Sub-Saharan Africa (SSA) is seen as a region of mass migration and population displacement caused by poverty, violent conflict, and environmental stress. However, empirical evidence is inconclusive regarding how SSA’s international migration progressed and reacted during its march to achieving the Sustainable Development Goals (SDGs). This article attempts to study the patterns and determinants of SSA’s international migration and the cause and effects on sustainable development by developing a Sustainability Index and regression models. We find that international migration was primarily intra-SSA to low-income but high-population-density countries. Along with increased sustainability scores, international migration declined, but emigration rose. Climate extremes tend to affect migration and emigration but not universally. Dry extremes propelled migration, whereas wet extremes had an adverse effect. Hot extremes had an increasing effect but were insignificant. SSA’s international migration was driven by food insecurity, low life expectancy, political instability and violence, high economic growth, unemployment, and urbanisation rates. The probability of emigration was mainly driven by high fertility. SSA’s international migration promoted asylum seeking to Europe with the diversification of origin countries and a motive for economic wellbeing. 1% more migration flow or 1% higher probability of emigration led to a 0.2% increase in asylum seekers from SSA to Europe. Large-scale international migration and recurrent emigration constrained SSA’s sustainable development in political stability, food security, and health, requiring adequate governance and institutions for better migration management and planning towards the SDGs.The online version contains supplementary material available at 10.1007/s11625-022-01116-z. Sub-Saharan Africa (SSA) has rich and diverse environmental and demographic potentials and has experienced rapid economic growth over ten years  in 2015 as the developmental agenda for global sustainable development covering 17 goals with 231 indicators that 193 countries have committed to, including SSA   along with their livelihoods and outcomes evolving from one status to another (in situ) over time. Migration is associated with the flow and redistribution of resources, such as human (e.g. labour), financial (e.g. cash and cattle), natural (e.g. land and water), and social (e.g. networks) capitals. Thus, it represents not only individual behaviour (e.g. proactive and reactive adaptation or autonomous-private and public-planned adaptation) . Although migration is a dynamic process  of a coupled system . It depAlthough the drivers of international migration differ across countries and contexts, it may take permanent migration into other countries or continents, which changes the number of expatriates and asylum seekers in the receiving countries. This work built a dataset of forty SSA countries (Table S2), referring to data integrity and consistency. The measurement of temporary and return migration is overlooked due to a lack of data.This study first built a sustainability index of twelve indicators. It is followed by a couple of regression models exploring the environmental and socioeconomic drivers of SSA’s international migration. Another regression model was developed by assuming that SSA's international migration might affect its expatriates in Europe and within SSA and influence its asylum seekers to Europe. Besides, a set of regression modes was composed to estimate the feedback effect of SSA's international migration on sustainable development.https://www.sdgindex.org).Indicators are selected for the composite Sustainability Index Table  from theIn terms of ‘Food security and sustainable agriculture’ in the index, we chose SDG 2 and its indicators 2.1 and 2.4 to guide the measurement via variables concerning dietary energy supply, livestock and crop production, arable land, and irrigation. For ‘Healthy lives’, life expectancy was selected referring to the description of SDG 3. Similarly, the urbanisation rate was chosen to reflect SDG 11.1 on access to adequate housing and essential services in cities. Variables, including per capita gross domestic product (GDP), Agro-GDP share, and unemployment rate, were employed to represent ‘Sustainable economy’ under SDG 8.1 and 8.5. The share of agricultural GDP indicates economic diversification, while the unemployment rate is defined as subtractive. ‘Peaceful societies’ are measured by variables about homicide and political stability under SDG 16.1, where rates of homicides are also defined as subtractive. Data were collected and transformed for those variables by taking the average of every five consecutive years. Missing values were supplemented by alternative data or dismissed with the entire case (or country). Eventually, twelve variables were derived for the Sustainability Index of the forty countries from 1990 to 2018. Although we chose those observable variables referring to the SDGs, the scope and extent are limited by data availability and measuring difficulty. It could be elaborated on in a future study.Based on Eq. , a Negatmth exploratory variable for the country p is the probability of a negative value of net international migration, nth exploratory variable for the country As described above, international migration in this work is the absolute value of net international migration to reflect the distance of an evolving system from its steady state. Nevertheless, it shows that those forty countries Table S2) can be defined as immigration counties  and emigration countries . They vary over time and have significant differences in their demographic, socioeconomic, and climatic conditions (Table S3). Emigration countries take the majority in SSA, including twenty-nine out of the forty countries, accounting for 68% of the total international migration. It is hence necessary to study the determinants of emigration probability and flow. A Heckman Selection model  in Europe and within SSA and make up a significant portion of asylum seekers to Europe. Hence, a set of Tobit regression models Table  was emplInverse Hyperbolic Sine transformation was applied for all dependent and exploratory variables to deal with skewness, remaining zero values, and to avoid stacking and disproportionate misrepresentation, given the unique properties of international migration. Marginal effects were presented to explain how dependent variables change when a specific exploratory variable changes while other covariates are constant. Robust standard errors were taken to obtain unbiased standard errors of coefficients under heteroscedasticity.A set of Ordinary Least Squares regression models Table  was deveMulticollinearity, residual normality, and robustness tests were conducted for the corresponding regression models (See S2 for details).Here, we analysed the dynamics of international migration, expatriates, and asylum seeking among SSA countries, and their progress in sustainable development under climate change from 1995 to 2020. After that, we identified the primary migration drivers regarding climate extremes, food security and agriculture, urbanisation, and peaceful societies, investigated the effect of SSA’s international migration on its expatriates within SSA countries and in EU-14 countries and on its asylum seekers in EU-14 countries, and examined the migration effect on the computed sustainability score and its five aspects (i.e. food security and agriculture (SDG2), healthy lives (SDG3), sustainable economy (SDG8), urbanisation (SDG11), and peaceful societies (SDG16)).From 1995 to 2020, the international migration from SSA was up to 26 million, with around 6.3 million people moving out. Among those forty countries, South Africa, Zimbabwe, Ethiopia, Guinea, Nigeria, Malawi, Angola, United Republic of Tanzania, Senegal, and Mali were the top ten, which account for 68% of the total SSA migration  and 'SDG11 urbanisation'  were identified regarding climate extremes, food security and agriculture, urbanisation, and peaceful societies . The international migration contributed to SDG8 sustainable economy and SDG11 urbanisation but undermined SDG2 food security and agriculture, SDG3 healthy lives, and SDG16 peaceful societies Below is the link to the electronic supplementary material.


