Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to disc...Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to discover, develop, and validate a novel saliva-based microRNA signature for early diagnosis and prediction of OC risk in oral potentially malignant disorders (OPMD).The Cancer Genome Atlas (TCGA) miRNA sequencing data and small RNA sequencing data of saliva samples were used to discover differentially expressed miRNAs. Identified miRNAs were validated in saliva samples of OC (n=50), OPMD (n=52), and controls(n=60) using quantitative real-time PCR. Eight differentially expressed miRNAs (miR-7-5p, miR-10b-5p, miR-182-5p, miR-215-5p,miR-431-5p, miR-486-3p, miR-3614-5p, and miR-4707-3p) were identified in the discovery phase and were validated. The efficiency of our eight-miRNA signature to discriminate OC and controls was:area under curve (AUC):0.954, sensitivity:86%, specificity:90%,positive predictive value (PPV):87.8%and negative predictive value (NPV):88.5%whereas between OC and OPMD was:AUC:0.911,sensitivity:90%, specificity:82.7%, PPV:74.2%and NPV:89.6%. We have developed a risk probability score to predict the presence or risk of OC in OPMD patients. We established a salivary miRNA signature that can aid in diagnosing and predicting OC,revolutionising the management of patients with OPMD. Together, our results shed new light on the management of OC by salivary miRNAs to the clinical utility of using miRNAs derived from saliva samples.展开更多
In this study,we employed a number of geospatial techniques to examine the spatiotemporal patterns and changes of environmental attitudes and place attachment values in the Gauteng province of South Africa.The data we...In this study,we employed a number of geospatial techniques to examine the spatiotemporal patterns and changes of environmental attitudes and place attachment values in the Gauteng province of South Africa.The data were obtained from the Gauteng City Region Observatory’s Quality of Life Survey collected at three separate points in time,namely 2013,2015,and 2017.Results indicated that wards(smallest administrative and analysis units)located on the urban periphery of Gauteng,which are generally less affluent,largely held more negative environmental attitudes and place attachment values during the three time periods.In contrast,centrally located wards,which are generally more affluent,expressed more positive environmental attitudes but less place attachment values,especially in 2017.The findings of this research not only highlight the complex spatio-temporal distribution of environmental attitudes and place attachment values throughout Gauteng but also empha-size the need for spatially targeted state interventions for future environmental planning within the province.展开更多
Discrete global grid systems have become an important component of Digital Earth systems.However,previously there has not existed an easy way to map between named places(toponyms)and the cells of a discrete global gri...Discrete global grid systems have become an important component of Digital Earth systems.However,previously there has not existed an easy way to map between named places(toponyms)and the cells of a discrete global grid system.The lack of such a tool has limited the opportunities to synthesize social place-based data with the more standard Earth and environmental science data currently being analyzed in Digital Earth applications.This paper introduces Wāhi,the first gazetteer to map entities from the GeoNames database to multiple discrete global grid systems.A gazetteer service is presented that exposes the grid system and the associated gazetteer data as Linked Data.A set of use cases for the discrete global grid gazetteer is discussed.展开更多
This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-...This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-mand response(DR)potentials.With advances in automated building management systems,this can be achieved seamlessly by a smart autonomous RL agent which takes the best action,for example,a change in HVAC temper-ature set point,necessary to change the electricity usage pattern of a building in response to demand response signals,and with minimal thermal comfort impact to customers.Previous research in this area has tackled only individual aspects of the problem using RL.Specifically,due to the challenges in implementing demand response with whole-building models,simpler analytical models which poorly capture reality have been used instead.And where whole-building models are applied,RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected.Thus,in this research,we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals.Our simulation results show that by applying reinforcement learning for normal HVAC operation,a maximum weekly energy reduction of up to 22%can be achieved compared to a handcrafted baseline controller.Furthermore,by employing a DR-aware RL controller during demand response periods,average power reductions or increases of up to 50%can be achieved on a weekly basis compared to the default RL controller,while keeping occupant thermal comfort levels within acceptable bounds.展开更多
基金supported by a joint GUIPRS/AHEAD Scholarship and GU Postgraduate Research Scholarshipcurrently receiving funds from Cancer Australia (APP1145657)+2 种基金the National Health and Medical Research Council (APP 2002576 and APP 2012560)the Garnett Passe and Rodney Williams FoundationNIH R21 and the RBWH Foundation。
文摘Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to discover, develop, and validate a novel saliva-based microRNA signature for early diagnosis and prediction of OC risk in oral potentially malignant disorders (OPMD).The Cancer Genome Atlas (TCGA) miRNA sequencing data and small RNA sequencing data of saliva samples were used to discover differentially expressed miRNAs. Identified miRNAs were validated in saliva samples of OC (n=50), OPMD (n=52), and controls(n=60) using quantitative real-time PCR. Eight differentially expressed miRNAs (miR-7-5p, miR-10b-5p, miR-182-5p, miR-215-5p,miR-431-5p, miR-486-3p, miR-3614-5p, and miR-4707-3p) were identified in the discovery phase and were validated. The efficiency of our eight-miRNA signature to discriminate OC and controls was:area under curve (AUC):0.954, sensitivity:86%, specificity:90%,positive predictive value (PPV):87.8%and negative predictive value (NPV):88.5%whereas between OC and OPMD was:AUC:0.911,sensitivity:90%, specificity:82.7%, PPV:74.2%and NPV:89.6%. We have developed a risk probability score to predict the presence or risk of OC in OPMD patients. We established a salivary miRNA signature that can aid in diagnosing and predicting OC,revolutionising the management of patients with OPMD. Together, our results shed new light on the management of OC by salivary miRNAs to the clinical utility of using miRNAs derived from saliva samples.
