Phytoplankton Community and Algal Toxicity at a Recurring Bloom in Sullivan Bay, Kabetogama Lake, Minnesota, USA

Kabetogama Lake in Voyageurs National Park, Minnesota, USA suffers from recurring late summer algal blooms that often contain toxin-producing cyanobacteria. Previous research identified the toxin microcystin in blooms, but we wanted to better understand how the algal and cyanobacterial community changed throughout an open water season and how changes in community structure were related to toxin production. Therefore, we sampled one recurring bloom location throughout the entire open water season. The uniqueness of this study is the absence of urban and agricultural nutrient sources, the remote location, and the collection of samples before any visible blooms were present. Through quantitative polymerase chain reaction (qPCR), we discovered that toxin-forming cyanobacteria were present before visible blooms and toxins not previously detected in this region (anatoxin-a and saxitoxin) were present, indicating that sampling for additional toxins and sampling earlier in the season may be necessary to assess ecosystems and human health risk

Deep Long Short-term Memory Structures Model Temporal Dependencies Improving Cognitive Workload Estimation

Using deeply recurrent neural networks to account for temporal dependence in electroencephalograph (EEG)-based workload estimation is shown to considerably improve day-to-day feature stationarity resulting in significantly higher accuracy (p \u3c .0001) than classifiers which do not consider the temporal dependence encoded within the EEG time-series signal. This improvement is demonstrated by training several deep Recurrent Neural Network (RNN) models including Long Short-Term Memory (LSTM) architectures, a feedforward Artificial Neural Network (ANN), and Support Vector Machine (SVM) models on data from six participants who each perform several Multi-Attribute Task Battery (MATB) sessions on five separate days spread out over a month-long period. Each participant-specific classifier is trained on the first four days of data and tested using the fifth’s. Average classification accuracy of 93.0% is achieved using a deep LSTM architecture. These results represent a 59% decrease in error compared to the best previously published results for this dataset. This study additionally evaluates the significance of new features: all combinations of mean, variance, skewness, and kurtosis of EEG frequency-domain power distributions. Mean and variance are statistically significant features, while skewness and kurtosis are not. The overall performance of this approach is high enough to warrant evaluation for inclusion in operational systems

Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks

Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three important contributions: (1) The performance of ensembles of individually-trained models is statistically indistinguishable from group-trained methods at most sequence lengths. These ensembles can be trained for a fraction of the computational cost compared to group-trained methods and enable simpler model updates. (2) While increasing temporal sequence length improves mean accuracy, it is not sufficient to overcome distributional dissimilarities between individuals’ EEG data, as it results in statistically significant increases in cross-participant variance. (3) Compared to all other networks evaluated, a novel convolutional-recurrent model using multi-path subnetworks and bi-directional, residual recurrent layers resulted in statistically significant increases in predictive accuracy and decreases in cross-participant variance

Right to Serve, Right to Lead: Lives and Legacies of the USCT

This is a catalog for an exhibit that follows the evolution of African-American participation in the Civil War, from slaves, to contrabands, to soldiers of the United States Colored Troops (USCT), as well as the lives of black veterans beyond the war, and their ultimate military and social legacy. Using a variety of period items, it creates a narrative that stretches from the Antebellum Period to the current day. In doing so, the exhibit shows how black sacrifice on the battlefield redefined the war\u27s purpose throughout the divided nation, how Jim Crowe suppressed the memory of black participation after Reconstruction, and how the illustrious African-American military tradition left by the USCT endures to this day in their modern heirs

Exploring the DNA-recognition potential of homeodomains

The recognition potential of most families of DNA-binding domains (DBDs) remains relatively unexplored. Homeodomains (HDs), like many other families of DBDs, display limited diversity in their preferred recognition sequences. To explore the recognition potential of HDs, we utilized a bacterial selection system to isolate HD variants, from a randomized library, that are compatible with each of the 64 possible 3′ triplet sites (i.e., TAANNN). The majority of these selections yielded sets of HDs with overrepresented residues at specific recognition positions, implying the selection of specific binders. The DNA-binding specificity of 151 representative HD variants was subsequently characterized, identifying HDs that preferentially recognize 44 of these target sites. Many of these variants contain novel combinations of specificity determinants that are uncommon or absent in extant HDs. These novel determinants, when grafted into different HD backbones, produce a corresponding alteration in specificity. This information was used to create more explicit HD recognition models, which can inform the prediction of transcriptional regulatory networks for extant HDs or the engineering of HDs with novel DNA-recognition potential. The diversity of recovered HD recognition sequences raises important questions about the fitness barrier that restricts the evolution of alternate recognition modalities in natural systems

