing the requisite heating for the conventional pretreatment and alcohol distillation processes, was shown to dominate the exergy loss. An overall energy balance with different potential distillation energy requirements shows that as much as 30% of the biomass energy content may be available in the future as a feedstock for thermochemical production of liquid fuels

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance