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Human behavior and performance in deep space exploration: next … – Nature.com

The next phase of human space exploration includes the establishment and habitation of a lunar gateway, a permanent base on the Moons surface, and exploratory crewed missions to Mars. As human activity in space moves from Low Earth Orbit (LEO) operations, such as those that take place on the ISS, to deep space exploration, the crews will face a different set of psychological challenges1. These include extended mission durations, increased distance from Earth, prolonged isolation and confinement, reduced size of crew quarters, lack of privacy, communication latency, need for increased autonomy in decision-making processes, and lack of short-term rescue possibilities, amongst other known and as yet unknown demands2,3.

There is good evidence to suggest that astronauts behavior, health, and performance can be impacted by the demands encountered in future space missions4. For the purposes of this work, issues of behavior, health, and performance are separated and considered according to discrete cognitive, affective, behavioral, social, and mental health components. While these function areas are related, distinguishing between them provides the opportunity for conducting well-specified and targeted psychological studies. Cognitive dimensions include issues of perception, vigilance, judgment, memory, and reaction time, amongst other executive functions5. When discussing cognition, there are clear synergies with the neurosciences. Affective experiences refer to emotions, feelings, and moods, which can be shaped by a persons physiology and subjective experience. Behaviors include observable individual and interpersonal actions and the execution of skilled performances. For instance, before they are executed, motor actions must be planned in the brain and rely on complex neuronal networks. Team-level social functions include relevant team process dynamics, such as experiences of cohesion and conflict. Mental health is a relevant component, which is related to the psychosocial, affective, cognitive, emotional, and physical challenges that astronauts face during missions. Mental health includes the importance of managing psychophysical stress and promoting well-being, both on individual and team levels.

This white paper is the result of a consensus among experts invited by the European Space Agency to update the roadmap for scientific research for the next decade6. The psychology working group (corresponding to the authors of this paper) was composed of experts in psychological science with a large research experience in the space context, currently engaged in research with ESA, in the space environment, analogue environments, and ground-based research. Research gaps were identified by the experts, referring to their direct research experience and to their knowledge of the scientific literature, and discussed to reach a consensus. The work specifically focused on the behavioral and performance aspects. Some of the relevant aspects that could impact the astronauts life, including the effects of space radiations, were not addressed by this group, as they have been included in other working groups report.

In this report, we broadly refer to long-duration (i.e., >30 days) space exploration (LDSE) missions, with a particular focus on deep space voyages, outside the earths atmosphere, which are distinct from current long missions in LEO. Even with this differentiation, it is worth keeping in mind that the psychological challenges of relatively near-Earth explorations, including incoming Moon missions, may be qualitatively different from those that will be experienced during long-distance deep space voyages, such as human missions to Marswhich also include long travels and an extreme routine. In the following, many of the open questions related to psychological function in space are framed in the context of LDSE missions. However, where we refer to space missions broadly, questions are pertinent to LEO, lunar and deep space missions. With respect to an understanding of human performance and behavior issues of spaceflight, the European Space Agency and its partners can build on several years of studies and experiences on the ISS and sub-orbital flights, as well as during simulations studies and in analogue environments. Deep space exploration, though, has some different characteristics that will require ad-hoc preparation and new studies to answer currently open questions. For this reason, further studies will be needed not only to be conducted during long-duration space missions, but also in other settings, including analogue environments and other isolated and confined settings. These environments share some similarities with the space context, including isolation and confinement. Some analogue missions are conducted in specifically designed facilities, such as HERA (Human Exploration Research Analog), the underwater research station NEEMO (NASA Extreme Environment Mission Operations), and the MARS500 isolation chamber. Other facilities include Antarctic stations, such as the Concordia base, an Italian/French research facility considered one of the most remote outposts on Earth. Most of these environments allow to study behavioral, physical, and team dynamics, and to test countermeasures that can be implemented in space missions. Other analogue environments allow to recreate specific challenges of space research, including radiations (e.g., the NASA Space Radiation Lab) and bedrest implications (e.g., the: envihab facility, in Cologne, Germany).

Risks to behavior, health, and performance during deep space exploration could be mitigated and astronaut function optimized with the application of effective countermeasures. However, research is required to identify and further develop or refine the strategies and approaches that might be used to enable astronauts to maintain elevated well-being and high-performance standards on LDSE missions. In the following section, we distinguish between questions related to basic issues of adaptation and countermeasures. The former largely deals with the understanding of psychological aspects of deep space exploration and the impact of unique deep space mission demands upon behavior, health, and performance. This includes the role of individual differences in adaptation, and broader mechanisms underlying individual and team phenomena that are relevant to human spaceflight7,8,9. Important information in this area could be inferred from different types of observational and correlational studies. Countermeasures deal with the specific actions and interventions that space agencies can enact to mitigate the risks of future missions. This might include a refined selection process or the application of inflight psychological training and support. While most questions in the white paper focus on pre-mission and inflight activities, it is also important to consider psychological experiences in the post-mission phase, to ensure that astronauts well-being is robust after the end of what are likely to be physically and psychologically demanding voyages. Astronauts constitute a very limited community on Earth. When addressing these fundamental questions, it is therefore critical to consider whether and how the findings can be transferred to the general public as many activities parallel, to some extent, what space travelers will undergo.

Most knowledge in space psychology has focused on short-duration mission, relatively close to Earth, and with synchronous contacts with mission control. New incoming space missions pose different challenges, in terms of psychological adaptation and the definition of countermeasures to mitigate risks.

To inform the identification of effective preparatory and preventative countermeasures for future space missions, there are several questions related to basic issues of psychological adaptation that need to be resolved. These questions relate to both in-mission dynamics influenced by the interaction between individual and team factors and contextual demands, and what happens in the post-mission phase. Open questions identified by the expert scientific group, largely reflect unknowns associated with missions beyond Low Earth Orbit (LEO).

For the ISS, there already exists a standard human behavior and performance competency framework for crewmembers10. There have also been efforts to standardize the psychological/psychosocial and behavioral data collected during space agency sponsored research activities11,12,13. This research has been used in ground-based studies in analogue environments, such as NASA-HERA, VaPER, AGBRESA, SIRIUS 19 & 20, as well as Antarctica. Standard measures have been used on the International Space Station in multiple expeditions14. These standard measures have included cognition batteries, personality surveys, and neuroscience assessments of sensorimotor measures, together with biomarkers, immune markers, and actigraphy. All these data will help create useful benchmarks for LDSE missions. Nevertheless, despite the important research and the progress made so far, there is a need to continue this research in analog environment and long-term spaceflight. In addition, the scope of this research should be enlarged with respect to including also physiological measures as possible indicators of mental health and performance state, as well as measures of team cohesion and climate which can capture team dynamics in the course of LDSE missions. Questions that must be addressed include:

What variables capture relevant information on cognitive, affective, behavioral function, and team functions? And what outcomes are informative about mental health issues?

What physiological biomarkers provide a valid insight to the psychological experience of astronauts?

To date, there have been many studies on spaceflight stressors that have utilized both space platforms and ground-based analogs. However, for LDSE missions the type and magnitude of stressors encountered will be different from those experienced in the past. Research is needed to evaluate the impact of such stressors on individual and team function. Certain stressors could be evaluated in analog environments (e.g., isolation, confinement, chronic threat, sleep deprivation, sensory deprivation), while the effects of others will need to be examined in space (microgravity, hypogravity). Research conducted during spaceflight, in analog environments and simulation studies, thus far, has already provided important insights into the effects of microgravity, prolonged confinement, and isolation, as well as sleep deprivation on human performance, well-being, and behavior15. However, the impact of stressors such as prolonged hypogravity, transitions between different levels of hypogravity, prolonged radiation exposure, chronic threat due to lacking rescue possibilities, extreme levels of crew autonomy, and Earth-out-of-view unique to LDSE missions, have not been addressed yet, or only to a very limited degree (e.g., effects of autonomy during simulation studies)16,17. Their impact on cognition, affective experiences, behavior and performance, team dynamics, mental health, and performance should be carefully assessed. Some of these stressors can be studied in analog environments (e.g., autonomy), others require spaceflight experiments during upcoming missions to the Moon (e.g., hypogravity) and still others might be only investigated during actual LDSE missions (e.g., Earth-out-of-view effects). Moreover, the relationship among stress biomarker dynamics (e.g., heart rate variability, cortisol), related biological processes, and individual and team function within these settings will have to be clearly unfolded.

While there have been many studies on individual differences in relation to human spaceflight, data that exist remains relatively limited in a predictive sense1. Additional research is needed to examine how baseline individual and team characteristics are likely to impact upon in-mission individual and team function. Moreover, day-to-day team performance indicators need further exploration in the context of extreme environments.

How do demographic criteria (e.g., age, gender identity, and ethnicity) influence adaptability and individual function during space missions?

What is the unique impact of sexuality on the psychological function of crewmembers in space?

How do individual psychological differences (e.g., personality, motivation, and values) influence adaptability and individual and team function during space missions?

What individual difference factors should be used to inform effective team composition decisions for LDSE missions?

How do team structure and composition influence crew adaptability and function during space missions?

How do social dynamics, values, norms, and culture influence crew adaptability and function during space missions?

How can we assess team performance and dynamics on a small scale, and how does that relate to the overall mission success?

The high levels of autonomy that will be encountered on LDSE missions mean that crew will have increased responsibility for their own self-care/self-management18. New research is needed to examine the coping and health and performance self-regulation strategies that individuals and teams use to maintain their function. Although there have been initial studies on coping in analog settings19, there is limited empirical data on what astronauts do to manage themselves and their teams during space missions (LDSE or otherwise). Open questions are:

What resources and equipment (e.g., food variety, entertainment systems) contribute to effective coping and self-regulation during LDSE missions?

What coping and regulatory strategies are effective for optimizing cognition, affective experiences, behavior and performance, individual and team function, and, more in general, to promote mental health during LDSE missions?

Integrative studies that examine the interactive effects of psychosocial factors alongside physiological responses and other features of the environment, such as habitat design and resource availability, are required to provide a deeper insight to the human experience in space. This is especially important for LDSE where new systems, equipment, and habitats will be used. Among the unanswered questions, we find:

How do spaceflight stressors, demographic criteria, individual differences, coping, and regulatory strategies interact to impact individual and team function during LDSE missions?

What factors predict the extent to which skill fade occurs during LDSE missions?

How do physical features of the environment (e.g., habitat, architecture, internal conditions, and plants) impact upon individual and team adaptation?

How do food perception (e.g., taste and olfaction), texture, and variety impact upon astronauts affective, experiences?

How do astronauts interact with reporting systems designed to capture safety-critical information (e.g., medication use)?

What individual and team factors impact upon compliance with reporting systems designed to capture safety-critical information (e.g., medication use)?

The post-mission phase has addressed by research one both overwinter missions in Antarctica (e.g., ref. 20), and NASA post-flight standard measures14. However, it still requires a structured and deepened exploration, which has been sometimes overlooked. Anecdotal reports from the astronauts of the lunar missions in the 1960ies and 70ies suggest, that the mental processing of such extreme experiences represents a challenge also after the actual mission. With LDSE missions, the importance of questions related to reintegration, recovery, and mental processing of the mission experiences will significantly raise. Specifically, crucial open questions which need to become addressed more systematically relate to what positive or negative after-effects might occur after prolonged spaceflights, and what regulatory strategies might be effective to support reintegration, recovery, and rehabilitation upon return from LDSE missions. For example, there is limited empirical information on how individuals cope during their return from space and what strategies they use to maintain their health and well-being during reintegration and recovery. Research is needed to identify the strategies that individuals use and what impact that has upon their cognition, affect, and behavior in the post-mission phase. Open questions include:

What individual coping and regulatory strategies are effective for optimizing cognition, affective experiences, behavior, and performance, and, more in general, mental health, during the return, transition, and recovery following LDSE missions?

How do social networks contribute to effective astronaut coping and self-regulation during their return?

How do individuals prepare themselves and their families to redeploy on new missions?

Addressing open questions related to basic issues of adaptation should provide the knowledge to develop effective countermeasures for the envisaged future space missions. Psychological countermeasures might target selection and training, in-mission, and post-mission phases. The emphasis in this white paper is on identification and testing and evaluating the impact of applied measures.

Current selection and training protocols have been designed for LEO missions. Research is needed to identify how individual and team psychological selection should be adapted for LDSE missions. Specifying and developing the training needed to ensure optimal crew function on LDSE is also needed. While existing processes might continue to have utility, this should be confirmed with empirical evidence. Questions that still have to be addressed include:

What individual difference factors inform on psychological suitability for LDSE missions?

How should psychological suitability be assessed during the assessment and selection of astronauts for LDSE missions?

What methods are available to inform the selection of psychologically compatible or incompatible teams?

Do these methods raise any ethical concerns?

How should current selection processes be adapted and validated to inform the effective psychological selection of crewmembers for LDSE missions?

What unique training protocols need to be developed and how should they be delivered (e.g., what strategies, tools, and techniques) to prepare individuals and teams to respond effectively to the demands of LDSE missions?

How should individuals and teams be trained to respond effectively to critical or off-nominal incidents when operating autonomously in space? What protocols and policies need to be developed?

How should approaches and methods for optimizing affective experiences and cognition (e.g., mind-body strategies, emotion regulation, and flexible coping) during space missions be trained?

How should approaches and methods for optimizing team function (e.g., communication, cooperation, collaboration, and conflict resolution) during space missions be trained?

How should astronauts be trained to deal with extreme and unexpected events (e.g., deaths and psychiatric issues) that might occur during LDSE missions?

Support during and after LDSE missions will rely on accurate monitoring, diagnosis, and deployment of effective countermeasures. Although research in these areas is currently being undertaken, there remain a number of open questions about how to best support individuals and teams in space. Studies conducted in microgravity and on ground-based analogs can be used to identify and evaluate the efficacy of approaches to support individuals and crew during and after return from LDSE missions.

What methods, measures, and metrics should be used to monitor individual and team function, sleep, and fatigue during space missions?

How should work/life balance be managed during different phases of a LDSE mission?

How can astronauts be supported and what resources do they need to allow them to rest and relax away from work tasks?

How should sleep and fatigue management skills for LDSE missions be trained and maintained?

What non-pharmaceutical approaches are effective for sleep and fatigue management during LDSE missions?

How should methods used to minimize skill fade and degradations in task performance during LDSE missions be administered?

How should astronauts be supported to maintain their motivation to engage in healthy behaviors (e.g., exercise) across the duration of a LDSE mission?

What and how should support be provided following the occurrence of extreme and unexpected events (e.g., deaths and psychiatric issues)?

How should approaches and methods for optimizing mental health, affective experiences, cognition, behavior, and performance (e.g., mind-body strategies, emotion regulation, and flexible coping) during space missions be maintained?

How should approaches and methods for optimizing team function (e.g., communication, cooperation, collaboration, and conflict resolution) during space missions be maintained?

How should autonomous and digital systems be used to effectively support individual and team functions during LDSE missions?

How do human factors impact upon autonomous and digital system interaction?

What features must be included in autonomous and digital systems for effective use in space?

How do trust and privacy impact the likelihood of astronauts engaging with autonomous and digitally delivered countermeasures?

What communication types/methods are effective as a mechanism for support during autonomous missions?

How should communications be adapted to effectively support team function during autonomous LDSE missions?

What family support mechanisms need to be established to minimize potential issues due to separation and lack of family contact during LDSE missions and what would be the optimal communication frequency and duration?

How should families and social groups be effectively-prepared to support those returning from space?

Psychosocial function of astronauts can be impacted by the system that the individual and team are operating in. The constraints of LDSE missions mean that new systems, architectures, and habitats will need to be developed. There are open questions about how to engineer and design the systems, architectures, and habitats to facilitate optimal function in space:

How should autonomous and digital systems be designed for use during LDSE missions? In particular, what would be the benefits of using virtual reality-based approaches?

How should communications be designed to effectively support individual functions during autonomous LDSE missions?

What architectural and habitat design features should be implemented to enhance individual and interpersonal function during LDSE missions?

What features should be considered and designed into safety-critical reporting systems (e.g., medication reporting systems)?

How might an astronauts connection to nature be established through architecture and habitat design?

Several of the identified knowledge gaps have direct relevance for micro- and hypogravity research. In particular, this holds true for a better understanding of the effects of hypogravity on human cognition and performance, which are already relatively well understood for some basic cognitive functions, but which lack knowledge with respect to higher executive functions or issues related to skill maintenance across different levels of (hypo-)gravity. The clear majority of the key knowledge gaps previously identified, however, relate to basic issues of individual or crew adaptation to long-term confinement and isolation and to effective countermeasures for maintaining well-being and performance of crews under such conditions. To close these knowledge gaps is of most relevance for future exploration missions to the Moon and Mars which will involve more extreme conditions of isolation and confinement than has been known from other environments, thus far. Even though the conditions of travel to the Moon will be more extreme than what we know from near-Earth orbital spaceflight and overwintering in Antarctica, they do not seem to be different in a qualitative sense (i.e., the demands are amplified rather than being especially unique). Thus, it might be expected that at least some of the current knowledge about the psychological effects of isolation and confinement as well as hypogravity might be generalized to missions in lunar orbits or even stays on the lunar surface. In contrast, future deep space missions to Mars will represent a qualitatively much more extreme change (e.g., with respect to autonomy, restricted means of crew-ground communication, lack of evacuation possibilities) compared to what has been known about effects of isolation and confinement from other fields already, and, thus, will provide completely new psychological challenges which currently are not well understood. In a sense, future missions to Mars will resemble past ambitious naval explorations, such as those conducted by Vespucci and Colombo, in which humans pushed their limits beyond a line that had never been crossed before21. However, on the other hand, we are arguably more prepared than a crew on a ship that did not know what they were about to find, as, we can prepare such missions using probes, satellites, and many other remote observation techniques. Among the preparation activity, psychological research addressing the knowledge identified gaps will be a fundamental step in any space program focusing on exploratory human missions to Mars and beyond. While microgravity and hypogravity pose serious challenges to the central nervous system22, most of the knowledge gaps about behavioral and psychological aspects are not related. Therefore, the research needed does not necessarily involve research during actual space missions. Naturalistically extreme environments on Earth (e.g., Antarctica) and, even better with respect to experimental control, simulation studies on the ground will, in many cases, provide appropriate environments for such research.

The research gaps highlighted in this report are in line with the ones identified by NASA23. The need to identify and validate countermeasures to promote health and performance, to define improved monitoring and assessment strategies, and to investigate and optimize team dynamics, for example, are shared concerns between this report and the NASAs Evidence Book for Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders23. Similar conclusions have been described in a recent NASA report24 about team research, highlighting the lack of data availability from the space context, and the need for further research on the topic, including studies in analogue environments and subject matter expert interviews.

Space travels magnify the challenges posed to a team of astronauts, such as confinement, and lack of external communication. However, there are also many situations that regular workers can face on the Earth and that includealthough to a lesser extent, some features astronauts can meet. For example, teams sometimes work in remote places, where communication is constrained. Therefore, more classical Earth-based activities can benefit from the transfer of this fundamental research.

Research conducted to fill knowledge gaps identified in the psychological phenomenon linked to space exploration may be applied to optimize the behavior, health, and performance of crewmembers in these extreme conditions. Once the processes that might contribute to the possible impairment have been identified, it could be envisaged to elaborate specific countermeasures that could help crewmembers to maintain and enhance their health and performance. For example, innovative and new technologies like virtual reality may be stimulated by this kind of challenge and be used to provide sensory stimulation and train cognitive and psychomotor performance of crew without there being a requirement to undertake live operations. New technologies (e.g., artificial intelligence) may also be used to reduce communication delays and, thus, mitigate isolation consequences.

As frequently observed with space research, many new devices, technology, or stress management techniques, may, once tested in space, be efficiently applied to adaptation and performance on Earth in specific conditions. For example, during the sanitary crisis period, some results concerning adaptation to isolation and confinement obtained in space or in polar environments have been useful for people during confinement periods. Some operational or mental strategies identified and validated in space may be transferred to life on Earth in isolation, confined, and extreme conditions. In many instances, this might be in settings that have societal important e.g., climate scientists, defense and security personnel, and anti-poaching wildlife rangers. Since constraints on the design of such techniques can be largely relaxed for Earth applications compared to Space applications, more flexibility is a promise for wider applications for the public. Finally, the space brand exerts great charm on the public and can be a channel for the promotion of societal and psychological improvements. For example, pro-environment behaviors studied and reported by the astronauts may be mimicked on Earth; well-being promotion strategies that are currently developed for space explorations, such as certain mind-body techniques, can also be implemented on the planet, following the examples from the space context. There are therefore several environments in which behavioral space research can have a positive impact on Earth research and society, including educational, organizational, professional, and recreational contexts. To facilitate these benefits, the communication strategy implemented by all the involved actors (national agencies, private companies, astronauts) should be mindful of these potential implications.

Table 1 reports the overarching categories representing the key open psychological research questions related to lunar and LDSE missions. Many of the open questions could be partly addressed in ground-based analogs. However, where the unique demands of missions beyond LEO and in deep space are relevant, ongoing research across various platforms will be needed. To effectively prepare for future LDSE missions, such as a Mars expedition, we suggest these questions should be addressed during a short to medium timeframe. There are certain unknowns that will only be elucidated over longer time periods and perhaps during a Mars mission itself. We recommend these timelines (3, 5, and 10 years) as a suggestion for addressing research gaps, although we are aware that research often requires longer times, so they do not necessarily correspond to expected research results.

The countermeasures below have all been used to mitigate the psychological demands of spaceflight. However, beyond a few initial studies, there has been a relatively limited attempt to empirically test the impact of their application (see Table 2). Before these methods can be considered evidence-based further space and/or analog-based research would be needed.

This white paper reported the results of a consensus statement among experts invited by ESA about the existing knowledge gaps on behavioral and performance topics of space research. This is particularly timely, as exploration missions are moving from low orbit to deep space destinations, with new psychological and team challenges forthcoming. While this is a non-systematic review of these research gaps, the working group consisted of experts in space psychology, who have been engaged in space research for ESA. Pre-, during-, and post-mission challenges and research gaps were considered, referring to promising countermeasures, either with preliminary evidence of their effectiveness, or to be developed and tested. The results summarize a set of challenges and questions to be addressed, but also some potential answers that have already been provided by the scientific community over decades of space psychology research. New empirical evidence is required to address most of these gaps, collected with specifically designed studies. It must be noted that to address the contextual constraints (e.g., number of active astronauts, or available analogue environment), some of these gaps can be tackled with thorough reviews or white papers incorporating all extant research findings. While space psychology is likely to have an important future, researchers should also be mindful of previously developed knowledge.

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Hear from the experts: using machine learning and AI to grow your … – SmartCompany

Simon Johnston, artificial intelligence and machine learning practice lead at AWS.

Across two events on February 9 and 16, SmartCompany and AWS dived into all things AI and machine learning. In his introduction speech, host Simon Crerar referenced the wide-eyed futurism of science fiction films like Blade Runner. According to keynote speaker Simon Johnston of AWS, that sci-fi future is already here. If you can take one thing away from todays presentation from myself it would be that the technology is good to go. Its not about the technology, in my opinion, its purely the application, the integration, the process.

Sharing their insights were speakers from AWS, Deloitte, Carsales and Nearmap. Lets take a closer look at a few of the key themes that emerged over the two days.

