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Modern life is essentially homochiral, containing D-sugars in nucleic acid backbones and L-amino acids in proteins. Since coded proteins are theorized to have developed from a prebiotic RNA World, the homochirality of L-amino acids observed in all known life presumably resulted from chiral transfer from a homochiral D-RNA World. This transfer would have been mediated by aminoacyl-RNAs defining the genetic code. Previous work on aminoacyl transfer using tRNA mimics has suggested that aminoacylation using D-RNA may be inherently biased toward reactivity with L-amino acids, implying a deterministic path from a D-RNA World to L-proteins. Using a model system of self-aminoacylating D-ribozymes and epimerizable activated amino acid analogs, we test the chiral selectivity of 15 ribozymes derived from an exhaustive search of sequence space. All of the ribozymes exhibit detectable selectivity, and a substantial fraction react preferentially to produce the D-enantiomer of the product. Furthermore, chiral preference is conserved within sequence families. These results are consistent with the transfer of chiral information from RNA to proteins but do not support an intrinsic bias of D-RNA for L-amino acids. Different aminoacylation structures result in different directions of chiral selectivity, such that L-proteins need not emerge from a D-RNA World.

 

Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance.

 

We propose a new class of haptic devices that provide haptic sensations by delivering liquid-stimulants to the user's skin; we call this chemical haptics. Upon absorbing these stimulants, which contain safe and small doses of key active ingredients, receptors in the user's skin are chemically triggered, rendering distinct haptic sensations. We identified five chemicals that can render lasting haptic sensations: tingling (sanshool), numbing (lidocaine), stinging (cinnamaldehyde), warming (capsaicin), and cooling (menthol). To enable the application of our novel approach in a variety of settings (such as VR), we engineered a self-contained wearable that can be worn anywhere on the user's skin (e.g., face, arms, legs). Implemented as a soft silicone patch, our device uses micropumps to push the liquid stimulants through channels that are open to the user's skin, enabling topical stimulants to be absorbed by the skin as they pass through. Our approach presents two unique benefits. First, it enables sensations, such as numbing, not possible with existing haptic devices. Second, our approach offers a new pathway, via the skin's chemical receptors, for achieving multiple haptic sensations using a single actuator, which would otherwise require combining multiple actuators (e.g., Peltier, vibration motors, electro-tactile stimulation). We evaluated our approach by means of two studies. In our first study, we characterized the temporal profiles of sensations elicited by each chemical. Using these insights, we designed five interactive VR experiences utilizing chemical haptics, and in our second user study, participants rated these VR experiences with chemical haptics as more immersive than without. Finally, as the first work exploring the use of chemical haptics on the skin, we offer recommendations to designers for how they may employ our approach for their interactive experiences. 

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species “known” to our CNN in contrast to images of the species that were “unknown” to our CNN.