University of California, Santa Cruz

Accurate dynamic object manipulation in a robotic hand remains a difficult task, especially when frictional slip is involved. Prior solutions involve extensive data collection to train complex models to control the hand that do not necessarily generalize to other slip circumstances. Our approach focuses on direct slip sensing using a tactile sensor with a capacitive array, coupled with a programmable system on a chip, capable of mode switching and sampling rate adjustment. We characterize the sensor's capacity to sense slip features at higher speeds and introduce a novel methodology for estimating motions. Low-level sensor reprogramming that couples multiple taxels improves slip avoidance and reaction time during rapid slip onset events. The technology also tracks dominant surface vibration frequencies resulting from stick-slip cycles, estimating speed and acceleration of smooth flat surfaces. Using a parallel-jaw robotic gripper, we demonstrate dynamic repositioning of objects lacking trackable surface features within the hand. The goal of this investigation is to support faster reasoning and reflexes for dynamic dexterous robots that experience directional in-hand slip.

Six decades after its conception, proton computed tomography (pCT) and proton radiography have yet to be used in medical clinics. However, good progress has been made on relevant detector technologies in the past two decades, and a few prototype pCT systems now exist that approach the performance needed for a clinical device. The tracking and energy-measurement technologies in common use are described, as are the few pCT scanners that are in routine operation at this time. Most of these devices still look like detector R&D efforts as opposed to medical devices, are difficult to use, are at least a factor of five slower than desired for clinical use, and are too small to image many parts of the human body. Recommendations are made for what to consider when engineering a pre-clinical pCT scanner that is designed to meet clinical needs in terms of performance, cost, and ease of use.

Despite elite, human free-divers achieving incredible feats in competitive free-diving, there has yet to be a study that compares consummate divers, (i.e. northern elephant seals) to highly conditioned free-divers (i.e., elite, competitive free-diving humans). Herein, we compare these two diving models and suggest that hematological traits detected in seals reflect species-specific specializations, while hematological traits shared between the two species are fundamental mammalian characteristics. Arterial blood samples were analyzed in elite, human, free-divers (n=14) during a single, maximal volitional apnea and in juvenile northern elephant seals (n=3) during rest-associated apnea. Humans and elephant seals had comparable apnea durations (~6.5 mins) and end-apneic arterial PO 2 (humans: 40.4±3.0mmHg (mean±SE), seals: 27.1±5.9mmHg; p=0.2). Despite similar increases in arterial PCO 2 (humans: 33±5%, seals: 16.3±5%; P=0.2), only humans experienced reductions in pH from baseline (humans: 7.45±0.01, seal: 7.39±0.02) to end apnea (humans: 7.37±0.01, seals: 7.38±0.02; p<0.0001). Hemoglobin P 50 was greater in humans compared to elephant seals (29.9±1.5 and 28.7±0.6mmHg, respectively; p=0.046). Elephant seals overall had higher COHb levels (5.9±2.6%) compared to humans (0.8±1.2%; p<0.0001); however, following apnea, COHb was reduced in seals (baseline: 6.1±0.3%, end-apnea: 5.6±0.3%), but was slightly elevated in humans (baseline: 0.7±0.1%, end-apnea: 0.9±0.1%; p<0.0002, both comparisons). Our data indicate that during static apnea, seals have reduced hemoglobin P 50 , greater pH buffering, and increased COHb levels. The differences in hemoglobin P 50 is likely due to the differences in the physiological environment between the two species during apnea, whereas enhanced pH buffering and higher COHb may represent traits selected for in elephant seals.

Trade-offs are crucial for species divergence and reproductive isolation. Trade-offs between investment in growth versus defense against herbivores are implicated in tropical forest diversity. Empirically exploring the role of growth-defense trade-offs in closely related species’ reproductive isolation can clarify the eco-evolutionary dynamics through which growth-defense trade-offs contribute to diversity. Costus villosissimus and C. allenii are recently diverged, interfertile, and partially sympatric neotropical understory plant species primarily isolated by divergent habitat adaptation. This divergent adaptation involves differences in growth rate, which may constrain investment in defense. Here, we investigate growth-defense trade-offs and how they relate to the divergent habitat adaptation that isolates these species. We characterize leaf toughness and chemistry, evaluate the feeding preferences of primary beetle herbivores in controlled trials and field-based experiments, and investigate natural herbivory patterns. We find clear trade-offs between growth and defense: slower-growing C. allenii has tougher leaves and higher defensive chemical concentrations than faster-growing C. villosissimus. Costus villosissimus has rapid growth-based drought avoidance, enabling growth in drier habitats with few specialist herbivores. Therefore, growth-defense trade-offs mediate synergistic biotic and abiotic selection, causing the divergent habitat adaptation that prevents most interspecific mating between C. villosissimus and C. allenii. Our findings advance understanding of ecological speciation by highlighting the interplay of biotic and abiotic selection that dictates the outcome of trade-offs.

