Characterizing and Predicting Submovements during Human Three-Dimensional Arm Reaches

We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs) to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration) of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces.     

Can adaptation lead to extinction?

Ever since J.B.S. Haldane proposed the idea, evolutionary biologists are aware that individual level adaptations do not necessarily lead to optimal population performance. A few deeply mathematical models, drawing from a diverse range of systems, even predict that individual selection can lead to the extinction of the whole population, a phenomenon which has become known as evolutionary suicide. Due to the complexity of both following adaptation and determining the exact cause of an extinction, evolutionary suicide has remained untested empirically. However, three recent empirical studies suggest that it may occur, and that suicide should be taken seriously as a potentially important evolutionary phenomenon. Here we ask whether or not evolutionary suicide can occur, briefly reviewing the theoretical and empirical evidence. We further highlight systems which may be used to test whether or not individual level selection can cause extinction.     

Issues regarding artificial neural network modeling for reactors and fermenters

In recent years researchers in many areas have used artificial neural networks (ANNs) to model a variety of physical relationships. While in many cases this selection appears sound and reasonable, one must remember than ANN modeling is an empirical modeling technique (based on data) and is subject to the limitations of such techniques. Poor prediction occurs when the training data set does not contain adequate "information" to model a dynamic process. Using data from a simulated continuous-stirred tank reactor, this paper illustrates four scenarios: (1) steady state, (2) large process time constant, (3) infrequent sampling, and (4) variable sampling rate. The first scenario is typical of simulation studies while the other three incorporate attributes found in real plant data. For the cases in which ANNs predicted well, linear regression (LR), one of the oldest empirical modeling techniques, predicted equally well, and when LR failed to accurately model/predict the data, ANNs predicted poorly. Since real plant data would resemble a combination of situations (2), (3), and (4), it is important to understand that empirical models are not necessarily appropriate for predictively modeling dynamic processes in practice.     

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.     

From the biology of the individual to the dynamics of the population: bridging the gap in fish early life studies

The mean properties of larval fish populations do not necessarily reflect the properties of the mean individual. For example, the change in mean length in a population with time may not reflect the average individual growth rate, since individual growth rates and survival probability are linked so that slow growing individuals suffer higher mortality. Hence, mean growth rate indicated from population data could be biased upwards. Factors which influence the magnitude and variability of individual growth rates can exert nonlinear effects on population survival. Two categories of process must be considered: first, the variability in exposure of the average individual as a consequence of individual variability in dispersal through a patchy environment; and second, the intrinsic variability between individuals expressed even under equal exposure conditions. These two aspects have been addressed independently, the first by lagrangian modelling of individual fish larvae linked to spatially resolved hydrodynamic models, and the second by strategic biological modelling. In this paper, progress towards the goal of individually based larval fish ecosystem models is reviewed, highlighting the space and time scales which may be important in such systems, and identifying the gaps in current knowledge of larval biology.     

Assessment of respiratory system mechanics by artificial neural networks: an exploratory study.

We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANN(MOD)) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient (R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANN(BIO) showed a performance expressed by R = 0.40 and ANN(MOD) by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.     

Identification and Discrimination of Oral Asaccharolytic Eubacterium spp. by Pyrolysis Mass Spectrometry and Artificial Neural Networks

Curie-point pyrolysis mass spectra were obtained from 29 oral asaccharolytic Eubacterium strains and 6 abscess isolates previously identified as Peptostreptococcus heliotrinreducens. Pyrolysis mass spectrometry (PyMS) with cluster analysis was able to clarify the taxonomic position of this group of organisms. Artificial neural networks (ANNs) were then trained by supervised learning (with the back-propagation algorithm) to recognize the strains from their pyrolysis mass spectra; all Eubacterium strains were correctly identified, and the abscess isolates were identified as un-named Eubacterium taxon C₂ and were distinct from the type strain of P. heliotrinreducens. These results demonstrate that the combination of PyMS and ANNs provides a rapid and accurate identification technique.     

Significance of time and language in view of the process of medical diagnostic processes. Philosophical methodologic preliminaries

The understanding of the patient is a prerequisite to philosophical and methodological thinking about the process of medical diagnosis-making. In regard to the philosophical discussion about the recently development of scientific knowledge, the comprehension of the medical object and the conceptions of health and illness has been modified. The human state of illness as a "Zeitgestalt" of intra- and intersubjective disturbances of communication is joined with the individuality and complexity of the subjective experiences of the patient. The modified conceptions of health and illness can help to overcome the division between subject and object, a consequence of Cartesian rationalistic thought. In this way new connections within medical thinking and a new comprehension of medical diagnosis appear. The medical diagnosis-making is simultaneously a process of knowledge to the physician and an instruction for his therapeutical action. The special significance of the medical diagnosis at all is the irreversibility and the temporal openness of the therapeutical action. Medical diagnosis is a form of communication leading to the action of the diagnosis-making physician and therefore a inseparable part of the physician-patient relation. The adequate valuation of the multi-dimensionality of the individual pathogenesis (physic, psychic, social) is possible only within this relation. The prediction of critical states presupposes the knowledge of the individual pathogenesis. Of course, such predictions are not completely quantifiable.     