Chemical informatics uncovers a new role for moexipril as a novel inhibitor of cAMP phosphodiesterase-4 (PDE4)

PDE4 is one of eleven known cyclic nucleotide phosphodiesterase families and plays a pivotal role in mediating hydrolytic degradation of the important cyclic nucleotide second messenger, cyclic 3′5′ adenosine monophosphate (cAMP). PDE4 inhibitors are known to have anti-inflammatory properties, but their use in the clinic has been hampered by mechanism-associated side effects that limit maximally tolerated doses. In an attempt to initiate the development of better-tolerated PDE4 inhibitors we have surveyed existing approved drugs for PDE4-inhibitory activity. With this objective, we utilised a high-throughput computational approach that identified moexipril, a well tolerated and safe angiotensin-converting enzyme (ACE) inhibitor, as a PDE4 inhibitor. Experimentally we showed that moexipril and two structurally related analogues acted in the micro molar range to inhibit PDE4 activity. Employing a FRET-based biosensor constructed from the nucleotide binding domain of the type 1 exchange protein activated by cAMP, EPAC1, we demonstrated that moexipril markedly potentiated the ability of forskolin to increase intracellular cAMP levels. Finally, we demonstrated that the PDE4 inhibitory effect of moexipril is functionally able to induce phosphorylation of the Hsp20 by cAMP dependent protein kinase A. Our data suggest that moexipril is a bona fide PDE4 inhibitor that may provide the starting point for development of novel PDE4 inhibitors with an improved therapeutic window

Using defined finger-finger interfaces as units of assembly for constructing zinc-finger nucleases

Zinc-finger nucleases (ZFNs) have been used for genome engineering in a wide variety of organisms; however, it remains challenging to design effective ZFNs for many genomic sequences using publicly available zinc-finger modules. This limitation is in part because of potential finger-finger incompatibility generated on assembly of modules into zinc-finger arrays (ZFAs). Herein, we describe the validation of a new set of two-finger modules that can be used for building ZFAs via conventional assembly methods or a new strategy-finger stitching-that increases the diversity of genomic sequences targetable by ZFNs. Instead of assembling ZFAs based on units of the zinc-finger structural domain, our finger stitching method uses units that span the finger-finger interface to ensure compatibility of neighbouring recognition helices. We tested this approach by generating and characterizing eight ZFAs, and we found their DNA-binding specificities reflected the specificities of the component modules used in their construction. Four pairs of ZFNs incorporating these ZFAs generated targeted lesions in vivo, demonstrating that stitching yields ZFAs with robust recognition properties

First results from Faint Infrared Grism Survey (FIGS): first simultaneous detection of Lyman-alpha emission and Lyman break from a galaxy at z=7.51

Galaxies at high redshifts provide a valuable tool to study cosmic dawn, and therefore it is crucial to reliably identify these galaxies. Here, we present an unambiguous and first simultaneous detection of both the Lyman-alpha emission and the Lyman break from a z = 7.512+/- 0.004 galaxy, observed in the Faint Infrared Grism Survey (FIGS). These spectra, taken with G102 grism on Hubble Space Telescope (HST), show a significant emission line detection (6 sigma) in multiple observational position angles (PA), with total integrated Ly{\alpha} line flux of 1.06+/- 0.12 e10-17erg s-1cm-2. The line flux is nearly a factor of four higher than the previous MOSFIRE spectroscopic observations of faint Ly{\alpha} emission at {\lambda} = 1.0347{\mu}m, yielding z = 7.5078+/- 0.0004. This is consistent with other recent observations implying that ground-based near-infrared spectroscopy underestimates total emission line fluxes, and if confirmed, can have strong implications for reionization studies that are based on ground-based Lyman-{\alpha} measurements. A 4-{\sigma} detection of the NV line in one PA also suggests a weak Active Galactic Nucleus (AGN), potentially making this source the highest-redshift AGN yet found. Thus, this observation from the Hubble Space Telescope clearly demonstrates the sensitivity of the FIGS survey, and the capability of grism spectroscopy to study the epoch of reionization.Comment: Published in ApJL; matches published versio