The modular bioreactor allows i) excellent control of the cell seeding process; ii) cell culture under controlled shear stress stimulus, and; iii) individual analysis of each cell type upon completion of the experiment. The advantages of the bioreactor detailed here are derived from the modification of a commercial porous membrane with an elastomeric wall specifically moulded in order to define the cell culture area, to act as a gasket that will fit into the bioreactor, and to provide improved mechanical robustness. The device presented herein has been designed to simulate the in vivo organization of a liver sinusoid and tested by co-culturing endothelial cells (EC) and hepatic stellate cells (HSC). The results show both an optimal morphology of the endothelial cells as well as an improvement in the phenotype of stellate cells, most probably due to paracrine factors released from endothelial cells. This device is proposed as a versatile, easy-to-use co-culture system that can be applied to biomedical research of vascular systems, including the liver.We describe a unique, versatile bioreactor consisting of two plates and a modified commercial porous membrane suitable for  More specifically, endothelial cells should be exposed to blood-flow derived shear stress, below hepatic stellate cells placed in the “Space of Disse”.The liver, in particular, has attracted much of the research in A variety of hepato-microfluidic bioreactors, mostly aimed at the study hepatocytes integrity, can be found in the literature in vivo organization to co-culture and individually analyze sinusoidal cells, necessary to study their integrity and their paracrine communications. In the present work, a microporous membrane separates two cell culture microfluidic chambers. This permits biomechanical stimulation in the upper compartment, paracrine interactions through the membrane, and static culture in the bottom compartment. Although previous studies have used a similar design However, none of the previously cited liver models have sufficient In our work, a bioreactor integrating a modified commercial membrane that separates two cell culture chambers is proposed to facilitate the study of the liver sinusoid. In particular, this bioreactor simulates the architecture and microcirculation in the liver in order to properly analyze the paracrine interactions between cells.This novel bioreactor is a modular system that comprises two transparent plates and a porous membrane that can be easily mounted and dismounted. To do that, the membrane is modified by adding an elastomeric wall . The walThe upper plate includes trenches where the elastomeric wall manufactured on the membrane will fit and a channel to perfuse the medium over the endothelial cell culture. To ensure a well-defined, laminar and homogeneous fluid flow along the whole channel area, and over the membrane, a parallel-plate flow chamber configuration was used The lower plate of the bioreactor contains a rectangular pool with no fluidic connection where a second cell type can be seeded while being protected from the shear stress during the cell-culture. This pool has the same area as that delimited by the elastomeric wall on the membrane. Moreover, an external pool was also designed to receive the excess of liquid from the inner pool once the bioreactor is closed.The present bioreactor aims to include a culture area large enough to allow optimal cell seeding and culture, along with multipurpose, post-experimental analysis. Although some molecular studies can be done in a single-cell, most biomedical research requires a decent amount of cells to cooperate and communicate such that a large range of molecular assays can be performed. For that, commercial hydrophilic microporous membrane filters  were chosen.2 (34×28.5 mm), a mould with the negative of the wall-structure was fabricated in Teflon by CNC machining. Next, the mold was filled with a UV-curable silicon-based elastomeric adhesive . Then, the membrane was placed between the mold and a polymethilmetacrylate (PMMA) cover. After a few seconds, the full-sandwiched system was placed under a UV Curing Light Lamp System  with irradiance of 62 mW cm−2 at the wavelength of 365 nm for 60 s in order to cure the silicone adhesive. Finally, the membrane attached with the cured elastomeric wall was gently peeled off from the mold.To manufacture the 2 mm×1.5 mm elastomeric wall on the membrane encompassing an area of 969 mmThe fabrication of the bioreactor was performed by CNC machining on a 5 mm (for the lower plate) and 8 mm thick (for the top plate where microfluidic connections are included) PMMA plates. In particular, the cell-culture pool on the lower plate had a depth of 1 mm, a distance fully adequate for paracrine interactions since it is much smaller than commonly used cell culture inserts. Meanwhile, the perfusion channel micromachined in the upper plate was 500 µm deep.In order to place the second cell type on the top of the lower plate, the PMMA surface needs to be modified The hepatic sinusoid was mimicked using endothelial cells and hepatic stellate cells previously validated as gold standard methods for vascular and hepatic research The bioreactor was assembled, as shown in 2) the full system was connected and the peristaltic pump switched on.In the current set-up, the peristaltic pump was placed outside the incubator to preserve its integrity. Then, the perfusion media was held at ambient conditions on its way from the flask to the bioreactor. This avoids bubble generation, especially if the perfusion media is stirred during preparation. To further avoid bubble generation problems, the media was degasified in a vacuum chamber before use and left in an incubator overnight. Once the media was stabilized under the desired conditions, , and by staining with DAF-FM (Invitrogen), respectively. Nuclei and membrane staining is necessary to evaluate the morphological state, the bonding and the alignment of the cells after shear stress stimulation. Similarly, the production of nitric oxide (NO) represents an excellent functional test to validate the improvement of the endothelial phenotype due to biomechanical stimulus (shear stress). Measurement of NO in real time has been described in depth elsewhere For HSC phenotype characterization, two major markers of their phenotype, alpha-smooth muscle actin (α-SMA) and pro-collagen I, were analyzed using qPCR The bioreactor presented in this work improves the usability of the reported bioreactors. In particular, the elastomeric structure that is manufactured over a porous membrane facilitates the experimental procedure as it defines the cell culture area and provides significant mechanical robustness to often delicate membranes. Then, cell seeding over the membrane can be performed outside the bioreactor, allowing the use of well-established protocols under static conditions. This results in improved test reliability since the viability of the cell culture will no longer affect the outcome.A final, but no less important, advantage of the device presented in this work is the possibility to separate cell types after the experiment. Since the cell cultures are placed on different substrates, they can easily be separated (membrane and lower plate), and tested independently as it was done in order to present the following results.As shown in Hepatic stellate cells co-cultured in the bioreactor with shear-stress stimulated endothelial cells exhibited a much lower expression of both phenotypic markers, α-SMA and pro-collagen I, than did cells co-cultured without biomechanical stimulation. This suggests that significant amelioration in the phenotype was due to paracrine signaling from stimulated endothelial cells . Considein vivo situation of the hepatic vasculature. The improved communication of hepatic cells and the modification in their phenotype validate the bioreactor design described here. Indeed, the effects of endothelial cells on hepatic stellate cells phenotype in a physiological-like environment is much prominent than those observed using ordinary co-culture assays The successful demonstration of the bioreactor described here enables improved study of the structure and function of the liver sinusoid. Permitting different cell types to be easily co-cultured under physiological conditions and separately analyzed afterwards simplifies and improves the reliability of clinical work on liver sinusoids. Importantly, our report demonstrates the improvement of hepatic stellate cells phenotype due to paracrine factors released from endothelial cells cultured under biomechanical stimulation. This has not been described in the literature to the best of our knowledge due to the unavailability of an easy-to-use bioreactor that allows cell co-culture under uniform and controlled shear stress stimulation.organ on a chip experimentation, as different cell types can be easily co-cultured in an in vivo environment. This is because of the excellent control of the seeding processes and the post-experimentation analysis possibilities; therefore, the applicability of this bioreactor in other vascular systems different from the liver is promising but will require further study.In addition, these successful results point out the feasibility of using the proposed bioreactor for basic


