‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Sequencing data has become a standard measure of diverse cellular activities. For example, gene expression is accurately measured by RNA sequencing (RNA-Seq) libraries, protein-DNA interactions are captured by chromatin immunoprecipitation sequencing (ChIP-Seq), protein-RNA interactions by crosslinking immunoprecipitation sequencing (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), DNase or MNase sequencing libraries. The processing of these sequencing techniques involves library-specific approaches. However, in all cases, once the sequencing libraries are processed, the result is a count table specifying the estimated number of reads originating from each genomic locus. Differential analysis to determine which loci have different cellular activity under different conditions starts with the count table and iterates through a cycle of data assessment, preparation and analysis. Such complex analysis often relies on multiple programs and is therefore a challenge for those without programming skills. We developed DEBrowser as an R bioconductor project to interactively visualize every step of the differential analysis, without programming. The application provides a rich and interactive web based graphical user interface built on R’s shiny infrastructure. DEBrowser allows users to visualize data with various types of graphs that can be explored further by selecting and re-plotting any desired subset of data. Using the visualization approaches provided, users can determine and correct technical variations such as batch effects and sequencing depth that affect differential analysis. We show DEBrowser’s ease of use by reproducing the analysis of two previously published data sets. DEBrowser is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.

/pdf/debrowser-interactive-differential-expression-analysis-and-3cxi6o31om.pdf

DEBrowser: interactive differential expression analysis and visualization tool for count data

Titanium dioxide (TiO2 ) is widely used in a variety of products including cosmetics. TiO2 in its nanoparticle form (nano-TiO2 ) is now the only form used as an ultraviolet (UV) filter in sunscreens, but also in some day creams, foundations and lip balms. While its efficacy as a UV filter is proven in the prevention of skin cancers and sunburns, some concerns have been raised about its safety. Indeed, considering its small size, nano-TiO2 is suspected to penetrate dermal, respiratory or gastrointestinal barriers, disseminate in the body and therefore constitute a potential risk to the consumer. At the skin level, most studies performed in humans or animals showed that nano-TiO2 did not penetrate beyond the outer layers of stratum corneum to viable cells and did not reach the general circulation, either in healthy or in compromised skin. The Scientific Committee on Consumer Safety (SCCS) considers nano-TiO2 as a non-sensitizer and as mild- or non-irritant to skin and concludes in no evidence of carcinogenicity (supported by the European Chemicals Agency), mutagenicity or reproductive toxicity after dermal exposure to nano-TiO2 . According to the SCCS, nano-TiO2 from sunscreens does not present any health risk when applied on the skin at a concentration up to 25%. However, the SCCS does not recommend the use of nano-TiO2 in formulations that may lead to exposure of the consumer's lungs by inhalation (sprayable products and powders). Indeed, even if human data are sparse and inconsistent, lung inflammation was reported in animals. In 2016, the EU Cosmetic Regulation made nano-TiO2 as an authorized UV filter, except in products that could lead to exposure of the lungs. After oral exposure, nano-TiO2 absorption and toxicity are limited. The incidental oral exposure to nano-TiO2 contained in lip balms is thus not expected to induce adverse health effects.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1111/jdv.15943

Safety of titanium dioxide nanoparticles in cosmetics

Biomedical application and hidden toxicity of Zinc oxide nanoparticles

Nanoparticles (NPs) have been shown to have good ability to improve the targeting and delivery of therapeutics. In the field of photodynamic therapy (PDT), this targeting advantage of NPs could help ensure drug delivery at specific sites. Among the commonly reported NPs for PDT applications, NPs from zinc oxide, titanium dioxide, and fullerene are commonly reported. In addition, graphene has also been reported to be used as NPs albeit being relatively new to this field. In this context, the present review is organized by these different NPs and contains numerous research works related to PDT applications. The effectiveness of these NPs for PDT is discussed in detail by collecting all essential information described in the literature. The information thus assembled could be useful in designing new NPs specific for PDT and/or PTT applications in the future.

/pdf/the-application-of-titanium-dioxide-zinc-oxide-fullerene-and-44t27r1dgp.pdf

The application of titanium dioxide, zinc oxide, fullerene, and graphene nanoparticles in photodynamic therapy

Batch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tests resulting in actual effects going unidentified. At their worst, they constitute confounds and render datasets useless. Attempting to remove batch effects will result in some of the biologically meaningful component of the measurement (i.e. signal) being lost. We present and benchmark a novel technique, called Harman. Harman maximises the removal of batch noise with the constraint that the risk of also losing biologically meaningful component of the measurement is kept to a fraction which is set by the user. Analyses of three independent publically available datasets reveal that Harman removes more batch noise and preserves more signal at the same time, than the current leading technique. Results also show that Harman is able to identify and remove batch effects no matter what their relative size compared to other sources of variation in the dataset. Of particular advantage for meta-analyses and data integration is Harman’s superior consistency in achieving comparable noise suppression - signal preservation trade-offs across multiple datasets, with differing number of treatments, replicates and processing batches. Harman’s ability to better remove batch noise, and better preserve biologically meaningful signal simultaneously within a single study, and maintain the user-set trade-off between batch noise rejection and signal preservation across different studies makes it an effective alternative method to deal with batch effects in high-throughput genomic datasets. Harman is flexible in terms of the data types it can process. It is available publically as an R package (
 https://bioconductor.org/packages/release/bioc/html/Harman.html
 ), as well as a compiled Matlab package (
 http://www.bioinformatics.csiro.au/harman/
 ) which does not require a Matlab license to run.

