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PubMed2006

Modeling adolescent drug-use patterns in cluster-unit trials with multiple sources of correlation using robust latent class regressions.

PURPOSE: The purpose of the study is to examine variation in adolescent drug-use patterns by using latent class regression analysis and evaluate the properties of an estimating-equations approach under different cluster-unit trial designs. METHODS: A set of second-order estimating equations for latent class models under the cluster-unit trial design are proposed. This approach models the correlation within subclusters (drug-use behaviors), but ignores the correlation within clusters (communities). A robust covariance estimator is proposed that accounts for within-cluster correlation. Performance of this approach is addressed through a Monte Carlo simulation study, and practical implications are illustrated by using data from the National Evaluation of the Enforcing Underage Drinking Laws Randomized Community Trial. RESULTS: The example shows that the proposed method provides useful information about the heterogeneous nature of drug use by identifying two subtypes of adolescent problem drinkers. A Monte Carlo simulation study supports the proposed estimation method by suggesting that the latent class model parameters were unbiased for 30 or more clusters. Consistent with other studies of generalized estimating equation (GEE) estimators, the robust covariance estimator tended to underestimate the true variance of regression parameters, but the degree of inflation in the test size was relatively small for 70 clusters and only slightly inflated for 30 clusters. CONCLUSIONS: The proposed model for studying adolescent drug use provides an alternative to standard diagnostic criteria, focusing on the nature of the drug-use profile, rather than relying on univariate symptom counts. The second-order GEE-type estimation procedure provided a computationally feasible approach that performed well for a moderate number of clusters and was consistent with prior studies of GEE under the generalized linear model framework.

Adolescent↗

PubMed2005

The effects of Tai Chi Chuan on physiological function and fear of falling in the less robust elderly: an intervention study for preventing falls.

The aim of this report is to investigate the effects of 8 weeks of intensive Tai Chi Chuan (TCC) training on physiological function and fear of falling (FOF) in the less-robust elderly. Forty-nine community-dwelling elderly, aged 60 or older, were classified randomly into a TCC training or control group. Physical performance measures (including one-leg stance, trunk flexion, and walking speed) and interviews were conducted before and after the intervention. The TCC group showed significant improvements in balance and flexibility, and a reduced FOF, when compared with the control group after the intervention. However, walking speed did not change significantly. The results suggest that a high-frequency, short-term TCC training program can improve balance, flexibility, and increase the confidence of less-robust elderly. These suggest the effectiveness of TCC for intervention as a means to prevent falling among high-risk elderly populations.

Accidental Falls↗

PubMed2006

Microfluidic electroporation of robust 10-microm vesicles for manipulation of picoliter volumes.

We present a new way to transport and handle picoliter volumes of analytes in a microfluidic context through electrically monitored electroporation of 10-25 microm vesicles. In this method, giant vesicles are used to isolate analytes in a microfluidic environment. Once encapsulated inside a vesicle, contents will not diffuse and become diluted when exposed to pressure-driven flow. Two vesicle compositions have been developed that are robust enough to withstand electrical and mechanical manipulation in a microfluidic context. These vesicles can be guided and trapped, with controllable transfer of material into or out of their confined environment. Through electroporation, vesicles can serve as containers that can be opened when mixing and diffusion are desired, and closed during transport and analysis. Both vesicle compositions contain lecithin, an ethoxylated phospholipid, and a polyelectrolyte. Their performance is compared using a prototype microfluidic device and a simple circuit model. It was observed that the energy density threshold required to induce breakdown was statistically equivalent between compositions, 10.2+/-5.0 mJ/m2 for the first composition and 10.5+/-1.8 mJ/m2 for the second. This work demonstrates the feasibility of using giant, robust vesicles with microfluidic electroporation technology to manipulate picoliter volumes on-chip.

Electroporation↗

PubMed2005

Robustness, evolvability, and optimality of evolutionary neural networks.

