Flame Retardant Nanocoatings for the Protection of Polyurethane Foam

Layer-by-layer (LbL) assembly is a simple technique capable of building multifunctional thin films on a variety of surfaces from dilute aqueous solutions. LbL coatings on polyurethane foam have been successful in reducing the flammability through environmentally friendly means. This technology provides a potential avenue for replacing halogenated flame retardants which are successfully used on foams, but present a toxic threat to health and the environment. A thin film nano-brick wall structure composed of chitosan and vermiculite clay was combined with an all-polymer film of chitosan and ammonium polyphosphate to form a stacked coating on polyurethane foam to reduce flammability. Individually, the coatings were able to reduce flammability of the foam, however the all-polymer coating was unable to prevent total degradation of the polyurethane due to inability to form char prior to the collapse of the foam. The nano-brick wall provided the necessary structure to allow the all-polymer coating to act and form an expanded insulating char layer that prevents flame spread across the surface of the polyurethane as well as reduce the peak heat release rate of the foam significantly. Incorporating carbon nanotubes into a LbL assembly allowed further reductions in polyurethane foam flammability. Only a few layers of nanotube-containing polymer layers were able to completely prevent flame propagation in both horizontal and vertical flame tests. Cone calorimetry revealed significant reductions in peak heat release rate as well as total smoke release. Reduction in heat release rates and smoke release are important factors towards extending escape time in a fire scenario. Barrier fabrics are commonly used to protect flammable materials. A polyelectrolyte complex was used to coat cotton fabric and prevented flame spread and ignition of underlying polyurethane foam. This study also highlights the importance of testing combined fabric and foam assemblies as pertaining to upholstered furniture. Cone calorimetry is a useful instrument to ascertain interactions between varying fabric and foam compositions and potentially will highlight an appropriate method for flame retarding the combination

The Incremental Information Content of \u3cem\u3eSAS No. 59\u3c/em\u3e Going-Concern Opinions

The purpose of this paper is to evaluate whether the expanded requirements of SAS No. 59 (A/CPA [1988]), which requires auditors to actively evaluate and report on a client\u27s going-concern status for the coming year, have allowed investors to make more accurate ex ante assessments of firms that eventually file for bankruptcy. We extend Chen and Church [1996] (hereafter CC), who conclude that SAS No. 34 (AICPA [1981]) subject to going-concern opinions have information value because they reduce the surprise associated with bankruptcy announcements. We hypothesize that if SAS No. 59 has achieved what was intended, going-concern opinions issued under SAS No. 59 should further reduce investor surprise at bankruptcy announcements. While we do not believe SAS No. 59 was issued for the specific purpose of helping users to predict bankruptcy, we do suggest that the increased auditor responsibility and improved communication should provide users with information that is of relatively higher quality. This argument is based on a number of important differences between SAS No. 34 and SAS No. 59

Hybrid deep learning approach to improve classification of low-volume high-dimensional data

Background: The performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in feature generation but requires large datasets to achieve high classification accuracy. Biology domains typically exhibit these challenges with numerous handcrafted features (high-dimensional) and small amounts of training data (low volume).Method: A hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these features to re-express the data for input to a non-deep learning method, which is trained to perform the final classification.Results: The approach is systematically evaluated to determine the best layer of the deep learning network from which to extract features and the threshold on training data volume that prefers this approach. Results from several domains show that this hybrid approach outperforms standalone deep and non-deep learning methods, especially on low-volume, high-dimensional datasets. The diverse collection of datasets further supports the robustness of the approach across different domains.Conclusions: The hybrid approach combines the strengths of deep and non-deep learning paradigms to achieve high performance on high-dimensional, low volume learning tasks that are typical in biology domains

Making adaptable systems work for mission operations: A case study

The Advanced Multimission Operations System (AMMOS) at NASA's Jet Propulsion Laboratory is based on a highly adaptable multimission ground data system (MGDS) for mission operations. The goal for MGDS is to support current flight project science and engineering personnel and to meet the demands of future missions while reducing associated operations and software development costs. MGDS has become a powerful and flexible mission operations system by using a network of heterogeneous workstations, emerging open system standards, and selecting an adaptable tools-based architecture. Challenges in developing adaptable systems for mission operations and the benefits of this approach are described

Extracellular ATP triggers proteolysis and cytosolic Ca²⁺ rise in Plasmodium berghei and Plasmodium yoelii malaria parasites.

