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PubMed2026

Precision targeting of teacher burnout using network-informed ecological momentary interventions.

Teacher well-being affects classroom functioning and workforce stability, yet generic digital programs rarely use person-specific affect dynamics to select support. This cluster-randomised trial evaluated whether micro-interventions selected from high expected influence (EI) nodes in teachers' contemporaneous affect networks produced larger changes in burnout-related EI and everyday happiness than content-matched random allocation. The objectives were to estimate allocation effects on changes in estimated network summaries and happiness, evaluate network change as a statistical mediator, examine personality moderation, and benchmark simpler allocation rules. A two-arm cluster randomised platform trial was conducted in 84 public schools across four urban districts in H Province. After a 14 day baseline of ecological momentary assessment (EMA), person specific partial correlation networks were estimated for happiness, exhaustion, detachment, efficacy and rumination. An optimisation engine prioritised three brief micro-intervention types per teacher according to baseline EI, while the active control received the same library without network information. EMA continued for 8 weeks; Bayesian multilevel models, permutation-based mediation, and benchmarking analyses were applied. EI-based targeting produced larger reductions in the composite EI-change index than active control (mean difference 0.11, 95% credible interval 0.08 to 0.14) and higher week 7 EMA happiness (4.4 points on a 0 to 100 scale, 95% credible interval 2.7 to 6.0), with a positive arm by week slope difference of 0.62 points per week (95% credible interval 0.39 to 0.85). Model-based mediation estimates were consistent with approximately one half of the happiness difference being statistically associated with change in the composite EI-change index (average conditional mediation estimate 3.5 points, 95% credible interval 2.0 to 5.2). Benchmarking showed smaller gains under severity, threshold, or group-level centrality rules. Effects were stronger among teachers higher in conscientiousness. The findings indicate that integrating EMA, network modelling, and EI-driven optimisation yields measurable gains beyond content-matched exposure, providing a proof of concept for district-scale precision mental health that requires prospective implementation testing. Replication in additional regions, expanded node sets, and longer follow up are warranted to assess durability and generalisability.

Female↗

PubMed2004

Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins.

Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. The networks have been tested and standard error of prediction (SEP) of 4.19% for alpha helix, 3.49% for beta sheet, and 3.15% for turns have been achieved. The results indicate that there is a significant decrease in the SEP for each type of structure parameter compared to previous works.

Algorithms↗

PubMed2003

Chronic nephropathies: individual risk for progression to end-stage renal failure as predicted by an integrated probabilistic model.

BACKGROUND/AIMS: To predict risk of end-stage renal disease (ESRD) in individual patients with chronic nephropathy. METHODS: Sequential use of univariate analyses and Cox regression to identify risk factors, artificial neural network to quantify their relative importance and Bayesian analysis to address uncertainty of relationships and incorporate ESRD prevalence information in 344 patients with chronic nephropathy enrolled in the Ramipril Efficacy In Nephropathy study. RESULTS: Serum creatinine (SC), 24-hour urinary protein excretion (UPE) and calcium-phosphorus (Ca*P) product were, in this order, the strongest time-adjusted ESRD predictors. Individual risk of ESRD ranged from near zero when SC and UPE were <1.66 mg/dl and <3 g/24 h, to 69% when SC, UPE and Ca*P were > or =2.41 mg/dl, > or =3 g/24 h and > or =32.64 mg2/dl2, respectively. Receiver operating characteristic curves showed that within lowest, middle and highest tertiles of basal SC (0.90-1.65, 1.66-2.40 and 2.41-6.30 mg/dl, respectively) the model accurately predicted ESRD (AUC = 0.80, 0.72 and 0.65; p = 0.0003, 0.0001 and 0.0022, respectively), quality of life or treatment costs. CONCLUSION: Integrated use of regression analysis and probabilistic models allows computation of individual risk of progression to ESRD and related utilities. This may help in optimizing care and costs in nephrology and other medical areas and designing trials in high-risk patients.

Analysis of Variance↗

PubMed2006

Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice.

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.

Animals↗

PubMed2005

Bayesian inference and attentional modulation in the visual cortex.

