Persistent organic pollutants (POPs) in fish species from different lakes of the lesser Himalayan region (LHR), Pakistan: The influence of proximal sources in distribution of POPs

Fish dwelling in remote mountain water systems are sensitive to long term exposure of POPs and can be used as an important bioindicator of POPs pollution in fragile mountain ecosystems. Current study aimed to investigate the concentrations and patterns of organic pollutants in fish tissues from different lakes of the Lesser Himalayan Region (LHR). OCPs, PCBs, PBDEs were analyzed in four common edible fish species of the LHR: Oncorhynchus mykiss, Labeo rohita, Hypophthalmichthys molitrix and Orechromis aureus. The fish were collected from lakes with different types of catchment areas (glacial, non-glacial mountain region and urban region) and extent of anthropogenic influence. The levels OCPs, PCBs and PBDEs analyzed in the selected fish species were in range of 0.21-587, 6.4-138 and 1.2-14 ng g^-1 lw, respectively. The ∑DDTs, higher chlorinated PCBs, tetra- and penta-BDEs were more prevalent in urban and remote lakes whereas pp'-DDE, lower chlorinated PCBs and BDE-47 and -99 were predominant in fish species from glacial lakes. ∑DDTs, ∑PCBs and ∑PBDEs showed statistically significant differences (p < 0.05) among species, trophic guilds (carnivore, herbivore and omnivore) and feeding regimes (surface, bottom and column feeder) and ∑HCH showed a significant difference only among trophic guilds. The stable isotope values of δ ¹⁵N and δ¹³C differed significantly among species for ∑HCH, ∑PCBs, ∑PBDEs (p < 0.05) and ∑DDT (p < 0.01). The range of δ¹³C values (-34 to -19‰) indicated the importance of littoral and pelagic sources of dietary carbon. Trophic position and dietary proxies were identified as important variables for explaining the variability of the studied compounds. Kohonen self-organizing maps (SOM) showed that in addition to trophic position and other physiological characteristics of fish, that the type of lakes and proximal sources of POPs were the most important predictors for distribution of organic contaminants in fish samples from LHR.

Environmental pollution influence to soil-plant-air system in organic vineyard: bioavailability, environmental, and health risk assessment

This study was performed in organic vineyard to assess integrated pollution in soil-plant-air system by potentially toxic elements (PTE). Concentrations of 26 PTE were determined in soil, grapevine, and air biomonitors (moss bags) using ICP-OES and ICP-MS. Environmental implication assessment of soil did not show pollution by PTE, except for B in samples collected in the middle of grapevine season (July). Despite low total Cd concentrations in soil, it has the highest influence on increase of environmental risk. Based on biological accumulation concentration (BAC), grapevine is not hyperaccumulator of PTE from soil. Advanced classification algorithm, Kohonen self-organizing map (SOM), was applied to compare environmental implications in organic with conventional vineyards. PTE concentrations were significantly lower in organic than conventional grapevine. PTE concentrations were higher in the outer (leaf and petiole) than in the inner grapevine parts (skin, pulp, and seed). Some airborne elements have an influence on outer grapevine parts, especially on leaves (ratio factor-RF > 1). Moss bag technique testified about lower enrichment of airborne elements compared with the conventional vineyard and urban microenvironments. Environmental and health risk assessments confirmed that organic production is harmless for field workers and grape consumers.

Morphological Properties of the Two Types of Caudate Interneurons: Kohonen Self-Organizing Maps and Correlation-Comparison Analysis