Biological pest control relies on interactions between herbivores and their natural enemies. Maintaining this ecosystem service requires considering herbivore and natural enemy interactions and their response to anthropogenic change at multiple scales. In this study, we used ecological networks to quantify the network structure of interactions between herbivorous insects and their parasitoids. We examined how herbivore host abundance, parasitism rates, and shifts in network structure relate to changes in local habitat management and landscape context. We sampled herbivores and parasitoids in Brassica oleracea plants at 22 urban gardens in the Central Coast of California. At each site, we measured local management characteristics (e.g., vegetation, ground cover, canopy cover) and quantified surrounding landscape composition (e.g., urban, natural, open, and agricultural cover). For the eight sites with large enough networks, we calculated three network structure metrics (interaction richness, vulnerability, and functional complementarity). We then used generalized linear and mixed models to examine relationships between herbivore host abundance, parasitism rates, garden management and landscape characteristics, and network metrics. We found that both local management and landscape composition influenced parasitism, while only local factors affected host abundance and network structure. Higher network interaction richness was marginally associated with enhanced parasitism rates for two host species and lower parasitism rates for one host species. Our results suggest that local garden management decisions may shift the structure of host–parasitoid networks, which may subsequently affect host parasitism rates, but outcomes for biological pest control will likely vary across host species.

Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater‐derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr⁻¹ for DIN, DON, and DIP, respectively. Despite large natural variability, total SGD fluxes likely exceed global riverine nutrient export. Fresh SGD is a small source of new nutrients, but saline SGD is an important source of mostly recycled nutrients. Nutrients exported via SGD via subterranean estuaries are critical to coastal biogeochemistry and a significant nutrient source to the oceans.

Although tool use may enhance resource utilization, its fitness benefits are difficult to measure. By examining longitudinal data from 196 radio-tagged southern sea otters (Enhydra lutris nereis), we found that tool-using individuals, particularly females, gained access to larger and/or harder-shelled prey. These mechanical advantages translated to reduced tooth damage during food processing. We also found that tool use diminishes trade-offs between access to different prey, tooth condition, and energy intake, all of which are dependent on the relative prey availability in the environment. Tool use allowed individuals to maintain energetic requirements through the processing of alternative prey that are typically inaccessible with biting alone, suggesting that this behavior is a necessity for the survival of some otters in environments where preferred prey are depleted.

In the United States, the dominant contemporary understanding of childhood is one that is cultivated through children's role as dependents, served by adults who are their providers. This framework obscures how children contribute to society through their learning and practice. This paper proposes a reconsideration of children's learning to advance the theoretical conceptualization of emotional labor so that children's contributions can be recognized. To advance an expansion of how adults understand what children's contributions to society are, I frame their social–emotional learning as the practice of emotional labor, not as them simply obtaining the skills of emotional intelligence. In doing so, I advocate for seeing children as more than learners, but also as contributors and producers.

Context Resource selection functions are powerful tools for predicting habitat selection of animals. Recently, machine-learning methods such as random forest have gained popularity for predicting habitat selection due to their flexibility and strong predictive performance. Objectives We tested two methods for predicting continental-scale, second-order habitat selection of a wide-ranging large carnivore, the mountain lion (Puma concolor), to support continent-wide conservation management, including estimating abundance, and to predict habitat suitability for recolonizing or reintroduced animals. Methods We compared a generalized linear model (GLM) and a random forest model using GPS location data from 476 individuals across 20 study sites in the western USA and Canada and remotely-sensed landscape data. We internally validated models and examined their ability to correctly classify used and available points by calculating area under the receiver operating characteristics (AUC). We performed leave-one-out (LOO) out-of-sample tests of predictive strength on both models. Results Both models suggested that mountain lions select for steeper slopes, areas closer to water, and with higher normalized difference vegetation index (NDVI), and against variables associated with human impact. The random forest model (AUC = 0.94) demonstrated that mountain lion habitat can be accurately predicted at continental scales, outperforming the traditional GLM model (AUC = 0.68). Our LOO validation provided similar results (x̄ = 0.93 for the random forest and x̄ = 0.65 for the GLM). Conclusions We found that the added flexibility of the random forest model provided deeper insights into how individual covariates impacted habitat selection across diverse ecosystems. Our LOO analyses suggested that our model can predict mountain lion habitat selection in unoccupied areas or where local data are unavailable. Our model thus provides a tool to support discussions and analyses relevant to continent-wide mountain lion conservation and management including estimating metapopulation abundance.

