Artificial neural network based identification of Campylobacter species by Fourier transform infrared spectroscopy

Two prototypes of artificial neural network (ANN), multilayer perceptron (MLP), and probabilistic neural network (PNN), were used to analyze infrared (IR) spectral data obtained from intact cells belonging to the species Campylobacter coli and Campylobacter jejuni. In order to establish a consistent identification and typing procedure, mid infrared spectra of these species were obtained by means of a Fourier transform infrared (FT-IR) spectroscope. FT-IR patterns belonging to 26 isolates subclassified into 4 genotypes were pre-processed (normalized, smoothed and derivatized) and grouped into training, verification and test sets. The two architectures tested (PNN, MLP) were developed and trained to identify or leave unassigned a number of IR patterns. Two window ranges (w(4), 1200 to 900 cm(-1); and w(5), 900 to 700 cm(-1)) in the mid IR spectrum were presented as input to the ANN models functioning as pattern recognition systems. No matter the ANN used all the training sets were correctly identified at subspecies level. For the test set, the four-layer MLP network was found to be specially suitable to recognize FT-IR data since it correctly identified 99.16% of unknowns using the w(4) range, and was fully successful in detecting atypical patterns from closely related Campylobacter strains and other bacterial species. The PNN network obtained lower percentages in assignation and rejection. Overall, ANNs constitute an excellent mathematical tool in microbial identification, since they are able to recognize with a high degree of confidence typical as well as atypical FT-IR fingerprints from Campylobacter spp.     

Quantitative analysis of single- vs. multiple-set programs in resistance training

The purpose of this study was to examine the existing research on single-set vs. multiple-set resistance training programs. Using the meta-analytic approach, we included studies that met the following criteria in our analysis: (a) at least 6 subjects per group; (b) subject groups consisting of single-set vs. multiple-set resistance training programs; (c) pretest and posttest strength measures; (d) training programs of 6 weeks or more; (e) apparently "healthy" individuals free from orthopedic limitations; and (f) published studies in English-language journals only. Sixteen studies generated 103 effect sizes (ESs) based on a total of 621 subjects, ranging in age from 15-71 years. Across all designs, intervention strategies, and categories, the pretest to posttest ES in muscular strength was (chi = 1.4 +/- 1.4; 95% confidence interval, 0.41-3.8; p < 0.001). The results of 2 x 2 analysis of variance revealed simple main effects for age, training status (trained vs. untrained), and research design (p < 0.001). No significant main effects were found for sex, program duration, and set end point. Significant interactions were found for training status and program duration (6-16 weeks vs. 17-40 weeks) and number of sets performed (single vs. multiple). The data indicated that trained individuals performing multiple sets generated significantly greater increases in strength (p < 0.001). For programs with an extended duration, multiple sets were superior to single sets (p < 0.05). This quantitative review indicates that single-set programs for an initial short training period in untrained individuals result in similar strength gains as multiple-set programs. However, as progression occurs and higher gains are desired, multiple-set programs are more effective.     

The effects of ten weeks of resistance and combined plyometric/sprint training with the Meridian Elyte athletic shoe on muscular performance in women

The purpose of this investigation was to examine the combined effects of resistance and sprint/plyometric training with or without the Meridian Elyte athletic shoe on muscular performance in women. Fourteen resistance-trained women were randomly assigned to one of 2 training groups: (a) an athletic shoe (N = 6) (AS) group or (b) the Meridian Elyte (N = 8) (MS) group. Training was performed for 10 weeks and consisted of resistance training for 2 days per week and 2 days per week of sprint/plyometric training. Linear periodized resistance training consisted of 5 exercises per workout (4 lower body, 1 upper body) for 3 sets of 3-12 repetition maximum (RM). Sprint/plyometric training consisted of 5-7 exercises per workout (4-5 plyometric exercises, 40-yd and 60-yd sprints) for 3-6 sets with gradually increasing volume (8 weeks) followed by a 2-week taper phase. Assessments for 1RM squat and bench press, vertical jump, broad jump, sprint speed, and body composition were performed before and following the 10-week training period. Significant increases were observed in both AS and MS groups in 1RM squat (12.0 vs. 14.6 kg), bench press (6.8 vs. 7.4 kg), vertical jump height (3.3 vs. 2.3 cm), and broad jump (17.8 vs. 15.2 cm). Similar decreases in peak 20-, 40-, and 60-m sprint times were observed in both groups (20 m: 0.14 vs. 0.11 seconds; 40 m: 0.29 vs. 0.34 seconds; 60 m: 0.45 vs. 0.46 seconds in AS and MS groups, respectively). However, when sprint endurance (the difference between the fastest and slowest sprint trials) was analyzed, there was a significantly greater improvement at 60 m in the MS group. These results indicated that similar improvements in peak sprint speed and jumping ability were observed following 10 weeks of training with either shoe. However, high-intensity sprint endurance at 60 m increased to a greater extent during training with the Meridian Elyte athletic shoe.     