Our previous study found that caudate and putaminal interneurons are morphologically very different, and that accordingly they could be divided in two separate clusters. In addition, it also demonstrated, as a collateral result, that the caudate cluster itself consists of two clusters of morphologically different interneurons. Hence, the objective of this study is a morphological description and subtle differing of morphologies of these two types of caudate interneurons, i.e., an investigation of those morphological traits which characterize them uniquely, and which would distinguish them. Binary two-dimensional images of caudate interneurons, taken from deceased adult human subjects, were analyzed by using 46 parameters, describing the morphology of interneurons. The parameters can be divided in the following classes: size (surface) of a neuron, neuronal shape, length of neuronal morphological compartments, dendritic branching, morphological organization, and complexity. The morphological determination of caudate interneurons was performed in a step-wise manner. The first step was the assignment of each individual neuron to an adequate cluster where it belonged according to morphological criteria. This was done by using the trained artificial neural network, Kohonen self-organizing map. After the clusters were formed, the analysis is further continued by the precise, feature-wise determination of morphological differences found between clusters of caudate interneurons and then finished by defining correlation-based, mutual, inter-parametric relations for each of the clusters. The first was performed by using single-factor analysis, and the second by correlation-comparison analysis. Single-factor analysis showed significance for 34 parameters (morphological features) that distinguish between the clusters. Correlation-comparison analysis extended the results of single-factor analysis by demonstrating significance for 198 inter-parametric correlation pairs that represent 19.13% of mismatched correlations of the first kind among the total number of correlations. This represents a significant inter-cluster separation zone. In addition, the analysis extracted one correlation of the second kind, namely, the DO-MDCBO, very highly significant (p&lt;0.001), positive (r=0.45) in the cluster I, while negative (r=-0.13), also significant (p&lt;0.05) in the cluster II. The two clusters of caudate interneurons were found to be significantly morphologically different. These differences, albeit not strong as the caudate-putaminal differences, are more numerous with respect to significant morphological properties defining them. They probably underlie, influence, and modulate different neurofunctional behavior of the two types of interneurons, which need to be further investigated by future studies.

Metabolic profile of injured human spinal cord determined using surface microdialysis

The management of patients having traumatic spinal cord injury would benefit from understanding and monitoring of spinal cord metabolic states. We hypothesized that the metabolism of the injured spinal cord could be visualized using Kohonen self-organizing maps. Sixteen patients with acute, severe spinal cord injuries were studied. Starting within 72 h of the injury, and for up to a week, we monitored the injury site hourly for tissue glucose, lactate, pyruvate, glutamate, and glycerol using microdialysis as well as intraspinal pressure and spinal cord perfusion pressure. A Kohonen map, which is an unsupervised, self-organizing topology-preserving neural network, was used to analyze 3366 h of monitoring data. We first visualized the different spinal cord metabolic states. Our data show that the injured cord assumes one or more of four metabolic states. On the basis of their metabolite profiles, we termed these states near-normal, ischemic, hypermetabolic, and distal. We then visualized how patients' intraspinal pressure and spinal cord perfusion pressure affect spinal cord metabolism. This revealed that for more than 60% of the time, spinal cord metabolism is patient-specific; periods of high intraspinal pressure or low perfusion pressure are not associated with specific spinal cord metabolic patterns. Finally, we determined relationships between spinal cord metabolism and neurological status. Patients with complete deficits have shorter periods of near-normal spinal cord metabolic states (7 ± 4% vs. 58 ± 12%, p < 0.01, mean ± standard error) and more variable injury site metabolic responses (metabolism spread in 70 ± 11 vs. 40 ± 6 hexagons, p < 0.05), compared with patients who have incomplete neurological deficits. We conclude that Kohonen maps allow us to visualize the metabolic responses of the injured spinal cord and may thus aid us in treating patients with acute spinal cord injuries.

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

BACKGROUND CONTEXT

Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns.

PURPOSE

To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM.

STUDY DESIGN

This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion.

PATIENT SAMPLE

A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification.

OUTCOME MEASURES

For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded.

METHODS

A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map.

RESULTS

The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types.

CONCLUSIONS

An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.

Artificial intelligence in sports biomechanics: new dawn or false hope?

This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements ('techniques') and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics. Key PointsExpert Systems remain almost unused in sports biomechanics, unlike in the similar discipline of gait analysis.Artificial Neural Networks, particularly Kohonen Maps, have been used, although their full value remains unclear.Other AI applications, including Evolutionary Computation, have received little attention.