The responses of neurons in cortical areas V2 and V4 can be significantly modulated by attention to particular locations within an input image. We show that such effects emerge naturally when perception is viewed as a probabilistic inference process governed by Bayesian principles and implemented in hierarchical cortical networks. The proposed model can explain a rich variety of attention-related responses in cortical area V4 including multiplicative modulation of tuning curves, restoration of neural responses in the presence of distracting stimuli, and influence of attention on neighboring unattended locations. Our results suggest a new interpretation of attention as a cortical mechanism for reducing perceptual uncertainty by combining top-down task-relevant information with bottom-up sensory inputs in a probabilistic manner.

Animals↗

PubMed2002

Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data.

This paper reports the methods and results of a computer-based search for causal relationships in the gene-regulation pathway of galactose metabolism in the yeast Saccharomyces cerevisiae. The search uses recently published data from cDNA microarray experiments. A Bayesian method was applied to learn causal networks from a mixture of observational and experimental gene-expression data. The observational data were gene-expression levels obtained from unmanipulated "wild-type" cells. The experimental data were produced by deleting ("knocking out") genes and observing the expression levels of other genes. Causal relations predicted from the analysis on 36 galactose gene pairs are reported and compared with the known galactose pathway. Additional exploratory analyses are also reported.

Animals↗

PubMed1997

Flat minima.

We present a new algorithm for finding low-complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to "simple" networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require gaussian assumptions and does not depend on a "good" weight prior. Instead we have a prior over input-output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second-order derivatives, it has backpropagation's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms conventional backprop, weight decay, and "optimal brain surgeon/optimal brain damage".

Algorithms↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2006

Lidar detection of underwater objects using a neuro-SVM-based architecture.

This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.

Algorithms↗

PubMed2006

Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale.

Many thousands of proteins encoded by the genome of Plasmodium falciparum, the causal organism of the deadliest form of human malaria, are of unknown function. It is of utmost importance that these proteins be characterized if we are to develop combative strategies against malaria based on the biology of the parasite. In an attempt to infer protein function on a genome-wide scale, we computationally modeled the P. falciparum interactome, elucidating local and global functional relationships between gene products. The resulting interaction network, reconstructed by integrating in silico and experimental functional genomics data within a Bayesian framework, covers approximately 68% of the parasite genome and provides functional inferences for more than 2000 uncharacterized proteins, based on their associations. Network reconstruction involved the use of a novel strategy, where we incorporated continuously updated, uniform reference priors in our Bayesian model. This method for generating interaction maps is thus also well suited for application to other genomes, where pre-existing interactome knowledge is sparse. Additionally, we superimposed this map on genomes of three apicomplexan pathogens--Plasmodium yoelii, Toxoplasma gondii, and Cryptosporidium parvum--describing relationships between these organisms based on retained functional linkages. This comparison provided a glimpse of the highly evolved nature of P. falciparum; for instance, a deficit of nearly 26% in terms of predicted interactions is observed against P. yoelii, because of missing ortholog partners in pairs of functionally linked proteins.

Animals↗

PubMed1995

Lesion size quantification in SPECT using an artificial neural network classification approach.

An artificial neural network (ANN) has been developed to determine the size of lesions detected in single photon emission computed tomographic images. The network is the Learning Vector Quantizer and is trained to perform size quantification based on image neighborhoods extracted around the lesions. The ANN is compared to the optimal, Bayesian algorithm developed to perform the same task using the unreconstructed, projection data. The performance of the neural network is evaluated at two different noise levels. The Bayesian algorithm provides the upper bound for size quantification performance against which the ANN is compared. In the ideal case where the Bayesian algorithm has explicit knowledge of the underlying distributions, its performance is superior to that of the neural network. However, in the more realistic case where the distributions need to be estimated from the same learning sample the ANN was trained on, the two algorithms have comparable performances.

Algorithms↗

PubMed2006

QBES: predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization.

