Abstract: Candida albicans is both a member of the healthy human microbiome and a major pathogen in immunocompromised individuals. Infections are typically treated with azole inhibitors of ergosterol biosynthesis often leading to drug resistance. Studies in clinical isolates have implicated multiple mechanisms in resistance, but have focused on large-scale aberrations or candidate genes, and do not comprehensively chart the genetic basis of adaptation. Here, we leveraged next-generation sequencing to analyze 43 isolates from 11 oral candidiasis patients. We detected newly selected mutations, including single-nucleotide polymorphisms (SNPs), copy-number variations and loss-of-heterozygosity (LOH) events. LOH events were commonly associated with acquired resistance, and SNPs in 240 genes may be related to host adaptation. Conversely, most aneuploidies were transient and did not correlate with drug resistance. Our analysis also shows that isolates also varied in adherence, filamentation, and virulence. Our work reveals new molecular mechanisms underlying the evolution of drug resistance and host adaptation. DOI: http://dx.doi.org/10.7554/eLife.00662.001

Abstract: Asphalt is the most recycled material in the USA at a re-use rate of 99%. However, by average only 10–20% reclaimed asphalt pavement (RAP) is used in a given mix design and large part of the RAP is degraded for use in lower value applications. The amount of RAP in asphalt mixtures can be significantly increased with the application of good RAP management practice, readily available modern production technologies and advanced knowledge of mix design. This paper summarises the state-of-the-art approaches for increasing the amount of RAP in asphalt mixtures above 40%. The production challenges and common pavement distresses of very high RAP content mixtures are identified and methods to optimise the mix design as well as production technology in order to allow manufacturing of such sustainable mixtures are described. The best practices for RAP management and economic benefits of high RAP use are also discussed.

Abstract: Bamboo, a fast-growing grass, has a higher strength-to-weight ratio than steel and concrete. The unique properties of bamboo come from the natural composite structure of fibers that consists mainly of cellulose microfibrils in a matrix of intertwined hemicellulose and lignin called lignin-carbohydrate complex (LCC). Here, we have used atomistic simulations to study the mechanical properties of and adhesive interactions between the materials in bamboo fibers. With this aim, we have developed molecular models of lignin, hemicellulose and LCC structures to study the elastic moduli and the adhesion energies between these materials and cellulose microfibril faces. Good agreement was observed between the simulation results and experimental data. It was also shown that the hemicellulose model has stronger mechanical properties than lignin while lignin exhibits greater tendency to adhere to cellulose microfibrils. The study suggests that the abundance of hydrogen bonds in hemicellulose chains is responsible for improving the mechanical behavior of LCC. The strong van der Waals forces between lignin molecules and cellulose microfibril is responsible for higher adhesion energy between LCC and cellulose microfibrils. We also found out that the amorphous regions of cellulose microfibrils are the weakest interfaces in bamboo fibrils. Hence, they determine the fibril strength.

Abstract: This article presents a tactical supply chain planning model that can be used to investigate tradeoffs between cost and environmental degradation including carbon emissions, energy consumption and waste generation. The proposed model also incorporates other aspects of real world supply chains such as multiple transport lot sizing and flexible holding capacity of warehouses. A solution methodology, the Nested Integrated Cross-Entropy (NICE) method, is developed to solve the proposed mixed-integer nonlinear mathematical model. The application of the model and solution method is investigated in an actual case problem. Analysis of the numerical results focuses on investigating the relationship between lean practices and green outcomes. We find that (1) not all lean interventions at the tactical supply chain planning level result in green benefits, and (2) a flexible supply chain is the greenest and most efficient alternative when compared to strictly lean and centralized situations.

Abstract: Reinforcement learning describes how a learning agent can achieve optimal behaviour based on interactions with its environment and reward feedback. A limiting factor in reinforcement learning as employed in artificial intelligence is the need for an often prohibitively large number of environment samples before the agent reaches a desirable level of performance. Learning from demonstration is an approach that provides the agent with demonstrations by a supposed expert, from which it should derive suitable behaviour. Yet, one of the challenges of learning from demonstration is that no guarantees can be provided for the quality of the demonstrations, and thus the learned behavior. In this paper, we investigate the intersection of these two approaches, leveraging the theoretical guarantees provided by reinforcement learning, and using expert demonstrations to speed up this learning by biasing exploration through a process called reward shaping. This approach allows us to leverage human input without making an erroneous assumption regarding demonstration optimality. We show experimentally that this approach requires significantly fewer demonstrations, is more robust against suboptimality of demonstrations, and achieves much faster learning than the recently developed HAT algorithm.

