Review Special Issues

Future trends in organic flour milling: the role of AI

  • Received: 16 August 2022 Revised: 11 November 2022 Accepted: 02 December 2022 Published: 07 December 2022
  • The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.

    Citation: Loïc Parrenin, Christophe Danjou, Bruno Agard, Robert Beauchemin. Future trends in organic flour milling: the role of AI[J]. AIMS Agriculture and Food, 2023, 8(1): 48-77. doi: 10.3934/agrfood.2023003

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  • The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance.



    Glioblastoma (or glioblastoma multiforme, GBM) is the most common and agammaessive adult glioma. It has been estimated that it accounts for approximately 15% of all primary brain and central nervous system tumors and, in particular, for 55% of all gliomas, presenting a more than 90% 2-years mortality [1].

    The GBM arises from astrocytes, the most abundant category of glial cells and almost half of the cells contained in the brain glia. From a morphological point of view, this kind of cells is characterized by a star-shape and the presence of ramified protrusions. To be more precise, within the GBM group it is possible to pinpoint two categories on the base of the clinical appearance: the primary and secondary glioblastoma subtypes. A GBM is defined primary (approximately 90%) if it develops rapidly de novo in elderly patients, without clinical or histologic evidence of a less malignant precursor lesion; in this case, patients have symptoms less than six months prior to diagnosis. Conversely, a GBM is defined secondary when it progresses from low-grade diffuse astrocytoma or anaplastic astrocytoma and generally it develops in younger patients with signs and/or symptoms for longer than six months and carries a significant better prognosis. Histologically, primary and secondary glioblastomas are indistinguishable, but they differ in their genetic and epigenetic profiles [2].

    Nowadays, the current standard protocol for the treatment of GBM is the maximally possible surgery resection of the tumor combined with radiotherapy and/or chemotherapy [2,3]. Surgical resection is case-specific and depends on the tumor size, shape, proximity to blood vessels and on the brain region importance in terms of life-function. Radiotherapy in some cases has limited effects, since cells may develop the ability to upregulating the DNA double-stranded break repair machinery, making the treatment ineffective [4]. However, in combination with chemotherapy the median survival rates increase up to 26.5% at 24 months, a vast improvement over the 10.4% with radiotherapy alone [5].

    Despite the great progress made in the last decades for the development of new imaging techniques for brain tumours and for genetic targeting of adjuvant therapies [5,6], the survival rate after clinical treatment of GBM has remained substantially unaltered. Notwithstanding, recent experiments on in-vitro system models of GBM have shown that the chemo-mechanical feedbacks due to interactions with the tumour microenvironment may play a key role in determining its invasiveness and its highly infiltrative growth.

    The most common system model for the avascular growth phase of solid tumours is the Multicellular Tumour Spheroid (MTS) [7,8,9]. The MTS is a three dimensional agammaegate of cancer cells either seeded in agarose gel or floating within a biological medium filled with nutrients. In [10], MTSs were grown within an inert matrix of agarose gel. Thus, the growing MTS is subjected to a mechanical stress due to the external spatial constraint of the matrix, that mimics the peritumoral stroma. It has been found that the apoptosis rate is directly proportional to the matrix stiffness, with a progressive inhibition on the proliferation rate up to growth saturation. Later works were able to quantify how the local distribution of mechanical stress affect the shape of tumor spheroids [11]. A novel approach has been proposed in [12] by immersing a floating MTS into an external medium enriched with a Dextran solution, that exerts an osmotic pressure independent on the size. The results confirmed the inhibiting effect of a compressive stress, the full reversibility to the stress-free growth curve as far as the Dextran is removed from the culture medium. The imposition of a osmotic stress affects the biological activity of the cells, with an external rim of proliferative cells with reduced apoptosis and a central core characterized by the reverse situation [13]. Regarding specifically the human glioblastoma multiforme, in [14] a set of experiments has been performed investigating the growth of U-87MG spheroids. In the free growth case, it has been shown that it is possible to distinguish between a first stages of growth characterized by an exponential/linear behavior and a second saturation phase where the radius reaches a steady value. On the other hand, the compression experiments performed with Dextran have revealed one more time both the inhibitory effect of stress on growth and the reversibility of this phenomenon.

    This inhibiting effect of osmotic stress on tumour cells has been also observed in a two-dimensional system model, where cells are cultured either individually or to form a monolayer. Although MTS give a more realistic representation of the force transmission between neighboring cells in a three-dimensional embedding, the monolayers are easier to handle experimentally, and can be used to investigate the single cell response in terms of morphology, cell cycle and adhesion. Using different osmotic conditions, gross morphological changes were quantified in cancer cells [15]: hyperosmotic media induce cells to have an elliptical and more tapered shape than cells cultured in isotonic conditions. For the hypotonic treatment, cells tend toward a more rounded morphology at late times. A similar behavior was observed in condrocytes exposed to osmotic shock gang, from hypertonic to hypotonic they exhibit a more spherical shape and membrane ruffles reduction [16]. Beyond the morphological observations, the effect of low osmotic pressure (1 kPa) exerted by Dextran on melanoma proliferation, growth and motility has been investigated in [17,18]. The reported results show that few functional properties of the cells are heavily affected by osmotic pressure: both proliferation and motility are reduced and significant changes in F-actin organization have been observed. These modifications are more evident in primary melanoma cell lines than in the metastatic one and they are supposed to be responsible for the cells elongation. Moreover, several studies have suggested that chemo-mechanical stimuli from the tumor micro–environment are responsible for alteration in the cell cycle. The effect of the hyposmotic condition has been studied on different colonic and pancreatic cell lines [19], highlighting a reversible growth arrest and, in some cases, the accumulation at the G1/S checkpoint of the cell cycle. The mechanism controlling hyperosmotic stress-induced growth arrest, while it is unknown, seemed not to affect the cell cycle in monolayers culture of murine colon cancer cells [20].

