A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

Evaluation of nanoplastics toxicity to the human placenta in systems

Following the discovery of plastics in the human placenta, this study evaluated the toxicity of ten different nanoplastics (NPs) in the human placenta. Since the placenta performs metabolic and excretion functions by the enzymatic system, the NPs were docked on these human enzymes including soluble epoxide hydrolase, uracil phosphoribosyltransferase, beta 1,3-glucuronyltransferase I, sulfotransferase, N-acetyltransferase 2, and cytochrome P450 1A1at their active sites with toxicity (binding affinity) determined and compared to control compounds. Density functional theory analysis were conducted on the NPs to identify their global reactivity descriptors and Artificial Neural Networks to predict toxicity based on reactivity descriptors. Polycarbonate (PC), polyethylene terephthalate (PET) and polystyrene (PS) showed the highest toxicity to all enzymes and thus the most toxic polymers due to the presence of an electron-withdrawing group in their aromatic rings, which demonstrated an improved recognition of the enzyme active site by pi- and alkyl interactions. A 2^10-6 fractional factorial design approach was used in conjunction with a fixed effects model to assess the primary and secondary effects of NPs in a composite system on binding affinity to the placental enzymes. The simulation results suggest that NPs mixture may pose significant risks to the placenta through inhibition of its key enzymes.

Study on Thermal Degradation Processes of Polyethylene Terephthalate Microplastics Using the Kinetics and Artificial Neural Networks Models

Because of its slow rate of disintegration, plastic debris has steadily risen over time and contributed to a host of environmental issues. Recycling the world’s increasing debris has taken on critical importance. Pyrolysis is one of the most practical techniques for recycling plastic because of its intrinsic qualities and environmental friendliness. For scale-up and reactor design, an understanding of the degradation process is essential. Using one model-free kinetic approach (Friedman) and two model-fitting kinetic methods (Arrhenius and Coats-Redfern), the thermal degradation of Polyethylene Terephthalate (PET) microplastics at heating rates of 10, 20, and 30 °C/min was examined in this work. Additionally, a powerful artificial neural network (ANN) model was created to forecast the heat deterioration of PET MPs. At various heating rates, the TG and DTG thermograms from the PET MPs degradation revealed the same patterns and trends. This showed that the heating rates do not impact the decomposition processes. The Friedman model showed activation energy values ranging from 3.31 to 8.79 kJ/mol. The average activation energy value was 1278.88 kJ/mol from the Arrhenius model, while, from the Coats-Redfern model, the average was 1.05 × 104 kJ/mol. The thermodynamics of the degradation process of the PET MPs by thermal treatment were all non-spontaneous and endergonic, and energy was absorbed for the degradation. It was discovered that an ANN, with a two-layer hidden architecture, was the most effective network for predicting the output variable (mass loss%) with a regression coefficient value of (0.951–1.0).

Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China

BACKGROUND

This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model and multivariate Long Short-Term Memory Neural Network (LSTM) models.

METHODS

Disability adjusted life year (DALY) was used to evaluate the disease burden of occupational pneumoconiosis. ARIMA model, DNN model and multivariate LSTM model were used to establish prediction model. Three performance evaluation metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the prediction effects of the three models.

RESULTS

From 1990 to 2021, there were 10,694 cases of pneumoconiosis patients in Tianjin, resulting in a total of 112,725.52 person-years of DALY. During this period, the annual DALY showed a fluctuating trend, but it had a strong correlation with the number of pneumoconiosis patients, the average age of onset, the average age of receiving dust and the gross industrial product, and had a significant nonlinear relationship with them. The comparison of prediction results showed that the performance of multivariate LSTM model and DNN model is much better than that of traditional ARIMA model. Compared with the DNN model, the multivariate LSTM model performed better in the training set, showing lower RMES (42.30 vs. 380.96), MAE (29.53 vs. 231.20) and MAPE (1.63% vs. 2.93%), but performed less stable than the DNN on the test set, showing slightly higher RMSE (1309.14 vs. 656.44), MAE (886.98 vs. 594.47) and MAPE (36.86% vs. 22.43%).

CONCLUSION

The machine learning techniques of DNN and LSTM are an innovative method to accurately and efficiently predict the burden of pneumoconiosis with the simplest data. It has great application prospects in the monitoring and early warning system of occupational disease burden.