Abstract: Because of aged binder, high reclaimed asphalt pavement (RAP) content mixes are susceptible to cracking failures and are less workable than virgin mixtures. The potential of six differently originated recycling agents was evaluated in this study to restore the desired binder properties using conventional binder test methods and the results were compared with performance-related test results of 100 % RAP mixture. Binder test results showed that application of organic products require much lower dose to provide the same softening effect as petroleum products. The workability of binder and mixture was improved compared to RAP, but remained lower than that of reference virgin mixture. All rejuvenated mixtures proved to be very rut resistant. Low temperature performance of RAP, measured by creep compliance at −10 °C, was improved with the application of all recycling agents and RAP mixes rejuvenated with waste vegetable products even performed equal or better than virgin mixture. Organic oil and both waste vegetable products provided the best performance in binder and mixture fatigue resistance tests as measured by linear amplitude sweep and fracture work density respectively. Overall, the organic products outperformed the petroleum based additives in most tests, which partly can be attributed to un-optimized recycling agent dose. It was observed that penetration test may be a good indicator for initial selection of optimum dose since the results provide indication of rut resistance as well as fatigue performance of mixture and can be easily predicted using an exponential relationship that was developed in the research.

Abstract: Accurate estimation of heart rates from photoplethysmogram (PPG) signals during intense physical activity is a very challenging problem. This is because strenuous and high intensity exercise can result in severe motion artifacts in PPG signals, making accurate heart rate (HR) estimation difficult. In this study we investigated a novel technique to accurately reconstruct motion-corrupted PPG signals and HR based on time-varying spectral analysis. The algorithm is called Spectral filter algorithm for Motion Artifacts and heart rate reconstruction (SpaMA). The idea is to calculate the power spectral density of both PPG and accelerometer signals for each time shift of a windowed data segment. By comparing time-varying spectra of PPG and accelerometer data, those frequency peaks resulting from motion artifacts can be distinguished from the PPG spectrum. The SpaMA approach was applied to three different datasets and four types of activities: (1) training datasets from the 2015 IEEE Signal Process. Cup Database recorded from 12 subjects while performing treadmill exercise from 1 km/h to 15 km/h; (2) test datasets from the 2015 IEEE Signal Process. Cup Database recorded from 11 subjects while performing forearm and upper arm exercise. (3) Chon Lab dataset including 10 min recordings from 10 subjects during treadmill exercise. The ECG signals from all three datasets provided the reference HRs which were used to determine the accuracy of our SpaMA algorithm. The performance of the SpaMA approach was calculated by computing the mean absolute error between the estimated HR from the PPG and the reference HR from the ECG. The average estimation errors using our method on the first, second and third datasets are 0.89, 1.93 and 1.38 beats/min respectively, while the overall error on all 33 subjects is 1.86 beats/min and the performance on only treadmill experiment datasets (22 subjects) is 1.11 beats/min. Moreover, it was found that dynamics of heart rate variability can be accurately captured using the algorithm where the mean Pearson’s correlation coefficient between the power spectral densities of the reference and the reconstructed heart rate time series was found to be 0.98. These results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities.

Abstract: Twenty years ago, law enforcement organizations lobbied to require data and communication services to engineer their products to guarantee law enforcement access to all data. After lengthy debate and vigorous predictions of enforcement channels “going dark,” these attempts to regulate security technologies on the emerging Internet were abandoned. In the intervening years, innovation on the Internet flourished, and law enforcement agencies found new and more effective means of accessing vastly larger quantities of data. Today, there are again calls for regulation to mandate the provision of exceptional access mechanisms. In this article, a group of computer scientists and security experts, many of whom participated in a 1997 study of these same topics, has convened to explore the likely effects of imposing extraordinary access mandates. We have found that the damage that could be caused by law enforcement exceptional access requirements would be even greater today than it would have been 20 years ago. In the wake of the growing economic and social cost of the fundamental insecurity of today’s Internet environment, any proposals that alter the security dynamics online should be approached with caution. Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect

Abstract: Robot manufacturing is currently highly specialized, time consuming, and expensive, limiting accessibility and customization. Existing rapid prototyping techniques (e.g., 3-D printing) can achieve complex geometries and are becoming increasingly accessible; however, they are limited to one or two materials and cannot seamlessly integrate active components. We propose an alternative approach called printable robots that takes advantage of available planar fabrication methods to create integrated electromechanical laminates that are subsequently folded into functional 3-D machines employing origami-inspired techniques. We designed, fabricated, and tested prototype origami robots to address the canonical robotics challenges of mobility and manipulation, and subsequently combined these designs to generate a new, multifunctional machine. The speed of the design and manufacturing process as well as the ease of composing designs create a new paradigm in robotic development, which has the promise to democratize access to customized robots for industrial, home, and educational use.