In a typical optimization problem, the main goal is to search for the appropriate values of the variables that provide the optimal solution of the given function. In artificial neural networks (ANN), this translates to the minimization of the error surface during training such that misclassification is minimized during generalization. However, since optimal training performance does not necessarily imply optimal generalization due to the possibility of overfitting or underfitting, we developed SEPA (Structure Evolution and Parameter Adaptation) which addressed these issues by simultaneously evolving ANN structure and weights. Since SEPA primarily relies on the perturbation function to bring variation in its population, this follow-up study aims to find out SEPAs evolvability, optimality, and robustness in other perturbation functions. Our findings indicate that SEPAs optimal generalization performances are stable and robust from the effect of the different perturbation functions. This is due to the feedback loop between its architecture evolution and weight adaptation such that any shortcoming of the former is compensated by the latter, and vice versa. Our results strongly suggest that proper ANN design requires simultaneous adaptation of ANN structure and weights to avoid one-sided or bias convergence to either the weight or architecture space.

Algorithms↗

PubMed2006

The genotypic complexity of evolved fault-tolerant and noise-robust circuits.

Noise and component failure is an increasingly difficult problem in modern electronic design. Bio-inspired techniques is one approach that is applied in an effort to solve such issues, motivated by the strong robustness and adaptivity often observed in nature. Circuits investigated herein are designed to be tolerant to faults or robust to noise, using an evolutionary algorithm. A major challenge is to improve the scalability of the approach. Earlier results have indicated that the evolved circuits may be suited for the application of artificial development, an approach to indirect mapping from genotype to phenotype that may improve scalability. Those observations were based on the genotypic complexity of evolved circuits. Herein, we measure the genotypic complexity of circuits evolved for tolerance to faults or noise, in order to uncover how that tolerance affects the complexity of the circuits. The complexity is analysed and discussed with regards to how it relates to the potential benefits to the evolutionary process of introducing an indirect genotype-phenotype mapping such as artificial development.

Algorithms↗

PubMed2004

Generic, highly selective and robust capillary electrophoresis method for separation of a racemic mixture of glitazone compounds.

A generic, highly selective, and robust capillary electrophoresis (CE) method was developed for separation of a racemic mixture of three available glitazone compounds (also known as thiazolidinediones) in active pharmaceutical ingredients (API) and tablets. The method separated the R and S enantiomers of balaglitazone, pioglitazone and rosiglitazone, and showed that the samples contained an equal (50:50) quantity of the enantiomers as a mixture. After a simple extraction of samples with acetonitrile:water (80:20), separation was performed using a combination of two cyclodextrins: sulfobuthylether-beta-cyclodextrin (SB-beta-CD) and dimethyl-beta-cyclodextrin (DM-beta-CD) in the electrolyte at pH 8.0. The method showed a very good specificity, and all separations were achieved with a resolution (Rs) over 3.0. The developed CE method was then validated. The Rs for the separations were 3.5 for balaglitazone enantiomers, 3.5 for pioglitazone enantiomers, and 3.7 for rosiglitazone. The squared correlation coefficients (r2) were found to be 0.999 for all three compounds. The range of the CE method (injection volume was approximately 4 nl) was demonstrated to be from 1.0 to 2.4 ng. The R.S.D. in the repeatability study was found to be less than 0.5 for area/area ratio (and 3.0% for area) for all three compounds. The R.S.D. in the intermediate precision study was found to be less than 0.7 for area/area ratio (and 4.5% for area) for all three compounds. Generally, the method showed good robustness. Resolution between the enantiomers peak was maintained acceptable throughout the small variations around the pH value of the buffer, different capillary, CE instrument and electrolytes ion strength capacity, but changes in concentration of cyclodextrins and acetonitrile showed significant effects on separations and affected the resolution. The validation results showed that the CE method was suitable for separation of the racemic mixtures of the three glitazone drugs. The CE method was then applied for routine test during the drug and formulation development work of balaglitazone. Due to the achieved results from this work, it is the authors' belief that this method can easily separate other glitazone racemic mixtures.

