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This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks. [ABSTRACT FROM AUTHOR]

EpCAM and its close relative Trop2 are well-known cell surface markers of carcinoma, but their potential role in cancer metastasis remains unclear. They are known, however, to downregulate myosin-dependent contractility, a key parameter involved in adhesion and migration. We investigate here the morphogenetic impact of the high EpCAM and Trop2 levels typically found in epithelial breast cancer cells, using spheroids of MCF7 cells as an in vitro model. Intriguingly, EpCAM depletion stimulated spheroid cohesive spreading, while Trop2 depletion had the opposite effect. Combining cell biological and biophysical approaches, we demonstrate that while EpCAM and Trop2 both contribute to moderate cell contractility, their depletions differentially impact on the process of "wetting" a substrate, here both matrix and neighboring cells, by affecting the balance of cortical tension at cell and tissue interfaces. These distinct phenotypes can be explained by partial enrichment at specific interfaces. Our data are consistent with the EpCAM-Trop2 pair acting as a mechanostat that tunes adhesive and migratory behaviours. Synopsis: Cell adhesion and cell cortex contractility are important determinants of the morphogenetic properties of solid tissues, including tumours. This study shows that EpCAM and Trop2, two closely related surface markers of carcinoma, play antagonistic roles, respectively opposing or favouring cohesion and collective migration of breast cancer cells. Both EpCAM or Trop2 repress myosin activity and cortical contractility. EpCAM tends to act preferentially on the cell cortex at free edges, Trop2 on matrix and cell adhesive contacts, subtly controlling the balance of tensions at interfaces. Consistently, the two molecules show differential enrichments at the corresponding cell cortices. MCF7 spheroids depleted of EpCAM extensively spread while remaining compact, while those depleted of Trop2 show lower cohesion and spreading than wild type spheroids. Effects of EpCAM and Trop2 can be accurately modelled based on the biophysical analogy with the phenomenon of wetting-dewetting. Two closely related cell surface proteins affect cortical tension in different domains and differentially regulate cell cluster cohesion and spreading. [ABSTRACT FROM AUTHOR]

Platelets are critical mediators of hemostasis and thrombosis. Platelets circulate as discs in their resting form but change shape rapidly upon activation by vascular damage and/or soluble agonists such as thrombin. Platelet shape change is driven by a dynamic remodeling of the actin cytoskeleton. Actin filaments interact with the protein myosin, which is phosphorylated on the myosin light chain (MLC) upon platelet activation. Actin-myosin interactions trigger contraction of the actin cytoskeleton, which drives platelet spreading and contractile force generation. Filamin A (FLNA) is an actin cross-linking protein that stabilizes the attachment between subcortical actin filaments and the cell membrane. In addition, FLNA binds multiple proteins and serves as a critical intracellular signaling scaffold. Here, we used platelets from mice with a megakaryocyte/platelet-specific deletion of FLNA to investigate the role of FLNA in regulating platelet shape change. Relative to controls, FLNA-null platelets exhibited defects in stress fiber formation, contractile force generation, and MLC phosphorylation in response to thrombin stimulation. Blockade of Rho kinase (ROCK) and protein kinase C (PKC) with the inhibitors Y27632 and bisindolylmaleimide (BIM), respectively, also attenuated MLC phosphorylation; our data further indicate that ROCK and PKC promote MLC phosphorylation through independent pathways. Notably, the activity of both ROCK and PKC was diminished in the FLNA-deficient platelets. We conclude that FLNA regulates thrombin-induced MLC phosphorylation and platelet contraction, in a ROCK- and PKC-dependent manner. [ABSTRACT FROM AUTHOR]

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) are powerful in vitro models to study the mechanisms underlying cardiomyopathies and cardiotoxicity. Quantification of the contractile function in single hiPSC-CMs at high-throughput and over time is essential to disentangle how cellular mechanisms affect heart function. Here, we present CONTRAX, an open-access, versatile, and streamlined pipeline for quantitative tracking of the contractile dynamics of single hiPSC-CMs over time. Three software modules enable: parameter-based identification of single hiPSC-CMs; automated video acquisition of >200 cells/hour; and contractility measurements via traction force microscopy. We analyze >4,500 hiPSC-CMs over time in the same cells under orthogonal conditions of culture media and substrate stiffnesses; +/− drug treatment; +/− cardiac mutations. Using undirected clustering, we reveal converging maturation patterns, quantifiable drug response to Mavacamten and significant deficiencies in hiPSC-CMs with disease mutations. CONTRAX empowers researchers with a potent quantitative approach to develop cardiac therapies. Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) are powerful in vitro models. Here the authors report CONTRAX, an open-access, versatile, and streamlined pipeline for quantitative tracking of the contractile dynamics of single hiPSC-CMs over time at increased throughput. [ABSTRACT FROM AUTHOR]