One big talking point of both sessions was AI/ML democratisation the idea that the technology is accessible to more businesses, budgets and skill levels than ever. Simon Johnston touched on how AWS uses a platform called Canvas for accessible education. If youre a business and youve got business analysts that know their part of the business really well, know their data and use cases but dont know machine learning, they shouldnt be prevented from developing these capabilities. Thats what Canvas allows.

Augustinus Nalwan, GM of AI, Data Science and Data Platform at Carsales, showed how that business is putting AI in the hands of just about every employee. Carsales began its AI journey in 2016, added a data science team in 2018 and, when the project brought success, the workload increased. The problem, according to Nalwan, is that hiring more data scientists and machine learning experts is extremely expensive. Instead, Carsales used existing Metaflow and Sagemaker ecosystems to automate workflows and upskill employees. At Carsales, 70% of AI models can be built using this platforms algorithm which does not require data scientists, said Nalwan. Anyone with good practice and guided by data scientists can perform this job. Carsales has even gone further, using AWS tools like Rekognition and Comprehend to allow those with no programming skills (such as marketing and finance teams) to train models such as spam message recognition.

Simon Johnston noted the recent, rapid growth of AI, talking about themes of data growth and increases in model sophistication. In the space of two years weve had a 1600x growth in the number of parameters. When you talk about ChatGPT and Open AI-type algorithms, theyre sitting around 175 billion parameters and itll continue to grow.

With such rapid growth has come both extreme complexity and, as Michael Bewley of aerial imaging company Nearmap has found, heavy processing requirements. Nearmap uses deep learning models to create incredibly detailed geospatial images which now total over 25 petabytes of data. Bewley says that, for businesses similarly leaning on ML, its wise to use cloud AI like AWS Sagemaker rather than taking everything on in-house. At some point theres a break point where local machines really start to suffer. Theyre great for freedom early on but then theres size and scale limitations. Cloud computing is really important. Probably the most important thing is, dont bring your legacy baggage with you on the transition to cloud.

In Melbourne, Simon Johnston asked the audience how many have or would be implementing machine learning into their business and about a quarter raised their hands. The question for those attendees, then, is how do we get started?

In the discussion panel, Alon Ellis of Deloitte pointed to a classic model of technological adoption, the Gartner hype cycle. Ellis says that, for businesses looking to effectively wield AI and ML tech, they need to avoid the distraction of hype and focus on practically applying these technologies. Its bringing it back to that business problem, getting really clear on how thats going to work, how youre going to alter the business going forward, what that might mean for different teams, different ways of working and capitalising on that so you can go from the hype through to the pragmatic, implemented outcome.

For AWS chief technologist Rada Stanic, jumping into AI and ML means getting your businesss data ready to go. The success of the project will rely on the quality and breadth of the data that you have. If the quality data is there and its ready, Ive seen proof of concepts happen in a couple of days, a week, to demonstrate that there is value in pursuing the project.

Learn about the 6 key trends driving Machine Learning innovation across Australian and New Zealand industries inclusive of improvements to Model Sophistication, Data Growth, ML Industrialisation, ML Powered Use Cases, Responsible AI and ML democratisation.

On-Demand Keynote Recording: View Here

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Top 10 Concepts and Technologies in Machine learning in 2023 – Analytics Insight

The top 10 concepts and technologies in machine learning in 2023 is a process of teaching computers to learn from data, without being explicitly programmed, Machine learning is a subject that is continuously evolving, with new ideas and technologies being created all the time. To remain ahead of the curve, data scientists should follow some of these sites to stay up to speed on the newest developments. This will assist you in comprehending how Technologies in machine learning can be used in practice and will provide you with ideas for possible applications in your own business or area of work.

Deep Neural Networks (DNN): Deep neural networks are a type of machine learning program that has existed since the 1950s. DNNs are capable of performing image identification, voice recognition, and natural language processing. They are made up of numerous hidden layers of neurons, each of which learns a representation of the incoming data. These models are then used to forecast the outgoing data.

Generative Adversarial Networks: GANs are a form of the generative model in which two competitive neural networks are trained against each other. One network attempts to create samples that appear genuine, while the other network determines whether those samples are derived from real or generated data. GANs have demonstrated tremendous success in the generation of pictures and videos. Gans are used to generating new data that resembles existing data but is entirely new. We can use GANs to generate new images from existing masterpieces created by renowned artists, also known as contemporary AI art. These artists are working with generative models to create masterpieces that have already been created.

Deep Learning: Deep learning is a type of machine learning that learns data models using numerous processing levels (typically hundreds). This enables computers to accomplish jobs that humans find challenging. Deep learning has been used in a wide range of applications, including computer vision, voice recognition, natural language processing, automation, and reinforcement learning.

COVID-19: Machine Learning and Artificial intelligence: Since January 2020, artificial intelligence (AI) has been used to identify COVID-19 instances in China. Wuhan University experts created this AI system. They developed a deep learning algorithm capable of analyzing data from phone calls, text messages, social media entries, and other sources.

Conversational AI or Conversational BOTS: It is a technology in which we talk to a chatbot and it processes the speech after detecting the voice input or text input and then enables a specific job or answer, such as

Machine Learning in Cybersecurity: Cybersecurity is the area in which it is ensured that an organization, or anyone for that matter, is secure from all security-related dangers on the Internet or in any network. An organization deals with a lot of complex data that needs to be protected from malicious dangers such as anyone attempting to breach into your computer or gain access to your data or unauthorized access, which is what cyber security is all about.

Machine learning and IoT: The different IOT procedures that we use in businesses are prone to errors; after all, it is a machine. If the system is not correctly designed or has a few flaws, it is destined to fail at some point. However, with machine learning, maintenance becomes much easier because all of the factors that may lead to a failure in the ID process are identified ahead of time and a new plan of action can be prepared for that matter, allowing companies to save a significant amount of money by lowering maintenance costs.

Augmented reality: The future of AI is augmented reality. Many real-life uses will benefit from the promise of augmented reality (AR).

Automated Machine Learning: Traditional machine learning model creation needed extensive subject expertise as well as time to create and compare hundreds of models. And it was more time-consuming, resource-intensive, and difficult. Automated machine learning aids in the rapid development of production-ready ML models.

Time-Series Forecasting: Forecasting is an essential component of any sort of company, whether it is sales, client desire, revenue, or inventory. When combined with automated ML, a suggested, high-quality time-series prediction can be obtained.

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Known Medicine is using machine learning to cure cancer – Utah Business

According to statistics from the National Cancer Institute, one in five men and one in six women in the United States are likely to die from cancer during their lifetime.

This year alone, the American Cancer Society estimates were in for a rough ride. In 2023, more than 1.9 million new cancer cases are expected to be diagnosed in the U.S., and nearly 610,000 are expected to die from cancer in the U.S. (about 1,670 deaths per day). Only heart disease outranks cancer, making cancer the second most common cause of death in the nation.

These chilling statistics make it clear why something must be done to save more lives from this hydra of a disease.

The founders of Known Medicine couldnt agree more. In 2020, the team launched the startup as a company dedicated to expediting the development of cancer treatments. As explained on its website, Known Medicines machine learning-based sensitivity assay, paired with -omics data, allows [the company] to identify predictive biomarkers and the most likely responders for any new drug.

Put in simpler terms, Katie-Rose Skelly, co-founder and CTO of Known Medicine, says the company is essentially trying to find out beforehand which patients will respond to which drug. This can help pharmaceutical companies design better clinical trials and improve the drugs chances for success.

To conduct its research, Known Medicine works closely with about 10 different cancer research centers that provide samples from patients who have authorized their use for research. The company breaks these tissue samples down into thousands of microtumors and doses each microtumor with a panel of over 100 drugs to see what works for each individual patient. The drugs include some that have already been approved, some that have failed previous clinical trials, some that pharmaceutical partners are interested in and some that Known Medicine might consider for in-licensing.