Cyclodextrins↗

PubMed2005

Robust interpretive optimisation in high-performance liquid chromatography considering uncertainties in peak position.

In the context of interpretive chromatographic optimisation, robustness is usually calculated by introducing deliberated shifts in the nominal optimal conditions and evaluating their effects on the monitored objective function, mimicking thus the experimental procedures used in method validation. However, such strategy ignores a major source of error: the uncertainties associated to the modelling step, that may give rise to deceiving results when conditions that were expected to yield baseline separation are reproduced in the chromatograph. Two approaches, based on the peak purity concept, are here proposed to evaluate the robustness of the objective function under the perspective of measurement errors and modelling. The first approach implements these uncertainties as an extra band broadening for each chromatographic peak. The second one implements them as peak fluctuations in simulated replicated assays, which gives rise to a distribution of peak purities, easily computed through Monte-Carlo simulations. Both approaches predict satisfactorily a decreased separation capability, with respect to the conventional approach, for those situations where the uncertainties in peak position make the objective function critical. The first approach is less optimistic and formally less rigorous than the second one, but its computation is simpler. It can be used to map the critical resolution regions, to be comprehensively appraised further by the slower, although more rigorous, Monte-Carlo approach.

Amino Acids↗

PubMed2005

A novel ensemble machine learning for robust microarray data classification.

Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).

Algorithms↗

PubMed2005

A tunable multivariable nonlinear robust observer for biological systems.

This paper presents a robust nonlinear asymptotic observer with adjustable convergence rate with a great potential of applicability for biological systems in which the main state variables are difficult and expensive to measure or such measurements do not exist. This observer scheme is based on the classical asymptotic observer, which is modified to allow the tuning of the convergence rate. It is shown that the proposed observer provides fast and satisfactory estimates when facing load disturbances, system failures and parameter uncertainty while maintaining the excellent robustness and stability properties of the classical asymptotic observer. The implementation of the tunable observer is carried out by numerical simulations of a mathematical model of an anaerobic digestion process used for wastewater treatment. The key results are examined and further developed.

Nonlinear Dynamics↗

PubMed2006

Generation of robust left-right asymmetry in the mouse embryo requires a self-enhancement and lateral-inhibition system.

The bilateral symmetry of the mouse embryo is broken by leftward fluid flow in the node. However, it is unclear how this directional flow is then translated into the robust, left side-specific Nodal gene expression that determines and coordinates left-right situs throughout the embryo. While manipulating Nodal and Lefty gene expression, we have observed phenomena that are indicative of the involvement of a self-enhancement and lateral-inhibition (SELI) system. We constructed a mathematical SELI model that not only simulates, but also predicts, experimental data. As predicted by the model, Nodal expression initiates even on the right side. These results indicate that directional flow represents an initial small difference between the left and right sides of the embryo, but is insufficient to determine embryonic situs. Nodal and Lefty are deployed as a SELI system required to amplify this initial bias and convert it into robust asymmetry.

Animals↗

PubMed2006

Robustness of modelled ozone exposures and doses.

This study evaluates the robustness of the AOTX and AF(st)Y indices for assessing the ozone-induced risk to vegetation. These indices represent the accumulated concentration and stomatal flux, respectively, above a threshold value. The robustness is expressed as the sensitivity to changes in inputs and the uncertainty due to input errors. The input data are taken from a regional-scale chemical transport model. Both indices show increasing sensitivity with increasing threshold values. The sensitivity depends on the threshold and the characteristics of the frequency distribution for concentrations and stomatal fluxes. AF(st)Y appears less sensitive than AOTX for the thresholds adopted for critical levels. The couplings between concentration gradients and deposition algorithms complicate the assessment of the total uncertainty. For AF(st)Y, the uncertainty due to the modelled stomatal conductance may sometimes increase, but sometimes decrease, the overall uncertainty significantly. In particular, the maximum stomatal conductance plays an important role in determining the uncertainty.

