Imaging technologies, which extend human vision capabilities, are such a natural part of our current everyday experience that we often take them for granted. However, the ability to capture images with new kinds of sensing devices that allow us to see more than what can be seen by the unaided eye has a relatively recent history. In the early 1800s, the first ever photograph was taken: an unassuming picture that required days of exposure to obtain a very grainy image. In the late 1800s, a photograph was used for the first time to see the movement of a running horse that the human eye alone could not see. In the following years, photography played a pivotal role in recording human history, ranging from influencing the creation of the first national parks in the United States all the way to documenting NASA's Apollo 11 mission to put a man on the Moon.
We are deeply concerned by the way in which our friend and colleague Professor Francisco Ayala has been forced to resign from the University of California, Irvine (UCI), after being accused of sexual harassment ("Prominent geneticist out at UC Irvine after harassment finding," M. Wadman, News, 29 June, https://scim.ag/AyalaResignation). The charges that have been raised against him have had appalling consequences. Those of us who are well acquainted with Professor Ayala know that he is an honorable person, who throughout his career has treated his friends, co-workers, and students in a respectful, egalitarian way. His lifelong commitment to teaching, research, and outreach on biological evolution has won him worldwide recognition. He has been a generous benefactor to the University of California and throughout his fruitful career has opened new fields of biological research, promoted mutual respect and independence between evolutionary studies and religious perspectives, played a key role in several major scientific organizations, and helped many Spanish-speaking female scholars and Hispanic students, in particular, both in the United States and throughout the world.
Next week, scientists working on artificial intelligence (AI) and games will be watching the latest human-machine matchup. But instead of a single pensive player squaring off against a computer, a team of five top video game players will be furiously casting magic spells and lobbing (virtual) fireballs at a team of five AIs called OpenAI Five. They'll be playing the real-time strategy game Dota 2 at The International in Vancouver, Canada, an annual e-sports tournament that draws professional gamers who compete for millions of dollars. In 1997, IBM's Deep Blue AI bested chess champion Garry Kasparov. In 2016, DeepMind's AlphaGo AI beat Lee Sedol, a world master, at the traditional Chinese board game Go.
This database provided a comprehensive record of opioids dispensed at California pharmacies to civilian, non–U.S. Department of Veterans Affairs, and non-institutionalized patients treated by clinicians in our sample. Descriptive and inferential statistics were carried out with the Stata software (6). The cmp command in Stata was used to compute a difference-in-differences estimator within a mixed-model two-part linear regression analysis (7). The difference-in-differences estimator compared the average change over time in milligram morphine equivalents (MMEs) dispensed for prescribers in the intervention group with the average change over time for prescribers in the control group.
Artificial intelligence (AI) explores different architectures that strive to exploit the powerful information-processing capability of the brain. Artificial neural networks use connected artificial components (mimicking the function of neurons and synapses) to process information and perform complex tasks such as written and spoken language and image recognition from vast datasets. All the networks require training, however, which usually has been done by computer, and the process can be very time-consuming. Hughes et al. developed an optical method in which the training process is done with laser light propagating through a complex network of paths patterned into an optical chip. The results bring the prospect of an optical chip–based AI platform operating at the speed of light a step closer.
Disregard for how the research could undermine the tribe's interests led to a lawsuit and out-of-court settlement. Science is a social enterprise. Many scientific programs interact with a wide range of communities and stakeholders to secure various types of access and permission, to seek cooperation and collaboration for scientific studies, to fulfill regulatory and ethical requirements, and to try to shape research strategies and to improve the translation of their findings into policy or practice. But these interactions are motivated disproportionately by the interests and goals of the scientific programs and less by the need to elicit and understand their implications for stakeholders. However, there is increasing recognition that substantive community and stakeholder engagement (CSE) can improve the performance, and even make or break the success, of some science programs by providing a means of navigating, and responding to, the complex social, economic, cultural, and political settings in which science programs are conducted.
Ten years ago, Nobel laureate Sydney Brenner remarked, "We don't have to search for a model organism anymore. Because we are the model organisms" (1). Indeed, over the past decade, we have deepened our understanding not only of how the genomic blueprint for human biology manifests physical and chemical characteristics (phenotype), but also of how traits can change in response to the environment. A better grasp of the dynamic relationship between genes and the environment may truly sharpen our ability to determine disease risk and response to therapy. A collection of human phenotypic data, and its integration with "omic" information (genomic, proteomic, transcriptomic, epigenomic, microbiomic, and metabolomic, among others), along with remote-sensing data, could provide extraordinary opportunities for discovery.
We report a simple, robust, and nondegrading protocol that achieves 40-plex protein staining in the same biological sample using off-the-shelf antibodies called iterative indirect immunofluorescence imaging (4i). In conjunction with high-throughput automated microscopy and computer vision, 4i allows highly reproducible multiplexed measurements from surface areas of several mm2 subsampled by pixels of 165 nm by 165 nm. This approach simultaneously captures functionally relevant properties that emerge at the cell population, cellular, and intracellular level. We developed a data-driven computer vision approach that generates multiplexed protein maps (MPMs). MPMs comprehensively quantify intracellular protein composition with high spatial detail in large numbers of single cells.
Two Parkinson's patients receive deep brain stimulation (DBS) in their subthalamic nuclei. Despite accurate electrode placement, one patient is able to stand up and walk effortlessly around the room while the other breaks down into uncontrolled sobbing that only stops once the stimulator is turned off. This paradox exposes one of the major roadblocks in developing therapies for brain disorders: the elaborate and diffuse nature of neural circuits. Physically proximal neurons are often engaged in functionally different pathways; whereas modulation of one pathway might be therapeutic, modulation of those surrounding it may produce debilitating side effects. The problem with high-amplitude electrical stimulation, as applied during DBS, is that it affects not only the activity of neurons around the electrode, but also the activity of neurons whose long extensions happen to pass by the electrode.
A multitude of brain disorders have debilitating impacts on the quality of life of a large patient populace, accounting for 30% of the global burden of disease (1). Most patients with brain disorders are unamenable to any form of treatment when first- and second-line interventions are ineffective (2, 3). Neuromodulation technologies can help millions of patients who suffer from such brain disorders. Deep brain stimulation (DBS) has proven highly effective in treating Parkinson's disease and obsessive-compulsive disorder (4) and shows great potential for other conditions, such as depression (5). However, being a surgical procedure, the deployment of DBS is limited by the potential for complications (6).