Evaluation and prediction method of railway passenger long-term vibration comfort under complex operating conditions

Vibration contributes large increases in railway passenger discomfort during long-term sitting. Discomfort caused by vibration may differ in different operation conditions. This paper conducted field measurements to investigate the interrelationships between the three. Participants completed a 240-min train journey with their whole-body vibration, subjective comfort ratings and train operating parameters being recorded. A large correlation was observed between the estimated vibration dose value and subjective comfort. The relationship that vibration magnitude significantly increases with increasing the train speed and tunnel density was also found and quantified. A vibration exposure limit of 2.08 m/s1.75 corresponding to the boundary between subjective ratings of comfortable and discomfortable was obtained. Based on the exposure limit and the quantified relationship, a vibration comfort prediction method that can calculate the passenger's maximum tolerance time under a given operation condition was proposed and may help in determining the optimal operating speed and tunnels distribution to alleviate vibration discomfort. Practitioner summary: Similar to the guide to effect of vibration on health in current standard, a vibration exposure limit regarding comfort was provided for reference when assessing long-term vibration comfort. Meanwhile, a prediction method was proposed for determining the best train operating speed and tunnels distribution, thereby alleviating railway passengers' vibration discomfort.

A transformer-based genomic prediction method fused with knowledge-guided module

Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.

Strength Degradation of Foamed Lightweight Soil Due to Chemical Erosion and Wet-Dry Cycle and Its Empirical Model

Foamed lightweight soils (FLS) have been extensively used as backfill material in the construction of transportation infrastructures. However, in the regions consisting of salt-rich soft soil, the earth structure made by FLS experiences both fluctuation of groundwater and chemical environment erosion, which would accelerate the deterioration of its long-term performance. This study conducted laboratory tests to explore the deterioration of FLS in strength after being eroded by sulfate attack and/or wet-dry cycling, where the influencing factors of FLS density, concentration of sulfate solution, and cation type (i.e., Na+ and Mg2+) were considered. An unconfined compressive test (UCT) was conducted, and the corrosion-resistant coefficient (CRC) was adopted to evaluate the erosion degree after the specimens experienced sulfate attack and/or dry-wet cycling for a certain period. The research results show that the erosion of the FLS specimen under the coupling effect of sulfate attack and dry-wet cycling was more remarkable than that only under chemical soaking, and Na2SO4 solution had a severe erosion effect as compared with MgSO4 solution when other conditions were kept constant. An empirical model is proposed based on the test results, and its reliability has been verified with other test results from the literature. The proposed model provides an alternative for engineers to estimate the strength deterioration of FLS on real structures in a preliminary design.

Tabletability and compactibility of α-lactose monohydrate powders of different particle size. II: predicted relationships

This study aimed to evaluate a material sparing method to predict tabletability and compactibility relationships. Seven α-lactose monohydrate powders with varying particle size were used as test materials. The compressibility of the powders was determined experimentally, whereas tabletability and compactibility profiles were derived both experimentally and predicted. In the prediction method, two experimental compression parameters (Kawakita b-1 and Heckel plastic stiffness) and a single tensile strength reference value were used, all necessary data obtained from a single compression experiment. For both predicted and experimental relationships, compaction and tableting parameters (performance indicators) were calculated. The correction for viscoelastic recovery was successful in generating compressibility profiles that corresponded to the series of experimental out-of-die tablet porosities. For both the tabletability and compactibility, the experimental and predicted profiles showed a high degree of similarity. Good correlations were obtained between the predicted and experimental compaction and tableting parameters. It is concluded that the hybrid prediction method is a material sparing method, which can give good approximations of tabletability and compactibility relationships. The prediction method has the potential to be included as a part of a protocol for the characterisation of the tableting performance of particulate solids.

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms.

A Novel Coupling Model of Physiological Degradation and Emotional State for Prediction of Alzheimer's Disease Progression

The prediction of Alzheimer's disease (AD) progression plays a very important role in the early intervention of patients and the improvement of life quality. Cognitive scales are commonly used to assess the patient's status. However, due to the complicated pathogenesis of AD and the individual differences in AD, the prediction of AD progression is challenging. This paper proposes a novel coupling model (P-E model) that takes into account the processes of physiological degradation and emotional state transition of AD patients. We conduct experiments on synthetic data to validate the effectiveness of the proposed P-E model. Next, we conduct experiments on 134 subjects with more than 10 follow-ups from the Alzheimer's Disease Neuroimaging Initiative. The prediction performance of the P-E model is significantly better than other state-of-the-art methods, which achieves the mean squared error of 7.137 ± 0.035. The experimental results show that the P-E model can well characterize the non-monotonic properties of AD cognitive data and can also have a good predictive ability for time series data with individual differences.

Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.

Thickness-Prediction Method Involving Tow Redistribution for the Dome of Composite Hydrogen Storage Vessels

Traditional thickness-prediction methods underestimate the actual dome thickness at polar openings, leading to the inaccurate prediction of the load-bearing capacity of composite hydrogen storage vessels. A method of thickness prediction for the dome section of composite hydrogen storage vessels was proposed, which involved fiber slippage and tow redistribution. This method considered the blocking effect of the port on sliding fiber tows and introduced the thickness correlation to predict the dome thickness at polar openings. The arc length corresponding to the parallel circle radius was calculated, and then, the actual radius values corresponding to the bandwidth were obtained by the interpolation method. The predicted thickness values were compared with the actual measured thickness. The maximum relative error of the predicted thickness was 4.19%, and the mean absolute percentage error was 2.04%. The results show that the present method had a higher prediction accuracy. Eventually, this prediction method was used to perform progressive damage analysis on vessels. By comparing with the results of the cubic spline function method, the analysis results of the present method approached the actual case. This showed that the present method improved the accuracy of the design.

Global Pattern of CD8+ T-Cell Infiltration and Exhaustion in Colorectal Cancer Predicts Cancer Immunotherapy Response

Background: The MSI/MSS status does not fully explain cancer immunotherapy response in colorectal cancer. Thus, we developed a colorectal cancer-specific method that predicts cancer immunotherapy response.

Methods: We used gene expression data of 454 samples (MSI = 131, MSI-L = 23, MSS = 284, and Unknown = 16) and developed a TMEPRE method that models signatures of CD8+ T-cell infiltration and CD8⁺ T-cell exhaustion states in the tumor microenvironment of colorectal cancer. TMEPRE model was validated on three RNAseq datasets of melanoma patients who received pembrolizumab or nivolumab and one RNAseq dataset of purified CD8⁺ T cells in different exhaustion states.

Results: TMEPRE showed predictive power in three datasets of anti-PD1-treated patients (p = 0.056, 0.115, 0.003). CD8⁺ T-cell exhaustion component of TMEPRE model correlates with anti-PD1 responding progenitor exhausted CD8⁺ T cells in both tumor and viral infection (p = 0.048, 0.001). The global pattern of TMEPRE on 454 colorectal cancer samples indicated that 10.6% of MSS patients and 67.2% of MSI patients show biological characteristics that can potentially benefit from anti-PD1 treatment. Within MSI nonresponders, approximately 50% showed insufficient tumor-infiltrating CD8⁺ T cells and 50% showed terminal exhaustion of CD8⁺ T cells. These terminally exhausted CD8⁺ T cells coexisted with signatures of myeloid-derived suppressor cells in colorectal cancer.

Conclusion: TMEPRE is a colorectal cancer-specific method. It captures characteristics of CD8⁺ T-cell infiltration and CD8⁺ T-cell exhaustion state and predicts cancer immunotherapy response. A subset of MSS patients could potentially benefit from anti-PD1 treatment. Anti-PD1 resistance MSI patients with insufficient infiltration of CD8⁺ T cells or terminal exhaustion of CD8⁺ T cells need different treatment strategies.

A Simplified Method for Predicting Pattern Match Ratio

Cognitive diagnostic test design (CDTD) has a direct impact on the pattern match ratio (PMR) of the classification of examinees. It is more helpful to know the quality of a test during the stage of the test design than after the examination is taken. The theoretical construct validity (TCV) is an index of the test quality that can be calculated without testing, and the relationship between the PMR and the TCV will be revealed. The TCV captures the three aspects of the appeal of the test design as follows: (1) the TCV is a measure of test construct validity, and this index will navigate the processes of item construction and test design toward achieving the goal of measuring the intended objectives, (2) it is the upper bound of the PMR of the knowledge states of examinees, so it can predict the PMR, and (3) it can detect the defects of test design, revise the test in time, improve the efficiency of test design, and save the cost of test design. Furthermore, the TCV is related to the distribution of knowledge states and item categories and has nothing to do with the number of items.

Selected Approaches to the Assessment of Environmental Noise from Railways in Urban Areas

Rail transport is the second most important way of transporting people and freights by land in the European Union. Rail noise affects around 12 million people in the European Union during the day and around 9 million at night. There are two possible ways to assess environmental noise: noise measurement in situ and prediction using mathematical models. The aim of the work is based on the performed measurements and selected noise predictions to evaluate the accuracy of the prediction models and assess their sensitivity to various aspects. Two measuring points in the Banská Bystrica Self-Governing Region, within Slovakia, were selected for measurement, which is characterized by increased mobility of the population. For prediction, the two methodologies were selected (Schall 03 and Methodical instructions for the calculation of sound pressure level from transport). The results show that the Schall 03 method is sensitive to the measurement location (the value reaches half of the significance level) and to the location–period interaction. The second prediction method is sensitive to systematic error (absolute term) and, such as Schall 03, to the location–period interaction. This method systematically overestimates the results. Results showed greater accuracy of both prediction models compared to the measured noise values than the results of the authors in other countries and conditions.

Equations for estimating the oxygen cost of walking in stroke patients: a systematic review

OBJECTIVE

To report all equations that can potentially be used to estimate the oxygen cost of walking (Cw) without using a respiratory gas exchange analyzer and to provide the level of reliability of each equation.

DATA SOURCES

Webline, Medline, Scopus, ScienceDirect, Bielefeld Academic Search Engine (BASE), and Wiley Online Library databases from 1950 to August 2019 with search terms related to stroke and oxygen cost of walking.

METHODS

This systematic review was reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the methodological quality of included studies was determined with the Critical Appraisal Skills Programme (CASP).

RESULTS

We screened 2065 articles, and 33 were included for full-text analysis. Four articles were included in the data synthesis (stroke individuals=184). Analysis reported 4 equations estimating Cw that were developed from logistic regression equations between Cw and self-selected walking speed. The equations differed in several methodological aspects (characteristics of individuals, type of equation, Cw reference measurement methods). The Compagnat et al. study had the highest quality (CASP score=9/9).

CONCLUSIONS

This literature review highlighted 4 equations for estimating Cw from self-selected walking speed. Compagnat et al. presented the best quality parameters, but this work involved a population restricted to individuals with hemispheric stroke sequelae.

NIHSS Consciousness Score Combined with ASPECTS is a Favorable Predictor of Functional Outcome post Endovascular Recanalization in Stroke Patients

Although revascularization rates after endovascular thrombectomy for large vessel acute ischemic stroke (AIS) are high (71%), only 46% of patients achieve functional independence at 90 days. The present study was designed to explore a new method for predicting the functional prognosis of AIS patients after endovascular recanalization. A total of 200 anterior circulation stroke patients who received endovascular therapy were enrolled. Logistic regression analysis of clinical characteristics on functional independence were performed. The predictive power of sub-items in National Institute of Health stroke scale (NIHSS) and the combination of NIHSS consciousness and Alberta Stroke Program Early CT Score (ASPECTS) on functional independence were assessed by Receiver Operating Characteristic (ROC) curves and the latter was compared with 3 previously published prediction models by AUC (the area under ROC curve). The AUC for the NIHSS consciousness score to predict functional independence was higher than whole NIHSS and other sub-items (0.716 v 0.705, 0.586, 0.573, 0.552 and 0.559). Low NIHSS consciousness score, high ASPECTS score, short time from onset to recanalization, and high rate of successful recanalization were demonstrated to be significantly associated with the functional independence (OR 0.697, 2.226, 0.994 and 28.643). The prediction power of the combination was significantly better than NIHSS and ASPECTS alone (AUC 0.793 v 0.705 and 0.752). Compared with 3 other prediction models, the combination was found to be the strongest predictor for functional independence (AUC 0.793 v 0.791, 0.671 and 0.564). NIHSS which has been shown to be a strong predictor of functional outcomes after endovascular recanalization is largely dependent on the consciousness component. NIHSS consciousness score combined with ASPECTS appears to be a favorable predictor of functional independence. These findings may have broad reaching effects for isolated centers around the world without advanced imaging for triage and prognostication.

Identification of Two Subgroups of FOLFOX Resistance Patterns and Prediction of FOLFOX Response in Colorectal Cancer Patients

To dissect gene expression subgroups of FOLFOX resistance colorectal cancer(CRC) and predict FOLFOX response, gene expression data of 83 stage IV CRC tumor samples (FOLFOX responder n = 42, non-responder n = 41) are used to develop a novel iterative supervised learning method IML. IML identified two mutually exclusive subgroups of CRC patients that rely on different DNA damage repair proteins and resist FOLFOX. IML was validated in two validation sets (HR = 2.6, p Value = 0.02; HR = 2.36, p value = 0.02). A subgroup of mesenchymal subtype patients benefit from FOLFOX. Different subgroups of FOLFOX nonresponders may need to be treated differently.

Study on Properties Prediction and Braiding Optimization of Axial Braided Carbon/Carbon Composite

It is well established that the microstructure has significant effects on the properties of axial braided C/C composites. In this study, a method coupling the homogenization method and finite element method (FEM) was proposed to predict the relationship between the microstructure characteristics and macroscopic properties. Based on the representative volume element (RVE) model, the periodic displacement boundary condition was introduced to predict the equivalent elastic properties of the RVE and component of C/C composite material, and the coefficient of thermal expansion (CTE) of the material was predicted by the energy prediction method. The predicted results were in good agreement with experimental results. By predicting the thermal and mechanical properties of the materials with different braiding spacing and fiber rod diameter, the variation of the properties with braiding spacing and fiber rod diameter was obtained. The research methods and results in this paper could provide important references for the optimization and rational application of composite materials.

Binding symmetry and surface flexibility mediate antibody self-association

Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients

BACKGROUND

Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice.

MATERIAL AND METHODS

Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness.

RESULTS

We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients.

CONCLUSION

We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.

An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices). Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.

Grain growth prediction based on data assimilation by implementing 4DVar on multi-phase-field model

We propose a method to predict grain growth based on data assimilation by using a four-dimensional variational method (4DVar). When implemented on a multi-phase-field model, the proposed method allows us to calculate the predicted grain structures and uncertainties in them that depend on the quality and quantity of the observational data. We confirm through numerical tests involving synthetic data that the proposed method correctly reproduces the true phase-field assumed in advance. Furthermore, it successfully quantifies uncertainties in the predicted grain structures, where such uncertainty quantifications provide valuable information to optimize the experimental design.

Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass.

Sticky Traps Baited with Synthetic Aggregation Pheromone Predict Fruit Orchard Infestations of Plautia stali (Hemiptera: Pentatomidae)

The brown-winged green bug, Plautia stali Scott, mainly reproduces on Japanese cedar or cypress cones in Japanese plantation forests during summer and autumn. It often depletes its food sources in forest habitats and moves to cultivated crops in large numbers. To establish an easy method for assessing the risk of fruit orchard infestation by P. stali, we conducted a 3-yr field survey that monitored the attraction of bugs to the synthetic P. stali aggregation pheromone using a sticky trap. We used a morphological indicator, variable body size depending on food intake, to estimate the nutritional status in nymphs, which showed that nymphs attracted to the synthetic pheromone were starving. Comparisons between increasing changes in the number of stylet sheaths left on the cones by P. stali and the number of trapped nymphs show that monitoring nymphs with the pheromone-baited sticky trap is useful for inferring conditions regarding food resources in forest habitats. The trend toward trapping second instars can provide a timely overview of resource competition for cones. Trapping middle-to-late (third-fifth) instars is a warning that the cones are finally depleted and that there is a high probability that adults will leave the forests and invade the orchards. In addition, trends in trapping adults suggest that there is a potential risk of orchard infestation by the pest and predict the intensity and period of the invasion. The pheromone-baited sticky trap is an easy but useful survey tool for predicting P. stali orchard infestations.

Novel isoforms of the bovine Nuclear factor I/X (CCAAT-binding transcription factor) transcript products and their diverse expression profiles

The aim of this study was to detect splicing variants of the bovine NFIX gene and determine their expression regulations. Through bioinformatics analysis, we predicted five isoforms of the bovine NFIX transcript product and validated their existence using cDNA pool and sequencing methods. The five isoforms had a common 5'-terminal sequence and various 3'-terminal sequences. Nuclear factor I family genes can activate or repress transcription by a highly variable C-terminal region. Thus, the five isoform products from a single gene may function differently. Quantitative PCR results showed that NFIX had highest expression in brain; medial expression in lung and muscle; and lower expression in spleen, kidney, liver and heart of both embryo and adult cattle. However, the expression levels NFIX in adult tissues were significantly decreased, and the diversity of its alternative splicing events was lower. Each isoform was expressed differently in different tissues at the embryo and adult stages. One of the isoforms (Nfix2) was not detected in tissues of adult cattle. In brain, another of the isoforms (Nfix3) was not detected, whereas the other four isoforms were highly expressed. In the embryo, of the five isoforms, the profile of the one labeled Nfix4 was the most similar to that of total Nfix, and we proved that it was the major isoform. This study is the first that has detected five novel isoforms of the bovine NFIX transcript products and that has examined their profiles at spatial and temporal levels, which will provide essential information for better understanding the bovine NFIX gene.

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at http://bioinf.uta.fi/PROlocalizer/ .

Multi-class subcellular location prediction for bacterial proteins

Two algorithms, based onBayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection.