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At least 1,225 records · Page 68Linked to original sources
PubMed2003

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations.

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.

Anti-Inflammatory Agents, Non-Steroidal↗
PubMed2006

Predicting the effect of missense mutations on protein function: analysis with Bayesian networks.

BACKGROUND: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. RESULTS: Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables. CONCLUSION: The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

Algorithms↗
PubMed2026

Integrated transcriptomic and metabolomic analysis reveals candidate regulatory networks associated with starch accumulation in tetraploid potato.

Potato (Solanum tuberosum L.) tuber starch is a major determinant of crop quality and industrial value, yet the regulatory mechanisms underlying starch accumulation in autotetraploid cultivars remain poorly resolved. Here, we performed integrated transcriptomic and metabolomic analyses using a segregating tetraploid population derived from parents with contrasting starch content. Extreme phenotypes were selected to systematically dissect the molecular basis of starch accumulation. Transcriptome profiling revealed extensive transcriptional reprogramming between high- and low-starch genotypes, with differentially expressed genes significantly enriched in carbohydrate metabolism, particularly the starch and sucrose metabolism pathway. Notably, multiple transcription factor families, including AP2/ERF, MYB, and bHLH, were prominently represented, suggesting coordinated regulatory control. Metabolomic analysis identified substantial metabolic divergence, with differentially accumulated metabolites predominantly enriched in starch and sucrose metabolism as well as secondary metabolic pathways. Most metabolites exhibited negative associations with starch content, indicating competitive carbon allocation between primary and secondary metabolism. Integrative multi-omics analysis further resolved a core regulatory module comprising key structural genes and transcription factors tightly associated with starch-related metabolites. In particular, genes involved in sucrose cleavage and ADP-glucose metabolism, together with trehalose-6-phosphate synthase (TPS) and UDP-glucose-associated pathways, emerged as critical nodes linking carbon flux to starch biosynthesis. Correlation network analysis suggested that AP2/ERF-, MYB-, and bHLH-type transcription factors modulate these pathways by coordinating structural gene expression and metabolic flux distribution. Collectively, our study establishes a transcriptional-metabolic framework for starch accumulation in tetraploid potato, highlighting the central role of carbon allocation and signaling intermediates in shaping starch content, and providing candidate targets for molecular breeding and genome editing.

Solanum tuberosum↗
PubMed2005

wolfPAC: building a high-performance distributed computing network for phylogenetic analysis using 'obsolete' computational resources.

wolfPAC is an AppleScript-based software package that facilitates the use of numerous, remotely located Macintosh computers to perform computationally-intensive phylogenetic analyses using the popular application PAUP* (Phylogenetic Analysis Using Parsimony). It has been designed to utilise readily available, inexpensive processors and to encourage sharing of computational resources within the worldwide phylogenetics community.

Algorithms↗
PubMed2002

Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks.

OBJECTIVE: To develop a computerised system that will assist the early diagnosis of fetal hypoxia and to investigate the relationship between the fetal heart rate variability and the fetal pulse oximetry recordings. DESIGN: Retrospective off-line analysis of cardiotocogram and FSpO2 recordings. SETTING: The Maternity Unit of the 2nd Department of Obstetrics and Gynaecology, Aretaieion Hospital, University of Athens. POPULATION: Sixty-one women of more than 37 weeks of gestation were monitored throughout labour. METHODS: Multiresolution wavelet analysis was applied in each 10-minute period of second stage of labour focussing on long term variability changes in different frequency ranges and statistical analysis was performed in the associated 10-minute FSpO2 recordings. Self-organising map neural network was used to categorise the different 10-minute fetal heart rate patterns and the associated 10-minute FSpO2 recordings. MAIN OUTCOME MEASURES: Umbilical artery pH of < or = 7.20 and Apgar score at 5 minutes of < or = 7 formed the inclusion criteria of the risk group. RESULTS: After using k-means clustering algorithm, the two-dimensional output layer of the self-organising map neural network was divided into three distinct clusters. All the cases that mapped in cluster 3 belonged in the risk group except one. The sensitivity of the system was 83.3% and the specificity 97.9% for the detection of risk group cases. CONCLUSIONS: A relationship between the fetal heart rate variability in different frequency ranges and the time in which FSpO2 is less than 30% was noticed. Fetal pulse oximetry seems to be an important additional source of information. Computerised analysis of the fetal heart rate monitoring and pulse oximetry recordings is a promising technique in objective intrapartum diagnosis of fetal hypoxia. Further evaluation of this technique is mandatory to evaluate its efficacy and reliability in interpreting fetal heart rate recordings.