BACKGROUND: Plasmodium has a complex cell biology and it is essential to dissect the cell-signalling pathways underlying its survival within the host. METHODS: Using the fluorescence resonance energy transfer (FRET) peptide substrate Abz-AIKFFARQ-EDDnp and Fluo4/AM, the effects of extracellular ATP on triggering proteolysis and Ca²⁺ signalling in Plasmodium berghei and Plasmodium yoelii malaria parasites were investigated. RESULTS: The protease activity was blocked in the presence of the purinergic receptor blockers suramin (50 μM) and PPADS (50 μM) or the extracellular and intracellular calcium chelators EGTA (5 mM) and BAPTA/AM (25, 100, 200 and 500 μM), respectively for P. yoelii and P. berghei. Addition of ATP (50, 70, 200 and 250 μM) to isolated parasites previously loaded with Fluo4/AM in a Ca²⁺-containing medium led to an increase in cytosolic calcium. This rise was blocked by pre-incubating the parasites with either purinergic antagonists PPADS (50 μM), TNP-ATP (50 μM) or the purinergic blockers KN-62 (10 μM) and Ip5I (10 μM). Incubating P. berghei infected cells with KN-62 (200 μM) resulted in a changed profile of merozoite surface protein 1 (MSP1) processing as revealed by western blot assays. Moreover incubating P. berghei for 17 h with KN-62 (10 μM) led to an increase in rings forms (82% ± 4, n = 11) and a decrease in trophozoite forms (18% ± 4, n = 11). CONCLUSIONS: The data clearly show that purinergic signalling modulates P. berghei protease(s) activity and that MSP1 is one target in this pathway

Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informative representation that addresses the need for defining relevant features. However, deep learning models are computationally expensive, and they require large training datasets to achieve good classification performance. Results One approach to addressing these challenges is to use a less complex deep learning network for feature selection and Machine Learning (ML) for classification. In the current study, we introduce a hybrid DL-ML approach that uses a deep neural network for extracting molecular features and a non-DL classifier to predict environmentally responsive transgenerational differential DNA methylated regions (DMRs), termed epimutations, based on the extracted DL-based features. Various environmental toxicant induced epigenetic transgenerational inheritance sperm epimutations were used to train the model on the rat genome DNA sequence and use the model to predict transgenerational DMRs (epimutations) across the entire genome. Conclusion The approach was also used to predict potential DMRs in the human genome. Experimental results show that the hybrid DL-ML approach outperforms deep learning and traditional machine learning methods

Flame Retardant Nanocoatings for the Protection of Polyurethane Foam

Layer-by-layer (LbL) assembly is a simple technique capable of building multifunctional thin films on a variety of surfaces from dilute aqueous solutions. LbL coatings on polyurethane foam have been successful in reducing the flammability through environmentally friendly means. This technology provides a potential avenue for replacing halogenated flame retardants which are successfully used on foams, but present a toxic threat to health and the environment. A thin film nano-brick wall structure composed of chitosan and vermiculite clay was combined with an all-polymer film of chitosan and ammonium polyphosphate to form a stacked coating on polyurethane foam to reduce flammability. Individually, the coatings were able to reduce flammability of the foam, however the all-polymer coating was unable to prevent total degradation of the polyurethane due to inability to form char prior to the collapse of the foam. The nano-brick wall provided the necessary structure to allow the all-polymer coating to act and form an expanded insulating char layer that prevents flame spread across the surface of the polyurethane as well as reduce the peak heat release rate of the foam significantly. Incorporating carbon nanotubes into a LbL assembly allowed further reductions in polyurethane foam flammability. Only a few layers of nanotube-containing polymer layers were able to completely prevent flame propagation in both horizontal and vertical flame tests. Cone calorimetry revealed significant reductions in peak heat release rate as well as total smoke release. Reduction in heat release rates and smoke release are important factors towards extending escape time in a fire scenario. Barrier fabrics are commonly used to protect flammable materials. A polyelectrolyte complex was used to coat cotton fabric and prevented flame spread and ignition of underlying polyurethane foam. This study also highlights the importance of testing combined fabric and foam assemblies as pertaining to upholstered furniture. Cone calorimetry is a useful instrument to ascertain interactions between varying fabric and foam compositions and potentially will highlight an appropriate method for flame retarding the combination