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25 results on '"Lanzoni, D."'

1. Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium.

Author

Fantasia, A., Rovaris, F., Abou El Kheir, O., Marzegalli, A., Lanzoni, D., Pessina, L., Xiao, P., Zhou, C., Li, L., Henkelman, G., Scalise, E., and Montalenti, F.

Subjects
PHASE transitions, GERMANIUM, DENSITY functional theory, MACHINE learning, DATABASES
Abstract

We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to β-Sn pressure-induced phase transformation, taken here as an illustrative example. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition

Author

Ge, G, Rovaris, F, Lanzoni, D, Barbisan, L, Tang, X, Miglio, L, Marzegalli, A, Scalise, E, Montalenti, F, Ge G., Rovaris F., Lanzoni D., Barbisan L., Tang X., Miglio L., Marzegalli A., Scalise E., Montalenti F., Ge, G, Rovaris, F, Lanzoni, D, Barbisan, L, Tang, X, Miglio, L, Marzegalli, A, Scalise, E, Montalenti, F, Ge G., Rovaris F., Lanzoni D., Barbisan L., Tang X., Miglio L., Marzegalli A., Scalise E., and Montalenti F.

Abstract

Closing the gap between experiments and simulations in the investigation of high-pressure silicon phase transitions calls for advanced, new-generation modeling approaches. By exploiting massive parallelization, we here provide molecular dynamics (MD) simulations of Si nanoindentation based on the Gaussian Approximation Potential (GAP). Results are analyzed by exploiting a customized Neural Network Phase Recognition (NN-PR) approach, helping to shed light on the phase transitions occurring during the simulations. Our results show that GAP provides a realistic description of silicon phase transitions. With the support of NN-PR method, the formation mechanism and stability of high-pressure phases are comprehensively studied. Additionally, we also show how simulations based on the less demanding and widely-used Tersoff potential are still useful to investigate the role played by the indenter tip modeling. However, high-pressure phases obtained with GAP are more consistent with observations made in nanoindentation experiments, removing a spurious phase that is shown by Tersoff simulations. This behavior is explained on the base of relative phase stability with comparison with Density Functional Theory (DFT) calculations. This work provides insight into the application of state-of-the-art Machine Learning (ML) methods on nanoindentation simulations, enabling further understanding of the phase transition mechanisms in silicon.

Published
2024

5. Deep Learning methods for the investigation of temporal evolution of materials

Author

Lanzoni, D, MONTALENTI, FRANCESCO CIMBRO MATTIA, LANZONI, DANIELE, Lanzoni, D, MONTALENTI, FRANCESCO CIMBRO MATTIA, and LANZONI, DANIELE

Abstract

I metodi Machine Learning (ML) in generale e i Neural Network (NN) in particolare rappresentano una delle più attive aree di ricerca degli ultimi anni. La fisica e le Scienze dei Materiali computazionali non sono state immuni a questo sviluppo: approcci NN e ML stanno rapidamente venendo adottati come nuovi strumenti, con la promessa di poter risolvere problemi che fino ad ora sono rimasti intrattabili. In questa tesi verranno presentati alcuni sviluppi di questa direzione di ricerca, concentrandosi sulla possibilità di attaccare problemi tradizionali da nuovi punti di vista. In particolare, la discussione verterà come sia possibile adattare di metodi Machine Learning “puri” per la simulazione di fenomeni mesoscala, come dislocazioni e l’evoluzione morfologica di superfici. Lo scopo è metodologico e la Tesi cerca di essere auto sussistente. In seguito all’introduzione dei concetti generali del Machine Learning e dei Neural Network, si affrontano diversi modelli fisici. I metodi sono presentati contestualmente all’applicazione e sono presentati dal più semplice al più sofisticato. Iniziando con l’approssimazione dell’interazione tra dislocazioni in film eteroepitassiali con semplici feedforward fully connected Neural Netowork, la tesi si sviluppa quindi verso la simulazione dell’evoluzione delle superfici di film coerenti e modelli phase field, utilizzando metodi convoluzionali e ricorrenti rispettivamente. Infine, viene presentata una applicazione di Generative Adversarial Networks ad un problema di meccanica stocastica., Machine Learning (ML) and Neural Networks (NN) specifically represent one of the most active areas of research in the last year. Computational physics and materials science have not been immune to this: Neural Networks and other ML approaches are being adopted as new tools, which promise to solve problems which have been intractable so far. In this Thesis, some advancements in this respect will be shown, focusing on the possibility of tackling traditional problems from new directions. In particular, the discussion will revolve around how to adapt pure Deep Learning approaches to the simulation of mesoscale phenomena such as dislocations and morphological evolution of surfaces. The scope is methodological, and the Thesis tries to be self-contained. After an introduction to basic Machine Learning and Neural Networks concepts, different physical models are tackled. Methods are presented contextually to applications and are introduced from the simplest to the most sophisticated. Starting from the approximation of dislocation interactions in heteroepitaxial films, the Thesis proceeds in showing how to simulate the evolution of coherent films free surfaces and phase field models, using convolutional and recurrent NN respectively. Finally, an application of Generative Adversarial Networks to a stochastic mechanics problem is presented.

Published
2024

7. Machine learning potential for interacting dislocations in the presence of free surfaces

Author

Lanzoni, D, Rovaris, F, Montalenti, F, Lanzoni D., Rovaris F., Montalenti F., Lanzoni, D, Rovaris, F, Montalenti, F, Lanzoni D., Rovaris F., and Montalenti F.

Abstract

Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations.

Published
2022

8. Accurate generation of stochastic dynamics based on multi-model generative adversarial networks

Author

Lanzoni, D, Pierre-Louis, O, Montalenti, F, Lanzoni, D, Pierre-Louis, O, and Montalenti, F

Abstract

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques.

Published
2023

9. Nutritional aspects of hemp-based products and their effects on the health and performance of monogastric animals.

Author

Lanzoni, D., Skrivanova, E., Pinotti, L., Rebucci, R., Baldi, A., and Giromini, C.

Subjects
*SCIENTIFIC literature, *HEALTH products, *ANIMAL health, *BREAST milk, *SUSTAINABLE agriculture, *RICE hulls, *FLAXSEED
Abstract

The article focuses on the nutritional aspects of hemp-based products and their effects on monogastric animals' health and performance. Topics include the nutritional value of hempseed, sustainable agriculture strategies involving hemp co-products, and the application of hemp-based products in the diets of swine, broilers, and laying hens, highlighting their potential benefits and challenges.

Published
2024

10. Computational analysis of low-energy dislocation configurations in graded layers

Author

Lanzoni, D, Rovaris, F, Montalenti, F, Lanzoni D., Rovaris F., Montalenti F., Lanzoni, D, Rovaris, F, Montalenti, F, Lanzoni D., Rovaris F., and Montalenti F.

Abstract

Graded layers are widely exploited in semiconductor epitaxy as they typically display lower threading dislocation density with respect to constant-composition layers. However, strain relaxation occurs via a rather complex distribution of misfit dislocations. Here we exploit a suitable computational approach to investigate dislocation distributions minimizing the elastic energy in overcritical constant-composition and graded layers. Predictions are made for SiGe/Si systems, but the methodology, based on the exact (albeit in two dimensions and within linear elasticity theory) solution of the stress field associated with a periodic distribution of defects, is general. Results are critically compared with experiments, when possible, and with a previous mean-field model. A progressive transition from one-dimensional to two-dimensional distributions of defects when continuous linear grading is approached is clearly observed. Interestingly, analysis of the low-energy distribution of dislocations reveals close analogies with typical pile-ups as produced by dislocation multiplication.