    In this work, we introduce a mechanical model that may support a deeper understanding of the mechano-biological features of cells cultured in monolayers in response to different osmotic conditions. The article is structured as follows. In Section 2, we describe the experimental method developed to perform in-vitro experiments on a GBM cell line. The main results, presented in Section 3, pertain the experimental characterization of the effects of the osmotic stress on the proliferation and the morphology of a cell monolayer. In Chapter 4, we develop a novel mathematical framework that explains the observed morphological transition triggered by a critical osmotic stress. The main results are summarized and discussed in Section 5.

    The experiments were performed at IFOM laboratory. A glioblastoma cell line, T98G, was obtained by ATCC (American Type Culture Collection) consortium. Cells were cultured under 37 ℃ and 5 % CO2 in Dulbecco's modified Eagle's medium plus Fetal Bovine Serum (FBS, 10 in volume), L-glutamine (2 mM), Sodium Pyruvate (1 mM), Non-Essential Amino-Acids (1 in volume) and Penicillin/Streptomycin (100 µg/mL).

    Dextran-containing mediums were prepared according to [14]. Briefly, high molecular weight purified Dextran (Mw = 100 kDa, from Sigma-Aldrich, code 0918) was dissolved in complete medium at room temperature till full solubilization. To exert 2 and 5 kPa of mechanical stress on cells, Dextran-containing mediums were prepared with a concentration of 32.5 and 55 g/L respectively.

    Hypotonic mediums were prepared according to [21]. Briefly, a solution of sterile water + 1 mM CaCl2 + 1 mM MgCl2 + 10 FBS was prepared. To obtain 50 hypotonic mediums, complete medium was diluted 1:1 with the aforementioned solution. To obtain 25 hypotonic solution, complete medium was diluted 1:1 with 50 hypotonic medium.

    At Day 0, T98G cells were seeded in 100-mm cell culture dishes, in 10 mL of complete medium. Around 4105 cells were seeded in each dish. After 8 hours, isotonic medium was replaced by the same amount of Dextran-containing or hypotonic medium. At Day 3 and 6, cells were imaged at 4X magnification using an EVOS-FL bright-field microscope (ThermoFisher Scientific), equipped with a high-sensitivity monochrome camera (1360 x 1024 pixels, 6.45 µm/pixels). Then, cells were detached by tripsinization and counted with a Multisizer 3 Coulter Counter (Beckman Coulter).

    To perform cell cycle analysis, collected cells were re–suspended in 0.3 mL of Phosphate-buffered saline (PBS) solution for every million of cells. Fixation was performed by adding 0.7 mL of cold Ethanol (EtOH 100) for every million of cells, dropwise while vortexing. DNA staining was performed washing cellular pellets once in PBS-BSA solution (1 w/v) and incubating them in 0.5 mL of PI solution (Propidium Iodide, 50 mg/mL in H20, light protected) plus RNAse A (0.25 mg/mL) overnight at 4 ℃. Collected cells were analyzed by DNA content with a Attune NxT Acoustic Focusing Cytometer (ThermoFisher). Quantification of cell cycle distribution and apoptosis was performed with FlowJo software.

    To understand the long-term effects of prolonged mechanical stimulation on the morphology and proliferation capacity of glioblastoma cells, we cultured T98G cells with Dextran-containing or hypotonic medium for 6 days.

    The use of Dextran-containing medium is a well-established method to apply an isotropic compression to single cells or multi-cellular spheroids. Indeed, this high-weight biopolymer is not able to penetrate cell membrane, but accumulates at cell surface, thus exerting a mechanical stress [12,14,20].

    At day 3, the number of cells growing in Dextran mediums (either 2 or 5 kPa) was smaller than in isotonic medium. Moreover, cells showed an elongated morphology and are more dispersed, without the formation of compact clusters. At day 6 the growth defect was even more evident, since the vast majority of living cells are aberrantly flattened with a certain amount of dead cells floating in the dish, see Figure 1. Conversely, cells growing in hypotonic medium (either 25 or 50) displayed a mild growth defect without appreciable morphological alterations compared to isotonic control, either at day 3 or day 6, see Figure 1.

    Figure 1.  Bright-field microscopy images of T98G cells grown in isotonic, Dextran-containing and hypotonic medium at Day 3 (left), at Day 6 (center), and 4x zoom at Day 3 (right), where the yellow lines depict the cell contours. The white scale bar is 1 mm.

    The resulting growth curves confirmed the observations by bright-field microscope. The treatment with 25 hypotonic medium had no major effect, whereas cells grown in 50 hypotonic medium showed a delay in proliferation compared to control. On the contrary, cells cultured in Dextran-containing medium experienced a strong growth defect, being almost stacked under 5 kPa of mechanical stress, Figure 2.

    Figure 2.  (Top) Growth curves of T98G cells in isotonic, Dextran-containing and hypotonic medium, from day 0 (cell seeding) to day 6. (Bottom) Cell cycle distribution of T98G cells grown in isotonic, Dextran-containing and hypotonic medium, at day 3 and day 6. Collected cells were stained with PI and analyzed for DNA content by flow citometry.

    To further characterize the effect on cell proliferation, we analyzed cell cycle progression of Dextran/hypotonic-treated cells. Prolonged culture in hypotonic medium caused an accumulation in G1 phase, which is likely the cause of the observed growth delay, Figure 2. Instead, Dextran-containing medium induced an initial accumulation in S phase at day 3, later resulting in a G1-phase accumulation at day 6, Figure 2. Moreover, analysis of fractional DNA content (sub-G1 analysis) highlighted that around 30 of cells underwent apoptosis when constantly cultured in presence of high mechanical stress (5 kPa), Figure 3. These results clearly highlight that prolonged mechanical stimuli impinge on the growth properties of glioblastoma cells on specific cell cycle phases, ultimately limiting the proliferative capacity of tumor cells.

    Figure 3.  Quantification of apoptotic cells in isotonic, Dextran-containing and hypotonic medium, at day 3 and day 6. Apoptotic cells were measured by sub-G1 analysis of DNA content.

