Solutions to a Radical Problem: Overview of Current and Future Treatment Strategies in Leber’s Hereditary Opic Neuropathy

Leber’s Hereditary Optic Neuropathy (LHON) is the most common primary mitochondrial DNA disorder. It is characterized by bilateral severe central subacute vision loss due to specific loss of Retinal Ganglion Cells and their axons. Historically, treatment options have been quite limited, but ongoing clinical trials show promise, with significant advances being made in the testing of free radical scavengers and gene therapy. In this review, we summarize management strategies and rational of treatment based on current insights from molecular research. This includes preventative recommendations for unaffected genetic carriers, current medical and supportive treatments for those affected, and emerging evidence for future potential therapeutics.     

Microbiota Composition in Diverticular Disease: Implications for Therapy

Gut microbiota (GM) composition and its imbalance are crucial in the pathogenesis of several diseases, mainly those affecting the gastrointestinal tract. Colon diverticulosis and its clinical manifestations (diverticular disease, DD) are among the most common digestive disorders in developed countries. In recent literature, the role of GM imbalance in the onset of the different manifestations within the clinical spectrum of DD has been highlighted. This narrative review aims to summarize and critically analyze the current knowledge on GM dysbiosis in diverticulosis and DD by comparing the available data with those found in inflammatory bowel disease (IBD). The rationale for using probiotics to rebalance dysbiosis in DD is also discussed.     

Stabilization of strict‐feedback nonlinear systems with input delay using closed‐loop predictors

In this paper, we consider the control problem of strict‐feedback nonlinear systems with time‐varying input and output delays. The approach is based on the usual observer/predictor/feedback approach, but the novelty is the use of the closed‐loop dynamics in the predictor. This approach allows to develop two designs, an instantaneous predictor and a delay differential equation‐based predictor, that both attain the same performance in terms of system trajectories and input signal as in the case with no delays. The design based on delay differential equations allows to build a cascade of predictors to deal with arbitrarily large delay bounds. The resulting controller is much simpler to implement than classical infinite‐dimensional predictors, and it is robust with respect to actuation and measurement disturbances. We illustrate the approach with an application to the control of a chaotic system with input delay. Copyright © 2016 John Wiley & Sons, Ltd.     

Rapid Detection of Wheat Blast Pathogen Magnaporthe oryzae Triticum Pathotype Using Genome-Specific Primers and Cas12a-mediated Technology

Wheat blast, caused by the fungus Magnaporthe oryzae Triticum (MoT) pathotype, is a devastating disease persistent in South America and Bangladesh. Since MoT generally fails to cause visual symptoms in wheat until the heading stage when the infection would have advanced, disease control by fungicide application solely based on the detection of visual symptoms is ineffective. To develop an accurate and sensitive method to detect MoT at the seedling and vegetative stages for disease control, we sequenced the genomes of two MoT isolates from Brazil and identified two DNA fragments, MoT-6098 and MoT-6099, that are present in the MoT genome but not in the genome of the rice-infecting Magnaporthe oryzae Oryzae (MoO) pathotype. Using polymerase chain reaction (PCR), we confirmed the specificity of the two markers in 53 MoT and MoO isolates from South America and Bangladesh. To test the efficiency of the two markers, we first established a loop-mediated isothermal amplification (LAMP) method to detect MoT at isothermal conditions, without the use of a PCR machine. Following this, we used the Cas12a protein and guide RNAs (gRNAs) to target the MoT-6098 and MoT-6099 sequences. The activated Cas12a showed indiscriminate single-stranded deoxyribonuclease (ssDNase) activity. We then combined target-dependent Cas12a ssDNase activation with recombinase polymerase amplification (RPA) and nucleic acid lateral flow immunoassay (NALFIA) to develop a method that accurately, sensitively, and cost-effectively detects MoT-specific DNA sequences in infected wheat plants. This novel technique can be easily adapted for the rapid detection of wheat blast and other important plant diseases in the field.     

Fast finite difference Poisson solvers on heterogeneous architectures

In this paper we propose and evaluate a set of new strategies for the solution of three dimensional separable elliptic problems on CPU–GPU platforms. The numerical solution of the system of linear equations arising when discretizing those operators often represents the most time consuming part of larger simulation codes tackling a variety of physical situations. Incompressible fluid flows, electromagnetic problems, heat transfer and solid mechanic simulations are just a few examples of application areas that require efficient solution strategies for this class of problems. GPU computing has emerged as an attractive alternative to conventional CPUs for many scientific applications. High speedups over CPU implementations have been reported and this trend is expected to continue in the future with improved programming support and tighter CPU–GPU integration. These speedups by no means imply that CPU performance is no longer critical. The conventional CPU-control–GPU-compute pattern used in many applications wastes much of CPU’s computational power. Our proposed parallel implementation of a classical cyclic reduction algorithm to tackle the large linear systems arising from the discretized form of the elliptic problem at hand, schedules computing on both the GPU and the CPUs in a cooperative way. The experimental result demonstrates the effectiveness of this approach.     

Sensory Characterization and Consumer Preference Mapping of Fresh Sausages Manufactured with Goat and Sheep Meat

The main objective of this study was the sensory characterization, by a taste and a consumers’ panel, of fresh sausages from 140 culled goats and 140 culled ewes. Species and type of preparation effects were studied. All data were previously analyzed by analysis of variance. Taste panel data were analyzed by a Generalized Procrustes Analysis (GPA). Consumers’ panel data were analyzed by Preference Mapping. The 1st 2 factors explained 88.22% of total variation in GPA. Different sausages samples were perfectly differentiated by assessors. Goat sausages have been considered harder, more fibrous, and less juicy than sheep sausages. The panelists observed that sheep sausages without paprika had greater intensity of flavor, tasted spicy, and had an off‐odor, while goat sausages with paprika were considered sweeter. Consumers’ panel did not show any preference for the different types of sausages. This means that all types of sausages can have market opportunity.     

Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is Magnetic Resonance Spectroscopy. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this paper, a Discrete Wavelet Transform procedure was applied to the pre-processing of spectra corresponding to several brain tumour pathologies. This procedure does not alleviate the high dimensionality of the data by itself. For this reason, dimensionality reduction was subsequently implemented using Moving Window with Variance Analysis for feature selection or Principal Component Analysis for feature extraction. The combined method yielded very encouraging results in terms of diagnostic discriminatory binary classification using Bayesian Neural Networks. In most cases, the classification accuracy improved on previously reported results.     

Adaptive local learning techniques for multiple-step-ahead wind speed forecasting

A massive deployment of wind energy in power systems is expected in the near future. However, a still open issue is how to integrate wind generators into existing electrical grids by limiting their side effects on network operations and control. In order to attain this objective, accurate short and medium-term wind speed forecasting is required.This paper discusses and compares a physical (white-box) model (namely a limited-area non hydrostatic model developed by the European consortium for small-scale modeling) with a family of local learning techniques (black-box) for short and medium term forecasting. Also, an original model integrating machine learning techniques with physical knowledge modeling (grey-box) is proposed.A set of experiments on real data collected from a set of meteorological sensors located in the south of Italy supports the methodological analysis and assesses the potential of the different forecasting approaches.     

On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines

In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques.This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.