Formal written land leasing contracts offer an alternative to land purchase for those farmers wishing to expand their land area and provide greater security relative to informal short-term rental agreements and are particularly important for beginning farmers with resources insufficient to purchase land. Formal land leasing contracts vary in terms of their duration, but there is limited understanding about the determinants of contract duration in developed countries. In this research, we use econometric techniques and transaction level data to explore the determinants of duration for agricultural land lease contracts for two regions in Ireland. Under the transaction cost economics approach, the research explores the role of legal status, price and non-price conditions in influencing the contract duration. Results indicate that the legal status of the tenant is a significant factor in influencing the duration. Provisions such as break clauses appear positively related to duration and confirm the theoretical expectation that long-term contracts create a demand for processes that enable adaptation over the course of long-term exchange. Patterns of land tenure can change due to various reforms or events. Large-scale changes in the pattern of land tenure have attracted more attention in the econometric literature relative to moderate changes  in Germany, the Czech Republic and Slovakia and relatively low rental shares (less than 35 per cent) in Poland, Portugal and Greece . As in the case of Ireland, a recent increase in land rental market activity is observed in Denmark and Italy (Eurostat 2020). In Norway, the land rental activity has expanded over a longer period of time Forbord . SckokaiThe increasing activity in land rental markets leads us to important questions about the duration of land rental agreements. In the USA, Bigelow et al  illustraThe type of farming system can influence the choice of land tenure and duration of possible rental agreements. In Ireland, tillage farming has historically occupied more rented land than dairy farming Conway . HoweverMuch of the recent literature about the agricultural land rental market in Ireland is concerned with the decision of individual farm holders to participate in agricultural land rental markets or with the determination of land rental prices  while the South-East region accounts for 15.6 per cent in Ireland. These two regions together account for approximately one-third of the agricultural land area in Ireland. Research about the land rental market in these two regions can therefore provide a useful guide to the situation in the Republic of Ireland as a whole.The research draws on the transaction cost economics approach and follows recent studies using transaction level data to analyse farmland markets including  with a condition that the land lease be of a minimum five years duration. These exemption limits increased substantially over time with variation according to the lease duration. For instance, the exemption limit for a five-year lease increased to €18,000 per annum in 2015 with higher limits for longer durations , the transaction is the basic unit of analysis where the transactions costs (TC) are the costs of negotiating, monitoring and governing exchanges.The intensity of the TC  affects the governance structure, within which the transaction is organized. For instance, a high degree of complexity, asset specificity and uncertainty generate high TC. This, in turn, will drive the transaction organization towards vertically integrated governance structures to save TC. On the contrary, a low degree of complexity, asset specificity and uncertainty generate low TC. This will spur the transaction organization towards (spot) markets. In the middle of the two extremes exist a combination of the three key variables, with different degrees of impact on TC and, consequently, a plethora of governance structures that Williamson names “hybrid” and that span from cooperatives, to consortia, from long-term contracts to franchising contracts, just to mention a few.Long-term contracts are hybrid structures, within which transactions are organized for efficient TC minimization. In the mainstream of transaction costs economics (TCE), it is a well-known theoretical result that the benefits of long-term contracting increase with the asset specificity required to undertake the transaction (need to secure the transaction) and decrease with the complexity and the uncertainty of the transaction (need for a flexible contract). TCE define asset specificity as “durable investments that are undertaken in support of particular transactions”  to define in advance the events that may affect the profitability of their transactional relationship and consequently exposes them to more or less important transaction costs depending on the asset specificity. Crocker and Masten  and BricA higher environmental uncertainty increases the probability of choosing short-term contractual relationship.High environmental uncertainty requires regulating all possible circumstances that can impact the transaction at stake. However, writing complex contracts can be costly and these costs bring about the adoption of imperfect agreements that only offer limited protection against opportunism. As a result, an emphasis has been put on short-term relational contact rather than long-term contract Mulherin . By the A higher transactions complexity and transaction frequency decrease the probability of choosing long-term contractual relationship.The choice of the duration of the contract also depends on the trade-off that the parties can find on the attributes of their transaction to minimize its transaction costs and optimize its profiles. This might occur because a higher frequency of payment or exchanges, for instance, can decrease transaction uncertainty and allow for a quick adaptation to changing circumstances with respect to the initial date of the contract.On the one hand, long-term contracting provides benefits, like the reduction in the cost of repeated bargaining and bigger willingness of transactors to take actions, whose value depends on the other party’s performance, thus better adapting to complexity. On the other hand, there are drawbacks on long-term contracting, stemming from the costs of anticipating, devising optimal responses to and specifying future contingencies (formation costs); and from the losses associated with efforts to enforce, evade or force a renegotiation of the contract’s terms and the “maladaptation” costs of failing to adjust to changing circumstances (execution costs).In this perspective, contract terms align ex ante marginal incentives and prevent wasteful efforts towards ex post redistribution of existing surplus. For instance, long-term contracts that specify the terms and conditions for future transactions ex ante represent a remedy for ex post performance problems. In this perspective, contract duration is a key synthesis indicator to understand the mechanisms that drive the parties’ reciprocal incentives and surplus redistribution of value, in the context of Irish agricultural contracts.Although a complete literature review goes beyond the scope of this paper, many recent studies in agricultural economics have explicitly followed the transaction cost approach. For instance, Bakucs et al  use thisSpecifically, this research will further our understanding regarding the determinants of contract duration in agricultural land rental markets. Few econometric studies have addressed the determinants of contract duration in agricultural land markets in developed countries. The exceptions include Bandiera  and AckeElsewhere, Ilbery et al  use qualThe section presents the data, the selected testable implications and empirical strategy.The dataset for this research is based on a large sample of land leasing transactions from the Property Services Regulatory Authority (PSRA) in Ireland. The pooled cross-sectional dataset contains information about transactions in the West and South-East NUTS 3 regions from 2013 to the onset of the COVID-19 pandemic in March 2020. The original data contained 3,644 transactions for agricultural land lease contracts in Ireland. However, 105 of these observations were deemed unusable mainly due to the absence of information about the parcel size or in a small number of instances where the declared parcel size is less than one acre, i.e. 0.405 hectares. This leaves a final dataset of 3,539 transactions.Each contract contains various types of information and provisions, spanning, among the others, on the agricultural activities performed by the tenant, rental value; dimension of the parcel; contract duration; payment frequency; type of tenant (sole trader or institution), geographical location of the parcels (county location), date of negotiations and entry into force of the contracts, a set of provisions that affect performance .The amount of agricultural land in medium- or long-term leases increased strongly from 2014 onwards and from quite a low base. This could be attributed to the abolition of milk quota and the expansion of tax incentives in 2015. Ireland is identified as the EU member state with the largest percentage increase in milk production since the abolition of the milk quota system .Complexity and uncertainty of the transaction increase TC. For this reason, the contract duration tends to be shorter in order to let the parties to adapt less costly to changing uncertain and complex circumstances. In our framework, “rent review”, “break” and “notice period” clauses represent the parties’ way to deal with uncertainty and complexity. The inclusion of these provisions in the contracts is expected to increase contracts duration.Following up the testable implications, our modelling reasoning, and related choice of the empirical estimation method, has also to include the assumption that a set of provisions is predetermined and exists before contract, while another group of provisions are determined within the contract, together with the duration. For this reason, we operationalize the relationship between contract duration as follows:Zi indicates instruments. In particular, we estimate a simultaneous equation model where the selected instruments  represent variables determined before contracting. Instrumental variable approach has been used elsewhere in the field of agricultural economics under the TCE approach , Yi indicates the endogenous variables, including terms of contract, and Lu et al . The selThe estimated relationship is simply exploratory in nature, with the objective to assess how (and how much) contract provisions affect the