Anatomic aortic anomalies are seen in many medical conditions and are known to cause disturbances in blood flow. Turner syndrome (TS) is a genetic disorder occurring only in females where cardiovascular anomalies, particularly of the aorta, are frequently encountered. In this study, numerical simulations are applied to investigate the flow characteristics in four TS patient- related aortic arches . The Quemada viscosity model was applied to account for the non-Newtonian behavior of blood. The blood is treated as a mixture consisting of water and red blood cells (RBC) where the RBCs are modeled as a convected scalar. The results show clear geometry effects where the flow structures and RBC distribution are significantly different between the aortas. Transitional flow is observed as a jet is formed due to a constriction in the descending aorta for the coarctation case. RBC dilution is found to vary between the aortas, influencing the WSS. Moreover, the local variations in RBC volume fraction may induce large viscosity variations, stressing the importance of accounting for the non-Newtonian effects. The circulatory system distributes oxygenated blood and nutrients to all parts of the body. Blood flows from the heart into the ascending aorta and then to the arch where three branches  supply blood to the upper part of the body. The remaining majority of the blood flow moves into the descending aorta to be distributed to the lower parts of the body. Changes in anatomy can result in abnormal blood flow pattern, which may lead to alteration of the forces acting on the vessel wall. This may also significantly change the distribution of red blood cells (RBCs). Anatomic vascular anomalies that may occur are changes in the shape of the arch, including changes in the aortic diameter or lengthening of certain portions of the arch. The aortic valve may also consist of two instead of three cusps that may cause the valves not to close or open properly, modifying the flow pattern as the blood enters the ascending aorta. These patterns of anomalies are often observed in Turner syndrome (TS) patients.TS is a genetic disorder occurring in 1:20,000 female live births. Cardiovascular diseases are common in this patient group. The most common defect in this patient population is characterized by an increase in the distance between the left common carotid and subclavian arteries that may include a flattening and kinking of the arch. This defect, described as an elongated transverse aorta (ETA), is found in approximately m2 Bondy , b. Accom2 Bondy  and Matum2 Bondy , ETA is m2 Bondy .The underlying reason for the cardiovascular defects found in TS along with the impact of these anomalies on the pathology of the disease is unknown. However, changes in flow patterns due to aortic anomalies as are found in TS in regions that are subjected to high velocities and shear stresses may have clinical implications. This in turn motivates further studies investigating the connection of changes in aortic anatomy, blood flow pattern and systemic blood pressure over time  averaged viscosity of the mixture.The effects of the blood viscosity can be modeled at different levels, depending on the assumptions made. A detailed model uses a continuum model for the plasma and a discrete approach for the cells, following each of these. This approach allows cell deformation by using a Volume of Fluid description of the cells, here used to validate the approach adopted in this study, assuming a mixture model. An intermediate approach is, for example, that of Jung and Hassanein  using a The four different patient-related aortic arches from TS are shown in Fig. The same inflow condition and outflow boundary conditions were applied in all cases to enable direct comparison of the flow fields due to differences in the aorta geometry in different TS patients.A no-slip condition was imposed at the walls with zero-gradient condition for the pressure and scalar transport. The walls were assumed to be rigid. One could consider using Fluid Structure Interaction (FSI). FSI has been carried out for studies of the carotid artery . A time-dependent inlet velocity  was applied at the location indicated by (A) in Fig. In order to ensure that the computed flow was not affected by the inlet boundary condition, seven cardiac cycles were completed for each geometry. Figure r is the radial coordinate. The parameters In the experimental work by Aarts et al. , the disp at the outlet was computed via the following condition:internal cell values (icv) near the boundary and p is not a constant value, rather a value evolving with the flow field, while being limited by the input mean value For all outlets,  using the medical-imaging-software MIMICSICEMCFD, consisting of a hybrid cell structure where prism layers were used at the walls in order to resolve the boundary layer, followed by a layer of tetrahedral cells acting as an interface between the prism layers and the hexahedral core. For TS normal, TS dilated, TS constriction and TS ETA, the computational grids consist of 5.8, 4.3, 5.4 and 4.7 million cells, respectively. A grid sensitivity study was performed to ensure a converged solution. Figure N is the number of samples.The computational grids were generated using AnsysThe computed flow field reveals a complex flow pattern, significantly different for the four aortic arches. Figure For TS normal and TS dilated, a recirculation zone develops as the flow enters the descending aorta at approximately the same time, near peak systole. This corresponds to what has been reported in the literature for a young healthy aorta assuming a Newtonian fluid flow  and most (TS constriction) chaotic flow behavior among the four geometries investigated. The sampling locations, displayed in Fig. ses Fig. .Figure p is the total pressure (Pa), Q is the volumetric flow rate  and the oscillatory shear index (OSI) (Chen and Lu se Sect. . An \docFigure The flow patterns, blood and RBC distribution into the branches reveal a clear geometry effect. In TS constriction and TS ETA, the flow is diverted to a greater extent into the branches as compared to TS normal and TS dilated.The flow in TS normal, and different TS variants are not fully turbulent even intermittently. Transitional flow with a limited range of scales may occur when a jet is formed, like in TS constriction.The flow influences the distribution of RBCs throughout the aorta and in the branches.In TS normal and TS dilated, the recirculation zone in the descending aorta appears as an isolated bubble with lower concentration of RBCs as compared to the surrounding flow.For TS constricted and TS ETA, the flow helps to mix the RBC concentration, displaying a lower, more homogeneously distributed volume fraction of RBCs.RBC dilution is found to vary between the geometries investigated, especially in the brachiocephalic branch, the left common carotid and the left subclavian branch, a feature that in the work by Wyk et al.  was repoThe results stress the importance of viscosity modeling as the observed local variations in RBC volume fraction may induce large local variations in viscosity.The mechanical losses estimated in the aortic arch were of the same order of magnitude for TS normal and TS ETA, but were increased for TS dilated and TS constriction compared to TS normal.TS normal maintains a TAWSS around 1.5 Pa throughout the entire arch, whereas the geometries displaying anatomic anomalies are all subjected to higher values of TAWSS.The flow in four TS patient-related aortas has been investigated using a mixture model solving for the full flow field. A mixture of plasma and RBCs was introduced at the inlet of the aortic arch allowing the local density to be computed as the RBCs were convected in the flow. The local density was coupled to the Quemada viscosity model to account for the non-Newtonian behavior of blood. This is a novel approach to study these complex flows. The main findings of this work can be summarized as follows:


Recently, Kenzo Tokunaga's group reported a novel restriction factor against HIV, MARCH8, which is highly expressed in terminally differentiated myeloid cells such as macrophages. Virus infection in macrophages was first observed in HIV-infected patients in the mid-1980s  constitute the reservoir by using patients or through laboratory experiments. In support of the hypothesis that macrophages are the HIV reservoir, HIV-infected macrophages were observed in HIV-infected patients with undetectable plasma viral loads . The existence of an HIV reservoir has been postulated since just after the establishment of ART  (Tada et al., HIV, particularly HIV-1, may have evolved a way of taking advantage of the effects of host restriction proteins such as SAMHD1 and MARCH8 (Figure The author confirms being the sole contributor of this work and approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer TU declared a shared affiliation, though no other collaboration, with the author MF to the handling Editor, who ensured that the process nevertheless met the standards of a fair and objective review. The reviewer SS declared a shared affiliation, though no other collaboration, with the author MF to the handling Editor, who ensured that the process nevertheless met the standards of a fair and objective review.