The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouth of the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastal system defined by its high suspended loads and relatively low colored dissolved organic matter. The typically high turbidity of these waters makes the BBAERONET-OC distinctive within the AERONET-OC network stations, providing exceptional opportunities not only for the validation of atmospheric correction algorithms but also for the development of regional algorithms for coastal complex waters. A SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM) instrument was deployed in January 2020 in the upper rail of a Mareograph Tower, a 15 m tall structure, located 10 miles away from the coast in optically deep waters. In this work we used the remote sensing reflectance (Rrs) derived from the BB-AERONET-OC measurements along with in situ hyperspectral radiometric data to classify optical water types (OWTs). We assigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., and OWTs were further characterized with the concentrations of optically significant components (chlorophyll-a and suspended particulate matter) and inherent optical properties (absorptions of phytoplankton, non-algal particles, and dissolved organic matter), retrieved from water samples obtained simultaneously with radiometric spectra. Based on a match-up exercise with in situ data, different schemes of atmospheric correction methods were applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operational product OLCI Level 2 European Space Agency (ESA) standard (hereafter referred to as “Standard Neural Network (NN)”) proves to be the most suitable atmospheric correction algorithm, which was then used to describe spatial patterns and temporal variability of the different OWTs in the region. The BB-AERONETOC site is located in a sharp transition between estuarine and coastal waters that present contrasting optical conditions: OWT 4 dominates over time (73.72% of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and 5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5, which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts the eutrophic coastal waters of the inner shelf. The variability of OWTs and the relative contribution of organic and inorganic compounds to the suspended material would be mostly related with the prevalence of northwest winds in the area, which would drive the export of estuarine sediments to the shelf. [ABSTRACT FROM AUTHOR]

Cell traction force plays a critical role in directing cellular functions, such as proliferation, migration, and differentiation. Current understanding of cell traction force is largely derived from 2D measurements where cells are plated on 2D substrates. However, 2D measurements do not recapitulate a vital aspect of living systems; that is, cells actively remodel their surrounding extracellular matrix (ECM), and the remodeled ECM, in return, can have a profound impact on cell phenotype and traction force generation. This reciprocal adaptivity of living systems is encoded in the material properties of biological gels. In this review, we summarize recent progress in measuring cell traction force for cells embedded within 3D biological gels, with an emphasis on cell–ECM cross talk. We also provide perspectives on tools and techniques that could be adapted to measure cell traction force in complex biochemical and biophysical environments. [ABSTRACT FROM AUTHOR]

The mechanical forces exerted by cells on their surrounding microenvironment are known as cellular traction forces. These forces play crucial roles in various biological processes, such as tissue development, wound healing and cell functions. However, it is hard for traditional techniques to measure cellular traction forces accurately because their magnitude (from pN to nN) and the length scales over which they occur (from nm to μm) are extremely small. In order to fully understand mechanotransduction, highly sensitive tools for measuring cellular forces are needed. Current powerful techniques for measuring traction forces include traction force microscopy (TFM) and fluorescent molecular force sensors (FMFS). In this review, we elucidate the force imaging principles of TFM and FMFS. Then we highlight the application of FMFS in a variety of biological processes and offer our perspectives and insights into the potential applications of FMFS. [ABSTRACT FROM AUTHOR]

In order to improve the security of power remote telemetry system, this paper studies the parameter identification method of power remote telemetry system based on real-time cross-section data and error-proof topology analysis. The method is based on a synchronization vector measurement unit. It uses the multi-source measurement method to fuse the real-time cross-section data of the power remote telemetry system, we reconstruct the fault-proof topology map of the power grid based on the fused real-time cross-section data. And according to the grid error prevention topology diagram, we set the grid line impedance parameters, calculate the sum and variance of voltage squares and so on. After the set parameters are established, the constraint conditions are divided, and the constraint conditions are transformed by linear function through quadratic programming. By judging whether the parameter identification function complies with its constraints, the parameter identification of the power remote telemetry system is realized. The experimental results show that this method can accurately obtain the real-time cross-section data of the power remote telemetry system. Moreover, it can accurately reconstruct the fault-proof topology of the power grid, and the maximum deviation of parameter identification is only 0.5223. [ABSTRACT FROM AUTHOR]

Social media applications have been ubiquitous in modern society, and their usage has grown exponentially over the years. With the widespread adoption of these platforms, social media has evolved into a significant origin of digital evidence in the domain of digital forensics. The increasing utilization of social media has caused an increase in the number of studies focusing on artifact (digital remnants of data) recovery from these platforms. As a result, we aim to present a comprehensive survey of the existing literature from the past 15 years on artifact recovery from social media applications in digital forensics. We analyze various approaches and techniques employed for artifact recovery, structuring our review on well-defined analysis focus categories, which are memory, disk, and network. By scrutinizing the available literature, we determine the trends and commonalities in existing research and further identify gaps in existing literature and areas of opportunity for future research in this field. The survey is expected to provide a valuable resource for academicians, digital forensics professionals, and researchers by enhancing their comprehension of the current state of the art in artifact recovery from social media applications. Additionally, it highlights the need for continued research to keep up with social media's constantly evolving nature and its consequent impact on digital forensics. [ABSTRACT FROM AUTHOR]

Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60 ∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s -1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (10.6076/D1630T, Loisel et al., 2023). [ABSTRACT FROM AUTHOR]