The Known Medicine team is currently working toward its first peer-reviewed journal publication, which will essentially provide proof of concept for the innovative work the company is doing. What well be able to show is that we can look at the patient that donated the tissue, see what drug they were given, see how they responded and identify whether that matches what we would have expected, Skelly says.

In other words, the initial publication will prove that Known Medicine can replicate patient responses and that its microtumors are a faithful representation of what the patients cells will do in the body.

From there, Known Medicines goal will be to aid in the drug development process. Skelly explains that most drugs currently fail in clinical trials, with just 3.4 percent of oncology drugs making it to market. This is often due to ineffective patient population selection.

If you try running a new anticancer drug on 100 patients, maybe 20 or 30 respond wellbetter than they would have to any other drug, Skelly says. But if you cant identify that 20 percent to 30 percent upfront, your drug is going to fail clinical trials.

Known Medicines platform will enable drug companies to identify trial candidates that are more likely to respond to their drugs. We can look at what kind of genetic signatures [patients] have, what kind of RNA expression levels they have, Skelly says. We can see if there is anything they can use to separate the patients who will respond from the patients who wont and only enroll the people who will respond in the clinical trials.

The concept for the company came as a collaboration between Skelly and Dr. Andrea Mazzocchi, who serves as the companys CEO. The co-founders initially met by chanceSkelly was working as a data scientist at Recursion, a Utah-based drug discovery digital biology company. Mazzocchi was pursuing her doctorate degree at Wake Forest and happened to be dating Skellys best friend at Recursion.

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Machine Vision: MERLIC 5.3 makes deep learning technologies … – Robotics Tomorrow

Object detection as a new deep learning feature in MERLIC 5.3 Expansion of easy-to-use functionalitiesAvailable from April 20, 2023

Munich, March 30, 2023 - With MVTec MERLIC, a software for machine vision, it is possible to both develop and solve complete machine vision applications even without any programming knowledge. On April 20, 2023, MVTec Software GmbH (www.mvtec.com), a leading international software manufacturer for machine vision, will launch the latest version 5.3 of its easy-to-use machine vision software MERLIC. For one thing, customers can look forward to a new deep learning feature. Secondly, user-friendliness has been further enhanced. These improvements are in line with the company's goal of offering powerful machine vision software for beginners as well.

New tool for deep-learning-based object detectionThe deep learning technology for object detection is now also available in MERLIC. The "Find Object" tool locates trained object classes and identifies them with a surrounding rectangle (bounding box). Touching or partially overlapping objects are also separated, which allows counting of objects. Labeling and training are possible without programming knowledge using the free MVTec Deep Learning Tool. The trained network can then be loaded into MERLIC and used with a single click.

Plug-in for Mitsubishi Electric MELSEC PLCs With MERLIC 5.3 it is possible to communicate directly with the widespread Mitsubishi Electric PLC via the MC/SLMP protocol. This is made possible by a newly developed plug-in included in MERLIC. This plug-in supports the Mitsubishi Electric iQR, iQF, L- and Q-series. MERLIC thus offers significant added value for customers working with Mitsubishi Electric PLCs.

Training functionalities in the MERLIC FrontendWith the new version 5.3 it is now possible to use training functionalities in the MERLIC Frontend even during runtime. For example, new matching models or code reading parameters can be trained. This means that the end customer can also perform training for other products directly on the production line in the MERLIC Frontend. This significantly increases flexibility and application possibilities.

Tool grouping for clearer workflowsMERLIC helps solve complex machine vision applications, even without programming knowledge. The visual Tool Flow supports this. To maintain an overview even with complex applications, it is now possible to group several tools into a virtual tool inside the Tool Flow.

Concise startup dialog for easy access to functions and machine vision applicationsUsability is one of MERLIC's unique selling points. To further strengthen this ease-of-use approach, a start dialog with thumbnails was integrated into the MERLIC Creator. This allows users to get an overview of their most recently opened MVApps. All standard examples are clearly displayed. Especially for new users, these offer an orientation to reliably create their own applications. In addition, helpful introductory material as well as the documentation can be easily accessed via quick links.

About MVTec Software GmbHMVTec is a leading manufacturer of standard software for machine vision. MVTec products are used in all demanding areas of imaging: semiconductor industry, surface inspection, automatic optical inspection systems, quality control, metrology, as well as medicine and surveillance. By providing modern technologies such as 3D vision, deep learning, and embedded vision, software by MVTec also enables new automation solutions for the Industrial Internet of Things aka Industry 4.0. With locations in Germany, the USA, and China, as well as an established network of international distributors, MVTec is represented in more than 35 countries worldwide. http://www.mvtec.com

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Machine learning combined with multispectral infrared imaging to guide cancer surgery – Medical Xpress

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

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Scientists employ multispectral emission profiles instead of the conventional fluorescence intensity profile to train machine learning models for accurately identifying tumor boundaries. Credit: Waterhouse et al, DOI: 10.1117/1.JBO.28.9.094804

Surgical tumor removal remains one of the most common procedures during cancer treatment, with about 45% of cancer patients undergoing this surgery at some point. Thanks to recent progress in imaging and biochemical technologies, surgeons are now better able to tell tumors apart from healthy tissue. Specifically, this is enabled by a technique called "fluorescence-guided surgery" (FGS).

In FGS, the patient's tissue is stained with a dye that emits infrared light when irradiated with a special light source. The dye preferentially binds to the surface of tumor cells, so that its light-wave emissions provide information on the location and extent of the tumor. In most FGS-based approaches, the absolute intensity of the infrared emissions is used as the main criterion for discerning the pixels corresponding to tumors. However, it turns out that the intensity is sensitive to lighting conditions, the camera setup, the amount of dye used, and the time elapsed after staining. As a result, the intensity-based classification is prone to erroneous interpretation.

But what if we could instead use an intensity-independent approach to classify healthy and tumor cells? A recent study published in the Journal of Biomedical Optics and led by Dale J. Waterhouse from University College London, U.K., has now proposed such an approach. The research team has developed a new technique that combines machine learning with short-wave infrared (SWIR) fluorescence imaging to detect precise boundaries of tumors.

Their method relies on capturing multispectral SWIR images of the dyed tissue rather than simply measuring the total intensity over one particular wavelength. Put simply, the team sequentially placed six different wavelength frequency (color) filters in front of their SWIR optical system and registered six measurements for each pixel. This allowed the researchers to create the spectral profiles for each type of pixel (background, healthy, or tumor). Next, they trained seven machine learning models to identify these profiles accurately in multispectral SWIR images.

The researchers trained and validated the models in vivo, using SWIR images with a lab model for an aggressive type of neuroblastoma. They also compared different normalization approaches aimed at making the classification of pixels independent of the absolute intensity such that it was governed by the pixel's spectral profile only.

Out of the seven tested models, the best performing model achieved a remarkable per-pixel classification accuracy of 97.5% (the accuracies for tumor, healthy, and background pixels were 97.1%, 93.5%, and 99.2%, respectively). Moreover, thanks to the normalization of the spectral profiles, the results of the model were far more robust against changes in imaging conditions. This is a particularly desirable feature for clinical applications since the ideal conditions under which new imaging technologies are usually tested are not representative of the real-world clinical environment.

Based on their findings, the team has high hopes for the proposed methodology. They anticipate that a pilot study on its implementation in human patients could help revolutionize the field of FGS. Additionally, multispectral FGS could be extended beyond the scope of the present study. For example, it could be used to remove surgical or background lights from images, remove unwanted reflections, and provide noninvasive ways for measuring lipid content and oxygen saturation. Moreover, multispectral systems enable the use of multiple fluorescent dyes with different emission characteristics simultaneously, since the signals from each dye can be untangled from the total measurements based on their spectral profile. These multiple dyes can be used to target multiple aspects of disease, providing surgeons with even greater information.

Future studies will surely unlock the full potential of multispectral FGS, opening doors to more effective surgical procedures for treating cancer and other diseases.

More information: Dale J. Waterhouse et al, Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning, Journal of Biomedical Optics (2023). DOI: 10.1117/1.JBO.28.9.094804

Journal information: Journal of Biomedical Optics

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Who’s schooling Congress on AI? – Axios

Congress gets a lot of flak for not being savvy on tech issues. But as artificial intelligence advancements heat up, some members are working hard to educate themselves.

Why it matters: AI has the potential to transform our society, and as lawmakers grapple with how to regulate the technology, companies are scrambling to inform their opinions.

Behind the scenes: Generative AI is heavy on lawmakers' minds on both sides of the aisle. Here's some of what we're hearing has been happening:

CEOs on the Hill: Sen. Mark Warner has met with OpenAI CEO Sam Altman and Scale AI CEO Alexandr Wang along with Tristan Harris, executive director of the Center for Humane Technology, per spokesperson Rachel Cohen.

What they're saying: We believe AI may represent the most consequential technology advancement of our lifetime. There is enormous interest in the opportunity ahead. And responsibilities for those of us who develop this technology. Were using this time to educate, be curious, and learn, a Microsoft spokesperson told Axios.

Threat level: Christopher Padilla, IBM's government and regulatory affairs vice president, said he worries there will be a techlash" and emphasized that consumer-facing AI, such as ChatGPT, brings about fundamentally different risks from the AI work his own company is doing.

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Will the Raspberry Pi 5 CPU Have Built-in Machine Learning? – MUO – MakeUseOf

Raspberry Pi has been at the forefront of single-board computers (SBCs) for quite some time. However, nearly four years after the launch of Raspberry Pi 4, a new model is on the horizon.

Previous Raspberry Pi iterations generally involved faster processors, more RAM, and with the Pi 4, improved IO. However, a lot of Pis are used for AI (artificial intelligence) and ML (machine learning) purposes, leading to a lot of speculation from DIY enthusiasts about the Raspberry Pi 5's built-in machine learning capabilities.

Whether the Raspberry Pi 5 gets built-in machine learning capabilities depends a lot on what CPU the board is based around. Raspberry Pi co-founder Eben Upton teased the future of custom Pi silicon back at the tinyML Summit 2021. Since then, an imminent Raspberry Pi 5 release with massive improvements to ML is looking very likely.

Up until Raspberry Pi 4, the development team had been using ARM's Cortex processors. However, with the release of the Raspberry Pi Pico in 2021 came the RP2040, the company's first in-house SoC (system-on-chip). While it doesn't have the same power as the Raspberry Pi Zero 2 W, one of the cheapest SBCs on the market, it does provide microcontroller capabilities similar to that of an Arduino.

The Raspberry Pi 2, Pi 3, and Pi 4 have used ARM's Cortex-A7, Cortex-A53, and Cortex-A72 processors respectively. These have increased the Pi's processing capabilities over each generation, giving each progressive Pi more ML prowess. So does that mean we'll see built-in machine learning on the Raspberry Pi 5's CPU?

While there's no official word on what processor will power the Pi 5, you can be pretty sure it'll be the most ML-capable SBC in the Raspberry Pi lineup and will most likely have built-in ML support. The company's Application Specific Integrated Circuit (ASIC) team has been working since on the next iteration, which seems to be focused on lightweight accelerators for ultra-low power ML applications.

Upton's talk at tinyML Summit 2021 suggests that it might come in the form of lightweight accelerators likely running four to eight multiply-accumulates (MACs) per clock cycle. The company has also worked with ArduCam on the ArduCam Pico4ML, which brings together ML, a camera, microphones, and a screen into a Pico-sized package.

While all the details about the Raspberry Pi 5 aren't yet confirmed, if Raspberry Pi sticks to its trend of incrementally upgrading its boards, the upcoming SBC can be a rather useful board that'll check a lot of boxes for ML enthusiasts and developers looking for cheap hardware for their ML projects.

The Raspberry Pi 5 could come with built-in machine learning support, which opens up a plethora of opportunities for just about anyone to build their own ML applications with hardware that's finally able to keep up with the technology without breaking the bank.

You can already run anything from a large language model (LLM) to a Minecraft server on existing Raspberry Pis. As the SBC becomes more capable (and accessible), the possibilities of what you can do with a single credit-card-sized computer will also increase.

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Application of machine learning in predicting non-alcoholic fatty liver … – Nature.com

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2023 CANADA GAIRDNER AWARDS RECOGNIZE WORLD-RENOWNED SCIENTISTS FOR TRANSFORMATIVE CONTRIBUTIONS TO RESEARCH IMPACTING HUMAN HEALTH – Yahoo Finance

TORONTO, March 30, 2023 /CNW/ - The Gairdner Foundation is pleased to announce the 2023 Canada Gairdner Award laureates, recognizing some of the world's most significant biomedical and global health research and discoveries.

Gairdner Les Prix Canada Gairdner Awards (CNW Group/Gairdner Foundation)

"Congratulations to all the 2023 Canada Gairdner Award recipients! The ground-breaking work of this year's laureates has resulted in innovative, globally accessible tools to fight diseases and improve our well-being. The work of two Canadian researchers Dr. Christopher Mushquash and Dr. Gelareh Zadeh especially stands out. Dr. Zadeh's research to better understand brain tumours and Dr. Mushquash's research on Indigenous-led mental health and substance use will be transformative in improving the quality of life of so many here in Canada and around the world."

- The Honourable Jean-Yves Duclos, Minister of Health

"Our government knows that in order to create a better future for all, we need to foster the research that will improve human health around the globe. It is why I want to congratulate the 2023 Canada Gairdner Awards recipients showcasing international excellence in science and research. I'm proud to highlight the two Canadians awarded for their world-class achievements including improving our understanding of brain tumour treatments and providing culturally appropriate mental health services for First Nations."

- The Honourable Franois-Philippe Champagne, Minister of Innovation, Science and Industry

"I wish to congratulate this year's award recipients for their groundbreaking research and the profound contributions that their discoveries will have. It is specifically noteworthy to see Dr Christopher Mushquash as a recipient of the 2023 Canada Gairdner Momentum Award. Chris's contributions to our understanding of mental health amongst Indigenous communities are already profoundly affecting the needs of Indigenous peoples. As a member of CIHR's Institute for Indigenous Peoples Health Advisory Board, Chris has provided the same critical thinking to advancing Indigenous research in Canada."

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- Dr. Michael J. Strong, President of the Canadian Institutes of Health Research

2023 Canada Gairdner International AwardThe five 2023 Canada Gairdner International Award laureates are recognized for seminal discoveries or contributions to biomedical science:

Demis Hassabis, CBE FRS FREng FRSA

Founder & CEO, DeepMind; Founder & CEO, Isomorphic Labs

John Jumper, PhD, MPhil

AlphaFold Lead and Senior Staff Research Scientist, DeepMind

Awarded "For developing AlphaFold, which has been heralded as an AI-based solution to the 50-year grand challenge of protein structure prediction and has culminated in the release of the most accurate and complete picture of the structure of the proteome, with enormous potential to accelerate biological and medical research."

The Work:

Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into from their amino acid sequence is known as the 'protein structure prediction problem' and has stood as a grand challenge in biology for the past 50 years. With their team at DeepMind, Demis Hassabis and John Jumper have developed the artificial intelligence (AI) system AlphaFold, which today can predict the structure of a protein, at scale and in minutes, down to atomic accuracy.

Hassabis had long suspected that protein structure prediction might be the perfect problem for AI to tackle. He was the project leader on the AlphaFold project from its inception in 2016 to its conclusion, and recruited Jumper to the project in late 2017. In 2018 the team was expanded, with Jumper becoming the new research lead, with the goal to re-design the system with a completely new architecture into what would become AlphaFold2. Together they co-supervised the subsequent projects to create the most accurate and complete picture of the human proteome and predict the structures of nearly all known proteins, and released an open-access database to make all of AlphaFold's predictions freely available to the scientific community.

