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How your phone can predict depression and lead to personalized treatment

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Study used data from cell phone apps and watches, brain activity and lifestyle factors to generate predictions of depression; results could lead to individualized treatment plans for mental health

According to the National Alliance on Mental Illness and the World Health Organization, depression affects 16 million Americans and 322 million people worldwide. Emerging evidence suggests that the COVID-19 pandemic is further exacerbating the prevalence of depression in the general population. With this trajectory, it is evident that more effective strategies are needed for therapeutics that address this critical public health issue.

In a recent study, publishing in the June 9, 2021 online edition of Nature Translational Psychiatry, researchers at University of California San Diego School of Medicine used a combination of modalities, such as measuring brain function, cognition and lifestyle factors, to generate individualized predictions of depression.

The machine learning and personalized approach took into account several factors related to an individual’s subjective symptoms, such as sleep, exercise, diet, stress, cognitive performance and brain activity.

“There are different underlying reasons and causes for depression,” said Jyoti Mishra, PhD, senior author of the study, director of NEATLabs and assistant professor in the Department of Psychiatry at UC San Diego School of Medicine. “Simply put, current health care standards are mostly just asking people how they feel and then writing a prescription for medication. Those first-line treatments have been shown to be only mild to moderately effective in large trials.

“Depression is a multifaceted illness, and we need to approach it with personalized treatment whether that be therapy with a mental health professional, more exercise or a combination of approaches.”

The one-month study collected data from 14 participants with depression using smartphone applications and wearables (like smart watches) to measure mood and lifestyle variables of sleep, exercise, diet and stress, and paired these with cognitive evaluations and electroencephalography, using electrodes on the scalp to record brain activity.

The goal was not to make any comparisons across individuals, but to model the predictors of each person’s daily fluctuations in depressed mood.

The researchers developed a new machine-learning pipeline to systematically identify distinct predictors of low mood in each individual.

As an example, exercise and daily caffeine intake emerged as strong predictors of mood for one participant, but for another, it was sleep and stress that were more predictive, while in a third subject, the top predictors were brain function and cognitive responses to rewards.

“We should not be approaching mental health as one size fits all. Patients will benefit by having more direct and quantified insight onto how specific behaviors may be feeding their depression. Clinicians can leverage this data to understand how their patients might be feeling and better integrate medical and behavioral approaches for improving and sustaining mental health,” said Mishra.

“Our study shows that we can use the technology and tools that are readily available, like cell phone apps, to collect information from individuals with or at risk for depression, without significant burden to them, and then harness that information to design personalized treatment plans.”

Mishra said next steps include examining if the personalized treatment plans guided by the data and machine learning are effective.

“Our findings could have broader implications than depression. Anyone seeking greater well-being could benefit from insights quantified from their own data. If I don’t know what is wrong, how do I know how to feel better?”

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Co-authors include: Rutvik Shah, Gillian Grennan, MariamZafar-Khan, Fahad Alim, Sujit Dey, all with UC San Diego; and Dhakshin Ramanathan with UC San Diego and the VA San Diego Medical Center.

Disclosure: Shah, Dey and Mishra have an Invention Disclosure filed for “Personalized Machine Learning of Depressed Mood using Wearables.”

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Source: https://bioengineer.org/how-your-phone-can-predict-depression-and-lead-to-personalized-treatment/

Bioengineer

Invention uses machine-learned human emotions to ‘drive’ autonomous vehicles

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FAU College of Engineering and Computer Science receives US utility patent for ‘adaptive mood control in semi or fully autonomous vehicles’

Americans have one of the highest levels of fear in the world when it comes to technology related to robotic systems and self-driving cars. Addressing these concerns is paramount if the technology hopes to move forward.

A researcher from Florida Atlantic University’s College of Engineering and Computer Science has developed new technology for autonomous systems that is responsive to human emotions based on machine-learned human moods. His solution, “Adaptive Mood Control in Semi or Fully Autonomous Vehicles,” has earned a very competitive utility patent from the United States Patent and Trademark Office for FAU.

Adaptive Mood Control provides a convenient, pleasant, and more importantly, trustworthy experience for humans who interact with autonomous vehicles. The technology can be used in a wide range of autonomous systems, including self-driving cars, autonomous military vehicles, autonomous airplanes or helicopters, and even social robots.

“The uniqueness of this invention is that the operational modes and parameters related to perceived emotion are exchanged with adjacent vehicles for achieving objectives of the adaptive mood control module in the semi or fully autonomous vehicle in a cooperative driving context,” said Mehrdad Nojoumian, Ph.D., inventor, and an associate professor in the Department of Computer and Electrical Engineering and Computer Science and director of the Privacy, Security and Trust in Autonomy Lab. “Human-AI/autonomy interaction is at the center of attention by academia and industries. More specifically, trust between humans and AI/autonomous technologies plays a critical role in this domain, because it will directly affect the social acceptability of these modern technologies.”

The patent, titled “Adaptive Mood Control in Semi or Fully Autonomous Vehicles,” uses non-intrusive sensory solutions in semi or fully autonomous vehicles to perceive the mood of the drivers and passengers. Information is collected based on facial expressions, sensors within the handles/seats and thermal cameras among other monitoring devices. Additionally, the adaptive mood control system contains real-time machine-learning mechanisms that can continue to learn the driver’s and passengers’ moods over time. The results are then sent to the autonomous vehicle’s software system allowing the vehicle to respond to perceived emotions by choosing an appropriate mode of operations such as normal, cautious or alert driving mode.

“One of the major issues with the technology of fully or semi-autonomous vehicles is that they may not be able to accurately predict the behavior of other self-driving and human-driving vehicles. This predication is essential to properly navigate autonomous vehicles on roads,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “Professor Nojoumian’s innovative and cutting-edge technology circumvents this problem by using machine learning algorithms to learn patterns of behaviors of people riding in these vehicles.”

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About FAU’s College of Engineering and Computer Science:

The FAU College of Engineering and Computer Science is internationally recognized for cutting edge research and education in the areas of computer science and artificial intelligence (AI), computer engineering, electrical engineering, bioengineering, civil, environmental and geomatics engineering, mechanical engineering, and ocean engineering. Research conducted by the faculty and their teams expose students to technology innovations that push the current state-of-the art of the disciplines. The College research efforts are supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Department of Defense (DOD), the Department of Transportation (DOT), the Department of Education (DOEd), the State of Florida, and industry. The FAU College of Engineering and Computer Science offers degrees with a modern twist that bear specializations in areas of national priority such as AI, cybersecurity, internet-of-things, transportation and supply chain management, and data science. New degree programs include Masters of Science in AI (first in Florida), Masters of Science in Data Science and Analytics, and the new Professional Masters of Science degree in computer science for working professionals. For more information about the College, please visit eng.fau.edu.

About Florida Atlantic University:

Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, the University serves more than 30,000 undergraduate and graduate students across six campuses located along the southeast Florida coast. In recent years, the University has doubled its research expenditures and outpaced its peers in student achievement rates. Through the coexistence of access and excellence, FAU embodies an innovative model where traditional achievement gaps vanish. FAU is designated a Hispanic-serving institution, ranked as a top public university by U.S. News & World Report and a High Research Activity institution by the Carnegie Foundation for the Advancement of Teaching. For more information, visit http://www.fau.edu.

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Source: https://bioengineer.org/invention-uses-machine-learned-human-emotions-to-drive-autonomous-vehicles/

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Semiconductor technology mitigates fire risk in electric vehicle batteries

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Convergence of semiconductor physics and electrochemistry leads to effective inhibition of dendrite formation using semiconducting passivation layers

Despite rapid development of electric vehicles (EVs), the safety of the lithium-ion (Li-ion) batteries remains a concern as they are as a fire and explosion risk. Among the various approaches to tackle this issue, Korean researchers have used semiconductor technology to improve the safety of Li-ion batteries. A research team from the Korea Institute of Science and Technology (KIST) led by Dr. Joong Kee Lee of the Center for Energy Storage Research has succeeded in inhibiting the growth of dendrites, crystals with multiple branches that cause EV battery fires by forming protective semiconducting passivation layers on the surface of Li electrodes.

When Li-ion batteries are charged, Li ions are transported to the anode (the negative electrode) and are deposited on the surface as Li metal; at this point, tree-like dendrites are formed. These Li dendrites are responsible for the uncontrollable volumetric fluctuations and leads to reactions between the solid electrode and the liquid electrolyte, which causes a fire. Unsurprisingly, this severely degrades battery performance.

To prevent dendrite formation, the research team exposed fullerene (C60), a highly electronic conductive semiconductor material, to plasma, resulting in the formation of semiconducting passivation carbonaceous layers between the Li electrode and the electrolyte. The semiconducting passivation carbonaceous layers allow Li-ions to pass through while blocking electrons due to generation of Schottky barrier, and by preventing electrons and ions from interacting on the electrode surface and inside, they stops the formation of Li crystals and the consequent growth of dendrites.

