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Transport for NSW trials machine learning to detect crash blackspots

#artificialintelligence

Transport for NSW has built a proof-of-concept using machine learning technology from Microsoft to identify potentially dangerous traffic intersections and fast-track remediation works. The'dangerous intersections' proof-of-concept, which took place last year, analysed telematic data collected from 50 vehicles travelling on Wollongong's roads over a 10-month period. The data – sent from the vehicles at a rate of 25 records a second – was used to pinpoint five previously unknown blackspots, with the two highest-risk now slated for upgrades later this financial year. TfNSW's data discovery program lead Julianna Bodzan came up with the idea while driving down the Mount Ousley descent on the Princes Highway – a notorious, four-and-a-half kilometre stretch of road leading into North Wollongong. She said the telematics data collected from the vehicles was compared with crash data from known blackspots to discern whether or not other intersections in the coastal city were potentially risky.


Robots aid rescue during Navy exercise - Australian Defence Magazine

#artificialintelligence

Autonomous Warrior Genesis – the first of Navy's flagship events exercising Robotics, Autonomous Systems and Artificial Intelligence (RAS-AI) – saw unmanned vehicles deployed by air, land and water to respond to a fictional Humanitarian and Disaster Relief (HADR) scenario on the Brisbane River. Minister for Defence Linda Reynolds said the exercise demonstrated Defence working with industry to integrate emerging technologies with Navy platforms to rapidly respond in emergency situations. "Australia's commitment to maintaining a strong and secure region is predicated on ongoing modernisation of Defence capability as new and disruptive technologies emerge," Minister Reynolds said. "As announced in the 2020 Force Structure Plan, the Government recognises the exploration of autonomous and un-crewed systems will further safeguard Australia's capability and achieve expanded reach across the region. "Using autonomous systems to respond to disaster scenarios is a potential game changer for Defence by providing the agility and technological edge to rapidly support our region in times of crisis.


Monash University and The Alfred to develop AI-based superbug detection system

ZDNet

Monash University and Alfred Hospital are developing an artificial intelligence-based system to improve the way superbugs are diagnosed, treated, and prevented. According to Monash University professor of digital health Christopher Bain, infections from superbugs kill 700,000 people every year and by 2050, the world could see 10 million deaths annually from previously treatable diseases. Superbugs are created when microbes evolve to become immune from the effects of antimicrobials. The project, which will be mainly based at The Alfred, has received AU$3.4 million from the federal government's Medical Research Future fund. According to the project's lead researcher, Antony Peleg, the project will look to integrate genomics, electronic healthcare data, and AI technologies to address antimicrobial resistance in the healthcare system.


Australia's Artificial Intelligence (AI) future: A call to Action

#artificialintelligence

Artificial intelligence (AI) is steadily becoming a familiar tool for many Australians. We have come to know it through our pocket voice assistants, like Siri and Alexa, and as the brains behind Google's predictive searches. Australian businesses, particularly in the mining sector, view it as a means to gain a competitive advantage, and we have even seen its potential to fight COVID-19. As AI begins to permeate every aspect of our lives, the Australian government has recognised the economic and social opportunities it affords us in its newly proposed AI Action Plan. The discussion paper, released on 29 October 2020, is the latest in a suite of Australian initiatives targeting AI regulation and development, following on from the AI Ethics Framework.


Digital Innovation Futures Victoria

#artificialintelligence

This year it's more important than ever to us to provide an opportunity for the industry to come together, to share best practices, and to get excited... Experts from Intellify share crucial strategies necessary to implement AI and ML projects within your organisation. About this Event Please note, this... Digital Cultural Adventures bring the Chinese Museum to your classroom! About this Event Schools can choose from a range of themed programs to learn a... Timely talks for software development managers, tech leaders, lead developers or software engineers looking to move up into a lead role. Industry 4.0 heavily impacted business models but are you, as a leader, ready to embrace, and keep up with, the changes required? Learn about the easy payroll solution for successful businesses CloudPayroll is a proven, cloud-based payroll solution, suitable for a MICRO size busi... Learn about the easy payroll solution for successful businesses CloudPayroll is a proven, cloud-based payroll solution, suitable for a MICRO size busi... Join us for a monthly interactive workshop where we cover various economic and technology trends as they impact your career.