Furthermore, the starch/PANI films are only selective to ammonia. Therefore, the starch/PANI films can be potentially applied as colourimetric ammonia sensors for intelligent food packaging.The use of petroleum-based plastics in food packaging leads to various environmental impacts, while spoilage of food and misinterpretation of food-date labelling account for food insecurity; therefore, a biopolymer capable of indicating food edibility is prepared to resolve these issues. In this research, starch/polyaniline (starch/PANI) biopolymer film was synthesised and investigated as an ammonia sensor for potential application as intelligent food packaging. FT-IR and XRD were used to confirm the composition of the biopolymer films, while UV-Vis spectrometry was applied to identify the oxidation state of PANI in emeraldine form. PANI was successfully incorporated into the starch matrix, leading to better thermal stability (TGA) but decreasing the crystallinity of the matrix (DSC). The performance of the polymer-film sensor was determined through ammonia-vapour sensitivity analysis. An obvious colour change from green to blue of starch/PANI films was observed upon exposure to the ammonia vapour. Starch/PANI 0.4% is the optimum composition, having the best sensor performance with good linearity (R There are countless adverse environmental impacts resulting from the large-scale production of petroleum-based plastics, including the risk of global warming . These hOn the other hand, food-safety issues have gained much public concern and attention. The contamination of foodstuffs due to microbial or chemical reactions compromises the quality and sensorial properties of the food, which may be harmful to human health . MoreoveIntelligent food packaging is defined as materials and articles that monitor the condition of packaged food or the environment surrounding the food ,15. VariRhinella arenarum of HCl-doped PANI nanofibre has been studied through the amphibian embryo toxicity (AMPHITOX) test, where it shows no toxicological effects even at a high concentration of 400 mgL−1 [Polyaniline (PANI) is a good candidate as it is simple, reliable, rapid, ecofriendly, and safe from leaching ,22,23. T00 mgL−1 . Further00 mgL−1 . As long00 mgL−1 . In addi00 mgL−1 ,27,28.PANI has chains that are arranged in an orderly way, with phenyl rings and nitrogen-containing groups alternating with each other, as shown in In a nutshell, starch-polymer matrix, which is biodegradable, is expected to resolve polymer pollution and to conserve fossil-fuel resources. However, their high electrical resistivity has become a stumbling block for the bio-based polymers to be adopted as a commercial food-freshness indicator. Thus, the noninvasive and leach-free PANI will be incorporated into the starch matrix for the ammonia-sensing property to indicate food freshness.Although PANI has been widely studied for its sensor properties, based on our knowledge, there is no published research regarding the sensor performance of starch/PANI biopolymer film as a colourimetric ammonia sensor. In this study, starch/PANI polymer films were synthesised to study their chemical and physical properties as an ammonia sensor for its potential application in the intelligent food-packaging industry.Ammonium persulfate (APS), aniline hydrochloride (ANI-HCl), acetone, hydrochloric acid, glycerol, and 30% ammonium hydroxide were purchased from Sigma Aldrich , while ethanol, methanol, toluene, and cassava starch were purchased from R&M Chemicals , Systerm , ChemSoln , and Cap Kapal ABC , respectively.Firstly, 100 mL of 0.25 M ammonium persulfate (APS) was added dropwise into 100 mL of 0.2 M of aniline chloride (ANI-HCl) under 0–5 °C. Both solutions were chilled in a refrigerator for 1 h prior to the dripping. The mixture of the solutions was left to be stirred overnight. After that, the PANI precipitate was filtered and washed thrice with 0.2 M HCl to remove unreacted monomer and APS, then washed thrice with acetone to remove excess HCl dopant, oligomers, and low-molecular-weight organic intermediate. The collected PANI was dried and ground into powder.−1 to 4000 cm−1 and ultraviolet-visible (UV-Vis) spectroscopy  from wavelengths 250 nm to 900 nm.The synthesised PANI was characterised using Fourier transform infrared spectroscopy (FT-IR)  in a range of wavenumbers from 650 cmInitially, 10 g of cassava starch, 5 g of glycerol, and the PANI  was added into 85 mL of distilled water. The suspension was stirred and sonicated. After sonication, the suspension was heated at 70 °C with constant stirring until it was gelatinised. The coagulation was poured into a mould and dried at 70 °C for 24 h. The thickness of the starch and starch/PANI films were in the range of 0.529 mm to 0.699 mm.The films were characterised through FT-IR spectroscopy. Deconvolution was conducted to identify overlapping peaks based on the Gaussian–Lorentzian model using the OMNIC 8 software provided by Thermo Fisher Scientific Inc. . Additionally, the crystallinity of the films was studied through X-ray diffraction (Bruker/D8 Advance) under the conditions of voltage 40 kV, X-ray wavelength (λ) 1.5406 nm, and current 30 mA. The diffractograms were smoothed based on a Savitzky–Golay filter using the Origin Pro 8.5 software by OriginLab Corporation . Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were carried out to study the thermal properties of the films STA 449 F3 Jupiter, NETZSCH, Selb, Germany).For sensor application, the starch/PANI films were exposed to the ammonia solutions  in a closed system for 30 min to study the films’ sensitivity towards ammonia at different concentrations. The optimum composition of starch/PANI film was then exposed to 1000 ppm ammonia solution at 30 °C, 4 °C, and −18 °C to accurately understand the applicability of the sensor at different storage temperatures. Sensor selectivity was studied by exposing starch/PANI films to different types of volatile solvents including ethanol, methanol, toluene, and acetone.0 = initial blue signal before exposure to ammonia vapour; G0 = initial green signal before exposure to ammonia vapour; B = blue signal after exposure to ammonia vapour; and G = green signal after exposure to ammonia vapour.The changes of green and blue appearances of the starch/PANI films were recorded using the ImageJ software developed by the National Institutes of Health USA. The change of colour signal of the films was calculated using the following equation:The sensitivity of the sensor was monitored based on the changes in colour signals mentioned in Equation (1) where the normalised change in colour signal was obtained by dividing the change in colour signal of the sample by the change in colour signal of control sample.−1 and 3206 cm−1 correspond to the N–H groups while the bands at 2902 cm−1 and 2899 cm−1 suggest the presence of aromatic C–H groups, including CH3 and CH2 [−1 and 1470 cm−1, respectively [−1 and 1239 cm−1 arises due to the aromatic conjugated C–N stretching in the quinoid ring, whereas 1109 cm−1 is due to the electron-like band in the quinoid unit of doped PANI [−1. Additionally, the C–H out-of-plane stretching deformation of the meta- and parasubstituted ring of the PANI leads to the bands at 875 cm−1 and 788 cm−1, respectively [The PANI synthesised through oxidative polymerisation was characterised by identifying the functional groups present in the polymer through FT-IR analysis . The int and CH2 . Moreoveectively . These bectively ,36. Moreped PANI ,37. The ectively .The UV-Vis spectrum of PANI is shown in −1 ranging from 3600 cm−1 to 3000 cm−1 is assigned to the stretching of hydroxyl groups in the starch, glycerol, and adsorbed water [−1 and its shoulder peak at 2883 cm−1 are attributed to the C–H bending of the anhydrous glucose unit (AGU) and CH2 deformation, respectively [−1 is due to the H–O–H stretching vibration of water molecules that were tightly bound towards the starch granule through intermolecular hydrogen bonds [−1 is associated with the C–O stretching of glycerol [−1 and 761 cm−1 show the presence of α-1,6-glycosidic linkage and α-1,4-glycosidic linkage, respectively [The starch/PANI films were fabricated through ex-situ polymerisation. ed water ,42. The ectively . On the en bonds ,44. Thisglycerol . Therefoectively ,46.−1) in the FT-IR spectra of starch/PANI (0.2–0.5%) which may arise due to the C–H vibration in PANI [The starch/PANI polymer films show similar FTIR spectra towards the starch film. This is in agreement with the previous studies by Azmi et al.  and Nand in PANI .The XRD diffractogram of starch and starch/PANI 0.4% polymer films is shown in In addition to the characteristic peaks of starch, the XRD spectrum of starch/PANI film also shows diffraction peaks at 2θ = 20.9°, 25.7°, and 27.2°. The periodicity parallel and perpendicular to the PANI chain direction was assigned to 2θ = 20.9°, and 25.7°, respectively ,52. The The thermal stability of the synthesised polymer films was evaluated through TGA analysis. The thermograms of TGA and differential thermogravimetry (DTG) are shown in The starch-polymer film exhibits three decomposition stages. The first decomposition stage at 120 °C is due to the loss of moisture on the surface of the polymer film and the water absorbed in the polymer film . The maiFor the starch/PANI  films, there is a similar degradation pattern with four decomposition stages. Similar to the starch film, the first stage of decomposition (116–140 °C) is due to the evaporation of moisture. There is a new decomposition stage at (189–236 °C) which occurs during the decomposition of HCl dopant . HoweverDespite the decomposition of PANI not being detected in the thermograms, the effect of PANI on the thermal stability of the polymer films was observed. The degradation temperature of the starch/PANI-polymer matrix (317–319 °C) is slightly higher compared to starch-polymer film (316 °C), where starch/PANI 0.4% exhibits the best thermal stability (319.8 °C).g) and melting temperatures (Tm) of the starch and starch/PANI films were identified through differential scanning calorimetry (DSC)  , Table 2 pattern .g steps, endothermic peaks were found in the DSC thermograms of the polymer films. The endothermic peak (277–292 °C) is attributed to the Tm of crystalline amylopectin and co-crystallised amylose [m is the temperature at which the crystalline region of the polymer turns amorphous; it is the characteristic peak for the crystalline materials [m peaks become smaller as the composition of PANI increases in the matrix. The reduction in the enthalpy of fusion (∆Hf) in the polymer films with higher composition of PANI shows that the crystalline characteristic was suppressed due to the disruption of the interaction between the starch-polymeric chains [f further proves that PANI was well-incorporated into the starch matrix.In addition to T amylose . Tm is taterials . The Tm c chains ,64. ThusThe sensor response against volatile ammonia in a noninvasive manner was monitored. 