Cell cycle analysis of primary sponge cell cultures

Proliferation of sponge cells is generally measured via cell counts or viability assays. However, more insight into the proliferative state of a sponge cell population can be obtained from the distribution of the cells over the different phases of the cell cycle. Cell cycle distribution of sponge cells was measured via flow cytometry after staining the DNA with propidium iodide. The five sponges studied in this paper all showed a large fraction of cells in G1/G0 compared to G2/M and S, indicating that cells were not actively dividing. In addition, some sponges also showed a large apoptotic fraction, indicating cell death. Additional apoptosis measurements, based on caspase activity, showed that harvesting and dissociation of sponge tissue to initiate a primary cell culture was directly correlated with an increase in apoptotic cells. This indicates that for the development of cell cultures, more attention should be given to harvesting, dissociation, and quality of starting material. Finally, cultivation conditions used were ineffective for proliferation, since after 2 d of cultivating Haliclona oculata cells, most cells shifted towards the apoptotic fraction, indicating that cells were dying. For development of in vitro sponge cell cultures, flow cytometric cell cycle analysis is a useful method to assess the proliferative state of a sponge cell culture and can be used to validate improvements in harvesting and dissociation, to select sponges with good proliferative capacities and to study the influence of culture conditions for stimulating cell growth

Mathematics Education in The United States of America, Finland, and Singapore: A Comparative Study

Education systems around the world must put quality instruction as a priority, even as society constantly changes. Countries have to put a bigger emphasis on the importance of science, technology, engineering, and mathematics (STEM) education in their schools as the years have progressed. Those who have a career in the STEM areas are an integral part of sustaining a country’s economy. However, not every country does the best job in teaching STEM effectively and appropriately to students. Although countries around the world teach mathematics, each one has a different approach, as seen with scores from PISA and TIMMS regarding The United States of America, Finland, and Singapore. By taking part in these tests, countries can learn from each other and possibly enhance their education regarding mathematics in their schools

A new deep sea coralline sponge from Turks and Caicos Islands: <i>Willardia caicosensis</i> gen. <i>et</i> sp. nov. (Demospongiae: Hadromerida)

A new coralline sponge, Willardia caicosensis, assigned to the family Timeidae, is described from the deep fore reef off the Turks & Caicos Islands, tropical western Atlantic Ocean, where it is common at depths ranging from 100 to 119 m. Individuals vary up to 15-20 cm in width. The relatively thin aragonitic skeleton is covered with delicate pillars up to + 1 mm. The living tissue is restricted to the spaces between pillars and a thin sheet lying above the calcareous skeleton. Exhalant canals converge upon regularly spaced central oscules on the sponge surface. Siliceous spicules include tylostyles and amphiasters which are secondarily embedded in the aragonitic moiety of the skeleton. In addition, ultrastructural characters of thechoanocytes, such as periflagellar sleeves are typical of the Order Hadromerida. Two types of cells with dense spherules are abundant in the mesohyl: sperulous cells packed with large heterogeneous inclusions, protruding at the surface of the sponge, and glycocytes with smaller ovoid corpuscles, mainly grouped along the basal calcareous skeleton. Rough collagen fibrils extend in tracts from the base of the sponge to the ectosome. Sparse bacteria are scattered in the mesohyl

Experimental investigation of the productivity of a wet separation process of traditional and bio-plastics

The separation process within a mechanical recycling plant plays a major role in the context of the production of high-quality secondary raw materials and the reduction of extensive waste disposal in landfills. Traditional plants for plastic separation employ dry or wet processes that rely on the different physical properties among the polymers. The hydraulic separator is a device employing a wet technology for particle separation. It allows the separation of two-polymer mixtures into two products, one collected within the instrument and the other one expelled through its outlet ducts. Apparatus performance were analyzed as a function of fluid and solid flow rates, flow patterns developing within the apparatus, in addition to the density, shape, and size of the polymers. For the hydraulic configurations tested, a two-way coupling takes place where the fluid exerts an influence on the plastic particles and the opposite occurs too. The interaction between the solid and liquid phases determines whether a certain polymer settles within the device or is expelled from the apparatus. Tests carried out with samples of increasing volumes of solid particles demonstrate that there are no significant differences in the apparatus effectiveness as far as a two-way interaction takes place. Almost pure concentrates of Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), and Polycarbonate (PC) can be obtained from a mixture of traditional polymers. Tests conducted on Polylactic Acid (PLA) and Mater-Bi® samples showed that the hydraulic separator can be effectively employed to separate bio-plastics from conventional plastics with remarkable grade and recovery