contract duration, attempting to interpret the results in a TCE framework. We adopt the two-stage least squares (2SLS) estimation techniques for estimating with STATA 16 the relationship between contract duration and contractual provisions.4Table Model 2 estimates contract duration as depending on a set of variables, in the case that the contract does not involve a monetary payment for the land lease. Selected Instruments are the same as in Model 2 for the sake of comparability.Results for model 1 show that if the contract includes a notice period clause and a “no concession” provision,When the land lease contract does not include a rent payment, as in Model 2, the contract duration is shorter. The inclusion of a break clause extends the contract duration. Also in this case, if the tenant is an individual , the contract duration is shorter.The overall diagnostic of the model indicates that estimates are correct; therefore, the explanatory power of the selected empirical specification is robust. In particular, the Durbin–Wu–Hausman test of endogeneity rejects the null hypothesis that variables are exogenous since p values are less than 0.05.The results are consistent with transaction costs economics theory. The parties have the incentive to mitigate long-term contract inflexibility through the negotiation of ex ante bargained terms and conditions, with provisions that allow for contingent adaptation. For instance, a zero rent may be balanced by the possibility to exit the contract, through the definition of a shorter time horizon. At the same time, long-term contracts can increase the risk of ex post maladaptation, thus creating demand for processes that enable adaptation over the course of long-term exchange. This motivates the inclusion of provisions such as break clauses and notice periods. However, long-term contracts can increase the risk of ex post maladaptation, thus creating demand for processes that enable adaptation over the course of long-term exchange.The provisions that regulate the frequency of the transaction (monthly payment) negatively affects the contract duration. This might occur because a higher frequency of payment (monthly) can decrease uncertainty and allow for a quick adaptation to changing circumstances with respect to the initial date of the contract. Under transaction cost economic theory, lower payment frequency increases transaction costs, and as predicted by this theory, this increases the contract duration/favours the adoption of longer terms contracts. In this perspective, the contracts that include provisions for a higher frequency of payment tend to be shorter.The contracts with break and notice period provisions are longer. The notice period is an adaptation (to uncertainty and changed contingencies) are therefore more procedural than a break clause. The notice period, for instance, can vary from one month to six months. On the contrary, the requirement to "re-appropriate the resource" is more immediate and quicker with a break clause. Those results show that the provisions are used as ex ante contractual remedies to possible ex post maladaptation, due to unexpected contingencies or even opportunistic behaviour in two different situations (rent/no rent). This suggests a relationship between the drivers that generate TC  and the selected (possibly) transaction cost minimizing governance structure, even if expected signs differ from standard theory. Such results differ from theory-based expectations and testable hypotheses, but are validated by the literature. The seminal paper by Cheung , for ins6In all estimated specifications, the results indicate that if the tenant is an individual , the contract duration is shorter. A shorter contract implies a governance that is closer to “market” transactions and therefore lowering TC. This may occur because contracts with individuals are less complex than those with incorporated farms. TC are lower and contracts are shorter. In addition, incorporated farms account for less than one per cent of all farm holdings in Ireland CSO . AnotherFinally, the no concession clause provision, as a proxy of financial and or physical assets specificity increases the contract duration, as predicted by the theory. If the tenant must incur in specific investments for improvements and cannot obtain a rent reduction in exchange, then higher TC, generated by asset specificity is minimized within a longer contract governance structure. In addition, the motivation for the inclusion of non-price provisions appears rational when CAP reform policies can potentially influence the land rental values and eligibility for subsidies. In this context, the inclusion of non-price provisions enables both the lessor and lessee to adapt to potentially changing policy circumstances.Overall, the results indicate that the long-term contracts are not TC minimizing governance structures, but are highly influenced by a set of regulatory incentives (tax exemptions and the CAP subsidies) that may overwhelm the boundaries of private bargaining. This may explain why the estimated coefficients for the contractual variables (selected and bargained upon by the parties) are not statistically significant and why the most "incisive" clauses are those that represent the "way out" (notice and break).The study has performed econometric analysis of (selected) factors that affect contract duration, with an application to agricultural land lease contracts in two regions in Ireland. Results corroborate TCE theory. Contracting parties, while negotiating upon duration, need to balance the need of securing a long-term horizon for the generation of mutual surplus from contracts execution and the need to guarantee flexibility to adapt to changing circumstances. Empirical findings support, in the case at issue, such a theoretical approach. The econometric results indicate that the presence of non-price provisions such as break clauses and notice periods increase the contract duration and provide the necessary flexibility for long-term contracts to be agreed and formally written.Concurrently, the low adoption rates for these provisions indicates that insufficient attention may be allocated to their inclusion in written contracts. This conclusion is supported by the fact that the practice of long-term farmland leasing is a new activity for many landowners and tenant farmers in Ireland. There is likely to be a high degree of inexperience in the setting of these formal written contracts. In theory, such inexperience could play a number of roles. It could lead to more conditions such as break clauses and frequency of payment as both parties exhibit additional caution. However, inexperience could also manifest itself in fewer conditions with implications for transaction costs at a later stage and this appears to be the more likely representation of the Irish agricultural land market. There appears to be an important role for policymakers and relevant stakeholders in improving education for farmers and landowners in relation to the inclusion of non-price provisions within land rental contracts and therefore optimize the duration of contracts.Furthermore, the statistically significant coefficients associated with variables that improve the flexibility of the contract allows us to argue that other factors , such as the policy context and incentives (tax incentives and subsidies), may drive the decision-making of both tenants and landowners. Indeed, when the tenant is an individual farmer , we observe the tendency to reduce the length of the contract. This behaviour can be justified by the need to manage the uncertainty coming from the recursive reform of the CAP. However, further research is required to uncover the contribution of policy uncertainty towards contract duration.This might imply that even if long-term contracts contain important TC minimizing provisions , they do not necessarily represent the most TC minimizing governance structure. Policy instruments "imposed" by economic or regulatory reform can override the concern for TC minimizing provisions that may be expected to emerge from the parties acting rationally and independently. The abolition of milk quota and the expansion of tax incentives have undoubtedly influenced the decision of many landowners and tenants to enter into the land leasing market, but this may be occurring in the absence of sufficient consideration for the importance of TC minimizing provisions and the adoption of TC minimizing governance structures that are different from long-term contracts (TC are not administrative costs).Until recently, most attention from researchers and policymakers with respect to the land rental market in Ireland has focused on the marginal economic gains from land market participation under the favourable conditions of rising land rental prices and expanding tax incentives. This reading of the land market is consistent with neoclassical economics where market equilibrium and the alignment of financial incentives are the main focus of the argument. This research shows the potential contribution of institutional economics under the TC approach where theory can be tested using administrative level data on complex aspects of land rental transactions, which are often ignored under standard economic theory. Obviously, the results (and suggested implications) are derived from an analysis that is based on data about transactions registered with the relevant regulatory authority. Some transactions may not be registered with the regulatory authority. Informal one year renting agreements are mainly excluded from the official register and these transactions lie outside the scope of the research.In this perspective, further research might focus both on (1) analysing the intended use on the duration of the rental contract  and (2) thinking, designing and comparing alternative governance structures  that might better implement the Irish reform of the land lease. The duration of rental contracts, in fact, is regulated in some of the EUSCs, which influences the responsiveness of the rental market to agricultural policy changes. The length of rental contracts is regulated by the government in Belgium and France (with a contract duration of nine years minimum), the Netherlands (six years minimum) and Spain (five years minimum). In several EUSCs (e.g. France), the renewal/ inheritance of rental contracts is also regulated. The prevalence of land renting is typically higher in countries with strict rental market regulations, such as Belgium and France. In these countries, formal rental markets are stickier and the time lag is longer in adjusting to policy changes.


Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class. Wheat is a staple crop used across the globe for a variety of food products. A reduction in the supply of wheat can lead to food shortages and malnutrition, especially in developing countries where it is a critical food crop . Wheat sPrevious research mainly focused on the classification of WRD ,6,7,8, whttps://github.com/dll-ncai/NUST-Wheat-Rust-Disease-NWRD (accessed on 20 June 2023).Our main contributions are summarized as follows. (1) We present a new multileaf semantic segmentation dataset for WRD. To the best of our knowledge, it is the first real-world multileaf crop field dataset with arbitrarily shaped disease regions, busy backgrounds, occlusions, and varying illumination conditions. Therefore, the images are of high resolution, with a maximum resolution of 6016 × 4000. The only other study related to semantic segmentation was carried out by Yang Li et al. (2022) , who preThe availability of well-annotated and diverse datasets plays a significant role in the learning of deep learning models . DiseasePlantVillage , DeepWeeThere is a limited number of small-scale wheat datasets available in the literature. Either these datasets are not publicly available or they are not widely adopted. The most recent and well-known wheat disease datasets are presented in The CDTS is the most recent and only research dataset related to the segmentation of wheat rust. This dataset targets the spore and spot segmentation and detection of rust disease. It is also a close-view image dataset, with a focus on spores and spots of rust disease. Every image has a single leaf with diseased and healthy parts, with segmentation masks available for background, diseased, and healthy parts . A rust The Kaggle research dataset is a freely available classification dataset. The data in this dataset have a close view, with a single leaf per image . The KagThe problem of wheat head detection and counting from a complex background was addressed by Khaki et al. (2022) . An instNiu et al. (2014)  proposedThe Mask-RCNN algorithm has also been used to identify the location of wheat rust using semantic segmentation of diseased leaves . Once a Ul Haq et al. 2022)  presente  presentKhanfri et al. (2018)  discusseImage-processing-based techniques have also been used in the literature for assessment of crop damage in wheat fields . Khatra Mi et al. (2020)  used a wThe wheat crop disease dataset presented in this study is a first-of-its-kind multileaf, open-source segmentation dataset of wheat rust disease collected from crop fields in Islamabad, Pakistan. The data were collected over one season of wheat crop, which was cultivated in November 2021 and harvested in May 2022. Under suitable conditions for rust, the disease starts its onset in February and continues to spread in March and April. The data were progressively obtained during this season in the morning and afternoon.The data on wheat rust were collected in collaboration with the National Agriculture Research Centre (NARC), Pakistan. NARC is located six kilometers southeast of the capital, Islamabad. Physical facilities including experimental farm areas, crop fields, and greenhouses are available to undertake the data collection process for research purposes. Images of the diseased and healthy crops were taken under different illumination conditions during the morning and afternoon. The camera-based images collected from the field are of varied resolutions, including a few images of 6016 × 4000 resolution, while the rest have a resolution of 4608 × 3456.The proposed dataset has properties such as high resolution, dense annotation, clear/fuzzy views of disease, arbitrarily shaped disease regions, manual annotation, image complexity, and varied image views. It is a real-world dataset comprising high-quality, high-resolution rust disease images. It clearly portrays the real-world and challenging nature of the diseased crop fields and was captured under natural field and weather conditions. It is a densely annotated dataset with various stages of rust disease (with respect to the spread of disease). Densely annotated images in the dataset comprise multiple diseased rust leaves in a single image.As it is a real-word dataset, it contains images with clear, fuzzy, and occluded views of WRD. As disease spread on leaves is not symmetric, there are arbitrarily shaped disease regions of rust in each image. This shape variation of disease regions is difficult to annotate due to its arbitrary nature; however, it portrays the true nature of real-world wheat fields.Ground truths are manually annotated for all instances at a fine-grained level for each image with high precision. Geometric shapes in annotation tools cannot capture the arbitrarily shaped disease regions in the dataset images. Therefore, we categorized the images into two categories with respect to the complexity of the detection of disease, i.e., easy and difficult .Furthermore, the dataset presents a varied view of the diseased wheat field. The images capture aerial, as well as ground and front, views of the field, with multiple leaves in a single image. In each image, the majority of the leaves are of the healthy wheat crop class, while a minority of the diseased class creates an imbalance between healthy and diseased leaves. A few annotated sample images from the proposed NWRD segmentation dataset are shown The images in the dataset are of high resolution relative to the patch size of 128 × 128 pixels that our segmentation model accepts. The area of a patch is very small relative to the total area of the image. This results in the creation of a large number of patches per image. A possible solution to reduce the number of patches is to increase the patch size; however, increasing the patch size also increases the number of computations. Another solution to this problem is to downsample the input images beforehand to a smaller size and use them for training. Downsampling is the reduction in the spatial resolution of an image. This process reduces the overall number of patches, reducing the training time.a is a parameter known as the filter radius, which governs the size of the kernel used in resizing an image. It determines the extent to which neighboring pixels affect the value of each pixel in the resized image. This method effectively reduces aliasing artifacts and preserves the details and sharpness of the original image [The process of downsampling results in a slight decrease in the F1 score, but this is a tradeoff with the benefit of faster training in the case of a large dataset. Downsampling can be performed using multiple methods; each method preserves information about lost pixels in a different way. For our experiments, we used Lanczos filtering, which uses a modified sinc filter to preserve the spatial information as much as possible. Lanczos filtering is a technique that is used to resize images while maintaining their visual quality. This is achieved by calculating new pixel values of the resized image based on a weighted average of neighboring pixels of the original image. The weights are calculated using a mathematical function known as the Lanczos kernel. The Lanczos kernel is defined as:al image .The real-world images that are captured containing diseased and healthy leaves of wheat are high-resolution images. The processing of these images is intensive and time-consuming. As a first step of preprocessing, we break down these images into multiple patches to feed to the model. We use a patch size of 128 × 128 pixels for both the images and binary masks. This means that we can directly train our segmentation model on these patches.We explored three different patching techniques: grid patching (GP), adaptive patching (AP), and adaptive patching with feedback (APF). The GP technique is used to divide a larger image into a grid of smaller patches. These patches do not overlap with each other. This technique is the most commonly used patching technique when working with high-resolution images in deep learning. The patches can then be easily preprocessed individually and used to train the convolutional models. However, the grid patching technique comes with a drawback: it generates a large number of patches. When a dataset has an imbalance between classes, like the NWRD dataset with imbalanced rust and non-rust classes, grid patching generates a lot of unnecessary patches that contain non-rust parts but do not contain rust parts. In this case, training segmentation models with GP comparatively takes more time training on the non-rust class than on the rust class and does not contribute much to the overall performance of the model. This unnecessary training on the non-rust part also results in an increase in the overall time taken by the training process.The AP technique is used to select the most useful patches for training from an image while ensuring that patches with the maximum information are extracted. This technique is used to reduce the number of patches that are input to the model, consequently reducing the overall training time of the model. Unlike grid patching, this technique of adaptive patching dynamically selects patches on runtime during training as required by the model.Adaptive patching selects only those patches from the images for training that contain specs of rust in them. The rest of the patches from the image are discarded. In The images from the NWRD dataset show that the ratio of rust and non-rust area within an image is not balanced. This introduces the problem of unbalanced data for rust and non-rust patches. If only adaptive patching is used where rust-based patches are input to reduce the computations, the model becomes biased towards rust and generates false positives on test images. To minimize this problem, we propose a new technique of adaptive patching with feedback in which rust and noise-based patches from the input images are selected for feature learning.The adaptive patching with feedback technique has two main steps. (1) It starts with a simple adaptive patching technique as discussed in the section above. It selects a few patches of the image for training that contain some traces of rust and discards other patches. This reduces the total number of patches by almost 10 times, consequently reducing the training time. It is noteworthy that only patches of a single class are obtained by this method, i.e., the disease class. Each rust patch obtained by this selective patching technique contains 128 × 128 pixels.