SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopy

This work presents a new deep learning architecture, SpectraNet-53, for quantitative analysis of fruit spectra, optimized for predicting Soluble Solids Content (SSC, in Brix). The novelty of this approach resides in being an architecture trainable on a very small dataset, while keeping a performance level on-par or above Partial Least Squares (PLS), a time-proven machine learning method in the field of spectroscopy. SpectraNet-53 performance is assessed by determining the SSC of 616 Citrus sinensi L. Osbeck 'Newhall' oranges, from two Algarve (Portugal) orchards, spanning two consecutive years, and under different edaphoclimatic conditions. This dataset consists of short-wave near-infrared spectroscopic (SW-NIRS) data, and was acquired with a portable spectrometer, in the visible to near infrared region, on-tree and without temperature equalization. SpectraNet-53 results are compared to a similar state-of-the-art architecture, DeepSpectra, as well as PLS, and thoroughly assessed on 15 internal validation sets (where the training and test data were sampled from the same orchard or year) and on 28 external validation sets (training/test data sampled from different orchards/years). SpectraNet-53 was able to achieve better performance than DeepSpectra and PLS in several metrics, and is especially robust to training overfit. For external validation results, on average, SpectraNet-53 was 3.1% better than PLS on RMSEP (1.16 vs. 1.20 Brix), 11.6% better in SDR (1.22 vs. 1.10), and 28.0% better in R2 (0.40 vs. 0.31).project NIBAP ALG-01-0247-FEDER-037303, project OtiCalFrut ALG-010247-FEDER-033652info:eu-repo/semantics/publishedVersio

Nanofiltration separation of polyvalent and monovalent anions in desalination brines

This work, as part of a global membrane process for the recovery of alkali and acids from reverse osmosis (RO) desalination brines, focuses on the nanofiltration (NF) separation of polyvalent and monovalent anions, more specifically sulfate and chloride. This pretreatment stage plays a key role in the whole recovery process. Working with model brines simulating the concentration of RO concentrates, 0.2–1.2 M chloride concentration and 0.1 M sulfate concentration, the experimental performance and modeling of the NF separation is reported. The study has been carried out with the NF270 (Dow Filmtec) membrane. The effect of operating pressure (500–2000 kPa), ionic strength (0.4–1.3 M) and chloride initial concentration (0.2–1.2 M) on the membrane separation capacity has been investigated. Finally, the Donnan Steric Pore Model (DSPM) together with experimentally determined parameters, effective pore radius (rp), thickness of the membrane effective layer (d) and effective membrane charge density (Xd), was proved accurate enough to satisfactorily describe the experimental results. In this work we provide for the first time the analysis of partitioning effects and transport mechanism in the NF separation of sulfate and chloride anions in concentrations that simulate those found in RO desalination brines.This work has been financially supported by projects CTQ2008-0690, ENE2010-15585 and CTM2011-23912 (co-financed by ERDF Funds).The authors would like to acknowledge SADYT, S.A. for providing assistance for this work

RegaDB: Community-driven data management and analysis for infectious diseases

Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB pr

Mechanisms governing the selectivity of NF membranes during the concentration of clavulanic acid

International audienc

Modelling the separation by nanofiltration of a multi-ionic solution relevant to an industrial process

International audienc

Nanofiltration of multi-ionic solutions: prediction of ions transport using the SEDE model

International audienceThis work focuses on the application of nanofiltration (NF) to the concentration of a pharmaceutical product, Clavulanate (), from clarified fermentation broths, which show a complex composition with six main identified ions (, , , , and ), glucose and glycerol. The solutes transport through the NF membrane pores was investigated using the SEDE (Steric, Electric and Dielectric Exclusion) model. This model was applied to predict the rejection rates of the initial feed solution and the final concentrated solution (10-fold concentrated solution). The best results were achieved with a single fitted parameter, (the dielectric constant of the solution inside pores) and considering that the membrane selectivity is governed by steric, electric (Donnan) and Born dielectric exclusion mechanisms. While the predicted intrinsic rejections of solutions comprising up to six ions and uncharged solutes were in good agreement with the experimental values, the deviations were much larger for the 10-fold concentrated solution

Production of extracellular L-asparaginase: from bioprospecting to the engineering of an antileukemic biopharmaceutical

The L-asparaginase (L-asparagine amino hydrolase, E.C.3.5.1.1) catalyzes the hydrolysis of L-aspargine into L-aspartic acid and ammonia. The effective depletion of L-asparine results in cytotoxicity for leukemic cell. Therefore the enzyme has been a clinically acceptable anti-tumour agent for the effective treatment of acute lymphoblastic leukemia (ALL) and lymphosarcoma. L-asparaginase production using microbial system had attracted considerable attention, owing to the cost effective and eco friendly nature. A wide range of microorganisms such as filamentous fungi, yeasts and bacteria have proved to be the good sources of the enzyme L-asparaginase. Thus, in this review mainly focuses on the biochemical aspects of L-asparaginase production, aiming to comprehend the physiochemical characteristics, such as stability, bioavailability, toxicity, allergenic aspects, application, and enzyme properties and kinetics of recombinant enzyme production by fermentation. Processes central to these biochemical aspects, including fermentation of L-asparaginase producing organisms and downstream processing of the enzyme are also discusse