Probabilistic algorithms for blind adaptive multiuser detection

In this paper, two probabilistic adaptive algorithmsfor jointly detecting active users in a DS-CDMA system arereported. The first one, which is based on the theory of hiddenMarkov models (HMM’s) and the Baum–Wech (BW) algorithm,is proposed within the CDMA scenario and compared withthe second one, which is a previously developed Viterbi-basedalgorithm. Both techniques are completely blind in the sense thatno knowledge of the signatures, channel state information, ortraining sequences is required for any user. Once convergencehas been achieved, an estimate of the signature of each userconvolved with its physical channel response (CR) and estimateddata sequences are provided. This CR estimate can be used toswitch to any decision-directed (DD) adaptation scheme. Performanceof the algorithms is verified via simulations as well as onexperimental data obtained in an underwater acoustics (UWA)environment. In both cases, performance is found to be highlysatisfactory, showing the near–far resistance of the analyzed algorithms.

Statistical reference criteria for adaptive signal processing in digital communications

A general criterion for the design of adaptive systemsin digital communications called the statistical reference criterionis proposed. The criterion is based on imposition of the probabilitydensity function of the signal of interest at the outputof the adaptive system, with its application to the scenario ofhighly powerful interferers being the main focus of this paper.The knowledge of the pdf of the wanted signal is used as adiscriminator between signals so that interferers with differingdistributions are rejected by the algorithm. Its performance isstudied over a range of scenarios. Equations for gradient-basedcoefficient updates are derived, and the relationship with otherexisting algorithms like the minimum variance and the Wienercriterion are examined.

Effect of Tocopherol Extract, Staphylococcus carnosus Culture, and Celery Concentrate Addition on Quality Parameters of Organic and Conventional Dry-Cured Sausages

The effects of the addition to sausage mix of tocopherols (200 mg/kg), a conventional starter culture with or without Staphylococcus carnosus, celery concentrate (CP) (0.23% and 0.46%), and two doses of nitrate (70 and 140 mg/kg expressed as NaNO(3)) on residual nitrate and nitrite amounts, instrumental CIE Lab color, tocol content, oxidative stability, and overall acceptability were studied in fermented dry-cured sausages after ripening and after storage. Nitrate doses were provided by nitrate-rich CP or a chemical grade source. The lower dose complies with the EU requirements governing the maximum for ingoing amounts in organic meat products. Tocopherol addition protected against oxidation, whereas the nitrate dose, nitrate source, or starter culture had little influence on secondary oxidation values. The residual nitrate and nitrite amounts found in the sausages with the lower nitrate dose were within EU-permitted limits for organic meat products and residual nitrate can be further reduced by the presence of the S. carnosus culture. Color measurements were not affected by the CP dose. Product consumer acceptability was not affected negatively by any of the factors studied. As the two nitrate sources behaved similarly for the parameters studied, CP is a useful alternative to chemical ingredients for organic dry-cured sausage production.

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

Background: Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements. Results: We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer- approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in Saccharomyces cerevisiae. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast Saccharomyces cerevisiae to heat shock Conclusion: Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.

Minimization of biosynthetic costs in adaptive gene expression responses of yeast to environmental changes

Yeast successfully adapts to an environmental stress by altering physiology and fine-tuning metabolism. This fine-tuning is achieved through regulation of both gene expression and protein activity, and it is shaped by various physiological requirements. Such requirements impose a sustained evolutionary pressure that ultimately selects a specific gene expression profile, generating a suitable adaptive response to each environmental change. Although some of the requirements are stress specific, it is likely that others are common to various situations. We hypothesize that an evolutionary pressure for minimizing biosynthetic costs might have left signatures in the physicochemical properties of proteins whose gene expression is fine-tuned during adaptive responses. To test this hypothesis we analyze existing yeast transcriptomic data for such responses and investigate how several properties of proteins correlate to changes in gene expression. Our results reveal signatures that are consistent with a selective pressure for economy in protein synthesis during adaptive response of yeast to various types of stress. These signatures differentiate two groups of adaptive responses with respect to how cells manage expenditure in protein biosynthesis. In one group, significant trends towards downregulation of large proteins and upregulation of small ones are observed. In the other group we find no such trends. These results are consistent with resource limitation being important in the evolution of the first group of stress responses.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Assessment of the levels of degradation in fat co- and by-products for feed uses and their relationships with some lipid composition parameters

This paper discusses the levels of degradation of some co- and byproducts of the food chain intended for feed uses. As the first part of a research project, 'Feeding Fats Safety', financed by the sixth Framework Programme-EC, a total of 123 samples were collected from 10 European countries, corresponding to fat co- and byproducts such as animal fats, fish oils, acid oils from refining, recycled cooking oils, and other. Several composition and degradation parameters (moisture, acid value, diacylglycerols and monoacylglycerols, peroxides, secondary oxidation products, polymers of triacylglycerols, fatty acid composition, tocopherols, and tocotrienols) were evaluated. These findings led to the conclusion that some fat by- and coproducts, such as fish oils, lecithins, and acid oils, show poor, nonstandardized quality and that production processes need to be greatly improved. Conclusions are also put forward about the applicability and utility of each analytical parameter for characterization and quality control.