    We finally provide a quantitative measurement of the single cell morphology in the different culture conditions. For this purpose we used the Fiji software [22] on the collected images to measure the perimeter pc and the area Ac for each cell in a sample area of dimension 278 x 252 pixels taken randomly in the central area of the dish. The collected measures allowed to compute an average circularity ratio Γ=4πAc/p2c, that is depicted in Figure 4.

    Figure 4.  Quantification of mean circularity ratio Γ in isotonic, Dextran-containing and hypotonic medium, at day 3 and day 6. Error bars describe the standard deviation, measuring about 20 cells per sample area.

    Notably, we find that there is no statistically significant difference between the morphology of the cells in hypotonic and in isotonic conditions, with a mean circularity value of about Γ=0.6. On the contrary, the cells immersed into a hypertonic solution have a significantly lower circularity ratio, with a mean value in the range Γ=0.250.35. Thus, the Dextran molecules are found to trigger a morphological transition towards an elongated shape, that is investigated in the following using a mechanical model.

    In this Section we derive a mathematical framework to model the mechano-biological responses observed in-vitro, focusing on the morphological changes of a single cell in response to hypertonic conditions.

    Living cells possess structural and physical properties that enable them to withstand physical forces exerted by the surrounding environment and to adapt themselves to external chemo-mechanical stimuli. At the time scale of interest (days), we can neglect the much faster active dynamics (seconds) of the cytoskeletal units of the cells [23] and the viscoelastic response (minutes) [24]. Thus, we model the adherent GBM cell at equilibrium as a continuous material possessing a solid-like elastic response and an internal microstructure depending on the preferential spatial arrangement of the cytoskeletal fibers.

    The cell occupies a portion Ω of the three-dimensional Euclidean space E in the current configuration, whilst Ω0 is the reference domain, that is assumed to have a smooth boundary Ω0.

    Let X and x be the material and current position vectors, and ei, i=1,2,3 be a orthonormal Cartesian base. The smooth vectorial mapping:

    x=ϕ(X),

    is taken to be twice differentiable, injective except possibly at the boundary Ω0 and orientation preserving [25], which means that the material cannot penetrate itself or reverse the orientation of material coordinates. We define the displacement vector field as:

    u=ϕ(X)X,

    and the deformation gradient tensor as:

    F(X)=ϕ(X)=xX,

    where the dependence of ϕ on time t drops off because we are at equilibrium.

    The single cell is assumed to have a homogeneous mass density per unit volume, thus neglecting the heterogeneity around its cortical layer, and to behave as a perfectly elastic material. Accordingly, we define a hyperelastic strain energy function Ψ per unit volume to describe its passive mechanical response, so that the first Piola-Kirchhoff stress tensor P reads:

    P=ΨF. (4.1)

    In the absence of bulk forces, the balances of linear and angular momentum impose the equation for the elasto–static equilibrium:

    Div P=0;FP=PTF; (4.2)

    where Div is the divergence operator material coordinates.

    The equilibrium equation is complemented by the following boundary condition at the boundary Ω:

    PN|Ω=pD JFTN; (4.3)

    where J=detF is the Jacobian of the mapping, N is the unit material normal vector, and pD is the osmotic pressure exerted by the Dextran molecules in the solution.

    The nonlinear elastic boundary value problem expressed by Eqs (4.2, 4.3) can be solved after giving the constitutive law of the strain energy function, that relates the mechanical stress to the local strain within the material.

    We assume that the cytoskeletal fibers are mainly oriented along two directions a and b in the material configuration, so that the strain energy function is in the form Ψ=Ψ(F,a,b). Even though fiber dispersion could be accounted using a more realistic model [26], this simpler assumption contains the minimal ingredients to describe the tensional pattern within the cytoskeleton [27].

    By application of the representation theorem of tensor functions [28,29] to enforce the material symmetry group, this functional dependence can be described with respect to the following invariants:

    I1=tr(FTF),I2=12((tr)2(FTF)tr(FTF)2),I3=det(FTF)I4=(Fa)(Fa);I5=(FTFa)(FTFa);I6=(Fb)(Fb);I7=(FTFb)(FTFb);I8=(Fa)(Fb) (4.4)

    We further assume that the anisotropic contributions due to the extensional response of the fibers (expressed by the dependence on the invariants I4,I5,I6,I7) are negligible with respect to the energy contribution due to the presence of crosslinks in the fiber network, that is encoded in the explicit dependence on I8. The contour length of the fibers is indeed much bigger than the average distance of fiber entanglement. Thus, the entropic contribution due to the fiber uncrimping is negligible with respect to the enthalpic terms corresponding to the fibers splay. Therefore, we assume the following strain energy function:

    W(F,a,b)=μ12(I13log(I3))μ22(I8ab)2log(I3), (4.5)

    where μ1 and μ2 are the material parameters that describe the isotropic and the anisotropic responses, respectively. The energy term multiplying μ1 describes a compressible neo-Hookean material, that is the minimal model used to describe the solid stress in tumour cells [30]. The anisotropic contribution contains a novel functional dependence on both I8 and I3, mimicking the passive response due to the fiber splay within the cytoskeleton, also affecting the cell compressibility. We remark that in the absence of deformation, i.e. for F=I, the anisotropic strain measure (I8ab) vanishes [31]. By simple application of the chain differentiation rule to Eq (4.5), the first Piola-Kirchhoff stress tensor is given by:

    P=W(F,a,b)F=μ1F(μ1+(I8ab)2μ2)FTμ22log(I3)(I8ab) (Fab+Fba), (4.6)

    where denotes the dyadic product between vectors.

    In order to make analytic calculations, we consider the simple case in which the cells initially occupies the domain Ω0=[0,L]×[0,L]×[0,H], thus it has a thin geometry with planar square section of width L, and an out-of-plane thickness H<L. We also assume that the fibers are initially orthogonal, so that a=e1 and b=e2. We derive in the following two basic solutions of the elastic boundary value problems representing the two morphologies observed in experiments.