Precision medicine endeavors to personalize treatments, considering individual variations in patient responses based on factors like genetic mutations, age, and diet. Integrating this approach dynamically, bioelectronics equipped with real-time sensing and intelligent actuation present a promising avenue. Devices such as ion pumps hold potential for precise therapeutic drug delivery, a pivotal aspect of effective precision medicine. However, implementing bioelectronic devices in precision medicine encounters formidable challenges. Variability in device performance due to fabrication inconsistencies and operational limitations, including voltage saturation, presents significant hurdles. To address this, closed-loop control with adaptive capabilities and explicit handling of saturation becomes imperative. Our research introduces an enhanced sliding mode controller capable of managing saturation, adept at satisfactory control actions amidst model uncertainties. To evaluate the controller’s effectiveness, we conducted in silico experiments using an extended mathematical model of the proton pump. Subsequently, we compared the performance of our developed controller with classical Proportional Integral Derivative (PID) and machine learning (ML)–based controllers. Furthermore, in vitro experiments assessed the controller’s efficacy using various reference signals for controlled Fluoxetine delivery. These experiments showcased consistent performance across diverse input signals, maintaining the current value near the reference with a relative error of less than 7% in all trials. Our findings underscore the potential of the developed controller to address challenges in bioelectronic device implementation, offering reliable precision in drug delivery strategies within the realm of precision medicine.

Differential scanning fluorimetry (DSF) is a technique that reports protein thermal stability via the selective recognition of unfolded states by fluorogenic dyes. However, DSF applications remain limited by protein incompatibilities with existing DSF dyes. Here we overcome this obstacle with the development of a protein-adaptive DSF platform (paDSF) that combines a dye library ‘Aurora’ with a streamlined procedure to identify protein–dye pairs on demand. paDSF was successfully applied to 94% (66 of 70) of proteins, tripling the previous compatibility and delivering assays for 66 functionally and biochemically diverse proteins, including 10 from severe acute respiratory syndrome coronavirus 2. We find that paDSF can be used to monitor biological processes that were previously inaccessible, demonstrated for the interdomain allostery of O-GlcNAc transferase. The chemical diversity and varied selectivities of Aurora dyes suggest that paDSF functionality may be readily extended. paDSF is a generalizable tool to interrogate protein stability, dynamics and ligand binding.

The interplay between local consistency and global consistency has been the object of study in several different areas, including probability theory, relational databases, and quantum information. For relational databases, Beeri, Fagin, Maier, and Yannakakis showed that a database schema is acyclic if and only if it has the local-to-global consistency property for relations, which means that every collection of pairwise consistent relations over the schema is globally consistent. More recently, the same result has been shown under bag semantics. In this paper, we carry out a systematic study of local vs. global consistency for relations over positive commutative monoids, which is a common generalization of ordinary relations and bags. Let K be an arbitrary positive commutative monoid. We begin by showing that acyclicity of the schema is a necessary condition for the local-to-global consistency property for K-relations to hold. Unlike the case of ordinary relations and bags, however, we show that acyclicity is not always sufficient. After this, we characterize the positive commutative monoids for which acyclicity is both necessary and sufficient for the local-to-global consistency property to hold; this characterization involves a combinatorial property of monoids, which we call the transportation property. We then identify several different classes of monoids that possess the transportation property. As our final contribution, we introduce a modified notion of local consistency of K-relations, which we call pairwise consistency up to the free cover. We prove that, for all positive commutative monoids K, even those without the transportation property, acyclicity is both necessary and sufficient for every family of K-relations that is pairwise consistent up to the free cover to be globally consistent.

The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful high-resolution nuclear magnetic resonance (NMR) spectroscopy of molecules under ambient conditions, including samples up to the micron-scale. The realization of these goals requires the development of spin detection techniques that are many orders of magnitude more sensitive than conventional NMR and MRI, capable of detecting and controlling nanoscale ensembles of spins. Over the years, a number of different technical approaches to NanoMRI have emerged, each possessing a distinct set of capabilities for basic and applied areas of science. The goal of this roadmap article is to report the current state of the art in NanoMRI technologies, outline the areas where they are poised to have impact, identify the challenges that lie ahead, and propose methods to meet these challenges. This roadmap also shows how developments in NanoMRI techniques can lead to breakthroughs in emerging quantum science and technology applications.