Artificial neural networks for decision-making in urologic oncology

Artificial neural networks (ANNs) are computational methodologies that perform multifactorial analyses, inspired by networks of biological neurons. Like neural networks, ANNs contain layers of simple points (nodes) of data that interract through carefully weighted connection lines. ANNs are "trained" and balanced by having been previously fed data, which the ANN uses as the means for adjusting its interconnections. Studies have shown that novel and highly accurate ANNs significantly enhance the ability to detect prostate cancer early (high sensitivity) while avoiding a greater number of unnecessary tissue samplings (high specificity). The use of ANNs in prostate cancer is ideal because of 1) multiple predicting factors that influence outcome; 2) the desire to offer individual consulting based on various tests; 3) the fact that prior logistic regression analysis results have had serious limitations in application; and 4) the need for an up-to-date tool that can apply easily to everyone. An ANN should be seen as an important tool that is complementary to the physician's personal knowledge and judgment in making decisions.     

The influence of spatial scale on cross-taxon congruence patterns and prediction accuracy of species richness

A comparison of species richness patterns of butterflies and birds was made using data from two grids of squares (small squares 137.5 km on a side and large squares 275 km on a side) covering western North America. Using geostatistical procedures, we found that the spatial patterns of species richness of these two taxa were related. The influence of grain size on the strength of this relationship was investigated by analysing the two data sets. For both data sets, the number of butterfly species in a square was a statistically significant predictor of the corresponding number of bird species. However, cross-validation techniques showed that the marginal improvement in prediction accuracy due to including butterflies as a predictor was greater in the large-square data. We explored the effect of areal extent on cross-taxon congruencies by investigating species richness patterns in four subsets of the small-square data. In regions with smaller areal extent, the cross-taxon congruence patterns were not substantially different from the pattern found in the full data set. Finally, using data-splitting techniques, we explored the relationships between prediction accuracy of species richness, sample size, areal extent of the sample, and grain size.     

Neural network for female mate preference,trained by a genetic algorithm

In some animals, males evolve exaggerated traits (e.g. the peacock''s conspicuous tail and display) because of female preference. Recently Enquist and Arak presented a simple neural network model for a visual system in female birds that acquires the ability to discriminate males of the correct species from those of the wrong species by training. They reported that the trained networks were attracted by ''supernormal stimuli'' where there was a greater response to an exaggerated form than to the images used as the correct species for training. They suggested that signal recognition mechanisms have an inevitable bias in response, which in turn causes selection on signal form. We here examine the Enquist and Arak model in detail. A three-layered neural network is used to represent the female''s mate preference, which consists of 6 by 6 receptor cells arranged on a regular square lattice, ten hidden cells, and one output cell. Connection weights of the network were modified by a genetic algorithm, in which the female''s fitness increases if she accepts a conspecific male but decreases if she accepts a male of a different species or a random image. We found that: (i) after the training period the evolved network was able to discriminate male images. Female preference evolves to favour unfamiliar patterns if they are similar to the images of the correct species (generalization); (ii) the speed and the final degree of learning depended critically on the choice of the random images that are rejected. The learning was much less successful if the random images were changed every generation than if 20 random images were fixed throughout the training period; (iii) the male of the same species used for training achieved the highest probability of being accepted by the trained network. Hence, contrary to Enquist and Arak, the evolved network was not attracted by supernormal stimuli.     

Modeling of pain using artificial neural networks

In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.     

Indicator taxa revisited: useful for conservation planning?