    By substituting Eq (4.6) into Eqs (4.2, 4.3), we search for a uniformly compressed solution, so that the deformation gradient is given by F=λI, with λ<1. We find that λ must be the real root solving :

    λ(λ21)+λ4pDμ1=0 (4.7)

    Due to the uniform compression, we remark that I8=0, so there is no anisotropic contribution to the stress. The solution of Eq (4.7) is shown in Figure 5 as a function of the dimensionless osmotic stress pD/μ1.

    Figure 5.  Uniform compressive stretch λ solving Eq (4.7) as a function of the dimensionless osmotic stress pD/μ1.

    We search now for an elongated solution of Eqs (4.2, 4.3) with the constitutive assumption given by Eq (4.5). Our ansatz for the mapping solution reads:

    x=(αX1+δX2)e1+βX2e2+γX3e3 (4.8)

    where α,β,γ,δ are four unknown parameters.

    Substituting Eq (4.8) into Eqs (4.3, 4.6), we find the following algebraic system of nonlinear equations:

    δ2(μ1+α2δ2μ2)+β2(μ1α2μ1+α2δ2μ2)αβ3γpDαβγδ2pD=0μ1β2μ1+α2δ2μ2αβγpD=0μ1γ2μ1+α2δ2μ2αβγpD=0δ(μ1+α2δ2μ2αβγpD2α2β2μ2log(αβγ))=0. (4.9)

    The solution of Eqs (4.9) can be numerically computed using the Newton's methods as a function of the dimensionless osmotic stress pD/μ1 at different anisotropy ratios μ2/μ1. In particular, we select the solutions such that δ does not vanish, so that the resulting morphology is characterized by a fiber splay causing the elongated shape observed in experiments.

    In this paragraph, we use energetic arguments to identify the onset of a morphology transition between the two basic solutions of the elastic boundary value problem.

    For this purpose we write the total mechanical energy E of the system in the spatial configuration as:

    E=ΩΨd3xpD(|Ω||Ω0|) (4.10)

    where the first term on the rhs is the total elastic energy within the cell and the second term is the mechanical work performed by the osmotic stress. In the following we use the subscripts c and el to indicate the corresponding values evaluated for the basic solutions given by Eq (4.7) and Eq (4.9), respectively.

    In Figure 6 we depict the total energies of the two basic solution as a function of the dimensionless osmotic stress pD/μ1 at a fixed anisotropy ratio μ2/μ1 = 0.15. We find that the uniformly compressed solution has a lower total energy compared to the elongated solution if pD/μ1 is lower than a critical value of about 1.2, beyond which the elongated solution is energetically favourable. This means that the cells keeps a symmetric rounded morphology whilst being squeezed by the outer the osmotic pressure, but a topological crossover occurs beyond this critical pressure value towards an elongated shape dictated by the fiber splay. An illustration of the experimental morphologies and the corresponding elastic solutions is depicted in Figure 7.

    Figure 6.  Total dimensionless energies per unit initial volume Ec/μ1(yellow line) and Eel/μ1 (blue line) of the uniformly compressed and the elongated solutions, respectively, as a function of the dimensionless osmotic stress pD/μ1, shown at μ2=0.15μ1. The two curves intersect at a critical level pcr=pD/μ1=1.2.
    Figure 7.  (Top) Zoomed microscopy images showing the red contours of the cells in isotonic (left) and hypertonic (right) conditions. (Bottom) Sketch of the corresponding homogeneous solutions of the elastic boundary problem.

    In Figure 8 we depict the critical value pcr=pD/μ1 of this morphological crossover as a function of the anisotropy ratio μ2/μ1, showing that the critical threshold for the osmotic pressure is of the same order as μ1 if μ2/μ1=O(1). Since for living cells the shear modulus is about 110 KPa [32], the critical osmotic pressure is of the same order as the one applied in experiments.

    Figure 8.  Critical dimensionless threshold pcr/μ1 of the osmotic stress for the morphological crossover as a function of the dimensionless anisotropic ratio μ2/μ1. The region above the curve is characterized by the energetic dominance of the elongated configuration, while the uniformly compressed solution is favoured below.

    Glioblastoma Multiforme (GBM) is one of the most common and agammaessive tumors and practically impossible to treat due to its high invasive and infiltrative growth, despite the great progress made in the last decades for the development of new imaging techniques for brain tumours and for genetic targeting of adjuvant therapies. Our results suggest the micro–environment is a key factor in GBM development owing to the mutual chemo-mechanical feedback exchanged with the tumour. In particular, we have proven new insights on the effect of different osmotic conditions on monolayers made of human glioblastoma cell line (T98G), particularly focusing on the effect on growth and on morphological changes. By means of in-vitro experiments, we have shown that the osmotic pressure exerted by a medium rich in Dextran (either 2 or 5 kPa) gives an inhibitory effect on growth compared to the standard isotonic medium. The relative effects on growth and proliferation become more evident at long times, where the majority of living cells flatten and a certain amount of dead cells float in suspension. In both hypertonic and hypotonic cases the results on proliferation were also confirmed by the analysis of the experimental growth curves. Moreover, we correlated the effect of pressure alteration on cell proliferation with the cell cycle progression of Dextran/hypotonic-treated cells. We observed that prolonged culture in hypotonic medium caused an accumulation in G1 phase, which is likely the cause of the observed growth delay; instead, the presence of Dextran in the medium induced an initial accumulation in S phase that definitively resulted in a a G1-phase accumulation at later time. In addition, the analysis of fractional DNA content showed that in presence of high mechanical stress (5 kPa) around 30 of cells did undergo apoptosis. Interestingly, we reported that cells undertake a peculiar elongated morphology in presence of Dextran, in agreement with previous results [15,17], and they appear more dispersed rather than gathered in compact clusters. On the contrary, the hypotonic treatment had only a mild effect on growth, without appreciable alteration of the cells morphology.

