Understanding cellular internalization pathways of silicon nanowires

BACKGROUND: Understanding how cells interact with nanomaterials is important for rational design of nanomaterials for nanomedicine and transforming them for clinical applications. Particularly, the mechanism for one-dimensional (1D) nanomaterials with high aspect ratios still remains unclear. RESULTS: In this work, we present amine-functionalized silicon nanowires (SiNW-NH2) entering CHO-β cells via a physical membrane wrapping mechanism. By utilizing optical microscopy, transmission electron microscopy, and confocal fluorescence microscopy, we successfully visualized the key steps of internalization of SiNW-NH2 into cells. CONCLUSION: Our results provide insight into the interaction between 1D nanomaterials and confirm that these materials can be used for understanding membrane mechanics through physical stress exerted on the membrane

Learning Segmentation Masks with the Independence Prior

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances' poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201

Analysis of Automated Fault Detection and Diagnostics Records as an Indicator of HVAC Fault Prevalence: Methodology and Preliminary Results

Faults in commercial buildings can cause energy waste and other performance problems such as reduced occupant comfort, reduced equipment longevity, and increased noise. However, it is currently unknown how commonly faults occur in different equipment types. This paper describes a method to estimate the prevalence of faults in air handling units, air terminal units, and rooftop units and the use of three metrics for summarizing results. This method was developed by the authors as part of a study which includes data from several automated fault detection and diagnostics (AFDD) data providers, providing a large sample with a wide range of building types, geographical locations, and equipment types. This dataset includes fault diagnoses from thousands of buildings throughout the United States, as well as anonymized metadata describing the building and equipment characteristics. The number of fault records is on the order of 106. We describe here how the data from different data providers can be processed and unified using a common taxonomy, and illustrate three metrics that can provide insights using this type of data. The methods developed for this study are illustrated here with preliminary data. This work supports a multi-year, multi-institutional project that will provide insight into the drivers of fault prevalence; for example, whether prevalence is correlated with characteristics like building type, building size, and geographical location (including related factors like local climate and utility rates). We discuss some of the challenges of harmonizing disparate outputs from multiple AFDD providers, the usefulness of applying a unifying fault taxonomy, and provide preliminary figures that illustrate three fault prevalence metrics

Bayesian Networks for Whole Building Level Fault Diagnosis and Isolation

Buildings consume more than 40% of primary energy in the U.S. and 57% of the energy usage in commercial and residential buildings are consumed by the heating, ventilation and air conditioning (HVAC) system.Malfunctioning sensors, components, and control systems, as well as degrading systems in HVAC and lighting systems are main reasons for energy waste and unsatisfactory indoor environment. In HVAC systems, faults occur in one component or equipmentcan also cause abnormality in other subsystems because of the coupling among different subsystems. Therefore, whole building level fault diagnosis methods is critical to locate fault root cause and isolate the fault. Bayesian network (BN) is a prevalent toolin fault diagnosis which can deal withprobabilistic reasoning of uncertainty. In this paper, a two-layer Bayesian network which consists of fault layer and fault symptom layer is developed to diagnose whole building HVAC system fault. Weather information based Pattern Matching (WPM) method which was employed in fault detection was also used to create baseline data and generate LEAK probability. BAS data from a campus building are collected to evaluate the effectiveness of the proposed method

Unaprijeđeni algoritam za praćenje putanje na neravnoj cesti

The path planning problem is an important problem in the research area of robot, games and group animation. This paper shows a 2.5-dimensional terrain grid which can reduce the amount of computation. By applying the fuzzy logic theory, the terrain trafficability of the rugged road can be evaluated based on different gradient, roughness, elevation difference; the trafficability factor can be achieved and applied to the heuristic function. The improved algorithm can solve the symmetry problem of path planning on uneven surfaces, reduce the search space.Problem planirana putanje je važan problem u istraživačkom području robotike, igara i grupne animacije. U ovom radu teren je predstavljen 2.5-dimenzionalnom mrežom što može smanjiti vrijeme računanja. Korištenjem teorije neizrazite logike prohodnost neravne ceste može se procijeniti na osnovu razlike gradijenata, nagiba i grbavosti, te se može odrediti faktor prohodnosti koji je primijenjiv na heurističku funkciju. Unaprijeđeni algoritam može riješiti problem simetrije kod planiranja putanje na neravnim površinama i smanjiti prostor pretraživanja

Second harmonic optical coherence tomography

Second harmonic optical coherence tomography, which uses coherence gating of second-order nonlinear optical response of biological tissues for imaging, is described and demonstrated. Femtosecond laser pulses were used to excite second harmonic waves from collagen harvested from rat tail tendon and a reference nonlinear crystal. Second harmonic interference fringe signals were detected and used for image construction. Because of the strong dependence of second harmonic generation on molecular and tissue structures, this technique offers contrast and resolution enhancement to conventional optical coherence tomography.Comment: 3 pages, 5 figures. Submitted on November 8, 2003, this paper has recently been accepted by Optics Letter