Role of immune-inflammatory and oxidative and nitrosative stress pathways in the etiology of depression: therapeutic implications

Accumulating data have led to a re-conceptualization of depression that emphasizes the role of immuneinflammatory processes, coupled to oxidative and nitrosative stress (O&NS). These in turn drive the production of neuroregulatory tryptophan catabolites (TRYCATs), driving tryptophan away from serotonin, melatonin, and Nacetylserotonin production, and contributing to central dysregulation. This revised perspective better encompasses the diverse range of biological changes occurring in depression and in doing so provides novel and readily attainable treatment targets, as well as potential screening investigations prior to treatment initiation. We briefly review the role that immune-inflammatory, O&NS, and TRYCAT pathways play in the etiology, course, and treatment of depression. We then discuss the pharmacological treatment implications arising from this, including the potentiation of currently available antidepressants by the adjunctive use of immune- and O&NS- targeted therapies. The use of such a frame of reference and the treatment benefits attained are likely to have wider implications and utility for depression-associated conditions, including the neuroinflammatory and (neuro)degenerative disorders.

In hot pursuit : fluctuating mating system and sexual selection in sand lizards

A changing climate is expected to have profound effects on many aspects of ectotherm biology. We report on a decade-long study of free-ranging sand lizards (Lacerta agilis), exposed to an increasing mean mating season temperature and with known operational sex ratios. We assessed year-to-year variation in sexual selection on body size and postcopulatory sperm competition and cryptic female choice. Higher temperature was not linked to strength of sexual selection on body mass, but operational sex ratio (more males) did increase the strength of sexual selection on body size. Elevated temperature increased mating rate and number of sires per clutch with positive effects on offspring fitness. In years when the “quality” of a female's partners was more variable (in standard errors of a male sexual ornament), clutches showed less multiple paternity. This agrees with prior laboratory trials in which females exercised stronger cryptic female choice when male quality varied more. An increased number of sires contributing to within-clutch paternity decreased the risk of having malformed offspring. Ultimately, such variation may contribute to highly dynamic and shifting selection mosaics in the wild, with potential implications for the evolutionary ecology of mating systems and population responses to rapidly changing environmental conditions.

Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.

Use of geographical information system and remote sensing techniques for planning culture-based fisheries in non-perennial reservoirs of Sri Lanka

 The presence of a wide areal extent of small-sized village reservoirs offers a considerable potential for the development of culture-based fisheries (CBFs) in Sri Lanka. To this end, this study uses geographical information systems (GISs) and remote sensing (RS) techniques to determine the morphometric and biological characteristics most useful for classifying non-perennial reservoirs for CBF development and for assessing the influence of catchment land-use patterns on potential CBF yields. The reservoir shorelines at full water supply level were mapped with a Global Positioning System to determine shoreline length and reservoir areal extent. The ratio of shoreline length to reservoir extent, which was reported to be a powerful predictor variable of CBF yields, could be reliably quantified using RS techniques. The areal extent of reservoirs, quantified with RS techniques (RS extent), was used to estimate the ratio of forest cover plus scrubland cover to RS extent and was found to be significantly related to the CBF yield (R2 = 0.400; P < 0.05). The results of this study indicated that morphometric characteristics and catchment land-use patterns, which might be viewed as indices of biological productivity, can be quantified using RS and GIS techniques. © 2014 Wiley Publishing Asia Pty Ltd.

The neuro-immune pathophysiology of central and peripheral fatigue in systemic immune-inflammatory and neuro-immune diseases

