nZVI particles production for the remediation of soil and water polluted by inorganic Lead

The present study deals with experiments of Pb removal by nano-Zero Valent Iron (nZVI) in aqueous solution and in soil. Synthetic Pb aqueous solutions were treated by nZVI, at a fixed Pb concentration of 100 mg L-1 , varying nanoparticles initial concentration in the range between 27 and 270 mg nZVI L-1 . A kinetic study was carried out: Pb adsorption followed a first order kinetic, and half life times between 11 and 26.66 min were determined. Soil samples were first characterized, and Pb speciation and concentration by sequential extractions was determined. Adsorption tests were then carried out at three selected amounts of nZVI, to allow Pb stabilization in the soil matrix. To evaluate the treatment efficiency, sequential extractions were also performed on the treated samples

Hexavalent chromium reduction in manganese-rich soils by ZVI nanoparticles: the influence of natural organic matter and manganese oxides

Hexavalent chromium reduction by nano Zero-Valent Iron (nZVI) has been proved fast and efficient, mainly due to nanoparticles large specific surface area and high chemical reactivity. In this work the influence of natural organic matter and manganese oxide was investigated, through a set of experimental tests carried out on a real polluted soils naturally rich in manganese. Soil samples were characterized in terms of initial concentration of Cr, Cr(VI), Mn, pH, and TOC and three different nZVI solutions were used (120, 360 and 600 mg nZVI L-1 ) for the treatment. At selected interval times (0, 5, 10, 15, 30, 60, 120 min) a slurry sample was filtered and Cr(VI) residual concentration and pH were measured. The same procedure was carried out on an artificial spiked soil, characterized by a similar TOC and poor of Mn. Furthermore the two soils were mixed with different amounts of leonardite, to evaluate the influence of NOM on treatment efficiency

Continuous production of KNO3 nanosalts for the fertilization of soil by means of a Spinning Disk Reactor

In this study the production of high soluble material nanoparticles was successfully performed by means of a spinning disk reactor (SDR). This result was possible due to the use of a potassium nitrate saturated solution, which was continuously recycled back to the reactor after removal of the produced solid nanoparticles. Several process configurations were checked. It appears to be mandatory that the recycled saturated solution must be free of residual nanoparticles since their presence would lead to heterogeneous nucleation. In this respect, a small amount of nitric acid was added to the stream to permit the residual nanoparticle dissolution. Moreover, a spiral wounded piping system was developed in order to increase both the contact time and the mixing condition of the saturated solution with the added acid before entering the SD

A complex pathway for 3 ' processing of the yeast U3 snoRNA

Mature U3 snoRNA in yeast is generated from the 3′-extended precursors by endonucleolytic cleavage followed by exonucleolytic trimming. These precursors terminate in poly(U) tracts and are normally stabilised by binding of the yeast La homologue, Lhp1p. We report that normal 3′ processing of U3 requires the nuclear Lsm proteins. On depletion of any of the five essential proteins, Lsm2–5p or Lsm8p, the normal 3′-extended precursors to the U3 snoRNA were lost. Truncated fragments of both mature and pre-U3 accumulated in the Lsm-depleted strains, consistent with substantial RNA degradation. Pre-U3 species were co-precipitated with TAP-tagged Lsm3p, but the association with spliced pre-U3 was lost in strains lacking Lhp1p. The association of Lhp1p with pre-U3 was also reduced on depletion of Lsm3p or Lsm5p, indicating that binding of Lhp1p and the Lsm proteins is interdependent. In contrast, a tagged Sm-protein detectably co-precipitated spliced pre-U3 species only in strains lacking Lhp1p. We propose that the Lsm2–8p complex functions as a chaperone in conjunction with Lhp1p to stabilise pre-U3 RNA species during 3′ processing. The Sm complex may function as a back-up to stabilise 3′ ends that are not protected by Lhp1p

Thyroid disease treatment prediction with machine learning approaches

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists' activities and improve the quality of the patients' life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient's treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%

Optimizing investments in national-scale forest landscape restoration in Uganda to maximize multiple benefits

