New Framework for Code-Mapping-based Reversible Data Hiding in JPEG Images

Code mapping (CM) is an efficient technique of reversible data hiding (RDH) in JPEG images, which embeds data by constructing the mapping relationship between used codes and unused codes in JPEG bitstream. In this paper, we present a new framework to design the CM-based RDH method. Firstly, to suppress the file size expansion and improve the applicability, a new code mapping strategy is proposed. Based on the proposed strategy, the mapped codes are redefined by customizing a new Huffman table thoroughly rather than selected from the unused codes in the original Huffman table. Afterwards, the key issue of designing the CM-based RDH method, i.e., constructing the code mapping, is converted into solving a combinatorial optimization problem. As a realization, a novel CM-based RDH method is introduced by employing the genetic algorithm (GA). Experimental results show that the efficacy of the proposed method with high embedding capacity and no signal distortion while suppressing file size expansion

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology

A Bayesian regression tree approach to identify the effect of nanoparticles' properties on toxicity profiles

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose- and time-response surface smoothing. The resulting posterior distribution is sampled by Markov Chain Monte Carlo. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physico-chemical properties and their marginal effect on toxicity. We illustrate the application of our method to the analysis of a library of 24 nano metal oxides.Comment: Published at http://dx.doi.org/10.1214/14-AOAS797 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Bayesian regression tree approach to identify the effect of nanoparticles properties on toxicity profiles

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose and time-response surfaces smoothing. The resulting posterior distribution is sampled via a Markov Chain Monte Carlo algorithm. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physico-chemical properties and their marginal effect on toxicity. We illustrate the application of our method to the analysis of a library of 24 nano metal oxides

Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments

A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a battery of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physico-chemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a data set on 24 metal oxides nanoparticles, characterized in relation to their electrical, crystal and dissolution properties

Comparison of the ocular surface microbiota between thyroid-associated ophthalmopathy patients and healthy subjects

PurposeThyroid-associated ophthalmopathy (TAO) is a chronic autoimmune disease. In this study, high-throughput sequencing was used to investigate the diversity and composition of the ocular microbiota in patients with TAO.MethodsPatients with TAO did not receive treatment for the disease and did not have exposed keratitis. Patients with TAO (TAO group) and healthy individuals (control group) were compared. All samples were swabbed at the conjunctival vault of the lower eyelid. The V3 to V4 region of the 16S rDNA was amplified using polymerase chain reaction and sequenced on the Illumina HiSeq 2500 Sequencing Platform. Statistical analysis was performed to analyze the differences between the groups and the correlation between ocular surface microbiota and the disease. The ocular surface microbiota of patients and healthy individuals were cultured.ResultsThe ocular surface microbiota structure of TAO patients changed significantly. The average relative abundance of Bacillus and Brevundimonas increased significantly in the TAO group. Corynebacterium had a significantly decreased relative abundance (P<0.05). Paracoccus, Haemophilus, Lactobacillus, and Bifidobacterium were positively correlated with the severity of clinical manifestations or disease activity (P<0.05). Bacillus cereus and other opportunistic pathogens were obtained by culture from TAO patients.ConclusionsThis study found that the composition of ocular microbiota in patients with TAO was significantly different from that in healthy individuals. The ocular surface opportunistic pathogens, such as Bacillus, Brevundimonas, Paracoccus, and Haemophilus in TAO patients, increase the potential risk of ocular surface infection. The findings of this study provide a new avenue of research into the mechanism of ocular surface in TAO patients

Interference in Autophagosome Fusion by Rare Earth Nanoparticles Disrupts Autophagic Flux and Regulation of an Interleukin-1β Producing Inflammasome

Engineered nanomaterials (ENMs) including multiwall carbon nanotubes (MWCNTs) and rare earth oxide (REO) nanoparticles, which are capable of activating the NLRP3 inflammasome and inducing IL-1β production, have the potential to cause chronic lung toxicity. Although it is known that lysosome damage is an upstream trigger in initiating this pro-inflammatory response, the same organelle is also an important homeostatic regulator of activated NLRP3 inflammasome complexes, which are engulfed by autophagosomes and then destroyed in lysosomes after fusion. Although a number of ENMs have been shown to induce autophagy, no definitive research has been done on the homeostatic regulation of the NLRP3 inflammasome during autophagic flux. We used a myeloid cell line (THP-1) and bone marrow derived macrophages (BMDM) to compare the role of autophagy in regulating inflammasome activation and IL-1β production by MWCNTs and REO nanoparticles. THP-1 cells express a constitutively active autophagy pathway and are also known to mimic NLRP3 activation in pulmonary macrophages. We demonstrate that, while activated NLRP3 complexes could be effectively removed by autophagosome fusion in cells exposed to MWCNTs, REO nanoparticles interfered in autophagosome fusion with lysosomes. This leads to the accumulation of the REO-activated inflammasomes, resulting in robust and sustained IL-1β production. The mechanism of REO nanoparticle interference in autophagic flux was clarified by showing that they disrupt lysosomal phosphoprotein function and interfere in the acidification that is necessary for lysosome fusion with autophagosomes. Binding of LaPO4 to the REO nanoparticle surfaces leads to urchin-shaped nanoparticles collecting in the lysosomes. All considered, these data demonstrate that in contradistinction to autophagy induction by some ENMs, specific materials such as REOs interfere in autophagic flux, thereby disrupting homeostatic regulation of activated NLRP3 complexes