    Thus, we have proposed a mathematical model for the single cell in order to understand the mechanical origin of the morphological transition observed in monolayers in presence of hypertonic stress. Despite the tumour consists in an agammaegate of cell, the importance of focusing on the single cell behaviour is due to the need of understanding how the micro-environment can influence the biological processes of the single during the migration process. Considering the time scale of interest (days), the adherent GBM cell is assumed to respond to external solicitation as a solid-like elastic material characterized by the presence in the bulk of an internal micro–structure that accounts for the preferential spatial arrangement of the cytoskeletal fibers, which are assumed to be mainly distributed around two orthogonal directions in the reference configuration. After stating the constitutive law of the strain energy density using the representation theorem of tensor functions, we computed two homogeneous solutions of the nonlinear elastic boundary value problem, corresponding to uniform symmetric compression and elongated shape observed in experiments.

    Through energetic considerations we suggested a plausible explanation of the morphology crossover between the two solutions, based on a competition between the isotropic response and the splay contribution given by the cytoskeletal fibers. The theoretical results are in good agreement with the experiments: the uniformly compressed solution is energetic favourable in presence of low osmotic stresses, whilst the elongated solution is dictated by the fiber splay, and it only occurs if the cell is subjected to an osmotic stress beyond a critical threshold, which is of the order of magnitude as the one used in experiments.

    This work has been supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) through the MFAG grant 17412 to PC, through IG#18621 to GS, and the Italian Ministry of Health (RF-2013-02358446) to GS. DA acknowledges the MIUR grant "Dipartimento di Eccellenza 2018-2022" (E11G18000350001).

    The authors declare no conflict of interest.



    [1] Déragon F (2016) La ferme biologique, un espace d'éducation relative à l'éco-alimentation et de construction du rapport à la terre[Thèse de Maitrise]: Université du Québec à Montréal.
    [2] Hughner RS, McDonagh P, Prothero A, et al. (2007) Who are organic food consumers? A compilation and review of why people purchase organic food. J Consum Behav 6: 94–110. https://doi.org/10.1002/cb.210 doi: 10.1002/cb.210
    [3] Willer H, Trávníček J, Meier C, et al. (2021) The World of Organic Agriculture Statistics and Emerging Trends 2021 Research Institute of Organic Agriculture FiBL, Frick, and IFOAM—Organics International, Bonn.
    [4] Cappelli A, Oliva N, Cini E (2020) Stone milling versus roller milling: A systematic review of the effects on wheat flour quality, dough rheology, and bread characteristics. Trends Food Sci Technol 97: 147–155. https://doi.org/10.1016/j.tifs.2020.01.008 doi: 10.1016/j.tifs.2020.01.008
    [5] Doblado-Maldonado AF, Pike OA, Sweley JC, et al. (2012) Key issues and challenges in whole wheat flour milling and storage. J Cereal Sci 56: 119–126. https://doi.org/10.1016/j.jcs.2012.02.015 doi: 10.1016/j.jcs.2012.02.015
    [6] Campbell GM (2007) Chapter 7—Roller milling of wheat. In: Salman AD, Ghadiri M, Hounslow MJ, Handbook of Powder Technology, Elsevier Science B.V., 383–419. https://doi.org/10.1016/S0167-3785(07)12010-8
    [7] Boudreau A, Ménard G (1992) Le Blé: éléments fondamentaux et transformation.
    [8] Mateos-Salvador F, Sadhukhan J, Campbell GM (2011) The normalised Kumaraswamy breakage function: A simple model for wheat roller milling. Powder Technol 208: 144–157. https://doi.org/10.1016/j.powtec.2010.12.013 doi: 10.1016/j.powtec.2010.12.013
    [9] FAO (1985) Standard for wheat flour.
    [10] IAOM (2018) Fundamentals of Flour Milling. Lenexa: International Association of Operative Millers, 422.
    [11] Steffan P (2012) An optimization model: Minimizing flour millers' costs of production by blending wheat and additives[Thèse de Maitrise]: Kansas State University.
    [12] Vrček Ⅳ, Čepo DV, Rašić D, et al. (2014) A comparison of the nutritional value and food safety of organically and conventionally produced wheat flours. Food Chem 143: 522–529. https://doi.org/10.1016/j.foodchem.2013.08.022 doi: 10.1016/j.foodchem.2013.08.022
    [13] Borghi B, Giordani G, Corbellini M, et al. (1995) Influence of crop rotation, manure and fertilizers on bread making quality of wheat (Triticum aestivum L.). Eur J Agron 4: 37–45. https://doi.org/10.1016/S1161-0301(14)80015-4 doi: 10.1016/S1161-0301(14)80015-4
    [14] Johansson E, Svensson G (1998) Variation in bread-making quality: Effects of weather parameters on protein concentration and quality in some Swedish wheat cultivars grown during the period 1975–1996. J Sci Food Agric 78: 109–118. https://doi.org/10.1002/(SICI)1097-0010(199809)78:1%3C109::AID-JSFA92%3E3.0.CO; 2-0 doi: 10.1002/(SICI)1097-0010(199809)78:1<109::AID-JSFA92>3.0.CO;2-0
    [15] Triboi E, Abad A, Michelena A, et al. (2000) Environmental effects on the quality of two wheat genotypes: 1. quantitative and qualitative variation of storage proteins. Eur J Agron 13: 47–64. https://doi.org/10.1016/S1161-0301(00)00059-9 doi: 10.1016/S1161-0301(00)00059-9
    [16] Köksal G, Batmaz İ, Testik MC (2011) A review of data mining applications for quality improvement in manufacturing industry. Expert Syst Appl 38: 13448–13467. https://doi.org/10.1016/j.eswa.2011.04.063 doi: 10.1016/j.eswa.2011.04.063
    [17] Misra NN, Dixit Y, Al-Mallahi A, et al. (2020) IoT, big data and artificial intelligence in agriculture and food industry. IEEE Int Things J 9: 6305–6324. https://doi.org/10.1109/JIOT.2020.2998584 doi: 10.1109/JIOT.2020.2998584
    [18] Kelleher JD (2019) Deep Learning (MIT Press Essential Knowledge series). The MIT Press, Illustrated edition, 296.
    [19] Fisher OJ, Watson NJ, Escrig JE, et al. (2020) Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Comput Chem Eng 140: 106881. https://doi.org/10.1016/j.compchemeng.2020.106881 doi: 10.1016/j.compchemeng.2020.106881
    [20] Branlard G, Dardevet M, Saccomano R, et al. (2001) Genetic diversity of wheat storage proteins and bread wheat quality. Euphytica 119: 59–67. https://doi.org/10.1023/A:1017586220359 doi: 10.1023/A:1017586220359
    [21] Maphosa L, Langridge P, Taylor H, et al. (2014) Genetic control of grain yield and grain physical characteristics in a bread wheat population grown under a range of environmental conditions. Theor Appl Genet 127: 1607–1624. https://doi.org/10.1007/s00122-014-2322-y doi: 10.1007/s00122-014-2322-y
    [22] Wrigley CW, Blumenthal C, Gras PW, et al. (1994) Temperature variation during grain filling and changes in wheat-grain quality. Funct Plant Biol 21: 875–885. https://doi.org/10.1071/PP9940875 doi: 10.1071/PP9940875
    [23] Mefleh M, Conte P, Fadda C, et al. (2020) From seed to bread: Variation in quality in a set of old durum wheat cultivars. J Sci Food Agric 100: 4066–4074. https://doi.org/10.1002/jsfa.9745 doi: 10.1002/jsfa.9745
    [24] Aydin N, Sayaslan A, Sönmez M, et al. (2020) Wheat flour milling yield estimation based on wheat kernel physical properties using artificial neural networks. Int J Intell Syst Appl Eng 8: 78–83. https://doi.org/10.18201/ijisae.2020261588 doi: 10.18201/ijisae.2020261588
    [25] Dowell FE, Maghirang EB, Xie F, et al. (2006) Predicting wheat quality characteristics and functionality using near-infrared spectroscopy. Cereal Chem 83: 529–536. https://doi.org/10.1094/CC-83-0529 doi: 10.1094/CC-83-0529
    [26] Caporaso N, Whitworth MB, Fisk ID (2018) Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl Spectrosc Rev 53: 667–687. https://doi.org/10.1080/05704928.2018.1425214 doi: 10.1080/05704928.2018.1425214
    [27] Assadzadeh S, Walker CK, McDonald LS, et al. (2022) Prediction of milling yield in wheat with the use of spectral, colour, shape, and morphological features. Biosyst Eng 214: 28–41. https://doi.org/10.1016/j.biosystemseng.2021.12.005 doi: 10.1016/j.biosystemseng.2021.12.005