Published
2020

11. Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty

Author

Lanzoni, D, Albani, M, Bergamaschini, R, Montalenti, F, Daniele Lanzoni, Marco Albani, Roberto Bergamaschini, Francesco Montalenti, Lanzoni, D, Albani, M, Bergamaschini, R, Montalenti, F, Daniele Lanzoni, Marco Albani, Roberto Bergamaschini, and Francesco Montalenti

Abstract

We use a convolutional, recurrent neural network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g., leading to the splitting of high aspect-ratio individual structures). The automatic smart augmentation of the training set and design of a hybrid simulation method are discussed.

Published
2022

12. A machine learning approach for studying low-energy dislocation distributions: methodology and applications to Ge/Si(001) films

Author

Lanzoni, D, Rovaris, F, Montalenti, F, Lanzoni, D, Rovaris, F, and Montalenti, F

Subjects
Machine Learning, Semiconductors, Neural Network, Dislocation, Dislocation Dyamic, Heteroepitaxial film
Abstract

Controlling the density of dislocations and their spatial distribution in semiconductor heterostructures is an important ingredient of microelectronics device design. Computer simulations can greatly help to shed light on the complex behavior of such line defects, also in view of the severe challenges posed by high-resolution, time-dependent experimental observations. In bulk systems and in ideal corrugation-free (i.e. perfectly flat) films, the dislocation stress fields can be obtained analytically. If one is interested in non-trivial nanostructures or more complex morphologies, numerical solutions via, e.g., Finite Element Methods (FEM) are needed, increasing the computational demand. Here we present a convenient Machine Learning-based approach which allows one to significantly speed up calculations of both forces and total elastic energies, while maintaining the accurate description of the FEM solution. We decompose both energy and forces for an arbitrary dislocation distribution in one and two body terms which can be obtained by simpler and computationally cheaper evaluations. For an assigned morphology, this permits one to use FEM on a reduced configuration space, exploited to build a training set. We then train on such set an artificial neural network (NN) to approximate dislocation energy functions, which allow for a faster evaluation of both energies and forces acting on a system of an arbitrary number of dislocations. This new approach is here tested in simulations of SiGe films on Si(100), a system of direct interest for the microelectronic industry. Specifically, we focus on the influence of surface undulations on the minimum-energy distribution for dislocations. This is achieved by standard Monte Carlo (MC) implementations where time-consuming FEM energy calculations are replaced by faster and cheaper NN approximations. Results are consistent with previous experimental and theoretical investigations. In particular, the formation of interfacial arrays of edge dislocations has been obtained for flat films, while isolated 60° dislocations are predicted for films with strong undulations. Importantly, the overall computation speed-up obtained using the NN functions exceeded three orders of magnitude. The NN functions have also been exploited to run dislocation dynamics simulations based on the fitted Peach-Koehler forces. Results for graded SiGe layers show the presence of a thermodynamic driving force towards the formation of dislocation pile-ups and are here illustrated. Our method is currently implemented in 2D only. Extensions to full 3D problems, however, are envisioned and could provide a valuable tool to explore large distribution of dislocations in complex geometries.

Published
2021

13. Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: extrapolation and prediction uncertainty

Author

Daniele Lanzoni, Marco Albani, Roberto Bergamaschini, Francesco Montalenti, Lanzoni, D, Albani, M, Bergamaschini, R, and Montalenti, F

Subjects
Condensed Matter - Materials Science, Cahn-Hilliard, Physics and Astronomy (miscellaneous), Condensed Matter - Mesoscale and Nanoscale Physics, Recurrent Neural Network, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Convolutional Neural Network, Computational Physics (physics.comp-ph), Machine Learning, Surface, Mesoscale and Nanoscale Physics (cond-mat.mes-hall), Prediction uncertainty, General Materials Science, Phase Field, Physics - Computational Physics
Abstract

We use a Convolutional Recurrent Neural Network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g. leading to splitting of high aspect-ratio individual structures). Automatic smart-augmentation of the training set and design of a hybrid simulation method are discussed., 11 pages, 7 figures

Published
2022

14. Machine learning potential for interacting dislocations in the presence of free surfaces

Author

Daniele Lanzoni, Fabrizio Rovaris, Francesco Montalenti, Lanzoni, D, Rovaris, F, and Montalenti, F

Subjects
Machine Learning, Multidisciplinary, Free surface, Dislocation, Linear Elasticity, Semiconductor, Potential, Epitaxial Film
Abstract

Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations.