Temporal separation of Cerenkov radiation and scintillation using artificial neural networks in Clinical LINACs

The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to determine the dose deposited in the scintillator volume. Artificial neural networks (ANNs) were trained and applied in a novel single probe method for the temporal separation of scintillation and Cerenkov radiation. Six dose profiles were measured using the ANN, with the dose profiles compared to those measured using background subtraction and an ionisation chamber. The average dose discrepancy of the ANN measured dose was 2.2% with respect to the ionisation chamber dose and 1.2% with respect to the background subtraction measured dose, while the average dose discrepancy of the background subtraction dose was 1.6% with respect to the ionisation chamber dose. The ANNs performance was degraded when compared with background subtraction, arising from an inaccurate model used to synthesise ANN training data.     

Localization of the coding region for an Epstein-Barr virus early antigen and inducible expression of this 60-kilodalton nuclear protein in transfected fibroblast cell lines.

Expression of a component of the Epstein-Barr virus early antigen (EA) complex has been studied in fibroblast cells transfected with both wild-type and P3HR-1 defective DNA fragments covering the BamHI-M-S region of the Epstein-Barr virus genome. Baby hamster kidney (BHK) cells transfected with the BglII-J fragment and stained with human serum that was positive for the diffuse component of EA [EA(D)] in an indirect immunofluorescence assay exhibited positive nuclear staining in 5% of the cell population. Cleavage of BglII-J before transfection with the restriction enzyme BglII, StuI, HindIII, or PvuII did not affect EA expression, whereas prior cleavage with BamHI or EcoRI reduced or eliminated synthesis of EA. These observations were confirmed by using individual cloned subfragments. A Bal 31 deletion clone (pTS1) in which the HindIII and StuI sites were eliminated retained activity, whereas a clone (pTS5) in which the deletion extended closer to the EcoRI site had greatly reduced activity. Transfection of the individual BamHI-M or BamHI-S fragments, which span BglII-J, also resulted in little or no EA expression. The 2.1-kilobase biologically active region defined by these experiments corresponds precisely to the BMLF1 open reading frame. Immunoblot analyses of BHK cells transfected with either P3HR-1 defective DNA clones or the BglII-J wild-type fragment identified the product of this EA(D) coding region as a family of polypeptides consisting of a major 60-kilodalton product and minor 45- and 50-kilodalton species. In latently Epstein-Barr virus-infected lymphocytes these early antigens are not expressed, but can be induced by treatment of the cultures with sodium butyrate or phorbol esters. Using the BglII-J and pTS6 clones that were positive in transient assays, we also established Neor coselected BHK and Vero cell lines which showed similar regulated expression of the 60-kilodalton EA(D) protein. In these cell lines constitutive expression of EA(D) was limited (0.1% positive by indirect immunofluorescence and undetectable by immunoblot analysis). However, expression of EA(D) could be induced by treatment with sodium butyrate. In the induced cultures, up to 30% of the cells were EA(D) positive by immunofluorescence, and there was a concomitant appearance of the 60-kilodalton EA(D) polypeptide.     

Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.     

The role of density-dependent individual growth in the persistence of freshwater salmonid populations

Theoretical and empirical models of populations dynamics have paid little attention to the implications of density-dependent individual growth on the persistence and regulation of small freshwater salmonid populations. We have therefore designed a study aimed at testing our hypothesis that density-dependent individual growth is a process that enhances population recovery and reduces extinction risk in salmonid populations in a variable environment subject to disturbance events. This hypothesis was tested in two newly introduced marble trout (Salmo marmoratus) populations living in Slovenian streams (Zakojska and Gorska) subject to severe autumn floods. We developed a discrete-time stochastic individual-based model of population dynamics for each population with demographic parameters and compensatory responses tightly calibrated on data from individually tagged marble trout. The occurrence of severe flood events causing population collapses was explicitly accounted for in the model. We used the model in a population viability analysis setting to estimate the quasi-extinction risk and demographic indexes of the two marble trout populations when individual growth was density-dependent. We ran a set of simulations in which the effect of floods on population abundance was explicitly accounted for and another set of simulations in which flood events were not included in the model. These simulation results were compared with those of scenarios in which individual growth was modelled with density-independent Von Bertalanffy growth curves. Our results show how density-dependent individual growth may confer remarkable resilience to marble trout populations in case of major flood events. The resilience to flood events shown by the simulation results can be explained by the increase in size-dependent fecundity as a consequence of the drop in population size after a severe flood, which allows the population to quickly recover to the pre-event conditions. Our results suggest that density-dependent individual growth plays a potentially powerful role in the persistence of freshwater salmonids living in streams subject to recurrent yet unpredictable flood events. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Applications of neural networks to recovery of biological products

