Athletic population with spondylolysis: review of outcomes following surgical repair or conservative management

Objective The study aims to critically review the outcomes associated with the surgical repair or conservative management of spondylolysis in athletes. Methods The English literature listed in MEDLINE/PubMed was reviewed to identify related articles using the term “spondylolysis AND athlete.” The criteria for studies to be included were management of spondylolysis in athletes, English text, and no year, follow-up, or study design restrictions. The references of the retrieved articles were also evaluated. The primary outcome was time to return to sport. This search yielded 180 citations, and 25 publications were included in the review. Results Treatment methods were dichotomized as operative and nonoperative. In the nonoperative group, 390 athletes were included. A combination of bracing with physical therapy and restriction of activities was used. Conservative measures allowed athletes to return to sport in 3.7 months (weighted mean). One hundred seventy-four patients were treated surgically. The most common technique was Buck's, using a compression screw (91/174). All authors reported satisfactory outcomes. Time to return to play was 7.9 months (weighted mean). There were insufficient studies with suitably homogenous subgroups to conduct a meta-analysis. Conclusion There is no gold standard approach for the management of spondylolysis in the athletic population. The existing literature suggests initial therapy should be a course of conservative management with thoracolumbosacral orthosis brace, physiotherapy, and activity modification. If conservative management fails, surgical intervention should be considered. Two-sided clinical studies are needed to determine an optimal pathway for the management of athletes with spondylolysis

Dynamic effects on capillary pressure-saturation relationships for two-phase porous flow: implications of temperature

Work carried out in the last decade or so suggests that the simulators for multiphase flow in porous media should include an additional term, namely a dynamic coefficient, as a measure of the dynamic effect associated with capillary pressure. In this work, we examine the dependence of the dynamic coefficient on temperature by carrying out quasi-static and dynamic flow simulations for an immiscible perchloroethylene–water system. Simulations have been carried out using a two-phase porous media flow simulator for a range of temperatures between 20 and 80 C. Simulation domains represent 3-D cylindrical setups used by the authors for laboratory-scale investigations of dynamic effects in two-phase flow. Results are presented for two different porous domains, namely the coarse and fine sands, which are then interpreted by examining the correlations between dynamic coefficient(s) and temperature, time period(s) required for attaining irreducible water saturation, and the dynamic aqueous/nonaqueous phase saturation and capillary pressure plots. The simulations presented here maintain continuity from our previous work and address the uncertainties associated with the dependency of dynamic coefficient(s) on temperature, thereby complementing the existing database for the characterization of dynamic coefficients and subsequently enabling the users to carry out computationally economical and reliable modeling studies

Computational modelling of two-phase porous flow: "effects of temperature on dynamic co-efficients"

Traditional continuum scale models for multiphase flow in porous media rely upon “Capillary Pressure, Saturation & Relative Permeability” relationships which do not necessarily illustrate the dynamic capillary pressure effects on the flow behaviour. As such, simulators for multiphase flow in porous media must include additional terms(s) associated with dynamic capillary pressures. For such reasons, investigations targeting at measurements of dynamic coefficients and its dependency on various physical parameters are of great interest. In this work we therefore examine the dependence of the dynamic coefficient τ on temperature T by carrying out quasi-static and dynamic flow simulations for an immiscible perchloroethylene (PCE)-water system exhibiting a drainage process. Simulations are carried out using a twophase porous media flow simulator STOMP for a range of temperatures between 20C-80C on 3-D cylindrical domains which correspond to laboratory scale domain set-ups used previously by the authors. Results are presented for coarse and fine sands at 40C and are interpreted by examining the correlations between dynamic coefficient(s) and temperature, time period(s) required for attaining residual saturation and the dynamic aqueous/non-aqueous phase saturation and capillary pressure plots. Our simulations maintain a continuity from our previous work and reduce the inconsistencies associated with the dependency of dynamic coefficient(s) on temperature which should subsequently enable the users to carry out computationally economical and reliable modelling studies at various length scales of observation

TRiC controls transcription resumption after UV damage by regulating Cockayne syndrome protein A