Motivation Cells adhering to the extracellular matrix can sense and respond to a wide variety of chemical and physical features of the adhesive surface. Traction force microscopy (TFM) allows determining the tensile forces exerted by the cells on their substrate with high resolution. Results To allow broad access of this techniques to cell biology laboratories we developed JeasyTFM, an open-source ImageJ package able to process multi-color and multi-position time-lapse pictures thus suitable for the automatic analysis of large TFM data. Availability and implementation JEasyTFM is implemented as an ImageJ plugin and available at: http://questpharma.u-strasbg.fr/JEasyTFM.html. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) bioprinting with suitable bioinks has become a critical tool for fabricating 3D biomimetic complex structures mimicking physiological functions. While enormous efforts have been devoted to developing functional bioinks for 3D bioprinting, widely accepted bioinks have not yet been developed because they have to fulfill stringent requirements such as biocompatibility and printability simultaneously. To further advance our knowledge of the biocompatibility of bioinks, this review presents the evolving concept of the biocompatibility of bioinks and standardization efforts for biocompatibility characterization. This work also briefly reviews recent methodological advances in image analyses to characterize the biocompatibility of bioinks with regard to cell viability and cell-material interactions within 3D constructs. Finally, this review highlights a number of updated contemporary characterization technologies and future perspectives to further advance our understanding of the biocompatibility of functional bioinks for successful 3D bioprinting. [ABSTRACT FROM AUTHOR]

AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. [ABSTRACT FROM AUTHOR]

Background: NMF are currently poorly evaluated in therapeutic decisions. A quantification of their severity would facilitate their integration. The objective of this study was to validate an autoquestionnaire evaluating the severity of non-motor fluctuations (NMF) in Parkinson's disease (PD)., Methods: Patients with PD were included in presurgical situation for deep brain stimulation of subthalamic nuclei. They participated in the PREDISTIM cohort (a study evaluating the predictive factors for therapeutic response of subthalamic stimulation in PD) in 17 centres in France. Our questionnaire, resulting from previous phases of development, included 11 non-motor symptoms (NMS). Their severity ranged from 0 to 10 and was assessed in OFF and then ON-Dopa to study their fluctuations., Results: 310 patients were included, of whom 98.8% had NMS and 98.0% had NMF. Each NMS was significantly improved by L-Dopa (decrease in severity score ranging from 43.1% to 69.9%). Fatigue was the most frequent and most severe NMS. NMS were considered more bothersome than motor symptoms by 37.5% of patients in OFF-Dopa and 34.9% in ON-Dopa., Conclusions: This is the first questionnaire allowing a real-time quantification of the severity of NMS and their fluctuation with levodopa. It was able to confirm and measure the effect of L-dopa and show differences according to the patients and the NMS. It differs from other questionnaires by its measurement at a precise moment of the severity of the NMS, allowing its use during pretherapeutic assessments.Our questionnaire has been validated to measure the severity of NMF. It will be able to quantify the non-motor effect of anti-parkinsonian treatments and could facilitate the integration of NMF in therapeutic decisions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Fibroblasts are a major cell population that perform critical functions in the wound healing process. In response to injury, they proliferate and migrate into the wound space, engaging in extracellular matrix (ECM) production, remodeling, and contraction. However, there is limited knowledge of how fibroblast functions are altered in diabetes. To address this gap, several state-of-the-art microscopy techniques were employed to investigate morphology, migration, ECM production, 2D traction, 3D contraction, and cell stiffness. Analysis of cell-derived matrix (CDM) revealed that diabetic fibroblasts produce thickened and less porous ECM that hindered migration of normal fibroblasts. In addition, diabetic fibroblasts were found to lose spindle-like shape, migrate slower, generate less traction force, exert limited 3D contractility, and have increased cell stiffness. These changes were due, in part, to a decreased level of active Rac1 and a lack of co-localization between F-actin and Waskott-Aldrich syndrome protein family verprolin homologous protein 2 (WAVE2). Interestingly, deletion of thrombospondin-2 (TSP2) in diabetic fibroblasts rescued these phenotypes and restored normal levels of active Rac1 and WAVE2-F-actin co-localization. These results provide a comprehensive view of the extent of diabetic fibroblast dysfunction, highlighting the regulatory role of the TSP2-Rac1-WAVE2-actin axis, and describing a new function of TSP2 in regulating cytoskeleton organization. [ABSTRACT FROM AUTHOR]

Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF. [ABSTRACT FROM AUTHOR]

In the course of the development of the nervous system, neuronal cells extend (grow) axons, which navigate over distances of the order of many cell diameters to reach target dendrites from other neurons and establish neuronal circuits. Some of the central challenges in biophysics today are to develop a quantitative model of axonal growth, which includes the interactions between the neurons and their growth environment, and to describe the complex architecture of neuronal networks in terms of a small number of physical variables. To address these challenges, researchers need new experimental techniques for measuring biomechanical interactions with very high force and spatiotemporal resolutions. Here we report a unique experimental approach that integrates three different high-resolution techniques on the same platform—traction force microscopy (TFM), atomic force microscopy (AFM) and fluorescence microscopy (FM)—to measure biomechanical properties of cortical neurons. To our knowledge, this is the first literature report of combined TFM/AFM/FM measurements performed for any type of cell. Using this combination of powerful experimental techniques, we perform high-resolution measurements of the elastic modulus for cortical neurons and relate these values with traction forces exerted by the cells on the growth substrate (poly acrylamide hydrogels, or PAA, coated with poly D-lysine). We obtain values for the traction stresses exerted by the cortical neurons in the range 30–70 Pa, and traction forces in the range 5–11 nN. Our results demonstrate that neuronal cells stiffen when axons exert forces on the PAA substrate, and that neuronal growth is governed by a contact guidance mechanism, in which axons are guided by external mechanical cues. This work provides new insights for bioengineering novel biomimetic platforms that closely model neuronal growth in vivo, and it has significant impact for creating neuroprosthetic interfaces and devices for neuronal growth and regeneration. [ABSTRACT FROM AUTHOR]