Published
2021

15. Computational Analysis of Low-Energy Dislocation Configurations in Graded Layers

Author

Francesco Montalenti, F Rovaris, Daniele Lanzoni, Lanzoni, D, Rovaris, F, and Montalenti, F

Subjects
Materials science, SiGe, General Chemical Engineering, 02 engineering and technology, Epitaxy, 01 natural sciences, Inorganic Chemistry, Condensed Matter::Materials Science, 0103 physical sciences, lcsh:QD901-999, General Materials Science, 010306 general physics, FIS/03 - FISICA DELLA MATERIA, dislocation, Condensed matter physics, business.industry, Linear elasticity, Relaxation (NMR), Elastic energy, modeling, 021001 nanoscience & nanotechnology, Condensed Matter Physics, heteroepitaxy, Stress field, Distribution (mathematics), Semiconductor, lcsh:Crystallography, Dislocation, 0210 nano-technology, business
Abstract

Graded layers are widely exploited in semiconductor epitaxy as they typically display lower threading dislocation density with respect to constant-composition layers. However, strain relaxation occurs via a rather complex distribution of misfit dislocations. Here we exploit a suitable computational approach to investigate dislocation distributions minimizing the elastic energy in overcritical constant-composition and graded layers. Predictions are made for SiGe/Si systems, but the methodology, based on the exact (albeit in two dimensions and within linear elasticity theory) solution of the stress field associated with a periodic distribution of defects, is general. Results are critically compared with experiments, when possible, and with a previous mean-field model. A progressive transition from one-dimensional to two-dimensional distributions of defects when continuous linear grading is approached is clearly observed. Interestingly, analysis of the low-energy distribution of dislocations reveals close analogies with typical pile-ups as produced by dislocation multiplication.

Published
2020
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16. Dietary supplementation with xylanase suppresses the antinutritional effect of nonstarch polysaccharides of flaxseed and increases bone strength in broiler chickens.

Author

Skřivan M, Englmaierová M, Marounek M, Taubner T, Lanzoni D, Bejčková K, Giromini C, and Baldi A

Subjects
Animals, Animal Nutritional Physiological Phenomena, Tibia drug effects, Bone Density drug effects, Flax chemistry, Chickens, Dietary Supplements analysis, Endo-1,4-beta Xylanases metabolism, Polysaccharides pharmacology, Animal Feed analysis
Abstract

The aim of this study was to determine the effects of xylanase and flaxseed the performance of chickens, digesta viscosity, nutrient retention, fatty acid profile in muscle, tibia strength and interrelations of these factors in broiler chickens fed a wheat-based diet. Seven hundred and twenty one-day-old Ross 308 cockerels were assigned to four treatments according to the contents of flaxseed (0 and 80 g/kg) and xylanase (0 and 0.1 g/kg) in the diet. Xylanase significantly decreased the intake of feed (p < 0.001), decreased feed conversion (p < 0.001), and reduced mortality (p = 0.050). In addition, xylanase significantly increased the retention of all nutrients (p = 0.010 -<0.001) except crude fibre, the fat content in breast meat (p = 0.029) and liver (p = 0.019) and the concentration of polyunsaturated fatty acids (PUFAs) in meat (p = 0.002). Flaxseed supplementation did not influence performance but decreased the retention of dry matter (p = 0.016), crude protein (p = 0.012), organic matter (p = 0.016) and nitrogen-free extract (p = 0.008). Xylanase in combination with flaxseed increased the content of n-3 fatty acids in the breast meat (p = 0.006). The lowest n-6/n-3 ratio (p = 0.001) was detected in the flaxseed and flaxseed combined with xylanase groups. Significant interaction effects of flaxseed and xylanase on tibia strength (p = 0.030) and tibia ash content (p = 0.009) were detected. The administration of xylanase or flaxseed alone increased tibia strength. Compared with the control diet, the addition of flaxseed to the diet increased the digesta viscosity (p = 0.043) in the ileum, whereas the addition of xylanase decreased the level of this indicator. It can be concluded that xylanase is an enzyme suitable for increasing nutrient availability, and in the case of its addition to a flaxseed diet, it can reduce the antinutritional effect of flaxseed by reducing the viscosity of the digesta and increasing the content of health-promoting n-3 PUFAs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Skřivan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2024
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17. Antioxidant capacity and peptidomic analysis of in vitro digested Camelina sativa L. Crantz and Cynara cardunculus co-products.