Abstract: The deuterium to hydrogen (D/H) ratio in strongly bound water or hydroxyl groups in ancient martian clays retains the imprint of the water of formation of these minerals. Curiosity’s Sample Analysis at Mars (SAM) experiment measured thermally evolved water and hydrogen gas released between 550°C and 950°C from samples of Hesperian era Gale crater smectite to determine this isotope ratio. The D/H value is 3.0 (±0.2) times the ratio in Standard Mean Ocean Water (SMOW). The D/H ratio in this ~3 billion year old mudstone that is half that of the present martian atmosphere but substantially higher than that expected in very early Mars indicates an extended history of hydrogen escape and desiccation of the planet.

Abstract: Diabetic foot ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smartphones with a high-resolution digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel wound image analysis system implemented solely on the Android smartphone. The wound image is captured by the camera on the smartphone with the assistance of an image capture box. After that, the smartphone performs wound segmentation by applying the accelerated mean-shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundary is found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on red–yellow–black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient. Experimental results on wound images collected in UMASS—Memorial Health Center Wound Clinic (Worcester, MA) following an Institutional Review Board approved protocol show that our system can be efficiently used to analyze the wound healing status with promising accuracy.

Abstract: In 2010, Gentry and Halevi presented the first FHE implementation. FHE allows the evaluation of arbitrary functions directly on encrypted data on untrusted servers. However, even for the small setting with 2048 dimensions, the authors reported a performance of 1.8 s for a single bit encryption and 32 s for recryption on a high-end server. Much of the latency is due to computationally intensive multi-million-bit modular multiplications. In this paper, we introduce two optimizations coupled with a novel precomputation technique. In the first optimization called partial FFT, we adopt Strassen’s FFT-based multiplication algorithm along with Barret reduction to speedup modular multiplications. For the encrypt primitive, we employ a window-based evaluation technique along with a modest degree of precomputation. In the full FFT optimization, we delay modular reductions and change the window algorithm, which allows us to carry out the bulk of computations in the frequency domain. We manage to eliminate all FFT conversion except the final inverse transformation drastically reducing the computation latency for all FHE primitives. We implemented the GH FHE scheme on two GPUs to further speedup the operations. Our experimental results with small parameter setting show speedups of 174, 7.6, and 13.5 times for encryption, decryption, and recryption, respectively, when compared to the Gentry–Halevi implementation. The speedup is enhanced in the medium setting. However, in the large setting, memory becomes the bottleneck and the speedup is somewhat diminished.

Abstract: Recent estimates suggest that >300 million people are afflicted by serious fungal infections worldwide. Current antifungal drugs are static and toxic and/or have a narrow spectrum of activity. Thus, there is an urgent need for the development of new antifungal drugs. The fungal sphingolipid glucosylceramide (GlcCer) is critical in promoting virulence of a variety of human-pathogenic fungi. In this study, we screened a synthetic drug library for compounds that target the synthesis of fungal, but not mammalian, GlcCer and found two compounds [ N ′-(3-bromo-4-hydroxybenzylidene)-2-methylbenzohydrazide (BHBM) and its derivative, 3-bromo- N ′-(3-bromo-4-hydroxybenzylidene) benzohydrazide (D0)] that were highly effective in vitro and in vivo against several pathogenic fungi. BHBM and D0 were well tolerated in animals and are highly synergistic or additive to current antifungals. BHBM and D0 significantly affected fungal cell morphology and resulted in the accumulation of intracellular vesicles. Deep-sequencing analysis of drug-resistant mutants revealed that four protein products, encoded by genes APL5 , COS111 , MKK1 , and STE2 , which are involved in vesicular transport and cell cycle progression, are targeted by BHBM. IMPORTANCE Fungal infections are a significant cause of morbidity and mortality worldwide. Current antifungal drugs suffer from various drawbacks, including toxicity, drug resistance, and narrow spectrum of activity. In this study, we have demonstrated that pharmaceutical inhibition of fungal glucosylceramide presents a new opportunity to treat cryptococcosis and various other fungal infections. In addition to being effective against pathogenic fungi, the compounds discovered in this study were well tolerated by animals and additive to current antifungals. These findings suggest that these drugs might pave the way for the development of a new class of antifungals.

Abstract: To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function which “explains” the motion of the human. To do this, we gather example trajectories from two participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict a human's motion for a given task by iteratively replanning a trajectory for a 23 DoF human kinematic model using the STOMP algorithm with the learned cost function in the presence of a moving collaborator. Our results suggest that our method outperforms baseline methods and generalizes well for tasks similar to those that were demonstrated.

Abstract: Solar water splitting in acidic solutions has important technological implications, but has not been demonstrated to date in a dual absorber photoelectrochemical cell. The lack of functionally stable water-oxidation catalysts (WOCs) in acids is a key reason for this slow development. The only WOCs that are stable at low pH are Ir-based systems, which are typically too expensive to be implemented broadly. It is now shown that this deficiency may be corrected by applying an ultra-thin monolayer of a molecular Ir WOC to hematite for solar water splitting in acidic solutions. The turn-on voltage is observed to shift cathodically by 250 mV upon the application of a monolayer of the molecular Ir WOC. When the molecular WOC is replaced by a heterogeneous multilayer derivative, stable solar water splitting for over 5 h is achieved with near-unity Faradaic efficiency.