Adult↗
PubMed2001

The EC Thematic Network on the Analysis of Thorium and its isotopes in Workplace Materials.

Accurate measurements of workplace exposure to 232Th and its progeny are required to estimate internal radiation doses received by persons working with thorium-containing materials. However, a small intercomparison carried out in the mid-nineteen nineties raised doubts about the reliability of results obtained by methods available for measurement of thorium. An EC-funded thematic network was therefore established to bring together experts in the field of thorium analysis in order to coordinate research activity and identify best analytical practice. requirements for reference materials. etc. This network has now successfully completed its work programme. which included a survey to determine future research needs; a series of intercomparisons to test the performance of methods for measuring thorium in workplace materials, and a workshop held to promote best practice and transfer information to regulatory authorities and industry. Results of the work have been used to make various recommendations concerning future needs in this field.

Humans↗
PubMed2005

Application of principle component analysis-artificial neural network for simultaneous determination of zirconium and hafnium in real samples.

Determination of zirconium and hafnium were done by applying singular value decomposition and a feed forward Neural Network Algorithm with back propagation of error. The determination of trace amounts of mixtures of Zr(IV) and Hf(IV) in various matrices (river, tap and industrial wastewater) were investigated by PC-ANN using the complexes formed between Alizarin Red S, Zr and Hf. The results showed that measurement is possible in the ranges of 0.03-3.4 and 0.2-7.0 microg ml-1 for Zr(IV) and Hf(IV), respectively. The detection limits were 0.02 and 0.08 microg ml-1 for Zr(IV) and Hf(IV), respectively. The results also show very good agreement between true and predicted concentration values and have the ability to use in routine analysis.

Anthraquinones↗
PubMed2000

Classification of reflectance spectra from pigmented skin lesions, a comparison of multivariate discriminant analysis and artificial neural networks.

Successful treatment of skin cancer, especially melanoma, depends on early detection, but diagnostic accuracy, even by experts, can be as low as 56% so there is an urgent need for a simple, accurate, non-invasive diagnostic tool. In this paper we have compared the performance of an artificial neural network (ANN) and multivariate discriminant analysis (MDA) for the classification of optical reflectance spectra (320 to 1100 nm) from malignant melanoma and benign naevi. The ANN was significantly better than MDA, especially when a larger data set was used, where the classification accuracy was 86.7% for ANN and 72.0% for MDA (p < 0.001). ANN was better at learning new cases than MDA for this particular classification task. This study has confirmed that the convenience of ANNs could lead to the medical community and patients benefiting from the improved diagnostic performance which can be achieved by objective measurement of pigmented skin lesions using spectrophotometry.

Algorithms↗
PubMed2004

Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling.

Transcriptional profiling using DNA microarrays has proved to be a valuable tool for dissecting bacterial adaptation to various environments, including human hosts. Analysis of genomes and transcriptomes of Pseudomonas aeruginosa shows that this bacterium possesses and expresses a core set of genes, including virulence factors, which allow it to thrive in a range of environments. Transcriptional regulators previously thought to control single virulence traits are now shown to regulate complex global signaling networks. Microarray-based research has led to the discovery of upstream regulators and downstream components of these pathways, as well as probed the response to antibiotics, environmental stresses and other bacteria. Independent studies have highlighted the role of media composition, the makeup of the physical environment and experimental methods in the outcome of microarray analyses. A compilation of all the published data clearly shows transcriptional regulation of genes in all functional classes. Under conditions examined to date, slightly more than a quarter of the genome is regulated, suggesting that P. aeruginosa may use much of its genome for conditions unexplored in the laboratory.

Animals↗
PubMed2000

Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.

Analysis of Variance↗
PubMed2005

Application of artificial neural network to fMRI regression analysis.

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Adult↗
PubMed2002

Activation of attention networks using frequency analysis of a simple auditory-motor paradigm.