2 value of 0.9725. However, the concentration range from 0 to 25 min shows better correlation (R2 = 0.9939), indicating that at the 30th minute, the sensor is saturated and is reaching the upper limit of ammonia-vapour detection [The B/G signal increased from 0.84 to 1.00 in the first 30 min and achieved constant B/G signal after that. The linear correlation for the concentration ranging from 0 to 30 min shows an Retection . TherefoThe sensitivity of starch/PANI films was studied within the ammonia-concentration range of 200–1000 ppm. After the exposure of starch/PANI films towards the ammonia solutions, there is a change of colour from green to blue which can be easily detected by the naked eye, as shown in +–H site, rendering it a –NH4+ group [The change of the colour of the polymer films can be attributed to the mechanism of the interaction between PANI in the form of emeraldine salt and the ammonia molecules . The amm4+ group . At the The colourimetric response of the starch/PANI films as an ammonia-vapour sensor was further quantitated using the RGB (Red Green Blue) model . As showOn the other hand, for the starch/PANI 0.1% to 0.4%, it can be seen that the sensor response improves more significantly when the concentration increases from 200 ppm (1.12–1.34) to 1000 ppm (1.24–1.67). At lower concentrations, the sensor responses of starch/PANI 0.1% to 0.4% are similar due to the limiting factor of ammonia concentration. Therefore, the increase in PANI concentration will not largely affect the sensor response, leading to a mild increase in sensor response. At higher concentrations, there are more ammonia molecules to interact with PANI and it is no longer a limiting factor, thus resulting in a more obvious increase in sensor response.Furthermore, it was also observed that the response of the starch/PANI films as ammonia sensors increases along with the concentration of the ammonia solution regardless of the polymer film’s composition. Referring to the mechanism in y,bσ = standard deviation of pseudo-blank; cm = slope of the curve.The parameters obtained from the analysis of the linear regressions  are tabup-value (<0.05) would determine that the regression does not happen by coincidence [p-values which are smaller than 0.01; thus, these regressions are significant. Besides, all the starch/PANI films show good linearity in ammonia sensing with coefficient correlation (R2) > 0.90 [An F-test was carried out to indicate the significance of the regression, where a small ncidence . The reg) > 0.90 . The sta) > 0.90 ,73,74. N) > 0.90 ,75. ThisIn general, the standard indicates that the total volatile basic nitrogen (TVB-N) should not be higher than 15,000 ppm for all types of meat, where the thresholds are higher for certain food products such as beef, fish, and crustaceans . TherefoTo investigate the performance of the sensors more accurately, the starch/PANI films were exposed to 1000 ppm ammonia solution at different temperatures including 30 °C, 4 °C, and −18 °C, imitating the storage conditions under room temperature, refrigerator temperature, and freezer temperature, respectively. Since the fluctuation of the weather might affect the room temperature, the analysis test for the sensor performance was set at 30 °C in a water bath to give a constant temperature regardless of the weather. Furthermore, it was set based on the average indoor air temperature in Malaysia, at around 27 °C to 33 °C .As shown in −3, which supports better vaporisation of ammonia solution. When the temperature of the air is cooled below 20 °C, where it exceeds its dew point temperature, the water vapour begins to condense and the vaporisation of ammonia is retarded [At a higher temperature, the air is capable of holding more water vapour. Specifically, at 30 °C, the water-vapour-holding capacity of the air is 30 gcmretarded . At evenretarded . This isretarded . Thus, aretarded . At loweTo investigate the selectivity of starch/PANI films towards ammonia vapour, the polymer films were exposed towards various volatile solvents including ethanol, methanol, toluene, and acetone . Starch/D), the molecular polar dipole–dipole forces (δP), and molecular hydrogen bonding (δH) through electron exchange [1/2) and ammonia (δ = 33.3 MPa1/2) is smaller compared to the other volatile solvents (δ = 18.2–29.6 MPa1/2). This shows that ammonia is more miscible in the starch-polymer matrix. Thus, the starch/PANI films show the best sensitivity towards ammonia and do not show colourimetric responses to the other volatile solvents.In addition to the reaction between the ammonia and the nitrogen of PANI, solvent solubility also plays a role in the selectivity of starch/PANI as an ammonia sensor. The solubility of the polymer matrix and solvents are estimated using the Hansen solubility parameters (HSP) (δ) which includes the solvent’s atomic dispersion forces (δexchange . The indexchange ,81. The exchange . The difA green and biodegradable ammonia-sensing film was developed for the potential indication of food spoilage in intelligent food packaging. The starch/PANI films were synthesised through ex-situ polymerisation and the PANI was successfully incorporated into the starch matrix. The PANI increases the thermal stability of the matrix and increases the amorphous characteristic of the film. Upon the exposure towards ammonia vapour, a significant colour change of the film from green to blue was observed with good linearity and precision. Furthermore, it shows sensitivity and has an LOD at even very low concentrations of ammonia. The starch/PANI 0.4% was found to be the optimum composition of the film, showing selectivity towards ammonia vapour. Thus, the environmentally friendly and user-friendly starch/PANI film has outstanding potential to be applied in intelligent food packaging.


Cell morphology was similar on all three gels. SCX expression was slightly reduced on the soft gels  and COL1A1 expression increased on the stiff gels . Culturing THP-1 macrophages on soft gels induced increased expression of IL1B , and IL8 expression was inhibited on the stiffer gels . We also found that culturing TDCs on plastic increased cell growth, altered cell morphology, and inhibited the expression of SCX, SOX9, MMP3, and COL3. We conclude that TDCs and macrophages respond to changes in matrix stiffness. The magnitude of responses measured in TDCs were minor on the range of substrate stiffness tested by the gels. Changes in THP-1 macrophages suggested a more inflammatory phenotype on substrates with non-physiological stiffness. Although cell response to subtle variations in matrix stiffness was moderate, it is possible that these alterations may contribute to the onset and progression of tendinopathy.Tendinopathy is characterised by pathological changes in tendon matrix composition, architecture, and stiffness, alterations in tendon resident cell characteristics, and fibrosis, with inflammation also emerging as an important factor in tendinopathy progression. The sequence of pathological changes in tendinopathy and the cellular effects of the deteriorating matrix are largely unknown. This study investigated the effects of substrate stiffness on tendon-derived cells (TDCs) and THP-1 macrophages using PDMS substrates representing physiological tendon stiffness (1.88 MPa), a stiff gel (3.17 MPa) and a soft gel (0.61 MPa). Human TDCs were cultured on the different gel substrates and on tissue culture plastic. Cell growth was determined by alamarBlue™ assay, cell morphology was analysed in f-actin labelled cells, and phenotypic markers were analysed by real-time PCR. We found that in comparison to TDCs growing on gels with physiological stiffness, cell growth increased on soft gels at 48 h (23%,  Tendons have a highly aligned and hierarchically organised extracellular matrix (ECM) , primariThe altered ECM composition and structure in tendinopathy results in tendons with inferior mechanical properties, which are prone to failure . PatholoStudies in matrix biology have demonstrated that the ECM microenvironment plays a key role in regulating the activity of cells. Substrate stiffness plays a crucial role in regulating cell growth, morphology , migratiin vitro have used very stiff tissue culture plastic (TCP)to better understand tendon cell biology, which is well known to induce TDC dedifferentiation , and incubated in digestion medium , dispase (0.5 mg/ml) , and collagenase II from Clostridium histolyticum  in a shaker incubator at 37°C overnight. Following digestion, cells were strained using a 70 μm cell strainer, resuspended in fresh DMEM/F12 with 10%FBS, seeded in T-75 tissue culture flasks, and incubated at 37°C/5%CO2. Cells were harvested at confluence and transferred to liquid nitrogen storage. Prior to use, passage 1 TDCs were revived and cultured to confluence in T-75 flasks, and then seeded on substrates of various stiffness.Healthy hamstring and biceps tendons were isolated from patients undergoing orthopaedic surgery . EthicalPoly-dimethyl siloxane (PDMS) resin and the crosslinking agent  were mixed in various ratios to prepare mixtures with different crosslinking concentration. It has been previously established that PDMS stiffness can be controlled by the concentration of the crosslinking agent , with in2 pieces of sectioned longitudinally along the direction of the fiber alignment to 300 µm thickness in a cryo-microtome. The elastic modulus of PDMS gels and the bovine tendon were measured using Asylum Research Atomic Force microscope (AFM) with SiNi tips .Bovine superficial digital flexor tendons, sourced from Wilson Hellaby-Auckland Meat Processor Ltd., were cut into 1 cmn = 3) and each PDMS stiffness gel group (n = 3–5) and were analyzed using the Asylum Research software (version 16.26). The force measurement in AFM is calculated by multiplying tip deflection with spring constant of the tip. The distance travelled by the tip was measured from the movement of the Piezo actuator height (Z). The indent portion of the curve was fitted into the hertz model (the tip assumed to have a pyramid shaped and the Poisson’s ratio (υ) of the material assumed to be 0.5) to calculate the elastic modulus of the substrates. Hertz’s contact stiffness measurement equation for micro-scale elastic modulus measurement in AFM using a pyramidal tip takes the following formThe elastic modulus measurement for all the slices and PDMS gels were conducted in an aqueous environment while hydrated in phosphate buffered saline. Prior to each experiment the tip was calibrated for stiffness and Inverse Optical Lever sensitivity in air and liquid environment. At least 30 indentations were performed at randomly chosen locations on the healthy, control bovine tendon slices , δ denotes the deformation of the tip, υ is Poisson’s ratio.2. The effect of substrate stiffness on TDC growth was quantified at 24 and 48 h. To quantify the cell growth, 5% alamarBlue™ (v/v) was added to the wells and incubated for 4 h at 37°C/5% CO2. At the end of the incubation period, 200 µl of alamarBlue™-containing conditioned medium from each well was transferred into a 96-well plate , and fluorescence determined in Synergy 2 multi-detection microplate reader . Following the first time point, medium in each well was refreshed and the assay was repeated at 48 h. The fluorescence background reading was subtracted, and the results were normalized to the that of cells on physiological stiffness after 24 h.Cell growth assays were carried out as previously described , using a4 cell/well in 24-well plates with the PDMS gels representing physiological, stiff and soft stiffness and incubated in DMEM/F12 with 5% FBS for 24 h. Cells were fixed in 4% paraformaldehyde for 20 min, and permeabilised with 