In this paper, the propagation and scattering of acoustic waves in a flexible wave-guide involving step discontinuity at an interface is considered. The emerging boundary value problem is non-Sturm-Liouville and is solved by employing a hybrid mode-matching technique. The physical scattering process and attenuation of duct modes versus frequency regime and change of height is studied. Moreover, the mode-matching solution is validated through a series of numerical experiments by testifying the power conservation identity and matching interface conditions. The noise control problem is a subject of technological and scientific concern in the modern industrialized society. Frequent sources of unwanted noise such as vehicle, aero-engines and heating, ventilation, and air conditioning (HVAC) systems contribute a lot in environmental nuisance. In particular the noise is generated by the mechanical devices like combustion engine or fans etc. which propagates through the networks of ducts to the outside world.In order to minimize the transmission of such unwanted noise the sound absorbent materials, acoustic lining or silencers and novel geometrical designs are significantly used The solution to the analogous problems with continuous geometry are usually tractable by means of Wiener-Hopf techniques. However, the envisaged model problem involves discontinuity in geometry at the junction of two wave bearing ducts, thereby impeding the use of a classical Wiener-Hopf technique which is inappropriate for the problems involving the discontinuity in geometry or a material property Initially mode-matching technique was introduced to solve the canonical problems associated with the Laplace or Helmholtz operators with imposed Dirichlet, Neumann or impedance type boundary conditions. Classically, the underlying eigen-systems are Sturm-Liouville (SL) and standard orthogonality relations lead to the solution of the problem. However, for the problems involving second or higher order boundary conditions the eigen-system becomes Non-SL and appearing modes do not satisfy the orthogonality conditions. Lawrie and Abrahams In this investigation, two incident duct modes, namely structure-borne and fluid-borne modes, are considered in an elastic plate bounded duct. The boundary value problem is reformulated in a non-dimensional form with respect to length In numerical section, we have debated the power distribution for elastic plate and membrane bounded ducts for both the fundamental and secondary mode incidents. Whereas Warren et al. The rest of the investigation is arranged in the following order. The next section is dedicated to formulate the boundary value problem governing the wave propagation in the wave-guide. A mode-matching solution is constructed in the subsequent section. The edge conditions and their implications on the scattering pattern are then discussed. Graphical results are presented to discuss the distribution of power against frequency and vertical discontinuity. Finally, the important contributions of the investigation are summarized.Consider a two dimensional infinite wave-guide consisting of two semi infinite duct sections with different heights. The lower wall of both duct sections is assumed to be acoustically rigid. The upper surface of inlet duct section consists of an elastic plate whereas that of the outlet duct section is a membrane. The upper surfaces of the inlet and outlet duct sections are connected by means of a vertical rigid strip and respectively meet the strip at heights Throughout this work, a harmonic time dependence Let The natural conditions in non-dimensional form at lower acoustically rigid wall for both duct regions are in vacuo plate wave-number and Since the upper surface of the inlet duct section comprises of an elastic plate, the boundary condition at surface Here On the other hand, since the upper surface of outlet duct section is assumed to be a membrane, following non-dimensional membrane boundary condition is imposed in vacuo on the membrane where In At the matching interface, In addition, the edge conditions are applied at the points where elastic plate and membrane are joined with rigid vertical strip. These conditions not only ensure a unique solution of the boundary value problem but also describe how the elastic plate or membrane are connected to the strip. The choice of edge conditions can significantly alter the scattered field. The zero displacement (resp. zero gradient) condition at membrane edge is The above mentioned class of boundary value problems having wave-bearing boundaries has been discussed in detail by many researchers, see for instance Let an incident wave of an arbitrary duct mode be propagating in inlet duct section from the negative The first term in The dispersion relations (13)–(14) can be solved numerically for a. For each root b. There is a finite number of real roots.c. There is an infinite number of imaginary roots.d. The complex roots The real and imaginary roots are taken by employing a convention that the positive roots, The above proposed eigen-system is non-SL system Note that The complex amplitudes of Finally, multiplying (19) with Similarly by invoking (11–12) into (9), multiplying with In A common assumption, when modeling wave-guide structures, is that the duct walls are clamped at the joint. In practice, however, the duct sections may be simply supported together. Therefore this section investigates different effects that arise when the edges are (a) clamped and (b) pin-jointed at the junction. The former edge conditions are characterized by zero membrane displacement and zero gradient while the latter by zero plate displacement and zero plate bending moment. A comprehensive list of appropriate edge conditions can be found, for example, in references In this case the elastic plate is connected along vertical rigid strip edge in the clamped connection. The appropriate edge conditions correspond to be the zero displacement and zero gradient. That is On multiplying (20) with For the case in which the plate is pin-jointed (simply supported) along the edge On imposing (29) in a similar fashion as for clamped edge condition, it is found that For the given non-SL system, (20) and (23) constitute a system of infinite number of linear algebraic equations which, together with the different values of In order to discuss wave propagation in similar structures as considered herein often requires the study of the power balance. There are two admissible means of energy propagation: through the fluid and along the flexible boundary. The convenient expressions for the  energy flux across an arbitrary vertical strip in a duct bounded above by an elastic plate and membrane, and below by a rigid wall are given by The dynamic interaction between a fluid and a structure is a major apprehension in many engineering problems. These problems include systems as diverse as offshore and submerged structures, storage tanks, bio-mechanical systems, ink-jet printers, aircrafts, and suspension bridges. The interaction can extremely change the dynamic properties of the structure. Therefore, it is desired to accurately model these diverse systems with the inclusion of the fluid-structure interaction. In order to see the fluid structure interaction, the fluid and structural equations need to be represented as energy equations for reflected and transmitted modes. This analysis presents a treatment of the interaction of an acoustic fluid with a flexible structures. The numerical results presented in this section consist of comparison between reflected and transmitted components of power against frequency and change of height, for both the structural-borne fundamental and the fluid-borne second mode incidence, and to validate the mode-matching technique, conditions are verified for the real and imaginary parts of pressure and velocity at the interface In the sequel we assume that the inlet duct contains elastic plate of aluminum with thickness In It is observed that for the case of fundamental mode incidence  at matching interface and validation of power balance identity (32) substantiate the validity of the mode-matching solution.b. It is important to note that for the fundamental mode incidence the pin-jointed edges minimize the power transmission as compared to the clamped edges. However for secondary mode incidence, it increases the rates of power distribution in duct sections.c. It is worth commenting that the choice of edge condition does not affect the attenuation of flexible duct modes. In fact, the choice of edge conditions imposed on the flexible boundaries at the junction significantly affects the transmission of energy along the duct. However, it does not affect the attenuation of flexible duct modes as can be visualized in An analytic solution to scattering problem of a plane acoustic wave propagating in a rectangular waveguide involving a step discontinuity is presented. It is a well-studied phenomenon


Behaviors are highly plastic and one aspect of this plasticity is behavioral changes over age. The presence of age-related plasticity in behavior opens up the possibility of between-individual variation in age-related plasticity  and genotype-age interaction (GxA). We outline the available approaches for quantifying GxA. We underline that knowledge of GxA for behaviors is an important step in reaching and understanding of the evolution of plasticity in behavior over lifetime. In particular, the heritability (repeatability) and/or the rank order of behavior across individuals are predicted to change across ages in presence of GxA. We draw on the theory of reaction norms to illustrate that GxA, when present, is likely to lead to developmental changes in the magnitude and possibly sign of the genetic correlation between behaviors . We present an overview of the literature on changes in the ranking of individuals’ behavior across ages, and in the correlation between behaviors. Although all studies were carried out on the phenotypic level, they overall suggest clear scope for increased study of GxA as a process explaining age-related plasticity in behaviors. Lastly, we throughout emphasize that many of the approaches and underlying theory of GxA is applicable to the study of IxA, which is informative as it presents the upper limit of GxA, but is also a more attainable target of study in many systems. Empirical work aimed at understanding IxA and GxA in behavior is needed in order to understand whether patterns predicted by theory on plasticity indeed occur for age-related plasticity of behavior. Behavior is often systematically affected by environmental conditions and by internal states. Such gradients driving plasticity may be an external environmental variable (e.g. temperature), but can also be the age of the organism. In thisFirstly, a range of students of behavior ask whether variation underlying between-individual differences measured at a certain age is representative for differences between these individuals at other ages. The (implicit or explicit) assumption in most studies is that a behavior shows constant between-individual or additive genetic variance over all age classes and that the ranking of trait values  does not change across age,4. ConsiSecondly, the study of GxA relates to fundamental aspects of evolutionary biology, because evolutionary theories of senescence ,10 prediThird, knowledge of GxA in behavior is of potential interest for those who aim to gain insight in the causes of variation in behavior on the proximate level in terms of either identifying genetic loci associated with variation in behavior or studying the physiological mechanisms underlying behavior. Behaviors without GxA are indicative of a clear consistency in behavior over ages, and hence signal a behavior where the proximate determinants  are likely to be the same independently of organismal age. In contrast, behaviors with GxA require a more careful dissection because these suggest that genetic associations and physiological mechanisms are acting in an age-dependent manner.The main aim of this paper is to flag the potential importance and relevance of considering between-individual and between-genotype variation in age-related plasticity in behavior (IxA and GxA respectively). To this end, we start with a short primer on quantitative genetics and then focus in some detail on approaches used in the estimation of GxA as well as the kind of information one obtains on the basis of these approaches. These approaches are established in quantitative genetics, but are applicable also in the estimation of IxA such that they are of interest even when the focus is mostly on understanding between-individual differences rather than genetic effects. We emphasize what the putative existence of GxA would imply for changes in the heritability (and repeatability) of behavior during the lifetime of individuals as well as the correlation in behavior across ages. Because the study of GxA in behavior is largely unexplored to date, we present an overview of the patterns in age-related changes in repeatability assuming it presents an upper limit of the heritability , and chaIn this section, we briefly summarize some of the core concepts in quantitative genetics. Some of the concepts mentioned in this section are explained in the glossary variance is the resultant of a compounding of different, hierarchically structured effects, each generating its own (co)variance. That is, phenotypic variance is partitioned into additive genetic (co)variances and other sources of (co)variances including residual (co)variances. To this end, information on the relatedness between individuals (i.e. a pedigree) is a minimal requirement for partitioning out the additive genetic (co)variances, but further tools may include a breeding design or artificial selection. By markA general approach for analysis of most of the pedigree or breeding design data described above, is to use a specific form of linear mixed model, where information on the relatedness between individuals is included. This so-called “animal model” uses individual-specific measurements to estimate quantitative genetic parameters and was originally developed by animal breeders, but is increasingly used in various fields ,28. A stA central prediction of GxA (or IxA) is that, when it is present in the population, the variance between genotypes  changes across ages Fig. . The treGxA (and IxA) can be present in one of two forms. One in which the ranking of the expected behaviors expressed by individuals is maintained across all ages considered Fig. a,b,c, inTypically, the study of plasticity of behavior over age, and variation in this plasticity, is based on repeated measures of behavior made on individuals during (part of) their lifetime. It is important to note that although each individual is likely to be a unique genotype, the demonstration of IxA does not constitute proof of underlying GxA. This is because, in broad terms, the expected behavior of an individual at a certain age is not only determined by its genotype, but also by its permanent environment. These permanent-environment effects may cause between-individual differences to have no additive genetic basis. Thus, the presence of plasticity in behavior itself does not inform whether there is variation in plasticity between individuals (IxA), nor does the presence of IxA inform on GxA. Instead, IxA and GxA are properties which need to be estimated explicitly. Provided information on the relatedness across individuals is available, quantitative genetic approaches, which we outline below, can be used to quantify GxA.The core interest in modelling IxA and GxA is in the (co)variances among and between age-specific performances, as outlined in the previous section. In this section, we provide a brief overview of possible approaches to study IxA and GxA in behavior with the main objective to clarify what kind of quantities may be estimated. Although the focus in this section is on GxA, the main approach and logic applies similarly to the estimation of IxA, except that in the latter case between-individual (co)variances are to be estimated instead of additive genetic (co)variances. In order to quantify IxA and GxA, one needs to measure a behavior which is repeatedly expressed by the same individual at different ages during its lifetime. Because the interest is in age-related changes, the focal behavior shows plasticity on the individual level . Plasticity on the individual or genetic level  than at age+1 and so on. Thus, the behavior is considered to have, what quantitative geneticists term, different character states [For estimation of GxA, the basic approach is to recognize that a behavior expressed at a certainr states . Conceptr states  for detaAV for the behavior at age classes 1, 2,… ω, where ω is the last age class considered. In the off-diagonal, the pair wise genetic covariances (AC) are denoted between the age classes. Again, depending on the design of the study, the full multivariate mixed model used to estimate G (eq. 1) will include other matrices which may or may not be similarly structured as G describing the (co)variances at other levels including the residual level.which is the genetic (co)variance matrix denoting in its diagonal the additive genetic variance G matrix (eq. 1) for age classes requires ω(ω + 1)/2 additive genetic (co)variances to the estimated, which rapidly becomes demanding. For example, three age classes require six, five age classes 15, and seven age classes requires 28 genetic parameters. One possibility to reduce complexity is to group some age classes into a priori determined biologically relevant age groups . Another approach is to use a function-valued trait approach to approximate the G matrix under simplifying assumptions requiring the estimation of a reduced set of parameters . The matrix eq.  for age gression , and witgression ,2. The Rtrix eq.  for age z for individual i as a function of its age age is, at the phenotypic level, described byThe hierarchical nature of plasticity can be illustrated by considering two general formulations of random regression. The trait which can be partitioned further intoFAGE denotes the inclusion of age as a factor in order to model age-specific means. Functions ind, faf and pef describe an orthogonal polynomial of order x on the level of the individual, additive genetic and permanent environment, respectively. These polynomials capture the deviations from the age-specific fixed effect means. In practice, the coefficients of the polynomial are assumed to stem from a multivariate normal distribution [i,ageε is the (age-specific) residual for individual i.where μ is the overall fixed-effect mean, ribution . Lastly G is potentially large. For example, when a second-order polynomial  are assumed, the RRAM estimates these coefficients as stemming from the multivariate normal covariance matrixThe reduction in the number of parameters when using a RRAM to estimate V and C are variance and covariance, respectively, in the breeding value for the intercept (a0), the linear slope (a1) and the quadratic slope (a2) of the reaction norms. For a polynomial of order x, aK will be of dimension x × x.where aK is obtained, it can be transformed to G for any ages considered as appropriate character states[aK, eq. (3) to G (eq. 1); or, when studying IxA, from indK to the individual matrix ID, [K). This is because even when the reaction norms show considerable crossing (low or negative correlation between elevation and slope), the critical aspect is whether this crossing occurs within the range of ages considered and on the variances in elevation and slope  which pailed in ).A RRAM will assume that the additive genetic variances behave as a smooth function over all ages, one order higher than the polynomial of the underlying reaction norms. For exaIn this section, we explore the literature on age-related behavioral plasticity to explore central predictions of GxA. Because there are few studies which have explicitly quantified GxA, we here focus on(1) changes in the repeatability of behavior over ages, assumed to be indicative of changes in between-individual variance over ages. Repeatability presents the upper limit of heritability . Assumin(2) We explore age-related changes in the ranking of behavior, which have been estimated primarily as the phenotypic correlation of a behavior expressed by individuals at different ages. Under the assumption that the behavior is heritable, and that age-specific residuals are not correlated, such phenotypic correlations should present the lower expectation of the genetic correlation in behavior between ages (cf. ).We performed a search in ISI Web of Knowledge using different combinations of keywords  and selected only studies on behavior reporting estimates of repeatability for, or rank-order consistency between consecutive ages or ontogenetic stages. However, most of these studies did not test the statistical difference between ages and only provided information on whether each point estimate differed significantly from 0 (p<0.05). In total, we found 39 publications ,40-77, sThe importance of quantifying changes in the repeatability of behavior with age has been recognized (e.g. ). BehaviFew studies specifically addressed the question of lifelong stability of repeatable behavioral differences in animals; repeatability of behavior at old age is typically not explored. For example, animal breeding research benefits from the largest body of literature on the development of personality, as the interest of breeders is to predict the behavior of animals or other traits of interest, based on behavior expressed early in life. However, many of these studies are restricted to a relatively short time-span (from birth to maturity) and do not explore the question of aging, which is of interest mainly for behavioral ecologists and psychologists. The same observation can be made in behavioral ecology where most studies on the development of behavior focus on the earliest life-stages, rather than the entire lifespan. Changes in repeatability over age have been investigated in a meta-analysis  where noThe correlation in behavior across age classes is regularly measured in studies on animal behavior. Typically, behavior is measured once at different ages and a (Spearman rank) correlation is calculated for the behavior between consecutive ages . However, studies done thus far are largely restricted to the phenotypic level and do not separate correlations due to additive effect of genes from residual correlations or permanent environmental correlations. Hence, changes in the rank order of individuals for certain behavioral traits across their lifetime do not necessarily indicate the presence of GxA. Indeed, phenotypic correlations typically underestimate the magnitude of genetic correlations, although their sign is generally consistent with the sign of genetic correlations [Taken together, the overview of literature of behavioral studies in this section suggests there is considerable scope for changes in the ranking of individual expression of behavior during development. In particular, the modal phenotypic correlation of a behavior in animals across ages was 0.3 . We calculate the expected correlation and its 95% credible interval between these behaviors in adults for 1000 genotypes randomly drawn from a multivariate normal distribution. We then assume that the correlation of each behavior across ages (rJUV-AD) varies from 0 (behavior in juveniles and in adults are completely uncorrelated) to 0.9 (behavior as a juvenile and an adult are highly correlated). Clearly, the expected correlation between the two behaviors in the adult stage rAD is always lower than in the juvenile stage (because E(rAD) = (rJUV-AD)2 rJUV), as illustrated by the expected value  is expected to change as the organism ages ,106. We When considering more than two ages, it becomes particularly clear how different patterns of changes in the correlation between behaviors during aging can be due to different types of GxA. To illuTheoretical considerations ,106 reviThe take-home message of this paper is that when there is plasticity in behavior over ages, there may be variation in age-related plasticity between individuals (IxA) and/or genotypes (GxA). Whenever there is variation in plasticity, there is the clear expectation that repeatability and/or heritability of a focal behavior varies over ages. In addition, it is likely that the ranking of individual-specific and/or breeding values for the focal behavior change as a function of age, thereby leading to low cross-age correlations in behavior, and changes in the correlation between multiple behaviors. Thus, calculation of the repeatability or heritability of a behavior or the correlation between multiple behaviors while pooling all age groups may give a misleading impression of the consistency in behavior or a behavioral syndrome. Our literature overview, based on phenotypic measures, indeed illustrates that the consistency of behavioral differences between individuals across ages may be low, despite a behavior being repeatable when measured at different ages. We believe that changes in repeatability and heritability over ages as well as appreciation of cross-age correlations are of importance if we are to properly understand the sources of behavioral variation from a lifetime perspective.We have outlined conceptual models, which illustrate why the presence of IxA and GxA is expected to lead to age-related changes in repeatability, heritability and low cross-age correlations in behavior. In addition, we briefly underlined the quantitative genetic approaches which provide accessible means to model age-related plastic changes in behavior on the individual and/or genetic levels. In general, there is a clear scope for increased uptake of quantitative genetic approaches in the study of behavior . We beliThe authors declare no competing interests. Publication costs for this article were funded by the German Research Foundation (FOR 1232) and the Open Access Publication Fund of Bielefeld and Muenster University.JEB conceived the study and wrote the first draft. BC performed the literature search and compiled the literature estimates. JEB and BC jointly wrote the paper, and its final version is approved by both authors.Glossary of terms used in this paperClick here for fileSchematic illustration of how changes in the age-specific expression of behavior by an individual are modelled as deviations from the age-specific meanClick here for fileOverview of literature estimates of the correlation of behavioral traits across age classesClick here for fileR script for producing the simulations in this paperClick here for fileOverview of the literature considering changes in the correlation between two or more behavioral traits across agesClick here for file


We propose a model of neurite growth to explain the differences in dendrite and axon specific neurite development. The model implements basic molecular kinetics, e.g., building protein synthesis and transport to the growth cone, and includes explicit dependence of the building kinetics on the geometry of the neurite. The basic assumption was that the radius of the neurite decreases with length. We found that the neurite dynamics crucially depended on the relationship between the rate of active transport and the rate of morphological changes. If these rates were in the balance, then the neurite displayed axon specific development with a constant elongation speed. For dendrite specific growth, the maximal length was rapidly saturated by degradation of building protein structures or limited by proximal part expansion reaching the characteristic cell size. Such models can assist in the understanding of how lower level dynamics can be projected to a system level outcome. These models involve construction of large scale networks mimicking the morphology and functions of particular brain circuits, such as models of thalamocortical system . The key difference of the existing models is in description of building protein delivery to the growth cone. In . It should be noted that usually the segments radius changes when the neurite branching occurs. These models reproduce a wide variety of developmental scenarios, for example dendritic trees formation accounting the calcium-dependent processes of MAPs (de-)phosphorylation . We assumed that the intracellular machinery regulating the synthesis compensates for building proteins utilized for the elongation . In other words, the concentration of tubulin heterodimers in the soma is sustained near a constant level  during their transport. Consequently, the number of structures can be formulated in the following form:To describe the evolution of the tubulin concentration along the neurite, the following approach was used. Let's consider number of tubulin structures moving along toward growth cone at constant velocity N0 is the number of structures localized in the proximal part of the neurite (near the cell soma), N(x)—at the distance x along the neurite , Vat is the speed of the active transport, and τ is a constant describing the rate of building material (tubulin structures) degradation. The resulting equation can be expressed in terms of concentration. Thus a concentration profile in case of shape independent model  can be represented asWhere, C0 denotes the tubulin concentration in the proximal part of the neurite  and C(x) is the profile of the tubulin concentration along the neuronal outgrowth. The concentration of the building material localized in a growth cone (Cgc) available for neurite elongation can be obtained from Equation (2) as Cgc = C(L) . Assuming that the intensity of microtubule polymerization is a function of available building protein, the rate of the elongation process in a quasi-steady-state approximation can be expressed in the following form:Where, Where, α and β are constants representing rates of neurite growth and contraction, respectively.Cgc = Ccr = b/a) the neurite growth stops (microtubule assembly is fully compensated by depolymerization process). Thus, the maximum neurite length is defined by the following valueIt follows from Equation (3) that the elongation rate decreases exponentially with increasing neurite length. Moreover, when the concentration in the growth cone reaches some critical level , which changes with the distance from the soma and total neurite length. Because cross-sectional area near the growth cone is assumed to be constant, the following boundary condition should be satisfied: R|x = L = r. Thus, using Equation (1), we obtained the following relationship:Next, we analyzed the effect of neurite geometry on the elongation dynamics. Our results suggested that the shape, e.g., neurite tapering with the distance from cell soma, may have a significant impact on the developmental dynamics. To account for this we made the following assumptions. First we assumed that the neurite cross-section near the growth cone is fixed and does not depend on the neurite length. Second, a possible source of soluble tubulin for neurite caliber enlargement may be a building material, which is released during depolymerization of microtubules transported. Thus, it does not reach the growth cone, while it can be used for the local changes in the neurite. Third, for simplicity, we assumed that the cross-section at any distance from the soma has a circular shape with a radius, S is the cross-section area and C is the concentration profile along the neurite , S denotes the cross-section area of the proximal part of the neuronal outgrowth. In this case, the concentration of building material localized in the growth cone (x = L) and available for neurite elongation is defined by the following Equation:R and r denote the radius of the cross-section near the cell soma and the growth cone, respectively. Also geometrical restriction (GR) introduced here meaning that proximal segment radius cannot exceed the soma size. Thus, tubulin concentration in the growth cone depends on the caliber ratio (CR) R/r.Where R dependence. First, we considered the linear dependence of the radius on the total length of the neurite, i.e., R = r (1 + k (L - x)), R = r (1+k L) where k is a tapering constant. It follows from Equation (6) thatTo illustrate how the neurite geometry influences the elongation dynamics, we investigated two options of 1 + kL)2, i.e., providing positive feedback. This is a cooperative effect of two processes. The first one is an increase of tubulin influx due to expansion of the proximal part. On the other hand, the increase of tubulin synthesis occurs, since, as previously noted, intracellular machinery maintains the tubulin concentration in soma at a fixed level. Thus, a feedback, stimulating the elongation process emerges. However, with increasing length, the portion of available tubulin decreases , hence slowing the elongation process. Note that this model prediction is in good agreement with experimental observations of dendrite growth  sufficient to compensate for the loss of tubulin associated with degradation during the transport over longer distances. Note also that the neurite segment cross-sectional area and its volume decrease with distance at the same rate as the number of tubulin structures moving toward the growth cone. Therefore, the building protein concentration in the growth cone can be sustained at some constant level regardless of the distance from the cell soma. Thus, in the case where k = τ/Vat, the model predicts a sustainable elongation process with a fixed elongation rate:Let us focus on the case of exact balance, i.e., As a validation of the presented hypothesis the comparison of numerical calculation results and experimental studies of axonal outgrowth is performed. Figure k < τ/Vat, the neurite elongation dynamics were quite similar to the linear case considered earlier. The proximal segment expansion resulted in an increase in the amount of building proteins transported from the soma to the growth cone. However, this increase compensated for only a part of the tubulin loss due to degradation. As a result, the elongation process slows down over time and stops when a maximal length is reached , the geometric constraint of limited proximal segment size provides a growth saturation factor.For s Figure . HoweverNote that for this case there is a bias of the balance toward positive feedback, e.g., building proteins influx exceeds the amount of tubulin needed to compensate its degradation with increasing the neurite length. Thus, the outgrowth elongation accelerates with the course of time. However, the growth rate is limited by the active transport velocity and accelerated growth is replaced by the steady elongation.We proposed a mathematical model of neurite development based on the microtubule cytoskeleton dynamics. The model incorporated basic molecular mechanisms underlying the elongation and demonstrated growth dynamics which is consistent with experimental studies of neural development .Hence, depending on the parameters of the growth kinetics, several scenarios corresponding to dendrite or axon growth can be achieved. These types include the dendrite specific kinematics with slowing speed and axon growth specificity with constant elongation speed over long distances.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Humans depend on clothing protection to minimize the thermal burden imposed on the body by the surrounding environments to which they may be exposed . The abA numerical approach  was used to study the steady-state transport phenomena within inclined clothing microclimates, formed by air layers trapped between skin and air-impermeable fabrics. Focus was put on the effect of microclimate inclination relative to gravity in order investigate how natural convection affects the local heat transport near the skin, for different body regions (or postures). The analysis addressed both the flow in the microclimate and in the surrounding environment, in order to analyse how heat is transported between the body and the environment.The flow patterns inside clothing microclimates were observed to strongly depend on the inclination of microclimate as it rotates relative to gravity direction. The multi-cell flows observed for the horizontal set-up (i.e. warm skin below the cold fabric), switched to single-cell flows for the vertical set-up, with substantial impact on the temperature maps observed inside the microclimates. This resulted in considerable changes on the local transport rates observed along the skin and along the clothing layers, with clear impact on both the fabric temperature and the external convective coefficient obtained along the clothing surface. The latter was found to differ from the correlation-based convective coefficient curve obtained along surfaces with constant-temperature.The obtained results show the importance of the microclimates inclination (i.e. position relative to gravity) in determining the transport processes across clothing. It also stress the importance of considering both the internal flows (i.e. inside clothing) and the external flows (i.e. outside clothing) when analysing or predicting the transport phenomena to/from the body. This is needed to obtain representative insight on the effects influencing the local transport rates between the body and the environment, and is critically relevant when studying ways to optimize the performance of products used near and around the body . For the latter, further investigation is needed to clarify the interactions between the microclimates inclination, dimension and shape relative to the body.