Tracking development at the cellular level

Science

We each developed from a single cell—a fertilized egg—that divided and divided and eventually gave rise to the trillions of cells, of hundreds of types, that constitute the tissues and organs of our adult bodies. Advancing our understanding of the molecular programs underlying the emergence and differentiation of these diverse cell types is of fundamental interest and will affect almost every aspect of biology and medicine. Recently, technological advances have made it possible to directly measure the gene expression patterns of individual cells ([ 1 ][1]). Such methods can be used to clarify cell types and to determine the developmental stage of individual cells ([ 2 ][2]). Single-cell transcriptional profiling of successive developmental stages has the potential to be particularly informative, as the data can be used to reconstruct developmental processes, as well as characterize the underlying genetic programs ([ 3 ][3], [ 4 ][4]). ![Figure][5] A genomic technique for tracking cellular development High-throughput single-cell genomic methods enable a global view of cell type diversifcation by transcriptome and epigenome CREDIT: N. DESAI/ SCIENCE FROM CAO ET AL. ([7][6]) AND BIORENDER When I began my doctoral studies in Jay Shendure's lab at the University of Washington, available single-cell sequencing techniques relied on the isolation of individual cells within physical compartments and thus were limited in terms of both throughput and cost. As a graduate student, I developed four high-throughput single-cell genomic techniques to overcome these limitations ([ 5 ][7]–[ 8 ][8]). Leveraging these methods, I profiled millions of single-cell transcriptomes from organisms, in species that included worms, mice, and humans. By quantifying the dynamics of embryonic development at single-cell resolution, I was able to map out the global genetic programs that control cell proliferation and differentiation at the whole-organism scale. By the 1980s, biologists had documented every developmental step in the nematode Caenorhabditis elegans , from a single-cell embryo to the adult worm, and mapped the connections of all of the worm's neurons ([ 9 ][9]). However, although the nematode worm has a relatively small cell number (558 cells at hatching), a comprehensive understanding of the molecular basis for the specification of these cell types remains difficult. To resolve cellular heterogeneity, I first developed a method to specifically label the transcriptomes of large numbers of single cells, which we called sci-RNA-seq (single-cell combinatorial indexing RNA sequencing) ([ 5 ][7]). This method is based on combinatorial indexing, a strategy using split-pool barcoding of nucleic acids to label vast numbers of single cells within a single experiment ([ 9 ][9]). In this study, I profiled nearly 50,000 cells from C. elegans at the L2 stage, which is more than 50-fold “shotgun cellular coverage” of its somatic cell composition. We further defined consensus expression profiles for 27 cell types and identified rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. This was the first study to show that single-cell transcriptional profiling is sufficient to separate all major cell types from an entire animal. C. elegans development follows a tightly controlled genetic program. Other multicellular organisms, such as mice and humans, have much more developmental flexibility. However, conventional approaches for mammalian single-cell profiling lack the throughput and resolution to obtain a global view of the molecular states and trajectories of the rapidly diversifying and expanding cell types. To investigate cell state dynamics in mammalian development, I developed an even more scalable single-cell profiling technique, sci-RNA-seq3 ([ 7 ][6]), and used it to trace the development path of 2 million mouse cells as they traversed diverse paths in a 4-day window of development corresponding to organogenesis (embryonic day 9.5 to embryonic day 13.5). From these data, we characterized the dynamics of cell proliferation and key regulators for each cell lineage, a potentially foundational resource for understanding how the hundreds of cell types forming a mammalian body are generated in development. This was, and remains, the largest publicly available single-cell transcriptional dataset. The sci-RNA-seq3 method enabled this dataset to be generated rapidly, within a few weeks, by a single individual. A major challenge regarding current single-cell assays is that nearly all such methods capture just one aspect of cellular biology (typically mRNA expression), limiting the ability to relate different components to one another and to infer causal relationships. Another technique that I developed, sci-CAR (single-cell combinatorial indexing chromatin accessibility and mRNA) ([ 6 ][10]), was created with the goal of overcoming this limitation, allowing the user to jointly profile the epigenome (chromatin accessibility) and transcriptome (mRNA). I applied sci-CAR to the mouse whole kidneys, recovering all major cell types and linking cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells. To further explore the gene regulatory mechanisms, I invented sci-fate ([ 8 ][8]), a new method that identifies the temporal dynamics of transcription by distinguishing newly synthesized mRNA transcripts from “older” mRNA transcripts in thousands of individual cells. Applying the strategy to cancer cell state dynamics in response to glucocorticoids, we were able to link transcription factors (TFs) with their target genes on the basis of the covariance between TF expression and the amount of newly synthesized RNA across thousands of cells. In summary, my dissertation involved developing the technical framework for quantifying gene expression and chromatin dynamics across thousands to millions of single cells and applying these technologies to profile complex, developing organisms. The methods that I developed enable such projects to be achievable by a single individual, rather than requiring large consortia. Looking ahead, I anticipate that the integration of single-cell views of the transcriptome, epigenome, proteome, and spatial-temporal information throughout development will enable an increasingly complete view of how life is formed. GRAND PRIZE WINNER Junyue Cao Junyue Cao received his undergraduate degree from Peking University and a Ph.D. from the University of Washington. After completing his postdoctoral fellowship at the University of Washington, Junyue Cao started his lab as an assistant professor and lab head of single-cell genomics and population dynamics at the Rockefeller University in 2020. His current research focuses on studying how a cell population in our body maintains homeostasis by developing genomic techniques to profile and perturb cell dynamics at single-cell resolution. CATEGORY WINNER: ECOLOGY AND EVOLUTION Orsi Decker Orsi Decker completed her undergraduate degree at Eötvös Loránd University in Budapest, Hungary. She went on to receive her master's degree in Ecology and Evolution at the University of Amsterdam. Decker completed her doctoral research at La Trobe University in Melbourne, Australia, where she investigated the extinctions of native digging mammals and their context-dependent impacts on soil processes. She is currently a postdoctoral researcher at La Trobe University, where she is examining how land restoration efforts could be improved to regain soil functions through the introduction of soil fauna to degraded areas. [www.sciencemag.org/content/370/6519/925.1][11] CATEGORY WINNER: MOLECULAR MEDICINE Dasha Nelidova Dasha Nelidova completed her undergraduate degrees at the University of Auckland, New Zealand. She completed her Ph.D. in neurobiology at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland. Nelidova is currently a postdoctoral researcher at the Institute of Molecular and Clinical Ophthalmology Basel, where she is working to develop new translational technologies for treating retinal diseases that lead to blindness. [www.sciencemag.org/content/370/6519/925.2][12] CATEGORY WINNER: CELL AND MOLECULAR BIOLOGY William E. Allen William E. Allen received his undergraduate degree from Brown University in 2012, M.Phil. in Computational Biology from the University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's life span. [www.sciencemag.org/content/370/6519/925.3][13] 1. [↵][14]1. D. Ramsköld et al ., Nat. Biotechnol. 30, 777 (2012). [OpenUrl][15][CrossRef][16][PubMed][17] 2. [↵][18]1. C. Trapnell , Genome Res. 25, 1491 (2015). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. D. E. Wagner et al ., Science 360, 981 (2018). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. K. Davie et al ., Cell 174, 982 (2018). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. J. Cao et al ., Science 357, 661 (2017). [OpenUrl][29][Abstract/FREE Full Text][30] 6. [↵][31]1. J. Cao et al ., Science 361, 1380 (2018). [OpenUrl][32][Abstract/FREE Full Text][33] 7. [↵][34]1. J. Cao et al ., Nature 566, 496 (2019). [OpenUrl][35][CrossRef][36][PubMed][37] 8. [↵][38]1. J. Cao, 2. W. Zhou, 3. F. Steemers, 4. C. Trapnell, 5. J. Shendure , Nat. Biotechnol. 38, 980 (2020). [OpenUrl][39][CrossRef][40][PubMed][41] 9. [↵][42]1. D. A. Cusanovich et al ., Science 348, 910 (2015). 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AI that can diagnose tinnitus from brain scans may improve treatment