(2) As a second step, a feedback loop is used, i.e., model predictions are merged with the target binary masks of input images in order to include noisy patches for training. The new mask then contains both areas that are annotated as rust in the original masks and areas that the model segmented as rust. This means that the adaptive patching algorithm can now pick up the pixels in the image that were part of the rust traces and the pixels that were classified as false positives by the model. The algorithm then includes patches according to the new mask for the upcoming training epochs. This trains the model in areas where rust is present, as well as in areas where the models struggled in the previous epochs. The training continues, with step 2 executed after constant intervals.In the deep learning paradigm, the more data, the better; therefore, data augmentation is useful to increase the size of a dataset by generating new variations of existing data samples. This will help with improving the accuracy and generalization capabilities of the detection model. Cheng et al. (2023)  proposedA UNet model with 4 encoders and 4 decoder blocks was trained with the preprocessed dataset. The model input size was set to 128 × 128 pixels. Images were split into patches with dimensions of 128 × 128 to be fed into the model for training. The model was trained with patch strides of different sizes. From the experimentation results, it was observed that the model did not perform best with a patch stride of 128. However, a patch stride of 32 appeared to have a good balance between the overall number of generated patches and the time taken for the training process to complete.During the training process, a forward pass is used to update the weights of the UNet segmentation model, and another forward pass is used for the purpose of feedback. The first forward pass uses the patches generated through adaptive patching; those patches are then passed through the data augmentation module to apply augmentation techniques to the patches. Once augmentation is done, in this forward pass, focal loss is used to update weights during backpropagation. The second forward pass uses grid patches obtained from the training dataset and generates prediction masks for the training data. The prediction masks are then merged with the target binary masks to form a new mask, which is then used by the adaptive patching algorithm to generate patches for the upcoming epochs. However, the second forward pass occurs after a fixed number of training epochs. In our experimentation, we observed that 5 epochs between the adaptive patching process yield better results in providing a good balance between time complexity and accuracy of the UNet segmentation model on the NWRD data.The performance of the segmentation model for wheat rust detection was evaluated using well-known evaluation metrics. The F1 score is the harmonic mean of precision and recall. It is a single metric that summarizes both precision and recall. The NWRD dataset presents images in which the majority is non-rust or healthy and a small portion comprises rust leaves. This means that there is a class imbalance problem prevalent in our data, while dealing with class-imbalanced data, choosing evaluation metrics sufficiently robust to class distribution is essential. F1 score is a helpful evaluation metric for imbalanced data because it provides a balanced measure of precision and recall. The F1 score is calculated using the following formula:Precision refers to the proportion of correctly identified rust disease cases  among all the predicted rust disease cases. In other words, it measures how accurate the model is when it predicts that a wheat crop has rust disease. A high precision value means that the model is making fewer false-positive errors, i.e., predicting that a wheat crop has rust disease when it actually does not.Recall, on the other hand, refers to the proportion of correctly identified rust disease cases among all the actual rust disease cases. In other words, it measures how well the model is able to identify all the wheat crops that actually have rust disease. A high recall value means that the model is making fewer false-negative errors, i.e., correctly identifying most of the wheat crops that have rust disease.The overall training process of UNet is divided into two stages: model training and a forward pass to generate predictions of the GPs for feedback. The preprocessing and the UNet model are implemented in Python and Pytorch, respectively. In our experiments, we explored the aforementioned performance parameters on 22 and 100 images of the dataset. The numbers of patches generated for the GP and AP are presented in RMSprop optimizer [ExponentialLR scheduler. Training was carried out for 500 epochs, utilizing a batch size of 64 and a stride of 32 and 128. Furthermore, we experimented with multiple loss functions, such as focal loss and dice loss. In our case, focal loss yielded the best results. The experiments were conducted on two NVIDIA Tesla V100 GPUs, each with 32 GB memory. The best qualitative results for disease detection in wheat crops using the NWRD dataset are shown in For UNet model training, we used the ptimizer , with thDatasets are instrumental in addressing complex research questions , and theThe only semantic segmentation research work related to wheat disease segmentation was carried out by Li et al. (2022) . In theiThe results show that the APF technique with the UNet segmentation model achieves an F1 score of 0.564 and an IoU of 0.438, while Octave-UNet with the APF technique yields an F1 score of 0.529 and an IoU of 0.316. The results clearly demonstrate that in the case of the NWRD dataset, which is a complex real-world WRD dataset, the APF patching technique with the UNet segmentation model outperforms Octave-UNet in terms of F1 and IoU scores.A comparison of our approach of APF and the UNet segmentation model with the Octave-UNet model indicates that for the real-world NWRD dataset, our technique performs better than the state-of-the-art Octave-UNet model. In their wheat segmentation work, the authors used an image filtering technique that discards all image patches with a pure background, i.e., no rust spores or spots. We argue that removing the background images from the dataset hinders the ability of the model to learn the actual trend of the onset of the disease. Consequently, the model will generate false positives when tested on real-world WRD field data. The early onset of WRD affects a minority of leaves while the majority of the leaves in the field are healthy. The model also needs to train on healthy images to generalize and distinguish between rust and non-rust parts more accurately.In conclusion, the UNet segmentation model, along with the APF technique, considerably reduces the time taken for training and produces comparable results. Hence, there lies a tradeoff between processing time and precision. Since the rust spots, as compared to the healthy part of the leaf in most of the images, are very small, the UNet model is not performing at its best. Our results demonstrate that a semantic segmentation approach can be used to estimate the infected area in a wheat field. The initial results of the segmentation are promising, with room for further improvement. This is the main motivation behind making the NWRD dataset public so that the research community can explore models and techniques to solve the problem of controlling WRD disease in wheat crops.The investigations carried out in this study will lead to new opportunities for crop monitoring—specifically segmentation of WRD in wheat crops. Since WRD is infectious, the segmentation algorithm will help estimate the infected plants in their initial stages. Furthermore, the open-source pretrained models can be mapped to other diseases in crops, such as ascochyta blight in cotton and bacterial leaf streak disease in corn, using the transfer learning approach. One of the limitations of our work is the class imbalance problem prevalent in the NWRD dataset. As a complex dataset focusing on the early onset of rust disease, the captured images contain a major portion of healthy leaves and a minor percentage of disease, resulting in the requirement of a multistage preprocessing and feature extraction process. In the future, we plan to improve the preprocessing module and the detection pipeline, which will consequently improve the segmentation results.In this work, we present an open-source semantic segmentation dataset of WRD named the NWRD. The dataset contains high-resolution (max. 6016 × 4000) multileaf images from wheat fields under various illumination conditions with complex backgrounds. Previous works in this domain have focused on single-leaf, low-resolution images. Since the input image size is quite large, we explored different compression techniques, such as down-sampling and patch-based segmentation, to reduce the memory footprint. We also implemented the UNet model with different patching strategies for semantic segmentation of the NWRD. We achieved 0.506 precision and a 0.557 F1 score for the rust class. We also compared the presented approach with state-of-the-art segmentation techniques specifically in the area of wheat disease segmentation. Our proposed preprocessing technique of adaptive patching with feedback (APF) along with the UNet model showed an improved F1 score and IoU as compared to Octave-UNet on the NWRD dataset. The achieved segmentation results are promising, with room for further improvement. Therefore, we made all explorations, models, and implementations open-source so that our approach can be easily adopted or further improved by the research community. In future work, we intend to improve the disease detection pipeline by applying fine-grained feature extraction techniques that can consequently improve the segmentation results.