0.5% triton-X overnight. For actin cytoskeleton staining, cells were incubated overnight with Alexa-Fluor™ 594 Phalloidin , following the manufacturer protocol. Cells were then incubated for 3 h with DAPI  for nuclear staining. Images of the fixed and stained cells were captured using Olympus CKX53 inverted fluorescence microscope using Olympus DP-72 camera. Cell periphery was manually traced from the images to calculate cell area, aspect ratio  and circularity (defined as http://rsb.info.nih.gov/ij/).TDCs were seeded at a density of 106 cells/well in 24-well plates containing the PDMS gels representing physiological, stiff and soft stiffness. Phorbol 12-myristate 13-acetate (PMA) (200 ng/ml) was added to the medium for 24 h to induce the differentiation of THP-1 cells to macrophages , were seeded at a density of 1.5×10rophages , confirm® Mini kit (QIAGEN). RNA was extracted following the manufacturer’s protocol, and on-column DNase digestion with the RNAse-Free DNase Set (QIAGEN) was used to eliminate DNA contamination. The RNA concentration and purity were measured using Nano-Drop Lite spectrophotometer (Thermo Fisher Scientific) with 260/280 absorbance value > 1.8 considered as acceptable. cDNA was synthesised with Superscript-III (Thermo Fisher Scientific). Gene expression was analysed in QuantStudio™ 5 Real Time PCR System, using multiplex PCR with FAM-labelled TaqMan™ assays for the target genes and VIC-labelled TaqMan™ assay for 18S rRNA, used as the endogenous control (Thermo Fisher Scientific). The relative expression level of the genes compared to cells cultured on the physiological stiffness PDMS gels was calculated by the 2-∆∆Ct method. The genes studied are listed in RNA was extracted from TDCs and THP-1 cells after 48 h of culture on PDMS gels representing physiological, stiff and soft stiffness using the RNeasySecreted IL-1β was quantified in conditioned media samples collected from THP-1 cells using DuoSet ELISA (R&D Systems) and following the manufacturer’s protocol. Absorbance of each well at 450 nm was read using Synergy 2 multi-detection microplate reader .GraphPad Prism 8.2.1 (GraphPad Software) was used for all statistical analysis. Data was assessed for normality using the Shapiro-Wilk test. Normally distributed data were analysed using either one-way analysis of variance (ANOVA) or two-way ANOVA, with post-hoc Dunnett’s or Tukey’s test. Tests were 2-tailed, and a 5% significance level was maintained throughout the study. For non-normal data Kruskal–Wallis test was performed.p = 0.11). The stiff gel had a 2.1-fold higher elastic modulus , and the soft gel had a 2.5-fold lower elastic modulus  than that of the tendon (p < 0.0001) . The elastic modulus of the physiological gel was similar to that of tendon, with a mean of 1.88 MPa  ( 0.0001) .p = 0.0005) on TCP than on the physiological-stiffness gel (p = 0.003) on soft substrate, and 45% greater (p < 0.0001) on TCP than on the physiological-stiffness gel. To examine whether the response to substrate stiffness in our experimental system is a characteristic of the TDCs or a more general cellular response, we repeated the experiment with mouse osteoblast-like MC3T3-E1 cells. The growth of MC3T3-E1 cells was similar on TCP, physiological, and soft gels, but significantly higher on the stiff gel   . There wSCX , and TDCs cultured on the stiff substrate had higher expression of COL1A1   . TDCs cuThe expression of IL1B, encoding the inflammatory cytokine IL-1β, was approximately 2-fold higher in THP-1 macrophages cultured on the soft substrate and on TCP than on the physiological-stiffness gel . Howeverin vitro system, we prepared substrates with physiological tendon stiffness, and two formulations that represented matrix stiffness in non-physiological condition: soft gels with 2.1-fold lower stiffness, and stiff gels with 2.5-fold higher stiffness. In comparison to TDCs culturing on physiological substrate stiffness, we found increased cell growth and reduced expression of SCX in TDCs cultured on the soft gels, and increased expression of COL1A1 in TDCs cultured on the stiff gels. Culturing THP-1 macrophages on soft gels induced the expression of IL1B, whereas IL8 expression was inhibited on the stiffer gels. We also found that culturing TDCs on tissue culture dishes induced increased cell growth and altered cell morphology and the signature of gene expression in comparison to the substrate with physiological stiffness, confirming that TCP is not suitable for maintaining tendon cell characteristics. Our findings suggest that changes in tendon matrix stiffness during tendinopathy play a role in altering resident cell behaviour. These alterations in cell behaviour are minor, but may contribute to the onset and progression of tendinopathy.Our study found that TDCs and THP-1 macrophages respond to changes in substrate stiffness. Using a PDMS gel based ex-vivo tensile testing (Although it is well established that tendinopathy affects tendon matrix stiffness, the exact magnitude of these changes at the substrate stiffness level, and their contribution to the progression of tendon disease, are still largely unknown. The tensile modulus of tendon tissue, however, has been well characterised by elastography or  testing . In tend testing . Higher  testing . Here, u testing . In the In vitro, TDC morphology is known to be regulated by substrate architecture (in vivo, native cells elongate and align along the collagen fibres (The main substrate stiffness-dependent effect in TDCs was a small increase in cell growth on a softer matrix, with no effect seen in TDCs cultured on the stiffer matrix. Hypercellularity is one of the main cellular hallmarks of tendinopathy . Our resitecture . Whereasn fibres , thus chn fibres .SCX, which encodes for a transcriptional factor essential for determining tendon cell fate. Previous studies have shown decreased SCX expression in tendinopathy (COL1A1 expression in TDCs cultured on the stiff substrate (COL1A1 encodes for the major component of type I collagen, which is the main structural protein of tendon (Minor effects on TDC gene expression profiles were also observed on different stiffness gels. In comparison to TDCs on physiological stiffness gels, TDCs on soft gels had lower expression of inopathy , and it ubstrate . COL1A1 f tendon , suggestf tendon . TDCs cuf tendon .Understanding the effect of matrix stiffness in immune cell response is important in the context of tendinopathy, with histological studies observing immune cell invasion into the diseased tendon . Our datThis study has several limitations. One potential limitation is that the native tendon stiffness was determined in bovine tendon slices. Direct measurements of tendon stiffness in samples of healthy tendon and samples from patients at different stages of tendinopathy would have allowed us to relate our findings to tendon disease in humans. However, obtaining clinical samples of human tendon that are of sufficient size and quality to allow for AFM measurements to be made is problematic. Therefore, given the evidence that tendon structure is conserved across species, we used the bovine tendon to estimate physiological stiffness of human tendon . The stiin vitro system that allows tailored substrate stiffness and can mimic tendon tissue. We found that TDCs and macrophages respond to changes in matrix stiffness, with softer substrate stiffness affecting TDC growth rate and gene expression, and THP-1 macrophages expressing higher levels of pro-inflammatory cytokines on substrates with non-physiological stiffness. These effects were relatively minor, and while they may contribute to the cellular changes observed in tendinopathy, it is likely that matrix stiffness, architecture and composition all work synergistically to regulate cell characteristics in healthy and tendinopathic tendon.Overall, we established a PDMS gel based


Objective: Tuberous sclerosis complex (TSC) is a multisystem neurocutaneous genetic disorder. The clinical manifestations are extensive and include neurological, dermatological, cardiac, ophthalmic, nephrological, and neuropsychiatric manifestations. The prediction and pathophysiology of neuropsychiatric disorders such as emotional symptoms, conduct problems, hyperactivity, and poor social behavior are poorly understood. The aim of the study was to diagnose neuropsychiatric symptoms in individuals with TSC, and to examine their possible correlations with quantity, magnitude, and spatial location of tubers and radial migration (RM) lines.Methods: The cohort comprised 16 individuals with TSC, aged 5–29 years, with normal or low normal intelligence. The participants or their parents were requested to fill Strengths and Difficulties Questionnaire (SDQ) and the TAND (TSC-associated neuropsychiatric disorders) Checklist for assessment of their neuropsychiatric symptoms. Correlations were examined between these symptoms and the magnitude, quantities, and locations of tubers and white matter RM lines, as identified in T2/FLAIR brain MRI scans.Results: The SDQ score for peer relationship problems showed correlation with the tuber load . Tuber load and learning difficulties correlated significantly in the temporal and parietal area. Mood swings correlated with tubers in the parietal area . RM lines in the temporal area correlated with abnormal total SDQ . Anxiety and extreme shyness were correlated with RM lines in the parietal area, r = 0.513, p < 0.05 and r = 0.593, p < 0.05, respectively. Hyperactive/inattention correlated negatively with RM lines in the parietal area .Conclusions: These observations may lead to future studies for precise localization of neuropsychiatric symptoms, thereby facilitating directed therapy. TSC1 and TSC2, located on chromosomes 9 and 16, respectively. The protein products of TSC1 and TSC2 (hamartin and tuberin) function together within the cell and have an inhibitory effect on the mammalian target of rapamycin (mTOR), a protein kinase that influences cell growth and division, and on the synthesis of proteins and other cell components (TSC2 mutations (Tuberous sclerosis complex (TSC) is a multisystem neurocutaneous genetic disorder caused by mutations in the tumor suppressor genes mponents . Geneticutations  and earlutations . StructuAll patients with TSC who attended our institute between May 2019 and March 2020 with normal or low normal intelligence and brain MRI performed <1 year before evaluation were included. The clinical diagnosis of TSC was made by a senior pediatric neurologist based on the 2012 International Tuberous Sclerosis Complex Consensus Group . IndividThe neuropsychiatric profile was evaluated using the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), in addition to the TAND (TSC-associated neuropsychiatric disorders) Checklist that was translated to Hebrew. This checklist is generally used during meetings with a neurologist in order to assess patients' problems and to help guide a conversation between the clinician and the family or the person with TSC. It consists mainly of a series of YES/NO items in six domains: behavioral, psychiatric, intellectual, academic, neuropsychological, and psychosocial.https://www.sdqinfo.org).The Strengths and Difficulties Questionnaire (SDQ) was used to evaluate children's behavioral and attentional problems. It is an internationally accepted behavioral screening questionnaire