#artificialintelligence

An artificial intelligence that can diagnose tinnitus based on the results of brain imaging, rather than subjective tests, may help improve treatments for the condition. Mehrnaz Shoushtarian at the Bionics Institute in Melbourne, Australia, and her colleagues have developed an algorithm that can detect whether a person has tinnitus, and also how severe it is. The AI can spot the presence of tinnitus with 78 per cent accuracy, and distinguish between mild and severe forms with 87 per cent accuracy. Chronic tinnitus affects around 15 per cent of adults. The condition is usually diagnosed by a hearing test, by self-reporting or based on a subjective questionnaire.


NSW Transport and Microsoft use machine learning and data to reduce road accidents

ZDNet

Transport for New South Wales and Microsoft have partnered to develop a proof of concept that uses data and machine learning to flag potentially dangerous intersections and reduce road accidents. As part of the proof of concept, Transport for NSW ran a trial in Wollongong to uncover five potentially risky intersections. It involved 50 vehicles generating more than a billion rows of data over a 10-month period, before Databricks and Azure were used to curate, ingest, and interpret the data. The telematics data was used to identify speed, harsh braking, harsh acceleration, and lateral movement just before the intersection. It was then compared to patterns of existing crash investigation data.


This giant coral reef is taller than the Empire State Building

Mashable

A research team onboard a Schmidt Ocean Institute (SOI) ship discovered a giant detached reef in the Great Barrier Reef, just off Cape York, Australia. The team used an underwater robot to explore and 3D map the reef, as well as the vast ecosystem it supports.