Comparison of Lumbo-Pelvic Kinematics During Trunk Forward Bending and Backward Return Between Patients with Acute Low Back Pain and Asymptomatic Controls

Background—Prior studies have reported differences in lumbo-pelvic kinematics during a trunk forward bending and backward return task between individuals with and without chronic low back pain; yet, the literature on lumbo-pelvic kinematics of patients with acute low back pain is scant. Therefore, the purpose of this study was set to investigate lumbo-pelvic kinematics in this cohort. Methods—A case-control study was conducted to investigate the differences in pelvic and thoracic rotation along with lumbar flexion as well as their first and second time derivatives between females with and without acute low back pain. Participants in each group completed one experimental session wherein they performed trunk forward bending and backward return at self-selected and fast paces. Findings—Compared to controls, individuals with acute low back pain had larger pelvic range of rotations and smaller lumbar range of flexions. Patients with acute low back pain also adopted a slower pace compared to asymptomatic controls which was reflected in smaller maximum values for angular velocity, deceleration and acceleration of lumbar flexion. Irrespective of participant group, smaller pelvic range of rotation and larger lumbar range of flexion were observed in younger vs. older participants. Interpretation—Reduced lumbar range of flexion and slower task pace, observed in patients with acute low back pain, may be the result of a neuromuscular adaptation to reduce the forces and deformation in the lower back tissues and avoid pain aggravation

Mahkota Dewa (Phaleria Macrocarpa) sebagai Antinefrotoksisitas “Dewa Penyelamat” dalam Penurunan Efek Samping Cisplatin

Cisplatin is one of the most widely used chemotherapeutic agent in cancer treatment, however it possess series of harmful adverse effects, most notably, nephrotoxicity. Due to that reason, a natural chemopreventive agent is needed to minimize cisplatin's toxicity, namely, Mahkota Dewa fruit (Phaleria marcocarpa) extract. This research aim to determine anti nephrotoxic effect of mahkota dewa fruit on Vero cells, model of renal cells. Cytotoxic assay of mahkota dewa's extract and cisplatin both single and combination was determined using MTT assay on HeLa cells and Vero cells. The cytotoxic assay resulted that IC50 value of cisplatin and Mahkota Dewa to HeLa cells were 18µM (5,4 µg/mL) and 845 µg/mL, respectively, whereas the IC50 value of cisplatin and Mahkota Dewa to Vero were 80 µM (24 µg/mL) and 730 µg/mL, respectively. The results indicated that cisplatin was more cytotoxic to HeLa cell in comparison to Vero cell. Combination treatment of mahkota dewa's extract at 183 µg/mL and cisplatin 284 µM showed increased viability of Vero cells. Therefore, combination treatment of cisplatin and mahkota dewa are able to decrease nephrotoxicity of cisplatin to renal cells

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.Comment: Proceedings of 8th Learning and Intelligent OptimizatioN (LION8) Conference, 201

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes. View Full-Tex

Analysis of gene expression and chemoresistance of CD133(+ )cancer stem cells in glioblastoma