Aim Indicators for biodiversity are needed to facilitate the identification of complementary reserve networks for biodiversity conservation. One widely adopted approach is to use indicator taxa, i.e. a single taxon such as birds or butterflies, despite the ongoing debate regarding their usefulness as indicators of broader biodiversity. Here we assess several aspects, such as influence of species number, of indicator taxa for three extensive data sets to improve our insight into the effectiveness of indicator taxa. Location Denmark, sub‐Saharan Africa and Uganda. Methods First, we investigate to what extent variation in species number between indicator taxa (e.g. 488 mammal spp. vs. 210 snake spp.) is causing the differences in effectiveness between indicator taxa. Second, we investigate whether indicator taxa are capable of outperforming indicator groups composed of random sets of species chosen among all taxa. Finally, we assess the correlation of specific properties such as mean range size of the indicator taxa to their effectiveness. We investigate these aspects of the effectiveness of indicator taxa through the separate analysis of three distinct distributional species data sets: sub‐Saharan Africa (4,039 spp.), Denmark (847 spp.) and Uganda (2,822 spp.). Results We overall found that indicator taxa comprising a greater number of species tend to perform better than indicator taxa with fewer species (e.g. 488 mammal spp. outperform 210 snake spp.), although there are some exceptions. Second, we found most indicator taxa to perform worse than indicator groups consisting of a comparable number of species selected among all taxa. Finally, the effectiveness of indicator taxa was seen to correlate poorly with selected distributional properties such as mean range size of the indicator taxa, suggesting that it is difficult to predict which taxa are efficient biodiversity indicators. Main conclusions Overall, these findings might suggest that focus should simply be on increasing the number of species among all taxa as basis for priority setting, rather than striving to obtain the ‘perfect’ indicator taxa.     

Adaptations in rat skeletal muscle following long-term resistance exercise training

The purpose of this investigation was to determine whether long-term, heavy resistance training would cause adaptations in rat skeletal muscle structure and function. Ten male Wistar rats (3 weeks old) were trained to climb a 40-cm vertical ladder (4 days/week) while carrying progressively heavier loads secured to their tails. After 26 weeks of training the rats were capable of lifting up to 800 g or 140% of their individual body mass for four sets of 12–15 repetitions per session. No difference in body mass was observed between the trained rats and age-matched sedentary control rats. Absolute and relative heart mass were greater in trained rats than control rats. When expressed relative to body mass, the mass of the extensor digitorum longus (EDL) and soleus muscles was greater in trained rats than control rats. No difference in absolute muscle mass or maximum force-producing capacity was evident in either the EDL or soleus muscles after training, although both muscles exhibited an increased resistance to fatigue. Individual fibre hypertrophy was evident in all four skeletal muscles investigated, i.e. EDL, soleus, plantaris and rectus femoris muscles of trained rats, but muscle fibre type proportions within each of the muscles tested remained unchanged. Despite an increased ability of the rats to lift progressively heavier loads, this heavy resistance training model did not induce gross muscle hypertrophy nor did it increase the force-producing capacity of the EDL or soleus muscles. Accepted: 17 September 1997     

Effects of short vs. long rest period between sets on elbow-flexor muscular endurance during resistance training to failure

This study aimed to examine short-term resistance training effects of resting period length between sets on maximal number of repetitions and mean velocity over a moderate-intensity (60% of the maximum voluntary isometric contraction [MVIC]) set to failure on elbow-flexor muscles. The MVIC and surface electromyographic activity (sEMG) were also measured. Twenty-one untrained subjects were divided into 3 groups: short rest between sets (1 minute; SR), long rest between sets (4 minutes; LR), and nontraining control group (CG). The SR and LR performed 3 sets to failure in an arm-curl machine, 2 days per week for 5 weeks, with moderate loads (60-75% of the MVIC). The LR completed a significantly higher (31.6%, p < 0.05) total training volume than the SR. Both training groups enhanced the maximal number of repetitions to failure, with no significant differences in the magnitude of gains. The posttraining average velocity achieved by the SR at 40, 50, 60, 70, 80, and 90% of the total number of repetitions completed was significantly higher (p < 0.05) than the corresponding average velocity achieved on pretraining conditions, whereas no significant differences were observed in the LR. No significant changes in the MVIC or sEMG were observed in any group. We conclude that short-term elbow-flexor resistance training to failure, allowing 1 or 4 minutes of rest between sets, induces similar gains concerning local muscular endurance. Nevertheless, only the SR training approach reduced the rate of decline in the average repetition velocity during a set to failure. This can be of some importance in sport modalities in which not only the maximal number of repetitions (e.g., muscle endurance), but also a greater maintenance of high repetition velocities, may be critical for performance.     