Control of D-lactic acid content in P(LA-3HB) copolymer in the yeast Saccharomyces cerevisiae using a synthetic gene expression system

The fully biobased polyhydroxyalkanoate (PHA) polymers provide interesting alternatives for petrochemical derived plastic materials. The mechanical properties of some PHAs, including the common poly(3-hydroxybutyrate) (PHB), are limited, but tunable by addition of other monomers into the polymer chain. In this study we present a precise synthetic biology method to adjust lactate monomer fraction of a polymer by controlling the monomer formation in vivo at gene expression level, independent of cultivation conditions. We used the modified doxycycline-based Tet-On approach to adjust the expression of the stereospecific D-lactate dehydrogenase gene (ldhA) from Leuconostoc mesenteroides to control D-lactic acid formation in yeast Saccharomyces cerevisiae. The synthetic Tet-On transcription factor with a VP16 activation domain was continuously expressed and its binding to a synthetic promoter with eight transcription factor specific binding sites upstream of the ldhA gene was controlled with the doxycycline concentration in the media. The increase in doxycycline concentration correlated positively with ldhA expression, D-lactic acid production, poly(D-lactic acid) (PDLA) accumulation in vivo, and D-lactic acid content in the poly(D-lactate-co-3-hydroxybutyrate) P(LA-3HB) copolymer. We demonstrated that the D-lactic acid content of the P(LA-3HB) copolymer can be adjusted linearly from 6 mol% to 93 mol% in vivo in S. cerevisiae. These results highlight the power of controlling gene expression and monomer formation in the tuning of the polymer composition. In addition, we obtained 5.6% PDLA and 19% P(LA-3HB) of the cell dry weight (CDW), which are over two- and five-fold higher accumulation levels, respectively, than reported in the previous studies with yeast. We also compared two engineered PHA synthases and discovered that in S. cerevisiae the PHA synthase PhaC1437_Ps6-19 produced P(LA-3HB) copolymers with lower D-lactic acid content, but with higher molecular weight, in comparison to the PHA synthase PhaC1Pre.

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P(LA-3HB) monomer structure was adjusted with controlled gene expression.

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Expression of D-lactate dehydrogenase (ldhA) was controlled with Tet-On approach.

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Lactic acid content in copolymer P(LA-3HB) was adjusted from 6 mol% up to 93 mol%.

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5.6% PDLA and 19% P(LA-3HB) of cell dry weight (CDW) were obtained in S. cerevisiae.

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PhaC1437_Ps6-19 P(LA-3HB) had lower D-lactic acid % than PhaC1Pre P(LA-3HB).

Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.

Artificial Neural Network Modeling of Glass Transition Temperatures for Some Homopolymers with Saturated Carbon Chain Backbone

The glass transition temperature (Tg) is an important decision parameter when synthesizing polymeric compounds or when selecting their applicability domain. In this work, the glass transition temperature of more than 100 homopolymers with saturated backbones was predicted using a neuro-evolutive technique combining Artificial Neural Networks with a modified Bacterial Foraging Optimization Algorithm. In most cases, the selected polymers have a vinyl-type backbone substituted with various groups. A few samples with an oxygen atom in a linear non-vinyl hydrocarbon main chain were also considered. Eight structural, thermophysical, and entanglement properties estimated by the quantitative structure-property relationship (QSPR) method, along with other molecular descriptors reflecting polymer composition, were considered as input data for Artificial Neural Networks. The Tg's neural model has a 7.30% average absolute error for the training data and 12.89% for the testing one. From the sensitivity analysis, it was found that cohesive energy, from all independent parameters, has the highest influence on the modeled output.

Production of D-lactic acid containing polyhydroxyalkanoate polymers in yeast Saccharomyces cerevisiae

Polyhydroxyalkanoates (PHAs) provide biodegradable and bio-based alternatives to conventional plastics. Incorporation of 2-hydroxy acid monomers into polymer, in addition to 3-hydroxy acids, offers possibility to tailor the polymer properties. In this study, poly(D-lactic acid) (PDLA) and copolymer P(LA-3HB) were produced and characterized for the first time in the yeast Saccharomyces cerevisiae. Expression of engineered PHA synthase PhaC1437_Ps6–19, propionyl-CoA transferase Pct540Cp, acetyl-CoA acetyltransferase PhaA, and acetoacetyl-CoA reductase PhaB1 resulted in accumulation of 3.6% P(LA-3HB) and expression of engineered enzymes PhaC1Pre and PctMe resulted in accumulation of 0.73% PDLA of the cell dry weight (CDW). According to NMR, P(LA-3HB) contained D-lactic acid repeating sequences. For reference, expression of PhaA, PhaB1, and PHA synthase PhaC1 resulted in accumulation 11% poly(hydroxybutyrate) (PHB) of the CDW. Weight average molecular weights of these polymers were comparable to similar polymers produced by bacterial strains, 24.6, 6.3, and 1 130 kDa for P(LA-3HB), PDLA, and PHB, respectively. The results suggest that yeast, as a robust and acid tolerant industrial production organism, could be suitable for production of 2-hydroxy acid containing PHAs from sugars or from 2-hydroxy acid containing raw materials. Moreover, the wide substrate specificity of PHA synthase enzymes employed increases the possibilities for modifying copolymer properties in yeast in the future.