Artificial neural networks (ANN) are being applied to recovery of products from fermentation broths. Recovery methods for which mathematical models are complex or non-existent are particularly suitable for control and analysis by ANNs. Use and potential of artificial neural networks for product recovery applications are reviewed.     

Two-dimensional fluorometry coupled with artificial neural networks: a novel method for on-line monitoring of complex biological processes

The use of two-dimensional scanning fluorometry as an on-line, noninvasive, in situ bioreactor monitoring technique is extended to complex bioprocesses using mixed cultures, with particular attention to biofilm systems. Using the example of spectra subtraction, it is demonstrated that established methods for fluorescence data analysis have a limited capability of utilizing overall fluorometric information. Artificial neural networks (ANNs) are introduced as a novel nonlinear and nonmechanistic technique for interpreting the highly complex fluorescence maps. It is shown that ANNs are able to infer process performance parameters in a pattern recognition approach, based on the entire fluorescence "fingerprint" of the biological system. The studies were carried out using an extractive membrane bioreactor (EMB) for the degradation of chlorinated organic compounds, operating with mixed cultures. Model pollutants em- ployed were 1,2-dichloroethane, 3-chloro-4-methylaniline, and p-toluidine.     

Muscular performance modeling of the upper limb in static postures

The purpose of the present paper is to describe and evaluate the polynomial models for predicting the muscular work capacity of the upper limb during sustained holding tasks. This research was concerned with the relationship between indicators of performance, i.e., specific posture or specific level of maximum voluntary contraction (MVC), and then modeling the functional data based on experimental results to estimate factors that may have an effect on task performance. To this end, we designed an experiment using 10 subjects in which each subject performed sustained isometric shoulder and elbow flexion endurance exercise under 27 conditions [3 shoulder angles (SA)x3 elbow angles (EA)x3 levels of %MVC]. Throughout all experiments, subjective perception of effort was assessed using the Borg scale, every 60, 30, and 10 s during the 20%, 40%, and 60% MVC tests, respectively. Proposal models were represented by three approaches: model A: estimation of endurance time (ET), with input variables such as SA, EA, and %MVC; model B: estimation of recommendation time (RT, the time during which the operator was able to maintain a position under the desired condition), with input variables such as SA, EA, %MVC, and required rate on the Borg scale; and model C: estimation of limit strength or %MVC, with input variables such as SA, EA, request limit time for work (LT), and required rate on the Borg scale. Statistical analysis indicated that the three proposal estimation models based on polynomial regression functions showed high significance (p<0.0001). The proposal models suggested and recommended the possibility of finding the best positions entailing the reduction and minimization of total muscular strain from manual material handling tasks in different work situations, with the consequent increase in work efficiency.     

The use of caesium sulphate density gradient centrifugation to analyse proteoglycans from human articular cartilages of different ages

Proteoglycan subunits from human articular cartilage were fractionated by caesium sulphate density gradient centrifugation. A single heterogeneous population of molecules was produced whose average density decreased with increasing age of the individual from which they were obtained. At no density did the carbohydrate composition of any adult fraction resemble that of any newborn fraction, although there was considerable overlap in density. However, there was a similarity in amino acid composition between the most dense proteoglycans from the adult and those of least density from the newborn. The carbohydrate content of a 2-year-old proteoglycan was intermediate in composition, with high density fractions resembling the newborn and low density fractions resembling the adult. In addition, the proteoglycans of lowest density in both the newborn and two year preparations showed additional bands on agarose/polyacrylamide gel electrophoresis resembling the adult material. These results indicate that while a core protein of adult composition may occur in the juvenile proteoglycan it need not necessarily be glycosylated in an adult manner, suggesting that glycosylation is to some extent independent of the origin of core protein heterogeneity.     