Reducing Embodied Carbon in the Built Environment: A Research Agenda

In spite of significant global efforts, the International Energy Agency suggests that buildings-related emissions are on track to double by 2050. Whilst operational energy efficiency continues to receive significant attention by researchers, a less well-researched area is the assessment of embodied carbon in the built environment in order to understand where the greatest opportunities for its mitigation and reduction lie. This paper reports on available mitigation strategies to tackle embodied carbon identified through a systematic review of the available academic evidence. It also investigates the scope and scale of current academic investigations to highlight where significant gaps are for impactful further research on the topic. In total, 17 mitigation strategies have been identified from within the existing literature which have been discussed individually. Results reveal that a one-size-fits-all approach is unlikely to yield beneficial results and future research should be diverse in breadth and scope, locally accurate, and significantly interdisciplinary

Benefits and challenges of visualising embodied and whole life carbon of buildings

Embodied and whole life carbon of buildings are increasingly gaining attention. However, embodied carbon calculation is still far from being common practice for sustainability assessment of buildings. Some of its greatest difficulties lie with the long life lifespan of buildings which implies a great unpredictability of future scenarios and high uncertainty of data. To help understand which life cycle stages should get the most attention when considering a building project, this paper proposes a new visualisation method based on Sankey diagrams for whole life carbon that allows one to cluster the carbon emitted in each of the life cycle stages as identified in current BS 15978 standards. With the proposed method, the carbon figures can be further broken down to account for building assemblies and components. Additionally, the method is equally suitable to account for physical quantities of what is embedded in buildings and their components. As such it can supplement some units of existing assessment methods (e.g. metal depletion measured in mass units of Feeq) and turn it into mass units of embodied steel. With such new metric, a life cycle assessment would include knowledge on flows as well as quantities. Such information could then be linked to the building permanently and smartly to be updated when necessary as the building evolves, changes, and gets upgraded, building on the theoretical foundations of the shearing layers of buildings. As such, this information could be embedded within BIM which is fully suitable to store parametric details for each building component

DeepRICH: Learning Deeply Cherenkov Detectors

Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data. In this paper we present DeepRICH, a novel deep learning algorithm for fast reconstruction which can be applied to different imaging Cherenkov detectors. The core of our architecture is a generative model which leverages on a custom Variational Auto-encoder (VAE) combined to Maximum Mean Discrepancy (MMD), with a Convolutional Neural Network (CNN) extracting features from the space of the latent variables for classification. A thorough comparison with the simulation/reconstruction package FastDIRC is discussed in the text. DeepRICH has the advantage to bypass low-level details needed to build a likelihood, allowing for a sensitive improvement in computation time at potentially the same reconstruction performance of other established reconstruction algorithms. In the conclusions, we address the implications and potentialities of this work, discussing possible future extensions and generalization.Comment: 14 pages, 9 figures, preprin

Bayesian Neural Networks With Maximum Mean Discrepancy Regularization

Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpretability, multi-task learning, and calibration. Because of the intractability of the resulting optimization problem, most BNNs are either sampled through Monte Carlo methods, or trained by minimizing a suitable Evidence Lower BOund (ELBO) on a variational approximation. In this paper, we propose a variant of the latter, wherein we replace the Kullback-Leibler divergence in the ELBO term with a Maximum Mean Discrepancy (MMD) estimator, inspired by recent work in variational inference. After motivating our proposal based on the properties of the MMD term, we proceed to show a number of empirical advantages of the proposed formulation over the state-of-the-art. In particular, our BNNs achieve higher accuracy on multiple benchmarks, including several image classification tasks. In addition, they are more robust to the selection of a prior over the weights, and they are better calibrated. As a second contribution, we provide a new formulation for estimating the uncertainty on a given prediction, showing it performs in a more robust fashion against adversarial attacks and the injection of noise over their inputs, compared to more classical criteria such as the differential entropy

Pixle: a fast and effective black-box attack based on rearranging pixels

Recent research has found that neural networks are vulnerable to several types of adversarial attacks, where the input samples are modified in such a way that the model produces a wrong prediction that misclassifies the adversarial sample. In this paper we focus on black-box adversarial attacks, that can be performed without knowing the inner structure of the attacked model, nor the training procedure, and we propose a novel attack that is capable of correctly attacking a high percentage of samples by rearranging a small number of pixels within the attacked image. We demonstrate that our attack works on a large number of datasets and models, that it requires a small number of iterations, and that the distance between the original sample and the adversarial one is negligible to the human eye