Effect of the food-to-microorganism (F/M) ratio on the formation and size of aerobic sludge granules

Laboratory experiments were carried out to investigate the effect of the sludge loading, or the food-to-microorganism (F/M) ratio, on the rate of aerobic granulation and the size of the granules in biological wastewater treatment. Four column batch reactors were used with a similar sludge suspended solids (SS) concentration of around 2000 mg/L. The reactors were fed with a glucose-based wastewater at different chemical oxygen demand (COD) concentrations, resulting in F/M ratios from 0.3 to 1.1 g COD/g SS-d. A higher F/M ratio appeared to promote faster formation of larger granules and a lower F/M ratio led to slower formation of smaller granules. Upon complete granulation, the granules became rather stable in size, and the mean diameter of the granules in different reactors increased from 1.2 to 4.5 mm linearly with the F/M ratio applied. Molecular analysis of the sludge did not show the domination of any particular bacterial species during the granulation process. It is apparent that applying different F/M ratios in different granulation stages, e.g., a higher F/M in the early stage and a reduced F/M in the later stage, can be an effective start-up strategy to facilitate rapid granule formation and sustain small and healthy granules in bioreactors. © 2011 Elsevier Ltd. All rights reserved.postprin

Granular activated carbon for aerobic sludge granulation in a bioreactor with a low-strength wastewater influent

Aerobic sludge granulation is rather difficult or impossible for the treatment of low-strength wastewater. In this study, a novel technique involving granular activated carbon (GAC) was developed for rapid aerobic granulation under a low organic loading condition. Laboratory experiments were conducted with two sequencing batch reactors (SBRs) running side by side. One reactor had fine GAC added to the sludge mixture, and the other had no GAC added. A low-strength organic wastewater with a chemical oxygen demand (COD) concentration of only 200 mg/L was used as the influent to the SBRs. The morphology, physical properties, and bacterial community structure of the sludge in the two reactors were characterized and compared throughout the experiments. The results showed that granules could not be formed in the SBR without added GAC. However, complete granulation was achieved in the SBR with GAC addition. Selective discharge of slow settling sludge was also essential to the granulation process. Adding GAC to the seed sludge mixture, together with the selective discharge of small and loose sludge flocs, facilitated the retention and growth of bacterial cells on GAC in attached-growth mode, leading to complete granulation. In addition, the use of GAC produced aerobic granules with strong cores to help maintain the long-term stability of mature granules. With granulation, the solid-liquid separation property of the sludge was greatly improved. Once granules were formed, the granules were quite stable and GAC addition was no longer needed. Therefore, adding GAC is a simple and effective strategy to initiate granule formation for complete sludge granulation in bioreactors treating low-strength organic wastewater. © 2011 Elsevier B.V. All rights reserved.postprin

Fate of aerobic bacterial granules with fungal contamination under different organic loading conditions

Aerobic sludge granulation is an attractive new technology for biological wastewater treatment. However, the instability of aerobic granules caused by fungal growth is still one of the main problems encountered in granular bioreactors. In this study, laboratory experiments were conducted to investigate the fate and transformation of aerobic granules under different organic loading conditions. Bacterial granules (2-3 mm) in a poor condition with fungi-like black filamentous growth were seeded into two 1 L batch reactors. After more than 100 d of cultivation, the small seed granules in the two reactors had grown into two different types of large granules (>20 mm) with different and unique morphological features. In reactor R1 with a high organic loading rate of 2.0 g COD L-1 d-1, the black filaments mostly disappeared from the granules, and the dominance of rod-shaped bacteria was recovered. In contrast, at a low loading of 0.5 g COD L-1 d-1 in reactor R2, the filaments eventually became dominant in the black fungal granules. The bacteria in R1 granules had a unique web-like structure with large pores of a few hundred μm in size, which would allow for effective substrate and oxygen transport into the interior of the granules. DNA-based molecular analysis indicated the evolution of the bacterial population in R1 and that of the eukaryal community in R2. The experimental results suggest that a high loading rate can be an effective means of helping to control fungal bloom, recover bacterial domination and restore the stability of aerobic granules that suffer from fungal contamination. © 2009 Elsevier Ltd. All rights reserved.postprin

Phytochemical constituents and antioxidant properties of acetone extract of Cleome gynandra (L.) growing in the Eastern Cape, South Africa.