Compact Michelson interferometers are well positioned to replace existing displacement sensors in the readout of seismometers and suspension systems, such as those used in contemporary gravitational-wave detectors. Here, we continue our previous investigation of a customised compact displacement sensor built by SmarAct that operates on the principle of deep frequency modulation. The focus of this paper is the linearity of this device and its subsequent impact on sensitivity. We show the three primary sources of nonlinearity that arise in the sensor: residual ellipticity, intrinsic distortion of the Lissajous figure, and distortion caused by exceeding the velocity limit imposed by the demodulation algorithm. We verify the theoretical models through an experimental demonstration, where we show the detrimental impact that these nonlinear effects have on device sensitivity. Finally, we simulate the effect that these nonlinearities are likely to have if implemented in the readout of the Advanced LIGO suspensions and show that the noise from nonlinearities should not dominate across the key sub-10 Hz frequency band. Interferometry sits at the forefront of high-precision displacement measurement. This technique sees use in a wide range of contexts concerned with the detection of weak signals, which originate from the minute scale of quantum mechanics through to the grand scale of astrophysical phenomena. Amongst the most notable interferometric devices are the contemporary gravitational-wave (GW) detectors at the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO)  and Advad 100 Hz .Although specialised detector facilities, such as GW detectors, can span kilometre scales, compact interferometric devices on the centimetre scale can be traced back to 1972 . Since tA thorough overview of notable interferometric sensors can be found in Ref. . In thisExisting GW detectors use a range of inertial and displacement sensors to improve the stability and provide active isolation and readout control of their suspension systems. However, the detectors stand to benefit from further improvements in inertial sensors. A notable way to improve these devices is with better sensors, with current devices being broadly limited at low frequencies by their readout noise. Interferometric sensors are well poised to address this problem, with one example, the Homodyne Quadrature Interferometer (HoQI) , alreadyThe path towards improved sensitivity in the key frequency band lies in the development of better sensors. The SmarAct interferometric sensor represents a good candidate to achieve this. However, to satisfy the requirements laid out in Ref. , it willIn this paper, we investigate the impact of nonlinear noise in Michelson-type interferometric displacement sensors when placed in applications where the root mean square (RMS) displacement exceeds the effective RMS displacement of the sensor’s own noise by multiple orders of magnitude. The range of a simple Michelson interferometer is already narrow, and its sinusoidal response means that the usable linear region yields an even smaller operating range. A number of different techniques exist in the literature  to extenThe sensor scheme is based around a custom-designed optomechanical assembly derived from the SmarAct C01 PICOSCALE sensing head. The sensor shown in A is the signal scale factor (akin to amplitude); C is the fringe contrast, a value in the range 0–1 corresponding to the level of mode matching of the interfering beams; m is the modulation index; and nth-order Bessel function of the first kind, and m limit, which necessarily extends the scheme to include the higher-order terms. This is important for us, as it gives us access to multiple signals with a cyclical sinusoidal dependence on the arm phase . It is P(t)=A[1+k and filter out the beat signals above DC with an appropriate low-pass filter, we can write the kth demodulated harmonic asOur particular scheme relies on a technique of demodulation at multiple harmonics of A, C, and m remain constant, the simplest way to proceed is to take a pair of Assuming we have a stable scheme such that m such that If we proceed with the first two harmonics, we obtain the arm phase by applying the four-quadrant arc tangent throughThe optical and digital layout of our scheme can be found in Figure 1 of Ref. . The optThe new photodiode has a slightly higher responsivity of Nonlinearities intuitively grow in severity with an increase in the range of measurement. We consider our sensor to be ‘long-range’ according to two specific criteria. First, the sensor can perform linear measurements over multiples of the FSR. This is a general property of a fringe-counting interferometer but, nonetheless, distinguishes our sensor from the non-fringe-counting interferometers that are more common in the field.Secondly, we consider the sensor long-range due to the comparatively large span of ‘null’ positions at which it can be set. In the experimental demonstration, we tune the frequency modulation depth so that we balance the power in the first two harmonics. This is a restriction we impose in order to maintain stationary noise over the full range of operation. However, this condition can be achieved over a range of different test-mass separations as long as the correct depth of frequency modulation can be reached with the given laser.With our particular laser, we consider the minimum test-mass distance to be m). These algorithms  and the good thermal properties of glass.Consequently, the dominant ghost beams arise from higher-order reflections, which can pick up significant phase fluctuations through propagation outside of the beam cube, such as the reflections from the measurement port’s antireflective coating causing the beam to pick up the measurement arm’s phase twice. The initial sensing head design measured the interference on the return path down the fibre through the laser input port, separated from the incoming field by a fibre circulator. This was deemed undesirable due to the potential coupling of additional noise from fibre backscatter and stress relaxations through parasitic interference. With the redesign, we somewhat mitigate this issue by propagating the light from the beam splitter to the photodiode through free space, although this has not eliminated the problem entirely. Ultimately, ghost beams can be problematic for high-precision applications, and their presence in our sensor is being investigated.A) in Equation  laser, where frequency modulation is achieved by a modulation of the supply current, which is also directly responsible for controlling the laser power. Even in cases where the laser power and frequency are controlled by separate means, the laser cavity PZT in our case, there remains a non-negligible coupling with amplitude in the frequency modulation channel. In such a case, the power amplitude term . Eventually, under sufficiently high displacement, other nonlinearities, most prominently the changing modulation index due to large The extension of the above treatment of ellipticity leads us to consider nonlinearities that occur due to a departure from an elliptical Lissajous shape altogether. According to our long-term observations, the sensor produces a high-fidelity elliptical Lissajous figure, and in most situations, the nonlinearities tend to be dominated by poorly fitted ellipse parameters. However, even with proper ellipse fitting in post processing, we find residual periodic error in the phase readout, which suggests that the Lissajous figure is not entirely elliptical.This departure of the Lissajous figure from a simple elliptical shape was previously observed in Ref. , where wWe sweep over the effective displacement by slowly actuating on the laser wavelength. A benefit of this method is that the modulation index does not change, as it would if the sweep were performed over true displacement, introducing the nonlinearity associated with a dynamically changing Lissajous figure due to a non-constant modulation index. This is achieved by setting the laser wavelength from one extreme to another . To determine the maximum order that must be included, we make use of the fact that the This is a familiar equation within our setup and is of the type already encountered in Equation . Thus, tr itself . TherefoIn a practical sense, this means that the maximum frequency that our original signal is significantly upconverted to is given by approximately The first constraint on on FPGAs ,35,36, wWe experimentally demonstrate the nonlinear degradation of sensitivity by driving the measurement mirror with a known, high-RMS signal and observing the sensor’s resulting spectrum. We make use of a moving magnet actuator, specifically a BOSEM shadow sensor  with theWe must isolate the sensor from environmental disturbances, such as seismic and acoustic couplings, as these generate uncontrolled and dominant sources of noise. To achieve this, we set up the mirror-sensor system in an acoustically isolated box on a common base plate placed on foam blocks for further vibration isolation. As the sensor only measures the relative displacement between itself and the mirror, the vibrational coupling to the readout can be suppressed by many orders of magnitude. In a departure from the setup described in Ref. , we isolWe use a ThorLabs LDC 205 C laser diode driver to provide the coil drive current. We monitor this drive current using the built-in control port of the current driver. This is essential, as we find that the current driver cannot naturally drive inductance linearly, which we verified by comparing the linearity of the coil drive against the linearity of driving an equivalent resistor. This actuator nonlinearity dominates over the nonlinearity of the sensor and therefore must be suppressed. We notice an even stronger nonlinearity when using a mirror-mounted PZT, which leads us to discount this otherwise much