Wearable Microfluidic Strain Sensor with Smartphone Integration

Millions of individuals each year suffer from hand function impairment because of strokes and hand injuries. If these injuries are left untreated, permanent disability can result. Therefore, rehabilitation is necessary to improve and restore the function of the hand. However, this requires prolonged and repeated visits with a physician and physical therapist. Additionally, clinically significant milestones in these treatment programs, such as a 10° improvement in joint range of motion, often do not necessarily translate to improved quality of life for patients who can no longer perform every day activities due to their impairments. Thus, we propose a low cost, easy to use strain-sensing device that uses a fun and interactive interface to help patients visualize and monitor the progress they make in their physical therapy programs. This device is designed and fabricated using microfluidic principles to detect small changes in strain caused by changes in joint range of motion without the use of complex electronic components, making it a sensitive, easy to use method of monitoring patient progress in rehabilitation programs. We were able to successfully create a device that detects small changes in strain caused by a bending joint. By using a simple smart-phone based characterization method we were able to prove that changes in strain induced by a bent joint are measurable by the device

Genetic and otolith isotopic markers identify salmon populations in the Columbia River at broad and fine geographic scales

Processes occurring in freshwater, estuarine, and marine habitats strongly influence the growth, survival and reproductive success of salmonids. Nonetheless, implementing an ecosystem model explicitly linking these important habitats has been hindered by the inability to track the source identity of individuals where they co-occur. Here we explore the development and integration of natural markers- molecular and isotopic to characterize the natal sources of Chinook salmon (Oncorhynchus tshawytscha) in the Mid and Upper Columbia River summer/fall-run (UCR Su/F) population. Microsatellite DNA markers identified the majority of juveniles collected in rivers and hatcheries in the Mid and Upper Columbia River watershed to the Summer/Fall-run population in this watershed with 90% posterior probabilities of group membership. Strontium isotopes (87Sr/86Sr) measured in the natal rearing portion of the otolith showed significant geographic variation among natal rivers and hatcheries. Natal sites exhibited a wide dynamic range in 87Sr/86Sr source signatures (0.7043–0.7142), such that on average 61% of individuals were correctly classified to the location from which they were collected. We found that multilocus genotypes and otolith 87Sr/86Sr ratios collected on the same individuals were complementary markers when applied in a hierarchy. Microsatellites successfully assigned individuals to the broader UCR Su/F genetic group and 87Sr/86Sr provided finer-scale geographic assignments to five natal river and hatchery groups nested within the UCR Su/F population. The temporal stability of both genetic and 87Sr/86Sr markers, together with the coast-wide microsatellite baseline currently being used for mixed-stock fisheries management supports the further development and integration of 87Sr/86Sr markers to potentially achieve finer levels of stock resolution. Stock identification at the scales of individual rivers and hatcheries would help elucidate the abundance, distribution, and the relative contributions of natal sources important for the recovery and spatial management of Chinook salmon

Blood meal metabarcoding of the argasid tick (Ornithodoros turicata Dugès) reveals extensive vector‐host associations

Abstract Molecular methods to understand host feeding patterns of arthropod vectors are critical to assess exposure risk to vector‐borne disease and unveil complex ecological interactions. We build on our prior work discovering the utility of PCR‐Sanger sequencing blood meal analysis that work well for soft ticks (Acari: Argasidae), unlike for hard ticks (Acari: Ixodidae), thanks to their unique physiology that retains prior blood meals for years. Here, we apply blood meal metabarcoding using amplicon deep sequencing to identify multiple host species in individual Ornithodoros turicata soft ticks collected from two natural areas in Texas, United States. Of 788 collected O. turicata, 394 were evaluated for blood meal source via metabarcoding, revealing 27 different vertebrate hosts (17 mammals, five birds, one reptile, and four amphibians) fed upon by 274 soft ticks. Information on multiple hosts was derived from 167 individual O. turicata (61%). Metabarcoding revealed mixed vertebrate blood meals in O. turicata while same specimens yielded only one vertebrate species using Sanger sequencing. These data reveal wide host range of O. turicata and demonstrate the value of blood meal metabarcoding for understanding the ecology for known and potential tick‐borne pathogens circulating among humans, domestic animals, and wildlife such as relapsing fever caused by Borrelia turicatae. Our results also document evidence of prior feeding on wild pig from an off‐host soft tick for the first time in North America; a critical observation in the context of enzootic transmission of African swine fever virus if it were introduced to the US. This research enhances our understanding of vector‐host associations and offers a promising perspective for biodiversity monitoring and disease control strategies

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants.

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants

<div><p>The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the <i>k</i>_cat and K_M values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.</p></div

Correlation between machine learning predictions and experimentally-determined kinetic constants.

<p><i>Top panels</i>: predicted versus experimentally-measured values for kinetic constants <i>k</i>_cat/K_M (A), <i>k</i>_cat (B), and 1/K_M (C). All values are relative to the wild type enzyme and on a log scale. The standard deviation (error bars) of the predicted values are calculated based on the prediction by 1000-fold cross validation for each point. The red line corresponds to linear regression and has been added for visualization purposes. <i>Bottom panels</i>: Histograms of experimentally-determined values in the data set (90, 80 and 80 samples for <i>k</i>_cat/K_M, <i>k</i>_cat, and K_M, respectively), along with the residual errors (scatter plot) between predicted and measured kinetic values.</p

Structure and catalyzed reaction of BglB.

<p>(A) Structure of BglB in complex with the modeled <i>p</i>-nitrophenyl-β-D-glucoside (pNPG) used for design. Alpha carbons of residues mutated shown as blue spheres. The image was drawn with PyMOL. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147596#pone.0147596.ref016" target="_blank">16</a>] (B) The BglB–catalyzed reaction on pNPG used to evaluate kinetic constants of designed mutants.</p

Active site model and conservation analysis of BglB.

<p>(A) Docked model of pNPG in the active site of BglB showing established catalytic residues (navy) and a selection of residues mutated (gold). A multiple sequence alignment of the Pfam database’s collection of 1,554 family 1 glycoside hydrolases was made and the sequence logo for (B) selected regions around specific residues discussed in the text and (C) over the entire BglB coding sequence is represented. The height for each amino acid indicates the sequence conservation at that position.</p

Most informative structural features predicting each kinetic constant.

<p>For each mutant, 10 out of 100 models were selected based on the lowest total system energy. Fifty-nine structural features were calculated for the selected models and the most informative features were selected based on a constrained regularization technique (elastic net with bagging; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147596#sec012" target="_blank">Methods</a>). The table contains features that have been assigned non-zero weights during training (9 for <i>k</i>_cat/K_M, 8 for <i>k</i>_cat, 10 for K_M). The weights are multiplied by a normalized form of the value (not shown), and can therefore indicate both a positive or negative relationship. For example, a negative weight for hydrogen bonding is consistent with a positive correlation to hydrogen bonding where a smaller number indicates more hydrogen bonding is occurring. Inversely, a positive weight for packing would indicate a positive correlation since a larger value indicates a system with fewer voids. The relative contribution of each feature in determining the kinetic constant is given as a normalized weight (columns 1–3). Column 4 provides a description of each feature, and columns 5 and 6 show the range of observed values in the training dataset. The full feature table is available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147596#pone.0147596.s007" target="_blank">S2 Table</a>. <i>ns = feature not selected by the algorithm</i>.</p