for 3- to 16-year-olds . Brann ep ≤ 0.05 was considered significant.All statistical analyses were conducted using IBM-SPSS v.27 . Associations were calculated for the scores of the SDQ elements with the numbers and areas of tubers and with RM lines using Spearman's correlation. A The study group consisted of 16 patients with TSC, aged 5–29 years (mean age = 13 years). Nine (56%) were males. Eleven patients (68%) had epilepsy, and seizures were controlled in 90% of them with oxcarbazepine, carbamazepine, lacosamide, levetiracetam, and valproic acid. Five patients (31%) had a history of infantile spasm, which was treated with vigabatrin. All patients had subependymal nodules (SEN) and one patient had subependymal giant cell astrocytoma (SEGA) .3. More tubers were found on the left than on the right hemisphere (mean area = 9.9 vs. 8.5 cm3). The area of tubers on the frontal area was the highest (mean area = 6.1 cm3), followed by the parietal (4.2 cm3), occipital (4.0 cm3), temporal (2.9 cm3), and the cerebellum areas (0.3 cm3). The mean number of RM lines per patient was 7.2 and the range was 1–15. A mean number of 2.1 RM lines was smaller than 0.5 cm3, and a mean number of 5.2 was larger than 0.5 cm3. The mean areas of RM lines were almost equal on the right and left hemispheres: 3.31 and 3.8 cm3, respectively. The mean areas of the RM lines were the largest on the frontal (2.81 cm3) and parietal areas (2.4 cm3), followed by the occipital (1.0 cm3), temporal (0.94 cm3), and the cerebellum areas (0.1 cm3).In total, the patients had 273 tubers. The mean number of tubers per patient was 17.2, the minimum number was 2, and the maximum was 39. The range of cyst-like cortical structures detected per patient was 0–22. Fourteen (5.1%) tubers were calcified; none of these were in patients younger than 8 years. The overall quantity, magnitude, and spatial location of the tubers are summarized in Psychiatric diagnoses were made according to the DSM-V criteria and were collected by the TAND Checklist. Fourteen (87%) had psychiatric disorders. ADHD, diagnosed in nine (57%) patients, was the most common diagnosis, followed by learning disabilities in eight (50%). Anxiety disorder was diagnosed in three patients, schizophrenia in two, major depressive disorder in two, obsessive compulsive disorder in one, and ASD in one patient. Behaviors of concern were reported in all but one patient (93%). For 10 (63%), the concern was regarding emotional behavior such as anxiety and depressive mood. Nine reported hyperactive behavior, 7 reported aggressive behavior, 7 peer problems, 4 eating problems, and 19 had sleeping problems. The correlations of the tubers and radial migration lines with the TAND Checklist are presented in r = 0.611, p < 0.05) and the tuber load . Tubers in the right and left hemispheres were correlated with learning difficulties ; the correlation coefficient relating to the left hemisphere was highly significant. Learning problems were also correlated with tuber area in the parietal  and temporal areas . The presence of RM lines in the right hemisphere was correlated with learning difficulties .Learning difficulties were correlated with the number of tubers per patient  and in the frontal area  and with RM line area >0.5 cm3 .ADHD was negatively correlated with RM lines in the left hemisphere  and with the area of the RM lines in the right hemisphere  and in the cerebellum . RM lines with an area >0.5 cm3 were correlated with sleep problems .Sleep problems were correlated with tuber area in the cerebellum . Anxiety and extreme shyness were correlated with RM lines in the parietal area .Mood swings correlated with tubers in the parietal area (SD = 7.14), p < 0.001]. The mean score for peer problems was also significantly higher, with a mean of 3.5 . The mean scores for emotional symptoms, conduct problems, hyperactivity, and prosocial behavior were outside the norm, but without statistical significance . The SDQ score of hyperactive/inattention correlated negatively with RM lines in the parietal area . RM lines in the temporal area correlated with abnormal total SDQ .Half of the participants (8/16) scored in the abnormal range of the SDQ for total difficulty. The data elicited from the respondents were tested for normality using the Kolmogorov–Smirnov statistic. The mean (SD) score for total SDQ was significantly higher than that in a normative population [14.44 (ificance . One of ificance , and sevNeuropsychiatric features of TSC present in up to 90% of individuals with TSC  and causCortical tubers are focal malformations that consist of various cellular abnormalities, including astrogliosis, dysmorphic neurons, and immature glial cells . These t3. Associations have been reported between ADHD and weaker function and structure of prefrontal cortex circuits, especially in the right hemisphere. The prefrontal association cortex plays a crucial role in regulating attention, behavior, and emotion; the right hemisphere is especially associated with behavioral inhibition (3 were found to correlate with sleeping disorders (White matter abnormalities have emerged as another important mechanism for brain dysfunction in TSC. Such abnormalities are associated with an overall decrease in measures of functional connectivity, such as a reduced interhemispheric synchrony, between the different regions of the brain , 24. Thehibition . Both RMisorders . By inteisorders , and proThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Helsinki Committee, Rabin Medical Center. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.RC and JG examined the patients and participated in revising the article. LK revised the brain MRI's and participated in revising the article. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Designing the physical coupling between the human body and the wearable robot is a challenging endeavor. The typical approach of tightening the wearable robot against the body, and softening the interface materials does not work well. It makes the task of simultaneously improving comfort, and anchoring the robot to the body at the physical human robot interaction interface (PHRII), difficult. Characterizing this behavior experimentally with sensors at the interface is challenging due to the soft-soft interactions between the PHRII materials and the human tissue. Therefore, modeling the interaction between the wearable robot and the hand is a necessary step to improve design. In this paper, we introduce a methodology to systematically improve the design of the PHRII by combining experimentally measured characteristics of the biological tissue with a novel dynamic modeling tool. Using a novel and scalable simulation framework, HuRoSim, we quantified the interaction between the human hand and an exoskeleton. In the first of our experiments, we use HuRoSim to predict complex interactions between the hand and the coupled exoskeleton. In our second experiment, we then demonstrate how HuRoSim can be coupled with experimental measurements of the stiffness of the dorsal surface of the hand to optimize the design of the PHRII. This approach of data-driven modeling of the interaction between the body and a wearable robot, such as a hand exoskeleton, can be generalized to other forms of wearable devices as well, demonstrating a scalable and systematic method for improving the design of the PHRII for future devices coupled to the body. Robots coupled to the human body assist in the rehabilitation of impaired mobility . This model, called HuRoSim, includes the viscoelastic properties of the human skin and soft tissue, along with an interface material, and attachment straps of the device. HuRoSim focuses on relative movement between the human and the robot, and forces generated at the PHRII as a result of applying loads across it. This approach can be used to compute optimal values of the design parameters, such as geometry, compliance, strap stiffness, pre-tension, etc., quickly by systematically varying them in simulation. It also enables the analysis of robot configurations and the effects of variations in human size and tissue properties.In the sections that follow, we first introduce our novel simulational framework, HuRoSim followed by two experiments to outline our novel method of optimizing the PHRII . In our HuRoSim is a dynamic simulation framework built in Matlab 2016 , to characterize the interaction behavior at the PHRII between the hand exoskeleton and the hand. HuRoSim is built as a lumped parameter system, arranged as a network of nodes in 3D space. These nodes are connected by constraints, each with its own set of parameters and properties. This modularity allows the user of HuRoSim the ability to reconfigure the nodes, and simulate a variety of PHRII systems. The user is also able to choose the complexity of the simulational representation of the PHRII, thus trading between computational load and accuracy.Our simulation model of the PHRII includes a number of features that can affect the interaction between the rigid reference structure of the robot and the human reference  structure. This includes: viscoelastic properties of the human tissue  PHRII dimensions and geometry, mechanical properties of the padding used at the interface, slip along the interface, and the mechanical properties including the initial force between the device and tissues or pre-tension of attachment straps.(Left) shows a schematic cross section view of a typical PHRII. The human reference structure  is shown in the middle, completely encased by soft tissue both above and below it. The bone in this figure is the proximal phalanx of the index finger, and it's dorsal surface (above the bone in this figure) can be approximated to a straight line. Over the bone is a layer of soft tissue and skin represented by arrows showing its mechanical compliance in compression and sliding. Above the finger is the rigid plate of the robot attachment represented by a solid line, and its compliant padding in contact with the skin on the dorsal surface is similarly represented by arrows showing its mechanical compliance.