simpler actuation scheme. To improve the actuator linearity, we design and implement a feedback system where we subtract the measured drive current from the desired setpoint drive to form an error signal that controls the current driver in-loop. This scheme suppresses the subkilohertz nonlinear noise by around two orders of magnitude compared to the free-running performance.We drive the current with uniform white noise, which is then band-limited with an eighth-order elliptical band-pass filter to produce a large, flat signal spectrum confined to a sharply cut off frequency window. With this scheme, we tune the displacement RMS to around We drive the signal injection digitally and shape this drive using digital filters using the same CDS architecture as that used to read out the sensor. Therefore, we can freely shift the amplitude and frequency band of the signal to investigate the different levels of nonlinear noise that appear at low frequencies. We also simulate the sensor response in the time domain to compare the measured noise with the nonlinear noise models derived in We have demonstrated the loss of sensitivity due to nonlinearities in a carefully contrived scenario. However, any scenario that requires access to the full proposed dynamic range of the sensor may encounter problems with the sensor linearity. We mentioned the application in inertial sensors, which is highly relevant to the field of GW detection. An even closer application is in the sensing of the multistage suspension systems that are found in all contemporary GW detectors. In this section, we take the example of the Advanced LIGO quadruple pendulum suspensions .The suspensions in the Advanced LIGO chambers are already placed within a seismically isolated environment on top of the so-called internal seismic isolation (ISI). The suspension stages provide progressively greater levels of vibration filtering, which means their motion is, over many frequencies, even smaller than the residual motion injected by the ISI. Therefore, this application is intuitively less susceptible to nonlinearities due to its low RMS displacement.We consider, specifically, the measurement of longitudinal displacement sensing of the top mass of the quadruple chain. This is the location of the sensing and control of most of the 24 suspension degrees of freedom and the location of the majority of the existing displacement sensors. We propose our device as a candidate to precisely replace these sensors in order to achieve the required 100 factor of improvement in the suspension sensing noise, as laid out in Ref. .We generate an ISI noise model based on the measured ISI displacement spectral density. We further introduce a fit to the ISI horizontal-to-top mass horizontal transfer function in order to estimate the representative relative displacement spectrum between the top mass and ISI. We pass this displacement spectrum through our model of the sensor nonlinearity, assuming a nominal elliptical error of 2%. As found in our investigations above, it is possible to reach a hard limit of the nonelliptical nonlinearities at this level. The simulated displacement spectrum with the corresponding nonlinear noise level is shown in Interferometric displacement sensors are poised to replace many existing electromagnetic and optical sensing schemes within applications requiring subpicometre levels of sensitivity. In this paper, we follow-up the investigation of our custom sensor manufactured by SmarAct reported in Ref. , with a We briefly lay out the key equations that describe the deep frequency modulation technique that we employ within our readout scheme. From this starting point, we show the possible origins of the nonlinear couplings to the sensor readout and analyse their impact on the displacement sensitivity. We find that an imprecise fit (or drift) of the ellipticity of the Lissajous figure constructed from the two orthogonal readout signals is often the dominant source of nonlinear noise for elliptical error in excess of 2%. We subsequently revisit our measurement of the current hard limit to the nonlinearity, which is generated by periodic error due to distortion of the Lissajous figure, which cannot be corrected in real time or in post processing. This leads to an effective elliptical error of 2%.We construct a band-limited, high-RMS scenario in which the nonlinear error can significantly exceed both the linear noise level and the true displacement spectrum at low frequencies. We conduct an experimental demonstration of this nonlinear noise and compare the results to a time-domain simulation, which show good agreement with each other. We also estimate an order-of-magnitude figure for the maximum nonlinear noise level in the presence of a broadband signal and find that it is consistent with measurement.Overall, the linearity of the sensor is something that should be carefully considered based on the precise parameters of a given application, particularly the RMS displacement and the required dynamic range. We find that the nonlinear noise may limit the sensitivity of inertial sensors if not managed well. However, we find that on relatively quiet platforms, such as the Advanced LIGO ISI, the linearity of the sensor is sufficient to ensure an SNR above unity within the detection band; thus, no significant improvements to the sensor performance are necessary.Despite the generally sufficient linearity of the sensor for applications at the heart of our investigation, there is certainly scope for further improvement of the sensor’s linearity. Future experimental work on the sensor we presented herein will focus on implementing more advanced algorithms that seek to improve long-term stability and, potentially, real-time correction and linearisation of the system.


We propose a three-dimensional computational model to simulate the transient deformation of suspended cancer cells flowing through a constricted microchannel. We model the cell as a liquid droplet enclosed by a viscoelastic membrane, and its nucleus as a smaller stiffer capsule. The cell deformation and its interaction with the suspending fluid are solved through a well-tested immersed boundary lattice Boltzmann method. To identify a minimal mechanical model that can quantitatively predict the transient cell deformation in a constricted channel, we conduct extensive parametric studies of the effects of the rheology of the cell membrane, cytoplasm and nucleus and compare the results with a recent experiment conducted on human leukaemia cells. We find that excellent agreement with the experiment can be achieved by employing a viscoelastic cell membrane model with the membrane viscosity depending on its mode of deformation (shear versus elongation). The cell nucleus limits the overall deformation of the whole cell, and its effect increases with the nucleus size. The present computational model may be used to guide the design of microfluidic devices to sort cancer cells, or to inversely infer cell mechanical properties from their flow-induced deformation. The dynamics of cancer cells flowing in microchannels is a fundamental problem that has attracted increasing attention in recent years. The problem is highly relevant to cancer metastasis, where cancer cells shed into the bloodstream from the primary tumour, travel through the blood or lymphatic circulations to distant organs and form secondary tumours. The fluid dynamics of cancer cells is also at the heart of the recent developments of microfluidic technologies for cancer diagnosis and monitoring. It has been well established that the mechanical properties of cancerous cells can be very different from their healthy counterparts Suresh . The difNumerical simulations provide an important alternative approach to study the dynamics of cancer cells in flows and they can complement experiments for the following reasons. Firstly, in microfluidics for mechanical characterization of cancer cells (e.g. the DC), experimental measurements must be fitted to model predictions to infer cell mechanical properties. Analytical solutions are always limited to small cell deformation in the Stokes flow regime. Large cell deformation in inertial flows, which is a common feature in high-throughput microfluidics, can only be solved by means of numerical simulations. Secondly, numerical simulations can provide crucial information about cell dynamics that cannot be measured conveniently in experiments, such as the flow fields around and inside individual cells. Thirdly, compared with trial-and-error experiments, numerical simulations enable faster, cheaper, and rational design and optimisation of microfluidic devices for cell sorting or mechanical characterization.Most previous numerical studies of cell dynamics under flows have been focused on red blood cells (RBCs), which consist of a thin membrane enclosing Newtonian haemoglobin solution  . These features suggest that the present mechanical model has under-represented viscous dissipation within the cell during its transient deformation. The results are not surprising. Several sources can contribute to the viscous dissipation of the cell, such as the cell membrane and cytoplasm. We will first consider the effect of the cytoplasm viscosity in Sect. As shown from Fig. In our numerical tests of this section, we keep the parameter values that have led to the best fit with the experiment in the steady cell profile while adjusting the cell cytoplasm viscosity. The spatial evolution of the deformation index of the cells with a wide range of cytoplasm viscosity (It has been suggested in previous studies that the cytoplasm of biological cells, e.g. leukocytes, may be shear-thinning, because it is a suspension of filaments and organelles (Tsai et al The poor performance of the hyperelastic cell model in predicting cell transient deformation presented in Sect. In this section, we consider a cell with a viscoelastic