BACKGROUND: Recently, a small population of cancer stem cells in adult and pediatric brain tumors has been identified. Some evidence has suggested that CD133 is a marker for a subset of leukemia and glioblastoma cancer stem cells. Especially, CD133 positive cells isolated from human glioblastoma may initiate tumors and represent novel targets for therapeutics. The gene expression and the drug resistance property of CD133 positive cancer stem cells, however, are still unknown. RESULTS: In this study, by FACS analysis we determined the percentage of CD133 positive cells in three primary cultured cell lines established from glioblastoma patients 10.2%, 69.7% and 27.5%, respectively. We also determined the average mRNA levels of markers associated with neural precursors. For example, CD90, CD44, CXCR4, Nestin, Msi1 and MELK mRNA on CD133 positive cells increased to 15.6, 5.7, 337.8, 21.4, 84 and 1351 times, respectively, compared to autologous CD133 negative cells derived from cell line No. 66. Additionally, CD133 positive cells express higher levels of BCRP1 and MGMT mRNA, as well as higher mRNA levels of genes that inhibit apoptosis. Furthermore, CD133 positive cells were significantly resistant to chemotherapeutic agents including temozolomide, carboplatin, paclitaxel (Taxol) and etoposide (VP16) compared to autologous CD133 negative cells. Finally, CD133 expression was significantly higher in recurrent GBM tissue obtained from five patients as compared to their respective newly diagnosed tumors. CONCLUSION: Our study for the first time provided evidence that CD133 positive cancer stem cells display strong capability on tumor's resistance to chemotherapy. This resistance is probably contributed by the CD133 positive cell with higher expression of on BCRP1 and MGMT, as well as the anti-apoptosis protein and inhibitors of apoptosis protein families. Future treatment should target this small population of CD133 positive cancer stem cells in tumors to improve the survival of brain tumor patients

Fluid-structure interaction simulation of prosthetic aortic valves : comparison between immersed boundary and arbitrary Lagrangian-Eulerian techniques for the mesh representation

In recent years the role of FSI (fluid-structure interaction) simulations in the analysis of the fluid-mechanics of heart valves is becoming more and more important, being able to capture the interaction between the blood and both the surrounding biological tissues and the valve itself. When setting up an FSI simulation, several choices have to be made to select the most suitable approach for the case of interest: in particular, to simulate flexible leaflet cardiac valves, the type of discretization of the fluid domain is crucial, which can be described with an ALE (Arbitrary Lagrangian-Eulerian) or an Eulerian formulation. The majority of the reported 3D heart valve FSI simulations are performed with the Eulerian formulation, allowing for large deformations of the domains without compromising the quality of the fluid grid. Nevertheless, it is known that the ALE-FSI approach guarantees more accurate results at the interface between the solid and the fluid. The goal of this paper is to describe the same aortic valve model in the two cases, comparing the performances of an ALE-based FSI solution and an Eulerian-based FSI approach. After a first simplified 2D case, the aortic geometry was considered in a full 3D set-up. The model was kept as similar as possible in the two settings, to better compare the simulations' outcomes. Although for the 2D case the differences were unsubstantial, in our experience the performance of a full 3D ALE-FSI simulation was significantly limited by the technical problems and requirements inherent to the ALE formulation, mainly related to the mesh motion and deformation of the fluid domain. As a secondary outcome of this work, it is important to point out that the choice of the solver also influenced the reliability of the final results

A Characterization of Scale Invariant Responses in Enzymatic Networks

An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions

Software Model Checking with Explicit Scheduler and Symbolic Threads

In many practical application domains, the software is organized into a set of threads, whose activation is exclusive and controlled by a cooperative scheduling policy: threads execute, without any interruption, until they either terminate or yield the control explicitly to the scheduler. The formal verification of such software poses significant challenges. On the one side, each thread may have infinite state space, and might call for abstraction. On the other side, the scheduling policy is often important for correctness, and an approach based on abstracting the scheduler may result in loss of precision and false positives. Unfortunately, the translation of the problem into a purely sequential software model checking problem turns out to be highly inefficient for the available technologies. We propose a software model checking technique that exploits the intrinsic structure of these programs. Each thread is translated into a separate sequential program and explored symbolically with lazy abstraction, while the overall verification is orchestrated by the direct execution of the scheduler. The approach is optimized by filtering the exploration of the scheduler with the integration of partial-order reduction. The technique, called ESST (Explicit Scheduler, Symbolic Threads) has been implemented and experimentally evaluated on a significant set of benchmarks. The results demonstrate that ESST technique is way more effective than software model checking applied to the sequentialized programs, and that partial-order reduction can lead to further performance improvements.Comment: 40 pages, 10 figures, accepted for publication in journal of logical methods in computer scienc