The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The  We have developed a methodology that combines standard computational analysis of gene expression data with knowledge in the literature to identify pathways of gene and protein interactions. We extract the knowledge from PubMed citations using a tool (SemRep) that identifies specific relationships between genes or proteins. We string together networks of individual interactions that are found within citations that refer to the target pathways. Upon this skeleton of interactions, we calculate the weight of the interaction with the gene expression data captured over multiple time points using state-of-the-art analysis algorithms. Not surprisingly, this approach of combining prior knowledge into the analysis process significantly improves the performance of the analysis. This work is most significant as an example of how the wealth of textual data related to gene interactions can be incorporated into computational analysis, not solely to identify this type of pathway (a gene regulatory network) but for any type of similar biological problem. Gene regulatory networks (GRNs) are DNA-encoded regulatory subsystems in the genome that coordinate input from activator and repressor transcription factors to control various biological functions, including development, cell differentiation, and response to environmental cues. They provide a systems level illustration of physiological function and are composed of modules at varying hierarchical levels The development of microarray technology allowed the discovery of gene regulation to move from individual interactions to thousands of interactions in parallel predications (subject-relation-object triples). These predications mostly represent mammalian and specifically human interactions, therefore we use quantitative data provided by analysis of publicly available human breast cancer microarray datasets using a genetic algorithm. We compare the fitness of our model to that of a high-performing model from the literature to determine the performance of our technique as well as another model based on time-delay correlation. We also compare our results to KEGG pathways and find not only included interactions but also interactions not included but validated in the literature. We then modified the components of our method to be compatible with yeast data and again compared with the state-of-the-art model. Our method provides a novel approach to enhancing microarray data analysis with knowledge from the literature, and is the first such approach to incorporate semantic relations. Combining microarray data with semantic relations provides a more accurate model of gene interactions directing the behavior of cells, and consequently an enriched understanding of molecular physiology. This supports the discovery of disease mechanisms, which can then be exploited for diagnostic and therapeutic development in medicine.We propose utilizing knowledge extracted from publications as a network of interactions and then applying microarray data to provide a measure of the quantitative effect of each of the individual interaction components. The qualitative knowledge at the core of our approach is provided by SemRep, a natural language processing system that extracts textual meaning from the biomedical literature in the form of semantic relations called t to be considered as depending only on the previous time point t-1. A Bayesian network is a representation of a joint probability distribution. When applying Bayesian networks to genetic regulatory systems, nodes are identified with genes and their expression levels, edges indicate interactions between genes, and conditional distributions describe these interactions. In the stochastic differential equation, the change of expression of a given target gene over two adjacent time points is equal to the accumulation of the weighted results of the sigmoid function of its regulator genes plus a random error. These computational models all reflect one or more aspects of the nature of genes and gene regulatory networks, but computational complexity limits the dimensionality of the modeled networks, and sensitivity to noise in gene expression measurement largely reduces their accuracy.Previous computational models used to reconstruct gene regulatory networks from microarray data have employed techniques such as Hidden Markov, Bayesian network, and stochastic differential equation models Genetic algorithms (GA) With the rapid rate of growth in the biology literature, text mining is increasingly seen as indispensable in managing and discovering new biological knowledge Bacillus subtilis involving these entities. For the latter task, participating groups could either directly construct a regulation network for the provided entities or extract the interactions from which such a network could be derived using a predefined algorithm that the organizers provided. While the results were encouraging, these tasks remain challenging as evidenced by the limited participation and the fact that both tasks presupposed that the entities involved were already known, thus, addressing only a fraction of the problem of network construction from the literature. From an opposing viewpoint, Miwa et al. As these text mining methods mature, they are increasingly applied to practical needs of biologists, in tasks such as database curation and pathway generation. For example, two tasks in the recent BioNLP 2013 Shared Task competition Escherichia coli K-12, to establish the network of causal relationships to evaluate the congruence between the literature and whole-genome expression profiles There have been several attempts at improving gene regulatory network modeling by incorporating existing knowledge. For example, Steele et al. used the correlation between different gene concept profiles to calculate the probability of edges in GRNs modeled by Bayesian networks Several books, book chapters, and journal reviews are available detailing the pros and cons of various modeling approaches for GRN inferencing The idea that the literature holds a wealth of data that can be systematically used in the development of hypotheses has been a point of discussion in biology for over a decade p53 induced the pro-apoptotic Bcl-2 family members Bik and Bak and downregulated the anti-apoptotic Bcl-xL protein. (PMID: 11313989)In addition to the upregulation of death receptors, p53-STIMULATES-Bikindicator rules that map lexical/syntactic constructions, such as verbs, nominalizations, and modifier-head structures, to UMLS Semantic Network relations. For example, one of those rules stipulates that the verb induce may indicate the relation STIMULATES, and this rule licenses mapping induced in the example above to this relation. The subject and object arguments  are recognized using MetaMap and ABGene and are then normalized to official EntrezGene symbols. Several studies have evaluated predications extracted by SemRep. For example, Ahlers et al. reported 73% precision and 55% recall in pharmacogenomics relations SemRep relies on the UMLS SPECIALIST Lexicon http://www.ncbi.nlm.nih.gov/geo/) http://genome-www.stanford.edu/cellcycle/) originally used in The breast cancer microarray data used in our experiments comes from NCBI's Gene expression omnibus (GEO: http://www.genome.jp/kegg/) For assessment of our methodology, we use the Kyoto Encyclopedia of Genes and Genomes  were used to guide the predication network generation and also in evaluating the resulting GRNs. These pathways are the p53  map  AND )) AND physiology[sh] AND metabolism[sh].” This procedure was repeated to provide a citation list for each pathway. The number of citations for each pathway is given in In the first step, predication network generation, predications containing gene-gene interactions  are extracted from the MEDLINE citations that supported manual curation of each KEGG pathway; these citations are listed in the pathway entry on the KEGG website. These predications are augmented with those from additional relevant citations, which we identify by first extracting the Medical Subject Heading (MeSH) terms for each citation identified in KEGG and then manually refining that list to eliminate non-specific terms such as Humans, Male, or Biological Models. The resulting MeSH terms formed the basis of our PubMed searches. The terms that occurred in a significant distribution across the citations were grouped with an “OR” in the query when they were roughly equivalent or part of a set of different subtopics. As an example, the query for p53. This example has been pruned to provide a small network for simplicity.To improve the reliability of our approach, we used three predication filtering mechanisms, explained below. These mechanisms result in a smaller set of predications extracted for each pathway, which forms subnetworks. These subnetworks, together, serve as the predication network. Bax and pro-caspase-3 are potential objects for p53 activity.Argument-predicate distance is the number of intervening noun phrases between an argument and its predicate in the sentence from which they were extracted. It has been shown that smaller argument distance leads to higher precision in extracting interactions p53 promoted Bax expression, which led to the activation of pro-caspase-3 and eventually to apoptosis in MCF10A-ras cells. (PMID: 5566875)3. Furthermore, the up-regulation of Bax has an object distance of 1 since it is the first noun phrase subsequent to the predicate ‘promoted’ and pro-caspase-3 has a distance of 2. Preferring an argument-predicate distance of 1 selects the predication p53 STIMULATES Bax, while eliminating the predication p53 STIMULATES pro-caspase-3. In our experiments, we limit both the predicate-subject distance and the predicate-object distance to 1, which decreased the number of predications to approximately 27% of the initial number of predications on average. The number of predications for each pathway after filtering using argument-predicate distance of 1 is shown in http://www.genenames.org/). If a predication argument cannot be mapped to a formal gene symbol, the predication is pruned. The numbers of predications after duplicate removal and normalization are given in After argument-distance filtering is applied, we normalize the set of predications by removing duplicate predications and mapping the subjects and objects to formal gene symbols based on the standard gene name dataset  and +1 (greatest stimulation), indicates the strength of interaction. A weight of zero indicates no interaction. The predications in the network define which genes have interactions and whether the interaction is inhibitory or stimulatory, but do not contribute to the determination of the weight. The absence of an interaction is represented in the matrix with a weight of zero but is not altered through subsequent generations. The direction of the interaction is maintained from the subject-object relation in the predication. We randomly generate 2000 chromosomes that contain different weights for the interactions in the predication network. The gene structure in each chromosome is the same, as well as the non-interacting/zero-weighted gene pairs, but the weights representing the strength of inhibition