Traction force microscopy (TFM) is one of the most successful and broadly-used force probing technologies to quantify the mechanical forces in living cells. The displacement recovery of the fluorescent beads within the gel substrate, which serve as the fiducial markers, is one of the key processes. The traditional methods of extracting beads displacements, such as PTV, PIV, and DIC, persistently suffer from mismatching and loss of high-frequency information while dealing with the complex deformation around the focal adhesions. However, this information is crucial for the further analysis since the cells mainly transmit the force to the extracellular surroundings through focal adhesions. In this paper, we introduced convolutional neural network (CNN) to solve the problem. We have generated the fluorescent images of the non-deformable fluorescent beads and the displacement fields with different spatial complexity to form the training dataset. Considering the special image feature of the fluorescent images and the deformation with high complexity, we have designed a customized network architecture called U-DICNet for the feature extraction and displacement estimation. The numerical simulation and real experiment show that U-DICNet outperforms the traditional methods (PTV, PIV, and DIC). Particularly, the proposed U-DICNet obtains a more reliable result for the analysis of the local complex deformation around the focal adhesions. [ABSTRACT FROM AUTHOR]

The function of the integrin family of receptors as central mediators of cell-extracellular matrix (ECM) and cell–cell adhesion requires a remarkable convergence of interactions and influences. Integrins must be anchored to the cytoskeleton and bound to extracellular ligands in order to provide firm adhesion, with force transmission across this linkage conferring tissue integrity. Integrin affinity to ligands is highly regulated by cell signaling pathways, altering affinity constants by 1000-fold or more, via a series of long-range conformational transitions. In this review, we first summarize basic, well-known features of integrin conformational states and then focus on new information concerning the impact of mechanical forces on these states and interstate transitions. We also discuss how these effects may impact mechansensitive cell functions and identify unanswered questions for future studies. [ABSTRACT FROM AUTHOR]

Extracellular matrix (ECM) elasticity is perceived by cells via focal adhesion structures, which transduce mechanical cues into chemical signalling to conform cell behavior. Although the contribution of ECM compliance to the control of cell migration or division is extensively studied, little is reported regarding infectious processes. We study this phenomenon with the extraintestinal Escherichia coli pathogen UTI89. We show that UTI89 takes advantage, via its CNF1 toxin, of integrin mechanoactivation to trigger its invasion into cells. We identify the HACE1 E3 ligase-interacting protein Optineurin (OPTN) as a protein regulated by ECM stiffness. Functional analysis establishes a role of OPTN in bacterial invasion and integrin mechanical coupling and for stimulation of HACE1 E3 ligase activity towards the Rac1 GTPase. Consistent with a role of OPTN in cell mechanics, OPTN knockdown cells display defective integrin-mediated traction force buildup, associated with limited cellular invasion by UTI89. Nevertheless, OPTN knockdown cells display strong mechanochemical adhesion signalling, enhanced Rac1 activation and increased cyclin D1 translation, together with enhanced cell proliferation independent of ECM stiffness. Together, our data ascribe a new function to OPTN in mechanobiology. Uropathogenic strains of Escherichia coli (UPEC) are a leading cause of urinary tract infections (UTIs) and invasion involves Rho GTPase members, notably Rac1, to drive actin cytoskeleton rearrangement leading to engulfment. Here, Petracchini et al. provide evidence of an ECM stiffnessmodulated role of Optineurin (OPTN), which regulates HACE1-dependant Rac1 activity and thus controls integrinmediated mechanotransduction and bacterial invasion. [ABSTRACT FROM AUTHOR]

For more than a couple of decades now, “force” has been recognized as an important physical parameter that cells employ to adapt to their microenvironment. Whether it is externally applied, or internally generated, cells use force to modulate their various actions, from adhesion and migration to differentiation and immune function. T lymphocytes use such mechano-sensitivity to decipher signals when recognizing cognate antigens presented on the surface of antigen presenting cells (APCs), a critical process in the adaptive immune response. As such, many techniques have been developed and used to measure the forces felt/exerted by these small, solitary and extremely reactive cells to decipher their influence on diverse T cell functions, primarily activation. Here, we focus on traction force microscopy (TFM), in which a deformable substrate, coated with the appropriate molecules, acts as a force sensor on the cellular scale. This technique has recently become a center of interest for many groups in the “ImmunoBiophysics” community and, as a consequence, has been subjected to refinements for its application to immune cells. Here, we present an overview of TFM, the precautions and pitfalls, and the most recent developments in the context of T cell immunology [ABSTRACT FROM AUTHOR]

Traction force, an active force generated by interaction between cell and extracellular matrix, which required TFM for characterization. Based on the strain field obtained by measuring substrate deformation data, traction force can be retrieved using inversion algorithms, providing important theoretical support and detection means for mechanism exploration, diagnosis and treatment of many diseases caused by traction force imbalance (such as cancer, tissue fibrosis, atherosclerosis, etc.). This experiment introduces this advanced experimental technology with distinct features of integration of biology and mechanics into the biomechanics experimental teaching. The corresponding experiment content, experiment steps and course arrangement are also adapted. [ABSTRACT FROM AUTHOR]

Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods, and newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named 'cPTV-Retracking', or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43–77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software. [ABSTRACT FROM AUTHOR]