(Left) is the antagonistic strap that is in contact with the skin on the palmar surface of the hand (below the bone in this figure). Identical to the considerations on the dorsal surface discussed previously, in this PHRII, we also have the two mechanically compliant regions of human soft tissue and device padding between the bone and the inelastic fibers of the strap which we consider as its reference geometry.The PHRII region is defined by this contact surface described above, and it extends from the rigid reference geometry of the human to the rigid component of the robot. Also visible in (Left) also shows individual sources of movement as colored arrows. The strain distribution in the human soft tissue and the device padding are shown in purple and blue, respectively. Shear strain in the padding is also accounted for in our model, but not explicitly highlighted here. The human skin can also glide over the underlying layers of fascia, as shown in red. The reaction forces to this movement come from two main sources: stretching of the skin itself, which is dominated by the viscoelastic properties of the dermis, and the connective tissue linking the dermis to the underlying structures in the hypodermis and fascia. Lastly, we also have surface effects of slip and lift-off at the actual interface surface, as shown in green.The (Right). This system can be viewed as being composed of three PHRII units, represented by the numbered circles. Each PHRII unit is connected to the human reference ground through series viscoelastic elements, and to each other either with compliant, or with inelastic constraints.All components in the PHRII unit are modeled as viscoelastic elements. We use lumped parameter models to represent the robot and strap structures as linked point masses, and the human bone as the reference ground. With this approach, we represent the soft structures in the PHRII as viscoelastic elements connected in series as shown in x-direction while the right represents normal or the y-direction. Therefore, Cy1 & Cy2 represent the compressive elastic properties of the soft tissue and padding, while Cx1 & Cx2 represent the stretching/sliding of skin over layers of fascia and the padding shear strain. Rx1, Ry1, Rx2, and Ry2 represent the viscous properties of each of these elements. The normal and the tangential strains are orthogonal and considered independently. However, they are still connected to the same inertia and reference ground. This individual unit is replicated at every PHRII and is used as the building block for the simulation environment. As seen in (Right), these units are coupled by both elastic and inelastic constraints, as well as external forces and this is the input to the primary lumped parameter model. The lumped parameter model is solved using a constrained ODE solved detailed in the next sub-section. The steady state and dynamic responses of the solver are the outputs which we use for further analysis.In our example system, the dorsum of the hand has two such PHRII units connected by an inelastic constraint (simulating a rigid body) The palmar surface has a single one which is connected to the dorsal link via another viscoelastic element (simulating the strap). We will be discussing the construction of more complex systems with this building block as we go forward. Note that we have made the assumption of coupling the elastic elements in series separately from the viscous ones. Condition checks were included in the simulation of the system to ensure that this does not create unrealistic behavior in exception cases. These include negative normal forces at the interface on rapid withdrawal of loading.We use experimentally obtained values for human tissue parameters where available in the literature. These include tensile viscoelastic properties of the skin , our simulation also uses smoothed piece-wise linear models for tissue viscoelastic properties wherever possible based upon the desired complexity.q's are the coordinates of each point, M is the overall inertia matrix, Q is the array of non-constraint forces for all points, and A & b are coefficients of the constraint equations after differentiating twice and grouping in the form of Equation 2). This method was chosen over penalty methods and simultaneous solutions for numerical stability and computational simplicity  solvers, and constraints are implemented with the option of simulating in both 2D and 3D using Lagrangian mechanics by Udwadia and Kalaba's method  and provides both steady state and transient responses. However, due to the slow and almost quasi-static movement speed that is used in rehabilitation, assistance, and assessment, our results focused largely on the steady state response.In this experiment, we study the case of a hand exoskeleton attached to the dorsal aspect of the hand. Using HuRoSim, we apply an extension moment load about the MCP joint to the virtual finger which is fixed at an angle. From this simulation study, we predict the kinematic movement of the coupled hand-exoskeleton system. We then replicate this set-up with the Maestro hand exoskeleton attached to a human hand to measure the actual kinematic effect of applying an isometric load across the MCP joint.We configured HuRoSim to study the effects of applying an isometric extension load across the MCP joint. The input load to the simulation is in the form of an applied moment from the exoskeleton (to a maximum of 0.5 Nm), and the studied outputs are the final displacements and forces present in the system on reaching a steady state.in-vivo measurements of the tissue properties of the surfaces of the human hand have not been widely studied, leading to a lack of available literature to obtain measurements from. This scarcity contributed to the motivation for indentation experiments in section 2.3.3 that gather in-vivo stiffness data of the hand dorsum for use by HuRoSim.The human and exoskeleton interface material parameters were gathered from literature sources, most notably  as a discrete array of springs representing the hand dorsum (kdorsum) and the PHRII (kpHRI), respectively. These two arrays of springs are in series with each other (Right). This system is loaded with a constant bias force normal to the surface representing a generalized net force from one or more straps. External loading is resolved into the generalized net force and moment vectors, explained in more detail in section 2.3.1. Through analysis of the simplified system under external load, we deduce the shape of the desired effective stiffness (keff) of this series spring model that allows us to minimize the peak pressures at the contact surface (Section 2.3.1). Using a numerical simulation environment, we compute the optimal effective stiffness gradient which satisfies the deduced shape profile from the analysis, and that also minimizes peak pressure (Section 2.3.2). Next, we quantify the stiffness profile of the experimenter's hand (kdorsum) through an indentation experiment with a robot (Section 2.3.3). The keff computed from the numerical simulation and kdorsum, obtained experimentally, are then used to compute the kpHRI, the optimal stiffness of the PHRII (Section 2.3.4).We model the PHRII Left as ch other Right. TFb), applied normally and at L/2 as seen in (Left).To design an optimal PHRII for the hand dorsum, we simplify the complex interaction at the interface as two plates of length L and uniform width sandwiched between the rigid reference plate of the Maestro robot, and the rigid human bone. The robot reference plate is held to the dorsum by a bias force mimicking a strap (Preaction) distribution balances the net force  and the external moment (ML) [(Right)].We then load the PHRII with an external force mimicking the reaction forces from the Maestro actuators. Using the principle of transmissibility, we express the applied external force to the attachment plate as a combination of an equivalent force and moment applied at the center of the plate, placed coaxially with the bias force. The resulting reaction pressure (Ci) along the contact surface between the two plates.Our objective is to balance the applied force and moment while minimizing peak reaction pressure  below the plate  to the external applied force (FL). The position of “x,” the center of the highest pressure region in the optimal distribution (Popt), and “Ppeak,” the magnitude of the highest pressure between the two uniform distributions, are computed in Equations (2) and (3).The boundaries of the regions of uniform pressure shift depending on the ratio of external applied moment :The optimal value of bias force, Popt), a hyperbolic distribution with high stiffness in the center of the plate which tapers off toward the edge in the direction of the applied moment, ML, is a solution to the equation, though its exact parameters are yet to be computed. We approximate this hyperbolic representation to a linear stiffness profile for our experiment, decreasing from the highest stiffness at the middle of the attachment (kmid), and symmetrically tapering off to a minimum at each edge (kedge). The symmetry allows the resulting profile to hold true for external moment loads in either direction. To obtain kpHRI, we need to obtain kdorsum in addition to knowing keff.By analyzing the possible solutions to achieve this optimal pressure,  and moment loads (ML) with varying kpHRI profiles to examine the resulting pressure distribution. The relative displacement of the PHRII with respect to the underlying bone, due to FL, was also captured for each stiffness profile, and these results are presented in the next section.For this experiment, the dorsum surface was discretized into 15 total points, with the PHRII interacting with the underlying human metacarpal through the stiffnesses Tissue stiffness on the human body is quantified by applying force over a range of displacement.kdorsum, we designed an indentation system comprised of a Phantom Premium 1.5 high force haptic renderer, which has a high positional accuracy of the end effector (7*10−6m). This was used along with an ATI Nano 17 force torque transducer (having a high force torque sensing accuracy of 0.001N) attached at the end of the linkage as an indenter to probe the hand dorsum  shows the regions selected for indentation.To measure d dorsum . We sele(Left). The wrist and arm supports were positioned to level the hand dorsum in the transverse anatomical plane. The phantom probe is then manually led once to each point marked on the dorsum for indentation. The probe uses these points as input into an interpolator to compute a spatial trajectory to follow. The phantom is driven in an open-loop position controlled configuration.The wrist and arm were supported in braces, and a consistent grasp object was used across trials to minimize its influence on stiffness distributions due to the changes in bone locations and muscle recruitment strategy, as seen in kindenter) was found to be 2.67 N/mm along the workspace. We account for this stiffness in the measurement of the hand dorsum stiffness (kdorsum) by modeling the interaction between the indentation system and the hand dorsum as two springs in series. The measured stiffness of the hand dorsum, kmeasured, (Right) is used along with kindenter to calculate the kdorsum Equation (5).Quantifying the stiffness of the indentation system in the direction normal to the hand dorsum demonstrated the need to account for this value in estimating the stiffness of the hand dorsum. The stiffness of the indentation system . This gives us a PHRII stiffness profile that should generate the minimum peak pressure, or the optimal pressure profile (Popt) on the hand dorsum for the given FL and ML.In our model, HuRoSim offered many insights into the interaction between the exoskeleton and the hand at the PHRII. Specifically, the simulation environment was able to predict the directions and magnitude of displacements and forces that one would expect on the PHRII. The following sections describe some of the observations predicted by HuRoSim that were also confirmed by empirical experiments with the Maestro hand exoskeleton under identical conditions.