or stimulation of pairs found in the predications are varied. The population of chromosomes is then evaluated with the fitness function in T indicates the number of time points in the microarray dataset, R is the number of microarray replicates at each time point, r is the current replicate, n is the number of genes in the pathway, K is the activation function, j and gene i, and i at time point t. Since the difference of the expression value of a gene over each time course is considered to be a direct function of only the joint effect of other genes in the pathway and the gene expression values used have been normalized between −1 and 1, we use a linear function as the activation function, effectively dropping the term. A visual representation of the ANN model is presented in In our experiments, we use an artificial neural network (ANN) to model the interaction of the genes in each pathway. In this model, the gene expression level of a given gene at a given time point is a function of all other genes at the previous time point see . In a coSaccharomyces species and processing relevant citations (resulting from the PubMed query “Saccharomyces[mh] AND cell cycle[major]”) to extract predications. All subsequent procedures were consistent with the breast cancer study except that the predications were not filtered by argument distance or document frequency due to the lower initial number of predications. The number of citations and predications at each step are given in MEDLINE contains many more citations related to human genes than yeast genes. A simple PubMed search for “yeast and gene” returns a little more than 86,000 citations while the query “human and gene” returns over a million. When combining either species term with “cell cycle” (a concept with a heavy amount of research done in yeast), there are over 96,000 citations related to human and just over 15,000 for yeast. In addition to this limitation, SemRep was designed to identify human genes and uses Entrez Gene database entries specific to humans for genes and proteins. Although this led to an initial study with human genes, we made modifications to SemRep to support a study with yeast. This required changing the data source to the Entrez Gene fungi dataset that contains p53 pathway. Comparison to a KEGG pathway provides an assessment of the contribution of our natural language processing techniques .Evaluating a gene regulatory network inference model for human data is challenging, since there is no gold standard providing a complete reference of gene connectivity. Therefore, we evaluated the implementation of our approach using breast cancer in two ways: (1) comparing the accuracy of the model against the highest-performing model reported in the literature, (2) comparing the resulting networks to KEGG pathways and literature that formed the basis for their extraction, paying particular attention to the −8). The RSP model performed significantly worse than both of the other models. This demonstrates that our model is significantly better in terms of fitting the microarray data.As a measure of accuracy of the model, we determine how well the model fits the data, in line with previous research p53 pathway had the highest ratio of common edges compared to the kgml network , so was chosen for further validation at the predication level.We assessed the value of gene regulatory networks generated by our approach by comparing them to KEGG pathways, both the kgml format downloaded from the KEGG website and manually using the search function in the online pathway map. Nodes, representing genes, and edges, representing interactions, were independently compared and the results are shown in p53 signaling pathway map. 49 of the 92 interactions contained a gene not included in the map, 7 had both genes present but no interaction, and 9 existed in the map but were not included in the kgml version.A comparison was made for each of the new interactions with the online KEGG p53 network against the literature in three ways: a) whether the literature asserts an interaction between the two at either a gene or protein level , b) whether we capture correctly the direction of the effect, i.e. stimulatory or inhibitory, as compared to the weight generated by GRN analysis of the microarray data, and c) combining the two above, whether we capture correctly both the presence of the interaction and the direction. In addition to assessing the accuracy of SemRep in capturing relevant interactions, this validation allows us to establish the specific contribution of using semantic predications over term co-occurrence. The comparison was limited to citations from which the predications were extracted, ranging from 1 to 87 citations for each interaction. Although there may exist another citation that would validate the resulting interaction, this approach was taken to limit the man-hours required to a reasonable amount while still allowing a reasonable possibility for validation. As seen in p53 network  was eliminated due to argument-distance filtering, while the other two (Jak1 INTERACTS_WITH STAT1 and GRB2 INTERACTS_WITH PTPN11) passed the argument-distance filter but not the document frequency filter. Note that the former (Jak1 INTERACTS_WITH PTPN11) occurred in a single document and, therefore, would also be eliminated in the subsequent document frequency filtering step, had it passed the predicate-argument distance filter.Finally, we investigated whether argument-predicate distance filtering had a detrimental effect on the results, since long distance syntactic dependencies are common in biomedical literature Comparison of model accuracy, but now on the yeast cell-cycle dataset, which contains 24 time points. We compared the performance of our model against that of Keedwell et al. in the same manner as mentioned in our In this work, we augmented microarray data with literature knowledge to infer gene regulatory networks, replacing the random selection of component genes generally used in similar modeling efforts. We use SemRep to extract information from the literature, which forms the backbone of the network. State-of-the-art genetic algorithm-based analysis of the microarray data was used to determine the strengths of the effects between gene-gene interactions in the network. Our model provides better fitness than the state-of-the-art model used as the basis for the genetic algorithm and fitness function components of our model and the difference between error rate of the models is statistically significant (p<0.05), demonstrating that literature-derived knowledge provides a significant advantage over random selection of genes in this task, as suggested in TP53, MDMD2, BAX, CDKN1A, GADD45A, and CDK2 in the p53 network) and known interactions  demonstrate the potential of this technique to replicate “known” pathways. Perhaps more importantly, we were able to identify new interactions that were not included in the KEGG maps, which is not surprising since these maps are curated and therefore provide only the most thoroughly established and important interactions as determined by the curators.Although some of the genes in the KEGG pathways did not appear in the results due to the strict filtering process, the presence of essential member genes in the resulting predication networks  is not included in the KEGG P53 pathway map. It is a pro-apoptotic protein discovered in 1995 and interacts strongly with BCL-2 and BCL-xLP53 has been shown to induce expression of BIK under certain conditions, especially in breast cancer cells, as in our microarray dataset p53 pathway result also included an interaction between MDM2 an CDKN1A with a weight of −0.987, i.e., very strongly inhibitory. MDM2 is shown to inhibit p53 in the KEGG p53 map but there is only an indirect inhibitory action of MDM2 on CDKN1A through P53 (by diminishing p53's activation of CDKN1A). However, a direct interaction as a negative regulator of CDKN1A is present in the literature Two of the highest-weighted new interactions included in the resulting A major limitation of this method is the accumulation of system errors. As shown in Currently our network backbone is limited to genes included in the predications, but this set could be expanded by incorporating genes from the microarrays that are similar to the genes provided by the predications, using an ensemble of machine learning techniques such as support vector machines, random forests, and prediction analysis of microarrays. This approach of classifying genes from the microarray into each pathway would additionally reduce bias in the technique by not limiting interactions to what is already known. It would be valuable to maintain a distinction between the literature-based genes and the expansion set because a significant expansion in the final resulting networks would suggest that what is known in these pathways is only a fraction of what is waiting to be discovered.Another technique to extend the set of genes would be to adopt more sophisticated predication filtering mechanisms. The current mechanisms were effective in significantly reducing the computational complexity; however, our limited analysis of the effect of argument-distance filtering showed that some relevant predications were also eliminated due to filtering. A potentially useful approach would be to train a classifier that can identify ‘good’ predications, based on predication features, such as the predicate type, indicator types, etc. Predicate-argument distance could also serve as a feature for such a classifier.Additionally, although we used interactions from KEGG to evaluate our GRN, such interactions from this and similar databases could be incorporated into our interaction network to augment the proposed interactions from literature with established interactions, expanding the utility of prior knowledge.Although our current method of selecting source citations for predication extraction yielded usable results, there are many possible permutations in the method and a systematic comparison of source citations and their resulting networks should be explored. Our current search method facilitates the generation of hypothetical member genes for established pathways, but this approach can also be used to generate novel pathways by specifying a gene set and/or biological functions in the predication citation search, thereby providing an appropriate predication network.Related work section), have been gaining in popularity in the BioNLP community, mostly due to available corpora SemRep representation of biomolecular interactions (subject-predicate-object triples) is simple, intuitively accessible and lends itself easily to downstream applications. On the other hand, more complex representations, particularly event representation (as discussed in http://www.cytoscape.org) against a Biogrid (http://thebiogrid.org) yeast interaction dataset. We used the Biogrid Saccharomyces_cerevisiae-3.2.109 tab file, which contains 339,921 interactions and experimental information from multiple database sources including Saccharomyces Genome Database, MINT, IntAct, and Pathway Commons. As seen in To assess the precision of interactions for the yeast study, we performed an automated comparison using Cytoscape Click here for additional data file.