Podosomes are mechanosensitive protrusive actin structures that are prominent in myeloid cells, and they have been linked to vascular extravasation. Recent studies have suggested that podosomes are hierarchically organized and have coordinated dynamics on the cell scale, which implies that the local force generation by single podosomes can be different from their global combined action. Complementary to previous studies focusing on individual podosomes, here we investigated the cell-wide force generation of podosome-bearing ER-Hoxb8 monocytes. We found that the occurrence of focal tractions accompanied by a cell-wide substrate indentation cannot be explained by summing the forces of single podosomes. Instead, our findings suggest that superimposed contraction on the cell scale gives rise to a buckling mechanism that can explain the measured cell-scale indentation. Specifically, the actomyosin network contraction causes peripheral in-plane substrate tractions, while the accumulated internal stress results in out-ofplane deformation in the central cell region via a buckling instability, producing the cell-scale indentation. Hence, we propose that contraction of the actomyosin network, which connects the podosomes, leads to a substrate indentation that acts in addition to the protrusion forces of individual podosomes. [ABSTRACT FROM AUTHOR]

Combining experiments with artificial intelligence algorithms, we propose a machine learning based approach called wrinkle force microscopy (WFM) to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is much simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells. A deep learning approach, termed wrinkle force microscopy, allows for conducting traction force microscopy by observing bright-field cell images but without using its conventional requirements such as fluorescent microbeads and confocal microscopy. [ABSTRACT FROM AUTHOR]

Cell rigidity sensing—a basic cellular process allowing cells to adapt to mechanical cues—involves cell capabilities exerting force on the extracellular environment. In vivo, cells are exposed to multi-scaled heterogeneities in the mechanical properties of the surroundings. Here, we investigate whether cells are able to sense micron-scaled stiffness textures by measuring the forces they transmit to the extracellular matrix. To this end, we propose an efficient photochemistry of polyacrylamide hydrogels to design micron-scale stiffness patterns with kPa/µm gradients. Additionally, we propose an original protocol for the surface coating of adhesion proteins, which allows tuning the surface density from fully coupled to fully independent of the stiffness pattern. This evidences that cells pull on their surroundings by adjusting the level of stress to the micron-scaled stiffness. This conclusion was achieved through improvements in the traction force microscopy technique, e.g., adapting to substrates with a non-uniform stiffness and achieving a submicron resolution thanks to the implementation of a pyramidal optical flow algorithm. These developments provide tools for enhancing the current understanding of the contribution of stiffness alterations in many pathologies, including cancer. [ABSTRACT FROM AUTHOR]

Mammalian cells have evolved complex mechanical connections to their microenvironment, including focal adhesion clusters that physically connect the cytoskeleton and the extracellular matrix. This mechanical link is also part of the cellular machinery to transduce, sense and respond to external forces. Although methods to measure cell attachment and cellular traction forces are well established, these are not capable of quantifying force transmission through the cell body to adhesion sites. We here present a novel approach to quantify intracellular force transmission by combining microneedle shearing at the apical cell surface with traction force microscopy at the basal cell surface. The change of traction forces exerted by fibroblasts to underlying polyacrylamide substrates as a response to a known shear force exerted with a calibrated microneedle reveals that cells redistribute forces dynamically under external shearing and during sequential rupture of their adhesion sites. Our quantitative results demonstrate a transition from dipolar to monopolar traction patterns, an inhomogeneous distribution of the external shear force to the adhesion sites as well as dynamical changes in force loading prior to and after the rupture of single adhesion sites. Our strategy of combining traction force microscopy with external force application opens new perspectives for future studies of force transmission and mechanotransduction in cells. [ABSTRACT FROM AUTHOR]

Recently, smart cities provide various services to citizens through the convergence of Information and Communications Technology and industries such as transportation, health care, and automobiles. Accordingly, the number of smart devices that use artificial intelligence technology to store the personal information of users to provide services efficiently is increasing. Smart devices can be used to acquire key evidence through digital forensics, which can also serve, as evidence in a court. In this study, we acquire and analyze user data stored in wearable devices by applying a data acquisition framework for smart devices. This study contributes to the acquisition of key evidence for investigations. [ABSTRACT FROM AUTHOR]

Essential cellular processes such as cell adhesion, migration and division strongly depend on mechanical forces. The standard method to measure cell forces is traction force microscopy (TFM) on soft elastic substrates with embedded marker beads. While in 2D TFM one only reconstructs tangential forces, in 2.5D TFM one also considers normal forces. Here we present a systematic comparison between two fundamentally different approaches to 2.5D TFM, which in particular require different methods to deal with noise in the displacement data. In the direct method, one calculates strain and stress tensors directly from the displacement data, which in principle requires a divergence correction. In the inverse method, one minimizes the difference between estimated and measured displacements, which requires some kind of regularization. By calculating the required Green's functions in Fourier space from Boussinesq-Cerruti potential functions, we first derive a new variant of 2.5D Fourier Transform Traction Cytometry (FTTC). To simulate realistic traction patterns, we make use of an analytical solution for Hertz-like adhesion patches. We find that FTTC works best if only tangential forces are reconstructed, that 2.5D FTTC is more precise for small noise, but that the performance of the direct method approaches the one of 2.5D FTTC for larger noise, before both fail for very large noise. Moreover we find that a divergence correction is not really needed for the direct method and that it profits more from increased resolution than the inverse method. [ABSTRACT FROM AUTHOR]

