With public and academic attention increasingly focused on the new role of machine learning in the health information economy, an unusual and no-longer-esoteric category of vulnerabilities in machine-learning systems could prove important. These vulnerabilities allow a small, carefully designed change in how inputs are presented to a system to completely alter its output, causing it to confidently arrive at manifestly wrong conclusions. These advanced techniques to subvert otherwise-reliable machine-learning systems--so-called adversarial attacks--have, to date, been of interest primarily to computer science researchers (1). However, the landscape of often-competing interests within health care, and billions of dollars at stake in systems' outputs, implies considerable problems. We outline motivations that various players in the health care system may have to use adversarial attacks and begin a discussion of what to do about them.
Film is a universal language of modern societies. Larger-than-life images, stories, ideas, and characters portrayed in films can speak across the globe. This makes science and technology--which have shaped the modern world but remain little understood and poorly integrated into mainstream culture--a rich subject for film and a goldmine for filmmakers. From the mad scientist films of the '20s and '30s to the postnuclear dystopias of the '50s; and from the ecological disaster flics of the '70s and '80s to the space adventures of recent years, films have periodically reflected society's hopes and fears about science. But we can do better when it comes to dramatizing the great, ongoing human enterprise to understand and enhance the world around and inside us.
Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two.
Cancer treatment decisions are increasingly based on the genomic profile of the patient's tumor, a strategy called "precision oncology." Over the past few years, a growing number of clinical trials and case reports have provided evidence that precision oncology is an effective approach for at least some children with cancer. Here, we review key factors influencing pediatric drug development in the era of precision oncology. We describe an emerging regulatory framework that is accelerating the pace of clinical trials in children as well as design challenges that are specific to trials that involve young cancer patients. Last, we discuss new drug development approaches for pediatric cancers whose growth relies on proteins that are difficult to target therapeutically, such as transcription factors. The landscape of genomic alterations in cancers that arise in children, adolescents, and young adults is slowly becoming clearer as a result of dedicated pediatric cancer genome-sequencing projects conducted over the past decade. Of particular note are two recent studies that produced a comprehensive picture of the genomic features that characterize many of the more common pediatric cancers (1, 2). Two major themes have emerged.
The past decade has witnessed a major increase in our understanding of the genetic underpinnings of childhood cancer. Genomic sequencing studies have highlighted key differences between pediatric and adult cancers. Whereas many adult cancers are characterized by a high number of somatic mutations, pediatric cancers typically have few somatic mutations but a higher prevalence of germline alterations in cancer predisposition genes. Also noteworthy is the remarkable heterogeneity in the types of genetic alterations that likely drive the growth of pediatric cancers, including copy number alterations, gene fusions, enhancer hijacking events, and chromoplexy. Because most studies have genetically profiled pediatric cancers only at diagnosis, the mechanisms underlying tumor progression, therapy resistance, and metastasis remain poorly understood. We discuss evidence that points to a need for more integrative approaches aimed at identifying driver events in pediatric cancers at both diagnosis and relapse. We also provide an overview of key aspects of germline predisposition for cancer in this age group. Approximately 300,000 children from infancy to age 14 are diagnosed with cancer worldwide every year (1). Some of the cancer types affecting the pediatric population are also seen in adolescents and young adults (AYA), but it has become increasingly clear that cancers in the latter age group have unique biological characteristics that can affect prognosis and therapy (2).
Adaptive behaviors ranging from self-assembly to self-healing showcase the ability of such systems to sense and adapt to dynamic environments based on signaling between living cells. This signaling takes on many forms--biochemical, mechanical, and electrical--and uncovering it has become as much the purview of regenerative medicine as of fundamental biology. We cannot reverse-engineer native tissues if we do not understand the fundamental design rules and principles that govern their assembly from the bottom up (1). Movement is fundamental to many living systems and driven primarily by skeletal muscle in human bodies. Disease or damage that limits the functionality of skeletal muscle severely affects human health, mobility, and quality of life.
Transmission of signals from external stimuli into the interior of cells relies on complex signaling pathways that must be efficient and precise--every facet of life depends on this. Cells use biomolecular interactions in signaling pathways to make crucial decisions about whether to transmit signals or filter them out as noise. How is this possible, given that biomolecular interactions are inherently stochastic processes? The activation of membrane receptors typically involves the phosphorylation of their intracellular domain and subsequent recruitment of adaptor molecules and downstream effectors. The effectors amplify the original signal by catalyzing a reaction.
How do we remember our experiences? The mental skill of bringing previously encountered people, events, and objects to mind is intuitive, but how neural circuits enable this episodic memory retrieval remains a fundamental question in neuroscience. On page 975 of this issue, Vaz et al. (1) use intracranial electrophysiological recordings in humans to identify a putative mechanism involved in memory retrieval: synchronized occurrence of high-frequency oscillations across brain regions. Their findings highlight the importance of dynamic interactions between brain areas in mediating complex cognitive processes and suggest a biomarker for pinpointing neural populations involved in different memories.
We used ultrahigh-speed synchrotron x-ray imaging to quantify the phenomenon of vapor depressions (also known as keyholes) during laser melting of metals as practiced in additive manufacturing. Although expected from welding and inferred from postmortem cross sections of fusion zones, the direct visualization of the keyhole morphology and dynamics with high-energy x-rays shows that (i) keyholes are present across the range of power and scanning velocity used in laser powder bed fusion; (ii) there is a well-defined threshold from conduction mode to keyhole based on laser power density; and (iii) the transition follows the sequence of vaporization, depression of the liquid surface, instability, and then deep keyhole formation. These and other aspects provide a physical basis for three-dimensional printing in laser powder bed machines.