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Painless and long-term sustained delivery of ocular drugs is enabled through the innovation of silicon nanoneedles. 

This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit model consisting of three interconnected components: LGN and two input layers (Layer 4Ca and Layer 6) of the primary visual cortex V1, covering several hundred hypercolumns. Anatomical structures are incorporated and physiological parameters from realistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consis- tent with experimental data. These results are obtained through parameter selection and model analysis. The effects of nonlinear recurrent excitation and inhibition are also dis- cussed. A feature that distinguishes our model from previous modeling work on surround suppression is that we have tried to reproduce realistic lengthscales that are crucial for quantitative comparison with data. Due to its size and the large number of unknown parame- ters, the model is computationally challenging. We demonstrate a strategy that involves first locating baseline values for relevant parameters using a linear model, followed by the intro- duction of nonlinearities where needed. We find such a methodology effective, and propose it as a possibility in the modeling of complex biological systems. 

Spatial and temporal trends of remotely sensed sea-ice cover, sea surface temperatures, chlorophyll-a concentration and primary production in the Baffin Bay, Davis Strait and Labrador Sea were analyzed for the 1998–2017 period. We found spatial variability in the trends of these cryospheric, biologic and oceanographic phenomena. For example, in the northern Baffin Bay, we observed decreases in annual sea-ice persistence, yet increases along the Labrador Sea-ice edge during winter, with the latter having significant correlations with broader atmospheric patterns. In general, we observed increases in summer sea surface temperatures across the study region, except a small area of cooling along the southern Greenlandic coast. We also found significant negative trends in April chlorophyll-a and primary production followed by significant positive trends for both biological phenomena in May, owing to anomalously high values in 2014 and 2015. Notably, we found a significant positive correlation between days of monthly sea ice presence in April with May primary production quantities. Finally, we found a significant positive trend in total annual primary production over the study period. This novel finding suggests an important relationship between the timing of breakup along the sea-ice edge and peaks in biological production. 

Neuroscience models come in a wide range of scales and specificity, from mean-field rate models to large-scale networks of spiking neurons. There are potential trade-offs between simplicity and realism, versatility and computational speed. This paper is about large-scale cortical network models, and the question we address is one of scalability: would scaling down cell density impact a network’s ability to reproduce cortical dynamics and function? We investigated this problem using a previously constructed realistic model of the monkey visual cortex that is true to size. Reducing cell density gradually up to 50-fold, we studied changes in model behavior. Size reduction without parameter adjustment was catastrophic. Surprisingly, relatively minor compensation in synaptic weights guided by a theoretical algo- rithm restored mean firing rates and basic function such as orientation selectivity to models 10-20 times smaller than the real cortex. Not all was normal in the reduced model cortices: intracellular dynamics acquired a character different from that of real neurons, and while the ability to relay feedforward inputs remained intact, reduced models showed signs of defi- ciency in functions that required dynamical interaction among cortical neurons. These findings are not confined to models of the visual cortex, and modelers should be aware of potential issues that accompany size reduction. Broader implications of this study include the importance of homeostatic maintenance of firing rates, and the functional consequences of feedforward versus recurrent dynamics, ideas that may shed light on other species and on systems suffering cell loss. 

Drawing from social capital theory, this study examines the extent to which stable versus new friendship patterns affect low income students’ educational aspirations in urban and rural high schools. Using whole school sociometric data (744 high school students over a two-year period), this study applies a social influence model to determine the effects of stable and newly established friendships on conformity regarding college-going aspirations. Findings indicate that urban students have more new friends and their educational aspirations increased, conforming to those of their newly established friends. In contrast, rural students have more stable friendships than the urban students and their educational aspirations conformed to those of their stable friends. This work shows that rural students tend not to change their school network size or nominations. However, urban students are more willing to include new students in their school networks which have a positive effect on raising their educational aspirations. 

Elementary processes associated with ionization of liquid water provide a framework for understanding radiation-matter interactions in chemistry and biology. Although numerous studies have been conducted on the dynamics of the hydrated electron, its partner arising from ionization of liquid water, H2O+, remains elusive. We used tunable femtosecond soft x-ray pulses from an x-ray free electron laser to reveal the dynamics of the valence hole created by strong-field ionization and to track the primary proton transfer reaction giving rise to the formation of OH. The isolated resonance associated with the valence hole (H2O+/OH) enabled straightforward detection. Molecular dynamics simulations revealed that the x-ray spectra are sensitive to structural dynamics at the ionization site. We found signatures of hydrated-electron dynamics in the x-ray spectrum.