Motile cells can use and switch between different modes of migration. Here, we use traction force microscopy and fluorescent labeling of actin and myosin to quantify and correlate traction force patterns and cytoskeletal distributions in Dictyostelium discoideum cells that move and switch between keratocyte‐like fan‐shaped, oscillatory, and amoeboid modes. We find that the wave dynamics of the cytoskeletal components critically determine the traction force pattern, cell morphology, and migration mode. Furthermore, we find that fan‐shaped cells can exhibit two different propulsion mechanisms, each with a distinct traction force pattern. Finally, the traction force patterns can be recapitulated using a computational model, which uses the experimentally determined spatiotemporal distributions of actin and myosin forces and a viscous cytoskeletal network. Our results suggest that cell motion can be generated by friction between the flow of this network and the substrate. SYNOPSIS: A combination of imaging and computational modeling is used to investigate the traction force patterns and the distribution of actin and myosin in three different modes of migration in Dictyostelium discoideum cells. The wave dynamics of actin and myosin critically determine the traction force patterns, cell morphology, and migration modes.Two types of keratocyte‐like motion are observed, consistent with two different propulsion mechanisms.A computational model reveals that cell motion can be generated by friction between the flow of the cytoskeletal network and the substrate. [ABSTRACT FROM AUTHOR]

Quantifying mechanical forces generated by cellular systems has led to key insights into a broad range of biological phenomena from cell adhesion to immune cell activation. Traction force microscopy (TFM), the most widely employed force measurement methodology, fundamentally relies on knowledge of the force-displacement relationship and mechanical properties of the substrate. Together with the elastic modulus, the Poisson's ratio is a basic material property that to date has largely been overlooked in TFM. Here, we evaluate the sensitivity of TFM to Poisson's ratio by employing a series of computer simulations and experimental data analysis. We demonstrate how applying the correct Poisson's ratio is important for accurate force reconstruction and develop a framework for the determination of error levels resulting from the misestimation of the Poisson's ratio. In addition, we provide experimental estimation of the Poisson's ratios of elastic substrates commonly applied in TFM. Our work thus highlights the role of Poisson's ratio underpinning cellular force quantification studied across many biological systems. Mechanical forces play important roles in cell biology and traction force microscopy (TFM) experiments have enabled quantification of the cell-generated forces when placed on substrates of distinct stiffnesses. Here the authors evaluate the effect of the Poisson's ratio- one of the main descriptors of the material's mechanical behaviour together with the Elastic Modulus, in the context of TFM experiments. [ABSTRACT FROM AUTHOR]

Many types of adherent cells are known to reorient upon uniaxial cyclic stretching perpendicularly to the direction of stretching to facilitate such important events as wound healing, angiogenesis, and morphogenesis. While this phenomenon has been documented for decades, the underlying mechanism remains poorly understood. Using an on-stage stretching device that allowed programmable stretching with synchronized imaging, we found that the reorientation of NRK epithelial cells took place primarily during the relaxation phase when cells underwent rapid global retraction followed by extension transverse to the direction of stretching. Inhibition of myosin II caused cells to orient along the direction of stretching, whereas disassembly of microtubules enhanced transverse reorientation. Our results indicate distinct roles of stretching and relaxation in cell reorientation and implicate a role of myosin II-dependent contraction via a microtubule-modulated mechanism. The importance of relaxation phase also explains the difference between the responses to cyclic and static stretching. [ABSTRACT FROM AUTHOR]

Background and Objectives: The National Cancer Database (NCDB) captures nearly 70% of all new cancer diagnoses in the United States, but there exists significant variation in this capture rate based on primary tumor location and other patient demographic factors. Prostate cancer has the lowest coverage rate of all major cancers, and other genitourinary malignancies likewise fall below the average NCDB case coverage rate. We aimed to explore NCDB coverage rates for patients with genitourinary cancers as a function of race. Materials and Methods: We compared the incidence of cancer cases in the NCDB with contemporary United States Cancer Statistics data. Results: Across all malignancies, American Indian/Alaskan Natives subjects demonstrated the lowest capture rates, and Asian/Pacific Islander subjects exhibited the second-lowest capture rates. Between White and Black subjects, capture rates were significantly higher for White subjects overall and for prostate cancer and kidney cancer in White males, but significantly higher for bladder cancer in Black versus White females. No significant differences were observed in coverage rates for kidney cancer in females, bladder cancer in males, penile cancer, or testicular cancer in White versus Black patients. Conclusions: Differential access to Commission on Cancer-accredited treatment facilities for racial minorities with genitourinary cancer constitutes a unique avenue for health equity research. [ABSTRACT FROM AUTHOR]

Cellular force generation and force transmission are of fundamental importance for numerous biological processes and can be studied with the methods of Traction Force Microscopy (TFM) and Monolayer Stress Microscopy. Traction Force Microscopy and Monolayer Stress Microscopy solve the inverse problem of reconstructing cell-matrix tractions and inter- and intra-cellular stresses from the measured cell force-induced deformations of an adhesive substrate with known elasticity. Although several laboratories have developed software for Traction Force Microscopy and Monolayer Stress Microscopy computations, there is currently no software package available that allows non-expert users to perform a full evaluation of such experiments. Here we present pyTFM, a tool to perform Traction Force Microscopy and Monolayer Stress Microscopy on cell patches and cell layers grown in a 2-dimensional environment. pyTFM was optimized for ease-of-use; it is open-source and well documented (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background. pyTFM can be used as a standalone Python package or as an add-on to the image annotation tool ClickPoints. In combination with the ClickPoints environment, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing forces, stresses, and their distribution. In this work, we also thoroughly analyze the accuracy and performance of the Traction Force Microscopy and Monolayer Stress Microscopy algorithms of pyTFM using synthetic and experimental data from epithelial cell patches. Author summary: The analysis of cellular force generation and transmission is an increasingly important aspect in the field of biological research. However, most methods for studying cellular force generation or transmission require complex calculations and have not yet been implemented in comprehensive, easy-to-use software. This is a major hurdle preventing a wider application in the field. Here we present pyTFM, an open-source Python package with a graphical user interface that can be used to evaluate cellular force generation in cells and cell colonies and force transfer within small cell patches and larger cell layers grown on the surface of an elastic substrate. In combination with the image annotation and tool ClickPoints, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing cellular forces, stresses, and their distribution. Additionally, pyTFM can be used as standalone python library. pyTFM comes with an extensive documentation (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background. [ABSTRACT FROM AUTHOR]