Author

Lanzoni D, Grassi Scalvini F, Petrosillo E, Nonnis S, Tedeschi G, Savoini G, Buccioni A, Invernizzi G, Baldi A, and Giromini C

Subjects
Brassicaceae chemistry, Angiotensin-Converting Enzyme Inhibitors pharmacology, Angiotensin-Converting Enzyme Inhibitors chemistry, Phenols analysis, Phenols chemistry, Peptides chemistry, Peptides analysis, Animals, Plant Extracts pharmacology, Plant Extracts chemistry, Animal Feed analysis, Proteomics methods, Antioxidants pharmacology, Antioxidants analysis, Antioxidants chemistry, Cynara chemistry
Abstract

In recent decades, the food system has been faced with the significant problem of increasing food waste. Therefore, the feed industry, supported by scientific research, is attempting to valorise the use of discarded biomass as co-products for the livestock sector, in line with EU objectives. In parallel, the search for functional products that can ensure animal health and performances is a common fundamental goal for both animal husbandry and feeding. In this context, camelina cake (CAMC), cardoon cake (CC) and cardoon meal (CM), due valuable nutritional profile, represent prospective alternatives. Therefore, the aim of this work was to investigate the antioxidant activity of CAMC, CC and CM following in vitro digestion using 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), Ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAC) assays. Total phenolic content (TPC) and angiotensin converting enzyme (ACE) inhibitory activity, actively involved in modulating antioxidant properties, were also studied. Further, a peptidomic analysis was adopted to substantiate the presence of bioactive peptides after in vitro digestion. The results obtained confirmed an interesting nutritional profile of CAMC, CC and CM and relevant antioxidant and ACE inhibitory activities. In particular, considering antioxidant profile, CM and CC revealed a significantly higher (10969.80 ± 18.93 mg TE/100 g and 10451.40 ± 149.17 mg TE/100 g, respectively; p < 0.05) ABTS value than CAMC (9511.18 ± 315.29 mg TE/100 g); a trend also confirmed with the FRAP assay (306.74 ± 5.68 mg FeSO 4 /100 g; 272.84 ± 11.02 mg FeSO 4 /100 g; 103.84 ± 3.27 mg FeSO 4 /100 g, for CC, CM and CAMC, respectively). Similar results were obtained for TPC, demonstrating the involvement of phenols in modulating antioxidant activity. Finally, CAMC was found to have a higher ACE inhibitory activity (40.34 ± 10.11%) than the other matrices. Furthermore, potentially bioactive peptides associated with ACE inhibitory, anti-hypertensive, anti-cancer, antimicrobial, antiviral, antithrombotic, DPP-IV inhibitory and PEP-inhibitory activities were identified in CAMC. This profile was broader than that of CC and CM. The presence of such peptides corroborates the antioxidant and ACE profile of the sample. Although the data obtained report the important antioxidant profile of CAMC, CC, and CM and support their possible use, future investigations, particularly in vivo trials will be critical to evaluate and further investigate their effects on the health and performance of farm animals., (© 2024. The Author(s).)

