Composite indicator for railway infrastructure management

Railway Engineering Science - The assessment and analysis of railway infrastructure capacity is an essential task in railway infrastructure management carried out to meet the required quality and...     

LncRNA H19 Impairs Chemo and Radiotherapy in Tumorigenesis

Various treatments based on drug administration and radiotherapy have been devoted to preventing, palliating, and defeating cancer, showing high efficiency against the progression of this disease. Recently, in this process, malignant cells have been found which are capable of triggering specific molecular mechanisms against current treatments, with negative consequences in the prognosis of the disease. It is therefore fundamental to understand the underlying mechanisms, including the genes—and their signaling pathway regulators—involved in the process, in order to fight tumor cells. Long non-coding RNAs, H19 in particular, have been revealed as powerful protective factors in various types of cancer. However, they have also evidenced their oncogenic role in multiple carcinomas, enhancing tumor cell proliferation, migration, and invasion. In this review, we analyze the role of lncRNA H19 impairing chemo and radiotherapy in tumorigenesis, including breast cancer, lung adenocarcinoma, glioma, and colorectal carcinoma.     

The Role of the Choroid in Stargardt Disease

Stargardt disease is the commonest juvenile macular dystrophy. It is caused by genetic mutations in the ABCA4 gene. Diagnosis is not always straightforward, and various phenocopies exist. Late-onset disease can be misdiagnosed with age-related macular disease. A correct diagnosis is particularly critical because of emergent gene therapies. Stargardt disease is known to affect retinal pigment epithelium and photoreceptors. Many studies have also highlighted the importance of the choroid in the diagnosis, pathophysiology, and progression of the disease. The choroid is in an integral relationship with the retinal pigment epithelium and photoreceptors, and its possible involvement during the disease should be considered. The purpose of this review is to analyze the current diagnostic tools for choroidal evaluation and the extrapolation of useful data for ophthalmologists and researchers studying the disease.     

Chronic-Antibiotics Induced Gut Microbiota Dysbiosis Rescues Memory Impairment and Reduces β-Amyloid Aggregation in a Preclinical Alzheimer’s Disease Model

Alzheimer’s disease (AD) is a multifactorial pathology characterized by β-amyloid (Aβ) deposits, Tau hyperphosphorylation, neuroinflammatory response, and cognitive deficit. Changes in the bacterial gut microbiota (BGM) have been reported as a possible etiological factor of AD. We assessed in offspring (F1) 3xTg, the effect of BGM dysbiosisdysbiosis in mothers (F0) at gestation and F1 from lactation up to the age of 5 months on Aβ and Tau levels in the hippocampus, as well as on spatial memory at the early symptomatic stage of AD. We found that BGM dysbiosisdysbiosis with antibiotics (Abx) treatment in F0 was vertically transferred to their F1 3xTg mice, as observed on postnatal day (PD) 30 and 150. On PD150, we observed a delay in spatial memory impairment and Aβ deposits, but not in Tau and pTau protein in the hippocampus at the early symptomatic stage of AD. These effects are correlated with relative abundance of bacteria and alpha diversity, and are specific to bacterial consortia. Our results suggest that this specific BGM could reduce neuroinflammatory responses related to cerebral amyloidosis and cognitive deficit and activate metabolic pathways associated with the biosynthesis of triggering or protective molecules for AD.     

A microfluidic approach for development of hybrid collagen-chitosan extracellular matrix-like membranes for on-chip cell cultures

To advance organ-on-a-chip development and other areas befitting from physiologically-relevant biomembranes,a microfluidic platform is presented for synthesis of biomembranes during gelation and investigation into their role as extracellular matrix supports.In this work,high-throughput studies of collagen,chitosan,and collagen-chitosan hybrid biomembranes were carried out to characterize and compare key properties as a function of the applied hydrodynamic conditions during gelation.Specifically,depending on the biopolymer material used,varying flow conditions during biomembrane gelation caused width,uniformity,and swelling ratio to be differently affected and controllable.Finally,cell viability studies of seeded fibroblasts were conducted,thus showing the potential for biological applications.     

Automatic bearing fault diagnosis based on one-class ν-SVM

Rolling-element bearings are among the most used elements in industrial machinery, thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals, an automatic method for bearing fault detection and diagnosis is presented in this work. Initially, a one-class ν-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime, only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method, two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.     

uBench: exposing the impact of CUDA block geometry in terms of performance

The choice of thread-block size and shape is one of the most important user decisions when a parallel problem is written for any CUDA architecture. The reason is that thread-block geometry has a significant impact on the global performance of the program. Unfortunately, the programmer has not enough information about the subtle interactions between this choice of parameters and the underlying hardware. This paper presents uBench, a complete suite of micro-benchmarks, in order to explore the impact on performance of (1) the thread-block geometry choice criteria, and (2) the GPU hardware resources and configurations. Each micro-benchmark has been designed to be as simple as possible to focus on a single effect derived from the hardware and thread-block parameter choice. As an example of the capabilities of this benchmark suite, this paper shows an experimental evaluation and comparison of Fermi and Kepler architectures. Our study reveals that, in spite of the new hardware details introduced by Kepler, the principles underlying the block geometry selection criteria are similar for both architectures.     

A parallel evolutionary algorithm for technical market indicators optimization

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.     

Thermo-Metallurgical Modeling of Nodular Cast Iron Cooling Process

A new numerical model to describe the microstructural evolution of a eutectic nodular cast iron during its cooling is presented. In particular, equiaxial solidification assuming an independent nucleation of austenite and graphite nodules is considered. In this context, the austenite has dendritic growth whereas the graphite grows with a spherical shape. After solidification occurs, the model assumes that the graphite nodules present in the cast iron continue growing since the carbon content in austenite decreases. Once the stable eutectoid temperature is reached, the alloy undergoes the austenite-ferrite transformation. The nucleation of the ferrite takes place at the contour of the spherical graphite nodules where austenite has low carbon concentration. A ferrite shell surrounding the graphite nodules is formed afterward by means of a process governed by carbon diffusion. Then, a ferrite-pearlite competitive transformation occurs when the temperature is below the metastable temperature. This thermo-metallurgical model is discretized and solved by means of the finite element method. The model allows the computation of cooling curves, fraction evolution for each component, and size and distribution of graphite nodules. The present numerical results are compared with experiments using standardized Quick-cup-type cups, and satisfactory numerical predictions of the final microstructure and cooling curves are achieved.