Strojno učenje i neuronske mreže u modeliranju zadržavanja polarnih farmaceutski aktivnih tvari nanofiltracijom i reverznom osmozom

The retention of polar pharmaceutical active compounds (PPhACs) by nanofiltration and reverse osmosis (NF/RO) membranes is of paramount importance in membrane separation processes. The retention of 21 PPhACs was correlated using artificial intelligence techniques: multi-layer perceptron (MLP), feedforward neural network with radial basis function (RBF), and support vector machine (SVM). A database of 541 retention values has been collected from the literature. The results showed a high predictive capacity of the MLP model for the retention of PPhACs by NF/RO with a very high correlation coefficient (R = 0.9714) and a very low root mean squared error (RMSE = 3.9139 %) for the entire data set. The comparison between the three models showed the superiority of the MLP model. The sensitivity analysis emphasised that the retention of PPhACs is governed by three interactions arranged in descending order: polarity interactions (hydrophobicity/hydrophilicity), electrostatic repulsion, and steric hindrance. This research suggests that the PPhACs retention on the NF/RO membrane strongly depends on the topological polar surface area.Zadržavanje polarnih farmaceutski aktivnih tvari (PPhAC) tijekom nanofiltracije i reverzne osmoze (NF/RO) od iznimne je važnosti u membranskim separacijskim procesima. Membransko zadržavanje 21 PPhAC-a korelirano je sa svojstvima PPhAC-a, karakteristikama membrane i uvjetima provedbe procesa filtracije. Pri tome su primijenjene tehnike umjetne inteligencije: višeslojni perceptron (MLP), neuronska mreža s radijalnom baznom funkcijom (RBF) i metoda potpornih vektora (SVM). Iz literature je prikupljena 541 vrijednost zadržavanja. Rezultati su pokazali visok kapacitet predviđanja MLP modela za cijeli skup podataka, s vrlo visokom vrijednošću koeficijenta korelacije (R = 0,9714) i vrlo niskom vrijednošću korijena srednje kvadratne pogreške (RMSE = 3,9139 %). Usporedba s preostala dva modela (RBF i SVM) pokazala je superiornost MLP modela. Analiza osjetljivosti ukazala je na to da zadržavanjem PPhAC-a upravljaju tri interakcije i to (padajućim redoslijedom): polarne interakcije (hidrofobnost/hidrofilnost), elektrostatsko odbijanje i steričke smetnje. Provedenoo istraživanje sugerira da zadržavanje PPhACs na NF/RO membrani snažno ovisi o topologiji polarne površine

Modeli umjetne neuronske mreže za predviđanje gustoće i kinematičke viskoznosti različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Usporedna analiza

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results. This work is licensed under a Creative Commons Attribution 4.0 International License.U ovom članku dva modela zasnovana na metodologiji umjetne neuronske mreže (ANN) optimizirana su za predviđanje gustoće (ρ) i kinematičke viskoznosti (μ) različitih sustava biogoriva i njihovih mješavina s dizelskim gorivom. Za razvoj tih modela upotrijebljena je eksperimentalna baza podataka od 1025 točaka, uključujući 34 sustava (15 čistih sustava, 14 binarnih sustava i 5 ternarnih sustava). Ti modeli koriste šest ulaza: temperatura (T) u rasponu od −10 do 200 °C, volumni udjeli (X1, X2, X3) u rasponu 0 – 1, a za razlikovanje tih sustava korištena je kinematička viskoznost pri 20 °C u rasponu 0,67 – 74,19 mm2 s–1 i gustoća pri 20 °C u rasponu 0,7560 – 0,9188 g cm–3. Najbolji rezultati dobiveni su arhitekturom {6-26-2: 6 neurona u ulaznom sloju – 26 neurona u skrivenom sloju – 2 neurona u izlaznom sloju}. Rezultati usporedbe eksperimentalnih i simuliranih vrijednosti u smislu korelacijskih koeficijenata bili su: R2 = 0,9965 za gustoću i R2 = 0,9938 za kinematičku viskoznost. Za provjeru točnosti dva prethodno razvijena modela ANN upotrijebljeno je 238 novih eksperimentalnih baza podataka s 4 sustava (2 čista sustava, 1 binarni sustav i 1 ternarni sustav). Rezultati performansi predviđanja s obzirom na korelacijske koeficijente bili su: R2 = 0,9980 za gustoću i R2 = 0,9653 za kinematičku viskoznost. Usporedba rezultata validacije s rezultatima drugih studija pokazuje da su modeli neuronske mreže dali znatno bolje rezultate. Ovo djelo je dano na korištenje pod licencom Creative Commons Imenovanje 4.0 međunarodna

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, Domain of Application and Prediction

International audienceQuantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext and the Root Mean Square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Artificial Neural Network-Based Equation to Predict the Toxicity of Herbicides on Rats

International audienceThe use of herbicides is increasing around the world. The benefits achieved by the use of these herbicides are indisputable. Despite their importance in agriculture, herbicides can be dangerous to the environment and the human health, depending on their toxicity, and the degree of contamination. Also, it is essential and evident that the risk assessment of herbicides is an important task in the environmental protection. The objective of this work was to investigate and implement an Artificial Neural Network (ANN) model for the prediction of acute oral toxicity of 77 herbicides to rats. Internal and external validations of the model showed high Q2 and r m 2 - values, in the range 0.782 – 0.997 for the training and the test. In addition, the major contribution of the current work was to develop artificial neural network-based equation to predict the toxicity of 13 other herbicides; the mathematical equation using the weights of the network gave very significant results, leading to an R2 value of 0.959. The agreement between calculated and experimental values of acute toxicity confirmed the ability of ANN-based equation to predict the toxicity for herbicides that have not been tested as well as new herbicide