文摘In this study,we employed a number of geospatial techniques to examine the spatiotemporal patterns and changes of environmental attitudes and place attachment values in the Gauteng province of South Africa.The data were obtained from the Gauteng City Region Observatory’s Quality of Life Survey collected at three separate points in time,namely 2013,2015,and 2017.Results indicated that wards(smallest administrative and analysis units)located on the urban periphery of Gauteng,which are generally less affluent,largely held more negative environmental attitudes and place attachment values during the three time periods.In contrast,centrally located wards,which are generally more affluent,expressed more positive environmental attitudes but less place attachment values,especially in 2017.The findings of this research not only highlight the complex spatio-temporal distribution of environmental attitudes and place attachment values throughout Gauteng but also empha-size the need for spatially targeted state interventions for future environmental planning within the province.
文摘Discrete global grid systems have become an important component of Digital Earth systems.However,previously there has not existed an easy way to map between named places(toponyms)and the cells of a discrete global grid system.The lack of such a tool has limited the opportunities to synthesize social place-based data with the more standard Earth and environmental science data currently being analyzed in Digital Earth applications.This paper introduces Wāhi,the first gazetteer to map entities from the GeoNames database to multiple discrete global grid systems.A gazetteer service is presented that exposes the grid system and the associated gazetteer data as Linked Data.A set of use cases for the discrete global grid gazetteer is discussed.
基金This research was funded by Australian Renewable Energy Agency(ARENA)as part of ARENA’s Advancing Renewables Program under Grant 2018/ARP017.
文摘This paper proposes a novel reinforcement learning(RL)architecture for the efficient scheduling and control of the heating,ventilation and air conditioning(HVAC)system in a commercial building while harnessing its de-mand response(DR)potentials.With advances in automated building management systems,this can be achieved seamlessly by a smart autonomous RL agent which takes the best action,for example,a change in HVAC temper-ature set point,necessary to change the electricity usage pattern of a building in response to demand response signals,and with minimal thermal comfort impact to customers.Previous research in this area has tackled only individual aspects of the problem using RL.Specifically,due to the challenges in implementing demand response with whole-building models,simpler analytical models which poorly capture reality have been used instead.And where whole-building models are applied,RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected.Thus,in this research,we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals.Our simulation results show that by applying reinforcement learning for normal HVAC operation,a maximum weekly energy reduction of up to 22%can be achieved compared to a handcrafted baseline controller.Furthermore,by employing a DR-aware RL controller during demand response periods,average power reductions or increases of up to 50%can be achieved on a weekly basis compared to the default RL controller,while keeping occupant thermal comfort levels within acceptable bounds.
基金ZC is supported by an early career fellowship from the National Health and Medical Research Council(NHMRC)of Australia(GNT1156444)PK is supported by a Medical Research Future Fund(MRFF)from the NHMRC of Australia(MRF1136427)+2 种基金PK and BR are supported by a MRFF Stem Cell Therapies grant(APP1201781)His institution has received speaker or consultancy fees and/or research grants from Biscayne,Eisai,GW Pharmaceuticals,LivaNova,Novartis,UCB Pharma and Zynerba.TOB is supported by a programme grant from the NHMRC of Australia(APP1091593)the Royal Melbourne Hospital Neuroscience Foundation.AAB,AA,YM,ZG,and XW report no conflicts of interest.
