Making the longest sugars: a chemical synthesis of heparin-related [4](n) oligosaccharides from 16-mer to 40-mer

The chemical synthesis of long oligosaccharides remains a major challenge. In particular, the synthesis of glycosaminoglycan (GAG) oligosaccharides belonging to the heparin and heparan sulfate (H/HS) family has been a high profile target, particularly with respect to the longer heparanome. Herein we describe a synthesis of the longest heparin-related oligosaccharide to date and concurrently provide an entry to the longest synthetic oligosaccharides of any type yet reported. Specifically, the iterative construction of a series of [4]n-mer heparin-backbone oligosaccharides ranging from 16-mer through to the 40-mer in length is described. This demonstrates for the first time the viability of generating long sequence heparanoids by chemical synthesis, via practical solution-phase synthesis. Pure-Shift HSQC NMR provides a dramatic improvement in anomeric signal resolution, allowing full resolution of all 12 anomeric protons and extrapolation to support anomeric integrity of the longer species. A chemically pure 6-O-desfulfated GlcNS-IdoAS icosasaccharide (20-mer) represents the longest pure synthetic heparin-like oligosaccharide

Road Context-aware Intrusion Detection System for Autonomous Cars

Security is of primary importance to vehicles. The viability of performing remote intrusions onto the in-vehicle network has been manifested. In regard to unmanned autonomous cars, limited work has been done to detect intrusions for them while existing intrusion detection systems (IDSs) embrace limitations against strong adversaries. In this paper, we consider the very nature of autonomous car and leverage the road context to build a novel IDS, named Road context-aware IDS (RAIDS). When a computer-controlled car is driving through continuous roads, road contexts and genuine frames transmitted on the car's in-vehicle network should resemble a regular and intelligible pattern. RAIDS hence employs a lightweight machine learning model to extract road contexts from sensory information (e.g., camera images and distance sensor values) that are used to generate control signals for maneuvering the car. With such ongoing road context, RAIDS validates corresponding frames observed on the in-vehicle network. Anomalous frames that substantially deviate from road context will be discerned as intrusions. We have implemented a prototype of RAIDS with neural networks, and conducted experiments on a Raspberry Pi with extensive datasets and meaningful intrusion cases. Evaluations show that RAIDS significantly outperforms state-of-the-art IDS without using road context by up to 99.9% accuracy and short response time.Comment: This manuscript presents an intrusion detection system that makes use of road context for autonomous car

IFN-gamma is associated with risk of Schistosoma japonicum infection in China.

Before the start of the schistosomiasis transmission season, 129 villagers resident on a Schistosoma japonicum-endemic island in Poyang Lake, Jiangxi Province, 64 of whom were stool-positive for S. japonicum eggs by the Kato method and 65 negative, were treated with praziquantel. Forty-five days later the 93 subjects who presented for follow-up were all stool-negative. Blood samples were collected from all 93 individuals. S. japonicum soluble worm antigen (SWAP) and soluble egg antigen (SEA) stimulated IL-4, IL-5 and IFN-gamma production in whole-blood cultures were measured by ELISA. All the subjects were interviewed nine times during the subsequent transmission season to estimate the intensity of their contact with potentially infective snail habitats, and the subjects were all re-screened for S. japonicum by the Kato method at the end of the transmission season. Fourteen subjects were found to be infected at that time. There was some indication that the risk of infection might be associated with gender (with females being at higher risk) and with the intensity of water contact, and there was evidence that levels of SEA-induced IFN-gamma production were associated with reduced risk of infection

Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function

In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive definite. Moreover, in certain settings, careful modeling of the covariance structure for irregular longitudinal data can be crucial in order to ensure no bias arises in the mean structure. Two common settings where this occurs are studies with ‘outcome-dependent follow-up’ and studies with ‘ignorable missing data’. ‘Outcome-dependent follow-up’ occurs when individuals with a history of poor health outcomes had more follow-up measurements, and the intervals between the repeated measurements were shorter. When the follow-up time process only depends on previous outcomes, likelihood-based methods can still provide consistent estimates of the regression parameters, given that both the mean and covariance structures of the irregular longitudinal data are correctly specified and no model for the follow-up time process is required. For ‘ignorable missing data’, the missing data mechanism does not need to be specified, but valid likelihood-based inference requires correct specification of the covariance structure. In both cases, flexible modeling approaches for the covariance structure are essential. In this paper, we develop a flexible approach to modeling the covariance structure for irregular continuous longitudinal data using the partial autocorrelation function and the variance function. In particular, we propose semiparametric non-stationary partial autocorrelation function models, which do not suffer from complex positive definiteness restrictions like the autocorrelation function. We describe a Bayesian approach, discuss computational issues, and apply the proposed methods to CD4 count data from a pediatric AIDS clinical trial. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

Swimming using surface acoustic waves

Microactuation of free standing objects in fluids is currently dominated by the rotary propeller, giving rise to a range of potential applications in the military, aeronautic and biomedical fields. Previously, surface acoustic waves (SAWs) have been shown to be of increasing interest in the field of microfluidics, where the refraction of a SAW into a drop of fluid creates a convective flow, a phenomenon generally known as SAW streaming. We now show how SAWs, generated at microelectronic devices, can be used as an efficient method of propulsion actuated by localised fluid streaming. The direction of the force arising from such streaming is optimal when the devices are maintained at the Rayleigh angle. The technique provides propulsion without any moving parts, and, due to the inherent design of the SAW transducer, enables simple control of the direction of travel

Mining the Context of Citations in Scientific Publications

Recent advancements in information retrieval systems significantly rely on the context-based features and semantic matching techniques to provide relevant information to users from ever-growing digital libraries. Scientific communities seek to understand the implications of research, its importance and its applicability for future research directions. To mine this information, absolute citations merely fail to measure the importance of scientific literature, as a citation may have a specific context in full text. Thus, a comprehensive contextual understanding of cited references is necessary. For this purpose, numerous techniques have been proposed that tap the power of artificial intelligence models to detect important or incidental (non-important) citations in full text scholarly publications. In this paper, we compare and build upon on four state-of-the-art models that detect important citations using 450 manually annotated citations by experts - randomly selected from 20,527 papers from the Association for Computational Linguistics corpus. Of the total 64 unique features proposed by the four selected state-of-the-art models, the top 29 were chosen using the Extra-Trees classifier. These were then fed it to our supervised machine learning based models: Random Forest (RF) and Support Vector Machine. The RF model outperforms existing selected systems by more than 10%, with 89% precision-recall curve. Finally, we qualitatively assessed important and non-important citations by employing and self-organizing maps. Overall, our research work supports information retrieval algorithms that detect and fetch scientific articles on the basis of both qualitative and quantitative indices in scholarly big data

Prediction of grout penetration in fractured rocks by numerical simulation

As fractures in rock significantly reduce the strength as well as the stiffness of the rock mass, grouting may be required to improve the performance of the rock mass in engineering or mining projects. During grouting, mortar of cement or other materials is injected into the rock mass so that the fractures can be filled up and the rock mass can act as an integral unit. Unlike water, grouts are usually viscous and behave as non-Newtonian fluids. Therefore, the equations describing the flow of grout are more complicated and the solutions are quite difficult to obtain. The problem is further aggravated by the fact that the fractures are mostly randomly distributed, and it is rarely possible to accurately define the fractures and the distribution patterns. In this paper, a numerical model is proposed for analyzing the grouting process. The model is based on the stochastic approach, and it can provide the depth of penetration and the fluid pressure due to the flow of grout, which is modeled as a Bingham fluid, in the fractured rock mass. Parametric studies have been carried out to investigate the effects of various factors on the depth of penetration, and a regression formula is developed for calculating the penetration depth. Experiments have been carried out and their results are used to validate the present method.published_or_final_versio

Effect of Heat Treatment on the Properties of Wood-Derived Biocarbon Structures

Wood-derived porous graphitic biocarbons with hierarchical structures were obtained by high-temperature (2200–2400 °C) non-catalytic graphitization, and their mechanical, electrical and thermal properties are reported for the first time. Compared to amorphous biocarbon produced at 1000 °C, the graphitized biocarbon-2200 °C and biocarbon-2400 °C exhibited increased compressive strength by ~38% (~36 MPa), increased electrical conductivity by ~8 fold (~29 S/cm), and increased thermal conductivity by ~5 fold (~9.5 W/(m·K) at 25 °C). The increase of duration time at 2200 °C contributed to increased thermal conductivity by ~12%, while the increase of temperature from 2200 to 2400 °C did not change their thermal conductivity, indicating that 2200 °C is sufficient for non-catalytic graphitization of wood-derived biocarbon

Discoidin domain receptor 1 kinase activity is required for regulating collagen IV synthesis

Discoidin domain receptor 1 (DDR1) is a receptor tyrosine kinase that binds to and is activated by collagens. DDR1 expression increases following kidney injury and accumulating evidence suggests that it contributes to the progression of injury. To this end, deletion of DDR1 is beneficial in ameliorating kidney injury induced by angiotensin infusion, unilateral ureteral obstruction, or nephrotoxic nephritis. Most of the beneficial effects observed in the DDR1-null mice are attributed to reduced inflammatory cell infiltration to the site of injury, suggesting that DDR1 plays a pro-inflammatory effect. The goal of this study was to determine whether, in addition to its pro-inflammatory effect, DDR1 plays a deleterious effect in kidney injury by directly regulating extracellular matrix production. We show that DDR1-null mice have reduced deposition of glomerular collagens I and IV as well as decreased proteinuria following the partial renal ablation model of kidney injury. Using mesangial cells isolated from DDR1-null mice, we show that these cells produce significantly less collagen compared to DDR1-null cells reconstituted with wild type DDR1. Moreover, mutagenesis analysis revealed that mutations in the collagen binding site or in the kinase domain significantly reduce DDR1-mediated collagen production. Finally, we provide evidence that blocking DDR1 kinase activity with an ATP-competitive small molecule inhibitor reduces collagen production. In conclusion, our studies indicate that the kinase activity of DDR1 plays a key role in DDR1-induced collagen synthesis and suggest that blocking collagen-mediated DDR1 activation may be beneficial in fibrotic diseases

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures