The spectral shift function and Levinson's theorem for quantum star graphs

We consider the Schr\"odinger operator on a star shaped graph with $n$ edges joined at a single vertex. We derive an expression for the trace of the difference of the perturbed and unperturbed resolvent in terms of a Wronskian. This leads to representations for the perturbation determinant and the spectral shift function, and to an analog of Levinson's formula

Integration of biology, ecology and engineering for sustainable algal‑based biofuel and bioproduct biorefinery

Despite years of concerted research efforts, an industrial-scale technology has yet to emerge for production and conversion of algal biomass into biofuels and bioproducts. The objective of this review is to explore the ways of possible integration of biology, ecology and engineering for sustainable large algal cultivation and biofuel production systems. Beside the costs of nutrients, such as nitrogen and phosphorous, and fresh water, upstream technologies which are not ready for commercialization both impede economic feasibility and conflict with the ecological benefits in the sector. Focusing mainly on the engineering side of chemical conversion of algae to biodiesel has also become obstacle. However, to reduce the costs, one potential strategy has been progressing steadily to synergistically link algal aquaculture to the governmentally mandated reduction of nitrogen and phosphorous concentrations in municipal wastewater. Recent research also supports the suppositions of scalability and cost reduction. Noticeably, less is known of the economic impact of conversion of the whole algae-based biorefinery sector with additional biochemical and thermochemical processes and integration with ecological constraints. This review finds that a biorefinery approach with integrated biology, ecology, and engineering could lead to a feasible algal-based technology for variety of biofuels and bioproducts

Induction of oil accumulation by heat stress is metabolically distinct from N stress in the green microalgae Coccomyxa subellipsoidea C169

Algae are often promoted as feedstock organisms to produce a sustainable petroleum fossil fuel alternative. However, to induce lipid accumulation most often requires a severe stress that is difficult to induce in large batch cultures. The objective of this study is to analyze and mathematically model heat stress on growth, chlorophyll content, triacylglyceride, and starch synthesis in algae. We initially screened 30 algal species for the most pronounced induction of lipid droplets from heat stress using confocal microscopy and mass spectroscopy techniques. One species, Coccomyxa subellipsoidea C169, was selected and subjected to further biochemical analyses using a jacketed bioreactor amended with 1% CO2 at 25ÊC, 30ÊC, 32ÊC, 33ÊC, 34ÊC, 35ÊC, and 36ÊC. Lipid and starch accumulation was less extreme than N stress. Growth was reduced above 25ÊC, but heat stress induced lipid droplet synthesis was negatively correlated with growth only past a demonstrated threshold temperature above 32ÊC. The optimal temperature for lipid accumulation was 35ÊC, which led to 6% of dry weight triglyceride content and a 72% reduction from optimal growth after 5 days. Fatty acid influx rates into triglycerides and 15N labeling of amino acids and proteins indicate that heat stress is mechanistically distinct from N stress. Thus, this study lends support to a novel hypothesis that lipid droplet triglycerides result from a redistribution of carbon flux as fatty acids to neutral storage lipids over membrane or other lipids

A task and performance analysis of endoscopic submucosal dissection (ESD) surgery

BACKGROUND: ESD is an endoscopic technique for en bloc resection of gastrointestinal lesions. ESD is a widely-used in Japan and throughout Asia, but not as prevalent in Europe or the US. The procedure is technically challenging and has higher adverse events (bleeding, perforation) compared to endoscopic mucosal resection. Inadequate training platforms and lack of established training curricula have restricted its wide acceptance in the US. Thus, we aim to develop a Virtual Endoluminal Surgery Simulator (VESS) for objective ESD training and assessment. In this work, we performed task and performance analysis of ESD surgeries. METHODS: We performed a detailed colorectal ESD task analysis and identified the critical ESD steps for lesion identification, marking, injection, circumferential cutting, dissection, intraprocedural complication management, and post-procedure examination. We constructed a hierarchical task tree that elaborates the order of tasks in these steps. Furthermore, we developed quantitative ESD performance metrics. We measured task times and scores of 16 ESD surgeries performed by four different endoscopic surgeons. RESULTS: The average time of the marking, injection, and circumferential cutting phases are 203.4 (σ: 205.46), 83.5 (σ: 49.92), 908.4 s. (σ: 584.53), respectively. Cutting the submucosal layer takes most of the time of overall ESD procedure time with an average of 1394.7 s (σ: 908.43). We also performed correlation analysis (Pearson's test) among the performance scores of the tasks. There is a moderate positive correlation (R = 0.528, p = 0.0355) between marking scores and total scores, a strong positive correlation (R = 0.7879, p = 0.0003) between circumferential cutting and submucosal dissection and total scores. Similarly, we noted a strong positive correlation (R = 0.7095, p = 0.0021) between circumferential cutting and submucosal dissection and marking scores. CONCLUSIONS: We elaborated ESD tasks and developed quantitative performance metrics used in analysis of actual surgery performance. These ESD metrics will be used in future validation studies of our VESS simulator

Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-Shot Learning

We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively learn a word representation such that the similarities between class and combination of attribute names fall in line with the visual similarity. Contrary to the traditional zero-shot learning approaches that are built upon attribute presence, our approach bypasses the laborious attributeclass relation annotations for unseen classes. In addition, our proposed approach renders text-only training possible, hence, the training can be augmented without the need to collect additional image data. The experimental results show that our method yields state-of-the-art results for unsupervised ZSL in three benchmark datasets. © 2017 IEEE

Finite-size scaling for non-linear rheology of fluids confined in a small space

We perform molecular dynamics simulations in order to examine the rheological transition of fluids confined in a small space. By performing finite-size scaling analysis, we demonstrate that this rheological transition results from the competition between the system size and the length scale of cooperative particle motion.Comment: 4pages, 8 figure

Microstructural defect properties of InGaN/GaN blue light emitting diode structures

Cataloged from PDF version of article.In this paper, we study structural and morphological properties of metal-organic chemical vapour deposition-grown InGaN/GaN light emitting diode (LED) structures with different indium (In) content by means of high-resolution X-ray diffraction, atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), photoluminescence (PL) and current-voltage characteristic (I-V). We have found out that the tilt and twist angles, lateral and vertical coherence lengths of mosaic blocks, grain size, screw and edge dislocation densities of GaN and InGaN layers, and surface roughness monotonically vary with In content. Mosaic defects obtained due to temperature using reciprocal lattice space map has revealed optimized growth temperature for active InGaN layer of MQW LED. It has been observed in this growth temperature that according to AFM result, LED structure has high crystal dimension, and is rough whereas according to PL and FTIR results, bandgap energy shifted to blue, and energy peak half-width decreased at high values. According to I-V measurements, it was observed that LED reacted against light at optimized temperature. In conclusion, we have seen that InGaN MQW structure's structural, optical and electrical results supported one another

Pregnancy and Crimean-Congo haemorrhagic fever

AbstractCrimean-Congo Hemorrhagic fever (CCHF) is a potentially fatal viral infection with reported case fatality rates of 5–30%. Humans become infected through tick bites, by contact with a patient with CCHF during the acute phase of infection, or by contact with blood or tissues from viraemic livestock. In this first report in the literature, we present the characteristics of three pregnant women with CCHF infection and the outcome of their babies. Transmission of the CCHF infection could be either intrauterine or perinatal. In endemic regions, CCHF infection should be considered in the differential diagnosis of HELLP syndrome (haemolytic anaemia, elevated liver enzymes, low platelet count), and obstetricians should be familiar with the characteristics of CCHF infection. In the aetiology of necrotising enterocolitis, CCHF should be considered

Lumping the approximate master equation for multistate processes on complex networks

Complex networks play an important role in human society and in nature. Stochastic multistate processes provide a powerful framework to model a variety of emerging phenomena such as the dynamics of an epidemic or the spreading of information on complex networks. In recent years, mean-field type approximations gained widespread attention as a tool to analyze and understand complex network dynamics. They reduce the model\u2019s complexity by assuming that all nodes with a similar local structure behave identically. Among these methods the approximate master equation (AME) provides the most accurate description of complex networks\u2019 dynamics by considering the whole neighborhood of a node. The size of a typical network though renders the numerical solution of multistate AME infeasible. Here, we propose an efficient approach for the numerical solution of the AME that exploits similarities between the differential equations of structurally similar groups of nodes. We cluster a large number of similar equations together and solve only a single lumped equation per cluster. Our method allows the application of the AME to real-world networks, while preserving its accuracy in computing estimates of global network properties, such as the fraction of nodes in a state at a given time