Environmental Exposure↗

PubMed2005

Robustness of the p53 network and biological hackers.

The p53 protein interaction network is crucial in regulating the metazoan cell cycle and apoptosis. Here, the robustness of the p53 network is studied by analyzing its degeneration under two modes of attack. Linear Programming is used to calculate average path lengths among proteins and the network diameter as measures of functionality. The p53 network is found to be robust to random loss of nodes, but vulnerable to a targeted attack against its hubs, as a result of its architecture. The significance of the results is considered with respect to mutational knockouts of proteins and the directed attacks mounted by tumour inducing viruses.

Computer Simulation↗

PubMed2004

Noise, delays, robustness, canalization and all that.

A biological system such as a developing embryo can withstand many perturbations. What is the basis of this robustness both against noise and mutation? Recent advances in modeling may throw new light on this old problem. First, recent theoretical and experimental work clearly demonstrates the importance of noise and time delays for the proper functioning of genetic networks: noise and delays are simply part of the normal operating constraints. By contrast, sweeping statements have been made recently about a so-called 'robustness' of biological processes, based on work that neglects noise and delays completely. I submit that studying the stability of complex biological systems with such omissions is an unnecessary, inadequate and potentially disastrous simplification. I review the existing alternatives and propose using them to construct a modeling framework that overcomes all serious limitations.

Computational Biology↗

PubMed2004

Elucidating mechanisms underlying robustness of morphogen gradients.

Morphogen gradients play a pivotal role in most phases of developmental patterning. To ensure proper patterning, reproducible gradients are established under diverse environmental conditions and genetic backgrounds. We refer to the capacity to buffer fluctuations in gene dosage or environmental conditions as 'robustness'. By theoretical analysis of mechanisms that facilitate robustness, it is possible to unravel the machinery responsible for generating the spatial distribution of morphogens.

Animals↗

PubMed2005

Reflections on the use of robust and least-squares non-linear regression to model challenge tests conducted in/on food products.

In this research, we question the straight-forward use of the classical sum of squared error criterion for identifying the typical parameters of a primary model (like growth rate mumax and lag time lambda) when applied to growth curves obtained in and on food products. Firstly, we base our reflections on 62 Listeria monocytogenes laboratory challenge tests collected in various environments (broth, crushed cold-smoked salmon, and surface of cold-smoked salmon slices). Whereas growth data in broth resulted in residual values consistent with a Gaussian distribution, growth data in the crushed product and even more on the surface of slices appeared different. Secondly, we propose the use of an alternative so-called robust non-linear regression method suitable when experimental error is non-normally distributed, which seems, according to this research, typical for microbial challenge tests in/on food products, and which lead to apparent outliers or leverage points in the experimental data. Properties of the robust regression procedure are illustrated on simulated data first, whereafter its use on the considered challenge tests is illustrated. To conclude, reflections on the assumptions and related realism underlying challenge tests and recommendations for fitting growth curves obtained in and on food products are presented.

Animals↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2006

A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures.

In this paper robust statistical procedures are presented for the analysis of skewed and heavy-tailed outcomes as they typically occur in health care data. The new estimators and test statistics are extensions of classical maximum likelihood techniques for generalized linear models. In contrast to their classical counterparts, the new robust techniques show lower variability and excellent efficiency properties in the presence of small deviations from the assumed model, i.e. when the underlying distribution of the data lies in a neighborhood of the model. A simulation study, an analysis on real data, and a sensitivity analysis confirm the good theoretical statistical properties of the new techniques.

Health Expenditures↗

PubMed2005

Multiple target screening method for robust and accurate in silico ligand screening.

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].

Databases, Factual↗