Transcription-blocking DNA lesions are removed by transcription-coupled nucleotide excision repair (TC-NER) to preserve cell viability. TC-NER is triggered by the stalling of RNA polymerase II at DNA lesions, leading to the recruitment of TC-NER-specific factors such as the CSA-DDB1-CUL4A-RBX1 cullin-RING ubiquitin ligase complex (CRLCSA). Despite its vital role in TC-NER, little is known about the regulation of the CRLCSA complex during TC-NER. Using conventional and cross-linking immunoprecipitations coupled to mass spectrometry, we uncover a stable interaction between CSA and the TRiC chaperonin. TRiC's binding to CSA ensures its stability and DDB1-dependent assembly into the CRLCSA complex. Consequently, loss of TRiC leads to mislocalization and depletion of CSA, as well as impaired transcription recovery following UV damage, suggesting defects in TC-NER. Furthermore, Cockayne syndrome (CS)-causing mutations in CSA lead to increased TRiC binding and a failure to compose the CRLCSA complex. Thus, we uncover CSA as a TRiC substrate and reveal that TRiC regulates CSA-dependent TC-NER and the development of CS

Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities

An artificial neural network (ANN) is presented for computing a parameter of dynamic two-phase flow in porous media with water as wetting phase, namely, dynamic coefficient (τ), by considering micro-heterogeneity in porous media as a key parameter. τ quantifies the dependence of time derivative of water saturation on the capillary pressures and indicates the rates at which a two-phase flow system may reach flow equilibrium. Therefore, τ is of importance in the study of dynamic two-phase flow in porous media. An attempt has been made in this work to reduce computational and experimental effort by developing and applying an ANN which can predict the dynamic coefficient through the “learning” from available data. The data employed for testing and training the ANN have been obtained from computational flow physics-based studies. Six input parameters have been used for the training, performance testing and validation of the ANN which include water saturation, intensity of heterogeneity, average permeability depending on this intensity, fluid density ratio, fluid viscosity ratio and temperature. It is found that a 15 neuron, single hidden layer ANN can characterize the relationship between media heterogeneity and dynamic coefficient and it ensures a reliable prediction of the dynamic coefficient as a function of water saturation

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work

Development of a predictive mathematical model for coupled Stokes–Darcy flows in cross-flow membrane filtration

Free flow regimes accompanied by porous walls feature commonly in a variety of natural processes and industrial applications such as groundwater flows, packed beds, arterial blood flows and cross-flow and dead-end filtrations. Cross-flow microfiltration or ultrafiltration processes are generally employed in a range of industrial situations ranging from oil to medical applications. The coupled free/porous fluid transport phenomenon plays an equally important role along with the particle transport mechanisms concerning the separation efficiency of cross-flow membrane filtration. To provide a theoretical background for the experimental outcomes of cross-flow filtration, a mathematically sound model is desired which can reliably represent the interfacial boundary whilst maintaining the continuity of flow field variables across the interface between the free and porous flow regimes. Notwithstanding the numerous attempts reported in the literature, the development of a generic mathematical model for coupled flows has been prohibited by the complexities of interactions between the free and the porous flow systems. Henceforth, the aim of present work is to gain a better mathematical understanding of the interfacial phenomena encountered in coupled free and porous flow regimes applicable to cross-flow filtration systems. The free flow dynamics can be justifiably represented by the Stokes equation whereas the non-isothermal, non-inertial and incompressible flow in a low permeability porous medium can be handled by the Darcy equation. Solutions to the system of partial differential equations (PDE’s) are obtained using the finite element method employing mixed interpolations for the primary field variables which are velocity and pressure. A nodal replacement scheme previously developed by the same authors has been effectively enforced as the boundary constraint at the free-porous interface for coupling the two physically different flow regimes in a single mathematical model. A series of computational experiments for permeability values of the porous medium ranging between 10-6 -10-12 m2 have been performed to examine the susceptibility of the developed model towards complex and irregular shaped geometries. Our results indicate that at high permeability values, the discrepancy in mass balance calculations is observed to be significant for a curved porous surface, which may be attributed to the inability of the Darcy equation to represent the flow dynamics in a highly permeable medium. At a low permeability, a very small amount of fluid permeated through the free/porous interface as most of the fluid leaves the domain through the free flow exit. The geometry and permeability of the free/porous interface are found to affect the amount of fluid passing through the porous medium significantly. All the numerical solutions that are presented have been theoretically validated for their accuracy by computing the overall mass continuity across the computational domains

A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model

Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F1-ATPase data set and a 4.5 Å resolution SecYEG–SecA complex data set. All of the models were automatically built by the method to Rfree values of between 28.9 and 39.9% and were free from the initial model bias