Forest loss and degradation globally has resulted in declines in multiple ecosystem services and reduced habitat for biodiversity. Forest landscape restoration offers an opportunity to mitigate these losses, conserve biodiversity, and improve human well-being. As part of the Bonn Challenge, a global effort to restore 350 million hectares of deforested and degraded land by 2030, over 30 countries have recently made commitments to national forest landscape restoration. In order to achieve these goals, decision-makers require information on the potential benefits and costs of forest landscape restoration to efficiently target investments. In response to this need, we developed an approach using a suite of ecosystem service mapping tools and a multi-objective spatial optimization technique that enables decision-makers to estimate the potential benefits and opportunity costs of restoration, visualize tradeoffs associated with meeting multiple objectives, and prioritize where restoration could deliver the greatest benefits.Wedemonstrate the potential of this approach in Uganda, one of the nations committed to the Bonn Challenge. Using maps of the potential benefits and costs of restoration and efficiency frontiers for optimal restoration scenarios, we were able to communicate how ecosystem services benefits vary spatially across the country and how different weights on ecosystem services objectives can affect the allocation of restoration across Uganda. This work provides a generalizable approach to improve investments in forest landscape restoration and illuminates the tradeoffs associated with alternative restoration strategies.UKAid from the UK government through the International Union for Conservation of Nature’s KnowFor program as well as by the Natural Capital Project, a partnership between the University of Minnesota, Stanford University, the World Wildlife Fund, and the Nature Conservancy. MG was supported by the National Research Foundation of South Africa (Grant Number 98889).http://http://iopscience.iop.org1748-9326am2017Plant Scienc

Scheduling M2M traffic over LTE uplink of a dense small cell network

We present an approach to schedule Long Term Evolution (LTE) uplink (UL) Machine-to-Machine (M2M) traffic in a densely deployed heterogeneous network, over the street lights of a big boulevard for smart city applications. The small cells operate with frequency reuse 1, and inter-cell interference (ICI) is a critical issue to manage. We consider a 3rd Generation Partnership Project (3GPP) compliant scenario, where single-carrier frequency-division multiple access (SC-FDMA) is selected as the multiple access scheme, which requires that all resource blocks (RBs) allocated to a single user have to be contiguous in the frequency within each time slot. This adjacency constraint limits the flexibility of the frequency-domain packet scheduling (FDPS) and inter-cell interference coordination (ICIC), when trying to maximize the scheduling objectives, and this makes the problem NP-hard. We aim to solve a multi-objective optimization problem, to maximize the overall throughput, maximize the radio resource usage and minimize the ICI. This can be modelled through a mixed-integer linear programming (MILP) and solved through a heuristic implementable in the standards. We propose two models. The first one allocates resources based on the three optimization criteria, while the second model is more compact and is demonstrated through numerical evaluation in CPLEX, to be equivalent in the complexity, while it performs better and executes faster. We present simulation results in a 3GPP compliant network simulator, implementing the overall protocol stack, which support the effectiveness of our algorithm, for different M2M applications, with respect to the state-of-the-art approaches

The Role of Histone H4 Biotinylation in the Structure of Nucleosomes

Background: Post-translational modifications of histones play important roles in regulating nucleosome structure and gene transcription. It has been shown that biotinylation of histone H4 at lysine-12 in histone H4 (K12Bio-H4) is associated with repression of a number of genes. We hypothesized that biotinylation modifies the physical structure of nucleosomes, and that biotin-induced conformational changes contribute to gene silencing associated with histone biotinylation. Methodology/Principal Findings: To test this hypothesis we used atomic force microscopy to directly analyze structures of nucleosomes formed with biotin-modified and non-modified H4. The analysis of the AFM images revealed a 13% increase in the length of DNA wrapped around the histone core in nucleosomes with biotinylated H4. This statistically significant (p,0.001) difference between native and biotinylated nucleosomes corresponds to adding approximately 20 bp to the classical 147 bp length of nucleosomal DNA. Conclusions/Significance: The increase in nucleosomal DNA length is predicted to stabilize the association of DNA with histones and therefore to prevent nucleosomes from unwrapping. This provides a mechanistic explanation for the gene silencing associated with K12Bio-H4. The proposed single-molecule AFM approach will be instrumental for studying the effects of various epigenetic modifications of nucleosomes, in addition to biotinylation

Missing value imputation for microarray gene expression data using histone acetylation information

<p>Abstract</p> <p>Background</p> <p>It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.</p> <p>Results</p> <p>The paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(<it>k</it>-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.</p> <p>Conclusion</p> <p>We demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.</p