Based on the well-investigated OnVu™ TTI kinetics, models were developed to adjust the label to different food products and predict the discolouration process under dynamic temperature conditions. After the successful validation under laboratory conditions, the applicability of the time temperature indicator (TTI) as shelf life indicator was tested in a national poultry chain. The TTI accurately reflected the temperature fluctuations occurring under real chain conditions. Shelf life predictions based on the discolouration of the TTIs were in accordance with the microbial shelf life of the product. The models were integrated in an online software tool to check for the compliance of the cold chain and predict the remaining shelf life of the product. The implementation of TTI and the software result in a valuable tool to support the decision-making process in the cold chain. The application of flexible shelf life enables the reduction of food waste in the meat supply chain. Temperature monitoring in food chains is of vital importance for ensuring safe and high-quality products and reducing food waste , 35, 40.Thus, the aim of the study was to validate the applicability of the photochromic OnVu™ TTI as an indirect freshness indicator for fresh poultry and to develop a TTI monitoring software tool to support the decision-making process in meat supply chains. Since this TTI is already well described and characterised, modelling and further developments of this study are based on the investigations of Kreyenschmidt et al. .In the first step of the present study, an activation model was developed to adapt the discolouration process of the label to products with different shelf lives. A second model was developed to predict the discolouration of the label under dynamic temperature conditions. Both models were validated under laboratory conditions. In a third step, the applicability of the TTI as a freshness indicator and shelf life indicator for fresh poultry meat was tested under practical conditions. Based on the results, a software tool was developed (step four). The software can be used to support the decision-making process in companies by delivering information about the compliance of the cold chain and about the remaining shelf life of the product in each step of the chain.The analysed photochromic TTI was the OnVu™ label B1 + 081126 . The label, or rather the pigmented water ink, is activated by UV light, resulting in a blue colouring of the label. The discolouration of the label is characterised by a colour change from blue to white. The discolouration time and the kinetics of the label depend on the amount of light used for the charging process. During activation, the TTI can generally be adjusted to different products by varying the charging time, resulting in a modified intensity and duration of the light pulse.L is the lightness, a is the red and green component, and b is the yellow and blue component of the label.For quantifying the colour change of the TTI and determining the shelf life of the label, the square value SV, Eq.  of the TA is the amplitude, k is the reaction rate (h−1), tr is the reversal point (h), and t is the time (h).The colour change of the label or the development of the square value as function of time can be described by a logistic model . The folk is the reaction rate (h−1), k0 is the constant (h−1), Ea is the activation energy (kJ mol−1), R is the ideal gas constant (8.314 J mol−1 K−1), and T is the absolute temperature (K).The Arrhenius equation was used to describe the temperature dependency of the label discolouration . Thus, tA detailed characterisation of the discolouration process and the activation energy of the label calculated by the Arrhenius Model  were given by , 36. DatIn the first step, an activation model was developed to adjust the label to products with different shelf lives at a certain temperature. Initially, the model predicts the discolouration process of the TTI for several isothermal temperature conditions in relation to TTI activation. Based on the TTI discolouration time (shelf life) and storage temperature, the model calculates the appropriate charging and required initial SV of a label.0) on the shelf life of the TTI was modelled by linear regression. Therefore, discolouration times for different temperatures were log transformed and modelled as a function of the SV0. The relation between discolouration time and SV0 was plotted for each investigated temperature (2–20 °C) and fitted to the following equation , and SV0 is the initial SV of the TTI.The influence of the initial SV (SVtion Eq. :4\docume0 and temperature [bact) and intercept (aact) were a function of temperature  were used for further modelling. As discolouration shows a twofold dependency of SVperature , the sloure Eqs.  and 6.5\0 in relation to a specific storage temperature and shelf life , atemp and btemp are the intercept and slope of the temperature-dependent aact , tr is the reversal point (h), and t is the time (h).To predict the discolouration of the TTI under fluctuating temperature conditions, a dynamic TTI model was developed. Based on the description of Kreyenschmidt et al. , the loglows Eq. :8\documeA and k. Thereby, the SV of the previous interval was applied , tr is the reversal point (h), and t is the time (h).For every interval, the reversal point was calculated with the particular values of lied Eq. .9\documen(t) = 1, the SV0 is inserted. By executing the TTI kinetics model, the discolouration process of the TTI can be modelled under dynamic temperature conditions, resulting in an exact prediction of the SV for any point in the cold chain. Thus, the model enables the calculation of check values and warning ranges for specific temperature limits at inspection and control points.For the first interval The validation of the TTI model was conducted with 120 TTIs which were positioned on the top, the four sides, and the bottom of two cardboard boxes. Each box (40 cm × 30 cm × 10 cm) was filled with 5 kg of poultry filet wrapped in polyethylene foil to simulate the delivery unit in the supply chain. One box with 60 TTIs was stored under fluctuating temperature conditions simulating the temperature profile of a German poultry chain : 24 h at0 of 57.9. This value was calculated with the TTI activation model described under 2.2.Based on the product characteristics  described by Bruckner et al. , the TTIR2, the bias factor, and the accuracy factor [After activation of the label, the boxes were stored in high precision low temperature incubators . The discolouration process was monitored every 24 h by measuring the SV of the TTIs as described in Sect. y factor .For assessing the suitability of the TTI as a shelf life indicator for fresh poultry meat, the TTI was tested under practical conditions. The field trial was conducted in a German poultry chain and comprised the monitoring of eight cardboard boxes in 4 weeks, containing 5 kg of poultry filet in each case. The chicken breast was packed aerobically in the boxes and wrapped in transparent foil.Due to improvements of hygienic conditions in the production plant , the sheThe boxes were distributed through the typical German poultry chain . After the last transportation step (butcher), each cardboard box was stored until the end of its shelf life at 4 °C in a cooling chamber in the laboratory . During the study, the storage temperature at the boxes was in the range of − 1 °C to + 12 °C and maximum temperatures between + 5 and + 15 °C.n = 168). Since Pseudomonas spp. is the specific spoilage organism (SSO) for aerobe-packed fresh poultry filet, investigations focused on this organism. A representative sample of 25 g of the meat was transferred to a stomacher bag. After adding 225 g chilled saline peptone diluents , the sample was homogenised for 60 s in a Stomacher 400 . An appropriate 10-fold dilution of the homogenate was prepared using saline peptone diluents. The suspension was tested for Pseudomonas spp. by surface spreading on Pseudomonas Agar Base  plus CFC supplement . After incubating the petri dishes aerobically at 25 ± 1 °C for 48 h, the microbial counts were determined. The product was classified as spoiled when Pseudomonas spp. reached a level of 7.5 log10 cfu/g.Measurements of the TTIs response were applied at several inspection and decision points in the chain, resulting in a complete monitoring of the whole cold chain. Additionally, microbial samples were taken at seven points  to investigate the applicability of TTIs as shelf life indicator. At each microbial inspection point, three poultry samples were taken per box , an online Software tool was developed to support the decision-making process in cold chains using TTIs. By integrating the activation and dynamic models, the TTI can be adjusted to specific products to calculate if the cold chain was in its limit. The software delivers information about the temperature history and the remaining shelf life of the product based on the measured TTI value. The tool also allows the definition of specific warning and action ranges for the incoming and outgoing inspection. For the software development, the main goal was to separate modelling from content management system, achieve portability and use open source tools exclusively. A main focus was the easy maintenance and ability for further developments. The online tool is embedded into a wordpress system. The backend programming was conducted with PHP and the frontend programming is a generated javascript (via coffeescript). Data exchange between frontend and backend is conducted via AJAX. A MySQL database was set up with a wordpress interface. Thus, flexible adjustments are possible, if discolouration kinetics of the TTI change with new developments or enhanced prototypes. Different TTI systems or food products can easily be added to the database. Thus, the tool can be extended to a variety of TTI systems and food products, and act as a universal tool to support cold chain management.After storing the labels under dynamic and constant temperature conditions, the measured TTI values were compared to the predicted TTI response. As shown in Fig. R2 was above 0.96 on all sides of box 1 and 2 . As stated by Kreyenschmidt et al. [The charging process under practical conditions showed a high reproducibility. The SVt et al. , constanR2 > 0.95). During the field trial, the TTI discolouration reflected the logged temperature accurately. The mean shelf life of the TTIs was varying for the different boxes between 174 and 192 h. These differences were caused by different placements of the investigated boxes in pallets and their location in the truck. The cyclical cooling system, which generates airflows between pallets and over the surfaces of the boxes for example, can result in different temperature conditions for the single units [In practice, the TTI model displayed a very accurate prediction of the discolouration process of the label for all investigated boxes and the different sides of the boxes. This was reflected by high goodness of fit values  between 2.28 to 3.86 log10 cfu/g at the beginning of storage. The development of Pseudomonas spp. in chicken breast filet during transportation and storage is shown in Fig. 10 cfu/g . The estimated mean shelf life is in accordance with the shelf life given by the producer and corresponds to the shelf life of aerobe-packed poultry estimated in previous studies [The TTI discolouration process was compared to the microbial growth to investigate the functional capability of the label as an indirect freshness indicator. The specific spoilage organism  studies , 37.Fig.Pseudomonas spp. on fresh chicken breast filet obtained with the Gompertz model are listed in Table Pseudomonas spp. The remaining shelf life of products can be affected significantly by varying initial microbial loads [Growth parameters of al loads . Thus, hal loads . FurtherPseudomonas spp. on poultry is shown in Fig. Aside from box 6, the shelf life predictions based on the TTI discolourations are in good accordance with the shelf life estimated by the microbiological investigations Table . The prehttps://ccm-network.com/tti-predictor-2/. By entering the product’s shelf life at a certain reference temperature, the initial SV value of the label