Background: Several wild vegetables have been reported for their therapeutic benefits in South Africa. Many of these plants including Cleome gynandra (L.) lack scientific reports on its significance in folkloric medicine. Therefore, this study was undertaken to evaluate quantitatively the compositions of phytochemicals and antioxidant properties of acetone extract of different parts of C. gynandra.Materials and Method: Antioxidant activities were assessed against ferric reducing power, ABTS (2, 2’- azino-bis-3-ethyl benzothiazoline-6- sulfonic acid) diammonium salt, DPPH (1, 1- diphenyl-2-picrylhydrazyl) and NO (nitric oxide) radical scavenging activities. Total phenolics, flavonoids, flavanols, proanthocyanidins, tannins, saponins and alkaloids were also investigated.Results: Amongst the different plant parts, the leaf extract had the highest concentration of total phenolics (126.79 ± 0.55 mg/g), flavonoids (40.58 ± 0.06 mg/g) and flavanols (42.41 ± 0.05 mg/g) while the stem extract had the highest amount of proanthocyanidins (419.01 ± 0.67 mg/g) compared to the leaves (403.29 ± 0.89 mg/g) and fruits (107.18 ± 0.59 mg/g). The reducing power of the extracts was significantly higher than that of the standard drugs used in a concentration dependent manner. The activities of the plant extracts against ABTS, DPPH and NO radicals were dose responsive with IC50 value of 0.2, 0.1 and 0.03 mg/g respectively.Conclusion: C. gynandra possesses high secondary metabolites which accounts for its strong antioxidant ability thus justifying its use as natural occurring antioxidants in folkloric medicine. The study encourages a regular consumption of this wild vegetable in order to avert oxidative stress related diseases.Key words: Cleome gynandra, natural antioxidant, polyphenolics, antioxidant activity, phytochemical constituents

On the Correlations between Flavour Observables in Minimal U(2)^3 Models

The stringent correlations between flavour observables in models with CMFV are consistent with the present data except for the correlation Delta M_{s,d}-epsilon_K. Motivated by the recent work of Barbieri et al, we compare the CMFV correlations with the ones present in a special class of models with an approximate global U(2)^3 flavour symmetry, constrained by a minimal set of spurions governing the breakdown of this symmetry and the assumption that only SM operators are relevant in flavour physics. This analog of CMFV to be called MU(2)^3 allows to avoid the Delta M_{s,d}-epsilon_K tension in question because of reduced flavour symmetry and implied non-MFV contributions to Delta M_{s,d}. While the patterns of flavour violation in K meson system is the same as in CMFV models, the CP-violation in B_{s,d} meson systems can deviate from the one in the SM and CMFV models. We point out a stringent triple S_{psi K_S}-S_{psi phi}-|V_ub| correlation in this class of models that could in the future provide a transparent distinction between different MU(2)^3 models and in the context of these models determine |V_ub| by means of precise measurements of S_{psi K_S} and S_{psi phi} with only small hadronic uncertainties. For fixed S_{psi K_S} the correlation between B(B^+ -> tau^+nu_tau) and S_{psi phi} follows. We also find that MU(2)^3 models could in principle accommodate a negative value of S_{psi phi}, provided |V_ub| is found to be in the ballpark of exclusive determinations and the particular MU(2)^3 model provides a 25% enhancement of epsilon_K. A supersymmetric U(2)^3 model worked out in the Barbieri-School appears to satisfy these requirements. However if B(B^+ -> tau^+nu_tau)>1.0 10^{-4} will be confirmed by future experiments only positive S_{psi phi} is allowed in this framework. We summarize briefly the pattern of flavour violation in rare K and B_{s,d} decays in MU(2)^3 models.Comment: 28 pages, 6 figures; v2: Few references and discussion on CP violation in B_s-> mu^+ mu^- added; v3: Several clarifying comments added, conclusions unchanged, version accepted for publication in JHE

Privacy-Preserving Trust Management Mechanisms from Private Matching Schemes

Cryptographic primitives are essential for constructing privacy-preserving communication mechanisms. There are situations in which two parties that do not know each other need to exchange sensitive information on the Internet. Trust management mechanisms make use of digital credentials and certificates in order to establish trust among these strangers. We address the problem of choosing which credentials are exchanged. During this process, each party should learn no information about the preferences of the other party other than strictly required for trust establishment. We present a method to reach an agreement on the credentials to be exchanged that preserves the privacy of the parties. Our method is based on secure two-party computation protocols for set intersection. Namely, it is constructed from private matching schemes.Comment: The material in this paper will be presented in part at the 8th DPM International Workshop on Data Privacy Management (DPM 2013

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201

Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images

Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min- imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech