ALBERT

Description: In an attempt to find a solution similar to the FDM 3D printers which would allow cost-effective and reliable additive manufacturing of metal components, this paper proposes a three-axis WAAM system capable of reliably printing small, near-net-shape metal objects. The system consists of gas metal arc (GMA) process equipment, a three-axis CNC positioning system, the interpass temperature control and forced cooling of the base plate and the deposit. The main challenge addressed is the minimisation of shape distortions caused by excessive heat accumulation when printing small objects. The interpass temperature control uses an IR pyrometer to remotely measure the last deposited layer and a control system to keep the interpass temperature below the predefined value by stopping the deposition after each layer in order to allow the deposit to cool. This results in a stable and more repeatable shape of the deposit, even when the heat transfer conditions are changing during the build-up process. The combination of adaptive interlayer dwell time and forced cooling significantly improves system productivity. Open-source NC control and path generation software is used, which enables fast and easy creation of the control code. Different control methods are evaluated through the printing of simple walls, and the printing accuracy is evaluated by printing small shell objects. As the results show, the interpass temperature control allows small objects to be printed at near-net shape with a deviation of 2%, which means that successful printing of 3D shapes can be achieved without trial and error approach.

Description: To prevent an overheating of the workpiece material and to increase the productivity in hot aluminum extrusion, the application of extrusion dies with conformal cooling channels manufactured additively by selective laser melting is known. Since, to date, the additive manufacturing processes are often accompanied with higher manufacturing time and costs in comparison to conventional subtractive methods, a new concept for a hybrid extrusion die is presented. Here, the large volume but geometrically simple die part, the die bridge, is manufactured conventionally by subtractive methods, and the smaller part with geometrical complexity, the tip of the mandrel, is built-up on it additively by laser melting. A further novelty of the developed die is the isolated feeding of the coolant up to the target area, close to die bearings, where the cooling shall be localized. Numerical and experimental investigations revealed that the profile’s exit temperature can be reduced locally and controlled which leads only to a moderate increase of the extrusion force. The experimental results show that the hybrid tools withstand the high mechanical and thermal loads which occur during hot aluminum extrusion.

Description: Machine tools have an impact on the environment due to their energy consumption. New strategies with focus on the reduction of the energy consumed by manufacturing processes have received significant attention owing to the rise of the electricity costs. This paper presents an experimental study related to the optimization of cutting parameters in turning of AISI 1018 steel. The aim of the study was to minimize the quantity of electrical energy required by the machine tool in order to perform the cutting operation. The material removal rate was set to a constant value in all the experimental trials so as to analyze the effect that the cutting parameters have on the energy consumed. Robust Design was used to determine the effects of the depth of cut, feed rate, and cutting speed on the energy required by the machine tool, considering two sources of noise in the experimental trials. The results of this work show that the techniques covered by the concept of Robust Design can be used to minimize the energy consumed and variation of the machining process.

Description: We propose a model for the statistical design of a variable sample size chi-squared control chart (VSS χ 2 control chart) for monitoring linear profiles. Performance measures of the proposed adaptive control chart are obtained through a Markov chain approach. Through a numerical example, which consists of a calibration application in a production process of semiconductors, the proposed chart is compared to the fixed parameter chi-squared control chart (FP χ 2 chart) to monitor the intercept and slope of the linear profile. From this example, it is possible to assess the potential benefits provided by the proposed chart. Also, considering simultaneous shifts in the intercept, the slope, and the standard deviation, a sensitivity analysis of the proposed chart for monitoring linear profiles is presented.

Description: The present investigation analyses the force and torque developing during friction stir spot welding (FSSW) of thermoplastic sheets varying the main process parameters. In addition, measurements of the tool temperature and those of the material close to the welding region were carried out to better understand the variation of the forces during FSSW and quality of the joints. Experimental tests involving an instrumented drilling machine were performed on polycarbonate sheets. The study involved the variation of dwell time, tool plunge rate and rotational speed. Mechanical characterization and dimensional analysis of the joints were performed in order to assess the influence of the process parameters on the joint quality under considered processing conditions. According to the achieved results, using low values of the plunging speed has beneficial effects on both the process (reduction in the force and torque) and the mechanical behaviour of the joints. Increasing the tool rotational speed results in reduced processing forces and higher material mixing and temperature. The dwell time has a negligible effect on developing forces while it highly influences the material temperature, dimension of the welded region and consequently the mechanical behaviour of the joint.

Description: This research studies the cellular manufacturing system (CMS) controlled by kanban mechanism which defective items are produced in any production run of each product and rework is carried out to transform them into serviceable items. We consider and compare two different policies for rework where in the first policy rework is completed within the same production cycle and in the second policy rework done after N production cycles. Recently Aghajani et al. (2012) explain policy 2 and proposed a mixed-integer nonlinear programming (MINLP) model for this policy. In order to minimize total cost, MINLP model was developed for policy 1 to find simultaneously the optimal number of kanban, batch size, and number of batches. The cost function includes the cost of setup, holding, and transportation. Due to the high combinatorial structure of the problem, particle swarm optimization (PSO), and simulated annealing (SA) algorithms as meta-heuristic methods are proposed to solve the problem and numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms. It is shown that both PSO and SA result are in a near optimal solution but the PSO algorithm gives a better performance than the SA method. Also, sensitivity analysis is carried out to study the effect of defective rate, holding cost, and setup cost variations on the total system cost is discussed the performance of these policies in different conditions.

Description: Tool condition monitoring has found its importance to meet the requirement of quality production in industries. Machined surface is directly affected by the extent of tool wear. Hence, by analyzing the machined surface, the information about the cutting tool condition can be obtained. This paper presents a novel technique for multi-classification of tool wear states using a kernel-based support vector machine (SVM) technique applied on the features extracted from the gray-level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull, and dull tool states by using Gaussian and polynomial kernels. The proposed method is found to be cost-effective and reliable for online tool wear classification.

Description: This study proposes a high-precision compensation system using an on-machine noncontact measuring system to improve the manufacturing accuracy and efficiency of large-diameter aspheric mirrors by reducing profile errors arising from tool setting errors and machine positioning errors. By measuring a standard hemisphere, the assembly tilt angle of the measurement sensor can be calibrated. The grinding wheel setting offset can be calculated by comparing the measured profile and the ideal profile, and the profile error caused by wheel offset can be reduced by adjusting the grinding origin coordinate. According to the normal unit vector and residual error in the Z direction of the measuring points, the normal residual errors corresponding to the grinding points could be generated as well as the compensation grinding numerical control (NC) program. An 800-mm-diameter K9 mirror was ground to verify the proposed compensation grinding method. The profile error was reduced from 65 to 35 μm during the semi-finish grinding stage. By using the compensation grinding path, the profile accuracy was improved from 35 to 8 μm in the fine grinding stage. The proposed compensation method effectively improves the profile accuracy and manufacturing efficiency for grinding large-diameter aspheric mirrors.

Description: In this paper, we will perform a comparison between two approaches of dimensional synthesis of parallel robots. The first one concerns the single-objective optimization approach; in this case, the dimensional synthesis is expressed by taking into account only one performance criterion but enables to get a final solution if it exists. The second one concerns the multi-objective optimization approach; it enables to simultaneously take into account several performance criteria. However, this approach appears to provide a set of solutions instead of a single expected final solution which should directly enable to carry out the structural synthesis. In fact, the search of a single final solution is postponed to a further step where the designers have to impose and/or restrict certain parameters. And we will establish if it is really necessary to make a multi-objective optimization approach or if a single-objective is sufficient to reach the objectives set in the specifications (user requirements). A discussion is proposed concerning the arising questions related to each approach and leading to the optimal dimensional synthesis. The PAR2 robot with two degree-of-freedom is used to exemplify the analysis and the comparison of the two approaches. The proposed comparison can be applied to any classes of parallel robots.

Description: In order to generate efficient tool path with given precision requirements, scallop height should be kept under a given limit, while the tool path should be as short as possible to reduce machining time. Traditional methods generate CC curves one by one, which makes the final tool path far from being globally optimal. This paper presents an optimal tool path generation model for a ball-end tool which strives to globally optimize a tool path with various objectives and constraints. Two scalar functions are constructed over the part surface to represent the path intervals and the feedrate (with directions). Using the finite element method (FEM), the tool path length minimization model and the machining time minimization model are solved numerically. The proposed method is also suitable for tool path generation on mesh surfaces. Simulation results show that the generated tool path can be direction parallel or contour parallel with different boundary conditions. Compared to most of the conventional tool path generation methods, the proposed method is able to generate more effective tool paths due to the global optimization strategy.

Description: Ultrasonic-assisted grinding, a promising processing technique for machining hard and brittle materials, is already quite extensively employed in manufacturing industries. However, the material removal mechanism in ultrasonic-assisted grinding is not yet fully understood, which hinders its further application. This study investigates the material removal process in ultrasonic-assisted scratching (UAS) of SiC ceramics using both simulation and experiment method, in order to detail the material removal mechanism in ultrasonic-assisted grinding. A conventional scratching (CS) test was also carried out, but without ultrasonic vibration for comparison. The simulated workpiece is modeled by smooth particle hydrodynamic (SPH) particles. Results show the following: (1) the SPH method is suitable to investigate the material removal mechanism during ultrasonic-assisted grinding of hard and brittle materials. (2) The profile of scratching trace in ultrasonic vibration (UV) is a sinusoidal path. UV vibrating in the direction vertical to the workpiece results in material removed in either a continuous or a discontinuous mode. UV vibrating in the direction parallel to the workpiece expands the cutting area. (3) The groove depth in UAS is much bigger than that in CS. (4) UV results in the impact of the abrasive grain on the workpiece, causing the deformation field to spread from the impact site and leading to deeper scratching depths and larger radial and lateral cracks.

Description: With the emergence of new materials, personalized requirements for product performance, and new application background in polymer material industry, a new manufacturing mode is supposed to be studied. Based on cloud computing (CC) and big data techniques, a specific cloud manufacturing (CMfg) mode of polymer material industry has been proposed, which is different from that of continuous industries and that of discrete industries. The critical technologies of CMfg, including forecasting and demand management, storage and transportation management, advanced process control, manufacturing execution system, enterprise resource planning, etc., have been discussed. Besides the service composition optimal-selection (SCOS) algorithm for flexible manufacturing and the flexible polymer manufacturing system (FPMS), a typical product mode of CMfg is studied. Finally as a case, computer-aided process planning for blending material (CAPP-BM) was explored and a kind of fast searching algorithm for blending material crafts was proposed. The algorithm was applied to search target craft in more than 60,000 sections of the standard processes, production data, and environmental data, and finished its search within 10 min.

Description: In this paper, an inverse heat conduction method is applied to estimate the amount of the energy ( F c ) transferred to the workpiece during electric discharge machining (EDM) process. Embedded thermocouples which were connected to a four channel data logger were utilized to measure the temperature of a specific location on a rectangular workpiece during the EDM process. After temperature measurements were done, the 2-D heat conduction model of the workpiece and the Levenberg-Marquardt (LM) scheme were used to determine the energy transferred to the workpiece. This inverse procedure facilitates the determination of the heat energy at discharge-workpiece interface in EDM processes, which yet is a challenge for existing numerical models. The obtained results showed that the energy transferred to the workpiece varies with the discharge current and pulse duration from 5 % up to 45 %, which shows that the value of F c is a function of discharge current and pulse duration and that the fixed value of energy assumed in majority of the previous researches is not in accordance with real EDM conditions. Furthermore, the effects of machining parameters such as discharge current and pulse duration on F c were studied. It was evident that the F c has a direct but non-linear relationship with both discharge current and pulse duration, while discharge current has a higher impact on F c .

Description: Thermal errors are major contributor to dimensional errors of a part during precision machining. Error compensation is an effective method to reduce thermal errors. Accurate modeling of thermal errors is a prerequisite for thermal error compensation. In this paper, five key temperature points of a computer numerical control (CNC) machine tool were selected based on grey relational analysis method (GRAM). One thermal error model based on the five key temperature points was proposed using artificial fish swarm and ant colony algorithm-based back-propagation neural network (AFSACA-BPN). AFS is applied to generate initial pheromone value of ACA, which improves the computational efficiency of BPNs and prediction accuracy of thermal error modeling. One thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. Experiment results show that the diameter error of the workpiece reduced from 23 to 10 μm after compensation.

Description: Experimental and viscoplastic finite element analysis (FEA) of thermo-mechanical plastic deformation in nonisothermal warm deep drawing is studied using SS304. A nonisothermal deep drawing tool is used in a servo-motor-controlled press. Drawability window of SS304 under elevated temperatures (25–225 °C) and low to high strain rates (drawing speeds of 2.5, 25, and 50 mm/s) were determined. A viscoplastic thermal material model is adopted for nonwork softening material behaviors, as seen in low-temperature forming of SS304, and found to be easily applicable and quite satisfactory. Tensile and equi-biaxial bulge tests were conducted for more accurate flow stress data to be used in FEA. Measured punch load–stroke and cup’s curvilinear thickness (rolling/transverse) curves were successfully compared with predictions from the nonisothermal FE model of the warm deep drawing.

Description: In this article, the effect of cooling media on the residual stresses (RS) induced by a solid-state welding process is scrutinized through measuring and comparing RS caused by friction stir welding (FSW) underwater and in open air using the non-destructive ultrasonic method for aluminum AA7075-T6. Underwater FSW as a solid-state welding method can extend the application of solid-state welding techniques in marine industry. Results reveal that the longitudinal and transverse RS reduce under the water compared to open air. This reduction in the longitudinal RS is the maximum within the nugget zone (about 17 %). Meanwhile, such reduction for the transverse RS reaches 70 % within the heat-affected zone. In addition, under both air and water, the longitudinal RS is several times greater than the transverse RS and is in tensile and compressive states inside and outside the nugget zone, respectively.

Description: Machining-induced residual stress is important for the part performance. In the literature, various predictive models have been proposed for residual stress in one-pass machining without considering the multi-pass aspect. This study describes the regeneration of residual stress in multi-pass machining with thermo-mechanical loadings, in the full elasto-plastic state, captured using the Neumann-Duhamel principle. The residual stress is then analysed satisfying elastic-plastic relaxation in-between layers and at the boundaries. Large experimental data in milling of AA2121-T3 agreed well with model predictions, thus supporting the consideration of initial stress functions, materials cyclic plasticity and compatibility to allow for residual stress prediction in multi-pass machining.

Description: Different calibration strategies based on network measurements have been studied to improve the accuracy of a laser tracker having the beam source in the rotating head, thus allows us to determine if nominal distances are needed. Moreover, the minimum gauge needed to ensure a calibration valid result is characterised. First, the laser tracker calibration performance, using only network measurements without any nominal data known, has been studied. Different strategies have then been carried out, using reflector gauges as nominal data in the calibration procedure to determine the more suitable gauge in terms of accuracy and efficiency. The reflectors have been measured from different positions of the laser tracker. The gauge reflectors have been measured too with a coordinate measuring machine for obtaining the nominal data. The objective function to be minimised in the identification parameter procedure has been developed for every strategy for the distance criterion (distances between every pair of reflectors must be constant regardless of the laser tracker position from which they are measured). Then, two criteria, distance criterion and coordinate criterion (the reflector positions measured by the laser tracker are expressed in the same reference system and are then compared), have been used to evaluate the calibration performance. The analysis developed shows the improvement accuracy of every strategy studied.

Description: by Eliseo Ferrante, Ali Emre Turgut, Edgar Duéñez-Guzmán, Marco Dorigo, Tom Wenseleers Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as “task partitioning”, whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.

Description: by Patrícia Santos-Oliveira, António Correia, Tiago Rodrigues, Teresa M Ribeiro-Rodrigues, Paulo Matafome, Juan Carlos Rodríguez-Manzaneque, Raquel Seiça, Henrique Girão, Rui D. M. Travasso Sprouting angiogenesis, where new blood vessels grow from pre-existing ones, is a complex process where biochemical and mechanical signals regulate endothelial cell proliferation and movement. Therefore, a mathematical description of sprouting angiogenesis has to take into consideration biological signals as well as relevant physical processes, in particular the mechanical interplay between adjacent endothelial cells and the extracellular microenvironment. In this work, we introduce the first phase-field continuous model of sprouting angiogenesis capable of predicting sprout morphology as a function of the elastic properties of the tissues and the traction forces exerted by the cells. The model is very compact, only consisting of three coupled partial differential equations, and has the clear advantage of a reduced number of parameters. This model allows us to describe sprout growth as a function of the cell-cell adhesion forces and the traction force exerted by the sprout tip cell. In the absence of proliferation, we observe that the sprout either achieves a maximum length or, when the traction and adhesion are very large, it breaks. Endothelial cell proliferation alters significantly sprout morphology, and we explore how different types of endothelial cell proliferation regulation are able to determine the shape of the growing sprout. The largest region in parameter space with well formed long and straight sprouts is obtained always when the proliferation is triggered by endothelial cell strain and its rate grows with angiogenic factor concentration. We conclude that in this scenario the tip cell has the role of creating a tension in the cells that follow its lead. On those first stalk cells, this tension produces strain and/or empty spaces, inevitably triggering cell proliferation. The new cells occupy the space behind the tip, the tension decreases, and the process restarts. Our results highlight the ability of mathematical models to suggest relevant hypotheses with respect to the role of forces in sprouting, hence underlining the necessary collaboration between modelling and molecular biology techniques to improve the current state-of-the-art.

Description: by Sayed-Rzgar Hosseini, Aditya Barve, Andreas Wagner All biological evolution takes place in a space of possible genotypes and their phenotypes. The structure of this space defines the evolutionary potential and limitations of an evolving system. Metabolism is one of the most ancient and fundamental evolving systems, sustaining life by extracting energy from extracellular nutrients. Here we study metabolism’s potential for innovation by analyzing an exhaustive genotype-phenotype map for a space of 10 15 metabolisms that encodes all possible subsets of 51 reactions in central carbon metabolism. Using flux balance analysis, we predict the viability of these metabolisms on 10 different carbon sources which give rise to 1024 potential metabolic phenotypes. Although viable metabolisms with any one phenotype comprise a tiny fraction of genotype space, their absolute numbers exceed 10 9 for some phenotypes. Metabolisms with any one phenotype typically form a single network of genotypes that extends far or all the way through metabolic genotype space, where any two genotypes can be reached from each other through a series of single reaction changes. The minimal distance of genotype networks associated with different phenotypes is small, such that one can reach metabolisms with novel phenotypes – viable on new carbon sources – through one or few genotypic changes. Exceptions to these principles exist for those metabolisms whose complexity (number of reactions) is close to the minimum needed for viability. Increasing metabolic complexity enhances the potential for both evolutionary conservation and evolutionary innovation.

Description: by Pengxing Cao, Ada W. C. Yan, Jane M. Heffernan, Stephen Petrie, Robert G. Moss, Louise A. Carolan, Teagan A. Guarnaccia, Anne Kelso, Ian G. Barr, Jodie McVernon, Karen L. Laurie, James M. McCaw Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus–innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control.

Description: Crystallographic texture considerably affects the formability of crystalline materials. In this paper, the effects of BCC ideal rolling fibers—including α ,

Description: In the processing of large and ultra-large forgings, the heated billets need to be properly placed on the lower forging die as quickly as possible before the plastic forming, or else the cooling of billets incurs enormous risks to the operation. This paper presents a novel methodology for examining the positioning status of billets on a forging die based on multi-body dynamics simulation and design of experiment (DOE). Using this method, the position and posture of a billet can be theoretically predicted after falling into the cavity of lower die from a manipulator with varying initial states. The method can also clarify the initial geometrical position parameters of the billet that should be strictly controlled in the operation of the manipulator above the lower die. Furthermore, finite element method (FEM) simulation can be used to analyze plastic deformations of the billets on the lower die surface with varying states, to attain in-depth understanding of the influence of the geometric states of billets in forming processes. A case study of forging with Al 7050 indicates that the method can provide a valuable reference for the rapid positioning of billets on the lower die.

Description: The ANSYS software is used to establish the electromagnetic-structural coupling model and predict the electromagnetic sheet forming process. In comparison with experimental result, the maximum simulation error, about 4.5 %, occurs at the sheet center. Then, the simulation method is used to analyze the effect of discharge voltage on thickness distribution. The results indicated that the location of the maximum thickness reduction transfers from sheet center to the region near the sheet center (A region) and then to the region corresponding to the die corner (B region) with the voltage increases, which also cause the first principle strain changed. In addition, lager magnetic force and the material at sheet flange restrained to flow are the two reasons for the thickness reduction at B region. While the direction of material flows changed by inertial effect is the reason for thickness reduction at A region.

Description: In this paper, the formability of two-layer (aluminum-st12 steel) sheets in the deep drawing process was investigated through numerical simulations and experiments. The purpose of this research was to obtain more formability in deep drawing process. The limit drawing ratio (LDR) was obtained in deep drawing of two-layer metallic sheets, with aluminum inner layer which was in contact with the punch and steel outer layer which was in contact with the die. Finite element simulations were performed to study the effect of parameters such as the thickness of each layer, value of die arc radius, friction coefficient between blank and punch, friction coefficient between blank and die, and lay up on the LDR. Experiments were conducted to verify the finite element simulations. The results indicated that the LDR was dependent on the mentioned parameters, so the LDR and as a result the two-layer metallic sheet formability could be increased by improvement of these parameters in deep drawing process.

Description: This study aims to investigate two peel demolding schemes through numerical simulations and experimental studies in order to improve the yield rate of the automated system for demolding of the polydimethylsiloxane (PDMS) micropillars with aspect ratio of 6. Numerical models based on the explicit dynamic finite element analysis by using LS-DYNA are developed to identify an optimal demolding scheme which can minimize the maximum stress of microstructures during demolding. A scale-up modeling approach is proposed to increase the numerical time-step for microscale problems in order to reduce the computational time. The experimental tests are also carried out which agree with the findings from numerical simulations. From this study, the roller-based demolding system is identified as the optimal approach in our analysis cases which can minimize the distortion and collapse of micropillars. The yield rate of the roller-based demolding system in our experimental study can be up to 99 %.

Description: The size effect in cutting process that the specific cutting energy increases rapidly and nonlinearly as the undeformed chip thickness (UCT) decrease is discussed. To facilitate the discussion, the specific cutting energy is analyzed by separating the cutting mechanism into two parts: shearing and extrusion. The size effect of materials such as dislocation starvation was introduced to explain the increase of specific cutting energy. In conventional cutting, shearing dominates the size effect. And as the UCT reduces, the effect of tool radius is not ignorable, and extrusion participates more in describing the size effect. When the UCT is on the nanometric scale, extrusion dominates the cutting process. Besides that, the cutting energy was further separated into surface generation energy, material disorder energy, and heat generation energy. Each of them was discussed individually. The results show that the size effect of materials plays a major role in the change of specific cutting energy. And the other aspects like surface generation and material disorder also determine the size effect in cutting process.

Description: Based on the crack mechanism of hot forming, the causes of cracks occurring during the hot forming of complex structural parts were investigated in this study. High temperature flow stress model of ultrahigh strength steel (UHSS) BR1500HS was established using the true stress–strain curves of BR1500HS in high-temperature tensile. A finite element model (FEM) was built to investigate the causes of defects in hot forming, particularly the necking occurring at the end parts in plan stress status. Then, hot forming process and structure optimizing methods were proposed. According to the results of numerical simulation, it can be concluded that the indirect hot forming process can avoid forming defects and optimize preforming drawing height to 24.5 mm. Through changing the end size of blank to control the metal flow, crack occurring at the end of parts can be solved, since the material in two-way tensile stress state can flow compensation in one direction and therefore reduce the flow resistance. The experimental results are in good agreement with numerical simulation results, which indicates that the proposed method can avoid defects and meet the design requirements.

Description: The deforming zone in the die determined by the cross-sectional shape of the final product plays a key role in the extrusion process affecting the extrusion pressure and product quality. Therefore, prediction of the optimal profile of the deforming region is the main objective for an effective extrusion process. In this study, using the analogy between the conventional plasticity theorem and electrostatics, the notion of equi-potential lines (EPLs) was applied to accurate representation and 3D design of the deforming region in the extrusion process of a complex section. To implement the analogy in the extrusion, the initial and final shapes were considered, and two different potentials were assigned between the inlet and outlet surfaces. Then, the EPLs were drawn that show the minimum work path between the entry and exit sections. The drawn EPLs were connected to build up a 3D-profile for the deforming region in the extrusion process. In addition, the EPLs were used in accurate representation of the deforming region using high-order polynomial curves. The effectiveness of the proposed method was examined using a complex section (U-shaped) from the literature. Then, the extrusion pressure for different profiles in the deforming region was analyzed numerically and experimentally. Moreover, the obtained polynomial curves were used in the upper bound (UB) solution for prediction of the extrusion pressure. There were reasonable agreements between the analytical, numerical, and experimental results. An acceptable reduction in the extrusion pressure for 3D modelling of the deforming region with the EPLs was reported. It was shown that the EPLs could be used for accurate representation of the deforming region in the extrusion of complex sections.

Description: by Paul M. Harrison, Laurent Badel, Mark J. Wall, Magnus J. E. Richardson Models of neocortical networks are increasingly including the diversity of excitatory and inhibitory neuronal classes. Significant variability in cellular properties are also seen within a nominal neuronal class and this heterogeneity can be expected to influence the population response and information processing in networks. Recent studies have examined the population and network effects of variability in a particular neuronal parameter with some plausibly chosen distribution. However, the empirical variability and covariance seen across multiple parameters are rarely included, partly due to the lack of data on parameter correlations in forms convenient for model construction. To addess this we quantify the heterogeneity within and between the neocortical pyramidal-cell classes in layers 2/3, 4, and the slender-tufted and thick-tufted pyramidal cells of layer 5 using a combination of intracellular recordings, single-neuron modelling and statistical analyses. From the response to both square-pulse and naturalistic fluctuating stimuli, we examined the class-dependent variance and covariance of electrophysiological parameters and identify the role of the h current in generating parameter correlations. A byproduct of the dynamic I-V method we employed is the straightforward extraction of reduced neuron models from experiment. Empirically these models took the refractory exponential integrate-and-fire form and provide an accurate fit to the perisomatic voltage responses of the diverse pyramidal-cell populations when the class-dependent statistics of the model parameters were respected. By quantifying the parameter statistics we obtained an algorithm which generates populations of model neurons, for each of the four pyramidal-cell classes, that adhere to experimentally observed marginal distributions and parameter correlations. As well as providing this tool, which we hope will be of use for exploring the effects of heterogeneity in neocortical networks, we also provide the code for the dynamic I-V method and make the full electrophysiological data set available.

Description: by Ariel Afek, Hila Cohen, Shiran Barber-Zucker, Raluca Gordân, David B. Lukatsky Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C . elegans and D . melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity.

Description: In this study, induction brazing was performed on diamond grits coated with amorphous NiCrBSi alloy (1.6-μm thick) deposited by physical vapor deposition (PVD). The brazing alloy exhibited better wetting toward the coated diamond grits than toward the uncoated diamond grits during induction brazing. The fine chromium-carbon compounds were evenly distributed between the brazed diamond grits with coating and the brazing alloy. However, the bulky chromium-carbon compounds were unevenly distributed between the brazed uncoated diamond grits and the brazing alloy. Cylindrical grinding of casting aluminum ZL102 plate with thickness of 15 mm was also performed using the brazed diamond burs fabricated with the coated diamond grits and uncoated diamond grits, respectively. The falloff percentage of brazed coated diamond grits was lower than that of brazed uncoated diamond grits. Accordingly, the temperature of processing arc area of the brazed diamond bur fabricated with the coated diamond grits was lower than that of the brazed diamond bur fabricated with the uncoated diamond grits, and its rate of removal of material was higher than that of the brazed diamond bur fabricated with the uncoated diamond grits.

Description: by The PLOS Computational Biology Staff

Description: by James Tamerius, Cécile Viboud, Jeffrey Shaman, Gerardo Chowell While a relationship between environmental forcing and influenza transmission has been established in inter-pandemic seasons, the drivers of pandemic influenza remain debated. In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children. For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period. Here we fit a series of transmission models to daily laboratory-confirmed influenza data in 32 Mexican states using MCMC approaches, considering a meta-population framework or the absence of spatial coupling between states. We use these models to explore the effect of environmental, school–related and travel factors on the generation of spatially-heterogeneous pandemic waves. We find that the spatial structure of the pandemic is best understood by the interplay between regional differences in specific humidity (explaining the occurrence of pandemic activity towards the end of the school term in late May-June 2009 in more humid southeastern states), school vacations (preventing influenza transmission during July-August in all states), and regional differences in residual susceptibility (resulting in large outbreaks in early fall 2009 in central and northern Mexico that had yet to experience fully-developed outbreaks). Our results are in line with the concept that very high levels of specific humidity, as present during summer in southeastern Mexico, favor influenza transmission, and that school cycles are a strong determinant of pandemic wave timing.

Description: by Alireza Alemi, Carlo Baldassi, Nicolas Brunel, Riccardo Zecchina Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns.

Description: by Sander Land, Steven A. Niederer Biophysical models of cardiac tension development provide a succinct representation of our understanding of force generation in the heart. The link between protein kinetics and interactions that gives rise to high cooperativity is not yet fully explained from experiments or previous biophysical models. We propose a biophysical ODE-based representation of cross-bridge (XB), tropomyosin and troponin within a contractile regulatory unit (RU) to investigate the mechanisms behind cooperative activation, as well as the role of cooperativity in dynamic tension generation across different species. The model includes cooperative interactions between regulatory units (RU-RU), between crossbridges (XB-XB), as well more complex interactions between crossbridges and regulatory units (XB-RU interactions). For the steady-state force-calcium relationship, our framework predicts that: (1) XB-RU effects are key in shifting the half-activation value of the force-calcium relationship towards lower [Ca 2+ ], but have only small effects on cooperativity. (2) XB-XB effects approximately double the duty ratio of myosin, but do not significantly affect cooperativity. (3) RU-RU effects derived from the long-range action of tropomyosin are a major factor in cooperative activation, with each additional unblocked RU increasing the rate of additional RU’s unblocking. (4) Myosin affinity for short (1–4 RU) unblocked stretches of actin of is very low, and the resulting suppression of force at low [Ca 2+ ] is a major contributor in the biphasic force-calcium relationship. We also reproduce isometric tension development across mouse, rat and human at physiological temperature and pacing rate, and conclude that species differences require only changes in myosin affinity and troponin I/troponin C affinity. Furthermore, we show that the calcium dependence of the rate of tension redevelopment k tr is explained by transient blocking of RU’s by a temporary decrease in XB-RU effects.

Description: by Jonas Paulsen, Odin Gramstad, Philippe Collas The three-dimensional (3D) structure of the genome is important for orchestration of gene expression and cell differentiation. While mapping genomes in 3D has for a long time been elusive, recent adaptations of high-throughput sequencing to chromosome conformation capture (3C) techniques, allows for genome-wide structural characterization for the first time. However, reconstruction of "consensus" 3D genomes from 3C-based data is a challenging problem, since the data are aggregated over millions of cells. Recent single-cell adaptations to the 3C-technique, however, allow for non-aggregated structural assessment of genome structure, but data suffer from sparse and noisy interaction sampling. We present a manifold based optimization (MBO) approach for the reconstruction of 3D genome structure from chromosomal contact data. We show that MBO is able to reconstruct 3D structures based on the chromosomal contacts, imposing fewer structural violations than comparable methods. Additionally, MBO is suitable for efficient high-throughput reconstruction of large systems, such as entire genomes, allowing for comparative studies of genomic structure across cell-lines and different species.

Description: by Hiroo Kenzaki, Shoji Takada Nucleosomes, basic units of chromatin, are known to show spontaneous DNA unwrapping dynamics that are crucial for transcriptional activation, but its structural details are yet to be elucidated. Here, employing a coarse-grained molecular model that captures residue-level structural details up to histone tails, we simulated equilibrium fluctuations and forced unwrapping of single nucleosomes at various conditions. The equilibrium simulations showed spontaneous unwrapping from outer DNA and subsequent rewrapping dynamics, which are in good agreement with experiments. We found several distinct partially unwrapped states of nucleosomes, as well as reversible transitions among these states. At a low salt concentration, histone tails tend to sit in the concave cleft between the histone octamer and DNA, tightening the nucleosome. At a higher salt concentration, the tails tend to bound to the outer side of DNA or be expanded outwards, which led to higher degree of unwrapping. Of the four types of histone tails, H3 and H2B tail dynamics are markedly correlated with partial unwrapping of DNA, and, moreover, their contributions were distinct. Acetylation in histone tails was simply mimicked by changing their charges, which enhanced the unwrapping, especially markedly for H3 and H2B tails.

Description: by Sebastian Bitzer, Jelle Bruineberg, Stefan J. Kiebel Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.

Description: When machining titanium alloys at cutting speeds higher than 60 m/min using cemented carbide cutting tools, the tool wears out rapidly. With the ever-increasing use of titanium alloys, it is essential to address this issue of rapid tool wear in order to reduce manufacturing costs. Therefore, the intention of this study was to investigate all possible tool wear mechanisms involved when using uncoated carbide cutting tools to machine Ti6Al4V titanium alloy at a cutting speed of 150 m/min under dry cutting conditions. Adhesion, diffusion, attrition, and abrasion were found to be the mechanisms associated with the cratering of the rake surface of the cutting tool. The plastic deformation of the cutting edge was also noticed which resulted in weakening of the rake surface and clear evidence has been presented. Based on this evidence, the process of the formation of the crater wear has been described in detail.

Description: Temperature and material flow behavior during friction spot welding of Alclad 7B04-T74 aluminum alloy were studied by both numerical simulation and welding experiment. The Alclad 7B04-T74 aluminum alloy sequentially experienced solid solution treatment at 465 °C, low temperature artificial aging at 120 °C, and high temperature artificial aging at 180 °C. During welding, the material which flowed into the sleeve cavity suffered from higher temperature, and the peak temperature in the stir zone was higher than the incipient melting temperature of the base material. Accordingly, the eutectic films along the grain boundaries can be observed in the stir zone after welding. The peak temperatures in the thermo-mechanically affected zone and the heat affected zone were lower than the solution temperature and higher than the artificial aging temperature of the base material. In the sleeve retreating stage of the welding process, the material in the sleeve cavity flowed downward out of the sleeve cavity, and then it flowed laterally and upward to fill the gap left by the retreating sleeve. Such a material flow path resulted in the “U-shaped” morphology of the bonding ligament, the upward curving of the hook, and the upward distortion of the grains in the thermo-mechanically affected zone.

Description: In order to reduce the adverse effects on environment and avoid health problems caused by the excessively used cutting fluids, a green machining technology, minimum quantity lubrication (MQL), is drawing more and more attention. The cryogenic minimum quantity lubrication (CMQL) technique which combines the advantages of cryogenic air and MQL can improve cooling and lubricating performances during machining H13 steel. Internal cooling cutters have been widely employed to feed the cutting medium to the cutting zone directly. In this research work, cutting forces and tool wear were analyzed during side milling H13 steel with three kinds of internal cooling milling cutters under CMQL condition. The experimental results showed that the milling cutter with double straight channel (DSC) performed best in extending tool life and reducing cutting forces. In the perspective of economy and environmental protection, internal cooling cutter with DSC is recommended in cutting of H13 steel under CMQL condition.

Description: Considering the traditional power amplifier has the disadvantage of poor reliability and flexibility, a three-level pulse-width modulation (PWM) power amplifier which is based on a novel field-programmable logic gate array (FPGA) algorithm and hardware solution is proposed. The power amplifier can provide various signals flexibly and realize rapid response of the magnetic suspension spindle in micro-electrical discharge machining (EDM). In this paper, the principle of three-level PWM amplifier with half bridge and full bridge power circuit is introduced. According to different functions, the amplifier is divided into four function modules which include PWM signal generator module, voltage signal convert module, bootstrap drive module, and power bridge module. PWM signal generator module is also divided into four sub-modules in term of a new FPGA algorithm. Voltage signals are converted by high-speed photo coupler HCPL-2630. IR2110S chips are applied to drive the half bridge and full bridge power circuits. According to Kirchhoff voltage law, when the period of PWM signals is 50 μs and the duty cycles are larger than 0.76 and 0.665, the average current of half bridge and full bridge are more than 3 and 4 A; however, the ripple of the half bridge and full bridge are still less than 0.25 and 0.2 A, this advantage is suitable for the control system of magnetic suspension spindle. Test results of the average current and ripple are close to theoretical value. The axial response frequency of the spindle can reach 125 Hz, using this power amplifier and the magnetic suspension spindle, micro EDM can be achieved in Z axis with 1.2 mm stroke.

Description: This paper presents an improved methodology for evaluating the position and orientation errors of airfoil sections of a manufactured aero-engine blade. The existing method estimates these errors by finding rigid-body transformations with translational and rotational parameters altogether to best match the inspection data points onto the design airfoil profiles. Such transformations lead to unreliable evaluation results due to combining the position and orientation errors with each other. This paper proposes to decouple the position and orientation errors in their evaluation in order to avoid the combining effect. To isolate the position error from the orientation error, an important location tolerance evaluation feature, the centroid of a manufactured airfoil section, must be correctly identified from the sectional inspection data points. Identifying the centroid location directly from discrete data points is subject to an error caused by biased area calculations on the pressure and suction sides of an airfoil. This work proposes to reconstruct a valid airfoil profile from the inspection data points for each airfoil section to overcome the area bias problem and to maintain consistency in identifying the centroid. With the centroid of each inspected airfoil section identified, the position error and the orientation error can then be evaluated in sequence. A series of case studies has been performed to demonstrate the effectiveness of the proposed methodology and how it is able to prevent wrongful rejection/acceptance of geometrically acceptable/unacceptable blades as well as incorrect modification of the related manufacturing processes.

Description: A three-dimensional (3D) micromechanical finite element (FE) model of machining of fiber-reinforced polymer (FRP) composites was developed in the paper. The FE modeling considers the three phases of a composite, in which the interphase between the fiber and matrix can realize interfacial debonding to represent the failure of composites and allow heat transfer. The machined surface observations and surface roughness measurements of carbon fiber-reinforced polymer (CFRP) composites at different fiber orientations were done firstly, and then, the model predictions of the machining responses, such as cutting force, temperature, and surface roughness, at different fiber orientations were compared with various experimental data for model validation. It is indicated that the three-phase micromechanical model is capable of precisely predicting machining responses and describing the failure modes of fiber shearing or bending related with fiber orientations in the chip formation process. To investigate the complex coupling influences of multiple machining parameters on the key responses of CFRP composites, the single-factor analyses of each machining parameter were first carried out, and then, the multi-factorial analysis of multiple machining parameters was performed based on the orthogonal design of experiment and the analysis of variance (ANOVA) to quantitatively compare the influences of these key machining parameters on the cutting force and surface roughness. It was found that the fiber orientation angle, depth of cut, and cutting speed prove to be the important factors affecting the cutting force and surface roughness and that the coupling effects of these machining parameters all are relatively negligible in the machining of CFRP composites.

Description: Assembly system complexity, especially welding system complexity introduced by auto-body product personalization is regarded as a major contributor of uncertainty in the system planning and designing. The welding system complexity is defined based on information entropy theory, the station-level integrated complexity model, and system-level complexity flow model are established to obtain the complexity source of welding system. Complexity source sensitivity indices are proposed to indentify key station and key equipment that contribute most to the complexity. Based on the application of auto-body side welding line case, the result indicates that the proposed complexity model and key complexity source identifying and diagnosing process can be used as the decision support tool of auto-body welding system.

Description: Single point incremental forming (SPIF) is a relatively new manufacturing process that has been recently used to form medical grade titanium sheets for implant devices. However, one limitation of the SPIF process may be characterized by dimensional inaccuracies of the final part as compared with the original designed part model. Elimination of these inaccuracies is critical to forming medical implants to meet required tolerances. Prior work on accuracy characterization has shown that feature behavior is important in predicting accuracy. In this study, a set of basic geometric shapes consisting of ruled and freeform features were formed using SPIF to characterize the dimensional inaccuracies of grade 1 titanium sheet parts. Response surface functions using multivariate adaptive regression splines (MARS) are then generated to model the deviations at individual vertices of the STL model of the part as a function of geometric shape parameters such as curvature, depth, distance to feature borders, wall angle, etc. The generated response functions are further used to predict dimensional deviations in a specific clinical implant case where the curvatures in the part lie between that of ruled features and freeform features. It is shown that a mixed-MARS response surface model using a weighted average of the ruled and freeform surface models can be used for such a case to improve the mean prediction accuracy within ±0.5 mm. The predicted deviations show a reasonable match with the actual formed shape for the implant case and are used to generate optimized tool paths for minimized shape and dimensional inaccuracy. Further, an implant part is then made using the accuracy characterization functions for improved accuracy. The results show an improvement in shape and dimensional accuracy of incrementally formed titanium medical implants.

Description: In electrical discharge machining (EDM) process, one of the most important aspects is the surface quality of the workpiece. When a uniform and thick recast layer is achieved with characteristics of low roughness, high hardness, and the absence of pores and micro-cracks, it acts as a kind of coating. Such surface is required by mold-making industry, where the molds are subjected to chemical and abrasive wear, and the surface needs to present high resistance against corrosion and abrasive forces. The use of powder particles suspended in the dielectric is a way to provide such improvement and, at the same time, avoiding the need for subsequent polishing. This work investigated the influence of silicon and manganese powders with fine particle sizes, using two different concentrations, suspended in the dielectric when EDM machining AISI H13 tool steel. It evaluated the surface roughness, hardness, and the chemical composition and micro-structure of the recast layer; using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) techniques. The best results were obtained for silicon powder; presenting the surface roughness improved about five times, when compared to the conventional EDM process, as well as a thick and uniform recast layer without micro-cracks and pores. The silicon and the manganese powders also promoted an increase of the recast layer hardness of about 40 % when compared to the conventional EDM process.

Description: It has been proven that error compensation is a key technique to improve machining accuracy. However, existing iteration and recursive compensation algorithm is difficult to realize. Hence, a simple and rapid compensation method is considerably necessary for engineering application. In this paper, a novel compensation strategy just by algebraic operation was first proposed for machining accuracy improvement. Error motion transformation was introduced to build the position-independent geometric error (PIGE) model according to homogeneous transformation matrix (HTM). Then, the analytical numerical control (NC) code expression with error compensation was derived and used for NC code generation. In addition, the presented method is appropriate for post-processing of non-orthogonal machine tool. At last, simulation and cutting experiment were demonstrated to verify the feasibility and effectiveness of the proposed method. Taking hemisphere surface as the test object, the simulation results showed that the effects of PIGEs could be eliminated by the proposed method. The experiment results with compensation indicated that the machining accuracy improved to about 14 % compared with those without compensation.

Description: The study aims to obtain the effect of forming parameters on multi-stage cold forging with 20MnTiB steel by performing a series of physical simulation and then verified by producing experiment of high-strength bolt. Physical simulation was performed through Gleeble 3500 compression tests; the mainly forming parameters such as strain rate (10 0 ∼10 1 ), deformation degree (20∼80 %), and number of stages were discussed. The results showed that the strain rate has little effect on the microstructure and the mechanical property. However, the number of stages and the deformation degree have an appreciable effect on the sample microstructure, of which the pearlite grain is fined and ferrite grain is elongated as fiber. The adiabatic thermal temperature rises from 20 to 142 °C with a 60 % deformation degree at a strain rate of 10 s −1 . Finally, the deformation properties of bolts can compare with the physical simulation results.

Description: In this paper, a visual, data-driven operational level lean maturity model is developed. The model can be used to assess level of lean maturity and to compare it to performance results in different axes of manufacturing cells in order to evaluate lean effectiveness. As demonstrated in this paper, to measure effectiveness of lean manufacturing, both inputs (tools and processes) and outputs (performance) are measured separately and analyzed together. A case study is carried out for gathering data, analysis, and explanatory study of results. Qualitative and quantitative data on lean capability and performance of two manufacturing cells is collected using historical data and audit. A scoring system based on the major and minor non-conformances is suggested to quantify the indicators of leanness. Minimum of fuzzy membership values is selected to calculate overall performance. Then, the results of leanness are compared with performance to highlight the gaps of lean effectiveness. Results of the study show that the developed model can be used to measure both leanness and lean effectiveness through assessment of lean performance. The model can be applied by practitioners as a framework to design and develop a company-specific lean maturity model.

Description: by Malachi Griffith, Jason R. Walker, Nicholas C. Spies, Benjamin J. Ainscough, Obi L. Griffith Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.

Description: by Arjun Bharioke, Dmitri B. Chklovskii Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.

Description: by Murat Alp, Vipan K. Parihar, Charles L. Limoli, Francis A. Cucinotta In this work, a stochastic computational model of microscopic energy deposition events is used to study for the first time damage to irradiated neuronal cells of the mouse hippocampus. An extensive library of radiation tracks for different particle types is created to score energy deposition in small voxels and volume segments describing a neuron’s morphology that later are sampled for given particle fluence or dose. Methods included the construction of in silico mouse hippocampal granule cells from neuromorpho.org with spine and filopodia segments stochastically distributed along the dendritic branches. The model is tested with high-energy 56 Fe, 12 C, and 1 H particles and electrons. Results indicate that the tree-like structure of the neuronal morphology and the microscopic dose deposition of distinct particles may lead to different outcomes when cellular injury is assessed, leading to differences in structural damage for the same absorbed dose. The significance of the microscopic dose in neuron components is to introduce specific local and global modes of cellular injury that likely contribute to spine, filopodia, and dendrite pruning, impacting cognition and possibly the collapse of the neuron. Results show that the heterogeneity of heavy particle tracks at low doses, compared to the more uniform dose distribution of electrons, juxtaposed with neuron morphology make it necessary to model the spatial dose painting for specific neuronal components. Going forward, this work can directly support the development of biophysical models of the modifications of spine and dendritic morphology observed after low dose charged particle irradiation by providing accurate descriptions of the underlying physical insults to complex neuron structures at the nano-meter scale.

Description: by Brinda Vallat, Carlos Madrid-Aliste, Andras Fiser Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Description: The wall thickness of hollow turbine blade has emerged as a significant cause of blade retirement. The precision of the final wall thickness of blade is mainly inherited from its corresponding wax pattern. The layout scheme of ceramic locators has a great influence on the wall thickness of wax pattern. A good layout of ceramic locators can significantly reduce the wall thickness shifting. To address this issue, a stable locator layout is needed to reduce the error transferring. The main purpose of this study is to find an optimal localization scheme for ceramic core. Firstly, the mathematical model of ceramic core localization was built based on the fixture design theory. Then, the optimal algorithm of locator layout design was studied. The D-optimality criterion has been chosen as optimal design criterion. Finally, two demonstration cases were presented. A localization scheme for real ceramic core was achieved and verified by using Monte-Carlo method. Moreover, the localization scheme was validated through experiments. Both simulation and experimental results indicated that the optimal localization can significantly reduce the input error.

Description: Inaccuracies in conventional tolerance characterization methods, which are based on worst-case and root-square-error methods, as well as inefficiencies in Monte Carlo computational methods of statistical tolerance analysis, require an accurate and efficient method of statistical analysis of geometric tolerances. Here, we describe a unified error distribution model for various types of geometric tolerance to obtain the distribution of the deviations in different directions. The displacement distributions of planes, straight lines, and points are analyzed based on distributions within tolerance zones. The distribution of the displacements of clearance fits is then determined according to the precedence of the assembly constraints. We consider the accumulated assembly variations and displacement distributions, and an analytical model is constructed to calculate the distribution of the deviations of the control points and the process capability index to validate the functional requirements. The efficiency of the method is shown by applying it to the assembly of a single-rod piston cylinder. The results are compared with other statistical methods of tolerance analysis. We find an improvement of approximately 20 % in tolerance analysis, and the process capability index of the assembly procedure was reduced by 10 %.

Description: This paper aims to reveal the material removal mechanisms of the elliptical vibration cutting (EVC) and present the predicted model of orthogonal cutting force. Further study of mechanism will be helpful to explain the phenomena that EVC can reduce the cutting force, lower cutting temperature, and improve the surface integrity. In each overlapping EVC cycle, almost all the parameters are time-varying, of which two important factors are focused: (i) transient thickness of cut and (ii) transient shear angle. The analysis model simplified the complex process of the EVC as conventional cutting (CC) which considering two transient variables. This paper presents a non-equidistant shear zone model to predict the shear angle, tool–chip friction angle, and shear stress in CC under the same conditions of the EVC. Then, the transient thickness of cut and transient shear angle are investigated. Thus, an analytical model of the force in EVC is proposed. The model is available to predict the cutting force of the EVC accurately without any experimental parameters in CC. In addition, experimental results available in the literature are conducted for comparison, which are in well agreement with the analysis model.

Description: by Po-Wei Chen, Luis L. Fonseca, Yusuf A. Hannun, Eberhard O. Voit The article demonstrates that computational modeling has the capacity to convert metabolic snapshots, taken sequentially over time, into a description of cellular, dynamic strategies. The specific application is a detailed analysis of a set of actions with which Saccharomyces cerevisiae responds to heat stress. Using time dependent metabolic concentration data, we use a combination of mathematical modeling, reverse engineering, and optimization to infer dynamic changes in enzyme activities within the sphingolipid pathway. The details of the sphingolipid responses to heat stress are important, because they guide some of the longer-term alterations in gene expression, with which the cells adapt to the increased temperature. The analysis indicates that all enzyme activities in the system are affected and that the shapes of the time trends in activities depend on the fatty-acyl CoA chain lengths of the different ceramide species in the system.

Description: With pervasive applications of new information technology, a larger number of manufacturing big data is generated. This paper considers the unrelated parallel scheduling problem within the background of “big data and cloud technology for manufacturing.” Traditional unrelated parallel problem has been extensively investigated, and the main objective has been to improve production efficiency. With regard to the environmental concern, there has been limited literature. Therefore, this paper considers an unrelated parallel machine scheduling problem with the objective of minimization to the total tardiness and energy consumption where the energy consumption on each machine is also unrelated parallel. First, we give a mathematical model of this problem. Second, ten heuristic algorithms are, respectively, proposed based on the priority rules, the energy consumption, and the combinational rules due to the complexity of this problem. Finally, in order to test the performance of these ten algorithms, computational experiments are designed. In the computational experiments, lots of instances are generated, and the computational results indicate that the algorithms based on the combinational rules outperform the ones based on the priority rules and energy consumption, with respect to the unrelated parallel scheduling problem proposed in this paper.

Description: by Pengyi Yang, Xiaofeng Zheng, Vivek Jayaswal, Guang Hu, Jean Yee Hwa Yang, Raja Jothi Cell signaling underlies transcription/epigenetic control of a vast majority of cell-fate decisions. A key goal in cell signaling studies is to identify the set of kinases that underlie key signaling events. In a typical phosphoproteomics study, phosphorylation sites (substrates) of active kinases are quantified proteome-wide. By analyzing the activities of phosphorylation sites over a time-course, the temporal dynamics of signaling cascades can be elucidated. Since many substrates of a given kinase have similar temporal kinetics, clustering phosphorylation sites into distinctive clusters can facilitate identification of their respective kinases. Here we present a knowledge-based CLUster Evaluation (CLUE) approach for identifying the most informative partitioning of a given temporal phosphoproteomics data. Our approach utilizes prior knowledge, annotated kinase-substrate relationships mined from literature and curated databases, to first generate biologically meaningful partitioning of the phosphorylation sites and then determine key kinases associated with each cluster. We demonstrate the utility of the proposed approach on two time-series phosphoproteomics datasets and identify key kinases associated with human embryonic stem cell differentiation and insulin signaling pathway. The proposed approach will be a valuable resource in the identification and characterizing of signaling networks from phosphoproteomics data.

Description: Increasing use of the nitinol (NiTi), the nickel titanium alloy is primarily due to the fact that the medical fraternity is looking more toward less invasive medical procedures. Microengineering features such as microslots, grooves, and profiles of size 0.5 mm and below are required in the NiTi alloy-based medical components, but the material offers tremendous manufacturing difficulty due to its superior mechanical properties. High-speed micro machining was viewed as a possible way to process the NiTi-based medical components without compromising the productivity and quality of the machined surface textures. A study was undertaken to characterize the high-speed micromachining process for the NiTi alloy. More specifically, the optimization of the machining process parameters with the objective of reducing the milling forces and burr formation was focused upon. The study unveiled that the understanding the tool-work interface behavior is critically important for maximizing the machining performance of the NiTi alloy. Machining behavior characterized in terms of low cutting forces and reduced burr size was achieved at 15 m/min of cutting speed when the NiTi alloy undergoes a transition from B2 phase to B19 phase.

Description: Rapid prototyping fabricates physical prototypes from three-dimensional designing models using the additive process with layers. Aims at reducing the inevitable volumetric error induced in phrase of model slicing which impacts the shape accuracy of fabricated entity, a fast determining scheme of optimal slicing orientation for least volumetric error is proposed. The work analyses the staircase effect between two consecutive layers, then infers a direct computing formula of volume deviation of a whole model. Introduces the term of area weighted normal to express the significant effect of facet area on volumetric error and converts the optimal orientation determining problem to the least absolute deviation linear regression issue. Employs prominent components analysis on weighted normal set to obtain an approximate orientation efficiently, then optimizes the solution through few searchings in neighboring orientation space. The validity and efficiency of the algorithm are evaluated on several examples. Results demonstrate that proposed algorithm consumes less than 32 % of computation load and adaptively obtains the optimal slicing orientation.

Description: Defects diagnosis and condition surveillance of production and manufacturing rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. Condition surveillance for gear and bearing defects diagnosis for all rotating machines is a serious job because they cause accidents and consequently great production losses. For gear and bearing faults, and early detection especially in the gearboxes, researchers in the conditional maintenance and vibratory analysis used different methods and techniques in signal processing, among those and in full rise, demodulation by wavelets multiresolution analysis (WMRA) and high-frequency resonance technique (HFRT), based on the Hilbert transform, which allows filtering and the demodulation at the same time. In this paper, we propose to make a precise diagnosis for gears and bearings combined faults detection and identification in a laboratory test rig which simulate a rotating machine like in the manufacturing processes using WMRA and HFRT techniques. First of all, we applied WMRA method on simulated signals of gear or bearing defects or the combination of them, then we applied it on real signals measured on a test rig of the LMS laboratory in the University of Guelma.

Description: In this paper, we propose a new fuzzy group multi-criteria decision making method and apply it to determine the critical path in a project network. The criteria used here are time (expected duration), cost, risk, and quality of the project activities that are considered critical in project management. As each criterion has its independent level of importance in the critical path selection, the weights of the project criteria are also considered in the analysis. Considering that the information in terms of various project activities and criteria weights are often incomplete and/or uncertain in real-world situations, the essential information in terms of the criteria and project activities are obtained using triangular fuzzy numbers and/or linguistic variables that are mapped to triangular fuzzy numbers, wherever appropriate. The proposed method involves fuzzy evaluation based on the fuzzy information of the possible project paths on each criterion leading to the strength and weakness index scores of the project paths. We define a measure of criticality termed as the total performance score of each project path obtained using its strength and weakness index scores. The path that has the highest measure of criticality is selected as the critical path. A numerical illustration is provided to demonstrate working of the proposed methodology. Further, a case study from manufacturing engineering industry is also presented to better justify the applicability and potentials of the proposed methodology. A comparison with closely related fuzzy multi-criteria decision methods for the critical path selection is done to analyze the performance of the proposed methodology.

Description: A chemical mechanical grinding (CMG) wheel was developed for planarization of silicon wafers, which consists of magnesium oxide (MgO) abrasives and calcium carbonate (CaCO 3 ) additives, mixed with 25 % weight percentage of magnesium chloride (MgCl 2 ) solution. It was shown that chemical reactions occurred during the grinding process, which formed a softened layer on the top of silicon substrate. The reactants could be much more easily removed by mechanical abrasion than the removal of Si phase itself. The newly developed wheel was able to produce a similar surface integrity to that obtained from chemical mechanical polishing (CMP), i.e., the CMG achieved a surface roughness of 0.5 nm in R a and a subsurface damage layer of 13 nm thick. The CMG process developed thus has great potential for back grinding or thinning of silicon wafers in order to replace CMP.

Description: The topography of fixed abrasive grinding pad has a significant effect on the process of grinding analysis. A new numerical modeling technique has been proposed to generate the grinding pad topography with spherical grains in this paper. The simulation result was given by software. Five fixed abrasive grinding pads with different grain sizes were measured by using a confocal scanning laser microscope. Comparing the results of simulation and the experiment, it can be concluded that the simulated profile of the grinding pad is corresponding well with that of the actual pad.

Description: Underwater friction stir welding is an alternative method to improve the mechanical properties of the weldments by controlling the temperature level. Owing to the limitation of temperature measurement in practice, the finite element modeling is the best tool to investigate the process. It is still not clearly known as to what extent the temperature field of joint is influenced by operational parameters in underwater friction stir welding. In this paper, finite element modeling of friction stir welding in the air and underwater were performed for Al6061-T6 alloys to control the thermal cycles. In addition to cooling effect, the influence of welding speed and rotational speed on the maximum temperature in workpiece was investigated. For this purpose, three-dimensional modeling has been done with ANSYS. The model results were then examined by experimental data, and a reasonable agreement was observed. It is found that due to water cooling effect, heat is dissipated in faster rate which leads to low peak temperature in underwater welding compared to normal welding in air, while such relationship was not seen in high welding speeds. The reason is that at high welding speeds, workpiece temperature decreases, and region of boiling water in underwater welding is reduced. This causes that heat will be dissipated from workpiece surface in faster rate. Tool rotational speed has significant effect on thermal cycles than welding speed. Moreover, in normal friction stir welding, the peak temperature diminishes with respect to welding speed in faster manner in comparison with welding in underwater.

Description: Laser drilling has swiftly become an economical and well-regulated substitute to conventional hole drilling methods such as wire EDM, punching, broaching, or other prevalent destructive processes, because of cleanliness, accurate results, precise holes, fast material removal rate, and possibility to make holes. Prompt expansion of laser technology in current years gave us facility to regulate laser input factors such as lamp current, pulse frequency, air pressure, and pulse width. The dimensional accuracy and quality of holes are very important for some specific applications of holes. Circularity of drilled hole at entry and exit, and taper are very important attributes which influence the quality of a drilled hole in laser drilling. For this reason, the experimentation based on central composite design is carried out on austenitic stainless steel for examining the effect of laser parameters, i.e., lamp current, pulse frequency, gas pressure, and pulse width, on the quality of drilled holes. A total of 31 experiments were carried out. Later, the models were predicted for output responses using response surface methodology and then tested for adequacy. It is found that the response surface methodology (RSM) predicted models are in close agreement with the experimental values. Hence, the models may be further used for optimization of process parameters using evolutionary algorithms.

Description: In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimization problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumulative distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘closed’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technological limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing operation; therefore, the economic objectives are minimized under imposed maximum probabilities of failure. In both situations, the efficiency and robustness of optimal solutions generated by the P-NSGA-II algorithm are analysed, discussed and compared with sequence of unconstrained minimization technique (SUMT) method.

Description: Process capability indices have been widely used in industries to assess the performance of the manufacturing processes. Various different multivariate capability indices have been introduced. In this paper, a new multivariate capability vector is proposed under the assumption of multivariate normality, to assess the production capability of the processes that involve multiple product quality characteristics. Also, we investigate the relation between this index and process centering, as well as the relation between this index and the lower and upper bounds of percentage of non-conforming items manufactured. Two real manufacturing data set are used to demonstrate the effectiveness of the proposed index.

Description: by Deborah A. Striegel, Manami Hara, Vipul Periwal Pancreatic islets of Langerhans consist of endocrine cells, primarily α, β and δ cells, which secrete glucagon, insulin, and somatostatin, respectively, to regulate plasma glucose. β cells form irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Due to the central functional significance of this local connectivity in the placement of β cells in an islet, it is important to characterize it quantitatively. However, quantification of the seemingly stochastic cytoarchitecture of β cells in an islet requires mathematical methods that can capture topological connectivity in the entire β-cell population in an islet. Graph theory provides such a framework. Using large-scale imaging data for thousands of islets containing hundreds of thousands of cells in human organ donor pancreata, we show that quantitative graph characteristics differ between control and type 2 diabetic islets. Further insight into the processes that shape and maintain this architecture is obtained by formulating a stochastic theory of β-cell rearrangement in whole islets, just as the normal equilibrium distribution of the Ornstein-Uhlenbeck process can be viewed as the result of the interplay between a random walk and a linear restoring force. Requiring that rearrangements maintain the observed quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that β-cell rearrangement is dependent on its connectivity in order to maintain an optimal cluster size in both normal and T2D islets.

Description: In this study, classical and vortex tube cooling methods are compared in the pocket machining of AA5083-H36 alloy with uncoated cemented carbide cutting tool. The effects of cutting speed, feed rate, axial/radial depth of cut and nose radius and their two-way interactions on the surface roughness, and the optimization of surface roughness are investigated via Taguchi method. The experiments conducted based on Taguchi’s L16 orthogonal array (OA) are assessed with analysis of variance (ANOVA) and signal to noise (S/N) ratio. As a result, in both cooling methods, it is obtained that roughness correlates negatively with cutting speed and radial depth of cut and positively with feed rate and axial depth of cut. While in the cooling with vortex tube, lower average R a values are observed in the experiments with the nose radius of 0.8 mm, in the classical cooling almost no change is obtained. Lastly, optimum roughnesses for the classical and vortex tube cooling are obtained as 0.164 and 0.188 μm, respectively.

Description: by Ghanim Ullah, Yina Wei, Markus A Dahlem, Martin Wechselberger, Steven J Schiff Cell volume changes are ubiquitous in normal and pathological activity of the brain. Nevertheless, we know little of how cell volume affects neuronal dynamics. We here performed the first detailed study of the effects of cell volume on neuronal dynamics. By incorporating cell swelling together with dynamic ion concentrations and oxygen supply into Hodgkin-Huxley type spiking dynamics, we demonstrate the spontaneous transition between epileptic seizure and spreading depression states as the cell swells and contracts in response to changes in osmotic pressure. Our use of volume as an order parameter further revealed a dynamical definition for the experimentally described physiological ceiling that separates seizure from spreading depression, as well as predicted a second ceiling that demarcates spreading depression from anoxic depolarization. Our model highlights the neuroprotective role of glial K buffering against seizures and spreading depression, and provides novel insights into anoxic depolarization and the relevant cell swelling during ischemia. We argue that the dynamics of seizures, spreading depression, and anoxic depolarization lie along a continuum of the repertoire of the neuron membrane that can be understood only when the dynamic ion concentrations, oxygen homeostasis,and cell swelling in response to osmotic pressure are taken into consideration. Our results demonstrate the feasibility of a unified framework for a wide range of neuronal behaviors that may be of substantial importance in the understanding of and potentially developing universal intervention strategies for these pathological states.

Description: by John R. Houser, Craig Barnhart, Daniel R. Boutz, Sean M. Carroll, Aurko Dasgupta, Joshua K. Michener, Brittany D. Needham, Ophelia Papoulas, Viswanadham Sridhara, Dariya K. Sydykova, Christopher J. Marx, M. Stephen Trent, Jeffrey E. Barrick, Edward M. Marcotte, Claus O. Wilke How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E . coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E . coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E . coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.

Description: by Shaun S. Sanders, Dale D. O. Martin, Stefanie L. Butland, Mathieu Lavallée-Adam, Diego Calzolari, Chris Kay, John R. Yates, Michael R. Hayden Palmitoylation involves the reversible posttranslational addition of palmitate to cysteines and promotes membrane binding and subcellular localization. Recent advancements in the detection and identification of palmitoylated proteins have led to multiple palmitoylation proteomics studies but these datasets are contained within large supplemental tables, making downstream analysis and data mining time-consuming and difficult. Consequently, we curated the data from 15 palmitoylation proteomics studies into one compendium containing 1,838 genes encoding palmitoylated proteins; representing approximately 10% of the genome. Enrichment analysis revealed highly significant enrichments for Gene Ontology biological processes, pathway maps, and process networks related to the nervous system. Strikingly, 41% of synaptic genes encode a palmitoylated protein in the compendium. The top disease associations included cancers and diseases and disorders of the nervous system, with Schizophrenia, HD, and pancreatic ductal carcinoma among the top five, suggesting that aberrant palmitoylation may play a pivotal role in the balance of cell death and survival. This compendium provides a much-needed resource for cell biologists and the palmitoylation field, providing new perspectives for cancer and neurodegeneration.

Description: by Liat Rockah-Shmuel, Ágnes Tóth-Petróczy, Dan S. Tawfik Systematic mappings of the effects of protein mutations are becoming increasingly popular. Unexpectedly, these experiments often find that proteins are tolerant to most amino acid substitutions, including substitutions in positions that are highly conserved in nature. To obtain a more realistic distribution of the effects of protein mutations, we applied a laboratory drift comprising 17 rounds of random mutagenesis and selection of M.HaeIII, a DNA methyltransferase. During this drift, multiple mutations gradually accumulated. Deep sequencing of the drifted gene ensembles allowed determination of the relative effects of all possible single nucleotide mutations. Despite being averaged across many different genetic backgrounds, about 67% of all nonsynonymous, missense mutations were evidently deleterious, and an additional 16% were likely to be deleterious. In the early generations, the frequency of most deleterious mutations remained high. However, by the 17th generation, their frequency was consistently reduced, and those remaining were accepted alongside compensatory mutations. The tolerance to mutations measured in this laboratory drift correlated with sequence exchanges seen in M.HaeIII’s natural orthologs. The biophysical constraints dictating purging in nature and in this laboratory drift also seemed to overlap. Our experiment therefore provides an improved method for measuring the effects of protein mutations that more closely replicates the natural evolutionary forces, and thereby a more realistic view of the mutational space of proteins.