can be calculated. To use the TTI as a shelf life control instrument, the user can measure the values of the TTI with a spectrophotometer in each step of the chain and integrate the value. To calculate the remaining shelf life, the date of packaging or the time since packaging has to be inserted. By comparing the measured SV of the TTI with the SV calculated by the model at the optimal storage temperature, the tool gives an output if the cold chain has been violated or is in its limit. Based on the measured TTI value, a calculation of the mean storage temperature occurred in the supply chain is given. Additionally, the remaining shelf life is calculated based on the optimal storage temperature for the product chosen in the first step.The developed TTI predictor addresses to all actors handling the product and allows to calculate dynamic shelf lives and support the decision-making process in supply chain. The TTI predictor is available for free on After implementing the TTI system into the supply chain, monitoring of the SV is crucial at inspection points, to avoid shelf life losses and deliver products meeting the minimum acceptance level of the customer. The definition of warning and control ranges for specific inspection points enables a comparison between an ideal TTI response and the current response in the chain. As a result, the software displays a traffic signal , showing if there was a problem with the cold chain and, consequently, with the shelf life: A green signal is shown when the measured SV is smaller than the computed SV, whereas a red signal is shown if the measured SV is higher than the computed SV. Thus, the green sign corresponds to a cold chain where the temperature was within its limits and the red sign shows temperature abuse affecting the shelf life of the product. The specific warning and control ranges for the SV or calculated mean temperature can be set flexible in the database. Thus, the tool is easily adaptable to the specific needs at inspection points of the particular cold chain.If the tool recognises that the cold chain was interrupted, a further option will open. The software is able to predict if it is possible to save the product by adjusting the cooling conditions. For meat products, adjusting the storage temperature is an efficient way to reduce food waste . By utilAfter the establishment of overall temperature monitoring along the distribution chain, the efficiency of information management has a similarly high impact on the success of the application of novel methods –25, 34. The TTI field trial and the validated TTI models showed that the TTI can support valuable management decisions in perishable food supply chains. As such, TTIs can deliver an important contribution to the monitoring of the shelf life of perishable products, and to the reduction of food waste. However, different technical, functional, and organisational requirements have to be assured prior to its integration within the supply chain.Generally, the study has shown that the TTIs, as well as the TTI kinetics model, are a useable and useful tool for temperature monitoring during processing and for the prediction of remaining shelf life in different steps of the supply chain. Furthermore, the high model performance and consistency in the TTIs discolouration process in this study are the basis for a successful implementation in a real cold chain and establishment of dynamic shelf life. TTI predictions of remaining shelf life were very satisfying in the present pilot study. The software should be extended for smartphone application. A colour measurement of the TTI directly with smartphones is desired for an improved work flow and dynamic shelf life calculation online. During implementation, a detailed adjustment to the particular needs of the actors in the cold chain is required.


In the original article, we neglected to include the funder São Paulo Research Foundation, FAPESP 2017/50216-0 to Henrique Ferreira.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


Plants have developed various acclimation strategies in order to counteract the negative effects of abiotic stresses (including temperature stress), and biological membranes are important elements in these strategies. Brassinosteroids (BR) are plant steroid hormones that regulate plant growth and development and modulate their reaction against many environmental stresses including temperature stress, but their role in modifying the properties of the biological membrane is poorly known. In this paper, we characterise the molecular dynamics of chloroplast membranes that had been isolated from wild-type and a BR-deficient barley mutant that had been acclimated to low and high temperatures in order to enrich the knowledge about the role of BR as regulators of the dynamics of the photosynthetic membranes. The molecular dynamics of the membranes was investigated using electron paramagnetic resonance (EPR) spectroscopy in both a hydrophilic and hydrophobic area of the membranes. The content of BR was determined, and other important membrane components that affect their molecular dynamics such as chlorophylls, carotenoids and fatty acids in these membranes were also determined. The chloroplast membranes of the BR-mutant had a higher degree of rigidification than the membranes of the wild type. In the hydrophilic area, the most visible differences were observed in plants that had been grown at 20 °C, whereas in the hydrophobic core, they were visible at both 20 and 5 °C. There were no differences in the molecular dynamics of the studied membranes in the chloroplast membranes that had been isolated from plants that had been grown at 27 °C. The role of BR in regulating the molecular dynamics of the photosynthetic membranes will be discussed against the background of an analysis of the photosynthetic pigments and fatty acid composition in the chloroplasts. Among the abiotic stresses, temperature stress is a global problem that mainly causes a decrease in the yield of most crop plants in agriculture and horticulture ,2. Some Many of the physiological phenomena that occur in cells could be due to the multidirectional effect of hormones on the plant metabolism, which is also the basis for the adaptability of organisms in order to function in changing environmental conditions. One plant hormone group is the brassinosteroids (BR), which are responsible for regulating both plant growth and development as well as their response to stress. BR belong to the steroid phytohormones that were first isolated from oilseed rape pollen in the 1970s . To datePhaeodactylum tricornutum, studies of the diadinoxanthin cycle have provided information about the molecular dynamics of the thylakoid membrane and about the influence of the diadinoxanthin cycle pigment on this effect  were 65.6 ± 0.2 and 64.9 ± 0.3, respectively for Delisa and mutant. In case of monolayers built from DGDG values of Alim were 55.2 ± 0.1 and 51.7 ± 0.2, respectively for Delisa and mutant. Usually, greater fluidity of cell membranes is considered as more favourable for a higher frost tolerance, which has been wider discussed in our earlier work [For the chloroplast membranes with an incorporated SASL-16 spin label, differences in the S parameter were observed between the mutant and the wild type that had been acclimated at 5 °C. The values of S parameter were significantly lower for the Delisa cultivar compared to its mutant in the entire tested temperature range and the differences were almost always statistically significant E. The saier work . The 522ier work , is, howier work . On the other hand, we have to mention that the 522DK mutant that was studied in the present experiment also had a better high-temperature tolerance than the wild-type Delisa , althoug0 and rotational correlation times τ2B and τ2C) provided additional information about the movement of a spin label and at the same time about the movement of FA in the interior of the membrane. The ΔH0 parameter provided information on the oscillation and rotation of a spin label and its rotation along the long axis of the molecule [0 indicate a greater freedom of the spin label motion. Our results show that there were generally no differences in the motion of the molecules (ΔH0 parameter) of the chloroplast membranes of the Delisa cultivar and its 522DK mutant at the tested temperatures  is in line with an increased rigidity of membranes [The values of τsurement D–I. It wild type D,E. For und 0 °C F. Moreovnd 10 °C G,H. For nd 10 °C I. When tembranes .We can assume that the greater motional freedom had FA included in Delisa’s chloroplast membranes compared to chloroplast membranes of 522DK. Therefore, all of the calculated parameters once again implied that the chloroplast membranes of 522DK at 20 °C and 5 °C were more ordered/less fluid than the chloroplast membranes of the Delisa cultivar. The final matter to discuss is what caused the differences in the molecular dynamics of the membranes between the mutant and its wild type. Because the components that are important for membrane structure and properties, such as FA, carotenoids and chlorophylls were comparable in the chloroplasts of the wild type and mutant , it is lBased on our experiment, the following main findings can be pointed out: (1) In the chloroplasts that were isolated from the barley wild-type Delisa and the mutant 522DK, regardless of the plant growth temperature, eight brassinosteroids at different concentrations were identified. Their diverse presence may indicate that they play some role in the functioning of these organelles. (2) Mutant 522DK is a BR-deficient mutant that has a lower accumulation of BR compared to the wild type in its leaf tissue ,20. Howe(3) Although electron paramagnetic resonance has been used to study the cell membrane properties and the influence of different factors on those properties , knowled


High-precision metal cutting is increasingly relevant in advanced applications. Such precision normally requires a cutting feed in the micron or even sub-micron dimension scale, which raises questions about applicability of concepts developed in industrial scale machining. To address this challenge, we have developed a device to perform linear cutting with force measurement in the vacuum chamber of an electron microscope, which has been utilised to study the cutting process down to 200 nm of the feed and the tool tip radius. The machining experiments carried out in-operando in SEM have shown that the main classical deformation zones of metal cutting: primary, secondary and tertiary shear zones—were preserved even at sub-micron feeds. In-operando observations and subsequent structural analysis in FIB/SEM revealed a number of microstructural peculiarities, such as: a substantial increase of the cutting force related to the development of the primary shear zone; dependence of the ternary shear zone thickness on the underlaying grain crystal orientation. Measurement of the cutting forces at deep submicron feeds and cutting tool apex radii has been exploited to discriminate different sources for the size effect on the cutting energy (dependence of the energy on the feed and tool radius). It was observed that typical industrial values of feed and tool radius imposes a size effect determined primarily by geometrical factors, while in a sub-micrometre feed range the contribution of the strain hardening in the primary share zone becomes relevant. Gaining a deeper understanding of the physical mechanisms which underpin metal cutting will help to reduce manufacturing costs and improve component quality. Interaction of the cutting tool with the workpiece imposes enormous deformation work in a small volume in a very short time, which complicates the study of the thermomechanical processes at the cutting edge by conventional means. Up till now this challenge has been addressed by post-machining structural analysis of the tool, workpiece10. Later, Heo (2004) has reported a linear micro-machining setup inside an SEM chamber12 able to control feed in the range of tens of microns. The process of machining of tungsten carbide by a polycrystalline diamond tool was studied and observations of cutting progress were made by taking the still images after stopping the process every 100 µm. In his works