Efficacy and safety of minimally invasive percutaneous nephrolithotomy(MPCNL) and standard channel percutaneous nephrolithotomy(SPCNL) in the treatment of kidney stones:A meta analysis

Objective: To evaluate the efficacy and safety of minimally invasive percutaneous nephrolithotomy(MPCNL) standard channel percutaneous nephrolithotomy(SPCNL) in the treatment of kidney stones. Methods: We searched pubmed, Cochrane Liabrary, CNKI, VIP , Wangfang database from January 1, 1992 to April 1, 2015. Screening of the two methods of treatment of kidney stones randomized controlled trials (RCT) and quality assessment, using revman5.2 software for statistical analysis. Results: A total of nine RCT, 1017 patients, 508 cases in MPCNL group, 509 cases in SPCNL group. The results show: the stones clearance , MPCNL group was lower than SPCNL group [OR=0.62, 95%CI(0.43, 0.91), P=0.01]; in terms of operative time, MPCNL group was longer than SPCNL group [MD = 14.23 , 95% CI (6.30, 22.16), P = 0.0004]; hospitalization time, blood loss, total complications, were no significant difference between the two group, the results are [MD = 0.88, 95% CI (-0.69 , 2.44), P = 0.27], [MD =-19.87, 95% CI (-64.36, 24.61), P = 0.38], [OR=1.28, 95%CI(0.90, 1.84), P=0.17]. Conclusion: the stone clearance rate:MPCNL group was lower than SPCNL group, the operative time of MPCNL group was longer which may affect postoperative recovery, the complications, hospital stay, blood loss, the two groups had no significant difference. So , we tend to standard channel percutaneous nephrolithotomy in the treatment of common type of kidney stones

Affine Disjunctive Invariant Generation with Farkas' Lemma

Invariant generation is the classical problem that aims at automated generation of assertions that over-approximates the set of reachable program states in a program. We consider the problem of generating affine invariants over affine while loops (i.e., loops with affine loop guards, conditional branches and assignment statements), and explore the automated generation of disjunctive affine invariants. Disjunctive invariants are an important class of invariants that capture disjunctive features in programs such as multiple phases, transitions between different modes, etc., and are typically more precise than conjunctive invariants over programs with these features. To generate tight affine invariants, existing constraint-solving approaches have investigated the application of Farkas' Lemma to conjunctive affine invariant generation, but none of them considers disjunctive affine invariants

An Efficient Multi-solution Solver for the Inverse Kinematics of 3-Section Constant-Curvature Robots

Piecewise constant curvature is a popular kinematics framework for continuum robots. Computing the model parameters from the desired end pose, known as the inverse kinematics problem, is fundamental in manipulation, tracking and planning tasks. In this paper, we propose an efficient multi-solution solver to address the inverse kinematics problem of 3-section constant-curvature robots by bridging both the theoretical reduction and numerical correction. We derive analytical conditions to simplify the original problem into a one-dimensional problem. Further, the equivalence of the two problems is formalised. In addition, we introduce an approximation with bounded error so that the one dimension becomes traversable while the remaining parameters analytically solvable. With the theoretical results, the global search and numerical correction are employed to implement the solver. The experiments validate the better efficiency and higher success rate of our solver than the numerical methods when one solution is required, and demonstrate the ability of obtaining multiple solutions with optimal path planning in a space with obstacles.Comment: Robotics: Science and Systems 202

Influence of coalification on methane diffusion dynamics in middle-high rank coals

Understanding the effect of coalification on the diffusion of methane in middle-high rank coals (MHRC) is fundamental for optimizing the coalbed methane (CBM) drainage strategies. Safe coal mining relies critically on this approach. Hence, CH4 adsorption/desorption experiments and a new methane diffusion model were used to study methane diffusion behavior in five different metamorphic degree coals from China. The results indicate that, as the vitrinite reflectance (Ro) increase, the Langmuir volume (VL) shows a downward trend at first, then increases. Coalification especially for the third coalification jump (Ro = 1.26%) shows a turning effect on diffusion dynamics of MHRC. The desorption volume and initial diffusion coefficient (D0) both show a slight decreasing, then rapid increasing trend. The D0 of five coals jumps at Ro = 1.26%. VL increases alongside that of the micropores volume. With the decrease of the Raman parameter ID1/IG, the VL, methane desorption volume within 7200 s and D0 all increase. Results show that coalification changes the microporous structure and the macromolecular structure of the coal, which finally affects the diffusion capacity of coal. Coalification has a positive effect for the gas extraction and the development of CBM for the semianthracites

