Autonomous Electron Tomography Reconstruction with Machine Learning

Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, compressed sensing tomography creates overly smoothed 3D reconstructions. Adding momentum into the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that compressed sensing is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based compressed sensing greatly reduces the required compute time$-$an 80% reduction was observed for the 3D reconstruction of SrTiO$_3$ nanocubes. Automated parameter selection is necessary for large scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials.Comment: 8 pages, 4 figure

Assessing PM(sub 2.5) Exposures with High Spatiotemporal Resolution Across the Continental United States

A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index and meteoroidal fields, are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed good model performance with total R2 of 0.84 on the left out monitors. Regional R2 could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily prediction of PM2.5 at 1 km 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short term and the long term

Serotoninergic modulation of sensory transmission to brainstem reticulospinal cells

Sensory inputs are subjected to modulation by central neural networks involved in controlling movements. It has been shown that serotonin (5‐HT) modulates sensory transmission. This study examines in lampreys the effects of 5‐HT on sensory transmission to brainstem reticulospinal (RS) neurons and the distribution of 5‐HT cells that innervate RS cells. Cells were recorded intracellularly in the in vitro isolated brainstem of larval lampreys. Trigeminal nerve stimulation elicited disynaptic excitatory responses in RS neurons, and bath application of 5‐HT reduced the response amplitude with maximum effect at 10 μm. Local ejection of 5‐HT either onto the RS cells or onto the relay cells decreased sensory‐evoked excitatory postsynaptic potentials (EPSPs) in RS cells. The monosynaptic EPSPs elicited from stimulation of the relay cells were also reduced by 5‐HT. The reduction was maintained after blocking either N‐methyl‐d‐aspartate (NMDA) or α‐amino‐3‐hydroxy‐5‐methylisoxazole‐4‐propionic acid (AMPA) receptors. The local ejection of glutamate over RS cells elicited excitatory responses that were only slightly depressed by 5‐HT. In addition, 5‐HT increased the threshold for eliciting sustained depolarizations in response to trigeminal nerve stimulation but did not prevent them. Combined 5‐HT immunofluorescence with axonal tracing revealed that the 5‐HT innervation of RS neurons of the middle rhombencephalic reticular nucleus comes mainly from neurons in the isthmic region, but also from neurons located in the pretectum and caudal rhombencephalon. Our results indicate that 5‐HT modulates sensory transmission to lamprey brainstem RS cells

Correlated Rounding of Multiple Uniform Matroids and Multi-Label Classification

We introduce correlated randomized dependent rounding where, given multiple points y^1,...,y^n in some polytope Psubseteq [0,1]^k, the goal is to simultaneously round each y^i to some integral z^i in P while preserving both marginal values and expected distances between the points. In addition to being a natural question in its own right, the correlated randomized dependent rounding problem is motivated by multi-label classification applications that arise in machine learning, e.g., classification of web pages, semantic tagging of images, and functional genomics. The results of this work can be summarized as follows: (1) we present an algorithm for solving the correlated randomized dependent rounding problem in uniform matroids while losing only a factor of O(log{k}) in the distances (k is the size of the ground set); (2) we introduce a novel multi-label classification problem, the metric multi-labeling problem, which captures the above applications. We present a (true) O(log{k})-approximation for the general case of metric multi-labeling and a tight 2-approximation for the special case where there is no limit on the number of labels that can be assigned to an object

Developing a global indicator for Aichi Target 1 by merging online data sources to measure biodiversity awareness and engagement

Due to the importance of public support in fostering positive outcomes for biodiversity, Aichi Biodiversity Target 1 aims to increase public awareness of the value of biodiversity and actions that help to conserve it. However, indicators for this critical target have historically relied on public-opinion surveys that are time-consuming, geographically restricted, and expensive. Here, we present an alternative approach based on tracking the use of biodiversity-related keywords in 31 different languages in online newspapers, social media, and internet searches to monitor Aichi Target 1 in real-time, at a global scale, and at relatively low cost. By implementing the indicator, we show global patterns associated with spatio-temporal variability in public engagement with biodiversity topics, such as a clear drop in conversations around weekends and biodiversity-related topic congruence across culturally similar countries. Highly divergent scores across platforms for each country highlight the importance of sourcing information from multiple data streams. The data behind this global indicator is visualized and publicly available at Biodiversity Engagement Indicator.com and can be used by countries party to the Convention on Biological Diversity (CBD) to report on their progress towards meeting Aichi Target 1 to the' Secretariat. Continued and expanded monitoring using this indicator will provide further insights for better targeting of public awareness campaigns.Peer reviewe

Distance Properties of Short LDPC Codes and their Impact on the BP, ML and Near-ML Decoding Performance

Parameters of LDPC codes, such as minimum distance, stopping distance, stopping redundancy, girth of the Tanner graph, and their influence on the frame error rate performance of the BP, ML and near-ML decoding over a BEC and an AWGN channel are studied. Both random and structured LDPC codes are considered. In particular, the BP decoding is applied to the code parity-check matrices with an increasing number of redundant rows, and the convergence of the performance to that of the ML decoding is analyzed. A comparison of the simulated BP, ML, and near-ML performance with the improved theoretical bounds on the error probability based on the exact weight spectrum coefficients and the exact stopping size spectrum coefficients is presented. It is observed that decoding performance very close to the ML decoding performance can be achieved with a relatively small number of redundant rows for some codes, for both the BEC and the AWGN channels