Kinetic and equilibrium studies of fluoride adsorption by a carbonaceous material from pyrolysis of waste sludge

The efficiency of the adsorption for fluoride by sludge from the treatment of starch industry wastewater was investigated. Batch experiments were conducted in order to determine the parameters that affect the adsorption process. The activation for waste sludge and specific surface area and porosity effects in enhancing the pyrolysis conditions were determined. The adsorption parameters of initial fluoride concentration, pH and adsorbent dosage were investigated with carbonaceous material. As a result of pyrolysis of samples treated with ZnCl₂ 11% m²/g; the specific surface area was reached. Correlation coefficient of 0.99 and 12.75 mg/g adsorption capacity and adsorption isotherm model were revealed as convenient. Experimental results show that the adsorption of fluoride waste sludge will be effective in many ways in which the adsorbent is applied

DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks

Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi-task feed-forward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction. DEEPred was optimized through rigorous hyper-parameter tests, and benchmarked using three types of protein descriptors, training datasets with varying sizes and GO terms form different levels. Furthermore, in order to explore how training with larger but potentially noisy data would change the performance, electronically made GO annotations were also included in the training process. The overall predictive performance of DEEPred was assessed using CAFA2 and CAFA3 challenge datasets, in comparison with the state-of-the-art protein function prediction methods. Finally, we evaluated selected novel annotations produced by DEEPred with a literature-based case study considering the 'biofilm formation process' in Pseudomonas aeruginosa. This study reports that deep learning algorithms have significant potential in protein function prediction; particularly when the source data is large. The neural network architecture of DEEPred can also be applied to the prediction of the other types of ontological associations. The source code and all datasets used in this study are available at: https://github.com/cansyl/DEEPred

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

„Die Rolle des „retinoic-acid-receptor-related orphan receptor α“ RORα bei der Systemischen Sklerose“

Diese Studie zeigt eine verminderte RORα-Expression bei SSc-Patienten, im Vergleich zu gesunden Kontrollprobanden. Die synthetischen RORα-Antagonisten, SR1001 und SR3335, werden sowohl in vitro, als auch in vivo als signifikant hemmend auf die Kollagensynthese charakterisiert. Die molekularen Wirkungszusammenhänge sind noch weitestgehend unklar und bedürfen weiterer Forschung

Low-velocity impact response of E-glass reinforced thermoset and thermoplastic based sandwich composites

This paper presents an experimental investigation on impact response of sandwich composite panels with thermoplastic and thermoset face-sheet. E-glass reinforced epoxy (thermoset) and polypropylene (thermoplastic) have been used to produce polymer composite face-sheets. PVC foam was used as a core material. Several low velocity impact tests were performed under various impact energies. Besides to the individual impact behavior of the thermoset and thermoplastic sandwich composites, the impact response of sandwich composites having hybrid sequences was also investigated. Along with images of damaged samples, variations of the impact characteristics such as absorbed energy, maximum contact force and maximum deflection of the samples are provided. Most particularly this study showed that sandwich composites must have the harmony between core and the face sheet material. The deformation required for core densification must be able to compensate by the face sheet material. (C) 2017 Elsevier Ltd. All rights reserved

Vanadium(V) Removal by Adsorption onto Activated Carbon Derived from Starch Industry Waste Sludge

This study examines the adsorption potential of activated carbons for vanadium (V) removal from aqueous solution. Activated carbons were produced via chemical activation of waste treatment sludge from the starch industry. Specific surface area and pore sizes of waste sludge samples were determined through chemical activation and pyrolysis. Experimental data indicated that sludge samples had micropore structure and specific surface area of up to 1196 m(2)/g. First-order and second-order models were applied to determine adsorption kinetics. Freundlich, Langmuir, and Dubinin-Radushkevich isotherms were used to analyze equilibrium data of adsorption. Equilibrium adsorption data showed the best fit to the Freundlich isotherm. Adsorption of vanadium (V) follows second-order kinetic models. Maximum adsorption was observed at pH 4.0. Langmuir adsorption capacity was found to be 37.17 mg/g. The results of the study indicated that activated carbon obtained from industrial sewage sludge was effective in removing vanadium from aqueous solutions, which creates a significant advantage for treatment of industrial wastewaters and management of solid wastes