Published
2024
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18. Accurate generation of stochastic dynamics based on multi-model generative adversarial networks.

Author

Lanzoni D, Pierre-Louis O, and Montalenti F

Abstract

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

Published
2023
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20. Total Phenolic Content and Antioxidant Activity of In Vitro Digested Hemp-Based Products.

Author

Lanzoni D, Skřivanová E, Rebucci R, Crotti A, Baldi A, Marchetti L, and Giromini C

Abstract

The growth of the world population has prompted research to investigate new food/feed alternatives. Hemp-based products can be considered excellent candidates. Hemp ( Cannabis sativa L.) is an environmentally sustainable plant widespread worldwide. Following the reintroduction of its cultivation, hemp is attracting interest, especially in the food/feed industry. To date, scientific research has mainly focused on its nutritional aspect. Therefore, the aim of the work was also to investigate the functional profile (total phenolic content (TPC) and antioxidant activity (Ferric- reducing antioxidant power (FRAP) and 2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS)) of hemp-based products (hempseeds (HSs), flowers, and HS protein extract), following methanol extraction and in vitro digestion, to study the behaviour of the molecules involved. The results show an interesting nutritional value, even when compared to matrices used in the food/feed industry, such as soy and flaxseeds. The functional profile revealed a very interesting TPC following methanol extraction for HSs, flowers, and HS protein extract, respectively, (550.3 ± 28.27; 2982.8 ± 167.78; and 568.9 ± 34.18 mg Tannic Acid Equivalent (TAE)/100 g). This trend was also confirmed for FRAP (50.9 ± 4.30; 123.6 ± 8.08; and 29.73 ± 1.32 mg Ascorbic Acid Equivalent (AAE)/100 g), recording values similar/higher than soy protein extract and flaxseeds (17.4 ± 1.55; and 10.4 ± 0.44 mg AAE/100 g). The results were also maintained following physiological digestion. These results, although promising, need further investigation, confirming what has been observed with different antioxidant activity assays and identifying individual molecules involved in functional pathways. This information will be necessary to gain a better understanding of the functional characteristics of these matrices for use in food/feed formulations.

Published
2023
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21. Role of Short Chain Fatty Acids to Counteract Inflammatory Stress and Mucus Production in Human Intestinal HT29-MTX-E12 Cells.

Author

Giromini C, Baldi A, Rebucci R, Lanzoni D, Policardi M, Sundaram TS, and Purup S

Abstract

Short chain fatty acids (SCFAs), especially butyrate (BUT), are known to promote intestinal health, but their role in the protection of intestinal barrier integrity is poorly characterized. The aim of the study was to set up an in vitro model of human colon epithelium using HT29-MTX-E12 cells to delineate the potential role of SCFAs under stress conditions. Accordingly, the HT29-MTX-E12 cells were differentiated for 42 days and subsequently exposed to dextran sulphate sodium (DSS). Further, the effects of BUT or its mixture with acetate and propionate (SCFAs-MIX) were tested to study proliferation, epithelial integrity and mucus production. The results showed that the concentration of 10% DSS for 24 h decreased the TEER about 50% compared to the control in HT29-MTX-E12 cells. The pre-treatment on HT29-MTX-E12 cells with BUT or SCFAs-MIX at specific concentrations significantly (p < 0.05) reduced the DSS-induced damage on epithelial cell integrity and permeability. Further, the treatment with specific concentrations of BUT and SCFAs-MIX for 24 h significantly promoted ZO-1, MUC2 and MUC5AC mRNA expression (p < 0.005). The present study demonstrated the suitability of HT29-MTX-E12 cells treated with DSS as an in vitro stress model of inflammatory bowel disease, which enabled us to understand the effect of bioactive SCFAs on the intestinal barrier.

Published
2022
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22. Antioxidant and Antimicrobial Activity of Algal and Cyanobacterial Extracts: An In Vitro Study.