Abstract: Outstanding mechanical properties of biological multilayered materials are strongly influenced by nanoscale features in their structure. In this study, mechanical behaviour and toughening mechanisms of abalone nacre-inspired multilayered materials are explored. In nacre's structure, the organic matrix, pillars and the roughness of the aragonite platelets play important roles in its overall mechanical performance. A micromechanical model for multilayered biological materials is proposed to simulate their mechanical deformation and toughening mechanisms. The fundamental hypothesis of the model is the inclusion of nanoscale pillars with near theoretical strength (σth ~ E/30). It is also assumed that pillars and asperities confine the organic matrix to the proximity of the platelets, and, hence, increase their stiffness, since it has been previously shown that the organic matrix behaves more stiffly in the proximity of mineral platelets. The modelling results are in excellent agreement with the available experimental data for abalone nacre. The results demonstrate that the aragonite platelets, pillars and organic matrix synergistically affect the stiffness of nacre, and the pillars significantly contribute to the mechanical performance of nacre. It is also shown that the roughness induced interactions between the organic matrix and aragonite platelet, represented in the model by asperity elements, play a key role in strength and toughness of abalone nacre. The highly nonlinear behaviour of the proposed multilayered material is the result of distributed deformation in the nacre-like structure due to the existence of nano-asperities and nanopillars with near theoretical strength. Finally, tensile toughness is studied as a function of the components in the microstructure of nacre.

Abstract: We introduce a CUDA GPU library to accelerate evaluations with homomorphic schemes defined over polynomial rings enabled with a number of optimizations including algebraic techniques for efficient evaluation, memory minimization techniques, memory and thread scheduling and low level CUDA hand-tuned assembly optimizations to take full advantage of the mass parallelism and high memory bandwidth GPUs offer. The arithmetic functions constructed to handle very large polynomial operands using number-theoretic transform (NTT) and Chinese remainder theorem (CRT) based methods are then extended to implement the primitives of the leveled homomorphic encryption scheme proposed by Lopez-Alt, Tromer and Vaikuntanathan. To compare the performance of the proposed CUDA library we implemented two applications: the Prince block cipher and homomorphic sorting algorithms on two GPU platforms in single GPU and multiple GPU configurations. We observed a speedup of 25 times and 51 times over the best previous GPU implementation for Prince with single and triple GPUs, respectively. Similarly for homomorphic sorting we obtained 12–41 times speedup depending on the number and size of the sorted elements.

Abstract: Stereotaxy is a neurosurgical technique that can take several hours to reach a specific target, typically utilizing a mechanical frame and guided by preoperative imaging. An error in any one of the numerous steps or deviations of the target anatomy from the preoperative plan such as brain shift (up to $20$ mm), may affect the targeting accuracy and thus the treatment effectiveness. Moreover, because the procedure is typically performed through a small burr hole opening in the skull that prevents tissue visualization, the intervention is basically “blind” for the operator with limited means of intraoperative confirmation that may result in reduced accuracy and safety. The presented system is intended to address the clinical needs for enhanced efficiency, accuracy, and safety of image-guided stereotactic neurosurgery for deep brain stimulation lead placement. The study describes a magnetic resonance imaging (MRI)-guided, robotically actuated stereotactic neural intervention system for deep brain stimulation procedure, which offers the potential of reducing procedure duration while improving targeting accuracy and enhancing safety. This is achieved through simultaneous robotic manipulation of the instrument and interactively updated in situ MRI guidance that enables visualization of the anatomy and interventional instrument. During simultaneous actuation and imaging, the system has demonstrated less than $15$ % signal-to-noise ratio variation and less than $0.20\%$ geometric distortion artifact without affecting the imaging usability to visualize and guide the procedure. Optical tracking and MRI phantom experiments streamline the clinical workflow of the prototype system, corroborating targeting accuracy with three-axis root mean square error $1.38\pm 0.45$ mm in tip position and $2.03\pm 0.58^\circ$ in insertion angle.

Abstract: We describe Team WPI-CMU's approach to the DARPA Robotics Challenge (DRC), focusing on our strategy to avoid failures that required physical human intervention. We implemented safety features in our controller to detect potential catastrophic failures, stop the current behavior, and allow remote intervention by a human supervisor. Our safety methods and operator interface worked: we avoided catastrophe and remote operators could safely recover from difficult situations. We were the only team in the DRC Finals that attempted all tasks, scored points (14/16), did not require physical human intervention (a reset), and did not fall in the two missions during the two days of tests. We also had the most consistent pair of runs. Much of the paper discusses lessons learned from the DRC.