Heo reported forces in the range of tens of Newtons measured in-situ11, however, no description of how this was done was therein reported. Recently Fang has developed an in-SEM linear cutting device with ultra-high precision positioning13, which allowed feed values down to 10 nm. This allowed the observation of a ductile to brittle cutting transition in monocrystalline silicon cut by a diamond tool. These few works, to the best of our knowledge, represent all approaches to in-situ SEM machining made to date. The limited progress in this area is due to the complexity of incorporating micro-devices inside an SEM chamber, and limited availability of this kind of equipment within the metal processing community.Structural features of metals, significant for cutting, have scales down to sub-100 nm and are assessed by different electron microscopy techniques. Thermomechanical processes, defining plasticity and thus governing cutting, also have characteristic length scales well below one micron. In an effort to access and visualise machining operations at the micro-/nano-scale, cutting devices have been developed inside Scanning Electron Microscopes (SEM). The first device of this kind was reported by a Japanese group in a series of works starting from 1989The advantage of in-SEM machining lies in the ability to perform a deep microstructural analysis of the cutting process based on a variety of spectroscopic and structural techniques provided by SEM. Cutting, in this case, is carried out in a vacuum chamber that prevents oxidation of microstructural features produced during cutting. Besides structural and compositional data, measurement of the forces involved in cutting is an even more important source of information because it relates to the energetics of the process. Such measurements have not been realised so far in micro-cutting devices on the scales of sub-micrometre feeds and micro-Newton forces.Kc). This quantity is a measure of the energy needed for cutting a unitary volume of material and has a specific standard value for each material14. In an ideal case, Kc would be independent of the cutting parameters.A comparative study of forces in the machining process at different scales requires an invariant to the cutting dimensions. A commonly accepted size-invariant parameter for multiscale studies is the so called specific cutting energy 15. Two main hypotheses were proposed to explain this phenomenon. One considers that the relative contribution of tool-workpiece friction increases while h decreases16, which leads to the higher values of the specific cutting energy at low feeds. This effect becomes relevant when h decreases to values close or below the tool edge radius. The latter is quantified by the relative tool sharpness (RTS), the ratio between h and the tool edge radius17. Several works have successfully described Kc as being solely a function of RTS, meaning it is a purely geometrical effect19. In contrast to this approach, it has been suggested that the material strengthening due to higher strain concentration20 at small scales can contribute to the specific energy increase22 at small tool radii. This suggestion is based on a discrete mechanics of solids, where a major increase in dislocation density causes material hardening. These two hypotheses introduce different dependencies of the Kc—in the first case it depends only on RTS, in the second case there should also be an independent contribution of the tool edge radius. Though several studies have been made to resolve this ambiguity between both hypotheses, a limited range of h and tool radii accessible in macroscopic machining has not allowed for a definite explanation so far. Understanding the underlaying mechanisms of this size effect may be important for optimisation of the cutting conditions and the tool geometry in practical applications.In reality, it has been experimentally observed that the In the present work the concept of a versatile micro-cutting in-SEM setup is developed, a prototypic device is constructed, and proof of concept experiments are performed. On top of structural and compositional analysis of the components involved in the cutting process, this prototype allows measurement of the cutting force. These microscopic measurements in combination with macroscopic cutting experiments of the same material  enable the study of size dependence of the specific cutting energy in a range of tool radii varying by two orders of magnitude. A simple model, quantitatively describing the behaviour of the cutting energy in the whole range of employed cutting parameters, is proposed and validated.This work is enabled by microscopic and macroscopic machining experiments. For the microscopic experiments a device for linear micro-machining has been designed and build ad-hoc for this work. The design of this in-SEM cutting device with force measurement is described in detail in “Macroscopic cutting experiments were performed on a linear bench (High Speed Machining Centre Lagun) with a dynamometer (TPUN 160308 TTM—uncoated), see “All experiments were performed on the same annealed and rolled Aluminium 7475 material.23, as shown in Fig. h = 1.18 µm at 0.6 mm/min cutting speed. After the PSZ is fully developed and a chip is being generated, the chip thickness here is ~ 4 µm. Development of the PSZ means that the material deformation is concentrated in a narrow region around a shear line  of 1.1 µm, and width of 12 µm. A characteristic feature of the curve is a substantially higher force at the beginning of cutting. A subsequent steady state after ~ 20 μm of cutting shows values which are 20–30% lower than the initial force.Cutting force measurements in micro-cutting experiments were made by tracking the workpiece displacement on SEM images as described in methods section. Figure This initial force increase may be attributed to a stick–slip movement or could correspond to the development of the PSZ, when the deformation is still not concentrated in the band of shearing. From here on, for further force comparisons and discussion about specific cutting force (cutting energy) the values of force in steady state will be used, i.e. in this example a mean value of points b,c,d and assuming an error of ± 10%.For the macroscopic case, the values of the cutting force were obtained by a force sensor. Three different edge radii  were used. For a proper comparison of the values of forces obtained in in-situ SEM and in the machining bench, the cutting forces were normalized to the cutting width in all the cases, which is in the order of micrometres for in-situ cutting and one millimetre for the machining bench.h for different tool tip radius for both micro and macro- experiments. For every tip radius the dependence is well approximated by a straight line. The coefficients of all linear fits (having the meaning of specific cutting energy) are very close to each other indicating close values of specific cutting energies in a broad range of tip radii. At the same time, the intersect terms of the fits differ by more than one order of magnitude for the smallest and largest tip radii, which is considered a signature of the size effect26. Moreover, as can be seen from Fig. h values, also indicating the dependence of the cutting energy on h.Figure h in the range of tens of microns12 and force measurements in the range of tens of Newtons, yet the working principle of the force sensing has been completely omitted making it impossible to verify. Much greater precision was achieved by Fang et al. with h sizes down to 10 nm13. In the current work, an in-SEM micro-cutting setup has been implemented with the function of measuring the cutting force by image monitoring. The device is constituted of simple and commercially available elements, as presented in Fig. low cost commercial availability of the elements and flexibility in elements arrangement in an SEM chamber, which allows utilization of the setup for various types of materials, various workpiece geometries and different kinds of experiments;displacement and force measurements performed by image analysis in the area of the tool tip, which eliminates the necessity for extreme stiffness and precision of the rest of the construction;possibility for in-operando observation of the cutting process including chip generation leading to a detailed view of the different stages of deformation;possibility for a direct link of observed structural features and cutting peculiarities to the force acting between tool and the workpiece;possibility for a-posteriori analysis of the internal structure of the machined surface, the chip and the tool using various capabilities of the electron/ion microscope in the inert vacuum environment without bringing the sample to the air.Previously, Heo et al. proposed a linear cutting in-SEM device, realizing While these individual aspects represent certain technological innovation with respect to previous works, the possibility to link mechanical and microstructural aspects in one experiment by the proposed setup represents, in the opinion of the authors, a much more significant advantage.27 it has been hypothesised that a chip generation is determined by the relation between the uncut chip thickness h and the tool edge radius rather than by h itself, however, this has been hardly confirmed experimentally as a sub-micron tool tip radius has been difficult to achieve in macroscopic machining. The in-SEM setup utilized in the present study allows to perform cutting within the micrometre range for h, yet keeping the cutting-edge radius smaller than h. In this process a chip generation identical to the macroscopic cutting is observed with all classical deformation regions present, which confirms the above hypothesis. The typical lamellae texture of the chips characteristic for macroscopic cutting is also present in microscopic cutting as shown in Fig. 28. It is remarkable that this zone in principle exists in micro-cutting experiments, as the cutting speed is a few orders of magnitude lower than in macroscopic case and the friction should not cause a substantial heating necessary for recrystallization. The observation of classical deformation structure in microscopic cutting corroborates its identical principles to macro cutting and thus supports the feasibility of utilization of in-SEM micro-cutting to study fundamental microscopic features of the machining process.In-operando microscopic observation of the cutting process allowed to approach metal cutting from a fundamental perspective. It can be confirmed that the cutting process in the nanometre regime is fundamentally similar to that at the macroscopic scale. Earlier29. Hence, the slip direction perpendicular to the surface can be favoured or not for strain propagation depending on the crystallographic orientation of the grain under cutting. Though such dependence may sound obvious, it has not been observed experimentally so far and would be difficult to observed if the tool radius exceeds the grain size, which is the typical case for macroscopic cutting.A few more observations are here outlined: Thus, it has been directly shown that the thickness of the deformed layer in TSZ changes from one grain of the workpiece to another, as represented on Fig. Besides relating force dependencies to the microstructural processes, force measurement in micro-cutting opens up the possibility of extending the study of energetics of cutting to extremely small dimensions, and thus to reveal individual phenomena contributing to the cutting energy. This will be discussed in the following chapter. For this discussion the cutting forces measured in the steady state will solely be used.As it was pointed out above, the possibility of measuring the cutting force during micro-cutting gives a unique opportunity to study the energetics of the cutting process on a scale range reduced by a few orders of magnitude and thus to reveal peculiarities passed unnoticed in a common macroscopic study.31. In all these works a linear increase of cutting force with increase of h has been observed; however, at h below the tool tip radius the nonlinear variation of the cutting force was noted. Moreover, a vertical offset of the force curves depending on the tool radius has been observed26, similar to what is shown in Fig. Experimental data on cutting forces obtained in this work and shown in Fig. 32. Specific cutting energy (here after denoted as Kc after Drucker33) is defined as the energy required to machine a unit volume of material and can be expressed through the common parameters of orthogonal cutting as (where Ec and Vc are the cutting energy and cut volume respectively):fc is the cutting force, h is the uncut chip thickness, w is the width of cut and d is the length of cut. Note that Kc has a dimension of pressure and can be interpreted as an internal pressure of the material, which the tool has to overcome in order to cut.Along with individual machining parameters like cutting forces, tool wear, material removal rate, temperature or chip ratio, an integral measure of the efficiency of machining, called specific cutting energy , was assigned as a reliable way to quantitatively evaluate machiningh, the values of the cutting energy Kc for Alu7475 obtained as the slope of the linear fits on Fig. At maximum 27 have argued that this effect has purely geometric nature, i.e. is solely determined by the ratio between tool radius and h, Weber et al.35 have suggested that a material hardening should be a reason for variation of cutting energy with size. In this case, the size effect due to strain hardening has been related to much higher strain gradients and thus to higher dislocation densities at small tool radii. Fang et al.36 have also suggested an extreme case of nanocutting at the feeds smaller than inter dislocation distance (tens of nanometres), when the hardening is caused by the starvation of strain carriers. We will not consider this last mechanism as this scale is far beyond the reach of the proposed microcutting setup. In the following, utilizing the force measurements from both micro- and macroscale cutting experiments, we will try to discriminate the factors determining size dependence of the specific cutting energy.The increase of the cutting energy at low h , though being noted, has not obtained a unanimous explanation. While e.g. Woon and RahmanKc calculated from the force measurements on Fig. c equal to K0. At small h exponent is approaching -0.5 in power law, which corresponds to the friction contribution as will be discussed later. The second feature on Fig. h the Kc depends on the tool radius, while above 50 µm the data for all tool radii coincide, as is expected at large RTS17.Figure Kc can be expressed as:Two main hypotheses have been used so far to explain the size effect in the specific cutting energy. As mentioned above, one is based on the contribution of the geometry of the tool (RTS) and friction, and the other refers to material hardening due to the shear strain occurring in PSZ. In this framework, let us consider both effects as individual components of the overall specific cutting energy. Hence, the total μ is a friction coefficient; R is a tool radius; b is the Burgers vector, G is the material’s shear modulus; α is a conversion factor of 0.5 as defined by Nix and Gao37; M is a parameter relating the flow stress to the shear modulus, for which a value of √3 is assumed according to von Mises yield criterion38; ρPSZ is a dislocation density, b is the step size of one dislocation and θ is a PSZ angle. Here the first bracket is related to the geometrical aspects of tool/workpiece interaction, in particular the second term describes the friction at the tool edge. It is notable that this term depends only on RTS, see further discussion. The second bracket is related to the properties of the material under cutting, in particular the second term describes the addition to the cutting energy due to the strain hardening in PSZ.“Kc becomes constant and equal K0. For the large tool radii R independently of h the strain hardening term is small and thus the size dependence of Kc is described by geometrical factors only (according to RTS concept). Only when both h and R are small, may the strain hardening term become significant and will start to contribute to the cutting energy.A few simple consequences can be directly derived from Eq. . Thus, aK0 and μ were fitted by minimising the mean square deviation simultaneously over the complete dataset. The resulting optimised value of K0 = 1.1 GPa is very close to the values determined from Fig. Verification of this analytical model against the data obtained in macro- and micro-cutting experiments is shown in Fig. h covered in experiments. On the one hand, this proves the validity of the proposed description, and on other hand, provides a method for the independent measurement of the friction coefficient and the specific cutting energy directly from the cutting experiments. Thus, the friction coefficient can be extracted from the slope of the curve at h smaller than the tool radius (independently of this radius), while the specific cutting energy corresponds to the asymptote of the curve at a value of h at least 10 times larger than the tool radius.Assuming the scattering of the experimental data, the model gives an adequate description in the whole range of the tool radii and Kc vs RTS, separating the contributions coming from tool edge geometry and material hardening. In these coordinates the hardening contribution corresponds to the vertical separation of the curves—if the hardening did not take place  or varying tool radius (shear modulus). It should be noted that only the measurement of cutting force is necessary to measure the friction by this approach, while the procedure used e.g. by Stakhniv et al. required both cutting and feed force measurement to compute the friction in the flank side40.The proposed model does not arbitrarily include such cutting parameter as cutting velocity, though it is intrinsically included as the potential velocity dependence of the material properties: friction coefficient and shear modulus. The fact that the model is able to describe the cutting process in the micro- and macro-cutting experiments (3 orders of magnitude range in cutting speed and 2 orders in sizes) with the same values for material properties indicate that these properties do not significantly change in these ranges. Moreover, they can be measured directly in cutting conditions by a series of experiments with varying h between 200 nm and 7 µm have been successfully performed.In summary, an in-SEM linear micro-cutting device has been designed and implemented by a simple arrangement of commercially available elements except for the cutting tool itself. A unique feature of the proposed device is its ability to provide cutting force measurements while the workpiece is fully accessible for SEM/FIB inspections. Proof of concept cutting experiments with h) and small cutting velocity. This confirms that the tool-workpiece interaction in microscale experiments is principally the same as in macroscale cutting, and thus the method can be used for studying industrially relevant processes.All classical deformation regions  are observed in micro-cutting experiments very similar to the macroscale. In particular, the SSZ observed in micro-cutting experiments has notably different structure to the rest of the chip , despite a very small uncut chip thickness .The cutting force was measured and plotted during machining by tracking of the workpiece displacement on quick stop SEM images on-line by a software developed in collaboration with Kleindiek Nanotechnik GmbH (see below).+ ion beams were used.The device was mounted on the sample stage inside an FEI Nanolab Helios 600 DualBeam FIB/SEM instrument  as in Fig. The exact measurements of feed (in terms of uncut chip thickness) and cutting width were made a posteriori from SEM images by measuring the geometry of the cutting path resulted from machining. For convenience, cutting direction was aligned with the tilt axis of the microscope, leading to an observation of the cutting process perpendicular to the cutting direction  from Fagor, model 8070 was used. For cutting force monitoring samples were mounted on a dynamometer Kistler 9129AA. The setup included a high precision dial gauge fixed on the tool-holder that measured the real feed in-process to ensure that the lowest feeds were achieved  with feeds between 1 and 0.001 mm. Three repetitions were done for each cutting condition leading to a total of 60 tests. The details and parameters of macroscopic cutting tests are listed in Table Kc includes the contributions of the friction of the tool edge against the workpiece Kfric, and of the increase of material strengthening in the PSZ KPSZ. The total Kc can be expressed as:K0 is the ground constant cutting energy corresponding to the conditions at which cutting energy experimentally does not depend on sizes, as detailed in the discussion.The model for 41 approach as in Eq. (K0 as a pressure and p the contact area (per unit width) projected normal to the workpiece surface . The approach in this work assumes a very short path distance associated with an environment rich in dislocation sources. Similar to nanoindentation tests, α is fixed to 0.5 as by Nix and Gao37. The dislocation density ρPSZ can be estimated from the number of dislocations N necessary to accommodate shear s divided by the volume (Eq. b (dislocation Burgers vector) needed to reach the total strain . The thickness a in the PSZ is defined as  in Eq.  and x is the displacement, as shown in Fig. The software has been designed and implemented for on-line assistance of the experiments made in-SEM. The software monitors the directory  where the images are stored during the experiment and processes the images on-line. It calculates the force based on the shift of workpiece held by a bending spring as shown in Fig. mentclass2pt{minim46. In order to track the position of the tool and the workpiece, the software follows the positions of user defined regions of interest (ROI) one for each of the tool and the workpiece, as in Fig. The tracking is performed using cross-correlation functionHanning filter to use the Hann function47 (Fig. Sobel filter to use the Sobel operator48 (Fig. In order to improve the sharpness and unambiguity of cross-correlation maximum, several adaptive image filters are implemented. Figure n47 Fig. c, the Sor48 Fig. d, or a cr48 Fig. e or 5 × r48 Fig. f convoluA successful tracking of the tool and workpiece displacements define the pairs of values for tool/workpiece movement and the force. This data can be plotted in various representations and monitored on-line: either force–displacement Fig.  or valid


In this paper, the dynamical behavior of a SEIR epidemic system that takes into account governmental action and individual reaction is investigated. The transmission rate takes into account the impact of governmental action modeled as a step function while the decreasing contacts among individuals responding to the severity of the pandemic is modeled as a decreasing exponential function. We show that the proposed model is capable of predicting Hopf bifurcation points for a wide range of physically realistic parameters for the COVID-19 disease. In this regard, the model predicts periodic behavior that emanates from one Hopf point. The model also predicts stable oscillations connecting two Hopf points. The effect of the different model parameters on the existence of such periodic behavior is numerically investigated. Useful diagrams are constructed that delineate the range of periodic behavior predicted by the model. S), exposed to the disease but not yet infectious (E), infectious (I), or removed (recovered or deceased) (R). The SEIR model has been used extensively in the literature to model many human infections such as Ebola [The spread of infectious diseases is a very complex phenomenon that depends on a large number of factors. Some of them are social, environmental, or economic, which are linked to human activities while other factors have to do with the nature of the pathogen causing the disease. With this complexity, it is not surprising that sometimes simple mathematical models are often used to understand the dynamics of spread of infectious diseases. The use of more complex models will necessarily involve very large number of parameters that are difficult to estimate, making the model predictions weak and uncertain. In this regard, compartmental models are often used to simplify the mathematical modeling of infectious diseases. In these models, the population is divided into compartments, with the assumption that every individual in the same compartment has the same characteristics . The modas Ebola , H1N1 [3as Ebola  and MERSas Ebola . Despiteas Ebola –9. Each as Ebola –18. Toolas Ebola , 11 analas Ebola –16. Chaoas Ebola , 18.In this paper we examine the bifurcation behavior of a SEIR model when the transmission rate takes explicitly into account governmental action and population response to the severity of the pandemic. Although a number of studies have included the effects of governmental action and individual response in models of COVID-19 , 19, theA final note is to be made about the usefulness of such models in the forecast of the spread of COVID-19 pandemic. Because of the absence of complete data for the disease in Saudi Arabia, the validation of the model could not be carried out. However, the values of model parameters that were used in the mathematical analysis were taken from the literature where a validation of a similar model was carried out for the Wuhan province in China . This prThe rest of this paper is organized as follows. In Sect. N). The model attempts to describe what may unfortunately be an endemic disease (which may persist in a population for long time). Therefore the vital dynamics  have to be incorporated in the model. The variable (S) is the fraction of susceptible individuals (those able to contract the disease), (E) the fraction of exposed individuals (those who have been infected but are not yet infectious), (I) the fraction of infective individuals (those capable of transmitting the disease) and (R) is the fraction of removed individuals (those who have recovered or deceased). The model assumes that recovered individuals do not revert to the susceptible class. It is also assumed that all newborns are susceptible with the birth rate set equal to the death rate which is assumed not to be related to the infectious disease.We consider the following SEIR model: β is the effective per capita contact rate of infected individuals. The incidence rate is therefore b is the rate of natural birth, α is the rate at which the exposed individuals become infective, so P) that mimics the public perception of the severity of the pandemic. It can be seen that the dynamics (Eq. (I) with e being the proportion of severe cases and The term ion (Eq. ) and a nics (Eq. ) of the P), we adopt in this paper a new expression for the transmission rate β that reflects the impact of governmental action and the public perception of the severity of the disease. A number of studies have considered specific forms of the transmission rate. Lin et al. [et al. [D is a state variable representing social behavioral dynamics. The first term in Eq. ) ref) refet aN)κ (Eq. ), especiI: S, E, R and P can be obtained accordingly.The model at steady state has two equilibria: A trivial one  can be C3 (Eqs. –25)). A). A27\do}C1 (Eq. ), it canμ), decreases the range of periodic behavior, as the Hopf point moves to larger values of κ) on the Hopf point is shown in Fig. κ) increases the range of periodic behavior as the Hopf point occurs at small values of The numerical analysis of the model is carried out using standard bifurcation techniques  with thech Eqs. –11)). T). T\docu1). T). Te of Eq.  will leae of Eq.  and up tentclass1pt{minimaα) or smaller values of the mean duration of public reaction (i.e. larger λ). On the other hand, the periodic behavior increases with the increase in the birth rate (larger b) or we have an increase in the rate of recovery (larger γ).The rest of the graphs of Fig. α (the inverse of the mean latent period) is chosen to be the main bifurcation parameter with the rest of model parameters set at their values of Table α. Figure μ) decreases the range between the two Hopf points and therefore decreases periodic behavior. The Hopf points will not exist beyond a critical point. On the contrary, increasing the strength of the public reaction (κ) increases the range of periodic behavior which will disappear if the strength falls below a critical point. The rest of the curves in Fig. λ. The same can be said about the effect of the birth rate b. Periodic behavior is expected only within a specific range of birth rate. Finally, when γ increases (i.e. the mean infection periodic decreases) the range of periodic behavior increases. The periodic behavior cannot exist if γ falls below a critical value. Another type of bifurcation behavior predicted by the model can be shown in Fig. mentclass2pt{minimThis paper has proposed and analyzed the stability of a SEIR model structured upon susceptible, exposed, infected and removed cases, in addition to a new social behavior variable that mimics the public perception of risk regarding the severity of the pandemic. We also proposed a new expression of the transmission rate that modeled the impact of governmental action as a step function, and the individual reaction as a decreasing exponential function. The model was shown to predict one and two Hopf points. There is a fundamental difference between the two predicted periodic behaviors. While the former would exist for a wide range of model parameters, the latter is generally confined between some critical values. Regardless of the type of periodic behavior (emanating from one or two Hopf points) it was found that periodic behavior will increase in range if the disease has a large latent period, or if the mean duration of the public reaction increases, or the birth rate is high or the rate of recovery increases. Both the governmental action and public reaction have strong effects on the periodic behavior. A periodic behavior would not exist if no governmental action is taken or if there is no individual reaction. The range of periodic behavior would increase with a decrease in the strength of the governmental action or an increase in the strength of the public reaction.


Malaria parasites invade healthy red blood cells (RBCs) during the blood stage of the disease. Even though parasites initially adhere to RBCs with a random orientation, they need to align their apex toward the membrane in order to start the invasion process. Using hydrodynamic simulations of a RBC and parasite, where both interact through discrete stochastic bonds, we show that parasite alignment is governed by the combination of RBC membrane deformability and dynamics of adhesion bonds. The stochastic nature of bond-based interactions facilitates a diffusive-like re-orientation of the parasite at the RBC membrane, while RBC deformation aids in the establishment of apex-membrane contact through partial parasite wrapping by the membrane. This bond-based model for parasite adhesion quantitatively captures alignment times measured experimentally and demonstrates that alignment times increase drastically with increasing rigidity of the RBC membrane. Our results suggest that the alignment process is mediated simply by passive parasite adhesion. Malaria is a dangerous mosquito-borne disease which kills nearly 0.5 million of people every year . It is c1−2μm , into thRBC invasion by merozoites is preceded by three key events: (i) initial attachment, (ii) re-orientation or alignment of the parasite such that its apex is facing the RBC membrane, and (iii) formation of a tight junction . The apeeriments  indicateAnother important aspect during merozoite alignment is the deformation of the RBC membrane. Dynamic membrane deformations of various magnitudes are often observed  and are 16s . A recenCa+ ions . This prCa+ ions , which sIn this paper, we focus on the passive compliance hypothesis  which asThe article is organized as follows. First, we introduce and calibrate our hydrodynamic model, where simulation parameters are tuned to quantitatively match several characteristics of the parasite motion at the RBC membrane from available experimental data by The RBC membrane is modeled as a network of  springs . Our RBCoite see  is approoite see (1)(rx2+reriments . Therefoeriments .Both RBC and parasite are immersed in a fluid and the hydrodynamic interactions are modeled by the dissipative particle dynamics (DPD) method . The intTo relate simulation units to physical units, a basic length scale is defined as the effective RBC diameter 133.5μm2  and τ≈0.3×10−19J  and η=1mTo better understand the effect of various adhesion properties on parasite alignment, several parameters such as bond formation and rupture rates, bond rigidity, and ligand densities are varied. For each fixed parameter set, a number of simulations are performed and the results are combined and/or averaged, which is necessary due to the stochastic nature of bond-based interaction as well as thermal fluctuation effects within the fluid. Note that each simulation is performed for a different random choice of parasite vertices which represent long and short ligands, while their densities remain fixed, see section ‘Methods and models’.A parasite adhered to the RBC membrane exhibits visually an irregular diffusive-like motion observed experimentally , which ieriments  suggest odel see . To caliodel see  is quanteriments , where ΔTo further characterize the parasite motion on the RBC membrane, the mean-squared displacement (MSD) of the parasite’s center of mass is computed in simulations and shown in g states . The traecreased .Recent experiments suggest that a successful RBC invasion strongly correlates not only with the distance between parasite apex and RBC membrane, but also with a perpendicular alignment of the merozoite toward the cell membrane . Furthernd 10nm . Based oThe choice of In experiments, merozoite alignment times are measured as time intervals between initial parasite adhesion and the beginning of invasion . Similar4 MC steps correspond to about Note that the sample size (about 100) in simulations is limited by the computational cost. A single simulation, corresponding to a total physical time of about r JURECA  at ForscA recent simulation study by An interesting observation is that the head distance and alignment angle in membrane , the pea RBC see  is shiftA further noteworthy result from simulations is that a successful alignment occurs more frequently in the concave areas of RBC dimples than at the convex rim of the membrane. This is due to the fact that the cell dimples have a favorable local curvature or a lower energy penalty for membrane wrapping , which lThe dynamic adhesive behavior of the parasite in the current stochastic bond-based model is in striking contrast to the previous adhesion model  based ongle bond . Thus, totential , the strate koff . A signiotential .There exist three different timescales which might be relevant for the parasite alignment: (i) bond lifetime After the detailed analysis of parasite alignment, let us consider a possible influence of the effective membrane thickness, characterized by To better understand the dependence of merozoite alignment on bond kinetics, the off-rate ions see , consisty 15 see . FurtherAnother bond parameter, which may affect parasite alignment, is the extensional rigidities of both bond types. Furthermore, we consider effect of the density of long ligands ions see . For offThe importance of different ligand densities discussed above triggers the question whether both ligand types are necessary. Simulations performed with only short ligands μ=3kBThe bending energy of the membrane is expressed as (9)Ubend=The last two terms in  the RBC , where kThe elasticity of a healthy RBC is characterized by the 2D shear modulus membrane . These vmembrane  and membmembrane .Interaction between parasite and RBC membrane has two components. The first part imposes excluded-volume interactions between the RBC and merozoite (i.e. no overlap between them), using the purely repulsive part of the Lennard-Jones (LJ) potentialThis potential acts between every pair of RBC and parasite vertices separated by a distance The attractive part of RBC-parasite interaction is modeled by a reversible two-state bond model. Bonds can form between RBC membrane vertices representing receptors and merozoite vertices corresponding to ligands, while existing bonds can also dissociate. These bonds represent RBC-parasite adhesion through existing agonists at the surface of these cells and can be formed by two different types of ligands:long ligands with an effective binding range short ligands with an effective binding range constant (i.e. length independent) on- and off-rates  method , where fThe conservative force models fluid compressibility, whereas the dissipative and random forces maintain a desired temperature of the system. The dissipative force also gives rise to fluid viscosity, which is generally measured in DPD by simulating a reversible-Poiseuille flow . The DPDSimulation domain with a size of The main simulation parameters are shown in 133.5μm2  and the 3×10−19J  and plasOne of the main foci of our study is to obtain distributions of parasite alignment times for various conditions, which requires a large number of simulations of merozoite alignment. In order to significantly reduce the computational effort, Monte-Carlo (MC) sampling of alignment times, which is guided by direct DPD simulations of RBC-parasite adhesion, is employed. The MC sampling is based on a two-dimensional probability map see e.g. , which cInitial parasite orientation is selected randomly by choosing a state Transitions between the neighboring states are modeled according to the Metropolis algorithm described above.MC procedure is stopped whenever pre-defined alignment criteria are reached, and the number of MC steps is interpreted as alignment time.Note that the MC sampling algorithm fulfills detailed balance, but does not account for hydrodynamic interactions. The fulfillment of detailed balance for the Metropolis algorithm in equilibrium means that changes between different states  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The paper by Hillringhaus et al. studies the invasion of red blood cells by malaria parasites (merozoites), a key element of their reproduction cycle during the blood stage of the disease. Building on earlier work demonstrating the importance of geometrical alignment of merozoites with the cell, adhesion to the cell membrane and binding by filaments, the present work develops a computational model that incorporates stochastic deformations of the cell membrane and the discrete nature of the adhesive bonds. By exploring the influence of various parameters, such as the bond kinetics and RBC membrane stiffness, it is shown that alignment times similar to those observed experimentally can be obtained purely by a passive mechanism that balances adhesion and membrane elasticity.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Suzanne Pfeffer as the Senior Editor The following individuals involved in review of your submission have agreed to reveal their identity: Michael Gomez (Reviewer #2).Thank you for submitting your article "Stochastic bond dynamics facilitates alignment of malaria parasite at erythrocyte membrane upon invasion" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.This manuscript studies the invasion of red blood cells (RBCs) by malaria parasites (merozoites), a key element of their reproduction cycle during the blood stage of the disease. While initial attachment of merozoites to RBCs occurs rapidly, the major factor that limits successful invasion is that the merozoite must align almost perpendicularly at its apex to the RBC membrane. Using static models, previous work  has demonstrated the importance of adhesion (arising from filaments on the merozoite surface that bind to the RBC membrane) and elasticity of the RBC membrane: together these enable partial wrapping of the membrane around the merozoite to aid alignment. This manuscript builds upon these studies to address the dynamics of alignment, developing a computational model that incorporates stochastic deformations of the RBC membrane and the discrete nature of the adhesive bonds. By exploring the influence of various parameters, such as the bond kinetics and RBC membrane stiffness, they demonstrate that alignment times similar to those observed experimentally can be obtained purely by a passive mechanism, namely the balance between adhesion and membrane elasticity.The manuscript is very well written and discusses the details of the model and its results thoroughly and clearly. The inclusion of dynamic effects and discrete bonds resolves major shortfalls of previous models. The resolution of the simulations is very impressive and enables new insight into what governs the timescale of alignment and hence the invasion process. In addition, it is clear that the framework developed here can be adapted and built upon in future work to explore other effects, as well as informing further experimental studies in this area. I therefore strongly recommend publication.The reviewers request that you address the following issues to enhance the present story.σ. It must be very carefully checked how a variation in σ affects (or not) the results. This is particularly important since the authors do not only aim at a qualitative explanation, but a fully quantitative prediction of the biophysical alignment process.1) A numerical parameter that could potentially strongly influence the results is the repulsion distance 2) The authors very briefly state in the Discussion that “simulations with only short bonds show that the parasite is quickly arrested…". The authors might include some data on that. Also, it triggers the question what happens if there are only long bonds? Or, to state the question somewhat deeper: is the two-bond combination really necessary to reproduce the alignment? Or can one imagine that a single bond, of whatever nature, reproduces the alignment equally well?3) Again, in the Discussion the authors mention the “stochastic motion observed experimentally”. Figure 2B only shows the average fixed-time displacement, it does not indicate whether that motion is directed or truly stochastic. The authors should find a way to substantiate their claim that the experimental motion is truly stochastic and not somehow directed. For example, one might try to identify the (signed) distribution of Δ d within some meaningful local coordinates and see if the average is 0. Other ways to demonstrate the stochastic nature are certainly possible as well.long and λshort (defined in Equation 12)? I would guess that for a given bond number, the ratio of these stiffnesses to the membrane bending stiffness controls the degree of wrapping, similar to the dimensionless adhesion strength defined by Dusgupta et al., 2014. I appreciate much remains unknown about the properties of the binding filaments, but at the very least the values of λlong and λshort chosen in the model (Table 2) need some discussion .4) The effect of bond kinetics on alignment is discussed in detail, but what about the influence of the bond spring stiffnesses, i.e. λapex and θ for the distributions in Figures 3A-B. I understand that these peaks correspond to a configuration with maximum contact area (as discussed in subsection “Membrane deformation and parasite dynamics”). Are the associated values of apexd and θ then just determined by the egg-like geometry of the parasite and RBC, or do they also depend on the mechanical properties? Since these most-likely values  play a key role in determining whether alignment occurs, it would be good to discuss what parameters control these values. I appreciate a thorough exploration of parameter space is not possible due to the computational time, but it would be worth at least having a qualitative idea.5) Similarly, it would be good to know what influences the most-likely values of d6) Did the authors examine where on the RBC membrane alignment is most likely to occur? I would expect it to be the region near the centre of the RBC where the membrane is concave-outward, since here the membrane would naturally curve towards the merozoite so that wrapping requires less bending energy. This concave-outward region only exists on one side of sickle-shaped RBCs, which could be a contributing factor (as well as the increased membrane stiffness) as to why sickle cell anemia gives some resistance to malaria.θ = π/2). This is not the case for a spherical shape, for which there is no preferred orientation, so that the merozoite may become more easily arrested with its apex pointing away from the RBC. Moreover, the tapering near the apex means there is naturally less material in its vicinity, so that alignment potentially could be achieved with an apex angle θ further from π.7) Similarly, is it possible to speculate on the influence of the merozoite shape on achieving alignment? For example, as well as providing an obvious advantage for initiating invasion, the egg shape means that the apex is titled towards the membrane in the most-likely configuration with maximum contact area ?8) Figure 4B: Why are there so many instances of very short alignment times, below the lower bound of 7 seconds observed experimentally by Yahata et al., 2012? Is this due to an effect being neglected or overestimated, or can the discrepancy be improved simply by tightening the alignment criteria  requires l << R. However, the schematic in Figure 3A suggests that if the discretisation length is halved so that each segment becomes two segments, the value of θ could easily change by an amount comparable to the tolerance in the alignment criterion, i.e. 0.2π. Could the authors comment on this?9) One concern is the sensitivity of the results to the discretisation used. This is because alignment requires that the distance between the merozoite apex and RBC membrane is very small and may become comparable to the discretisation length of the RBC membrane. The alignment criteria also necessitate examining small changes in the apex angle  1) A numerical parameter that could potentially strongly influence the results is the repulsion distance σ. It must be very carefully checked how a variation in σ affects (or not) the results. This is particularly important since the authors do not only aim at a qualitative explanation, but a fully quantitative prediction of the biophysical alignment process.σ can be thought of as an effective membrane thickness (imagine a surface constructed from overlapping spheres with a diameter σ), to which additional interaction range leff is added. Normally, σ should be selected as small as possible for a given resolution length of both the RBC membrane and parasite (about 0.2 µm in our models). We selected σ=0.2 µm such that no overlap between the cells is guaranteed and the interacting surface is smooth enough. Thus, a little effect of the exact σ value on the adhesion results is expected.First, let us point out that the repulsion interaction is required to prevent the overlap between discrete models of the parasite and membrane. The repulsive distance σ=0.15 µm) and larger (σ=0.3 µm) repulsion distances than σ=0.2 µm used previously. The results indicate that σ can have an effect on the number of bonds between the RBC and parasite as it affects the binding range defined as 21/6σ + leff. Differences in results  are rather small for σ=0.15 µm and σ=0.2 µm. The case with σ=0.3 µm exhibits a larger number of bonds than for σ=0.2 µm, resulting in stronger membrane deformations. Nevertheless, fixed-time displacement characteristics of the parasite remain nearly unaffected by σ. We have added these new results related to different σ values to the manuscript.In order to test this expectation, we have performed two new sets of simulations for a smaller (2) The authors very briefly state in the Discussion that “simulations with only short bonds show that the parasite is quickly arrested…". The authors might include some data on that. Also, it triggers the question what happens if there are only long bonds? Or, to state the question somewhat deeper: is the two-bond combination really necessary to reproduce the alignment? Or can one imagine that a single bond, of whatever nature, reproduces the alignment equally well?koff rates show that the parasite is not able to achieve significant wrapping by the membrane, because such ligands are too short to facilitate progressive membrane attachment over a curved parasite surface. This limitation is directly connected to the density of available receptors on the RBC surface, which is determined in our model by the membrane resolution. For the same reason, parasite mobility is impaired, as