In a major scientific advance, in 2020 AlphaFold2 was recognized as a solution to the 50-year grand challenge of protein structure prediction by the organizers of the biennial Critical Assessment of Protein Structure Prediction (CASP).

The Impact:

AlphaFold has culminated in the creation of structure predictions for over 200 million proteins - nearly every protein known to science - which DeepMind have made freely available through the AlphaFold Protein Structure Database (AlphaFold DB).

Designed in partnership with European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), the AlphaFold DB serves as a 'google search' for protein structures, providing researchers with instant access to predicted models of the proteins they're studying, which has the potential to accelerate every field of study in biology.

Since launch, the AlphaFold DB has already been accessed by 1 million researchers and users in 190 countries. The program dramatically reduces the time scientists typically spend determining protein structure and demonstrates the impact AI can have on scientific discovery and its potential to accelerate progress in some of the most fundamental fields that explain and shape our world. Further, this research will help to better our understanding of disease, and accelerate the development of new targeted drugs.

Bonnie L. Bassler, PhDSquibb Professor and Chair, Department of Molecular Biology, Princeton University; Howard Hughes Medical Institute Investigator

E Peter Greenberg, PhDEugene and Martha Nester Endowed Professor of Microbiology, Department of Microbiology and Molecular & Cellular Biology Program, University of Washington School of Medicine

Michael R. Silverman, PhDEmeritus Investigator, The Agouron Institute; Emeritus Adjunct Professor, Scripps Institution of Oceanography

Awarded "For their discoveries of how bacteria communicate with each other and surrounding non-bacterial cells, providing a new paradigm for how microbes behave and yielding novel avenues for therapeutics against infectious diseases."

The Work:

Bacteria are found everywhere from soil to water to the human body. Despite their simple single-cell forms, bacteria are sophisticated organisms that are remarkably adaptable to changing conditions. Bacteria play crucial roles in medicine, both as members of the microbiome, increasingly understood to contribute to human health, and as major causes of disease. The discovery of how bacteria communicate with one another, coined "quorum sensing" by Dr. Greenberg and his colleagues, is foundational. Drs. Bassler, Greenberg and Silverman are awarded for a combined body of work that spawned an unexpected field in microbiology and are also recognized for their individual discoveries that underpin its implications for all of biology, human health and disease.

Quorum-sensing studies began with an obscure bioluminescent marine bacterium called Vibrio fischeri. In the 1970s, Dr. J. Woodland Hastings and colleagues described a signaling chemical of then unknown structure that stimulated collective glowing after the Vibrio fischeri bacteria had reached a particular population density. This finding was one of the first clues that bacteria could communicate using chemical "words", but it lay dormant for a decade until Silverman, exploiting the power of genetics, identified the genes involved in this signaling pathway and characterized their functions. Silverman's elegant analyses of the role each component played provided the world's first quorum-sensing circuit and the foundation for thousands of similar circuits identified later.

Widely thought to be a function specific to Vibrio fischeri, this phenomenon did not initially gain much traction. Indeed, the idea that bacteria could communicate was deemed highly improbable. But Greenberg was intrigued and trained with Hastings before he later independently further characterized the genes Silverman had identified, and discovered a similar quorum sensing signal that controlled virulence in the pathogenic bacterium Pseudomonas aeruginosa. The term "quorum sensing" was born as he demonstrated that this phenomenon was indeed bacterial communication, and not isolated to Vibrio fischeri. He not only showed that other bacteria exhibited quorum sensing, but he also discovered nearly all major steps in its mechanism.

It was Bassler who then brought Hastings' and Silverman's findings to an unprecedented level by showing that quorum sensing is not an exception but the rule in the bacterial world. What's more, the principal reason bacteria are so successful is that they rarely act alone. Quorum sensing turns out to be essential to many aspects of bacterial virulence and antimicrobial resistance. Initially with Silverman then later independently, Bassler discovered entirely new types of quorum-sensing signal molecules, mechanisms of detection and response to those molecules, and the profound influence quorum sensing exerts over the behaviour of many bacterial species. Moving to the human health front, Bassler demonstrated that it was possible to hijack quorum-sensing mechanisms to control virulence in globally important pathogens. She also made the stunning discovery that quorum-sensing communication is not restricted to bacteria. She found that bacteria can communicate across species and, moreover, quorum sensing underlies bacterial interactions with viruses and other types of cells. For example, she showed that human gut cells use quorum sensing to communicate with resident microbiome bacteria to defend the body against invading pathogens.

The Impact:

A new field of microbiology has emerged and the discoveries of Bassler, Greenberg and Silverman are at the heart of it, shaping and defining the field we now know as quorum sensing. They have independently and collaboratively revolutionized the way we think about bacteria, completely overturning the paradigm that bacteria act independently of each other.

The originality and elegance of their work led to novel and unexpected discoveries in the field time again, laying the groundwork for a deeper understanding of the microbial world with clinical ramifications that are being realized today. For example, Greenberg's work showed promise in targeting difficult infections such as those associated with cystic fibrosis and Bassler's small-molecule therapies are much less vulnerable to development of antimicrobial resistance than are traditional antibiotics because her strategies target the quorum-sensing mechanism rather than bacterial growth. With the recent recognition that microbes are foundational to the vitality of all corners of the biosphere, understanding their biology is crucial. Bassler's work in particular has provided vital mechanistic underpinnings that foster a growing understanding of the human microbiome, the niches in which different organisms thrive, and how behavior and competition within these niches is affected during disease.

All of this serves as pivotal in understanding how the microbiome influences our health and wellbeing and provides insight into novel ways to harness microbial communities to promote health and prevent disease. Bassler, Greenberg and Silverman have undoubtedly paved the way for unprecedented new possibilities for biological solutions to the world's most pressing problems in health, food, energy, and the environment.

2023 John Dirks Canada Gairdner Global Health Award

The 2023 John Dirks Canada Gairdner Global Health Award laureate is recognized for outstanding achievements in global health research:

Jos Belizn, MD, PhD

Senior Scientist, Department of Research in Maternal and Child Health, Institute for Clinical Effectiveness and Health Policy (IECS) Argentina; Superior Researcher at the National Scientific and Technical Research Council of Argentina (CONICET); Researcher, Bone Biology Laboratory, School of Medicine, University of Rosario, Argentina.

Awarded "For the development of innovative, evidence-based and low-cost global interventions in maternal and child health during the perinatal period that improve wellbeing and care during pregnancy, reduce morbidity and mortality, and promote equity in vulnerable populations."

The Work:

Dr. Jos Belizn is a trailblazer in the field of maternal and child health research in Latin America and internationally, focused on the perinatal period and its relevance to community health and the life cycle. His work spans basic research to international clinical studies, demonstrating the full cycle of scientific effort and leading to innovative, evidence-based and low-cost interventions. These interventions promote equity by improving maternal and child health in vulnerable populations. Through his work within these communities, he educates and empowers pregnant people, and witnesses real life health problems, which informs his outstanding scientific contributions.

Dr. Belizn discovered the connection between calcium intake and a decrease in the risk of hypertensive disorders of pregnancy (HDP) by observing Guatemalan Mayan women, where the prevalence of HDP was low, and their traditional cooking methods. Taking his observations further, he led numerous animal and human studies to confirm the association and basic studies to explain the mechanisms. He then planned and implemented international clinical trials in underdeveloped and developed country settings, which led to policy formulations at the highest international level and grassroots efforts to improve adherence to these guidelines.

This is just one of many examples of his extensive and comprehensive work to improve the wellbeing and care of people during pregnancy and interventions to reduce severe maternal morbidity and mortality. Dr. Belizn was the first to document, as well as design, test and implement landmark interventions addressing the issue of unnecessary increased use of Caesarean section. This is a complex and multifactorial challenge affecting not only high-income but also low- and middle-income countries, where associated risks can extend many years beyond delivery and are higher in those with limited access to comprehensive obstetric care. His research has also led to the decrease in unnecessary routine episiotomy worldwide, including in Canada and the US.

