Fungi produce a variety of microbial volatile organic compounds (MVOCs) during primary and secondary metabolism. The fungus, Aspergillus flavus, is a human, animal and plant pathogen which produces aflatoxin, one of t...Fungi produce a variety of microbial volatile organic compounds (MVOCs) during primary and secondary metabolism. The fungus, Aspergillus flavus, is a human, animal and plant pathogen which produces aflatoxin, one of the most carcinogenic substances known. In this study, MVOCs were analyzed using solid phase microextraction (SPME) combined with GCMS from two genetically different A. flavus strains, an aflatoxigenic strain, NRRL 3357, and a non-aflatoxigenic strain, NRRL 21882. A PDMS/CAR SPME fiber was used over 30 days to observe variations in MVOCs over time. The relative percentage of individual chemicals in several chemical classes (alcohols, aldehydes, esters, furans, hydrocarbons, ketones, and organic acids) was shown to change considerably during the varied fungal growth stages. This changing chemical profile reduces the likelihood of finding a single chemical that can be used consistently as a biomarker for fungal strain identification. In our study, discriminant analysis techniques were successfully conducted using all identified and quantified MVOCs enabling discrimination of the two A. flavus strains over the entire 30-day period. This study underscores the potential of using SPME GCMS coupled with multivariate analysis for fungi strain identification.展开更多
Increases in the frequency of extreme weather and climate events and the severity of their impacts on the natural environment and society have been observed across the globe in recent decades. In addition to natural c...Increases in the frequency of extreme weather and climate events and the severity of their impacts on the natural environment and society have been observed across the globe in recent decades. In addition to natural climate variability and greenhouse-induced climate change, extreme weather and climate events produce the most pronounced impacts. In this paper, the climate of three island countries in the Western Pacific: Fiji, Samoa and Tuvalu, has been analysed. Warming trends in annual average maximum and minimum temperatures since the 1950s have been identified, in line with the global warming trend. We present recent examples of extreme weather and climate events and their impacts on the island countries in the Western Pacific: the 2011 drought in Tuvalu, the 2012 floods in Fiji and a tropical cyclone, Evan, which devastated Samoa and Fiji in December 2012. We also relate occurrences of the extreme weather and climate events to phases of the El Niño-Southern Oscillation (ENSO) phenomenon. The impacts of such natural disasters on the countries are severe and the costs of damage are astronomical. In some cases, climate extremes affect countries to such an extent that governments declare a national state of emergency, as occurred in Tuvalu in 2011 due to the severe drought’s impact on water resources. The projected increase in the frequency of weather and climate extremes is one of the expected consequences of the observed increase in anthropogenic greenhouse gas concentration and will likely have even stronger negative impacts on the natural environment and society in the future. This should be taken into consideration by authorities of Pacific Island Countries and aid donors when developing strategies to adapt to the increasing risk of climate extremes. Here we demonstrate that the modern science of seasonal climate prediction is well developed, with current dynamical climate models being able to provide skilful predictions of regional rainfall two-three months in advance. The dynamic climate model-based forecast products are now disseminated to the National Meteorological Services of 15 island countries in the Western Pacific through a range of web-based information tools. We conclude with confidence that seasonal climate prediction is an effective solution at the regional level to provide governments and local communities of island nations in the Western Pacific with valuable assistance for informed decision making for adaptation to climate variability and change.展开更多
Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. I...Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. In investigating prospects for improving the skill of TC seasonal prediction in the South Indian and South Pacific Oceans, including the Australian Region, we used linear regression to model the relationship between the annual number of cyclones and three indices (SOI, NI?O3.4 and 5VAR) describing the strength of the El Ni?o-Southern Oscillation (ENSO). The correlation between the number of Australian Region (90?E - 160?E) TCs and the indices was strong (3-month 5VAR ?0.65, NI?O3.4 ?0.62 and SOI +0.64), and a cross-validation assessment demonstrated that the models which used July-August-September indices and the temporal trend as the predictors performed well. The predicted number of TCs in the Australian Region for 2010/2011 and 2011/2012 seasons was 14 (11 recorded) and 12, respectively. We also found that the correlation between the numbers of TCs in the western South Indian region (30?E to 90?E) and the eastern South Pacific region (east of 170?E) and the indices was weak, and it is therefore not sensible to build linear regression forecast models for these regions. We conclude that for the Australian Region, the new statistical model provides prospects for improvement in forecasting skill compared to the statistical model currently employed at the National Climate Centre, Australian Bureau of Meteorology. The next step towards improving the skill of TC seasonal prediction in the various regions of the Southern Hemisphere will be undertaken through analysis of outputs from the dynamical climate model POAMA (Predictive Ocean-Atmosphere Model for Australia).展开更多
The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to de...The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.展开更多
文摘Fungi produce a variety of microbial volatile organic compounds (MVOCs) during primary and secondary metabolism. The fungus, Aspergillus flavus, is a human, animal and plant pathogen which produces aflatoxin, one of the most carcinogenic substances known. In this study, MVOCs were analyzed using solid phase microextraction (SPME) combined with GCMS from two genetically different A. flavus strains, an aflatoxigenic strain, NRRL 3357, and a non-aflatoxigenic strain, NRRL 21882. A PDMS/CAR SPME fiber was used over 30 days to observe variations in MVOCs over time. The relative percentage of individual chemicals in several chemical classes (alcohols, aldehydes, esters, furans, hydrocarbons, ketones, and organic acids) was shown to change considerably during the varied fungal growth stages. This changing chemical profile reduces the likelihood of finding a single chemical that can be used consistently as a biomarker for fungal strain identification. In our study, discriminant analysis techniques were successfully conducted using all identified and quantified MVOCs enabling discrimination of the two A. flavus strains over the entire 30-day period. This study underscores the potential of using SPME GCMS coupled with multivariate analysis for fungi strain identification.