it is largely mediated by bond formation/dissociation at the edge of adhesion area between the parasite and the membrane. Therefore, the model with only short ligands does not reproduce proper parasite alignment.This is a very interesting and important point. The previously included brief statement about short bonds was based on a single simulation, providing an inaccurate description of the complexity of the system. We have performed a number of additional simulations with only short and long ligands separately. Simulations with only short ligands for several different Simulations with only long ligands show that the parasite mobility and alignment can be well reproduced. Therefore, the presence of long bonds aids in the stabilization of parasite adhesion and the enhancement of parasite motion, such that long bonds serve as some sort of effective leverages. Theoretically, a model with only long ligands would be sufficient to reproduce the proper parasite alignment; however, current biomolecular knowledge about parasite coating does not support the presence of many bonds with a length of about 100 nm. We speculate that short bonds are necessary (i) to stabilize parasite adhesion, as the density of long ligands is likely low, and (ii) to bring the two cells in sufficiently close contact to facilitate the formation of a tight junction required for invasion. Thus, the presence of both ligand types is likely necessary for a successful invasion.We have incorporated these new results and their Discussion into the manuscript.3) Again, in the Discussion the authors mention the “stochastic motion observed experimentally”. Figure 2B only shows the average fixed-time displacement, it does not indicate whether that motion is directed or truly stochastic. The authors should find a way to substantiate their claim that the experimental motion is truly stochastic and not somehow directed. For example, one might try to identify the (signed) distribution of Δ d within some meaningful local coordinates and see if the average is 0. Other ways to demonstrate the stochastic nature are certainly possible as well.Unfortunately, the amount of data (only 20 points) for the fixed-time displacement from experiments does not allow us to assess whether the motion is truly stochastic or not. Therefore, we modified the text stating that the parasite motion in experiments visually resembles an irregular motion, but it may also be partially directed. In simulations, the motion is stochastic and determined by the probability map (not a uniform distribution) in Figure 4A.long and λshort (defined in Equation 12)? I would guess that for a given bond number, the ratio of these stiffnesses to the membrane bending stiffness controls the degree of wrapping, similar to the dimensionless adhesion strength defined by Dusgupta et al., 2014. I appreciate much remains unknown about the properties of the binding filaments, but at the very least the values of λlong and λshort chosen in the model (Table 2) need some discussion .4) The effect of bond kinetics on alignment is discussed in detail, but what about the influence of the bond spring stiffnesses, i.e. λWe thank the reviewers for making this point. Our original expectation was that bond stiffnesses play a secondary role, because we employ constant (force independent) bond rates. To clarify this point, we have performed simulations for softer and stiffer bonds, which confirm that the reviewer’s expectations are qualitatively correct. Bonds with a larger stiffness lead to more membrane wrapping in comparison to soft bonds. The physical mechanism is that stiffer bonds facilitate a smaller distance between the membrane and the parasite at the edge of adhesion area between them, which favors further wrapping by the formation of additional bonds. Therefore, the spring stiffness in our model can mediate distance-limited bond formation at the edge of adhesion area between the parasite and the membrane, which is connected to membrane bending rigidity and the degree of wrapping. We have added these results into the revised manuscript with a short discussion.apex and θ for the distributions in Figures 3A-B. I understand that these peaks correspond to a configuration with maximum contact area (as discussed in subsection “Membrane deformation and parasite dynamics”). Are the associated values of dapex and θ then just determined by the egg-like geometry of the parasite and RBC, or do they also depend on the mechanical properties? Since these most-likely values  play a key role in determining whether alignment occurs, it would be good to discuss what parameters control these values. I appreciate a thorough exploration of parameter space is not possible due to the computational time, but it would be worth at least having a qualitative idea.5) Similarly, it would be good to know what influences the most-likely values of dapexd and θ are mainly determined by the egg-like shape, or also depend on the mechanical properties of the membrane, we have computed apexd and θ distributions for a parasite adhered to membranes of various rigidities. Clearly, for the case of a rigid membrane, the most likely apexd and θ values are determined by the parasite shape corresponding to a configuration with maximum contact area. In comparison to the soft membrane, the peak in apexd for the rigid RBC is shifted further away from zero. This indicates that the degree of wrapping also affects the most likely values of apexd and θ. Therefore, in addition to the egg-like shape of a parasite, RBC membrane properties, such as bending rigidity, shear elasticity, and local curvature , do affect the most likely values of apexd and θ.To check whether the most likely values of We discuss now this issue in the revised manuscript in more detail.6) Did the authors examine where on the RBC membrane alignment is most likely to occur? I would expect it to be the region near the centre of the RBC where the membrane is concave-outward, since here the membrane would naturally curve towards the merozoite so that wrapping requires less bending energy. This concave-outward region only exists on one side of sickle-shaped RBCs, which could be a contributing factor (as well as the increased membrane stiffness) as to why sickle cell anemia gives some resistance to malaria.The RBC-membrane location of possible preferred adhesion is an important issue. We have re-checked the simulation data for the most likely alignment spot, and indeed the alignment occurs more often in one of the RBC dimples. The main reason for this is exactly the fact that the parasite wrapping is stronger in those regions due to a favorable local curvature and therefore, a lower energy penalty for membrane wrapping.We have included an additional figure into the revised manuscript to discuss this point.7) Similarly, is it possible to speculate on the influence of the merozoite shape on achieving alignment? For example, as well as providing an obvious advantage for initiating invasion, the egg shape means that the apex is titled towards the membrane in the most-likely configuration with maximum contact area . This is not the case for a spherical shape, for which there is no preferred orientation, so that the merozoite may become more easily arrested with its apex pointing away from the RBC. Moreover, the tapering near the apex means there is naturally less material in its vicinity, so that alignment potentially could be achieved with an apex angle θ further from π.We agree with the reviewer that this is a very interesting question, and clearly parasite shape should make a difference. One of the possible advantages of the egg shape is that it favors a preferred orientation, where the apex is relatively close to the membrane; another possibility is that the subsequent entry into the cell is facilitated by the pointed shape. However, the motion toward the successful alignment has some energy penalty and therefore, the probability of aligned configuration is smaller than that of the preferred orientation. For a spherical shape, there is no preferred orientation and the rotational motion does not have a significant energy penalty, which could potentially result in many stochastic trajectories to be far from the aligned configuration. Furthermore, for the same effective adhesion strength, the number of bonds can be different for different parasite shapes, which would affect the average displacement. As a result, all these contributions need to be carefully considered, and currently different hypotheses for the effect of different shapes on parasite alignment are too speculative and need a more thorough investigation. We are planning to study the effect of parasite shape on its alignment, but we prefer to avoid any speculations about this issue in the current manuscript.8) Figure 4B: Why are there so many instances of very short alignment times, below the lower bound of 7 seconds observed experimentally by Yahata et al., 2012? Is this due to an effect being neglected or overestimated, or can the discrepancy be improved simply by tightening the alignment criteria (e.g. requiring θ > 0.9π rather than 0.8π)?The range between 7 s and 44 s for parasite alignment from Yahata et al., 2012, is based on 10 experimental observations. So, the discrepancy can be just due to the lack of statistics of experimental data. Furthermore, our model is calibrated by other experiments . We have tried to tighten the criteria, as suggested by the reviewer, but it seems not to affect the distribution of alignment times, it just reduces the total number of successful alignment events. Therefore, further experiments and potentially model improvements are needed to clarify whether there is a discrepancy and/or some effects are neglected or overestimated.We have added a short discussion of this issue to the revised manuscript.9) One concern is the sensitivity of the results to the discretisation used. This is because alignment requires that the distance between the merozoite apex and RBC membrane is very small and may become comparable to the discretisation length of the RBC membrane. The alignment criteria also necessitate examining small changes in the apex angle θ from π. For example, the change in the angle of the normal vector to the RBC membrane from one triangular face to the next scales as l/R, where l is the characteristic size of the triangles and R is the typical radius of curvature of the membrane. For the change in angle to be small (so that discretisation effects are negligible) requires l << R. However, the schematic in Figure 3A suggests that if the discretisation length is halved so that each segment becomes two segments, the value of θ could easily change by an amount comparable to the tolerance in the alignment criterion, i.e. 0.2π. Could the authors comment on this?0=0.2 µm. The half circumference length of a parasite (corresponding to angle π) is πR, which is equal to about 12 l0 for R=0.75 µm, such that our angle resolution with respect to the parasite size is 0.1π. Therefore, we use 0.2π for the alignment criteria, which is large enough to avoid strong discretization effects. Simulations with a finer discretization are possible, but they become very expensive computationally.We completely agree with the reviewer that the discretization length affects the tightness of the alignment criteria. Note that the schematic in Figure 3A is not up to scale. In our simulations, the average discretization length of the RBC membrane is about l


