The mammalian neocortex is one of the most intricate entities found in nature, both in terms of structure and function. It is the brain region responsible for the execution of high-order functions, including sensory perception, motor control, cognition, and speech. Its development is equally complex because it requires that millions to billions (depending on the species) of neurons assemble in distinct layers and connect with exquisite precision to perform complicated information processing operations. During embryonic development, formation of the cerebral cortex involves the migration of excitatory neurons generated in the ventricular zone toward the cortical plate, where they establish their final position in six well-defined horizontal layers consisting of different types of neurons and architecture. Along this migratory phase, developing neurons undergo a morphological transition from multipolar shape to bipolar morphology.
Nanosize pores can turn semimetallic graphene into a semiconductor and, from being impermeable, into the most efficient molecular-sieve membrane. However, scaling the pores down to the nanometer, while fulfilling the tight structural constraints imposed by applications, represents an enormous challenge for present top-down strategies. Here we report a bottom-up method to synthesize nanoporous graphene comprising an ordered array of pores separated by ribbons, which can be tuned down to the 1-nanometer range. The size, density, morphology, and chemical composition of the pores are defined with atomic precision by the design of the molecular precursors. Our electronic characterization further reveals a highly anisotropic electronic structure, where orthogonal one-dimensional electronic bands with an energy gap of 1 electron volt coexist with confined pore states, making the nanoporous graphene a highly versatile semiconductor for simultaneous sieving and electrical sensing of molecular species.
Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.
The recent development of single-cell genomic techniques allows us to profile gene expression at the single-cell level easily, although many of these methods have limited throughput. Rosenberg et al. describe a strategy called split-pool ligation-based transcriptome sequencing, or SPLiT-seq, which uses combinatorial barcoding to profile single-cell transcriptomes without requiring the physical isolation of each cell. The authors used their method to profile 100,000 single-cell transcriptomes from mouse brains and spinal cords at 2 and 11 days after birth. Comparisons with in situ hybridization data on RNA expression from Allen Institute atlases linked these transcriptomes with spatial mapping, from which developmental lineages could be identified.
For more than half a century, U.S. government officials have considered disaster scenarios, such as the consequences of a nuclear bomb going off in Washington, D.C. Only now, instead of following fixed story lines and predictions assembled ahead of time, they are using computers to play what-if with an entire artificial society: an advanced type of computer simulation called an agent-based model. Today's version of the nuclear attack model includes a digital simulation of every building in the area affected by the bomb, as well as every road, power line, hospital, and even cell tower. The model includes weather data to simulate the fallout plume. And the scenario is peopled with some 730,000 agents.
Lithium (Li) metal electrodes are not deployable in rechargeable batteries because electrochemical plating and stripping invariably leads to growth of dendrites that reduce coulombic efficiency and eventually short the battery. It is generally accepted that the dendrite problem is exacerbated at high current densities. Here, we report a regime for dendrite evolution in which the reverse is true. In our experiments, we found that when the plating and stripping current density is raised above 9 milliamperes per square centimeter, there is substantial self-heating of the dendrites, which triggers extensive surface migration of Li. We show that repeated doses of high-current-density healing treatment enables the safe cycling of Li-sulfur batteries with high coulombic efficiency.
What are our memories made of? Plato suggested imagining a block of wax in our soul, where perceptions and thoughts leave impressions that we can remember as long as they have not been erased. This historic metaphor captures the transience of some memories and the stability of others, and it illustrates the brain's plasticity. The mechanisms of memory formation and retention remain a key question in neuroscience. Groundbreaking work on the rodent hippocampus (a network in the temporal lobe) revealed that certain neurons form transiently stable representations of places (1).
In science news around the world, South Sudan announces it has eliminated Guinea worm disease despite the civil war that is ravaging the country. Uber suspends its research on autonomous vehicles (AVs) after a pedestrian is struck by one of its AVs and dies; a person was sitting behind the AV's steering wheel, but the car was said to be in self-driving mode. The Intergovernmental Panel on Climate Change establishes a task force to consider ways to improve gender equity within the organization, such as recruiting more female scientists to serve as authors of its authoritative reports on climate science. U.K. researchers develop the first wearable magnetoencephalography brain scanner, which can detect the weak magnetic fields emitted by communicating neurons in real time, offering an alternative to large, bulky scanning machines that require research subjects to lie inside and remain still.
Helping students develop skills in both critical thinking and scientific reasoning is fundamental to science education. However, the relationship between these two constructs remains largely unknown. Dowd et al. examined this issue by investigating how students' critical thinking skills related to scientific reasoning in the context of undergraduate thesis writing. The authors used the BioTAP rubric to assess scientific reasoning and the California Critical Thinking Skills Test to assess critical thinking. Results support the role of inference in scientific reasoning in writing, while also revealing other aspects of scientific reasoning (epistemological considerations and writing conventions) not related to critical thinking.
In April, two semiautonomous drones, developed by Saildrone, a marine tech startup based in Alameda, California, in close collaboration with the National Oceanic and Atmospheric Administration in Washington, D.C., are set to return from an 8-month tour of the Pacific Ocean. This the first scientific test for the drones, which are powered only by the wind and sun, in the Pacific Ocean. The voyage is an important step in showing that such drones, carrying 15 different sensors, could help replace an aging and expensive array of buoys that are the main way scientists sniff out signs of climate-disrupting El Niño events. If successful, scientists envision fleets of similar drones spreading across the ocean, inviting thoughts of what it could be like to do oceanography without a ship.