文摘Increases in the frequency of extreme weather and climate events and the severity of their impacts on the natural environment and society have been observed across the globe in recent decades. In addition to natural climate variability and greenhouse-induced climate change, extreme weather and climate events produce the most pronounced impacts. In this paper, the climate of three island countries in the Western Pacific: Fiji, Samoa and Tuvalu, has been analysed. Warming trends in annual average maximum and minimum temperatures since the 1950s have been identified, in line with the global warming trend. We present recent examples of extreme weather and climate events and their impacts on the island countries in the Western Pacific: the 2011 drought in Tuvalu, the 2012 floods in Fiji and a tropical cyclone, Evan, which devastated Samoa and Fiji in December 2012. We also relate occurrences of the extreme weather and climate events to phases of the El Niño-Southern Oscillation (ENSO) phenomenon. The impacts of such natural disasters on the countries are severe and the costs of damage are astronomical. In some cases, climate extremes affect countries to such an extent that governments declare a national state of emergency, as occurred in Tuvalu in 2011 due to the severe drought’s impact on water resources. The projected increase in the frequency of weather and climate extremes is one of the expected consequences of the observed increase in anthropogenic greenhouse gas concentration and will likely have even stronger negative impacts on the natural environment and society in the future. This should be taken into consideration by authorities of Pacific Island Countries and aid donors when developing strategies to adapt to the increasing risk of climate extremes. Here we demonstrate that the modern science of seasonal climate prediction is well developed, with current dynamical climate models being able to provide skilful predictions of regional rainfall two-three months in advance. The dynamic climate model-based forecast products are now disseminated to the National Meteorological Services of 15 island countries in the Western Pacific through a range of web-based information tools. We conclude with confidence that seasonal climate prediction is an effective solution at the regional level to provide governments and local communities of island nations in the Western Pacific with valuable assistance for informed decision making for adaptation to climate variability and change.
文摘Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. In investigating prospects for improving the skill of TC seasonal prediction in the South Indian and South Pacific Oceans, including the Australian Region, we used linear regression to model the relationship between the annual number of cyclones and three indices (SOI, NI?O3.4 and 5VAR) describing the strength of the El Ni?o-Southern Oscillation (ENSO). The correlation between the number of Australian Region (90?E - 160?E) TCs and the indices was strong (3-month 5VAR ?0.65, NI?O3.4 ?0.62 and SOI +0.64), and a cross-validation assessment demonstrated that the models which used July-August-September indices and the temporal trend as the predictors performed well. The predicted number of TCs in the Australian Region for 2010/2011 and 2011/2012 seasons was 14 (11 recorded) and 12, respectively. We also found that the correlation between the numbers of TCs in the western South Indian region (30?E to 90?E) and the eastern South Pacific region (east of 170?E) and the indices was weak, and it is therefore not sensible to build linear regression forecast models for these regions. We conclude that for the Australian Region, the new statistical model provides prospects for improvement in forecasting skill compared to the statistical model currently employed at the National Climate Centre, Australian Bureau of Meteorology. The next step towards improving the skill of TC seasonal prediction in the various regions of the Southern Hemisphere will be undertaken through analysis of outputs from the dynamical climate model POAMA (Predictive Ocean-Atmosphere Model for Australia).
基金This work was funded by the EPSRC Grant"Improving the product development process through integrated revision control and twinning of digital-physical models during prototyping",reference:EP/R032696/1.
文摘The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.