    [28] Delwiche SR, Souza EJ, Kim MS (2013) Limitations of single kernel near-infrared hyperspectral imaging of soft wheat for milling quality. Biosyst Eng 115: 260–273. https://doi.org/10.1016/j.biosystemseng.2013.03.015 doi: 10.1016/j.biosystemseng.2013.03.015
    [29] Unlersen MF, Sonmez ME, Aslan MF, et al. (2022) CNN–SVM hybrid model for varietal classification of wheat based on bulk samples. Eur Food Res Technol 248: 2043–2052. https://doi.org/10.1007/s00217-022-04029-4 doi: 10.1007/s00217-022-04029-4
    [30] Zhu J, Li H, Rao Z, et al. (2023) Identification of slightly sprouted wheat kernels using hyperspectral imaging technology and different deep convolutional neural networks. Food Control 143: 109291. https://doi.org/10.1016/j.foodcont.2022.109291 doi: 10.1016/j.foodcont.2022.109291
    [31] Gucbilmez CM, Sahin M, Akcacik AG, et al. (2019) Evaluation of GlutoPeak test for prediction of bread wheat flour quality, rheological properties and baking performance. J Cereal Sci 90: 102827. https://doi.org/10.1016/j.jcs.2019.102827 doi: 10.1016/j.jcs.2019.102827
    [32] Sisman CB, Ergin AS (2011) The effects of different storage buildings on wheat quality. J Appl Sci 11: 2613–2619. https://doi.org/10.3923/jas.2011.2613.2619 doi: 10.3923/jas.2011.2613.2619
    [33] Jia C, Sun D-W, Cao C (2001) Computer simulation of temperature changes in a wheat storage bin. J Stored Prod Res 37: 165–177. https://doi.org/10.1016/S0022-474X(00)00017-5 doi: 10.1016/S0022-474X(00)00017-5
    [34] Kibar H (2015) Influence of storage conditions on the quality properties of wheat varieties. J Stored Prod Res 62: 8–15. https://doi.org/10.1016/j.jspr.2015.03.001 doi: 10.1016/j.jspr.2015.03.001
    [35] Posner E, Deyoe C (1986) Changes in milling properties of newly harvested hard wheat during storage. Cereal Chem 63: 451–456.
    [36] Campbell GM, Sharp C, Wall K, et al. (2012) Modelling wheat breakage during roller milling using the Double Normalised Kumaraswamy Breakage Function: Effects of kernel shape and hardness. J Cereal Sci 55: 415–425. https://doi.org/10.1016/j.jcs.2012.02.002 doi: 10.1016/j.jcs.2012.02.002
    [37] Posner ES, Hibbs AN (2005) Wheat flour milling: American Association of Cereal Chemists, Inc. https://doi.org/10.1094/1891127403
    [38] Yoon BS, Brorsen BW, Lyford CP (2002) Value of increasing kernel uniformity. J Agric Resour Econ 27: 481–494.
    [39] González-Torralba J, Arazuri S, Jarén C, et al. (2013) Influence of temperature and r.h. during storage on wheat bread making quality. J Stored Prod Res 55: 134–144. https://doi.org/10.1016/j.jspr.2013.10.002 doi: 10.1016/j.jspr.2013.10.002
    [40] Catterall P (1998) Flour milling. In: Cauvain SP, Young LS, Technology of Breadmaking, Boston, MA: Springer US., 296–329. https://doi.org/10.1007/978-1-4615-2199-0_12
    [41] Tibola CS, Fernandes JMC, Guarienti EM (2016) Effect of cleaning, sorting and milling processes in wheat mycotoxin content. Food Control 60: 174–179. https://doi.org/10.1016/j.foodcont.2015.07.031 doi: 10.1016/j.foodcont.2015.07.031
    [42] Magyar Z, Véha A, Pepó P, et al. (2019) Wheat cleaning and milling technologies to reduce DON toxin contamination. Acta Agraria Debreceniensis 0: 89–95. https://doi.org/10.34101/actaagrar/2/3684 doi: 10.34101/actaagrar/2/3684
    [43] Bettge A, Rubenthaler G, Pomeranz Y (1989) Air‐aspirated wheat cleaning in grading and in separation by functional properties. Cereal Chem 66: 15–18.
    [44] Hayta M, Çakmakli Ü (2001) Optimization of wheat blending to produce breadmaking flour. J Food Process Eng 24: 179–192. https://doi.org/10.1111/j.1745-4530.2001.tb00539.x doi: 10.1111/j.1745-4530.2001.tb00539.x
    [45] Elevi B, Öztürk H, Kacir Z (2017) Optimization of wheat and flour blending for cost minimization by using mathematical modelling. Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 264–272.
    [46] Kweon M, Martin R, Souza E (2009) Effect of tempering conditions on milling performance and flour functionality. Cereal Chem 86: 12–17. https://doi.org/10.1094/CCHEM-86-1-0012 doi: 10.1094/CCHEM-86-1-0012
    [47] Warechowska M, Markowska A, Warechowski J, et al. (2016) Effect of tempering moisture of wheat on grinding energy, middlings and flour size distribution, and gluten and dough mixing properties. J Cereal Sci 69: 306–312. https://doi.org/10.1016/j.jcs.2016.04.007 doi: 10.1016/j.jcs.2016.04.007
    [48] Hook SCW, Bone GT, Fearn T (1982) The conditioning of wheat. The effect of increasing wheat moisture content on the milling performance of uk wheats with reference to wheat texture. J the Sci Food Agric 33: 655–662. https://doi.org/10.1002/jsfa.2740330711 doi: 10.1002/jsfa.2740330711
    [49] Doblado-Maldonado AF, Flores RA, Rose DJ (2013) Low moisture milling of wheat for quality testing of wholegrain flour. J Cereal Sci 58: 420–423. https://doi.org/10.1016/j.jcs.2013.08.006 doi: 10.1016/j.jcs.2013.08.006
    [50] Noort MWJ, van Haaster D, Hemery Y, et al. (2010) The effect of particle size of wheat bran fractions on bread quality—Evidence for fibre-protein interactions. J Cereal Sci 52: 59–64. https://doi.org/10.1016/j.jcs.2010.03.003 doi: 10.1016/j.jcs.2010.03.003
    [51] Cappelli A, Guerrini L, Parenti A, et al. (2020) Effects of wheat tempering and stone rotational speed on particle size, dough rheology and bread characteristics for a stone-milled weak flour. J Cereal Sci 91: 102879. https://doi.org/10.1016/j.jcs.2019.102879 doi: 10.1016/j.jcs.2019.102879
    [52] Parrenin L, Danjou C, Agard B, et al. (2022) Predicting the moisture content of organic wheat in the first stage of tempering. IFAC-PapersOnLine 55: 678–683. https://doi.org/10.1016/j.ifacol.2022.09.484 doi: 10.1016/j.ifacol.2022.09.484
    [53] Fang C, Campbell GM (2003) On predicting roller milling performance Ⅴ: Effect of moisture content on the particle size distribution from first break milling of wheat. J Cereal Sci 37: 31–41. https://doi.org/10.1006/jcrs.2002.0476 doi: 10.1006/jcrs.2002.0476
    [54] Lin S, Gao J, Jin X, et al. (2020) Whole-wheat flour particle size influences dough properties, bread structure and in vitro starch digestibility. Food Funct 11: 3610–3620. https://doi.org/10.1039/C9FO02587A doi: 10.1039/C9FO02587A