The Impact:

Dr. Belizn has undoubtedly improved maternal and childbirth outcomes and made a difference in the lives of pregnant people and their children. His discovery of the importance of calcium intake alone has significant potential as three billion people lack access to adequate calcium intake worldwide. Reaching the scientific community, health systems decision-makers, international organizations, practitioners, health-care providers and local communities, he has overturned practices, introduced more effective and equitable practices, and spearheaded global policies that will contribute to more equitable societies. His work has informed various World Health Organization recommendations, which have been adopted by many countries around the world. Dr. Belizn goes above and beyond, ensuring that these best practices are known and used at the community level.

As an international expert, Belizn's innovation and rigorous research from basic science to implementation has had a profound impact on global health and motivated researchers' careers and actions worldwide over the last five decades. His work has sparked and will continue to lead to important developments in this sector as he demonstrates the importance of representation from low- and middle-income countries in global health research.

2023 Canada Gairdner Momentum Award

The 2023 Canada Gairdner Momentum Award laureates are mid-career investigators recognized for exceptional scientific research contributions with continued potential for impact on human health.

Christopher Mushquash, Ph.D., C.Psych

Professor, Department of Psychology, Lakehead University; Psychologist, Dilico Anishinabek Family Care; Vice President Research, Thunder Bay Regional Health Sciences Centre; Chief Scientist, Thunder Bay Regional Health Research Institute

Awarded "For Indigenous-led mental health and substance use research that leads to culturally and contextually appropriate services for Indigenous children, adolescents, and adults."

The Work:

Dr. Christopher Mushquash brings together his clinical experience as a psychologist and his community-based participatory approach to research to meet community needs and improve systems and services that make a difference in people's lives. His innovative work focuses on Indigenous mental health and substance use through evidence-based practices that align with First Nations values. This approach ensures his research and its outcomes are culturally and contextually appropriate for people in First Nations, as well as those in rural and northern communities. Through large team collaborations and partnerships with communities, government and academia, Dr. Mushquash addresses various aspects of mental health for Indigenous communities, such as mental health, substance use, trauma, and general mental wellness. The overarching goals of his research are rooted in the four interconnected directions and include identifying culturally and contextually appropriate targets of intervention, developing methods of measuring community outcomes; developing and testing of interventions that incorporate culture-based knowledge with scientific methods; and the sharing of knowledge among Indigenous and academic communities, clinicians, and policymakers. These themes come together to form a holistic framework to improve not only systems and services but also research involving Indigenous communities. By putting the communities at the forefront of his work, Dr. Mushquash demonstrates the importance of understanding unique contexts and issues experienced by individuals in Indigenous communities. He has effectively shifted the relationship between communities and researchers, enabling more meaningful and relevant research and advancing the understanding of mental health in Indigenous communities. Systems and services are thus better equipped to address the needs of Indigenous, rural and northern communities in a culturally- and contextually-appropriate manner.

The Impact:

Dr. Mushquash champions culturally and contextually appropriate mental health and substance use services for Indigenous communities. His high-calibre work has improved the lives of many Indigenous communities and influenced national mental health and addiction understandings as he brings together western and Indigenous methodologies. His team conducted the first Canadian study of adverse childhood experiences in First Nations adults seeking residential treatment for substance use difficulties. The outcomes enhanced the understanding of the nature of developmental and intergenerational trauma in First Nations people and improved clinical care for those with substance use difficulties. His research has also upended conventional understandingsof mental health in Indigenous families and established best practices for engaging Indigenous people in research. Furthermore, his research has directly influenced federal funding policy in remote First Nations communities. As a leader in his field, Dr. Mushquash has advanced mental health across Canada, garnering various awards, honours and appointments in recognition of his research and clinical expertise. His devotion to the profession and Indigenous mental health can be seen in the impact of his work in changing Canadian policy, educating professionals working with First Nations people, and, more importantly, bettering the quality of life and care of many Indigenous youth and communities.

Gelareh Zadeh, MD, PhD, FRCS(C), FAANS

Professor and Neurosurgery Division Chair, Dan Family Chair in Neurosurgery, Wilkins Family Chair in Brain Tumor Research, Department of Surgery, Temerty Faculty of Medicine, University of Toronto; Head, Division of Neurosurgery, Toronto Western Hospital, Sprott Department of Surgery, University Health Network; Co-Director, Krembil Brain Institute, University Health Network; Senior Scientist, Princess Margaret Cancer Centre, University Health Network

Awarded "For advancing the molecular and genomic understanding of brain tumours, leading to better ways of discriminating, classifying and managing brain tumour subtypes with potential to transform the clinical care of the disease."

The Work:

Dr. Gelareh Zadeh is a neurosurgeon and senior scientist who combines her in-depth clinical knowledge of brain cancer with clinical and translational research to improve the diagnosis and management of adult brain tumours. Dr. Zadeh's research program applies advanced genomic and epigenomic profiling to further our understanding of the molecular regulators of brain tumours and to develop tools that can refine biomarkers of diagnosis to predict treatment responses and ultimately, improve patient outcomes.

Dr. Zadeh's research focuses on advancing knowledge of brain tumours through integration of multiple platforms of genomic analysis. This includes her research incorporating the largest-ever data analysis of meningiomasthe most common type of brain tumour, which has limited treatment options. She co-founded and leads the International Consortium on Meningiomas (ICOM), which provides researchers around the world with access to meningioma samples and data sets, as well as research expertise and collaborations. ICOM also helps to raise awareness of the importance of research funding into this disease. Dr. Zadeh's discoveries in this field have led to new classification criteria that are biologically and clinically relevant, with the potential to outperform the current standard classification system developed by the World Health Organization. Specifically, her research has shown that meningiomas can be classified into four molecular groups, which reveals biological insights into how the cancer behaves. Using molecular features that reflect tumour behavior, the new classification criteria more accurately predicts cancer recurrence. Dr. Zadeh's lab has also produced a comprehensive body of work on neuronal tumours, including schwannomas and peripheral nerve tumours. By performing the first integrated molecular analysis of schwannomas, her group established the genomic and epigenomic road map for sporadic and neurofibromatosis type 2 (NF2)-related schwannomas and identified a novel fusion protein that can be used for diagnostic, prognostic and therapeutic benefit. Similarly, Dr. Zadeh's research has shown that the transformation of benign neuronal tumours to malignant cancers occurs via two independent molecular pathways, both of which can be therapeutically targeted. Another key contribution of her work has demonstrated the utility of plasma-based biomarkers for diagnosis, discrimination and determination of response to treatment, for a wide variety of brain tumours.

The Impact:

Dr. Zadeh exemplifies an extraordinary commitment to advancing our understanding of brain tumour biology to improve patient outcomes. Her team has made significant strides in understanding how molecular features influence tumour management and has identified novel approaches to reduce the negative side effects of brain tumour treatments. Additionally, her team has identified plasma biomarkers that can help to diagnose intracranial tumours, predict treatment response and detect early recurrence, as well as potential drugs to treat malignant brain tumours. Her work is having a considerable impact in the diagnosis and clinical management of brain tumours and is giving hope to individuals affected by brain cancer.

About the Gairdner Foundation:

The Gairdner Foundation, established in 1957, is dedicated to fulfilling James A. Gairdner's vision to recognize major research contributions to the treatment of disease and alleviation of human suffering. Through annual prestigious Canada Gairdner Awards, the Gairdner Foundation celebrates the world's most creative and accomplished researchers whose work is improving the health and wellbeing of people around the world. Since its inception, 410 awards have been bestowed on laureates from over 40 countries, and of those awardees, 96 have gone on to receive Nobel Prizes.

The Gairdner Foundation believes in coming together to openly discuss science to better engage the public, understand the problems we face, and work together to find solutions. Since its founding, a number of outreach events and programs have been developed with the goal of inspiring the next generation of scientific innovators and fostering an informed society.

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Cision

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