Solvent accessibility, one of the key properties of amino acid residues in proteins, can be used to assist protein structure prediction. Various approaches such as neural network, support vector machines, probability profiles, information theory, Bayesian theory, logistic function, and multiple linear regression have been developed for solvent accessibility prediction. In this article, a much simpler quadratic programming method based on the buriability parameter set of amino acid residues is developed. The new method, called QBES (Quadratic programming and Buriability Energy function for Solvent accessibility prediction), is reasonably accurate for predicting the real value of solvent accessibility. By using a dataset of 30 proteins to optimize three parameters, the average correlation coefficients between the predicted and actual solvent accessibility are about 0.5 for all four independent test sets ranging from 126 to 513 proteins. The method is efficient. It takes only 20 min for a regular PC to obtain results of 30 proteins with an average length of 263 amino acids. Although the proposed method is less accurate than a few more sophisticated methods based on neural network or support vector machines, this is the first attempt to predict solvent accessibility by energy optimization with constraints. Possible improvements and other applications of the method are discussed.

Protein Conformation↗

PubMed2026

Introgression among maternal lineages inferred from complete mitogenomes and molecular dating helps resolve phylogeography of European roe deer.

BACKGROUND: The European roe deer (Capreolus capreolus) is one of the most widespread ungulates in Europe, with a phylogeographic structure mainly shaped by Pleistocene glacial cycles and secondary contacts with the Siberian roe deer (C. pygargus). METHODS: We sequenced 52 complete mitogenomes of C. capreolus from Slovenia, Poland and France, and combined them with 24 publicly available sequences of C. capreolus and C. pygargus, yielding an alignment of 76 genomes representing 59 haplotypes (42 from C. capreolus and 17 from C. pygargus). Phylogeographic structure was assessed using a median-joining network, and divergence times were estimated using a time-calibrated Bayesian phylogeny based on mitochondrial coding regions, incorporating published ancient C. pygargus mitogenomes. We additionally screened mitochondrial protein-coding genes for selection. RESULTS: The haplotype network recovered the three major European roe deer clades (Eastern, Central, and Western) and detected Central-clade haplotypes in France. Two Polish haplotypes (Cp9 and Cp10), detected in C. capreolus, clustered within the C. pygargus mitochondrial lineage, supporting mitochondrial introgression. Time-calibrated phylogenies placed introgressed haplotypes within established C. pygargus lineages. Selection analyses provided limited evidence for episodic positive selection restricted to a small number of codons. CONCLUSIONS: Whole mitogenomes improve resolution of roe deer phylogeography and reveal introgressed maternal lineages, while time-calibrated phylogenies and selection tests add evolutionary context for interpreting mtDNA diversity in genus Capreolus.

Animals↗

PubMed2003

Clustering ensembles of neural network models.

We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. In some cases this summary may even yield better function estimates. We present a method to find representative models through clustering based on the models' outputs on a data set. We apply the method on an ensemble of neural network models obtained from bootstrapping on the Boston housing data, and use the results to discuss bootstrapping in terms of bias and variance. A parallel application is the prediction of newspaper sales, where we learn a series of parallel tasks. The results indicate that it is not necessary to store all samples in the ensembles: a small number of representative models generally matches, or even surpasses, the performance of the full ensemble. The clustered representation of the ensemble obtained thus is much better suitable for qualitative analysis, and will be shown to yield new insights into the data.

Algorithms↗

PubMed2006

Molecular phylogeny of musk deer: a genomic view with mitochondrial 16S rRNA and cytochrome b gene.

The phylogenetic status of the infra order Pecora is controversial, even though it is supported by paleontological, morphological, and molecular evidence. We analyzed two mitochondrial genes (i.e., 16S rRNA and cytochrome b) to resolve the phylogenetic position of pecoran species, i.e., the Bovidae, Cervidae, and Moschidae endemic to the Indian subcontinent. We used phylogenetic analysis based on different algorithms, including neighbor joining, maximum parsimony, Bayesian inference, maximum likelihood, minimum evolution, median joining network, along with multidimensional scaling, and DNA word analysis. Our results established the basal position of Tragulidae and the monophyly of the infra order Pecora within the Suborder Ruminantia. Our results also demonstrated that Bovidae, Cervidae, and Moschidae are allied with the placement of musk deer as more closely related to bovids than to cervids. Molecular dating based on sequence analysis shows that the radiation of Pecora occurred during the early Oligocene and that the majority of the pecoran families radiated and dispersed rapidly during the Oligocene/Miocene transition.