Analysis of acute explosive training modalities to improve lower-body power in baseball players

Complex training is the simultaneous combination of heavy resistance training and plyometrics. The objective of this study was to test the effects of complex training vs. heavy resistance or plyometric interventions alone on various power-specific performance measures. Forty-five male division II junior college baseball players participated in 3 separate 4-week resistance training interventions. Subjects were randomly assigned to one of three groups. In a counterbalanced rotation design, each group participated in complex, heavy resistance, and plyometric training interventions. Each individual was tested in 20-yd (SP20), 40-yd (SP40), 60-yd (SP60), vertical jump, standing broad jump, and T-agility measures pre- and post-4-week training interventions. There was no statistical significant difference (p = 0.11) between groups across all performance measures. Review of each distinct training intervention revealed greater percent improvements in SP20 (0.55; -0.49; -0.12), SP40 (0.26; -0.72; -1.33), SP60 (0.27; 0.15; -0.27), standing broad jump (1.80; 0.67; 1.1), and T-agility (2.33; 1.23; -0.04) with complex training interventions than with the heavy resistance or plyometric training interventions, respectively. Plyometric-only training showed greater percent changes in vertical jump (1.90) than with complex (0.97) or heavy resistance training (0.36). The present results indicate that complex training can provide strength and conditioning professionals equal, if not slightly greater, improvements in muscular power than traditional heavy resistance- and plyometric-only interventions in moderately trained athletes. Complex training can be another valuable method for short-term power and speed improvements in athletes in isolation or in conjunction with other power development methods.     

Effect of appetitive training on brain lysine level and incorporation into nuclear proteins

The content of free lysine in the brains of mice increased significantly during an appetitive training in which the mice were trained to touch a bar in order to get sweetened milk. The free lysine level reached a maximum at 20–30 min of training, and returned to control levels at 60 min. The specific activity of free lysine was significantly lower in the brains of trained mice than in controls at 20 and 30 min after either subcutaneous or intracerebral administration of the isotopically labeled compound. Subcutaneously injected radioactive lysine disappeared more rapidly from the blood of trained mice than from the blood of control mice during the interval from 20 to 60 min after injection. The specific activities of brain nuclear proteins from trained mice were significantly greater than those of controls after 20 min or more of training. These protein differences were more marked when expressed as relative specific activities that were corrected for changes of specific activity of free lysine that occurred during training.     

Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates

Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist.     

Increased number of forced repetitions does not enhance strength development with resistance training

Some research suggests that strength improvements are greater when resistance training continues to the point at which the individual cannot perform additional repetitions (i.e., repetition failure). Performing additional forced repetitions after the point of repetition failure and thus further increasing the set volume is a common resistance training practice. However, whether short-term use of this practice increases the magnitude of strength development with resistance training is unknown and was investigated here. Twelve basketball and 10 volleyball players trained 3 sessions per week for 6 weeks, completing either 4 x 6, 8 x 3, or 12 x 3 (sets x repetitions) of bench press per training session. Compared with the 8 x 3 group, the 4 x 6 protocol involved a longer work interval and the 12 x 3 protocol involved higher training volume, so each group was purposefully designed to elicit a different number of forced repetitions per training session. Subjects were tested on 3- and 6-repetition maximum (RM) bench press (81.5 +/- 9.8 and 75.9 +/- 9.0 kg, respectively, mean +/- SD), and 40-kg Smith Machine bench press throw power (589 +/- 100 W). The 4 x 6 and 12 x 3 groups had more forced repetitions per session (p < 0.01) than did the 8 x 3 group (4.1 +/- 2.6, 3.1 +/- 3.5, and 1.2 +/- 1.8 repetitions, respectively), whereas the 12 x 3 group performed approximately 40% greater work and had 30% greater concentric time. As expected, all groups improved 3RM (4.5 kg, 95% confidence limits, 3.1- 6.0), 6RM (4.7 kg, 3.1-6.3), bench press throw peak power (57 W, 22-92), and mean power (23 W, 4-42) (all p < or = 0.02). There were no significant differences in strength or power gains between groups. In conclusion, when repetition failure was reached, neither additional forced repetitions nor additional set volume further improved the magnitude of strength gains. This finding questions the efficacy of adding additional volume by use of forced repetitions in young athletes with moderate strength training experience.     

Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins

Conventional multivariate statistical techniques (hierarchical cluster analysis, linear discriminant analysis) and unsupervised (Kohonen Self Organizing Map) and supervised (Bayesian network) artificial neural networks were compared for as tools for the classification and identification of 352 SDS-PAGE patterns of whole cell proteins of lactic acid bacteria belonging to 22 species of the genera Lactobacillus, Leuconostoc, Enterococcus, Lactococcus and Streptococcus including 47 reference strains. Electrophoretic data were pre-treated using the logistic weighting function described by Piraino et al. [Piraino, P., Ricciardi, A., Lanorte, M. T., Malkhazova, I., Parente, E., 2002. A new procedure for data reduction in electrophoretic fingerprints of whole-cell proteins. Biotechnol. Lett. 24, 1477-1482]. Hierarchical cluster analysis provided a satisfactory classification of the patterns but was unable to discriminate some species (Leuconostoc, Lb. sakei/Lb. curvatus, Lb. acidophilus/Lb. helveticus, Lb. plantarum/Lb. paraplantarum, Lc. lactis/Lc. raffinolactis). A 7x7 Kohonen self-organizing map (KSOM), trained with the patterns of the reference strains, provided a satisfactory classification of the patterns and was able to discriminate more species than hierarchical cluster analysis. The map was used in predictive mode to identify unknown strains and provided results which in 85.5% of cases matched the classification obtained by hierarchical cluster analysis. Two supervised tools, linear discriminant analysis and a 23:5:2 Bayesian network were proven to be highly effective in the discrimination of SDS-PAGE patterns of Lc. lactis from those of other species. We conclude that data reduction by logistic weighting coupled to traditional multivariate statistical analysis or artificial neural networks provide an effective tool for the classification and identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins.     

The influence of volume of exercise on early adaptations to strength training

The purpose of this investigation was to compare the effects of single-set strength training and 3-set strength training during the early phase of adaptation in 18 untrained male subjects (age, 20-30 years). After initial testing, subjects were randomly assigned to either the 3L-1U group (n = 8), which trained 3 sets in leg exercises and 1 set in upper-body exercises, or the 1L-3U group (n = 10), which trained 1 set in leg exercises and 3 sets in upper-body exercises. Testing was conducted at the beginning and at the end of the study and consisted of 2 maximal isometric tests (knee extension and bench press) and 6 maximal dynamic tests (1 repetition maximum [1RM] tests). Subjects trained 3 days per week for 6 weeks. After warm-up, subjects performed 3 leg exercises and 4 upper-body exercises. In both groups, each set consisted of 7 repetitions (reps) with the load supposed to induce muscular failure after the seventh rep (7RM load). After 6 weeks of training, 1RM performance in all training exercises was significantly increased (10-26%, p < 0.01) in both groups. The relative increase in 1RM load in the 3 leg exercises was significantly greater in the 3L-1U group than in the 1L-3U group (21% vs. 14%, p = 0.01). However, the relative increase in 1RM load in the 3 upper-body exercises was similar in the 3L-1U group (16%) and the 1L-3U group (14%). These results show a superior adaptation to 3-set strength training, compared with 1-set strength training, in leg exercises but not in upper-body exercises during the early phase of adaptation.     

Pattern recognition in flow cytometry.