(Left) shows the angular displacement of the exoskeleton dorsum attachment on application of moment load by the exoskeleton about the MCP joint. HuRoSim predicted an angular displacement that increased approximately proportional to the applied moment load, and that was reduced on increased strap pretension. Physical experiments under the same conditions confirmed both trends as seen in (Right). The experimental values of angular displacement under low strap pretension matched the predicted values quite closely. However, under higher pretension, where the non-linear behavior of human soft tissue becomes more pronounced, there is expected divergence between the predicted and experimental values.The effect of varying the applied moment loading and the strap pretension were studied in both the HuRoSim environment, as well as in the physical experiment. In addition, when the exoskeleton applies an extension torque about the MCP joint, HuRoSim also predicted an increase in pressure at the location on the PHRII on the hand dorsum closer to the joint while the location further away from the MCP joint experiences a reduction in pressure. The inverse is true when the actuator applies a flexion torque about the MCP joint. HuRoSim indicated that the magnitude of these increases with respect to the applied moment was found to be dependent on the length of the exoskeleton attachment and on the geometry of the PHRII at the contact locations on the hand dorsum.As a consequence of the distribution of pressures described above, HuRoSim predicted that the end of the PHRII closest to the MCP joint would lift off and lose contact with the skin surface on the application of extension torques by the exoskeleton. When we replicated the simulation empirically with the Maestro hand exoskeleton, we observed the lift off phenomenon predicted by HuRoSim .The effect of varying strap stiffness was studied using the simulation environment to identify the optimal strap stiffness to minimize both output metrics (relative displacement and forces across the interface). HuRoSim's initial results indicated that the best performance on both metrics occurred with effectively inelastic straps, or straps with extremely high stiffness.This result was unexpected since pilot user feedback with the maestro exoskeleton reported that mild elasticity was the most comfortable. Exploring different variables in the system, it was observed that in the actual human hand, co-contraction of muscles in response to the applied load leads to an increase in the thickness of the muscle body as it is flexed, and resulting changes in the cross sectional thickness of the hand. These also vary depending on the musculature of the individual subject and the magnitude of voluntary contraction. When the simulation was tested with approximate values of the cross section variation, a slightly reduced strap stiffness was found to produce lower pressure distributions with minimal trade-off in the relative displacement values.The values of this optimal reduced stiffness, however, are dependent on the magnitude of cross section increase due to co-contraction. Future work can perform the simulation using magnitudes of cross sectional variation sourced from literature or experiment to inform design values of this optimal stiffness.kdorsum was measured to be 1.0876 ± 0.40 N/mm over a range of from 0.54 to 1.59 N/mm. The region of the dorsum above the metacarpal bones was found to be stiffer (1.1285 ± 0.43 N/mm) than the region between the bones (1.0060 ± 0.36 N/mm) that accommodate soft tissue. Increasing force of grasp led to an increase in measured dorsum stiffness . The percentage improvement increases with increasing applied moment for the same value of bias force. Additionally, the optimal value of stiffness profile gradient is seen to increase with the applied moment. This corroborates well with our analytical prediction of the a load dependent optimal value of stiffness gradient.Looking at stiffness profile, the highest values of peak pressure are observed at the highest applied moment load for all effective stiffness profiles. However, for any given fixed applied moment, we observe an improvement in peak pressure at the dorsum when the effective stiffness between the human bone and the robot is varied in a gradient from the center to the edge of the interface, as seen in (Right) shows the effect on relative displacement due to a change in stiffness profiles with the applied force and moment loading held constant for each of the plotted lines. Contrary to the effect on peak pressure shown earlier, the relative displacement increases with the stiffness profile gradient.When considering the effect of these same variables on the relative displacement, it becomes evident that we have a trade-off between our performance metrics of comfort and relative displacement. x-axis of the stiffness profile gradient. It shows that increasing the stiffness profile gradient to improve the peak pressure across the interface has an adverse effect on the relative displacement. Thus, a practical design would be one in which we choose a trade-off between the two desired outcomes and pick our stiffness gradient based on an optimal weighting between the two.This is more clearly illustrated in We presented a novel method to optimize the design of the PHRII between a wearable exoskeleton and the human hand. By characterizing the mechanical properties of tissues at a few locations on the hand dorsum, we demonstrated how limited experimental data can be used to optimize the design of the PHRII through the use of a custom simulation environment, HuRoSim.For this simulation, input parameters of interest were strap pre-tension and the applied moment load. Important simulation output parameters were the relative displacement between the exoskeleton and the human hand, and human comfort (measured as peak pressure across the dorsum surface).Relative displacement was found to increase non-linearly with increasing applied moment. Relative displacement was similarly found to decrease with increasing strap pre-tension. This relation is expected since almost all of the stiffness in the human system, as well as the padding and straps are non-linear and increase with increasing strain. It is important to note that the magnitudes and directions of the above effects are configuration dependent and can change drastically with different link lengths of the robot exoskeleton, MCP joint angle, and other joints in the system as well. These dependencies, however, are very easily accepted as inputs to the numerical simulation model if we wish to study them independently.These results are the first of its kind for the hand. The trends observed here for this PHRII study agree with results observed by other experiments conducted on the lower limbs  to have a greater control over the design of the dynamic solver. Future versions of this environment can be implemented on commercial multi-body physics platforms to build a real-time system capable of solving continuous dynamics of the system. The experimental characterization of the tissue properties was quasi-static and can be extended to quantify dynamic response of the tissue to loading. Modeling the tissue response in greater detail can enable the design of PHRII optimized for dynamic loads applied during task performance.HuRoSim's modular construction enables it to not be limited to applications of just the hand, but to any human-robot coupling where both comfort and positional accuracy are of importance. Other upper extremety examples such as supernumary fingers  for wearable robots coupled to the body. We demonstrated how a novel simulation environment (HuRoSim) can be utilized with experimental data to improve the design of the PHRII for optimal comfort and performance. By utilizing this approach, design loops can leverage the power of limited experimental measurements that inform fast and inexpensive simulation tools to generate design recommendations.Our approach fills an important gap in the literature between difficult and error-prone experimental characterization of the behavior of the pHRII, and expensive iterative physical prototyping for the design of the interface between the human body and the device.We demonstrate that reduced order models informed by limited experimental measurements of human tissue provide sufficient information to optimize PHRII design. This work represents the beginning of a step change in the design of PHRII for robots attached to the body. It demonstrates how complex physical interactions between man and machine can be simplified using first principles to generate actionable recommendations for reduced design time and complexity.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional Review Board, University of Texas at Austin. Study number: 2013-05-0126. The patients/participants provided their written informed consent to participate in this study.RJ contributed to the development of the simulation engine of the platform under the guidance of AD. GM contributed to experimental acquisition of the hand dorsum properties. RJ and GM contributed to analysis of the data from simulation and physical experiments. All authors contributed to writing, reviewing, and proofreading the manuscript.RJ was employed by the company Harmonic Bionics Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Starch gelatinization has been widely studied previously, but there is still a lack of systematical research on the relationship between the degree of starch gelatinization (DSG) and its physicochemical and structural properties. In this study, potato starch samples with DSG ranging from 39.41% to 90.56% were obtained by hydrothermal treatment. The thermal, rheological, and structural properties, as well as the water-binding capacity of samples were investigated. A starch solution with a DSG of 39.41% was partially sedimented at room temperature, while starch with a DSG of 56.11% can form a stable paste with a fine shear-thinning property, as well as samples with a DSG larger than 56.11%. The endothermic enthalpy, gelatinization range, and short-range ordered structure of starch were negatively correlated with DSG, whereas onset gelatinization temperature, apparent viscosity, and water-binding capacity were positively correlated. The viscoelasticity of starch gels was negatively correlated with the DSG after full gelatinization (DSG > 39.41%). Starch granules gradually lose their typical shape and less birefringence can be observed with increasing DSG. Hydrothermal treatment has a more significant effect on the amount of exposed hydroxyl groups than the ordered and amorphous structures of partially gelatinized starch. This study built linear correlations between starch physicochemical properties and the DSG and provided comprehensive insight into the characteristics of partially gelatinized potato starch. Potato starch is widely used in the food industry owing to its low cost, abundant availability, and more importantly, its unique physicochemical properties compared to other commercially available starches ,2. PotatPotato starch can be gelatinized in hot water by breaking the intermolecular hydrogen bond and destroying the arrangement of micelle structure in starch granules. After being fully gelatinized, the starch granules are porous with broken hydrogen bonds, birefringence disappears, and the crystalline order is lost through rapid drying at high temperature . The degNevertheless, there have been studies on partial starch gelatinization induced by pre-heating for different times/temperatures or ball milling for different times. Studies reported that the pre-gelatinization treatment significantly changed the granule size distribution and rheological properties of starch dispersions ,12,13,14The objective of this study is to understand the relationship