Author

Frazzini S, Scaglia E, Dell'Anno M, Reggi S, Panseri S, Giromini C, Lanzoni D, Sgoifo Rossi CA, and Rossi L

Abstract

Algae and cyanobacteria, other than their nutritional value, possess different beneficial properties, including antioxidant and antimicrobial ones. Therefore, they can be considered functional ingredients in animal feed and natural substitutes for antibiotics. The aim of this study was to evaluate the antioxidant and antimicrobial capacity against porcine O138 E. coli of Ascophyllum nodosum, Chlorella vulgaris, Lithotamnium calcareum, Schizochytrium spp. as algal species and Arthrospira platensis as cyanobacteria. The antioxidant capacity was determined by ABTS Radical Cation Decolorization Assay testing at three different concentrations (100%; 75%; 50%). The growth inhibition effect of the extracts at concentrations of 25%, 12.5%, 6%, 3% and 1.5% against porcine O138 E. coli was genetically characterized by PCR to detect the presence of major virulence factors; this was evaluated by following the microdilution bacterial growth method. The ABTS assay disclosed that Ascophyllum nodosum was the compound with the major antioxidant properties (57.75 ± 1.44 percentage of inhibition; p < 0.0001). All the extracts tested showed growth inhibition activity at a concentration of 25%. Among all extracts, A. nodosum was the most effective, showing a significant growth inhibition of E. coli; in particular, the log10 cells/mL of E. coli used as a control resulted in a significantly higher concentration of 25% and 12.5% after 4 h (8.45 ± 0.036 and 7.22 ± 0.025 log10 cells/mL, respectively; p < 0.005). This also suggests a dose-dependent relationship between the inhibitory activity and the concentration. Also, a synergistic effect was observed on antioxidant activity for the combination of Ascophyllum nodosum and Lithotamnium calcareum (p < 0.0001). Moreover, to determine if this combination could affect the viability of the IPEC-J2 cells under the normal or stress condition, the viability and membrane integrity were tested, disclosing that the combination mitigated the oxidative stress experimentally induced by increasing the cell viability. In conclusion, the results obtained highlight that the bioactive compounds of algal species are able to exert antioxidant capacity and modulate O138 E. coli growth. Also, the combination of Ascophyllum nodosum and Lithotamnium calcareum species can enhance their bioactivity, making them a promising functional feed additive and a suitable alternative to antibiotics.

Published
2022
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23. Single-Cell Phenotypic and Molecular Characterization of Circulating Tumor Cells Isolated from Cryopreserved Peripheral Blood Mononuclear Cells of Patients with Lung Cancer and Sarcoma.

Author

Vismara M, Reduzzi C, Silvestri M, Murianni F, Lo Russo G, Fortunato O, Motta R, Lanzoni D, Giovinazzo F, Miodini P, Pasquali S, Suatoni P, Pastorino U, Roz L, Sozzi G, Cappelletti V, and Bertolini G

Subjects
Biomarkers, Tumor metabolism, Epithelial Cell Adhesion Molecule genetics, Humans, Leukocytes, Mononuclear metabolism, Retrospective Studies, Carcinoma, Non-Small-Cell Lung genetics, Lung Neoplasms pathology, Neoplastic Cells, Circulating pathology, Sarcoma
Abstract