It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the  Facial images convey a substantial amount of information such as the individual's identity, ethnicity, gender, age, and emotional state Aging affects the appearance of a face in diverse ways. It has been observed that every person has a personalized aging pattern depending on numerous factors such as genetics, ethnicity, dietary habits, environmental conditions, and stress level There are two aspects of building an age-invariant face recognition system: (1) facial age estimation and (2) age-separated face recognition. Accurate age estimation is crucial in a variety of situations such as the need to automatically estimate the age of an individual buying alcohol or cigarettes. In an extensive literature review on age estimation by humans, Rhodes The problem of perceived facial age has also been studied by computer vision researchers. Kwon and Lobo Some researchers have dedicated their research on finding the effect of group bias on the performance of age estimation. Anastasi and Rhodes The other aspect of facial aging is face recognition across aging. Lanitis et al. The above mentioned research efforts in computer vision domain suggest that there is a vast scope of improvement in developing automated systems which can mitigate the effect of facial aging. The objective of this research is to study the process of facial aging from the perspective of human cognition and to take cues from it to improve face recognition algorithms. This would enable us to achieve age invariance in facial recognition. Key contributions of this research are listed below.1. Analyzed human performance for estimating facial age. Specifically, weevaluated faces pertaining to different age groups and analyzed facial age estimation results;investigated facial cues that are utilized by humans to precisely estimate the age of people belonging to various age groups; andanalyzed participant's gender, age, and ethnicity bias on the ability to predict age.2. Studied human performance for recognizing age-separated images. Specifically, wedetermined if humans are able to recognize age-separated images of the same person;assessed which local features are employed for the above mentioned face recognition task; andevaluated if the cues gathered from the human study can be utilized to develop an age-invariant face recognition algorithm.3. Developed an algorithm that incorporates the observations obtained by analyzing the results of the first two studies.The proposed algorithm first estimates the age group of a probe image. In this research, we have focused on estimating the age group rather than the exact age. This can potentially help in (1) indexing images across ages, and (2) learning important characteristics such as features for every age-group, which can be utilized during automatic face recognition.Once the age group is estimated, texture features are extracted for different facial regions. For matching, the weights associated with each facial region captured from human responses are used to combine the information for decision making. The experimental results suggest that incorporating human performance in algorithm enhances the capabilities of automatic face recognition.To the best of our knowledge, this research is the first to study various aspects of facial aging, analyze human perception of aging facial features, and integrate these findings in an automatic face recognition algorithm.The study was conducted at Amazon's Mechanical Turk (MTurk), which is an online crowd-sourcing platform. At MTurk, only individuals who are above 18 years of age can register and work as participants. We follow the policies of MTurk which clearly transfer the rights of any survey to the requester and the participants are informed of this at the time of their online registration. The participants' consent to fill and submit the survey is taken as their willingness to participate in our study. Further, at the beginning of the study, we also inform the participants that their responses would be used for research and analysis purposes. The images shown in the paper belong to the authors of the paper and they are used for illustrative purposes. The authors in this manuscript have given written consent to publish their images. All the procedures used in the current study are approved by the Indraprastha Institute of Information Technology (IIIT)-Delhi Ethics Board.requesters to post tasks such as surveys, studies, and experiments which are, in turn, completed by the participants or workers. The participants are paid an amount fixed by the requester upon successful completion of the task. A research conducted by Buhrmester et al. Amazon's MTurk is a platform that enables researchers to conduct research by offering features such as: a unified participant compensation model, participants having diverse demographics, an efficient procedure of study design, participant enrollment, and data gathering. MTurk allows the researchers or the 366 Indian adults (Mean Age (M)  = 33.45 years, Standard Deviation in Age (SD)  = 11.67 years, 149 males, 217 females),81 Caucasian adults ,29 Asian (non-Indians) adults ,3 African adults , and3 participants with undisclosed ethnicity .The responses from all the participants have been analyzed in the study in order to preserve the diversity in the responses.The stimuli faces have been selected from 36 male and 18 female subjects from the FG-Net Facial Aging Database For evaluation, 10 sets of assignments are created and one set is randomly assigned to every participant. Each set contains three questions.The first question contains five facial images and the participants are asked to estimate the age group from the given face image. Similar to a previous research Five images of various facial regions such as the T-region, binocular region, chin region, eyes portion masked, and T-region masked are shown to the participants. They have to estimate the age group corresponding to every facial part individually. In the last set of questions, five pairs of age-separated images are shown to the participants and they are asked to determine if the pair of images belongs to the same individual or not. Some sample images are shown in Each participant is randomly assigned one of the 10 sets. The participant is supposed to answer the three questions in the Stimuli section. There is no time constraint on the participant to submit the responses. Each participant sees a face image and an identity only once to ensure there is no bias. In all the questions, a mixture of stimuli from different ethnic groups and ages is presented to each participant.The analysis of responses obtained are classified into four categories and key observations are discussed in this section.The responses on predicting age group based on the face stimuli presented to participants are summarized in a stimulus-response confusion matrix shown in Sensitivity,Specificity,Discriminability index (Information entropy.Sensitivity (or accuracy) represents the true positive performance specificityIn response to different visual stimuli, the participants need to make a decision on the correct age group. For each face stimulus shown, the participants have to be able to discriminate one among ten age groups which represents the perceptual judgment of each participant. The strongest response denotes the signal and represents the actual age group while the remaining nine alternatives denote noise or uncertainty distributed among other response categories. The distance between the means of the signal and the noise distributions are compared against the standard deviation of the noise distribution to compute the discriminability index  and noi  are calsignal,  ).(1)(2)bits and are summarized in Dividing Low values of accuracy for older age groups such as We also compared the human performance with an independently trained automatic algorithm. The same images are evaluated using Face++ In order to examine the existence of any group bias in age group estimation; ethnicity, gender and age group of the participants are compared with the stimuli ethnicity, gender and age group and the results are documented in Z-test of proportions identify faces belonging to their own race and 1.56 times less likely to be falsely matched. As per our opinion, the above statement may only hold true for recognizing faces when there is no significant age variation, as the faces used in their study did not have any aging variations.The results in Z-test of proportions at 95% confidence level also shows that gender of the participant does not act as a bias in the task of age group estimation. The result coheres with the observations made by Cross et al. To examine if gender influences the judgment of perceived facial age, the gender of the participant is compared with the gender of the face stimuli. As shown in Z-test of proportions It can be observed from 100% for infants and toddlers  for face recognition , that can estimate the age of any given query face. For this purpose, a variant of LBP termed as Three Patch LBP (TPLBP) Given a labeled training set of faces, TPLBP descriptor is extracted for each of the faces. This descriptor of the training set is given as input to the Support Vector Regressor (SVR - Support Vector Machine in Regression Mode) for implementing an age prediction function. Kernel testing After estimating the age group of the query image, the next task is to match the identity of the image with gallery (database) images. The face matching algorithm is explained below:Face Parts Extraction: Full face, binocular region, T-region, T-region masked, and chin region are obtained for the input image using facial landmark detection.LBP Calculation: For each of the facial region, uniform circular LBP features Score Fusion via Learned Weights: The five match scores corresponding to each face recgion are combined using score fusion In this research, we compute the weights learned from the accuracies obtained from human performance evaluation. Using Let If the age of the individual in the gallery lies in the age group 0–5 years and the predicted age of the probe image comes out to be in the range of 6–10 years, then using row 1 of Three datasets are used for evaluating the performance of the above described approach: IIIT-Delhi facial aging database All the datasets are divided into two partitions, approximately 30% of the subjects are used for training and the remaining unseen 70% for testing. The training partition is used for training the SVR model for age estimation. Two experiments For the IIIT-Delhi facial aging dataset, the proposed fusion rule HPFS  outperforms traditional fusion schemes for both sets of experiments. However, for experiments using the oldest images as probe, COTS (Row 15), Face++ From In order to highlight the error produced by the age estimation algorithm used in our proposed approach, It should be noted that the objective of these experiments is to illustrate that using existing facial feature extraction approach, the human perception based scheme can improve the performance of face recognition with aging variations. It is our assertion that the same framework can be applied to other facial feature extraction algorithms as well and similar improvements may be observed. However, there may be other ways to incorporate these findings/observations in an automatic face recognition algorithm.oldest image as probe experiment on the IIIT-Delhi and FG-Net databases. Currently, we are extending the algorithm to further improve the performance, specifically with both age and weight variations There are a few limitations of our research and some questions are yet to be explored. The proposed algorithm for age-invariant face recognition yields lower results that Face++ Faces undergo significant variations during the lifetime on an individual. This research attempts to analyze how humans perceive facial age and their ability to estimate age. The results indicate that age estimation for newborns and toddlers is easiest and a person's gender or ethnicity does not affect the performance of age group estimation. The research presents the effect of facial regions such as binocular region, T-region, and mouth on the age prediction accuracy. As a global feature, full face achieves good performance in age-separated face recognition. Using selected feature cues gathered from the research, we propose the human perception based weighted score fusion rule to enhance the face recognition accuracy with age variations. The proposed algorithm demonstrates improvement in accuracy on three facial aging databases when compared with existing approaches and commercial face recognition systems.