membrane. The membrane elasticity follows the same SK law that is used in Sect. We first employ a Newtonian membrane viscosity model and consider a cell with different levels of constant membrane viscosity. Figure n determines shear-thinning or -thickening. The strain rate We then consider a cell with its membrane following shear-thinning or -thickening rheology. The empirical model of Drury and Dembo  has beenDI in experiments. One example of our simulations is shown in Fig. DI in the straight channel downstream due to the high membrane viscosity.Using Eq.  over a wDI in the upstream converging channel.In view of the above, a shear-thickening membrane model seems to be more promising in that the cell membrane viscosity will generally drop when the cell is flowing downstream the straight channel. However, the membrane strain rate remains high in the straight channel for a long distance, till Based on the observation from Fig. Tr, which is always three for a Newtonian fluid. However, Tr can be orders of magnitude higher for polymeric fluids in an effect commonly called “strain hardening” (Bird et al Basing the membrane viscosity on the flow-type parameter follows from an extensive rheological literature on the response of polymeric liquids to different flow types (Fuller and Leal a along the a. We find excellent agreement with the experiment when using Eq.  with \doNote that the membrane viscosity of leukaemia cells has not been reported previously. In Fig. Since the cytoplasmic viscosity of leukaemia HL-60 cells is not known, in Fig. So far in our simulations we have fixed the nucleus size at Not surprisingly, a larger cell nucleus decreases the overall deformation of the cell Fig. a. HoweveNevertheless, it has been known that cancer cells typically have a larger nucleus than healthy cells. We expect that the effect of the nucleus membrane stiffness on the overall cell deformation in a microfluidic channel will become increasingly significant with the size of the nucleus. A large and stiff cell nucleus could be the dominant factor in limiting the cell deformation, in particular in small channels with the cross-sectional dimension being similar to that of the cell.The present study aims to determine the minimum set of attributes that must be included in a mechanical model to quantitatively predict the transient deformation of suspended cancer cells flowing through a constricted channel. To achieve this goal, we have conducted extensive and systematic numerical simulations, using a range of models with increasing complexity, and compared the simulation results with a recent experiment where the transient profiles of a human leukaemia HL-60 cell in a constricted microchannel were clearly recorded. We find that the hyperelastic cell membrane model, using the SK law, can only recover the steady-state deformation of the cell in the straight channel. Excellent agreement with the experiment in transient cell dynamics can be achieved by properly accounting for the membrane viscoelasticity. Specifically, the membrane viscosity of the cell in elongational deformation should be higher than that when the cell deformation is shear-dominant, by a factor of about five. The cell nucleus, with a stiffer membrane than the cell membrane, tends to reduce the overall cell deformation. However, its effect is small when the nucleus is not too large, i.e. A limitation of the present study is that the model has only been validated against a leukaemia cell line. As a general model, of course, it needs to be tested on other cell types and in other flow geometries. Such broad validation is not possible at present, due to the lack of quantitative experimental data on the transit of suspended cells in a fluid medium. Other experiments such as micropipette aspiration of cells, cell squeezing through narrow constrictions, or cell migration on substrates, in ECM and through confinement, involve cell response that might fundamentally differ from the present situation where the cell is carried passively by the flow without touching the solid wall. To use such data for validation, the model must be equipped with additional features, to consider, for instance, the stress fibres in adherent, active cell cortex deformation, cell-ECM and cell-wall interactions (Borau et al Regarding practical applications, the present computational model has the potential of inferring the mechanical properties of subcellular components of suspended cancer cells from their transient flow-induced deformation. By fitting the steady deformed cell profile to computational results, one can obtain the cell membrane shear and dilatational moduli. The spatial evolution of the cell deformation will enable one to estimate the viscosities of the cell membrane and cytoplasm. These have been demonstrated in the present study on the HL-60 cells. For further validation, however, our method of inverse analysis should be tested against cell types for which the parameters to be inferred are already known. At present, no such benchmark experimental data are available in the literature. Thus validated, the computational model may facilitate the design and optimisation of microfluidic devices for mechanical characterization or sorting of suspended cancer cells.


For a better understanding of the pathophysiology of spondyloarthropathy (SpA), a detailed anatomical description of the sacroiliac joint is required because sacroiliitis is the earliest and most common sign of SpA and an essential feature for the diagnosis of ankylosing spondylitis. Beyond the anatomy, the histopathology of sacroiliac entheses and immunological mechanisms involved in sacroiliitis are crucial for a better understanding of disease causation. In this narrative review, we discuss the core anatomical, histological, and immunohistological observations involved in the development of sacroiliitis, focusing particularly on imaging-based information associated with sacroiliitis. Finally, we try to answer the question of whether at the sacroiliac joint, enthesitis precedes synovitis and subchondral bone changes in SpA. Axial spondyloarthritis (axSpA) includes patients with no radiographic structural changes [non-radiographic axSpA (nr-axSpA)] and radiographic structural changes [radiographic axial SpA (r-axSpA) or ankylosing spondylitis (AS)]. Known as chronic inflammatory rheumatism, it affects mainly the axial skeleton by causing inflammatory and potentially structural changes in the sacroiliac joint (SIJ), which is responsible for inflammatory back pain or buttock pain and stiffness.A poor understanding of the etiology behind SpA has led to a wide diversity of clinicians’ views, as the exact mechanism remains debatable.Clinically, SpA is a family of joint disorder inflammation affecting sites where the tendons or ligaments insert into the bone, known as enthesis. The major gene involved in several key pathogenic steps is HLA-B27. AS is the most characteristic form of SpA. It predictably occurs in the SIJ. This condition is often classified as SIJ dysfunction and is diagnosed as articular inflammation named sacroiliitis. However, the etiopathogenesis of AS, notably the progression from inflammation to osteoproliferation, is as yet poorly elucidated.Although the pathological conditions causing SIJ dysfunction are consistently combinations of inflammatory and joint/bone degeneration, it is not clear whether inflammation is a consequence of a primary enthesis lesion or resultant from immunological/genetic factors. In fact, it is not known why/where inflammation begins and why/where ossification occurs. Herein, we review the literature in order to provide some insights into the following subjects: 1) normal anatomy of the SIJ and its variants; 2) histopathology and immunohistology of sacroiliitis in axSpA and correlation with imaging; 3) clinical correlation between X-ray and MRI in axSpA assessment of the primary lesions involved; 4) does enthesitis precede synovitis at the SIJ?Herein, we provide a full description of the SIJ articular and ligamentous regions and innervations. Such information complemented with the joint imaging analysis is essential to understand the pathological process initiation and progression.The articular part of the SIJ is roughly kidney-shaped, wider anteriorly than is posteriorly with an oblique orientation toward the front and top. It is bordered by a capsule. There is a joint cavity containing a minimal amount of synovial fluid with a joint capacity ranging from 0.5 to 2 mL . On stanIt is important to mention that there are many anatomical variations within the articular region of the SIJ, which may contribute to the initiation and development of degenerative abnormalities. This is also a hurdle for clinical imaging interpretation  Prassop. The accImportantly, these variations of the norm could be responsible for mechanical stress on the homolateral sacroiliac and also on the contralateral sacroiliac in the case of a unilateral defect . Contralvs. 