Caspase Inhibition with XIAP as an Adjunct to AAV Vector Gene-Replacement Therapy: Improving Efficacy and Prolonging the Treatment Window

AAV-mediated gene therapy in the rd10 mouse, with retinal degeneration caused by mutation in the rod cyclic guanosine monophosphate phosphodiesterase β-subunit (PDEβ) gene, produces significant, but transient, rescue of photoreceptor structure and function. This study evaluates the ability of AAV-mediated delivery of X-linked inhibitor of apoptosis (XIAP) to enhance and prolong the efficacy of PDEβ gene-replacement therapy.Rd10 mice were bred and housed in darkness. Two groups of animals were generated: Group 1 received sub-retinal AAV5-XIAP or AAV5-GFP at postnatal age (P) 4 or 21 days; Group 2 received sub-retinal AAV5-XIAP plus AAV5- PDEβ, AAV5-GFP plus AAV5- PDEβ, or AAV- PDEβ alone at age P4 or P21. Animals were maintained for an additional 4 weeks in darkness before being moved to a cyclic-light environment. A subset of animals from Group 1 received a second sub-retinal injection of AAV8-733-PDEβ two weeks after being moved to the light. Histology, immunohistochemistry, Western blots, and electroretinograms were performed at different times after moving to the light.Injection of AAV5-XIAP alone at P4 and 21 resulted in significant slowing of light-induced retinal degeneration, as measured by outer nuclear thickness and cell counts, but did not result in improved outer segment structure and rhodopsin localization. In contrast, co-injection of AAV5-XIAP and AAV5-PDEβ resulted in increased levels of rescue and decreased rates of retinal degeneration compared to treatment with AAV5-PDEβ alone. Mice treated with AAV5-XIAP at P4, but not P21, remained responsive to subsequent rescue by AAV8-733-PDEβ when injected two weeks after moving to a light-cycling environment.Adjunctive treatment with the anti-apoptotic gene XIAP confers additive protective effect to gene-replacement therapy with AAV5-PDEβ in the rd10 mouse. In addition, AAV5-XIAP, when given early, can increase the age at which gene-replacement therapy remains effective, thus effectively prolonging the window of opportunity for therapeutic intervention

Heterogeneous Information Saliency Features' Fusion Approach for Machine's Environment Sounds Based Awareness

International audienc

Sparse non-rigid registration of 3D shapes

Non-rigid registration of 3D shapes is an essential task of increasing importance as commodity depth sensors become more widely available for scanning dynamic scenes. Non-rigid registration is much more challenging than rigid registration as it estimates a set of local transformations instead of a single global transformation, and hence is prone to the overfitting issue due to underdetermination. The common wisdom in previous methods is to impose an ℓ2-norm regularization on the local transformation differences. However, the ℓ2-norm regularization tends to bias the solution towards outliers and noise with heavy-tailed distribution, which is verified by the poor goodness-of-fit of the Gaussian distribution over transformation differences. On the contrary, Laplacian distribution fits well with the transformation differences, suggesting the use of a sparsity prior. We propose a sparse non-rigid registration (SNR) method with an ℓ1-norm regularized model for transformation estimation, which is effectively solved by an alternate direction method (ADM) under the augmented Lagrangian framework. We also devise a multi-resolution scheme for robust and progressive registration. Results on both public datasets and our scanned datasets show the superiority of our method, particularly in handling large-scale deformations as well as outliers and noise

Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion

With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models use auditory features like log-mel spectrogram (LM) and mel frequency cepstral coefficient (MFCC), or raw waveform to train deep neural networks for environment sound classification (ESC) are unsatisfactory. In this paper, we first propose two combined features to give a more comprehensive representation of environment sounds Then, a fourfour-layer convolutional neural network (CNN) is presented to improve the performance of ESC with the proposed aggregated features. Finally, the CNN trained with different features are fused using the Dempster–Shafer evidence theory to compose TSCNN-DS model. The experiment results indicate that our combined features with the four-layer CNN are appropriate for environment sound taxonomic problems and dramatically outperform other conventional methods. The proposed TSCNN-DS model achieves a classification accuracy of 97.2%, which is the highest taxonomic accuracy on UrbanSound8K datasets compared to existing models