The purpose of this study was to devise a paradigm that stimulates attention using a frequency-based analysis of the data acquired during a motor task. Six adults (30-40 years of age) and one child (10 years) were studied. Each subject was requested to attend to "start" and "stop" commands every 20 s alternatively and had to respond with the motor task every second time. Attention was stimulated during a block-designed, motor paradigm in which a start-stop commands cycle produced activation at the fourth harmonic of the motor frequency. We disentangled the motor and attention functions using statistical analysis with subspaces spanned by vectors generated by a truncated trigonometric series of motor and attention frequency. During our auditory-motor paradigm, all subjects showed activation in areas that belong to an extensive attention network. Attention and motor functions were coactivated but with different frequencies. While the motor-task-related areas were activated with slower frequency than attention, the activation in the attention-related areas was enhanced every time the subject had to start or end the motor task. We suggest that although a simple block-designed, auditory-motor paradigm stimulates the attention network, motor preparation, and motor inhibition concurrently, a frequency-based analysis can distinguish attention from motor functions. Due to its simplicity the paradigm can be valuable in studying children with attention deficit disorders.

Adult↗
PubMed2000

Current evidence for the use of paediatric antiretroviral therapy--a PENTA analysis. Paediatric European Network for the Treatment of AIDS Steering Committee.

UNLABELLED: The introduction of combination antiretroviral therapy has been associated with a dramatic clinical improvement in children with human immunodeficiency virus infection. However, the uptake of antiretroviral therapy has been variable across Europe. The Paediatric European Network for the Treatment of AIDS Steering Committee has performed a systematic literature review of paediatric antiretroviral therapy trials. An analysis of the evidence base for the commencement and maintenance of antiretroviral therapy was produced. Suggestions for when to commence antiretroviral therapy, which drugs to start with and how to monitor and sequence drug regimens are given. CONCLUSION: The aim of these guidelines is to help in obtaining equity of access to a uniformly high standard of care for children with human immunodeficiency virus infection in all European countries.

Acquired Immunodeficiency Syndrome↗
PubMed2005

Functional stoichiometric analysis of metabolic networks.

MOTIVATION: An important tool in Systems Biology is the stoichiometric modeling of metabolic networks, where the stationary states of the network are described by a high-dimensional polyhedral cone, the so-called flux cone. Exhaustive descriptions of the metabolism can be obtained by computing the elementary vectors of this cone but, owing to a combinatorial explosion of the number of elementary vectors, this approach becomes computationally intractable for genome scale networks. RESULT: Hence, we propose to instead focus on the conversion cone, a projection of the flux cone, which describes the interaction of the metabolism with its external chemical environment. We present a direct method for calculating the elementary vectors of this cone and, by studying the metabolism of Saccharomyces cerevisiae, we demonstrate that such an analysis is computationally feasible even for genome scale networks.

Algorithms↗
PubMed2026

Multi-Omics Analysis Reveals Molecular Networks and Key Pathways Associated with Cysteine- and Methionine-Mediated Biosynthesis of Sulfur-Containing Flavor Metabolites in Lentinula edodes.

Lentinula edodes is renowned for its unique aroma, which is characterized by various volatile sulfur-containing flavor metabolites (SCFMs). Cysteine and methionine could enhance the SCFMs biosynthesis in L. edodes; however, the underlying metabolic pathways remain unclear. To bridge this gap, integrated proteomic and metabolomic analysis were performed to decipher pathways through which cysteine and methionine regulate SCFM biosynthesis. Results showed that exogenous cysteine and methionine supplementation significantly increased the content of lenthionine, the key aroma compound of shiitake mushrooms. Both treatments induced substantial changes in the proteomic and metabolomic profiles. Proteomic analysis revealed that differentially expressed proteins were predominantly enriched in cysteine and methionine metabolism and sulfur metabolism following cysteine treatment, whereas methionine treatment mainly affected proteins associated with tryptophan metabolism and sulfur metabolism. Metabolomic analysis showed that differentially accumulated metabolites were significantly enriched in D-amino acid metabolism and cysteine and methionine metabolism, with glutathione metabolism specifically enriched under cysteine treatment. Integrated omics analysis further uncovered distinct sulfur metabolite-protein regulatory networks under different sulfur nutrition and identified treatment-specific hub proteins. These findings establish a molecular regulatory framework linking SCFM biosynthesis with broader primary metabolic pathways involved in sulfur intermediate generation and regulation, providing new insights into the potential regulatory networks underlying SCFM formation in L. edodes.