*fullerene : a particular physical form of carbon in which 60 carbon atoms are connected by single and double bonds in a pentagonal shape to form a soccer ball-like shape

The stability of the electrodes with the semiconducting passivation carbonaceous layers was tested using Li/Li symmetric cells in extreme electrochemical environments where typical Li electrodes remain stable for up to 20 charge/discharge cycles. The newly developed electrodes showed significantly enhanced stability, with Li dendrite growth suppressed for up to 1,200 cycles. Moreover, using a lithium cobalt oxide (LiCoO2) cathode in addition to the developed electrode, approximately 81% of the initial battery capacity was maintained after 500 cycles, representing an improvement of approximately 60% over conventional Li electrodes.

Lead researcher Dr. Joong Kee Lee said, “The effective suppression of dendrite growth on Li electrodes is instrumental for improving battery safety. The technology for developing highly safe Li-metal electrodes proposed in this study provides a blueprint for the development of next-generation batteries that do not pose a fire risk.” As Dr. Lee explains, his team’s next goal is improving the commercial viability of this technology, “We aim to make the fabrication of the semiconducting passivation carbonaceous layers more cost-effective by substituting fullerene with less expensive materials.”

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This research was carried out as part of a KIST’s institutional R&D project and a mid-career researcher project. It also received funding as an outstanding new overseas research project from the National Research Foundation of Korea with the support of the Ministry of Science and ICT (MSIT). The results of this study are published in the latest issue of ‘ACS Energy Letters‘ (IF: 19.003, Top 1.852% in JCR), a highly respected international journal in the field of materials science.

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Source: https://bioengineer.org/semiconductor-technology-mitigates-fire-risk-in-electric-vehicle-batteries/

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Drone footage reveals social secrets of killer whales

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Killer whales have complex social structures including close “friendships”, according to a new study that used drones to film the animals.

The findings show that killer whales spend more time interacting with certain individuals in their pod, and tend to favour those of the same sex and similar age.

The study, led by the University of Exeter and the Center for Whale Research (CWR), also found that the whales become less socially connected as they get older.

“Until now, research on killer whale social networks has relied on seeing the whales when they surface, and recording which whales are together,” said lead author Dr Michael Weiss, of the University of Exeter.

“However, because resident killer whales stay in the social groups into which they’re born, how closely related whales are seemed to be the only thing that explained their social structure.

“Looking down into the water from a drone allowed us to see details such as contact between individual whales.

“Our findings show that, even within these tight-knit groups, whales prefer to interact with specific individuals.

“It’s like when your mom takes you to a party as a kid – you didn’t choose the party, but you can still choose who to hang out with once you’re there.”

Patterns of physical contact – one of the social interactions the study measured – suggest that younger whales and females play a central social role in the group. The older the whale, the less central they became.

The new research built on more than four decades of data collected by CWR on southern resident killer whales, a critically endangered population in the Pacific Ocean.

“This study would not have been possible without the amazing work done by CWR,” said Professor Darren Croft, of Exeter’s Centre for Research in Animal Behaviour.

“By adding drones to our toolkit, we have been able to dive into the social lives of these animals as never before.

“We were amazed to see how much contact there is between whales – how tactile they are.

“In many species, including humans, physical contact tends to be a soothing, stress-relieving activity that reinforces social connection.

“We also examined occasions when whales surfaced together – as acting in unison is a sign of social ties in many species.

“We found fascinating parallels between the behaviour of whales and other mammals, and we are excited about the next stages of this research.”

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The start of this drone project – including the purchase of one of the drones used in this study – was made possible by a crowd-funding campaign supported by members of the public, including University of Exeter alumni.

Results from the new study are based on 651 minutes of video filmed over ten days.

The study’s use of drones was conducted under research permits issued by the US National Marine Fisheries Service, and all pilots were licensed under the US Federal Aviation Administration.

The research team included the universities of York and Washington, and the Institute of Biophysics, and the study was partly funded by the Natural Environment Research Council (NERC).

The study, published in the journal Proceedings of the Royal Society B, is entitled: “Age and sex influence social interactions, but not associations, within a killer whale pod.”

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Source: https://bioengineer.org/drone-footage-reveals-social-secrets-of-killer-whales/

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PSMA-targeted radiotracer pinpoints metastatic prostate cancer across anatomic regions

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Credit: Images courtesy of Lantheus Holdings, Inc., Billerica, MA.

