Protective Effects of Two Halophilic Crude Extracts from Pseudomonas zhaodongensis and Bacillus stratosphericus against Memory Deficits and Anxiety- and Depression-Like Behaviors in Methionine-Induced Schizophrenia in Mice Focusing on Oxidative Stress Status.

Recently, the implication of oxidative stress in behavioral-like disorders has received a lot of attention. Many studies were interested in searching for new natural compounds with protective effects on behavioral-like disorders by focusing on oxidative stress as the main causal factor. Here, we assess the potential effect of cell-free extracts from halophilic bacteria on memory, anxiety, and depression-related behaviors in mice, as well as on cognitive deficits, negative symptoms, and some oxidative stress biomarkers in methionine-induced mice models of schizophrenia. Firstly, crude extracts of bacteria isolated from the Dead Sea were screened for their effects on memory and anxiety- and depression-like behaviors through Y-maze, elevated plus maze, and forced swimming test, respectively, using two doses 60 mg/kg and 120 mg/kg. Then, 120 mg/kg of two bacterial crude extracts, from two strains designated SL22 and BM20 and identified as Bacillus stratosphericus and Pseudomonas zhaodongensis, respectively, with significant contents of phenolic and flavonoid-like compounds, were selected for the assessment of cognitive and negative symptom improvement, as well as for their effects on oxidative stress status in methionine-induced mice models of schizophrenia using six groups (controls, methionine, crude extracts solely, and combinations of crude extracts and methionine). Results showed that the administration of the crude extracts caused a significant increase in the spontaneous alternations in the Y-maze task, the time spent in open arms of the elevated plus maze, and a decrease in immobility time in the forced swimming test in comparison with the control group. Furthermore, the administration of bacterial extracts seemed to diminish disorders related to cognitive and negative symptoms of schizophrenia and to improve the oxidative state in the temporal lobes, in comparison with the methionine group. Our findings suggest substantial antioxidant and anti-neuropsychiatric effects of the crude extracts prepared from Pseudomonas zhaodongensis strain BM20 and Bacillus stratosphericus strain SL22 and that further studies are needed to purify and to determine the active fraction from the extracts.

Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative.

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning discriminative properties can be a challenging task, particularly when dealing with complex data such as X-ray images, typically used for knee OA diagnosis. In this paper, we introduce a Discriminative Regularized Auto Encoder (DRAE) that allows to learn both relevant and discriminative properties that improve the classification performance. More specifically, a penalty term, called discriminative loss is combined with the standard Auto-Encoder training criterion. This additional term aims to force the learned representation to contain discriminative information. Our experimental results on data from the public multicenter OsteoArthritis Initiative (OAI) show that the developed method presents potential results for early knee OA detection.

Centrality in Complex Networks with Overlapping Community Structure.

Identifying influential spreaders in networks is an essential issue in order to prevent epidemic spreading, or to accelerate information diffusion. Several centrality measures take advantage of various network topological properties to quantify the notion of influence. However, the vast majority of works ignore its community structure while it is one of the main features of many real-world networks. In a recent study, we show that the centrality of a node in a network with non-overlapping communities depends on two features: Its local influence on the nodes belonging to its community, and its global influence on the nodes belonging to the other communities. Using global and local connectivity of the nodes, we introduced a framework allowing to redefine all the classical centrality measures (designed for networks without community structure) to non-overlapping modular networks. In this paper, we extend the so-called "Modular Centrality" to networks with overlapping communities. Indeed, it is a frequent scenario in real-world networks, especially for social networks where nodes usually belong to several communities. The "Overlapping Modular Centrality" is a two-dimensional measure that quantifies the local and global influence of overlapping and non-overlapping nodes. Extensive experiments have been performed on synthetic and real-world data using the Susceptible-Infected-Recovered (SIR) epidemic model. Results show that the Overlapping Modular Centrality outperforms its alternatives designed for non-modular networks. These investigations provide better knowledge on the influence of the various parameters governing the overlapping community structure on the nodes' centrality. Additionally, two combinations of the components of the Overlapping Modular Centrality are evaluated. Comparative analysis with competing methods shows that they produce more efficient centrality scores.

Fractional Brownian Motion and Rao Geodesic Distance for Bone X-Ray Image Characterization.

Osteoporosis diagnosis has attracted particular attention in recent decades. Textured images from the microarchitecture of osteoporotic and healthy subjects show a high degree of similarity, increasing the difficulty of classifying such textures. Thus, the evaluation of osteoporosis from the bone X-ray images presents a major challenge for pattern recognition and medical applications. The purpose of this paper is to use the fractional Brownian motion (fBm) model and the probability density function of its increments to compute a similarity measure with the Rao geodesic distance to classify trabecular bone X-ray images. When evaluated on synthetic fBm images (test vectors) with the well-known Hurst parameter H, the proposed method met our expectations in which a good classification of the synthetic images was achieved. A clinical study was conducted on textured bone X-ray images from two different female populations of osteoporotic patients (fracture cases) and control subjects. Using the proposed method, an area under curve rate of 97% was achieved.