Methionine↗
PubMed2003

Activity-dependent feedforward inhibition modulates synaptic transmission in a spinal locomotor network.

The analysis of synaptic properties in neural networks has focused on the properties of individual synapses. As a result, little is known of how neural assemblies arise from the connectivity and functional properties of different classes of network neurons. I examined synaptic properties in the lamprey locomotor network. Here I show that, in addition to their monosynaptic inputs to motor neurons, a proportion of the excitatory network interneurons (EINs) evoke an activity-dependent disynaptic feedforward inhibitory input. Connections from the excitatory interneurons to small ipsilateral inhibitory interneurons were found that could account for the feedforward inhibition. Both synapses in the disynaptic pathway exhibited activity-dependent facilitation during physiologically relevant spike trains, which could contribute to the delayed, activity-dependent development of the feedforward IPSP. Although it was not as common as the feedforward inhibition, the excitatory interneurons could also evoke feedforward excitatory inputs in motor neurons. EIN inputs to motor neurons usually depress during spike trains. In connections in which a delayed IPSP occurred, blocking the feedforward inhibition in motor neurons or preventing the activation of the disynaptic pathway abolished the depression of the direct EPSP during the spike train and could reveal an underlying facilitation. The feedforward inhibition thus heterosynaptically depressed the direct excitatory input to motor neurons. Activity-dependent heterosynaptic effects acting within network cellular assemblies can thus influence the integration of synaptic inputs in motor neurons. This could help to terminate ipsilateral motor neuron spiking during network activity.

Action Potentials↗
PubMedDate not supplied

A multi-user networked database for analysis of clinical and temperature data from patients treated with simultaneous radiation and ultrasound hyperthermia.

A database was developed using commercially available development software that allows the entry of clinical data and automatically analyses temperature and power data from a commercial ultrasound hyperthermia system. The database can be accessed via network connections by more than one authorized user, thus facilitating the entry, management, and analysis of clinical data. The software automatically estimates ultrasound induced temperature artifacts and calculates thermal dose parameters such as T90s, equivalent minutes at 43 degrees, and time at or above index temperatures using the corrected temperatures. These parameters also become part of the database. Digital photographs of treatment setup, probe placement, and tumour or normal tissue response can be included in the database for documentation and reference. Ultrasound diagnostic images that document the depth and reproducibility of probe placement can be scanned into the PC and included in the database as well. This short communication documents experiences developing this tool that may be useful to other investigators.

Combined Modality Therapy↗
PubMed1999

Neural network pattern recognition analysis of graft flow characteristics improves intra-operative anastomotic error detection in minimally invasive CABG.

OBJECTIVE: The intra-operative assessment of the quality of anastomosis in minimally invasive coronary artery bypass surgery (CABG) is critical. Recent investigations demonstrated that flow probes used intra-operatively to assess anastomotic errors may give the surgeon a false sense of confidence as only severely stenotic anastomoses (>90%) could be reliably detected. We developed a neural network system using graft flow data and assessed its potential to improve anastomotic error detection. METHODS: Mammary to LAD grafts (n = 46) were constructed in mongrel dogs off-pump. Continuous beat-to-beat graft flow was recorded using transit-time flow probes. Various degrees of anastomotic stenoses (0-100%) were created by an additional suture. The degree of anastomotic stenosis was confirmed by postoperative angiography. A learning vector quantization neural network was created using heart rate, mean aortic pressure, mean systolic, maximum systolic, minimum systolic, mean diastolic, maximum diastolic, minimum diastolic, and mean graft flows. In addition, a spectral analysis of the flow waveforms was performed and the magnitude and phase of the first five harmonics were used to further develop the neural network. RESULTS: The neural network pattern recognition system was 94% accurate in detecting any stenosis >50%. To validate the model, a testing set was used with 20% of the data values, and the accuracy remained at 100% above chance alone. CONCLUSION: Pattern recognition of transit-time flow probe tracings using neural network systems can detect anastomotic errors significantly better than the surgeon's visual assessment, thereby improving the clinical outcome of minimally invasive CABG.

Anastomosis, Surgical↗