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

Perceptions of Clickbait: A Q-Methodology Approach

Clickbait is “content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page” (“clickbait,” n.d.). The term is also generally used to refer specifically to the attention-grabbing headlines. Critics of clickbait argue that clickbait is shallow, misleading, and ubiquitous – “a new word that has become synonymous with online journalism” (Frampton, 2015). It is the subject of a small, but growing number of studies in disciplines ranging from linguistics, communications, and information sciences. Palau-Sampio (2016) analyzed linguistic strategies associated with tabloid journalism in the Spanish digital newspaper Elpais.com, concluding that there is a trend towards lower quality news reporting. In their research on Danish news sites, Blom & Hansen (2015) identified forward-referencing, specifically the use of empty pronouns to create an information gap, as a feature of clickbait headlines. Chen, Conroy & Rubin (2015) proposed that automatic identification of clickbait could draw upon three types of features: a) lexico-semantic and pragmatic linguistic patterns (e.g. unresolved pronouns, affective and suspenseful language, action words, overuse of numerals, and reverse narratives), b) incongruent image placement with a possible emotional load, and c) user reading and commenting behavior. An effort in automated identification of clickbait by Potthast, et al. (2016) achieved 79% accuracy on Twitter tweets. But debate still rages over what the word actually means (Gardiner, 2015)

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