86% (358/417) of men (p < 0.001). The accessory joints were associated with the risk of dorsal sclerosis.Sex and anatomical joint form can have a major impact on the development of degenerative lesions of the sacroiliac joints. The male pelvis is typically longer and narrower with a more conical shape than the female pelvis. Due to a more posterior inclination of the sacrum, the cartilaginous articular surface is located more horizontally and is generally smaller in women than in men. Anatomical variants are significantly more prevalent in women . Only 38via enthesitic involvement but probably also in mechanical pathologies related to abnormal mechanical traction. A distinction is made between the deep ligaments located on the anterior part of the joint and between the bony parts (interosseous ligament) and the superficial ligaments composed of short and long posterior sacroiliac ligaments. The anterior ligament is attached to the anterior part of the joint and capsule. It is relatively thin and presents a thin region useful for the lumbosacral plexus clinical infiltration procedure. The interosseous ligament is thicker and stronger covering the entire joint areas. The posterior short sacroiliac ligaments are stretched between the iliac crest and sacrum, whereas the posterior long sacroiliac ligament is strained between the posterosuperior iliac spine and the third and fourth sacral tubercle on the sacrum. The latter ligament plays a major role in stabilizing the joint during these movements, and hyperlaxity of this ligament can create hypermobility and pain  joints is supported by no more than three illustrations from two cases at advanced or terminal stages, neither demonstrating active disease”. He was one of the first to use the term “enthesopathy” in SpA but never applied the term to the SIJ in fact, only to the spine. On the SIJ, he reported the case of a young patient with a short duration of symptoms (4 years), who died at the age of 22 years. SIJ serial sections histology investigation showed two main characteristics: the joint was first ankylosed by bony bridges less than 1 mm thick, spanning the periphery of the joint at intervals, and partly composed of woven bone indicating an ossification of the fibrous tissue. Second, there was continuous endochondral ossification of the articular cartilages at the iliac side , CD8+ T cells, and CD14+ macrophages. Cellular infiltration, notably T cells and macrophages, was also observed in another German study in early SpA  has been shown to be one of the key factors for joint remodeling . In a mi+ multinuclear osteoclasts, and the levels of VEGF, caspase-3, and MMP-3 were significantly higher than in the controls  and endochondral ossification  at baseline were the two main risk factors for radiological structural damage , which demonstrated that only 63.3% of them presented a progression of structural damage upon radiographic assessment. Multiple regression analyses showed that cartilage pannus invasion  and the level of objective inflammation, as shown by the CRP values and MRI findings, and sex distribution in mainly cross-sectional studies . Yet, itThe response rate to certolizumab (CZP) or other anti-TNF drugs is similar in both nr-axSpA and AS patients . FurtherIn a study of 41 early SpA patients, no correlation was found between patient characteristics  and MRI abnormality scores . HoweverDue to its high diagnostic performance, MRI of the SIJ is an established tool to help rheumatologists diagnose SpA. It is also the best tool to detect pathological changes in the bone marrow and in enthesitis and to identify a follow-up of the pre-structural changes on radiography and computed tomography (CT) . NeverthMRI that includes T1-weighted sequences is as accurate as CT. It detects structural changes such as bone erosions, subchondral sclerosis, and ankyloses. Fluid-sensitive sequences such as T2-weighted fat-suppressed, T2-weighted Dixon, and short-tau inversion recovery (STIR) are used to detect bone marrow edema (BME), synovitis, enthesitis, and capsulitis .For several years, radiologists have used new sequences with the Dixon method based on chemical shifts to perform further uniform fat suppression with fewer magnetic artifacts. From a single acquisition, suppression of the fat signal provides one image with four contrasts: i) Dixon in-phase: water and fat are imaged, like T1 and T2; ii) Dixon out: water and fat are imaged, but out of phase; iii) Dixon water: only water without fat is imaged, like FAT-SAT; and iv) Dixon fat: only fat is imaged. When used as a fat-suppressed image, the Dixon fat image can be combined with other sequences of various weightings to provide fat suppression .The use of gadolinium contrast agents is not recommended by the EULAR for SpA .A correct MRI SIJ lesion interpretation should consider all sequences, notably two perpendicular plane slices that include the oblique coronal plane  and oblique axial plane. It should also involve all entire lesions ..Moreover, lesions should highly characterize SpA and refer to it as the specific anatomical region. Any small, isolated lesion should be interpreted with caution .Overall, we can distinguish two types of lesions: lesions related to SpA activity and those related to structural damage.The main lesion that is related to SpA activity is BME alongside inflammation imaged with T1-weighted sequence bone marrow contrast enhancement  or T2-weighted sequence sensitive for free water  or T2 fat sat.According to the ASAS group, BME is defined as a hyperintense signal on STIR images and often as a hypointense signal on T1 images. The sacral interforaminal bone marrow signal provides the sign of a normal signal in the bone marrow . SacroilOther lesions can be visualized such as capsulitis , alongside enthesitis. These are detected as high signals in the bone marrow and/or soft tissue on STIR, a bright signal in the joint space on STIR images, and an increased signal on STIR visualized after contrast medium administration, resulting from increased signals in the joint space of the cartilaginous portion of the sacroiliac joint and inflammation at the site of erosion .The presence of only synovitis, enthesitis, or capsulitis without BME does not permit a positive MRI-SI diagnosis . In factIn patients with SpA, BME can occur in all quadrants and all three sections of the joint and sometimes bilaterally .Structural damage at the SIJ includes erosions, osteophytes, fat lesions, ankylosis, and sclerosis.Erosions are defined as bony defects in the subchondral bone. It corresponds to a full-thickness loss of the dark appearance of the subchondral cortex at its expected location. It is also accompanied by a loss of signal on a T1W non–fat-suppressed sequence when compared with the normal bright appearance of the adjacent bone marrow . They ar.Fat metaplasia has a homogeneous bright signal seen on a T1W non–fat-suppressed sequence. It is found in the subchondral bone region, brighter than the regular bone marrow, and sharply distinct along its non-articular border with the normal bone marrow . They arA bright signal on the T1-weighted with an intensity greater than that of the normal bone marrow observed in erosion or confluent erosions region suggests fat metaplasia in an erosion cavity or backfill. It is often accompanied by a total loss of the dark facade of the subchondral cortex  and is clearly demarcated from the adjacent bone marrow by an irregular band of dark signal reflecting sclerosis at the border of the original erosion . This leSclerosis appears as an area with a hypointense signal in all sequences , without signal enhancement after contrast medium administration. Sclerosis attributable to SpA should extend at least 5 mm from the SI joint space since small areas of periarticular sclerosis can be observed in healthy subjects  .Ankylosis matches with an abnormally bright signal on a T1W non–fat-suppressed sequence together with a similar signal intensity to the bone marrow at the expected SIJ space location. It bridges the joint resulting in a continuous bone marrow signal between the ilium and sacrum. It is also associated with full-thickness loss of the dark appearance on both sides of the joint at the subchondral cortex . AnkylosBone buds include images with an abnormally bright signal on a non–fat-suppressed sequence T1W. The signal has a similar intensity to that of the bone marrow, located at the expected region of the SIJ space and continuous with the subchondral bone of either the ilium or sacrum but not both. A full-thickness loss of the dark appearance of the subchondral cortex on the corresponding side of the joint is also observed in the same region .Backfill and ankylosis may be considered pathognomonic for axSpA. Yet, they are not relevant for improving early diagnosis because of their late occurrence diagnosis .As indicated above, all lesions with their location and distribution in the SIJ and their combination must be taken into account for MRI interpretation. The different lesion assessment combinations advance MRI diagnostic capacities. The combination of BME, fat lesion, erosion, and sclerosis, associated two by two, particularly when located centrally or posteriorly, is diagnostically important for SpA .It has been demonstrated that SpA is associated with primary enthesitis . InitiatSeveral authors have however challenged the involvement of the enthesis organ in the SIJ in AS. They point out that enthesis is neither the predominant process nor the initial pathologic process in sacroiliitis of AS . Two othIt is beyond the scope of this study to report the utility of ultrasound in the assessment of sacroiliitis . UltrasoHowever, in patients with limitations or contraindications to MRI, ultrasound is an alternative and there is no doubt of its value in guiding sacroiliac joint injections . By contMoreover, in the early stages of sacroiliitis, which was one of the topics of the present article, it is difficult to adequately visualize the lower third of the sacroiliac joint, a site where the inflammatory process is supposed to begin .Sacroiliitis of AS involves syndromes at the entheses, synovium, cartilage, and underlying subchondral bone. At the developed stage, the clinical symptoms of AS are clearly defined, whereas, at the early stages, the initial events are much more complex. Each of the enthesis organs, that is, the cartilage and fibrocartilage, and the related subchondral bone are connected and could potentially contribute to the disease process; at this research stage, no single organ can be designated as the unique cause of AS.Further research with higher numbers of biopsy samples from early cases is required to define the signaling pathways responsible for the pathology initiation and draw definitive conclusions about the histopathogenesis.