Background: The isolation of circulating tumor cells (CTCs) requires rapid processing of the collected blood due to their inherent fragility. The ability to recover CTCs from peripheral blood mononuclear cells (PBMCs) preserved from cancer patients could allow for retrospective analyses or multicenter CTC studies., Methods: We compared the efficacy of CTC recovery and characterization using cryopreserved PMBCs vs fresh whole blood from patients with non-small cell lung cancer (NSCLC; n = 8) and sarcoma (n = 6). Two epithelial cellular adhesion molecule (EpCAM)-independent strategies for CTC enrichment, based on Parsortix® technology or immunomagnetic depletion of blood cells (AutoMACS®) were tested, followed by DEPArray™ single-cell isolation. Phenotype and genotype, assessed by copy number alterations analysis, were evaluated at a single-cell level. Detection of target mutations in CTC-enriched samples from frozen NSCLC PBMCs was also evaluated by digital PCR (dPCR)., Results: The use of cryopreserved PBMCs from cancer patients allowed for the retrospective enumeration of CTCs and their molecular characterization, using both EpCAM-independent strategies that performed equally in capturing CTC. Cells isolated from frozen PBMCs were representative of whole blood-derived CTCs in terms of number, phenotype, and copy number aberration profile/target mutations. Long-term storage (≥3 years) did not affect the efficacy of CTC recovery. Detection of target mutations was also feasible by dPCR in CTC-enriched samples derived from stored PBMCs., Conclusions: Isolating CTCs from longitudinally collected PBMCs using an unbiased selection strategy can offer a wider range of retrospective genomic/phenotypic analyses to guide patients' personalized therapy, paving the way for sample sharing in multicenter studies., (© American Association for Clinical Chemistry 2022. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Published
2022
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24. Machine learning potential for interacting dislocations in the presence of free surfaces.

Author

Lanzoni D, Rovaris F, and Montalenti F

Abstract

Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations., (© 2022. The Author(s).)

Published
2022
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25. Copy number alterations analysis of primary tumor tissue and circulating tumor cells from patients with early-stage triple negative breast cancer.

Author

Silvestri M, Dugo M, Vismara M, De Cecco L, Lanzoni D, Vingiani A, Folli S, De Santis MC, de Braud F, Pruneri G, Di Cosimo S, and Cappelletti V

Subjects
Adult, Aged, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Biopsy, Breast pathology, Breast surgery, Cohort Studies, Drug Resistance, Neoplasm genetics, Female, Humans, Mastectomy, Middle Aged, Mutation, Neoplasm Staging, Phylogeny, Prognosis, Triple Negative Breast Neoplasms diagnosis, Triple Negative Breast Neoplasms genetics, Triple Negative Breast Neoplasms pathology, Whole Genome Sequencing, Antineoplastic Combined Chemotherapy Protocols pharmacology, DNA Copy Number Variations drug effects, Neoadjuvant Therapy, Neoplastic Cells, Circulating pathology, Triple Negative Breast Neoplasms therapy
Abstract

Triple negative breast cancer (TNBC) is characterized by clinical aggressiveness, lack of recognized target therapy, and a dismal patient prognosis. Several studies addressed genomic changes occurring during neoadjuvant chemotherapy (NAC) focusing on somatic variants, but without including copy number alterations (CNAs). We analyzed CNA profiles of 31 TNBC primary tumor samples before and after NAC and of 35 single circulating tumor cells (CTCs) collected prior, during and after treatment by using next-generation sequencing targeted profile and low-pass whole genome sequencing, respectively. In pre-treatment tissue samples, the most common gains occurred on chromosomes 1, 2 and 8, and SOX11 and MYC resulted the most altered genes. Notably, amplification of MSH2 (4/4 versus 0/12, p < 0.01) and PRDM1 and deletion of PAX3 (4/4 versus 1/12, p < 0.01) significantly characterized primary tumors of patients with pathological complete response. All patients with paired pre- and post-NAC samples reported a change in post-treatment CNAs compared to baseline, despite they showed at least one common alteration. CNAs detected after treatment involved genes within druggable pathways such as EGFR, cell cycle process and Ras signaling. In two patients, CTCs shared more alterations with residual rather than primary tumor involving genes such as MYC, BCL6, SOX2, FGFR4. The phylogenetic analysis of CTCs within a single patient revealed NAC impact on tumor evolution, suggesting a selection of driver events under treatment pressure. In conclusion, our data showed how chemoresistance might arise early from treatment-induced selection of clones already present in the primary tumor, and that the characterization of CNAs on single CTCs informs on cancer evolution and potential druggable targets., (© 2022. The Author(s).)

Published
2022
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