Cultural variations complicate psychiatric care, especially for migrant children. Transcultural psychotherapy (TCP) is an original psychotherapeutic technique developed to address complex situations of resistant mental disorders in the context of migration. This research will aim to assess the efficacy, the acceptability, and describe the therapeutic processes of TCP for the treatment of depression in first or second generation of migration children and adolescents.Mixed method study using a multicenter, Bayesian randomized clinical trial with blinded evaluation of the primary outcome. Two parallel groups of 40 children or adolescents from 6 to 20 years old and their family will be included. In the experimental group, patients will attend six sessions of transcultural therapy in addition to usual care.The improved Clinical Global Impression scale scores at 6 months will be compared across groups. Qualitative analysis of families and therapists' interviews will allow to specify the therapeutic processes and acceptability of the therapy.The findings will encourage the development and routinization of TCP for second‐line use and its adaption as a first‐line technique in this population. CDRS‐RChildren's Depression Rating Scale‐RevisedCGIClinical Global ImpressionCNILCommission Nationale de l'Informatique et des Liberté iCGIimproved Clinical Global ImpressionSDstandard deviationSTAI‐CState‐Trait Anxiety Inventory for ChildrenTCPtranscultural psychotherapy1Migrant children represent 9.4% (47.3 million) of the European population and come, for a large part, from low‐ and middle‐income countries. In France, one in three children born has at least one parent who comes from elsewhere and 17% of children under 18 are migrants INSEE, . This prIn Europe, migrant children have been shown to exhibit a 5%–10% higher risk of internalizing problems in various studies .This is a multicenter, parallel group, Bayesian randomized clinical trial with blinded evaluation of the primary outcome and semi‐structured interviews for qualitative approach. The primary outcome—remission—will be assessed after the end of the follow‐up of the last included participant by three independent experts unaware of the received treatment who will watch the filmed semi‐structured clinical interview conducted by a psychologist at 34 weeks after inclusion . The study was approved by the Research Ethics Board of Sud Mediterranean IV  and meet the requirements of the French and European General Data Protection Regulation (CNIL). The study is funded by a grant from Programme Hospitalier de Recherche Clinique—PHRC 2018 —and is registered on the website 2.1Participants are recruited in six outpatient hospitals and psychological care centers in France in which every main therapist providing TCP has followed the same training. The study involves first and second generation of migrant children and adolescents suffering from depression resistant to a first‐line therapy, their families, and the therapists. The recruitment is planned to be from January 2020 to July 2022.2.1.1Children and adolescents (the participants) referred to the transcultural consultation of one of the recruiting centers because they suffer from a depressive disorder resistant to standard management are eligible to be included in the study. TCP involves parents and siblings, so they are also present and active at each endpoint measure and involved in the semi‐structured interviews on acceptability of the therapy.As mentioned above, the diagnosis of depressive disorders in migrant children and adolescent may be hindered by the effects of the culture . The core family is invited with any member of the extended family or other people they feel close to. The group is a variable‐geometry setting which may vary from 2 to 10 participants, depending on the patient origin and the complexity of the situation. For example, a specific smaller group is designed to receive unaccompanied minors who could be intimidated with too many therapists. On the contrary, a bigger group is designed for receiving most of Sub‐Saharan African migrant families, as sickness is considered as a family and social concern and treated in the social group in many traditional societies. More information is provided in Appendix 2.3Participants are randomized with a 1:1 allocation ratio to “usual care and waiting list for TCP for at least 34 weeks” (control group) or “usual care and TCP beginning in the 6 following weeks” . Every participant continues his/her usual treatment with the first‐line medical team throughout the duration of the study. As it is a pragmatic real‐life study, usual treatment may vary from one patient to another and from one first‐line medical team to another. It may include psychotherapy , medications, or institutional care .® v7.0, validated software using pseudorandom numbers generator, and the randomization is performed electronically (IWRS) using the Cleanweb application (Télémédecine) software.The randomization is centralized, stratified according to the recruitment center, with an unknown block size except from the statistician of the research center unit who generated this allocation sequence independently from the investigators and the statistician of the study. The allocation sequence was prepared using NQuery Advisor2.42.4.1The severity score on the iCGI is used to determine the remission of the depression  after inclusion as a 6‐week delay is needed to organize the first TCP session. In the control group, it is measured 34 weeks after inclusion.This measure consists in a filmed semi‐structured clinical interview conducted by a psychologist trained in transcultural care, in the presence of an interpreter speaking the native language of the family. The interview guide has been constructed by specialists of transcultural care in childhood and adolescence Appendix . It expl2.4.2At each collection time points, the iCGI severity scale is used to measure the severity of depression, the Children's Depression Rating Scale‐Revised (CDRS‐R) is used to assess the level of depressive symptoms, and the State‐Trait Anxiety Inventory for Children (STAI‐C) is used to assess the level of anxiety symptoms  are collected, and the psychologist conducts a 5‐minute filmed semi‐structured interview with the participant and his/her family for the iCGI assessment.For each participant fulfilling the inclusion criteria, randomization is then conducted. For each referred patient not included in the study, sociodemographic data and the reason for nonenrollment are collected.2.5.3Participants from both groups continue their usual care provided by the referent medical team outside the inclusion center.In the experimental group, participants receive TCP in addition to usual care, which consists of five sessions every 7 weeks  ± 1 week. The verbatim of all therapy sessions is collected depending of each group habits: audio recording and full transcription or verbatim transcription during the therapy session, as well as details of each session (number of therapists and their profession).In both groups, an assessment is conducted by a psychologist at W6, W13, W20, W27, and W34 . The same assessment process as at the baseline visit is performed.2.5.4In the control group, TCP can be initiated or not, depending on clinical criteria. No new assessment will be performed after the W34 assessment. The research ends at W34 after the last measures for this group.In the experimental group, TCP can be interrupted or continued depending on clinical criteria. Three qualitative semi‐structured interviews are organized between W34 and W44: one with the family (family interview); one with the therapists of the transcultural group (focus group); and one with the first‐line therapist(s) in charge of the participant who accompanied him during the therapy . These interviews explore the subjective experience of transcultural care, its acceptability, and the factors that each participant is highlighting as therapeutic in the TCP. The interviews are between 1 and 2 h long. At W52, a new assessment time is organized exactly in the same way as the previous follow‐up visits. The research ends at W52 after the last measures for this group.2.6Eighty participants will be enrolled in the study: 40 in experimental group and 40 in control group.p[1−p]/n) where p = 0.195 and n is the number of virtual subjects per group; Spiegelhalter, Abrams, & Myles, α = 0.05). As TCP is already used for a long time in practice in several centers, the Bayesian design is preferable for ethical reasons (less patients to include), for economical ones and for feasibility (centers could be reluctant to include too many patients).In principle, the sample size in Bayesian designs do not need to be formally justified. Nonetheless, for information and for equivalence with a frequentist design, the following calculation was performed. An a priori distribution was estimated from the elicitation of expert opinions in a pilot study. To limit allegiance bias , experts were asked to estimate the effect of the care as pessimistically as possible. The following distribution was found for estimates of the difference in the percentage of remission in the two randomized groups: 10%–0%: 0; 0%–10%: 1 expert; 10%–20%: 12 experts; 20%–30%: 4 experts; 30%–40%: 3 experts; 40%–50%: 0. The mean (±SD) of this distribution is 0.195 (±0.0825) and corresponds an a priori of 23 “virtual” subjects per group , and the sponsor's appointed representatives for the study. They will meet once a year to define the overall organization of the study, coordinate the information, monitor the research process, suggest procedures to be followed during the study, recommend changes to the protocol during the study if necessary. Every important protocol modification will be amended on No particular risk of any adverse event has been identified that could be caused by a psychotherapy properly conducted, however adverse events will be collected and described in the study report and any serious adverse event will be reported to the sponsor regardless of causality.33.1Analyses will be performed according to the intention to treat (ITT) principle and no interim analysis will be performed. For the primary objective, as the iCGI severity score ≥4 is an inclusion criterion, all patients will have at least a measure at W0 and will be included in the ITT analysis. For patients with missing data on the iCGI assessment at W34, in case of less than 5% of missing data, the last observation carried forward will be used as imputation method, multiple imputations will be used otherwise , the pessimistic posterior distribution (mixture of observed data and pessimistic a priori distribution), and from the distribution of observed data (uninformative prior). The 95% credible interval of efficacy will be estimated in these three situations.3.1.2t‐test comparing the mean of the score  at W34 and W52 in the experimental group.They will be analyzed using a frequentist perspective, but they will be considered as exploratory in nature. For this reason, no adjustment for multiple testing will be done. Score  change from baseline to W34 will be analyzed using a mixed‐effects model for repeated measures with treatment group, visit, and treatment group‐by‐visit interaction as fixed effects and the baseline score and center as covariates. The persistence of the efficacy of TCP over time will be evaluated using paired Student's 3.2© software. Next, the textual data will be analyzed using the statistical clustering procedures encoded in ALCESTE® software , and on social bonds . The consequences in adulthood on individual and social functioning and healthcare costs are large  is representative of the multiple situations usually encountered in the target population of TCP. Subgroup analyses will unfortunately not be sufficiently powered due to the small sample size. The diagnosis of depression will be made by the first‐line therapist referring the patient as diagnostic criteria for adolescent depression are too instable and are considerably influenced by culture. Clinical characteristics will be collected to describe the clinical pictures of the participants. Finally, the a priori distribution is obtained from expert's opinion and not from any source of objective data. This can be considered obviously as a methodological limitation, but it is also a way to include all source of knowledge in the evaluation of a treatment, which is ethically interesting.www.icmje.org/coi_disclosure.pdf and declare no support from any organization for the submitted work; no financial relationships with any organization that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.All authors have completed the ICMJE uniform disclosure form at Appendix S1 The transcultural psychotherapy settingClick here for additional data file.Appendix S2 iCGI protocolClick here for additional data file.Appendix S3 Semi‐structured interview guidesClick here for additional data file.Appendix S4 Questionnaire of sociodemographic characteristics and clinical history of the depressionClick here for additional data file.