/pdf/risk-conscious-correction-of-batch-effects-maximising-1qob7xishr.pdf

Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets

The application of sunscreen is a critical component of a sun-safe strategy, however the possibility of unexpected, adverse outcomes resulting from long-term use of sunscreens containing nanoparticles of titanium dioxide (TiO2) and zinc oxide (ZnO) has not yet been examined. Here, immune-competent hairless mice were exposed over a 36-week period to weekly topical applications of sunscreens containing nanoparticles of ZnO or TiO2, or no metal oxide nanoparticles, with or without subsequent exposure to ultraviolet radiation (UVR). Control groups received no sunscreen applications, with or without UVR. Mice exposed to UVR in the absence of sunscreen developed statistically significant incidences of histologically-diagnosed malignant and benign skin neoplasms, whereas no statistically significant adverse biological outcomes were found in mice treated with the sunscreens containing ZnO or TiO2 nanoparticles. Elevated levels of Ti were detected in the livers of mice treated with sunscreen containing TiO2 nanoparticles compared to untreated control, but total Zn concentrations did not significantly alter in any major organs except for the skin of mice treated with ZnO sunscreen. Exposure to UVR did not have a significant impact on examined tissue concentrations of Zn or Ti. Few to no transcriptional changes were found in ZnO or TiO2-treated groups, but mice treated with the sunscreen containing only organic filters showed substantial gene disregulation. Taken together with previous work, this long-term study provided no basis to avoid the use of sunscreens containing metal oxide nanoparticles.

/pdf/long-term-exposure-to-commercially-available-sunscreens-ya4ur2wq56.pdf

Long-term exposure to commercially available sunscreens containing nanoparticles of TiO2 and ZnO revealed no biological impact in a hairless mouse model.

Coatings applied to the surfaces of zinc oxide (ZnO) nanoparticles have been shown to decrease the capacity of uncoated nanoparticles to generate free radicals, as well as to reduce their solubility, and may be utilized in safetyby- design approaches to decrease ZnO nanoparticle toxicity. The liver is a target organ for ZnO nanoparticles, or ionic zinc released from ZnO nanoparticles, whether the route of entry is by dermal absorption, ingestion, injection or inhalation. Liver injury and fibrosis have been demonstrated in vivo in response to ZnO nanoparticle treatment, and cell injury has also been shown in vitro using immortalized hepatocyte cell lines. Despite their activation being a critical step for liver fibrosis, however, the response of human hepatic stellate cells (hHSCs) to ZnO nanoparticles has not yet been reported. Here, hHSCs were treated with four types of commercially-available ZnO nanoparticles suspended in cell culture medium. Two types were uncoated nanoparticles of different sizes, and the other two types with similar particle size carried different surface coatings. Cells were also treated with pre-dissolved ZnSO4 to provide a comparison of the cellular responses induced by ionic zinc. A systems biology approach was utilized whereby the activation of cell signaling pathways, changes in the transcriptome, and alterations in cellular function were assessed over a 24 h period. Treatment with both types of uncoated ZnO nanoparticles mobilized pathways and responses centered on cellular stress, survival, and apoptosis. Both surface coatings conferred almost complete protection against ZnO nanoparticle-induced cytotoxicity. Ionic zinc had little effect on these cells indicating that toxicity is mainly induced by the nanoparticles. The results presented here support the use of surface coatings in commercial applications of ZnO nanoparticles where there is human exposure.

/pdf/surface-coatings-protect-against-the-in-vitro-toxicity-of-2l1hz4bcuu.pdf

Surface Coatings Protect against the In vitro Toxicity of Zinc OxideNanoparticles in Human Hepatic Stellate Cells

The widespread adoption of advanced technologies, such as Artificial Intelligence (AI), Machine Learning, and Robotics, is rapidly increasing across the globe. This accelerated pace of change is drastically transforming various aspects of our lives and work, resulting in what is now known as Industry 4.0. As businesses integrate these technologies into their daily operations, it significantly impacts their work tasks and required skill sets. However, the approach to technological transformation varies depending on location, industry, and organization. However, there are no published methods that can adequately forecast the adoption of technology and its impact on society. It is essential to prepare for the future impact of Industry 4.0, and this requires policymakers and business leaders to be equipped with scientifically validated models and metrics. Data-driven scenario planning and decision-making can lead to better outcomes in every area of the business, from learning and development to technology investment. However, the current literature falls short in identifying effective and globally applicable strategies to predict the adoption rate of emerging technologies. Therefore, this paper proposes a novel parametric mathematical model for predicting the adoption rate of emerging technologies through a unique data-driven pipeline. This approach utilizes global indicators for countries to predict the technology adoption curves for each country and industry. The model is thoroughly validated, and the paper outlines highly promising evaluation results. The practical implications of this proposed approach are significant because it provides policymakers and business leaders with valuable insights for decision-making and scenario planning.

/pdf/a-cognitive-model-for-technology-adoption-1xhv0add.pdf

Fariborz Sobhanmanesh

Papers

Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets

Long-term exposure to commercially available sunscreens containing nanoparticles of TiO2 and ZnO revealed no biological impact in a hairless mouse model.

Surface Coatings Protect against the In vitro Toxicity of Zinc OxideNanoparticles in Human Hepatic Stellate Cells

A Cognitive Model for Technology Adoption