Reflections on clinical gait analysis

Clinical gait analysis allows the measurement and assessment of walking biomechanics, which facilitates the identification of abnormal characteristics and the recommendation of treatment alternatives. The predominant methods for this analysis currently include the tracking of external markers placed on the patient, the monitoring of patient/ground interaction (e.g. ground reaction forces), and the recording of muscle electromyographic (EMG) activity, all during gait. These data allow the computation of stride and temporal parameters, joint/segment kinematics, joint kinetics, and EMG plots that are used to gain a better understanding of a patient's walking difficulties. Gait interpretation involves a systemic evaluation of each of these types of data, noting both corroborating and conflicting information while identifying functionally significant deviations from the normal. Understanding the etiology of these abnormalities allows the formulation of a treatment plan that may involve physical therapy, bracing, and/or surgery. This process is challenging because of the complexity of the motion, neuromuscular involvement of the patient (e.g. dynamic spasticity), variability of treatment outcome, and on occasion, uncertainty about the quality of the gait data. The experience of the interpretation team with respect to gait biomechanics, a particular patient population, and the effectiveness of different treatment modalities is the principal determinant of the success of this approach. The clinical gait analysis process continues to evolve positively. It has become more comprehensive and meaningful because of an improved understanding of normal gait biomechanics and more rigorous data collection/reduction protocols that complement accumulated clinically relevant experience.     

Action of pertussigen (pertussis toxin) on serum IgE and on Fc epsilon receptors on lymphocytes

Pertussigen (pertussis toxin (PT] is one of the most effective stimulators of IgE production in mice and rats. Employing flow microfluorimetric analysis (FMF), we showed that PT increases the percentage of blood and spleen lymphocytes with IgE on their surface. The percentage of IgE-bearing cells in the spleen of normal untreated C57Bl/10SCN mice of various ages varied from 2.2 to 12.2%, with an average value of 6.1 +/- 5.4%. In mice treated with 400 ng of PT and 1 mg of chicken egg albumin (EA), the percentage of these cells increased, 14 days after immunization, to an average value of 31.1 +/- 2.2%. Immunization of mice with PT alone increase the percentage of IgE-bearing cells only slightly (13.1 +/- 2.2% of the splenic lymphocytes) while injection of 1 mg of EA alone did not have any detectable action. As little as 6 ng of PT, when given simultaneously with 1 mg of EA, increased the percentage of IgE-bearing lymphocytes. A booster dose of 10 micrograms of EA given on Day 14 induced a further increase in the percentage of these cells even when as little as 0.039 ng of PT had been given at the time of initial immunization. PT was effective when given 4 days before or 5 days after EA. EA was effective when given 4 days before or 4 days after PT, but not 8 days after. The increase in IgE-bearing cells was mainly due to cytophilic binding of IgE to receptors for the epsilon chain of IgE (Fc epsilon) on the surface of lymphocytes rather than to a greater number of IgE-producing cells. This was shown by removing the IgE from Fc epsilon receptors by acid treatment which reduced the percentage of IgE-bearing cells to nearly normal values. The antibodies of IgE class with specificity to EA were increased dramatically, while antibodies with specificity to PT were not detected.     

The use of Artificial Neural Networks to classify primate vocalizations: a pilot study on black lemurs

The identification of the vocal repertoire of a species represents a crucial prerequisite for a correct interpretation of animal behavior. Artificial Neural Networks (ANNs) have been widely used in behavioral sciences, and today are considered a valuable classification tool for reducing the level of subjectivity and allowing replicable results across different studies. However, to date, no studies have applied this tool to nonhuman primate vocalizations. Here, we apply for the first time ANNs, to discriminate the vocal repertoire in a primate species, Eulemur macaco macaco. We designed an automatic procedure to extract both spectral and temporal features from signals, and performed a comparative analysis between a supervised Multilayer Perceptron and two statistical approaches commonly used in primatology (Discriminant Function Analysis and Cluster Analysis), in order to explore pros and cons of these methods in bioacoustic classification. Our results show that ANNs were able to recognize all seven vocal categories previously described (92.5–95.6%) and perform better than either statistical analysis (76.1–88.4%). The results show that ANNs can provide an effective and robust method for automatic classification also in primates, suggesting that neural models can represent a valuable tool to contribute to a better understanding of primate vocal communication. The use of neural networks to identify primate vocalizations and the further development of this approach in studying primate communication are discussed. Am. J. Primatol. 72:337–348, 2010. © 2009 Wiley‐Liss, Inc.     

Frpm artificial individuals to global patterns

Artificial Life is a model of biological systems that describes lives archived by computer simulation, chemical substrates or any other non-biological substrates. Artificial Life simulation adopts a bottom-up approach in which behavior of lower-level entities (e.g. molecules, cells and individuals) is all that is programed; global patterns (e.g. evolutionary patterns observed at the level of the population and the community) can emerge as a result of interaction among lower-level entities. Artificial Life simulations will be used not only to test ecological and evolutionary hypotheses explaining real organisms but also to show the validity of general theories, processes and concepts such as natural selection, theories of complexity, hierarchical relations and self-organization.