Quantifying cell generated mechanical forces is key to furthering our understanding of mechanobiology. Traction force microscopy (TFM) is one of the most broadly applied force probing technologies, but its sensitivity is strictly dependent on the spatio-temporal resolution of the underlying imaging system. In previous works, it was demonstrated that increased sampling densities of cell derived forces permitted by super-resolution fluorescence imaging enhanced the sensitivity of the TFM method. However, these recent advances to TFM based on super-resolution techniques were limited to slow acquisition speeds and high illumination powers. Here, we present three novel TFM approaches that, in combination with total internal reflection, structured illumination microscopy and astigmatism, improve the spatial and temporal performance in either two-dimensional or three-dimensional mechanical force quantification, while maintaining low illumination powers. These three techniques can be straightforwardly implemented on a single optical set-up offering a powerful platform to provide new insights into the physiological force generation in a wide range of biological studies. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'. [ABSTRACT FROM AUTHOR]

Cells exert traction forces on the extracellular matrix to which they are adhered through the formation of focal adhesions. Spatial-temporal regulation of traction forces is crucial in cell adhesion, migration, cellular division, and remodeling of the extracellular matrix. By cultivating cells on polyacrylamide hydrogels of different stiffness we were able to investigate the effects of substrate stiffness on the generation of cellular traction forces by Traction Force Microscopy (TFM), and characterize the molecular dynamics of the focal adhesion protein zyxin by Fluorescence Correlation Spectroscopy (FCS) and Fluorescence Recovery After Photobleaching (FRAP). As the rigidity of the substrate increases, we observed an increment of both, cellular traction generation and zyxin residence time at the focal adhesions, while its diffusion would not be altered. Moreover, we found a positive correlation between the traction forces exerted by cells and the residence time of zyxin at the substrate elasticities studied. We found that this correlation persists at the subcellular level, even if there is no variation in substrate stiffness, revealing that focal adhesions that exert greater traction present longer residence time for zyxin, i.e., zyxin protein has less probability to dissociate from the focal adhesion. [ABSTRACT FROM AUTHOR]

Cell-extracellular matrix sensing plays a crucial role in cellular behavior and leads to the formation of a macromolecular protein complex called the focal adhesion. Despite their importance in cellular decision making, relatively little is known about cell-matrix interactions and the intracellular transduction of an initial ligand-receptor binding event on the single-molecule level. Here, we combine cRGD-ligand-decorated DNA tension sensors with DNA-PAINT super-resolution microscopy to study the mechanical engagement of single integrin receptors and the downstream influence on actin bundling. We uncover that integrin receptor clustering is governed by a non-random organization with complexes spaced at 20–30 nm distances. The DNA-based tension sensor and analysis framework provide powerful tools to study a multitude of receptor-ligand interactions where forces are involved in ligand-receptor binding. Relatively little is known about cell-matrix interactions and the intracellular transduction of an initial ligand-receptor binding event on the single-molecule level. Here authors combine ligand-decorated DNA tension sensors with DNA-PAINT super-resolution microscopy to study the mechanical engagement of single integrin receptors and the downstream influence on actin bundling. [ABSTRACT FROM AUTHOR]

The ability of cells to generate mechanical forces, but also to sense, adapt to, and respond to mechanical signals, is crucial for many developmental, postnatal homeostatic, and pathophysiological processes. However, the molecular mechanisms underlying cellular mechanotransduction have remained elusive for many decades, as techniques to visualize and quantify molecular forces across individual proteins in cells were missing. The development of genetically encoded molecular tension sensors now allows the quantification of piconewton-scale forces that act upon distinct molecules in living cells and even whole organisms. In this review, we discuss the physical principles, advantages, and limitations of this increasingly popular method. By highlighting current examples from the literature, we demonstrate how molecular tension sensors can be utilized to obtain access to previously unappreciated biophysical parameters that define the propagation of mechanical forces on molecular scales. We discuss how the methodology can be further developed and provide a perspective on how the technique could be applied to uncover entirely novel aspects of mechanobiology in the future. [ABSTRACT FROM AUTHOR]