A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station

The time required to image large samples is an important limiting factor in SPM-based systems. In multiprobe setups, especially when working with biological samples, this drawback can make impossible to conduct certain experiments. In this work, we present a feedfordward controller based on bang-bang and adaptive controls. The controls are based in the difference between the maximum speeds that can be used for imaging depending on the flatness of the sample zone. Topographic images of Escherichia coli bacteria samples were acquired using the implemented controllers. Results show that to go faster in the flat zones, rather than using a constant scanning speed for the whole image, speeds up the imaging process of large samples by up to a 4x factor.

Influence of process parameters on part quality and mechanical properties for DMLS and SLM with iron-based materials

Rapid manufacturing is an advanced manufacturing technology based on layer-by-layer manufacturing to produce a part. This paper presents experimental work carried out to investigate the effects of scan speed, layer thickness, and building direction on the following part features: dimensional error, surface roughness, and mechanical properties for DMLS with DS H20 powder and SLM with CL 20 powder (1.4404/AISI 316L). Findings were evaluated using ANOVA analysis. According to the experimental results, build direction has a significant effect on part quality, in terms of dimensional error and surface roughness. For the SLM process, the build direction has no influence on mechanical properties. Results of this research support industry estimating part quality and mechanical properties before the production of parts with additive manufacturing, using iron-based powders

Genome-wide analysis of adaptive molecular evolution in the carnivorous plant Utricularia gibba

The genome of the bladderwort Utricularia gibba provides an unparalleled opportunity to uncover the adaptive landscape of an aquatic carnivorous plant with unique phenotypic features such as absence of roots, development of water-filled suction bladders, and a highly ramified branching pattern. Despite its tiny size, the U. gibba genome accommodates approximately as many genes as other plant genomes. To examine the relationship between the compactness of its genome and gene turnover, we compared the U. gibba genome with that of four other eudicot species, defining a total of 17,324 gene families (orthogroups). These families were further classified as either 1) lineage-specific expanded/contracted or 2) stable in size. The U. gibba-expanded families are generically related to three main phenotypic features: 1) trap physiology, 2) key plant morphogenetic/developmental pathways, and 3) response to environmental stimuli, including adaptations to life in aquatic environments. Further scans for signatures of protein functional specialization permitted identification of seven candidate genes with amino acid changes putatively fixed by positive Darwinian selection in the U. gibba lineage. The Arabidopsis orthologs of these genes (AXR, UMAMIT41, IGS, TAR2, SOL1, DEG9, and DEG10) are involved in diverse plant biological functions potentially relevant for U. gibba phenotypic diversification, including 1) auxin metabolism and signal transduction, 2) flowering induction and floral meristem transition, 3) root development, and 4) peptidases. Taken together, our results suggest numerous candidate genes and gene families as interesting targets for further experimental confirmation of their functional and adaptive roles in the U. gibba's unique lifestyle and highly specialized body plan.

Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

Foliar photochemical processes and carbon metabolism under favourable and adverse winter conditions in a Mediterranean mixed forest, Catalonia (Spain)

Evergreen trees in the Mediterranean region must cope with a wide range of environmental stresses from summer drought to winter cold. The mildness of Mediterranean winters can periodically lead to favourable environmental conditions above the threshold for a positive carbon balance, benefitting evergreen woody species more than deciduous ones. The comparatively lower solar energy input in winter decreases the foliar light saturation point. This leads to a higher susceptibility to photoinhibitory stress especially when chilly (< 12 C) or freezing temperatures (< 0 C) coincide with clear skies and relatively high solar irradiances. Nonetheless, the advantage of evergreen species that are able to photosynthesize all year round where a significant fraction can be attributed to winter months, compensates for the lower carbon uptake during spring and summer in comparison to deciduous species. We investigated the ecophysiological behaviour of three co-occurring mature evergreen tree species (Quercus ilex L., Pinus halepensis Mill., and Arbutus unedo L.). Therefore, we collected twigs from the field during a period of mild winter conditions and after a sudden cold period. After both periods, the state of the photosynthetic machinery was tested in the laboratory by estimating the foliar photosynthetic potential with CO2 response curves in parallel with chlorophyll fluorescence measurements. The studied evergreen tree species benefited strongly from mild winter conditions by exhibiting extraordinarily high photosynthetic potentials. A sudden period of frost, however, negatively affected the photosynthetic apparatus, leading to significant decreases in key physiological parameters such as the maximum carboxylation velocity (Vc,max), the maximum photosynthetic electron transport rate (Jmax), and the optimal fluorometric quantum yield of photosystem II (Fv/Fm). The responses of Vc,max and Jmax were highly species specific, with Q. ilex exhibiting the highest and P. halepensis the lowest reductions. In contrast, the optimal fluorometric quantum yield of photosystem II (Fv/Fm) was significantly lower in A. unedo after the cold period. The leaf position played an important role in Q. ilex showing a stronger winter effect on sunlit leaves in comparison to shaded leaves. Our results generally agreed with the previous classifications of photoinhibition-tolerant (P. halepensis) and photoinhibitionavoiding (Q. ilex) species on the basis of their susceptibility to dynamic photoinhibition, whereas A. unedo was the least tolerant to photoinhibition, which was chronic in this species. Q. ilex and P. halepensis seem to follow contrasting photoprotective strategies. However, they seemed equally successful under the prevailing conditions exhibiting an adaptive advantage over A. unedo. These results show that our understanding of the dynamics of interspecific competition in Mediterranean ecosystems requires consideration of the physiological behaviour during winter which may have important implications for long-term carbon budgets and growth trends.

Neural mechanisms underlying adaptive actions after slips

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory thetabeta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.