between the DSG and its physicochemical and structural properties. Thus, we prepared 14 partially gelatinized potato starch samples with relatively evenly distributed DSG data points by hydrothermal treatment. The water-binding capacity and thermal, rheological, and structural properties were analyzed, and the correlations between these physicochemical properties of starch and its DSG were established.Potato starch (PS) containing 89.32% starch, 0.27% protein, and 0.42% ash, based on the dry matters of samples, was purchased from Aladdin Bio-Chem Technology Co., Ltd. . Moisture content was determined by drying samples to a constant weight in a 105 °C oven (AOAC 925.09). Crude protein was determined by the Kjeldahl method, with the total nitrogen content being converted to protein content by a conversion factor of 6.25 (AOAC 979.09). Ash content was determined by dry mineralization of samples in a muffle oven at 550 °C for 8 h (AOAC 923.03), and starch content was determined by a total starch assay kit .w/w) was thoroughly stirred for 1, 3, 6, 9, 12, 15, or 18 min, separately. Heating conditions were determined based on pre-experiments and the onset gelatinization temperature of native potato starch. The selected heating temperature provides starch samples with different DSGs in a convenient time period. Starch samples with a higher DSG are difficult to obtain when treated with a lower temperature in a relatively short time period, while starch is gelatinized too quickly when treated with a higher temperature. We therefore chose 59 °C and 60 °C as the temperatures and obtained a series of starch samples with different DSGs. After hydrothermal treatment, the beakers were quickly moved into ice water and cooled to room temperature before being put in the freezer. These partially gelatinized starch dispersions were freeze-dried for around 48 h using a vacuum freeze dryer  [Deionized water was stirred in beakers at 300 rpm using a thermostatic water bath maintained at 59 ± 1 °C and 60 ± 1 °C, separately. Native starch powder was added after the deionized water reached the fixed temperature. Each batch of dispersion at a concentration of 10%  . Starch 0), peak temperature (Tp), conclusion temperature (Tc), and endothermic enthalpy (ΔH) were calculated from the DSC curves using the equipment software. Gelatinization range (R) was computed as (Tc – T0), and the peak height index (PHI) was calculated by the ratio ΔH/(Tp – T0), as previously described [native and ΔHsample are the enthalpy change due to gelatinization of native and modified starches, respectively.The thermal properties of starch samples were investigated using a differential scanning calorimeter . Powder samples (3 mg) were weighed into aluminum pans, and 10 μL of distilled water was added before the pans were hermetically sealed. The pans were equilibrated at room temperature for 2 h before heating from 20 °C to 100 °C at a rate of 5 °C/min ,19. A seescribed ,22.DSG g) for 10 min. Free water was removed from the wet starch which was then continuously drained for 10 min at room temperature (20 °C). The wet starch was calculated using the difference between wet starch with tubes and empty tubes. The WBC of starch granules was calculated using the following formula:1 and W2 are the dry weight of original starch and wet weight of starch, respectively.The WBC of starch samples was measured as previously described  with sli−1 at a resolution of 4 cm−1 with an accumulation of 64 scans using the attenuated total reflectance (ATR) accessory. All spectra were baseline corrected automatically by OMNIC 8.2 and deconvoluted from 1200 to 800 cm−1 with a half-bandwidth of 19 cm−1 and an enhancement factor of 1.9. Intensity measurements were performed on the deconvoluted spectra by recording the peak height of absorbance bands from the baseline. The ratio of absorbance at 1047/1022 cm−1 and 1022/995 cm−1 was used to estimate the short-range ordered structure of starch [The short-range molecular order of starch samples was determined using a FTIR spectrometer . FTIR spectra were recorded from 4000 to 600 cmf starch .The microstructures of the starch samples were observed using field-emission environmental scanning electron microscopy  with a 10-KV acceleration voltage. Powder samples were added to double-sided adhesive tape mounted on an aluminum stub and sprayed with gold. Samples were photographed at 500× and 1000× magnification.The birefringence of starch samples was observed under polarized light with a binocular microscope  at a magnification of 100×. The starch samples were transferred onto a slide glass and a drop of water was then added to each sample before observation.w/w) concentration after hydrothermal treatment . Starch suspensions at 10%  is the consistency index, γ (s−1) is the shear rate, and n is the flow index [Steady shear tests were programmed to increase the shear rate from 0.01 sequation ,26.(3)σIn the temperature sweep test, the strain and frequency were set at 0.5% and 1 Hz, respectively (within the linear viscoelastic region). The temperature was set from 25 °C to 75 °C at a heating rate of 2 °C/min and cooled from 75 °C to 25 °C at a rate of 5 °C/min. The storage modulus (G′), loss modulus (G″), and loss tangent (tanδ = G″/G′) were obtained as a function of temperature .Frequency sweep tests were conducted in situ after the temperature sweep. Mechanical spectra of starch gel samples were recorded in the range of 0.1–100 rad/s with 10 points per decade, and the controlled variable was set at 5% strain (within the linear viscoelastic region) ,28. All In Equations (4) and (5), K′, K″, n′, and n″ are the corresponding fitting parameters, respectively, and ω is the angular frequency (Hz). K′ and K″ reflect the elastic and viscous properties, respectively. n′ and n″ are referred to as the frequency exponents and can provide useful information regarding the viscoelastic nature of materials .The physicochemical properties of potato starch that depend on its DSG were analyzed. Scatter diagrams were plotted, and the linear variation regression equation and correlation coefficient were calculated using Excel. SPSS was used to conduct the bivariate correlation test of the two datasets; the P value was obtained to determine the significance level of linear correlation between the starch properties and the DSG.Data are reported as averages of triplicate observations and expressed as the mean ± standard deviation. ANOVA followed by Tukey’s test at the 0.05 significance level was used to evaluate the differences among sample means. ANOVA was conducted using SPSS 18.0 . All graphs were generated using Excel and Origin 9.0, ΔH, R, and PHI are shown in P and Tc of the starch samples showed no significant differences (data not shown). The T0 and ΔH of the native potato starch samples were 56.94 °C and 12.73 J/g, respectively, which were values close to previous reports [0 of the partially gelatinized starch samples was higher than that of native potato starch, and it increased as the heating time and temperature increased. The gelatinization temperature of the partially gelatinized starch modified by spray drying or high hydrostatic pressure was also reported to be higher than that of the native starch [The DSC thermograms in  reports ,30. The e starch ,31. The e starch ,32. Howee starch . Specifie starch . Samples−1 reflected changes in polymer conformation and the hydration of processed starches [−1 were associated with the ordered and amorphous structures of starch, respectively. The band at 995 cm−1 was mainly caused by the bending vibration of C-OH, corresponding to the hydrogen bond structure formed between the hydroxyl groups of starch macromolecules. The FTIR spectra of potato starch samples clearly show that, as pre-heating time increased, the intensities of the peak around 1022 cm−1 gradually increased, the peak around 1047 cm−1 became flatter, and the peak around 995 cm−1 significantly decreased. The ratio 1022/995 cm−1 significantly increased, while the ratio 1047/1022 cm−1 slightly decreased as heating temperature and time increased  of the starch pastes increased significantly with increased heating time and temperature. This indicated that starch pastes with higher DSGs showed higher apparent viscosity. All flow behavior indexes (n) were less than 1.0, further indicating the pseudoplastic and shear-thinning behavior of the samples [The flow curves of starch solutions/pastes are presented in  samples . The n v samples . A high  samples .0) obtained from the DSC measurements.2 and R″2) of G′ and G″ were all above 0.86. The K′ of all starch gel samples was higher than K″ at the same angular frequency, consistent with the changes of G′ and G″. K′ and K″ decreased with the increase of pre-heating time, indicating that the viscoelastic moduli of starch gels decreased with increasing DSG after full gelatinization. The tanδ of starch gel samples ranged from 0.06 to 0.24  differing. The prominence detection of the two datasets analyzed was less than 0.01, indicating that these starch properties were significantly correlated with the DSG when the significance level was below 0.01. Both the T0 from the DSC test and the temperature at peak tanδ from the temperature sweep of the rheometer were positively correlated with the DSG . Thus, the value of 1022/995 cm−1 from the FTIR better represents the DSG of partially gelatinized potato starch after hydrothermal treatment. The maximum point of G′ in the reheating process, K′, and K″ of potato starch samples with DSGs higher than 39.41% were also negatively correlated with their DSG, indicating that the viscoelasticity of starch gels decreased with increasing DSG . However, partially gelatinized potato starch with a DSG of 56.11% can form stable paste with a fine shear-thinning property, as well as starch samples with a DSG larger than 56.11%. The apparent viscosity, WBC, and gelatinization onset of partially gelatinized potato starch increased as the DSG increased. Higher starch DSG led to the loss of the short-range ordered structure, and we also found that hydrothermal treatment has a more significant effect on the amount of exposed hydroxyl groups than the ordered and amorphous structures of starch. Partially gelatinized potato starch prepared under certain conditions (form uniform starch pastes in cold water with a low DSG) could form starch gels with relatively high viscoelasticity after reheating. The loss of oval or elliptical structures and the disappearance of birefringence of the starch granules can be clearly observed with increasing DSG. A correlation analysis between the starch physicochemical properties and the DSG further confirmed their linear relationships. The findings are expected help researchers better design the processing conditions for partially gelatinized potato starch and to provide information to better formulate food systems with added potato starch. However, since a heating process is normally required in food processing, further study is still needed to investigate the gelatinization and retrogradation of partially gelatinized potato starch with different DSGs during the reheating process. The molecular mechanism leading to the corresponding gelling behaviors still needs to be revealed.Native and partially gelatinized potato starch (DSG = 39.41%) cannot form uniform pastes in cold water (10%,