Intercellular Movements Research Articles

Linking Metastatic Potential and Viscoelastic Properties of Breast Cancer Spheroids via Dynamic Compression and Relaxation in Microfluidics.

The growth and invasion of solid tumors are associated with changes in their viscoelastic properties, influenced by both internal cellular factors and physical forces in the tumor microenvironment. Due to the lack of a comprehensive investigation of tumor tissue viscoelasticity, the relationship between such physical properties and cancer malignancy remains poorly understood. Here, the viscoelastic properties of breast cancer spheroids, 3D (in vitro) tumor models, are studied in relation to their metastatic potentials by imposing controlled, dynamic compression within a microfluidic constriction, and subsequently monitoring the relaxation of the imposed deformation. By adopting a modified Maxwell model to extract viscoelastic properties from the compression data, the benign (MCF-10A) spheroids are found to have higher bulk elastic modulus and viscosity compared to malignant spheroids (MCF-7 and MDA-MB-231). The relaxation is characterized by two timescales, captured by a double exponential fitting function, which reveals a similar fast rebound for MCF-7 and MCF-10A. Both the malignant spheroids exhibit similar long-term relaxation and display residual deformation. However, they differ significantly in morphology, particularly in intercellular movements. These differences between malignant spheroids are demonstrated to be linked to their cytoskeletal organization, by microscopic imaging of F-actin within the spheroids, together with cell-cell adhesion strength.

Two aquaporins, PIP1;1 and PIP2;1, mediate the uptake of neonicotinoid pesticides in plants

Neonicotinoids (NEOs), a large class of organic compounds, are a type of commonly used pesticide for crop protection. Their uptake and accumulation in plants are prerequisites for their intra- and intercellular movements, transformation, and function. Understanding the molecular mechanisms that underpin NEO uptake by plants is crucial for effective application, which remains elusive. Here, we demonstrate that NEOs enter plant cells primarily through the transmembrane symplastic pathway and accumulate mainly in the cytosol. Two plasma membrane intrinsic proteins discovered in Brassica rapa, BraPIP1;1 and BraPIP2;1, were found to encode aquaporins (AQPs) that are highly permeable to NEOs in different plant species and facilitate NEO subcellular diffusion and accumulation. Their conserved transport function was further demonstrated in Xenopus laevis oocyte and yeast assays. BraPIP1;1 and BraPIP2;1 gene knockouts and interaction assays suggested that their proteins can form functional heterotetramers. Assessment of the potential of mean force indicated a negative correlation between NEO uptake and the energy barrier of BraPIP1;1 channels. This study shows that AQPs transport organic compounds with greater osmolarity than previously thought, providing new insight into the molecular mechanisms of organic compound uptake and facilitating innovations in systemic pesticides.

Designing Sustainable Flexible Manufacturing Cells with Multi-Objective Optimization Models

Having sustainable and flexible features is crucial for manufacturing companies considering the increasing competition in the globalized world. This study considers three aspects of sustainability, namely economic, social, and environmental factors, in the design of flexible manufacturing cells. Three different multi-objective integer mathematical programming models were developed with the objective of minimizing the costs associated with carbon emissions, inter-cellular movements, machine processing, machine replacement, worker training, and additional salary (bonus). Simultaneously, these models aim to minimize the carbon emission amount of the cells within the environmental dimension scope. The developed models are a goal programming model, an epsilon constraint method, and an augmented epsilon constraint (AUGMECON) method. In these models, alternative routes of parts are considered while assigning parts to machines. The results are obtained using the LINGO 20.0 optimization program with a developed illustrative example. The obtained results are tested and compared by performing sensitivity analyses. The sensitivity analyses include examinations of the effects of changes in part demands, machine capacity values, carbon limit value, and the maximum number of workers in cells.

A new approach for determining optimum number of cell, cell formation and intra-machine cell layout with considering operation sequence

Cellular manufacturing has been effective in implementing Group Technology Philosophy so as to design a manufacturing system. In cellular manufacturing, it is imperative for the machines to be grouped and the parts to be assigned as part families depending on the level of similarity. Several research works have been carried out on the challenges faced in cell formation and their variants. Yet, based on zero-one machine-product occurrence matrix as the study's contribution data, only a small number of researchers have come out with a solution for overcoming feasibility evaluation and cell arrangement problems. An innovative similarity coefficient approach was proposed in the study to integrate feasibility evaluation, cell creation and intra-machine cell layout design by taking operation sequence into account. For tackling the challenges, an efficient heuristic solution has been proposed to use the similarity coefficient. The purpose of this study is to use eigenvalues of the resemblance matrix of coefficient and Kaiser's law to determine the appropriate sum of cells in the machine-product occurrence matrix. It is followed by rearranging the machines as well as the product depending on similarity value. The intra-machine cell layout design depending on flow matrix is attained. In addition, the proposed approach takes into consideration the intercellular movements, backtracking movements, number of operation and voids. The performance of the proposed approach has been assessed with renowned bench mark problem derived from previous literature and the findings have been contrasted to the CASE and CLASS algorithm. From the computational result, it could be inferred that the suggested approach is found to offer better (or) equal solution than the prevailing method. To add more, a real-world industrial case has been suggested to make evident the manner in which the proposed approach functions and the benefits attained by implementing the same.

A Mathematical Programming Approach for Multi-Objective Optimization in Designing Flexible Manufacturing Cells

The study aims to design flexible manufacturing cells with routing flexibility. A weighted mixed-integer linear mathematical programming model that aims to find optimal routing of parts in flexible manufacturing cells under constraints such as minimum machine utilization rate, maximum machine utilization rate, tool capacities, the utilization rate of workers, and labor-system unbalance is developed. The mathematical programming model aims to minimize the weighted sum of five objective functions: (1) the total number of intracellular movements; (2) the total number of intercellular movements; (3) the total workload unbalance of the machine system; (4) the total number of tools in all machines in the cells; and (5) the total labor-system workload unbalance. The main contribution of this study is to obtain these five objectives simultaneously, which have not been encountered to handle together before. By integrating these factors, the study presents a comprehensive approach to optimizing the design of flexible manufacturing cells. This study also has the potential to enhance system performance by addressing these factors. An illustrative problem tests the developed model, and the LINGO 17.0 optimization program is used to solve the generated mathematical programming model. Moreover, the related sensitivity analysis is performed with some parameters to examine the obtained results.

A Novel Mathematical Model for Tillage Production Planning and Facility Layout Design

Cell formation and layout design are important steps in the implementation of production systems. Existing models focus mainly on the problem of cell formation and machine layout design and cell location in a dynamic environment, with the exception of a few recent studies. Also, in many existing articles, binary information has been used for cell formation and other information such as production volume, operation sequence and lower production costs have played a role in the structure of existing models. In this paper, a nonlinear programming model under possible dynamic conditions is presented. The objectives of the model include minimizing the total costs of intracellular and intercellular movements of parts, the existence of exceptional parts, intercellular layout of machines, operating costs, fixed costs of machines and the cost of the difference in demand estimated by the model from its expected value. A genetic algorithm is used to solve and the results are compared with the optimal solution.

Fuzzy Integrated Cell Formation and Production Scheduling Considering Automated Guided Vehicles and Human Factors

In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems including the scheduling of parts within cells in a cellular manufacturing system (CMS) where several automated guided vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs’ collision as well as parts pickup/delivery. This article investigates the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and intercellular movements of parts. Due to the NP-hardness of the problem, a hybrid genetic algorithm (GA/heuristic) and a whale optimization algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and the other two well-known algorithms, i.e., particle swarm optimization and ant colony optimization, in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem.

Nested Bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest

Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their impacts on each other classically. For this reason, a novel bi-level model is designed to make decentralized decisions. Because of the importance of minimizing voids and exceptional element in the cellular manufacturing system, it is considered as a leader at the first level and the assignment of human resources is considered as a follower at the second level. To achieve product innovation and synergy among staff in the objective function at the second level, increasing the worker’s interest in order to cooperate with each other is considered too. Given the NP-Hard nature of cell formation and bi-level programming, nested bi-level genetic algorithm and particle swarm optimization are developed to solve the mathematical model. Various test problems have been solved by applying these two methods and validated results have been shown the efficiency of the proposed model. Also, real experimental comparisons have been presented. These results in contrast with previous works have been shown the minimum amount of computational time, cell load variation, total intercellular movements, and total intracellular movements of this new method. These effects have an important role in order to the improvement of cellular manufacturing behavior.