    [55] Tóth Á, Prokisch J, Sipos P, et al. (2006) Effects of particle size on the quality of winter wheat flour, with a special focus on macro‐ and microelement concentration. Commun Soil Sci Plant Anal 37: 2659–2672. https://doi.org/10.1080/00103620600823117 doi: 10.1080/00103620600823117
    [56] Pagani MA, Marti A, Bottega G (2014) Chapter 2 : Wheat milling and flour quality evaluation. In: Zhou W, Hui YH, De Leyn I, et al., Bakery Products Science and Technology, John Wiley & Sons, Ltd. 20–49. https://doi.org/10.1002/9781118792001.ch2
    [57] Campbell GM, Webb C (2001) On predicting roller milling performance: Part Ⅰ. The breakage equation. Powder Technol 115: 234–242. https://doi.org/10.1016/S0032-5910(00)00348-X doi: 10.1016/S0032-5910(00)00348-X
    [58] Campbell GM, Bunn PJ, Webb C, et al. (2001) On predicting roller milling performance: Part Ⅱ. The breakage function. Powder Technol 115: 243–255. https://doi.org/10.1016/S0032-5910(00)00349-1 doi: 10.1016/S0032-5910(00)00349-1
    [59] Cappelli A, Mugnaini M, Cini E (2020) Improving roller milling technology using the break, sizing, and reduction systems for flour differentiation. Lwt-Food Sci Technol 133: 110067. https://doi.org/10.1016/j.lwt.2020.110067 doi: 10.1016/j.lwt.2020.110067
    [60] Dal-Pastro F, Facco P, Bezzo F, et al. (2015) Data-based multivariate modeling of a grain comminution process. Comput Aided Chem Eng 37: 2219–2224. https://doi.org/10.1016/B978-0-444-63576-1.50064-9 doi: 10.1016/B978-0-444-63576-1.50064-9
    [61] Dal-Pastro F, Facco P, Bezzo F, et al. (2016) Data-driven modeling of milling and sieving operations in a wheat milling process. Food Bioprod Process 99: 99–108. https://doi.org/10.1016/j.fbp.2016.04.007 doi: 10.1016/j.fbp.2016.04.007
    [62] Fang Q, Biby G, Haque E, et al. (1998) Neural network modeling of physical properties of ground wheat. Cereal Chem 75: 251–253. https://doi.org/10.1094/CCHEM.1998.75.2.251 doi: 10.1094/CCHEM.1998.75.2.251
    [63] Stefan E-M, Voicu G, Constantin G, et al. (2018) Effects of wheat seeds characteristics on roller milling process—a review.
    [64] Fang C, Campbell GM (2003) On predicting roller milling performance Ⅳ: Effect of roll disposition on the particle size distribution from first break milling of wheat. J Cereal Sci 37: 21–29. https://doi.org/10.1006/jcrs.2002.0475 doi: 10.1006/jcrs.2002.0475
    [65] Campbell GM, Fang C, Muhamad Ⅱ (2007) On predicting roller milling performance Ⅵ: Effect of kernel hardness and shape on the particle size distribution from first break milling of wheat. Food Bioprod Process 85: 7–23. https://doi.org/10.1205/fbp06005 doi: 10.1205/fbp06005
    [66] Hook SCW, Bone GT, Fearn T (1982) The conditioning of wheat. The influence of roll temperature in the bühler laboratory mill on milling parameters. J Sci Food Agric 33: 639–644. https://doi.org/10.1002/jsfa.2740330709 doi: 10.1002/jsfa.2740330709
    [67] Fang Q, Haque E, Spillman CK, et al. (1998) Energy requirements for size reduction of wheat using a roller mill. Trans ASAE 41: 1713–1720. https://doi.org/10.13031/2013.17314 doi: 10.13031/2013.17314
    [68] Kalitsis J, Minasny B, Quail K, et al. (2021) Application of response surface methodology for optimization of wheat flour milling process. Cereal Chem 98: 1215–1226. https://doi.org/10.1002/cche.10474 doi: 10.1002/cche.10474
    [69] Oliver J, Blakeney A, Allen H (1993) The colour of flour streams as related to ash and pigment contents. J Cereal Sci 17: 169–182. https://doi.org/10.1006/jcrs.1993.1017 doi: 10.1006/jcrs.1993.1017
    [70] Banu I, Stoenescu G, Ionescu V, et al. (2011) Estimation of the baking quality of wheat flours based on rheological parameters of the Mixolab Curve. Czech J Food Sci 29: 35–44. https://doi.org/10.17221/40/2009-CJFS doi: 10.17221/40/2009-CJFS
    [71] Dowell FE, Maghirang EB, Pierce RO, et al. (2008) Relationship of bread quality to kernel, flour, and dough properties. Cereal Chem 85: 82–91. https://doi.org/10.1094/CCHEM-85-1-0082 doi: 10.1094/CCHEM-85-1-0082
    [72] Parenti O, Guerrini L, Zanoni B (2020) Techniques and technologies for the breadmaking process with unrefined wheat flours. Trends Food Sci Technol 99: 152–166. https://doi.org/10.1016/j.tifs.2020.02.034 doi: 10.1016/j.tifs.2020.02.034
    [73] Parenti O, Guerrini L, Cavallini B, et al. (2020) Breadmaking with an old wholewheat flour: Optimization of ingredients to improve bread quality. Lwt-Food Sci Technol 121: 108980. https://doi.org/10.1016/j.lwt.2019.108980 doi: 10.1016/j.lwt.2019.108980
    [74] Torbica A, Blazek KM, Belovic M, et al. (2019) Quality prediction of bread made from composite flours using different parameters of empirical rheology. J Cereal Sci 89: 102812. https://doi.org/10.1016/j.jcs.2019.102812 doi: 10.1016/j.jcs.2019.102812
    [75] Cappelli A, Guerrini L, Cini E, et al. (2019) Improving whole wheat dough tenacity and extensibility: A new kneading process. J Cereal Sci 90: 102852. https://doi.org/10.1016/j.jcs.2019.102852 doi: 10.1016/j.jcs.2019.102852
    [76] Ktenioudaki A, Butler F, Gallagher E (2010) Rheological properties and baking quality of wheat varieties from various geographical regions. J Cereal Sci 51: 402–408. https://doi.org/10.1016/j.jcs.2010.02.009 doi: 10.1016/j.jcs.2010.02.009
    [77] Oliver JR, Allen HM (1992) The prediction of bread baking performance using the farinograph and extensograph. J Cereal Sci 15: 79–89. https://doi.org/10.1016/S0733-5210(09)80058-1 doi: 10.1016/S0733-5210(09)80058-1
    [78] Abbasi H, Emam-Djomeh Z, Seyedin S (2011) Application of artificial neural network and genetic algorithm for predicting three important parameters in bakery industries. Int J Agric Sci Res 2: 51–64.
    [79] Różyło R, Laskowski J (2011) Predicting bread quality (bread loaf volume and crumb texture). Pol J Food Nutr Sci 61: 61–67. https://doi.org/10.2478/v10222-011-0006-8 doi: 10.2478/v10222-011-0006-8
    [80] Gómez Sarduy J, Viego P, Diaz Torres Y, et al. (2018) A new energy performance indicator for energy management system of a wheat mill plant. Int J Energy Econ Policy 8: 324–330.
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