Many patients with systemic immune-inflammatory and neuro-inflammatory disorders, including depression, rheumatoid arthritis, systemic lupus erythematosus, Sjögren's disease, cancer, cardiovascular disorder, Parkinson's disease, multiple sclerosis, stroke, and chronic fatigue syndrome/myalgic encephalomyelitis, endure pathological levels of fatigue. The aim of this narrative review is to delineate the wide array of pathways that may underpin the incapacitating fatigue occurring in systemic and neuro-inflammatory disorders. A wide array of immune, inflammatory, oxidative and nitrosative stress (O&NS), bioenergetic, and neurophysiological abnormalities are involved in the etiopathology of these disease states and may underpin the incapacitating fatigue that accompanies these disorders. This range of abnormalities comprises: increased levels of pro-inflammatory cytokines, e.g., interleukin-1 (IL-1), IL-6, tumor necrosis factor (TNF) α and interferon (IFN) α; O&NS-induced muscle fatigue; activation of the Toll-Like Receptor Cycle through pathogen-associated (PAMPs) and damage-associated (DAMPs) molecular patterns, including heat shock proteins; altered glutaminergic and dopaminergic neurotransmission; mitochondrial dysfunctions; and O&NS-induced defects in the sodium-potassium pump. Fatigue is also associated with altered activities in specific brain regions and muscle pathology, such as reductions in maximum voluntary muscle force, downregulation of the mitochondrial biogenesis master gene peroxisome proliferator-activated receptor gamma coactivator 1-alpha, a shift to glycolysis and buildup of toxic metabolites within myocytes. As such, both mental and physical fatigue, which frequently accompany immune-inflammatory and neuro-inflammatory disorders, are the consequence of interactions between multiple systemic and central pathways.

Lipid abundance in zebrafish embryos is regulated by complementary actions of the endocannabinoid system and retinoic acid pathway

The endocannabinoid system (ECS) and retinoic acid (RA) signaling have been associated with influencing lipid metabolism. We hypothesized that modulation of these pathways could modify lipid abundance in developing vertebrates and that these pathways could have a combinatorial effect on lipid levels. Zebrafish embryos were exposed to chemical treatments altering the activity of the ECS and RA pathway. Embryos were stained with the neutral lipid dye Oil-Red-O (ORO) and underwent whole-mount in situ hybridization. Mouse 3T3-L1 fibroblasts were differentiated under exposure to RA modulating chemicals and subsequently stained with ORO and analyzed for gene expression by qRT-PCR. ECS activation and RA exposure increased lipid abundance and the expression of lipoprotein lipase. Additionally, RA treatment increased expression of CCAAT/enhancer binding protein alpha. Both ECS receptors and RA receptor subtypes were separately involved in modulating lipid abundance. Finally, increased ECS or RA activity ameliorated the reduced lipid abundance caused by peroxisome proliferator-activated receptor gamma (PPARγ) inhibition. Therefore, the ECS and RA pathway influence lipid abundance in zebrafish embryos and have an additive effect when treated simultaneously. Furthermore, we demonstrated that these pathways act downstream or independently of PPARγ to influence lipid levels. Our study shows for the first time that the RA and ECS pathways have additive function in lipid abundance during vertebrate development.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.

A nonparametric model of financial system and economic growth

In this paper, we show that in the proposed models for economic growth, the financial system variables are generally nonparametric. We, thus, use a nonparametric panel data model to estimate the financial system-economic growth relationship. Our results suggest that as long as a country's domestic credit and private credit are above their cross-sectional mean they have a positive effect on GDP growth. We also discover that market capitalisation positively and significantly impacts GDP growth, while stocks traded (with the exception of OECD countries) has a statistically insignificant effect on GDP growth.

Multi-scale simulation using a hybrid mass-spring system and finite element model

Ply-scale finite element (FE) models are widely used to predict the performance of a composite structure based on material properties of individual plies. When simulating damage, these models neglect microscopic fracture processes which may have a significant effect on how a crack progresses within and between plies of a multidirectional laminate. To overcome this resolution limitation a multi-scale modelling technique is employed to simulate the effect micro-scale damage events have on the macro-scale response of a structure. The current paper discusses the development and validation of a hybrid mass-spring system and finite element modelling technique for multi-scale analysis. The model developed here is limited to elastic deformations; however, it is the first key step towards an efficient multi-scale damage model well suited to simulation of fracture in fibre reinforced composite materials. Various load cases have been simulated using the model developed here which show excellent accuracy compared to analytical and FE results. Future work is discussed, including extension of the model to incorporate damage modelling.