BACKGROUND: Analytical flow cytometry (AFC), by quantifying sometimes more than 10 optical parameters on cells at rates of approximately 10(3) cells/s, rapidly generates vast quantities of multidimensional data, which provides a considerable challenge for data analysis. We review the application of multivariate data analysis and pattern recognition techniques to flow cytometry. METHODS: Approaches were divided into two broad types depending on whether the aim was identification or clustering. Multivariate statistical approaches, supervised artificial neural networks (ANNs), problems of overlapping character distributions, unbounded data sets, missing parameters, scaling up, and estimating proportions of different types of cells comprised the first category. Classic clustering methods, fuzzy clustering, and unsupervised ANNs comprised the second category.We demonstrate the state of the art by using AFC data on marine phytoplankton populations. RESULTS AND CONCLUSIONS: Information held within the large quantities of data generated by AFC was tractable using ANNs, but for field studies the problem of obtaining suitable training data needs to be resolved, and coping with an almost infinite number of cell categories needs further research.     

Hypertrophic response to unilateral concentric isokinetic resistance training.

The purposes of this study were to 1) determine the effect of concentric isokinetic training on strength and cross-sectional area (CSA) of selected extensor and flexor muscles of the forearm and leg, 2) examine the potential for preferential hypertrophy of individual muscles within a muscle group, 3) identify the location (proximal, middle, or distal level) of hypertrophy within an individual muscle, and 4) determine the effect of unilateral concentric isokinetic training on strength and hypertrophy of the contralateral limbs. Thirteen untrained male college students [mean age 25.1 +/- 6.1 (SD) yr] volunteered to perform six sets of 10 repetitions of extension and flexion of the nondominant limbs three times per week for 8 wk, using a Cybex II isokinetic dynamometer. Pretraining and posttraining peak torque and muscle CSA measurements for both the dominant and nondominant limbs were determined utilizing a Cybex II isokinetic dynamometer and magnetic resonance imaging scanner, respectively. The results indicated significant (P less than 0.0008) hypertrophy in all trained muscle groups as well as preferential hypertrophy of individual muscles and at specific levels. None of the muscles of the contralateral limbs increased significantly in CSA. In addition, significant (P less than 0.0008) increases in peak torque occurred for trained forearm extension and flexion as well as trained leg flexion. There were no significant increases in peak torque, however, for trained leg extension or for any movement in the contralateral limbs. These data suggest that concentric isokinetic training results in significant strength and hypertrophic responses in the trained limbs.     

Taxonomic Resolution and Quantification of Freshwater Macroinvertebrate Samples from an Australian Dryland River: The Benefits and Costs of Using Species Abundance Data

In studies using macroinvertebrates as indicators for monitoring rivers and streams, species level identifications in comparison with lower resolution identifications can have greater information content and result in more reliable site classifications and better capacity to discriminate between sites, yet many such programmes identify specimens to the resolution of family rather than species. This is often because it is cheaper to obtain family level data than species level data. Choice of appropriate taxonomic resolution is a compromise between the cost of obtaining data at high taxonomic resolutions and the loss of information at lower resolutions. Optimum taxonomic resolution should be determined by the information required to address programme objectives. Costs saved in identifying macroinvertebrates to family level may not be justified if family level data can not give the answers required and expending the extra cost to obtain species level data may not be warranted if cheaper family level data retains sufficient information to meet objectives. We investigated the influence of taxonomic resolution and sample quantification (abundance vs. presence/absence) on the representation of aquatic macroinvertebrate species assemblage patterns and species richness estimates. The study was conducted in a physically harsh dryland river system (Condamine-Balonne River system, located in south-western Queensland, Australia), characterised by low macroinvertebrate diversity. Our 29 study sites covered a wide geographic range and a diversity of lotic conditions and this was reflected by differences between sites in macroinvertebrate assemblage composition and richness. The usefulness of expending the extra cost necessary to identify macroinvertebrates to species was quantified via the benefits this higher resolution data offered in its capacity to discriminate between sites and give accurate estimates of site species richness. We found that very little information (<6%) was lost by identifying taxa to family (or genus), as opposed to species, and that quantifying the abundance of taxa provided greater resolution for pattern interpretation than simply noting their presence/absence. Species richness was very well represented by genus, family and order richness, so that each of these could be used as surrogates of species richness if, for example, surveying to identify diversity hot-spots. It is suggested that sharing of common ecological responses among species within higher taxonomic units is the most plausible mechanism for the results. Based on a cost/benefit analysis, family level abundance data is recommended as the best resolution for resolving patterns in macroinvertebrate assemblages in this system. The relevance of these findings are discussed in the context of other low diversity, harsh, dryland river systems.