Animals↗

PubMed2001

An application of artificial neural intelligence for personal dose assessment using a multi-area OSL dosimetry system.

Significant advances have been made in recent years to improve measurement technology and performance of phosphor materials in the fields of optically stimulated luminescence (OSL) dosimetry. Pulsed and continuous wave OSL studies recently carried out on alpha-Al2O3:C have shown that the material seems to be the most promising for routine application of OSL for dosimetric purposes. The main objective of the study is to propose a new personal dosimetry system using alpha-Al2O3:C by taking advantage of its optical properties and energy dependencies. In the process of the study, a new dose assessment algorithm was developed using artificial neural networks in hopes of achieving a higher degree of accuracy and precision in personal OSL dosimetry system. The original hypothesis of this work is that the spectral information of X- and gamma-ray fields may be obtained by the analysis of the response of a multi-element system. In this study, a feedforward neural network using the error back-propagation method with Bayesian optimization was applied for the response unfolding procedure. The validation of the proposed algorithm was investigated by unfolding the 10 measured responses of alpha-Al2O3:C for arbitrarily mixed photon fields which range from 20 to 662 keV.

Algorithms↗

PubMedDate not supplied

Improved system for object detection and star/galaxy classification via local subspace analysis.

The two traditional tasks of object detection and star/galaxy classification in astronomy can be automated by neural networks because the nature of the problems is that of pattern recognition. A typical existing system can be further improved by using one of the local Principal Component Analysis (PCA) models. Our analysis in the context of object detection and star/galaxy classification reveals that local PCA is not only superior to global PCA in feature extraction, but is also superior to gaussian mixture in clustering analysis. Unlike global PCA which performs PCA for the whole data set, local PCA applies PCA individually to each cluster of data. As a result, local PCA often outperforms global PCA for data of multi-modes. Moreover, since local PCA can effectively avoid the trouble of having to specify a large number of free elements of each covariance matrix of gaussian mixture, it can give a better description of local subspace structures of each cluster when applied on high dimensional data with small sample size. In this paper, the local PCA model proposed by Xu [IEEE Trans. Neural Networks 12 (2001) 822] under the general framework of Bayesian Ying Yang (BYY) normalization learning will be adopted. Endowed with the automatic model selection ability of BYY learning, the BYY normalization learning-based local PCA model can cope with those object detection and star/galaxy classification tasks with unknown model complexity. A detailed algorithm for implementation of the local PCA model will be proposed, and experimental results using both synthetic and real astronomical data will be demonstrated.

Astronomy↗

PubMed2006

Neural networks for detection and classification of walking pattern changes due to ageing.

With age, gait functions reflected in the walking patterns degenerate and threaten the balance control mechanisms of the locomotor system. The aim of this paper is to explore applications of artificial neural networks for automated recognition of gait changes due to ageing from their respective gait-pattern characteristics. The ability of such discrimination has many advantages including the identification of at-risk or faulty gait. Various gait features (e.g., temporal-spatial, foot-ground reaction forces and lower limb joint angular data) were extracted from 12 young and 12 elderly participants during normal walking and these were utilized for training and testing on three neural network algorithms (Standard Backpropagation: Scaled Conjugate Gradient; and Backpropagation with Bayesian Regularization, BR). Receiver operating characteristics plots, sensitivity and specificity results as well as accuracy rates were used to evaluate performance of the three classifiers. Cross-validation test results indicate a maximum generalization performance of 83.3% in the recognition of the young and elderly gait patterns. Out of the three neural network algorithms, BR performed superiorly in the test results with best sensitivity, selectivity and detection rates. With the help of a feature selection technique, the maximum classification accuracy of the BR attained 100%, when trained with a small subset of selected gait features. The results of this study demonstrate the capability of neural networks in the detection of gait changes with ageing and their potentials for future applications as gait diagnostics.

Adult↗