Discovery of Dithioacetal Derivatives Containing Sulfonamide Moiety of Novel Antiviral Agents by TMV Coat Protein as a Potential Target.

Tobacco mosaic virus coat protein (TMV CP) plays an important role in viral replication, translation, and intracellular and intercellular movements. Thus, TMV CP could be regarded as a potential target for antiviral agents. In this study, in order to find out whether dithioacetal derivatives act on the CP target, a series of dithioacetal derivatives containing sulfonamide moiety was first designed and synthesized. Bioassay results demonstrated that Y14, Y18, and Y21 exhibited excellent activities against TMV, with half-maximal effective concentrations (EC50) of the curative, protective, and inactivate activities being 183.0 ± 3.2, 252.3 ± 2.6, and 63.8 ± 1.2 μg/mL, 270.6 ± 3.7, 249.7 ± 3.5, and 57.7 ± 1.4 μg/mL, and 329.5 ± 1.5, 269.2 ± 3.7, and 48.1 ± 2.0 μg/mL for Y14, Y18, and Y21, respectively, which were higher than those for the control agents ningnanmycin (331.0 ± 2.8, 271.0 ± 2.8, and 77.4 ± 1.3 μg/mL, respectively) and d2 (471.5 ± 1.4, 447.2 ± 2.1, and 91.7 ± 1.8 μg/mL, respectively). Transmission electron microscopy showed that the particle morphology of TMV was destroyed by Y21, and microscale thermophoresis (MST) showed that Y21 bonded to CP with a dissociation constant (Kd) of 9.7 ± 1.7 μM. Then, molecular docking and MST further illustrated that Y21 had a weak binding affinity with the TMV mutant protein (Kd = 561.3 ± 83.2 μM). Thus, we deduced that the dithioacetal derivative Y21 may inhibit TMV activity by binding TMV CP. This work provides some new insights for the design and optimization of novel anti-TMV agents.

An integrated model of cell formation and scheduling problem in a cellular manufacturing system considering automated guided vehicles' movements

In this paper, an integrated mathematical model for cellular manufacturing system (CMS) incorporating cell formation problem (CFP) and intra-cell scheduling is considered. It is assumed that in order to make the flexibility in handling system, automated guided vehicles (AGVs) are responsible for transferring the exceptional parts from one cell to another cell. Employing the AGVs in CMS can be challenging from mathematical model's point of view. In other words, despite the common constraints in CFP and intra-cell scheduling, several constraints such as AGVs' movement, preventing the AGVs' collision as well as parts' pickup/delivery by AGVs must be taken to account. There is no comprehensive model including the role of AGVs in a CMS. Hence, we endeavour to formulate CFP, scheduling and role of AGVs at the same time. The objective function is to minimise the sum of the maximum completion time (makespan) and inter-cellular movements of parts. The proposed nonlinear model is transformed to a linear form in order to solve it for optimality. Eventually, two small-size computational experiments are generated and ran on the GAMS.9 commercial software to show the efficiency and accuracy of the proposed model.

Machine Cell Formation with Alternative Routings based on Genetic Algorithm

In automated batch type production systems, machine-part cell formation problems (CFP) have long drawn attention of researchers. The objective of CFP in cellular manufacturing system (CMS) is to identity machine cells and part families in order to minimize the intercellular movements of parts as well as maximize the utilization of machines. Optimum cell formation results reduction in total production times, in-process inventories, material handling cost, labor cost/times, paper works, number of machine set-ups, set-up times. It also simplifies process plans, management and improves product quality, productivity, utilization of resources. Since the modern manufacturing machines are generally multifunctional, so the processing of parts can be performed by number of alternative routes. The objectives of this study are to determine the optimal machine cells with optimal processing route and balanced machine cells (minimum cell load variation). Here a genetic algorithm heuristic approach is presented for six benchmark problems with multiple process routes, sequence of processes and parts volume. Computational outcome show that the proposed heuristic gives better result comparatively with the well-known existing methods in terms of total intercellular movements.

Genetic Algorithm approach for Machine Cell Formation with Alternative Routings

In cellular manufacturing systems, study and optimization of machine cell formation (CF) problems have long drawn attention of researchers. Optimum CF results in reduction of overall processing time, material handling cost, labor cost, in-process inventories and number of set-ups requirements. Also, it simplifies process plans and improves product quality. Since the modern manufacturing machines in a cell are generally multifunctional, the processing of parts are performed following alternative processing routes. The objective of study is to determine the optimal alternative processing route in order to minimize the total intercellular movements of parts in CF problems. Intercellular movements of parts depend on many factors such as parts volume including batch size and number of batches, sequence of processes and routes of production. In this paper, a genetic algorithm heuristic is presented for the CF problem with multiple process routes, sequence of processes and parts volume. Computational experimentation was performed with five benchmark problems. The results demonstrate that the performance of the proposed approach in terms of total intercellular movements of parts and best route selection are either better or competitive with the well-known existing methods.

A New Mathematical Model in Cell Formation Problem with Consideration of Inventory and Backorder: Genetic and Particle Swarm Optimization Algorithms

Cell Formation (CF) is the initial step in the configuration of cell assembling frameworks. This paper proposes a new mathematical model for the CF problem considering aspects of production planning, namely inventory, backorder, and subcontracting. In this paper, for the first time, backorder is considered in cell formation problem. The main objective is to minimize the total fixed and variable costs, including the machine related costs, intercellular movements, deviation between the levels of cell utilizations, inventory, backorder, and sub-contracting costs. The presented mathematical model is validated using GAMS software, and various test problems are solved by Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) algorithm. The performance of the algorithms is compared with the results obtained by the GAMS. The results demonstrate, there is no significant difference between the results of algorithms. Finally, some sensitive analyses are carried out to analyze the effects of backorder and inventory holding costs.

A Heuristic Approach for Machine Cell Formation Problems with Alternative Routings

Cell Formation (CF) is an important problem in today's automated batch type production systems. It reduces material handling cost, processing time, labor requirement, in-process inventories, number of set-ups, simplifies process plan, and increases quality of product. Manufacturing equipments of automated manufacturing systems are highly multifunctional. As a result, production processes can be done by multiple process routes. Optimum cell formation can lead to more independent cells and less intercellular movement of parts. Previous research reveals that most of the researchers considered single route, unique volume of parts, non-sequential Cell Formation Problem (CFP). However, intercellular movements of parts depend on parts volume, sequence of processes, and routes. In this paper, a heuristic approach based on Euclidean Distance matrix is proposed for CF in multiple routes, process sequential and parts volume (included with batch size and number of batches) problems. Computational experiments were performed with five benchmark problem sets taken from the literature and the results demonstrate that the performances of the proposed heuristic in terms of intercellular movements of parts are either better than or competitive with the well-known existing algorithms.

A hybrid iterated local search and variable neighborhood descent heuristic applied to the cell formation problem

The Cell Formation Problem is an NP-hard optimization problem that consists of grouping machines into cells dedicated to producing a family of product parts, so that each cell operates independently and inter-cellular movements are minimized. Due to its high computational complexity, several heuristic methods have been developed over the last decades. Hybrid methods based on adaptations of popular metaheuristic techniques have shown to provide good performance in terms of solution quality. This paper proposes a new approach for solving the Cell Formation Problem using the group efficacy objective function. Our method is based on the Iterated Local Search metaheuristic coupled with a variant of the Variable Neighborhood Descent method that uses a random ordering of neighborhoods in local search phase. We consider two types of constraints on the minimum cell size, comparing them with several well-known algorithms in the literature. Computational experiments have been performed on 35 widely used benchmark instances with up to 40 machines and 100 parts. The proposed algorithm, besides obtaining solutions at least as good as any reported results, was able to find several optimal solutions and improve the group efficacy for some instances with unknown optima.

A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy

The cell formation problem determines decomposition of the manufacturing cells of a production system. Machines are assigned to the cells to process one or more part families so that each cell is operated independently and the inter-cellular movements are minimized. This paper proposes a new algorithm for grouping problems (bin packing, graph coloring, scheduling, etc.) which is a grouping version of an almost new algorithm (league championship algorithm (LCA)), and we used it to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 35 most widely used benchmark problem instances from literature and compare the performance of grouping LCA (GLCA) and several well-known algorithms published. The proposed algorithm can reach the best solution for 29 of the 35 benchmark problems and differs with the best-known solution of three benchmark problems only with 0.7 % average gap. We also used a new method to find the number of initial cells. The results show that GLCA may hopefully be a new approach for such kinds of difficult-to-solve problems. Moreover, a real-world industrial case is provided to show how the proposed algorithm works. Considering the performance of the GLCA algorithm on all test problems, the proposed algorithm should thus be useful to both practitioners and researchers.

Genome-Wide siRNA Screen Identifies Complementary Signaling Pathways Involved in Listeria Infection and Reveals Different Actin Nucleation Mechanisms during Listeria Cell Invasion and Actin Comet Tail Formation.

ABSTRACTListeria monocytogenes enters nonphagocytic cells by a receptor-mediated mechanism that is dependent on a clathrin-based molecular machinery and actin rearrangements. Bacterial intra- and intercellular movements are also actin dependent and rely on the actin nucleating Arp2/3 complex, which is activated by host-derived nucleation-promoting factors downstream of the cell receptor Met during entry and by the bacterial nucleation-promoting factor ActA during comet tail formation. By genome-wide small interfering RNA (siRNA) screening for host factors involved in bacterial infection, we identified diverse cellular signaling networks and protein complexes that support or limit these processes. In addition, we could precise previously described molecular pathways involved in Listeria invasion. In particular our results show that the requirements for actin nucleators during Listeria entry and actin comet tail formation are different. Knockdown of several actin nucleators, including SPIRE2, reduced bacterial invasion while not affecting the generation of comet tails. Most interestingly, we observed that in contrast to our expectations, not all of the seven subunits of the Arp2/3 complex are required for Listeria entry into cells or actin tail formation and that the subunit requirements for each of these processes differ, highlighting a previously unsuspected versatility in Arp2/3 complex composition and function.

Cell design and loading with alternative routing in cellular reconfigurable manufacturing systems

This paper deals with the design and loading of a Cellular Reconfigurable Manufacturing System (CRMS) in the presence of alternative routing. A CRMS consists of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools (RMT) and Computer Numerical Control (CNC) machines, a setup station and an automatic material handling and storage system. Each RMT has a library of feasible auxiliary machine modules for achieving a particular operational capability of the RMT. Each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The machine cell design problem involves the grouping of machines into machine cells. This problem is performed once, prior to the cell loading, using two cell formation methods from the literature. Once the resulting cells are physically implemented, a mixed-integer linear programming problem is developed for determining the routing mix (i.e. which proportion of each part type to be assigned to each alternative route) and the tool and module allocation (i.e. how many auxiliary modules and tools of each available type to be assigned to each RMT and CNC machine within each formed cell) in order to minimize total intercellular movements of parts. An illustrative problem and experimental results are presented.

New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements

This article presents a new nonlinear integer programming model for dynamic cell formation problem in cell manufacturing system. Difference between this model and other models developed thus far lies in implementation of the idea of more material flow in shorter distance in formation of cells. The presented model is NP-hard. A new strategy known as simulated annealing embedded in branch and cut was developed to solve the problem. In comparison with standard branch and cut, results of solving different problems showed speed and efficiency of simulated annealing embedded in branch and cut.

Metabolism of cholesterol in health and disease of the retina

AbstractPurpose Cholesterol is the main sterol in the vertebrate retina.Methods Deposits of free cholesterol and cholesteryl esters at the basement of RPE are hallmarks of aging in humans, and AMD, the leading cause of vision loss in the Western world.Results Cholesterol in the neuroretina originates from in situ synthesis and extra‐retinal sources. The relative contribution of cholesterol coming from the circulation and local biosynthesis remains unknown. Tight junctions between retinal pigment epithelial (RPE) cells limit the intercellular movements of molecules to water and small molecules. RPE cells express various lipoprotein and scavenger receptors which can promote the recognition of cholesterol‐rich lipoprotein and enhance the entry of cholesterol in the neurosensory retina. Both the neurosensory retina and RPE cells express proteins which participate to cholesterol export in other tissues than the retina, including CYP46A1. CYP46A1 would represent a mechanism of cholesterol removal from neurons, by catalyzing the hydroxylation of cholesterol at position C24. Other mechanisms than CYP46A1 have been described in the retina, including formation of 7‐ketocholesterol or 27‐hydroxycholesterol.Conclusion Interestingly a single nucleotide polymorphism in cyp46a1 gene was associated with a significant risk for glaucoma, the second leading cause of blindness worldwide.