Reston, VA (Embargoed until 3:00 p.m. EDT, Tuesday, June 15, 2021)–A phase III clinical trial has validated the effectiveness of the prostate-specific membrane antigen (PSMA)-targeted radiotracer 18F-DCFPyL in detecting and localizing recurrent prostate cancer. Approved by the U.S. Food and Drug Administration last month, the radiotracer identified metastatic lesions with high positive predictive values regardless of anatomic region, adding to the evidence that PSMA-targeted radiotracers are the most sensitive and accurate agents for imaging prostate cancer. This study was presented at the Society of Nuclear Medicine and Molecular Imaging (SNMMI) 2021 Annual Meeting.

Prostate cancer patients have high levels of PSMA expression, which makes PSMA an effective target for imaging the disease. In previous studies, the novel positron emission tomography (PET) imaging agent 18F-DCFPyL was found to bind selectively with high affinity to PSMA. To demonstrate the diagnostic performance of 18F-DCFPyL for regulatory approval, a prospective, multicenter study was conducted in 14 sites across the United States and Canada.

The study sought to determine the positive predictive value (the probability that patients with a positive screening test actually have the disease) and detection rate of 18F-DCFPyL PET/computed tomography (CT) by anatomic region, specifically the prostate/prostate bed, pelvic lymph nodes, and regions outside the pelvis. Study participants included men who had rising prostate-specific antigen (PSA) levels after local therapy as well as negative or equivocal conventional imaging results.

Patients were imaged with 18F-DCFPyL PET/CT, then imaged again after 60 days to verify suspected lesions using a composite “standard of truth,” which consisted of histopathology, correlative imaging findings and PSA response. Comparing findings between the 18F-DCFPyL imaging and the “standard of truth,” the positive predictive value and detection rate were measured.

18F-DCFPyL-PET/CT was found to successfully detect and pinpoint metastatic lesions with high positive predictive value, regardless of their location in the body, in men with biochemically recurrent prostate cancer who had negative or equivocal baseline imaging. Higher positive predictive values were observed in extra-pelvic lymph nodes and bone compared to soft tissue regions.

With the recent approval of 18F-DCFPyL (now referred to as piflufolastat F-18) by the FDA, the impact of this research may be realized in the very near future. As these agents become more widely available, patients with newly diagnosed, recurrent, and metastatic prostate cancer may have new therapeutic approaches available to them. The results of the study will be presented at the SNMMI meeting by Steven Rowe, MD, PhD, associate professor of radiology and radiological science at Johns Hopkins University in Baltimore, Maryland.

Abstract 123. “A Phase 3 study of 18F-DCFPyL-PET/CT in Patients with Biochemically Recurrent Prostate Cancer (CONDOR): An Analysis of Disease Detection Rate and Positive Predictive Value (PPV) by Anatomic Region,” Steven Rowe and Michael Gorin, Johns Hopkins, Baltimore, Maryland; Lawrence Saperstein, Yale School of Medicine, New Haven, Connecticut; Frederic Pouliot, Departement de Chirurgie, Division d’Urologie, University of Quebec, Quebec, Canada; David Josephson, Tower Urology, Cedars Sinai Medical Center, Los Angeles, California; Peter Carroll, UCSF, San Francisco, California; Jeffrey Wong, City of Hope, Sierra Madre, California; Austin Pantel, University of Pennsylvania Health System, Philadelphia, Pennsylvania; Morand Piert, University of Michigan, Ann Arbor, Michigan; Kenneth Gage, Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Steve Cho, University of Wisconsin-Madison, Madison, Wisconsin; Andrei Iagaru, Stanford University, Stanford, California; Janet Pollard, University of Iowa Hospital, Iowa City, Iowa; Vivien Wong, Jessica Jensen and Nancy Stambler, Progenics Pharmaceuticals, Inc., New York, New York; Michael Morris, Memorial Sloan-Kettering Cancer Center, New York, New York; and Barry Siegel, Washington University School of Medicine, St. Louis, Missouri.

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All 2021 SNMMI Annual Meeting abstracts can be found online at https://jnm.snmjournals.org/content/62/supplement_1.

About the Society of Nuclear Medicine and Molecular Imaging

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and medical organization dedicated to advancing nuclear medicine and molecular imaging, vital elements of precision medicine that allow diagnosis and treatment to be tailored to individual patients in order to achieve the best possible outcomes.

SNMMI’s members set the standard for molecular imaging and nuclear medicine practice by creating guidelines, sharing information through journals and meetings and leading advocacy on key issues that affect molecular imaging and therapy research and practice. For more information, visit http://www.snmmi.org.

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Source: https://bioengineer.org/psma-targeted-radiotracer-pinpoints-metastatic-prostate-cancer-across-anatomic-regions/

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