The small molecular inhibitor of formin FH2 domains, SMIFH2, is widely used in cell biological studies. It inhibits formin-driven actin polymerization in vitro, but not polymerization of pure actin. It is active against several types of formin from different species. Here, we found that SMIFH2 inhibits retrograde flow of myosin 2 filaments and contraction of stress fibers. We further checked the effect of SMIFH2 on non-muscle myosin 2A and skeletal muscle myosin 2 in vitro, and found that SMIFH2 inhibits activity of myosin ATPase and the ability to translocate actin filaments in the gliding actin in vitro motility assay. Inhibition of non-muscle myosin 2A in vitro required a higher concentration of SMIFH2 compared with that needed to inhibit retrograde flowand stress fiber contraction in cells.We also found that SMIFH2 inhibits several other non-muscle myosin types, including bovine myosin 10, Drosophila myosin 7a and Drosophila myosin 5, more efficiently than it inhibits formins. These off-target inhibitions demand additional careful analysis in each case when solely SMIFH2 is used to probe formin functions. [ABSTRACT FROM AUTHOR]

Advances in methods for determining the forces exerted by cells while they migrate are essential for attempting to understand important pathological processes, such as cancer or angiogenesis, among others. Precise data from three-dimensional conditions are both difficult to obtain and manipulate. For this purpose, it is critical to develop workflows in which the experiments are closely linked to the subsequent computational postprocessing. The work presented here starts from a traction force microscopy (TFM) experiment carried out on microfluidic chips, and this experiment is automatically joined to an inverse problem solver that allows us to extract the traction forces exerted by the cell from the displacements of fluorescent beads embedded in the extracellular matrix (ECM). Therefore, both the reconstruction of the cell geometry and the recovery of the ECM displacements are used to generate the inputs for the resolution of the inverse problem. The inverse problem is solved iteratively by using the finite element method under the hypothesis of finite deformations and nonlinear material formulation. Finally, after mathematical postprocessing is performed, the traction forces on the surface of the cell in the undeformed configuration are obtained. Therefore, in this work, we demonstrate the robustness of our computational-based methodology by testing it under different conditions in an extreme theoretical load problem and then by applying it to a real case based on experimental results. In summary, we have developed a new procedure that adds value to existing methodologies for solving inverse problems in 3D, mainly by allowing for large deformations and not being restricted to any particular material formulation. In addition, it automatically bridges the gap between experimental images and mechanical computations. [ABSTRACT FROM AUTHOR]

Quantifying small, rapidly evolving forces generated by cells is a major challenge for the understanding of biomechanics and mechanobiology in health and disease. Traction force microscopy remains one of the most broadly applied force probing technologies but typically restricts itself to slow events over seconds and micron-scale displacements. Here, we improve >2-fold spatially and >10-fold temporally the resolution of planar cellular force probing compared to its related conventional modalities by combining fast two-dimensional total internal reflection fluorescence super-resolution structured illumination microscopy and traction force microscopy. This live-cell 2D TIRF-SIM-TFM methodology offers a combination of spatio-temporal resolution enhancement relevant to forces on the nano- and sub-second scales, opening up new aspects of mechanobiology to analysis. Quantifying rapid and small cellular forces is a major challenge in mechanobiology. Here, the authors show a >2-fold spatially and >10-fold temporally force sampling improvement combining traction force microscopy with total internal reflection fluorescence super-resolution structured illumination microscopy. [ABSTRACT FROM AUTHOR]

Quantifying small, rapidly progressing three-dimensional forces generated by cells remains a major challenge towards a more complete understanding of mechanobiology. Traction force microscopy is one of the most broadly applied force probing technologies but ascertaining three-dimensional information typically necessitates slow, multi-frame z-stack acquisition with limited sensitivity. Here, by performing traction force microscopy using fast single-frame astigmatic imaging coupled with total internal reflection fluorescence microscopy we improve the temporal resolution of three-dimensional mechanical force quantification up to 10-fold compared to its related super-resolution modalities. 2.5D astigmatic traction force microscopy (aTFM) thus enables live-cell force measurements approaching physiological sensitivity. Quantifying rapidly progressing three-dimensional forces generated by cells remains a major challenge in mechanobiology. Here, the authors show that combining traction force microscopy with astigmatic imaging permits sensitive out-of-plane force estimation on the second timescale. [ABSTRACT FROM AUTHOR]

In this study, we couple intracellular signalling and cell-based mechanical properties to develop a novel free boundary mechanobiological model of epithelial tissue dynamics. Mechanobiological coupling is introduced at the cell level in a discrete modelling framework, and new reaction–diffusion equations are derived to describe tissue-level outcomes. The free boundary evolves as a result of the underlying biological mechanisms included in the discrete model. To demonstrate the accuracy of the continuum model, we compare numerical solutions of the discrete and continuum models for two different signalling pathways. First, we study the Rac–Rho pathway where cell- and tissue-level mechanics are directly related to intracellular signalling. Second, we study an activator–inhibitor system which gives rise to spatial and temporal patterning related to Turing patterns. In all cases, the continuum model and free boundary condition accurately reflect the cell-level processes included in the discrete model. [ABSTRACT FROM AUTHOR]

A large amount of data, called the big data, generated by the devices that are part of the Internet of Things, is expected in the coming years. This scenario creates challenges for sending, processing, and storing all data centrally in the cloud. Recent works propose a decentralization of the processing and storage of this data in local devices close to the user to solve such challenges. This paradigm, called dew computing, has been gaining attention from academia. Several works apply this proposal through devices such as desktops, laptops, and smartphones. However, after a systematic review, no studies were found that applied this proposal to smart wearable devices. Thus, this work shows the research, evaluation, analysis, and discussion of smartwatches for the dew computing environment. The results of this work showed that smartwatches could extend local device functionalities through performing services, cooperating with decentralizing cloud computing, and helping to reduce the negative impacts of the big data. [ABSTRACT FROM AUTHOR]